def plot(model, real, ax):
### open files
atm = open_ncfile(home+'tas/historical/'+real+'/tas_'+model+ \
'_'+real+'_nino34.nc')
ocean = open_ncfile(home+'tos/historical/'+real+'/tos_'+model+ \
'_'+real+'_nino34.nc')
if model == 'HadGEM2':
tos = ocean.variables['tos'][:1752,:,:]
tas = atm.variables['tas'][:1752,:,:]
else:
tos = ocean.variables['tos'][:,:,:]
tas = atm.variables['tas'][:,:,:]
### make arrays one dimensional for correlation and scatterplot
tas = tas.flatten()
tos = tos.flatten()
### make arrays two dimensional for linear regression
X = tas.reshape(-1, 1)
Y = tos.reshape(-1, 1)
### Linear regression
reg = LinearRegression()
reg.fit(X, Y)
Y_pred = reg.predict(X)
### Information about linear regression model
X2 = sm.add_constant(X)
est = sm.OLS(Y, X2)
est2 = est.fit()
print(est2.summary())
### Pearson correlation
corr, p_value = pearsonr(X, Y)
### Plot
ax.scatter(tas, tos, marker='o', s=2, c='k')
ax.plot(tas, Y_pred, label='Reg. coeff: '+str("%.2f" % reg.coef_[0][0])+
'\nIntercept: '+ str("%.2f" % reg.intercept_[0]) +
'\nCorr. coeff: '+ str("%.2f" % corr[0]) +
'\nP-value: '+ str("%.2f" % p_value[0]))
if ax in (ax3,ax4,ax5):
ax.set_xlabel('Tas [K]')
else:
pass
ax.set_xlim(287,304)
ax.set_ylim(287,304)
ax.set_ylabel('Tos [K]')
ax.set_title(real)
ax.legend(loc='best', frameon=False)
plt.suptitle(model)
def read_gsat_1pctCO2(indir, models, ignore):
"""
Read GSAT array from different model files, save anomaly in one ouput array with one dimension being total number
of runs, the other being the time
:param indir: directories where GSAT files are stored
models: dict containing models' characteristics
ignore: list of models to ignore
:return: GSAT anomaly array (years, nb of runs)
"""
nmodels = len(models)
nMembers = np.ma.zeros(nmodels)
timN = 140
indir_1pctCO2 = indir + '1pctCO2/'
indir_piC = indir + 'piControl/'
# -- Initialize GSAT
gsat_anom = np.ma.masked_all((timN, nmodels - len(ignore)))
imod = 0
for i, model in enumerate(models):
if model['name'] not in ignore:
# Read GSAT 1pctCO2
file_gsat_CO2 = glob.glob(indir_1pctCO2 + '*' + model['name'] +
'*.nc')[0] # GSAT File
fgsatCO2 = open_ncfile(file_gsat_CO2, 'r')
gsatread_CO2 = fgsatCO2.variables['GSAT'][0:timN]
print('- Reading GSAT of %s' % (model['name'], ))
# Read GSAT PiControl
file_gsat_piC = glob.glob(indir_piC + '*' + model['name'] +
'*.nc')[0] # GSAT File
fgsatpiC = open_ncfile(file_gsat_piC, 'r')
gsatread_piC = fgsatpiC.variables['GSAT'][:]
# Compute and Save GSAT anomaly
gsat_anom[:, imod] = gsatread_CO2 - np.ma.average(gsatread_piC,
axis=0)
imod = imod + 1
nMembers[i] = 1
print('Total number of models:', np.sum(nMembers))
return gsat_anom
def read_toe_1pctCO2(varread, indir, models, ignore, ndomains):
"""
Read ToE array from different model files, save in one ouput array with one dimension being total number
of models
:param varread: string, ToE variable name in files
indir: directory of files to read (one file per model)
models: dict containing models' characteristics
ignore: list of models to ignore
ndomains: number of domain names
:return: varToEA: numpy array (number of total runs, number of domains) ToE in the Atlantic
varToEP numpy array (number of total runs, number of domains)
varToEI numpy array (number of total runs, number of domains)
nMembers: numpy array containing number of members per model, dimension=nb of models
"""
nmodels = len(models)
# -- Initialize varToE containing ToE
varToEA = np.ma.masked_all((nmodels, ndomains))
varToEP = np.ma.masked_all((nmodels, ndomains))
varToEI = np.ma.masked_all((nmodels, ndomains))
nMembers = np.ma.zeros(nmodels)
for i, model in enumerate(models):
if model['name'] not in ignore:
# Read file
file_CO2piC = glob.glob(indir + '/' + '*' + model['name'] +
'*.nc')[0]
fpiC = open_ncfile(file_CO2piC, 'r')
# Read ToE (basin, domain)
toeread = fpiC.variables[varread][:]
print('- Reading ToE of ' + model['name'])
# Save ToE
varToEA[i, :] = toeread[1, :]
varToEP[i, :] = toeread[2, :]
varToEI[i, :] = toeread[3, :]
nMembers[i] = 1
print('Total number of models:', np.sum(nMembers))
# Remove rows with masked values (due to ignored model(s))
if len(ignore) != 0:
idx = np.argwhere(nMembers == 0)[0]
if len(idx) != 0:
idx = idx[0]
varToEA_bis = np.delete(varToEA, idx, 0) # Delete row at index idx
varToEA = np.ma.masked_greater(
varToEA_bis, 200) # Now turn back into a masked array
varToEP_bis = np.delete(varToEP, idx, 0)
varToEP = np.ma.masked_greater(varToEP_bis, 200)
varToEI_bis = np.delete(varToEI, idx, 0)
varToEI = np.ma.masked_greater(varToEI_bis, 200)
return varToEA, varToEP, varToEI, nMembers
def ammann_preprocessing():
file = RAW_DATA_FOLDER + "ammann2003b_volcanics.nc"
nc = open_ncfile(file)
aod_var = nc.variables['TAUSTR'][:, :]
aod = np.ndarray((1, len(aod_var[:, 0]), len(aod_var[0, :])))
aod[0] = aod_var
wavelength = [550]
time = nc.variables['time'][:]
years = []
# Convert yyyymm format to year format using center of each month
for index, element in enumerate(time):
fractional_year = float(
str(element)[0:4]) + (float(str(element)[4:6]) - 0.5) / 12
fractional_year = round(fractional_year, 3)
years.append(fractional_year)
time = years
time_units = 'years'
latitude = nc.variables['lat'][:]
aod_obj = AodData(aod, latitude, time, time_units, wavelength)
aod_obj.save_as_nc(PROCESSED_DATA_FOLDER + 'processed_ammann.nc')
def evolv2k_preprocessing():
file = RAW_DATA_FOLDER + "eVolv2k_v3_EVA_AOD_-500_1900_1.nc"
nc = open_ncfile(file)
aod_var = nc.variables['aod550'][:, :]
aod = np.ndarray((1, len(aod_var[:, 0]), len(aod_var[0, :])))
aod[0] = aod_var
wavelength = [550]
time_units = 'years'
time = nc.variables['time']
latitude = nc.variables['lat']
aod_obj = AodData(aod, latitude, time, time_units, wavelength)
aod_obj.save_as_nc(PROCESSED_DATA_FOLDER + 'processed_eVolv2k.nc')
def ugh(period, step):
file = open_ncfile('MODIS_GPP_' + period + '.nc')
lat = file.variables['lat'][:]
lon = file.variables['lon'][:]
veg = file.variables['Gpp_500m'][step, :, :]
# create dataset
ds = xr.Dataset(
{
'GPP':
xr.DataArray(data=veg,
dims=['lat', 'lon'],
coords=[lat, lon],
attrs={
'long_name': 'GPP',
'units': 'kgC m-2'
})
},
attrs={
'Conventions': 'CF-1.6',
'Institution': 'Land Processes Distributed Active '
'Archive Center (LP DAAC)',
'Source': 'AppEEARS v2.53',
'Title': 'MOD44B.006 for aid0001'
})
ds['lat'].attrs = {
'units': 'degrees_north',
'long_name': 'latitude',
'standard_name': 'latitude',
'axis': 'Y'
}
ds['lon'].attrs = {
'units': 'degrees_east',
'long_name': 'longitude',
'standard_name': 'longitude',
'axis': 'X'
}
ds.to_netcdf('GPP_' + str(step) + '.nc',
encoding={
'lat': {
'dtype': 'double'
},
'lon': {
'dtype': 'double'
},
'GPP': {
'dtype': 'float32'
}
})
def read_toe_rcp85(varread, listfiles, ignore, ndomains):
"""
Read ToE array from different model files, save in one ouput array with one dimension being total number
of runs
:param varread: string, ToE variable name in files
listfiles: list of files to read (one file per model)
ignore: list of models to ignore
ndomains: number of domain names
:return: varToEA: numpy array (number of total runs, number of domains) ToE in the Atlantic
varToEP numpy array (number of total runs, number of domains)
varToEI numpy array (number of total runs, number of domains)
nMembers: numpy array containing number of members per model, dimension=nb of models
"""
nruns = 0
nrunmax = 100
nmodels = len(listfiles)
nMembers = np.ma.zeros(nmodels)
# Initialize varToE containing ToE of all runs
varToEA = np.ma.masked_all((nrunmax, ndomains))
varToEP = np.ma.masked_all((nrunmax, ndomains))
varToEI = np.ma.masked_all((nrunmax, ndomains))
for i in range(nmodels):
file_toe = listfiles[i]
ftoe = open_ncfile(file_toe, 'r')
name = os.path.basename(file_toe).split('.')[1]
if name not in ignore:
# Read ToE (members, basin, domain)
toeread = ftoe.variables[varread][:]
nMembers[i] = int(toeread.shape[0])
print('- Reading ToE of %s with %d members' % (name, nMembers[i]))
nruns1 = int(nruns + nMembers[i])
run_labels = ftoe.variables['run_label'][:]
#print(' ',run_labels)
# Save ToE
varToEA[nruns:nruns1, :] = toeread[:, 1, :]
varToEP[nruns:nruns1, :] = toeread[:, 2, :]
varToEI[nruns:nruns1, :] = toeread[:, 3, :]
nruns = nruns1
print('Total number of runs:', nruns)
varToEA = varToEA[0:nruns, :]
varToEP = varToEP[0:nruns, :]
varToEI = varToEI[0:nruns, :]
return varToEA, varToEP, varToEI, nMembers
def tau_map_preprocessing():
file = RAW_DATA_FOLDER + "tau_map_2012-12.nc"
nc = open_ncfile(file)
aod_var = nc.variables['tau'][:, :]
aod = np.ndarray((1, len(aod_var[:, 0]), len(aod_var[0, :])))
aod[0] = aod_var
wavelength = [550]
time_units = 'days'
time = nc.variables['month'][:]
latitude = nc.variables['lat'][:]
aod_obj = AodData(aod, latitude, time, time_units, wavelength)
aod_obj.save_as_nc(PROCESSED_DATA_FOLDER + 'processed_tau_map.nc')
def plot_gm_aod(dataset, time='years', fig=None, ax=None):
# open the data
nc = open_ncfile(dataset)
wavelength = nc.variables['wavelength'][0]
gm_aod = nc.variables['GM_AOD'][0, :]
time_data = nc.variables[time][:]
# Remove high value data masks
gm_aod = np.ma.masked_where(gm_aod > 100, gm_aod)
# Plot the data
title = ("Global Mean AOD Trend at Wavelength: " + str(wavelength) + 'nm')
xlabel = ("Time (" + time + ")")
ylabel = ("Global Mean AOD at lambda = " + str(wavelength))
fig = figure(title=title, x_axis_label=xlabel, y_axis_label=ylabel)
fig.line(time_data, gm_aod)
return fig
def volmip_preprocessing():
file = RAW_DATA_FOLDER + "CMIP_VOLMIP550_radiation_v4_1850-2018.nc"
nc = open_ncfile(file)
alt = nc.variables['altitude'][:]
wl1_earth = nc.variables['wl1_earth'][:]
wl1_sun = nc.variables['wl1_sun'][:]
lat = nc.variables['latitude'][:]
ext_earth = nc.variables['ext_earth'][:, :, :, :]
ext_sun = nc.variables['ext_sun'][:, :, :, :]
wavelength = [550]
time_units = 'months'
time = nc.variables['month'][:]
# EXT dimensions go wl1, lat, alt, time, need to rearrange
# Want time, wl1, lat, alt
ext_earth = np.transpose(ext_earth, (3, 0, 1, 2))
ext_sun = np.transpose(ext_sun, (3, 0, 1, 2))
# Want to iterate over all altitudes, in case altitude differences are not consistently spaced.
# Build the aod arrays and initialize to zeros, so we can add to them
aod_earth = np.zeros((len(time), len(wl1_earth), len(lat)))
aod_sun = np.zeros((len(time), len(wl1_sun), len(lat)))
# Extrapolate past edge of altitude, in order to get a change in altitude for first point
last_alt = alt[0] - (alt[1] - alt[0])
# Sum ext*d(alt) over all altitudes
for alt_index, alt in enumerate(alt):
aod_earth += ext_earth[:, :, :, alt_index] * (alt - last_alt)
aod_sun += ext_sun[:, :, :, alt_index] * (alt - last_alt)
last_alt = alt
# Use aod_sun because it is the one at 550nm
aod = np.transpose(
aod_sun,
(1, 0,
2)) # Rearrange again - AodData expects wavelength, time, latitude
aod_obj = AodData(aod, lat, time, time_units, wavelength)
aod_obj.save_as_nc(PROCESSED_DATA_FOLDER + 'processed_volmip.nc')
def CMIP6_preprocessing():
file = RAW_DATA_FOLDER + "CMIP_1850_2014_extinction_550nm_strat_only_v3.nc"
nc = open_ncfile(file)
alt = nc.variables['altitude'][:]
lat = nc.variables['latitude'][:]
ext = nc.variables['ext550'][:, :, :]
wavelength = [550]
time_units = 'months'
time = nc.variables['month'][:] # months since jan 1 1850
# NOTE the nc file data says this is in months since jan 1960, but it appears that is incorrect.
# Add wavelength dimension to extension, only 1 because ext is at 550
ext_tmp = np.zeros((1, len(lat), len(alt), len(time)))
ext_tmp[0, :, :, :] = ext
ext = ext_tmp
# EXT dimensions go wl1, lat, alt, time, need to rearrange
# Want time, wl1, lat, alt
ext = np.transpose(ext, (3, 0, 1, 2))
# Want to iterate over all altitudes, in case altitude differences are not consistently spaced.
# Build the aod arrays and initialize to zeros, so we can add to them
aod = np.zeros((len(time), 1, len(lat)))
# Extrapolate past edge of altitude, in order to get a change in altitude for first point
last_alt = alt[0] - (alt[1] - alt[0])
# Sum ext*d(alt) over all altitudes
for alt_index, alt in enumerate(alt):
aod += ext[:, :, :, alt_index] * (alt - last_alt)
last_alt = alt
# Use aod_sun because it is the one at 550nm
aod = np.transpose(
aod,
(1, 0,
2)) # Rearrange again - AodData expects wavelength, time, latitude
aod_obj = AodData(aod, lat, time, time_units, wavelength)
aod_obj.save_as_nc(PROCESSED_DATA_FOLDER + 'processed_cmip6.nc')
varToEA_2 = np.ma.masked_all((nrunmax, len(domains)))
varToEP_2 = np.ma.masked_all((nrunmax, len(domains)))
varToEI_2 = np.ma.masked_all((nrunmax, len(domains)))
# -- Loop over models
if use_piC == True:
indir = indir_rcppiC
else:
indir = indir_rcphn
listfiles = glob.glob(indir + method_noise_rcp + '/*.nc')
nmodels = len(listfiles)
for i in range(nmodels):
file_toe = listfiles[i]
ftoe = open_ncfile(file_toe, 'r')
name = os.path.basename(file_toe).split('.')[1]
# If use same runs in vs. histNat as in vs. PiControl, take out deficient models
if (runs_rcp
== 'all') or (runs_rcp == 'same' and name != 'GISS-E2-R'
and name != 'FGOALS-g2' and name != 'MIROC-ESM'):
# Read ToE (members, basin, domain)
toe1read = ftoe.variables[var + 'ToE1'][:]
toe2read = ftoe.variables[var + 'ToE2'][:]
nMembers[i] = toe2read.shape[0]
print('- Reading ToE of %s with %d members' % (name, nMembers[i]))
nruns1 = nruns + nMembers[i]
# Save ToE
y1 = 33; y2 = 38
elif focus_1pctCO2 == '2*CO2':
y1 = 69; y2 = 74
else:
y1 = 134; y2 = 140
imodel = 13 # Choose model index in model list (modelsDef.py)
# for imodel in range(16):
# -- Choose work files
if name == 'Durack & Wijffels':
indir = '/data/ericglod/Density_binning/Obs_Prod_density_april16/'
file = 'DurackandWijffels_GlobalOceanChanges-NeutralDensity_1950-2000_120209_11_46_11_beta.nc'
data = indir + file
fh2d = open_ncfile(data, 'r')
if name == 'mme_hist':
indir = '/data/ericglod/Density_binning/Prod_density_april15/mme_hist/'
file_2d = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon2D.nc'
file_1d = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon1D.nc'
data_2d = indir + file_2d
data_1d = indir + file_1d
fh2d = open_ncfile(data_2d, 'r')
fh1d = open_ncfile(data_1d, 'r')
if name == 'mme_hist_histNat':
indirh = '/data/ericglod/Density_binning/Prod_density_april15/mme_hist/'
fileh_2d = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon2D.nc'
import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages from matplotlib.patches import Polygon from netCDF4 import Dataset as open_ncfile t1 = time.time() #-- retrieve start time #-- define path, grid and variable diri = '/Users/k204045/NCL/PyNGL/User_Guide_examples/' #-- data directory fname = 'ta_ps_850.nc' #-- data file gname = 'r2b4_amip.nc' #-- grid info file VarName = 'ta' #-- variable name #-- open data and grid file and read data and coordinate variable f = open_ncfile(diri + fname,'r') #-- add data file g = open_ncfile(diri + gname,'r') #-- add grid file (not contained in data file!!!) #-- read first timestep and level of variable 'ta' variable = f.variables['ta'] #-- first time step, lev, ncells var = variable[0,0,:] #-- ta [time,lev,ncells] var = var - 273.15 #-- convert to degrees Celsius title = 'Matplotlib: ICON model data' #-- plot title string #-- define _FillValue and missing_value if not existing missing = -1e20 if not hasattr(var,'_FillValue'): var._FillValue = missing #-- set _FillValue if not hasattr(var,'missing_value'):
def proj_map(kind, plt, ax, minmax, clevsm, clevsm_bold, lat, lon, cmap, isopyc, sliced_density, var1,
var2 = None, var3 = None):
isopyc_idx = np.argmin(np.abs(sliced_density - isopyc))
if kind == 'hist-histNat':
var_hist = np.squeeze(var1[:,isopyc_idx,:])
var_histNat = np.squeeze(var2[:,isopyc_idx,:])
# Difference
var_diff = np.ma.average(var_hist[-5:, :], axis=0) - np.ma.average(var_histNat, axis=0)
# Climatology
var_mean = np.ma.average(var_hist, axis=0)
elif kind == 'Durack':
var_diff = np.squeeze(var1[isopyc_idx,:])
var_mean = np.squeeze(var2[isopyc_idx,:])
#Error field
var_diff_er = np.squeeze(var3[isopyc_idx,:])
var_diff_er = var_diff_er*1.1 # to account for a potential underestimation of the error determined by a bootstrap analysis
var_diff_er = var_diff_er*2.58 # 99% level
not_signif_change = np.where(np.absolute(var_diff)<var_diff_er, 1, 0)
elif kind == 'hist':
var = np.squeeze(var1[:,isopyc_idx,:,:])
# look at difference between 1950 and end to compare with obs
var_diff = np.ma.average(var[-5:,:], axis=0) - np.ma.average(var[0:5,:], axis=0)
# Climatology
var_mean = np.ma.average(var, axis=0)
else :
var_obs = np.squeeze(var1[:,isopyc_idx,:,:])
var_diff = np.ma.average(var_obs[-5:,:], axis=0) - np.ma.average(var_obs[0:5,:], axis=0)
# Climatology
var_mean = np.ma.average(var_obs, axis=0)
# Create meshgrid
lon2d, lat2d = np.meshgrid(lon, lat)
# Levels for shade plot
levels = np.linspace(minmax[0],minmax[1],minmax[2])
# Format for contour labels
levfmt = '%.0f'
if abs(clevsm[1] - clevsm[0]) < 1:
levfmt = '%.1f'
if abs(clevsm[1] - clevsm[0]) < 0.1:
levfmt = '%.2f'
# Read grid for coloring continents
f2 = open_ncfile('/home/ysilvy/Density_bining/Yona_analysis/data/140807_WOD13_masks.nc', 'r')
landsea = f2.variables['landsea'][:]
# Create mask
sea_mask = landsea != 1
landsea = np.ma.array(landsea, mask=sea_mask)
landsea[landsea == 1] = 0.2
# Basemap
map = Basemap(projection='cyl', llcrnrlon=20, llcrnrlat=-70., urcrnrlon=380, urcrnrlat=70, ax=ax)
map.drawmapboundary(fill_color='1')
map.drawparallels(np.arange(-60, 61, 20.), labels=[1, 0, 1, 0], linewidth=0.5)
map.drawmeridians(np.arange(-180, 180, 60), labels=[0, 0, 0, 1], linewidth=0.5)
# Draw filled contours of diff
cnplot = map.contourf(lon2d, lat2d, var_diff, cmap=cmap, levels = levels, latlon=True, extend='both')
if kind == 'Durack':
map.fillcontinents(color='black')
# -- Plot areas where data is not significant
error_plot = map.contourf(lon2d, lat2d, not_signif_change, levels=[0.25,0.5,1.5], colors='None',
hatches=['','....'], edgecolor='0.6', linewidth=0.0, latlon=True)
else :
# Draw continents
pc2 = map.pcolormesh(lon2d, lat2d, landsea, shading='flat', cmap=plt.cm.gray, latlon=True)
# Draw mean contours
cpplot1 = map.contour(lon2d, lat2d, var_mean, clevsm, colors = 'black', linewidths=0.5, latlon=True)
#plt.clabel(cpplot1, inline=1, fontsize=10, fmt=levfmt)
cpplot2 = map.contour(lon2d, lat2d, var_mean, clevsm_bold, colors='black', linewidths=2, latlon=True)
plt.clabel(cpplot2, inline=1, fontsize=12, fontweight='bold', fmt=levfmt)
# Indicate which isopycnal we're projecting on
ax.text(0.1, 0.85, '$\gamma = %.1f$' %(isopyc,), transform=ax.transAxes, fontweight='bold',color='w', fontsize=17)
return cnplot, levels
def plot(model, name, experiment, position, v):
folderIN = home + '/TRENDY/S2/' + v + '/bootstrap/'
folderlpj = home + '/lpj_guess/runs/global_monthly_CRUNCEP/bootstrap/'
fname_cp = 'ensmean_annual_1960-2013_CP_detrend.nc'
fname_ep = 'ensmean_annual_1960-2013_EP_detrend.nc'
#-- open net-cdf and read in variables
if model == 'LPJ-GUESS':
if experiment == 'CP':
trendy = open_ncfile(folderlpj + v + '_' + fname_cp)
else:
trendy = open_ncfile(folderlpj + v + '_' + fname_ep)
lat = trendy.variables['Lat'][:]
lon = trendy.variables['Lon'][:]
elif model == 'TRENDY':
if experiment == 'CP':
trendy = open_ncfile(folderIN + fname_cp)
else:
trendy = open_ncfile(folderIN + fname_ep)
lat = trendy.variables['latitude'][:]
lon = trendy.variables['longitude'][:]
else:
if experiment == 'CP':
trendy = open_ncfile(folderIN + model + '_' + fname_cp)
else:
trendy = open_ncfile(folderIN + model + '_' + fname_ep)
lat = trendy.variables['latitude'][:]
lon = trendy.variables['longitude'][:]
var = trendy.variables[v][0, :, :]
if v in ('mnee'):
var = var * (-1000)
else:
var = var * 1000
plt.subplot(5, 4, position)
#-- create map
map = Basemap(projection='cyl',
llcrnrlat=-60.,
urcrnrlat=84.,
resolution='c',
llcrnrlon=0.,
urcrnrlon=360.)
#-- draw coastlines and edge of map
map.drawcoastlines(linewidth=0.2)
x, y = map(*np.meshgrid(lon, lat))
cut_data = var[:-1, :-1]
#-- define the colormap
if v in ('resp', 'ter'):
cmap = plt.cm.BrBG_r
else:
cmap = plt.cm.BrBG
#-- extract all colors colormap
cmaplist = [cmap(i) for i in range(cmap.N)]
#-- create the new map
cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)
#-- define the bins and normalize
levels = np.arange(-28, 32, 4)
norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
#-- draw filled contours
cnplot = map.pcolormesh(x, y, cut_data, cmap=cmap, norm=norm)
#-- plot title for subplots
plt.title(name + ' ' + experiment, fontsize=12)
#-- add colourbar
plt.subplots_adjust(top=0.95,
left=0.02,
right=0.98,
bottom=0.11,
wspace=0.05,
hspace=0.1)
cax = plt.axes([0.1, 0.08, 0.8, 0.02])
cbar = fig.colorbar(cnplot, orientation='horizontal', cax=cax)
cbar.ax.tick_params(labelsize=10)
plt.colorbar(ticks=levels, cax=cax, orientation='horizontal')
if v in ('resp', 'ter'):
cbar.set_label('Composite anomaly TER [gC]', fontsize=13)
elif v in ('dist'):
cbar.set_label('Composite anomaly DIST [gC]', fontsize=13)
elif v in ('nbp', 'mnee'):
cbar.set_label('Composite anomaly NBP [gC]', fontsize=13)
elif v in ('mgpp', 'gpp'):
cbar.set_label('Composite anomaly GPP [gC]', fontsize=13)
else:
pass
# Data path
indirh = '/data/ericglod/Density_binning/Prod_density_april15/Raw/mme_hist/mme/'
fileh = 'cmip5.' + name + '.historical.ensm.an.ocn.Omon.density.ver-' + model['file_end'] + '.nc'
else:
indirh = '/data/ericglod/Density_binning/Prod_density_april15/Raw/mme_hist/mme/'
fileh = 'cmip5.multimodel_All.historical.ensm.an.ocn.Omon.density_3D.nc'
# for hist - histNat, mme or not ready so use ensemble mean for now
#fileh = 'cmip5.GFDL-ESM2M.historical.ensm.an.ocn.Omon.density.ver-v20130226.nc'
if name == 'mme_hist_histNat':
indirhn = '/data/ericglod/Density_binning/Prod_density_april15/Raw/mme_histNat/mme/'
filehn = 'cmip5.GFDL-ESM2M.historicalNat.ensm.an.ocn.Omon.density.ver-v20110601.nc'
datahn = indirhn + filehn
fhn = open_ncfile(datahn,'r')
datah = indirh + fileh
fh = open_ncfile(datah,'r')
# Read variables
lat = fh.variables['latitude'][:]
lon = fh.variables['longitude'][:]
# ------ Define work and variables ---------
varname = defVarmme('salinity'); v = 'S'
#varname = defVarmme('temp'); v = 'T'
density = np.array([19.0, 19.2, 19.4, 19.6, 19.8, 20.0, 20.2, 20.4, 20.6, 20.8, 21.0, 21.2,
# -------------------------------------------------------------------------------
# -- Define variable properties
var = varname['var']
minmax = varname['1dminmax']
clevsm = varname['clevsm']
legVar = varname['legVar']
unit = varname['unit']
minmax = varname['1dminmax']
if ToE_Method == 'agreehist':
minmax=[-1.,1.]
#
# -- Open netcdf files
nc2dh = open_ncfile(inDirh + '/' + file2dh)
if ToE_Method == 'usehistNat':
nc2dhn = open_ncfile(inDirhn + '/' + file2dhn)
# -- Read variables
if ToE_Method == 'agreehist':
# Read model agreement variables
tvaraa = nc2dh.variables[var + 'Agree'][:, 1, :, :].squeeze()
tvarap = nc2dh.variables[var + 'Agree'][:, 2, :, :].squeeze()
tvarai = nc2dh.variables[var + 'Agree'][:, 3, :, :].squeeze()
if ToE_Method == 'usehistNat':
print 'use histNat'
# Read var in bowl for hist and histnat
tvaraah = nc2dh.variables[var + 'Bowl'][:, 1, :, :].squeeze()
tvaraph = nc2dh.variables[var + 'Bowl'][:, 2, :, :].squeeze()
tvaraih = nc2dh.variables[var + 'Bowl'][:, 3, :, :].squeeze()
#varname = defVar('persist')
#varname = defVar('heatcontent')
iniyear = 1860
finalyear = 2005
deltay = 10.
# -------------------------------------------------------------------------------
# file inits
os.chdir(inDirh)
listFiles = glob.glob('cmip5.*_zon2D.nc')
var = varname['var']
# find dimensions
fi = open_ncfile(inDirh+'/'+listFiles[0])
print inDirh+'/'+listFiles[0]
isond0 = fi.variables['isondepth'] ; # Create variable handle
latN = isond0.shape[3]
levN = isond0.shape[2]
basN = isond0.shape[1]
timN = isond0.shape[0]
levr = fi.variables['lev'][:]
lats = fi.variables['latitude'][:]
# init cumulated number of members
nruns = 0
nrunmax = 200
# init arrays for ToE
toe1,toe2 = [np.ma.ones([nrunmax,basN,levN,latN], dtype='float32')*1. for _ in range(2)]
# -- Initialize varnoise hist and histNat containing std of all runs for each basin
varnoise_ha = np.ma.masked_all((nrunmax, len(domains)))
varnoise_hp = np.ma.masked_all((nrunmax, len(domains)))
varnoise_hi = np.ma.masked_all((nrunmax, len(domains)))
varnoise_hna = np.ma.masked_all((nrunmax, len(domains)))
varnoise_hnp = np.ma.masked_all((nrunmax, len(domains)))
varnoise_hni = np.ma.masked_all((nrunmax, len(domains)))
# -- Loop over models
for i, model in enumerate(models):
# Read file
file = 'cmip5.' + model['name'] + '.noise_domains_hist_histNat.nc'
f = open_ncfile(indir_noise + file, 'r')
# Read noise (members, basin, domain)
varstdh = f.variables[var+'stdh'][:]
varstdhn = f.variables[var+'stdhn'][:]
nMembers_h[i] = varstdh.shape[0]
nMembers_hn[i] = varstdhn.shape[0]
nruns1_h = nruns_h + nMembers_h[i]
nruns1_hn = nruns_hn + nMembers_hn[i]
print '- Reading', model['name'], 'with', nMembers_h[i], 'hist members and', nMembers_hn[i], 'histNat members'
print ''
# Save noise
varnoise_ha[nruns_h:nruns1_h,:] = varstdh[:,1,:]
varnoise_hp[nruns_h:nruns1_h,:] = varstdh[:,2,:]
varnoise_hi[nruns_h:nruns1_h,:] = varstdh[:,3,:]
if name == 'mme':
file2d_1pctCO2 = 'cmip5.multimodel_piCtl.1pctCO2.ensm.an.ocn.Omon.density_zon2D.nc'
file1d_1pctCO2 = 'cmip5.multimodel_piCtl.1pctCO2.ensm.an.ocn.Omon.density_zon1D.nc'
file2d_piC = 'cmip5.multimodel_1pct.piControl.ensm.an.ocn.Omon.density_zon2D.nc'
file1d_piC = 'cmip5.multimodel_1pct.piControl.ensm.an.ocn.Omon.density_zon1D.nc'
else :
models = defModelsCO2piC()
model = models[imodel] # Iterate
file2d_1pctCO2 = 'cmip5.' + model['name'] + '.1pctCO2.ensm.an.ocn.Omon.density.ver-' + model['file_end_CO2'] + '_zon2D.nc'
file1d_1pctCO2 = 'cmip5.' + model['name'] + '.1pctCO2.ensm.an.ocn.Omon.density.ver-' + model['file_end_CO2'] + '_zon1D.nc'
file2d_piC = 'cmip5.' + model['name'] + '.piControl.ensm.an.ocn.Omon.density.ver-' + model['file_end_piC'] + '_zon2D.nc'
file1d_piC = 'cmip5.' + model['name'] + '.piControl.ensm.an.ocn.Omon.density.ver-' + model['file_end_piC'] + '_zon1D.nc'
f2dCO2 = open_ncfile(indir_1pctCO2 + file2d_1pctCO2,'r')
f1dCO2 = open_ncfile(indir_1pctCO2 + file1d_1pctCO2,'r')
f2dpiC = open_ncfile(indir_piC + file2d_piC,'r')
f1dpiC = open_ncfile(indir_piC + file1d_piC,'r')
# ----- Work ------
varname = defVarmme('salinity'); v = 'S'
#varname = defVarmme('temp'); v = 'T'
#varname = defVarmme('depth'); v = 'Z'
multStd = 2. # detect ToE at multStd std dev of piControl
labBowl = ['piControl', '2*CO2']
import matplotlib as mpl import matplotlib.pyplot as plt from matplotlib.backends.backend_pdf import PdfPages from matplotlib.patches import Polygon from netCDF4 import Dataset as open_ncfile t1 = time.time() #-- retrieve start time #-- define path, grid and variable diri = '/Users/k204045/NCL/PyNGL/User_Guide_examples/' #-- data directory fname = 'ta_ps_850.nc' #-- data file gname = 'r2b4_amip.nc' #-- grid info file VarName = 'ta' #-- variable name #-- open data and grid file and read data and coordinate variable f = open_ncfile(diri + fname,'r') #-- add data file g = open_ncfile(diri + gname,'r') #-- add grid file (not contained in data file!!!) #-- read first timestep and level of variable 'ta' variable = f.variables['ta'] #-- first time step, lev, ncells var = variable[0,0,:] #-- ta [time,lev,ncells] var = var - 273.15 #-- convert to degrees Celsius title = 'Matplotlib: ICON model data' #-- plot title string #-- define _FillValue and missing_value if not existing missing = -1e20 if not hasattr(var,'_FillValue'): var._FillValue = missing #-- set _FillValue if not hasattr(var,'missing_value'):
multStd = 2. # detect ToE at multStd std dev of piControl labBowl = ['piControl', '1pctCO2'] valmask = 1.e20 iniyear = 0 finalyear = 140 deltay = 10. # ----- Variables ------ # Choose random file to read only the basic variables and properties common to all files file = 'cmip5.' + models[0]['name'] + '.1pctCO2.ensm.an.ocn.Omon.density.ver-' + models[0]['file_end_CO2'] + '_zon2D.nc' f = open_ncfile(indir_1pctCO2 + file,'r') lat = f.variables['latitude'][:]; latN = lat.size density = f.variables['lev'][:]; levN = density.size time = f.variables['time'][:]; timN = time.size var = varname['var_zonal'] # Define variable properties legVar = varname['legVar'] # ----- Compute ToE for each model ------ # -- Initialize toe toe_a = np.ma.ones((len(models), levN, latN))*1. toe_p = np.ma.ones((len(models), levN, latN))*1. toe_i = np.ma.ones((len(models), levN, latN))*1.
"""
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset as open_ncfile
from maps_matplot_lib import defVarmme, custom_div_cmap, averageDom
from modelsDef import defModelsCO2piC
from libToE import ToEdomain1pctCO2vsPiC
# ----- mme -----
# mme 1pct CO2
indir_1pctCO2 = '/data/ericglod/Density_binning/Prod_density_april15/mme_1pctCO2/'
file = 'cmip5.multimodel_piCtl.1pctCO2.ensm.an.ocn.Omon.density_zon2D.nc'
data = indir_1pctCO2 + file
fCO2mme = open_ncfile(data,'r')
# mme PiControl
indir_piC = '/data/ericglod/Density_binning/Prod_density_april15/mme_piControl/'
file = 'cmip5.multimodel_1pct.piControl.ensm.an.ocn.Omon.density_zon2D.nc'
data = indir_piC + file
fpiCmme = open_ncfile(data,'r')
# Read main variables
lat = fCO2mme.variables['latitude'][:]
time = fCO2mme.variables['time'][:]
varname = defVarmme('salinity'); v = 'S'
density = fCO2mme.variables['lev'][:]
var = varname['var_zonal_w/bowl']
models = defModelsCO2piC()
multStd = 2. # detect ToE at multStd std dev of histNat labBowl = ['histNat', 'hist'] valmask = 1.e20 iniyear = 1860 finalyear = 2005 deltay = 10. # ----- Variables ------ # Choose random file to read only the basic variables and properties common to all files file = 'cmip5.' + models[0]['name'] + '.historical.ensm.an.ocn.Omon.density.ver-' + models[0]['file_end_hist'] + '_zon2D.nc' f = open_ncfile(indir_toe_1 + file,'r') lat = f.variables['latitude'][:]; latN = lat.size density = f.variables['lev'][:]; levN = density.size time = f.variables['time'][:]; timN = time.size var = varname['var_zonal_w/bowl'] # Define variable properties legVar = varname['legVar'] # ----- Read ToE for each model ------ nruns = 0 # Initialize total number of runs nrunmax = 100 nMembers = np.ma.empty(len(models)) # Initialize array for keeping nb of members per model # -- Initialize ToE arrays containing ToE of all runs
# density domain
domrho = [21., 26., 28.] # min/mid/max
delrho = [.5, .2]
#
# -------------------------------------------------------------------------------
# -- Define variable properties
var = varname['var']
minmax = varname['minmax']
clevsm = varname['clevsm']
legVar = varname['legVar']
unit = varname['unit']
# -- Open netcdf files
nc1d = open_ncfile(indir + '/' + file1d)
nc2d = open_ncfile(indir + '/' + file2d)
# -- Read variables
# Restrict variables to bowl
if restrictBowl:
varb = 'Bowl'
else:
varb=''
tvara = nc2d.variables[var + varb][:, 1, :, :].squeeze()
tvarp = nc2d.variables[var + varb][:, 2, :, :].squeeze()
tvari = nc2d.variables[var + varb][:, 3, :, :].squeeze()
lev = nc2d.variables['lev'][:]
lat = nc2d.variables['latitude'][:]
# Read model agreement variables
if modelAgree:
####################################################################################################################
# To add a new dataset, nothing beyond this point needs to be altered.
####################################################################################################################
# Reorder the data as needed
if order != [0, 1, 2, 3, 4, 5]:
files = [files[i] for i in order]
names = [names[i] for i in order]
colours = [colours[i] for i in order]
authors = [authors[i] for i in order]
links = [links[i] for i in order]
# Build a list of dictionaries containing the datasets and their relevant data
Datasets = []
for index, file in enumerate(files):
nc = open_ncfile(files[index])
current_set = {
'name': names[index],
'file': file,
'nc data': nc,
'AOD': nc.variables['aod'][0][:][:],
'GM_AOD': nc.variables['gm_aod'][0][:],
'Latitude': nc.variables['latitude'][:],
'Time': nc.variables['years'][:]
}
Datasets.append(current_set)
# Convert list to a dictionary for easy name lookup later
Datasets = {names[i]: Datasets[i] for i in range(len(Datasets))}
# Build a list of the plot tabs we are creating
tabs = []
def read_gsat_rcp85(indir, listfiles, ignore):
"""
Read GSAT array from different model files, save anomaly in one ouput array with one dimension being total number
of runs, the other being the time
:param indir: directories where GSAT files are stored
listfiles: list of ToE files to read (one file per model) to keep same order as when reading ToE
ignore: list of models to ignore
:return: GSAT anomaly array (years, nb of runs)
"""
nruns = 0
nrunmax = 100
nmodels = len(listfiles)
nMembers = np.ma.empty(nmodels)
timN = 240
# -- Initialize GSAT
gsat_anom = np.ma.masked_all((timN, nrunmax))
for i in range(nmodels):
file_toe = listfiles[
i] # Read toe file in the same order to retrieve model name
name = os.path.basename(file_toe).split('.')[1]
ftoe = open_ncfile(file_toe, 'r')
run_labels = ftoe.variables[
'run_label'][:] # Read run labels of model i
if name != 'HadGEM2-ES':
iystart = 11
else:
iystart = 2
if name not in ignore:
# Read GSAT
file_gsat = glob.glob(indir + 'GSAT.*' + name +
'*.nc')[0] # GSAT File
fgsat = open_ncfile(file_gsat, 'r')
gsatread = fgsat.variables['GSAT'][:]
run_labels_GSAT = fgsat.variables['members_name'][:]
nMembers[i] = int(gsatread.shape[1])
print('- Reading GSAT of %s with %d members' % (name, nMembers[i]))
# print(' gsatread shape : ',gsatread.shape)
nruns1 = int(nruns + nMembers[i])
# Re-organize order of members so that it's the same as ToE array
gsatread_cor = np.ma.masked_all_like(gsatread)
for k in range(int(nMembers[i])):
idx_cor = list(run_labels).index(run_labels_GSAT[k])
gsatread_cor[:, idx_cor] = gsatread[:, k]
#print(' ',k,run_labels_GSAT[k], run_labels[k], run_labels[idx_cor])
# Save GSAT
gsatread_anom = gsatread_cor - np.ma.average(
gsatread_cor[0:50, :],
axis=0) # Anomaly relative to first 50 years
gsat_anom[:, nruns:nruns1] = gsatread_anom[
iystart:, :] # Keep 1861-2005
# print(' gsatanom shape : ',gsat_anom.shape)
nruns = nruns1
print('Total number of runs:', nruns)
gsat_anom = gsat_anom[:, 0:nruns]
return gsat_anom
method_noise = 'average_piC' # Average PiC in the specified domains then determine the std of this averaged value domains = ['Southern ST', 'SO', 'Northern ST', 'North Atlantic', 'North Pacific'] multStd = 2. # detect ToE at multStd std dev of histNat iniyear = 0 finalyear = 140 deltay = 10. # ----- Variables ------ # -- Choose random file to read only the basic variables and properties common to all files file = 'cmip5.' + models[0]['name'] + '.1pctCO2.ensm.an.ocn.Omon.density.ver-' + models[0]['file_end_CO2'] + '_zon2D.nc' f = open_ncfile(indir_1pctCO2 + file,'r') lat = f.variables['latitude'][:]; latN = lat.size density = f.variables['lev'][:]; levN = density.size time = f.variables['time'][:]; timN = time.size basinN = 4 var = varname['var_zonal'] # -- Define variable properties legVar = varname['legVar'] unit = varname['unit'] # ----- Average signal and noise and compute ToE for each model ------ for i, model in enumerate(models):
import matplotlib.pyplot as plt
from netCDF4 import Dataset as open_ncfile
from maps_matplot_lib import defVarmme, zonal_2D, custom_div_cmap
from scipy import interpolate
# ----- Workspace ------
indir = '/data/ericglod/Density_binning/Prod_density_april15/mme_hist/'
file_2D = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon2D.nc'
file_1D = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon1D.nc'
name = 'mme_hist'
data_2D = indir + file_2D
data_1D = indir + file_1D
f2D = open_ncfile(data_2D,'r')
f1D = open_ncfile(data_1D,'r')
# ----- Variables ------
# -- Read variables
lat = f2D.variables['latitude'][:]
density = np.array([19.0, 19.2, 19.4, 19.6, 19.8, 20.0, 20.2, 20.4, 20.6, 20.8, 21.0, 21.2,
21.4, 21.6, 21.8, 22.0, 22.2, 22.4, 22.6, 22.8, 23.0, 23.2, 23.4, 23.6,
23.8, 24.0, 24.2, 24.4, 24.6, 24.8, 25.0, 25.2, 25.4, 25.6, 25.8, 26.0,
26.1, 26.2, 26.3, 26.4, 26.5, 26.6, 26.7, 26.8, 26.9, 27.0, 27.1, 27.2,
27.3, 27.4, 27.5, 27.6, 27.7, 27.8, 27.9, 28.0, 28.1, 28.2, 28.3, 28.4, 28.5])
varname = defVarmme('salinity')
#varname = defVarmme('temp')
#depth = defVarmme('depth')
domrho = [21., 26., 28.] # min/mid/max # # ------------------------------------------------------------------------------- # -- Define variable properties var = varname['var'] minmax = varname['minmax'] clevsm = varname['clevsm'] legVar = varname['legVar'] unit = varname['unit'] # -- Open netcdf files model 1 print print file1H2d nchist2d = open_ncfile(indirHist + '/' + file1H2d) nchist1d = open_ncfile(indirHist + '/' + file1H1d) print file1HN2d nchistn2d = open_ncfile(indirHistN + '/' + file1HN2d) nchistn1d = open_ncfile(indirHistN + '/' + file1HN1d) print agreelev = 0.6 # not used # -- Read variables # Restrict variables to bowl tvar = nchist2d.variables[var] tvarn = nchistn2d.variables[var] lev = nchist2d.variables['lev'][:] lat = nchist2d.variables['latitude'][:]
varToEA = np.ma.masked_all((nrunmax, len(domains)))
varToEP = np.ma.masked_all((nrunmax, len(domains)))
varToEI = np.ma.masked_all((nrunmax, len(domains)))
# -- Loop over models
if use_piC == True:
indir = indir_rcppiC
else:
indir = indir_rcphn
listfiles = glob.glob(indir + method_noise_rcphn + '/*.nc')
nmodels = len(listfiles)
for i in range(nmodels):
file_toe = listfiles[i]
ftoe = open_ncfile(file_toe, 'r')
name = os.path.basename(file_toe).split('.')[1]
# Read ToE (members, basin, domain)
toe2read = ftoe.variables[var + 'ToE2'][:]
nMembers[i] = toe2read.shape[0]
print('- Reading ToE of %s with %d members' % (name, nMembers[i]))
nruns1 = nruns + nMembers[i]
# Save ToE
varToEA[nruns:nruns1, :] = toe2read[:, 1, :]
varToEP[nruns:nruns1, :] = toe2read[:, 2, :]
varToEI[nruns:nruns1, :] = toe2read[:, 3, :]
nruns = nruns1
print('Total number of runs:', nruns)
varname = defVarmme('salinity'); v = 'S'
multStd = 2. # detect ToE at multStd std dev of histNat
use_piC = False # Over projection period, signal = RCP-average(histNat), noise = std(histNat)
# use_piC = True # Over projection period, signal = RCP-average(PiControl), noise = std(PiControl)
iniyear = 1860
finalyear = 2100
deltay = 10.
# Choose random file to read only the basic variables and properties common to all files
file = 'cmip5.' + models[1]['name'] + '.historicalNat.ensm.an.ocn.Omon.density.ver-' + \
models[1]['file_end_histNat'] + '_zon2D.nc'
f = open_ncfile(indir_histNat + file,'r')
lat = f.variables['latitude'][:]; latN = lat.size
density = f.variables['lev'][:]; levN = density.size
print(density)
timN = 240
var = varname['var_zonal_w/bowl']
basinN = 4
# Define variable properties
legVar = varname['legVar']
# ----- Compute zonal ToE for each simulation ------
nMembers = np.ma.zeros(len(models)) # Initialize array for keeping nb of members per model
"""
PyEarthScience: PyNGL contour plot example
- filled contour over map plot
- rectilinear grid (lat/lon)
- colorbar
09.10.15 kmf
"""
from mpl_toolkits.basemap import Basemap, cm
import matplotlib.pyplot as plt
from netCDF4 import Dataset as open_ncfile
import numpy as np
#-- open netcdf file
nc = open_ncfile('/Users/k204045/NCL/general/data/new_data/rectilinear_grid_2D.nc')
#-- read variable
var = nc.variables['tsurf'][0,:,:]
lat = nc.variables['lat'][:]
lon = nc.variables['lon'][:]
#-- create figure and axes instances
fig = plt.figure(figsize=(8,8))
ax = fig.add_axes([0.1,0.1,0.8,0.9])
#-- create map
map = Basemap(projection='cyl',llcrnrlat= -90.,urcrnrlat= 90.,\
resolution='c', llcrnrlon=-180.,urcrnrlon=180.)
#-- draw coastlines, state and country boundaries, edge of map
"""
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset as open_ncfile
from maps_matplot_lib import defVarDurack, zonal_2D, custom_div_cmap
# ----- Workspace ------
indir = '/data/ericglod/Density_binning/Obs_Prod_density_april16/'
file = 'DurackandWijffels_GlobalOceanChanges-NeutralDensity_1950-2000_120209_11_46_11_beta.nc'
name = 'Durack & Wijffels'
data = indir + file
f = open_ncfile(data,'r')
# ----- Variables ------
# -- Read variables
density = f.variables['density'][:]
lat = f.variables['latitude'][:]
varname = defVarDurack('salinity')
#varname = defVarDurack('temp')
# -- Define variable properties
minmax = varname['minmax_zonal']
clevsm = varname['clevsm_zonal']
clevsm_bold = varname['clevsm_bold']
legVar = varname['legVar']
#name = 'Ishii'
name = 'mme'
if name == 'Ishii' :
indir = '/data/ericglod/Density_binning/Obs_Prod_density_april16/'
file = 'obs.Ishii.historical.r0i0p0.an.ocn.Omon.density.ver-1.latestXCorr.nc'
### Ishii : 1945 to 2013
else :
indir = '/data/ericglod/Density_binning/Prod_density_april15/Raw/mme_hist/'
file = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_2D.nc'
### mme : 1861 to 2006
data = indir + file
f = open_ncfile(data,'r')
# Read grid for coloring continents
f2 = open_ncfile('/home/ysilvy/Density_bining/Yona_analysis/data/140807_WOD13_masks.nc', 'r')
# ----- Variables ------
# Read variables
lat = f.variables['latitude'][:]
lon = f.variables['longitude'][:]
ptopsigma = f.variables['ptopsigmaxy'][:]
ptopsalinity = f.variables['ptopsoxy'][:]
landsea = f2.variables['landsea'][:]
def proj_map_zonal_changes(kind, zonal_change, plt, ax1, ax2, minmax, clevsm, lat, lon, cmap, isopyc,
sliced_density, var1, var2 = None):
isopyc_idx = np.argmin(np.abs(sliced_density - isopyc))
if kind == 'model':
var_hist = np.squeeze(var1[:,isopyc_idx,:])
var_histNat = np.squeeze(var2[:,isopyc_idx,:])
# Difference
var_diff = np.ma.average(var_hist[-6:, :], axis=0) - np.ma.average(var_histNat[-6,:, :], axis=0)
# Climatology
var_mean = np.ma.average(var_hist, axis=0)
elif kind == 'Durack':
var_diff = np.squeeze(var1[isopyc_idx,:])
var_mean = np.squeeze(var2[isopyc_idx,:])
else:
var_obs = np.squeeze(var1[:,isopyc_idx,:])
var_diff = np.ma.average(var_obs[-6:,:], axis=0) - np.ma.average(var_obs[0:5,:], axis=0)
# Climatology
var_mean = np.ma.average(var_obs, axis=0)
# Build zonal mean(s) and remove from var_diff
zonal_mean = np.ma.average(var_diff, axis=1)
zonal_mean_2D = np.tile(zonal_mean,(len(lon),1))
zonal_mean_2D = np.transpose(zonal_mean_2D)
if zonal_change == 'global':
var_diff = var_diff - zonal_mean_2D
if zonal_change == 'basin' and kind == 'Durack' :
# Read basin mask
f3 = open_ncfile('/home/ysilvy/data/DurackandWijffels_GlobalOceanSurfaceChanges_1950-2000_mask.nc', 'r')
basin_mask = f3.variables['basin_mask'][:]
# Build zonal means for each basin
mask_p = basin_mask != 1
mask_a = basin_mask != 2
mask_i = basin_mask != 3
var_diff_p = np.ma.array(var_diff, mask=mask_p)
var_diff_a = np.ma.array(var_diff, mask=mask_a)
var_diff_i = np.ma.array(var_diff, mask=mask_i)
zonal_mean_p = np.ma.average(var_diff_p, axis=1)
zonal_mean_a = np.ma.average(var_diff_a, axis=1)
zonal_mean_i = np.ma.average(var_diff_i, axis=1)
zonal_mean_p = np.tile(zonal_mean_p, (len(lon),1)); zonal_mean_p = np.transpose(zonal_mean_p)
zonal_mean_a = np.tile(zonal_mean_a, (len(lon), 1)); zonal_mean_a = np.transpose(zonal_mean_a)
zonal_mean_i = np.tile(zonal_mean_i, (len(lon), 1)); zonal_mean_i = np.transpose(zonal_mean_i)
var_diff[basin_mask==1] = var_diff[basin_mask==1] - zonal_mean_p[basin_mask==1]
var_diff[basin_mask==2] = var_diff[basin_mask==2] - zonal_mean_a[basin_mask==2]
var_diff[basin_mask==3] = var_diff[basin_mask==3] - zonal_mean_i[basin_mask==3]
# Create meshgrid
lon2d, lat2d = np.meshgrid(lon, lat)
# Levels for shade plot
#levels = MaxNLocator(nbins=minmax[2]).tick_values(minmax[0], minmax[1])
levels = np.linspace(minmax[0], minmax[1], minmax[2])
# Read grid for coloring continents
f2 = open_ncfile('/home/ysilvy/Density_bining/Yona_analysis/data/140807_WOD13_masks.nc', 'r')
landsea = f2.variables['landsea'][:]
# Create mask
sea_mask = landsea != 1
landsea = np.ma.array(landsea, mask=sea_mask)
landsea[landsea == 1] = 0.2
# Basemap
map = Basemap(projection='cyl', llcrnrlon=20, llcrnrlat=-70, urcrnrlon=380, urcrnrlat=70, ax=ax1)
map.drawmapboundary(fill_color='1')
map.drawparallels(np.arange(-60, 61, 20.), labels=[1, 1, 0, 0], linewidth=0.5)
map.drawmeridians(np.arange(-180, 180, 60), labels=[0, 0, 0, 1], linewidth=0.5)
# Draw filled contours of diff
cnplot = map.contourf(lon2d, lat2d, var_diff, cmap=cmap, levels = levels, latlon=True, extend='both')
if kind == 'Durack':
map.fillcontinents(color='black')
else :
# Draw continents
pc2 = map.pcolormesh(lon2d, lat2d, landsea, shading='flat', cmap=plt.cm.gray, latlon=True)
# Indicate which isopycnal we're projecting on
ax1.text(0.1, 0.85, '$\gamma = %.1f$' %(isopyc,), transform=ax1.transAxes, fontweight='bold',color='w', fontsize=17)
# Plot zonal mean
ax2.plot(zonal_mean, lat, color='purple', linewidth=1.5)
# Set axis parameters
ax2.set_xlim(minmax[0], minmax[1])
ax2.set_ylim(-70,70)
ax2.tick_params(
axis='both', # changes apply to both axes
which='both', # both major and minor ticks are affected
top='off', # ticks along the top edge are off
labelleft='off',
#left='off',
right='off',
labelright='off')
ax2.spines['left'].set_position('zero')
ax2.spines['right'].set_color('none')
ax2.spines['top'].set_color('none')
ax2.set_yticks([-60,-40,-20,0,20,40,60])
labels = ax2.get_xticklabels()
plt.setp(labels, rotation=45, fontsize=10)
ax2.grid(True)
return cnplot, levels
# ----- Work ------
varname = defVarmme('salinity'); v = 'S'
# varname = defVarmme('temp'); v = 'T'
iniyear = 1860
finalyear = 2005
# ----- Variables ------
domains = ['Southern ST', 'SO', 'Northern ST', 'North Atlantic', 'North Pacific']
# Choose random file to read only the basic variables and properties common to all files
file = 'cmip5.' + models[0]['name'] + '.historicalNat.r1i1p1.an.ocn.Omon.density.ver-' + models[0]['file_end_histNat'] + '_zon2D.nc'
f = open_ncfile(indir_histNat + file,'r')
lat = f.variables['latitude'][:]; latN = lat.size
density = f.variables['lev'][:]; levN = density.size
time = f.variables['time'][:]; timN = time.size
basinN = 4
var = varname['var_zonal_w/bowl']
legVar = varname['legVar']
unit = varname['unit']
# ----- Compute noise and average in domains for each run, then save in output file for each model -----
# == Historical and historicalNat + PiControl in histvshistNat boxes (for RCP8.5 ToE calculation) ==
nMembers_h = np.ma.empty(len(models)) # Initialize array for keeping nb of members per model
indir_piC = '/data/ericglod/Density_binning/Prod_density_april15/mme_piControl/'
models = defModelsCO2piC()
# ----- Work ------
varname = defVarmme('salinity')
v = 'S'
multStd = 2. # detect ToE at multStd std dev of histNat
# -- Choose random file to read only the basic variables and properties common to all files
file = 'cmip5.' + models[0][
'name'] + '.1pctCO2.ensm.an.ocn.Omon.density.ver-' + models[0][
'file_end_CO2'] + '_zon2D.nc'
f = open_ncfile(indir_CO2 + file, 'r')
lat = f.variables['latitude'][:]
latN = lat.size
lev = f.variables['lev'][:]
levN = lev.size
time = f.variables['time'][:]
timN = time.size
var = varname['var_zonal_w/bowl']
basinN = 4
# Define variable properties
legVar = varname['legVar']
unit = varname['unit']
# ----- Compute zonal ToE for each model ------
markers = ['o','v','^','8','s','p','h','D','d','*','>']
var = varname['var_zonal_w/bowl']
# ----- Read ToE and noise for each run ------
# ToEhn, ToEpiC, noisehn, noisepiC
listfiles_rcphn = glob.glob(indir_rcphn + method_noise + '/*.nc')
nmodels = len(listfiles_rcphn)
model_names = np.empty(nmodels).astype('S20')
for i in range(nmodels):
# Read ToE RCP8.5 vs. histNat (members, basin, domain)
filetoehn = listfiles_rcphn[i]
ftoehn = open_ncfile(filetoehn)
name = os.path.basename(filetoehn).split('.')[1]
model_names[i] = name
print(name)
toehnread = ftoehn.variables[var + 'ToE2'][:]
# Read noise histNat
if method_noise == 'average_histNat':
# Here we read the std of the averaged histNat for all runs, then take the max as our noise
filenoise = 'cmip5.' + name + '.noise_domains_hist_histNat.std_of_average.nc'
fnoise = open_ncfile(indir_noise + filenoise,'r')
varstdhn = fnoise.variables[var+'stdhn'][:] # Already averaged in the domains (members,basin,domain)
varnoisehn = np.ma.max(varstdhn,axis=0)
else:
# Read histNat ensemble mean
filehn = glob.glob(indir_histNat + 'cmip5.' + name + '.'+'*.zon2D.nc')[0]
domains = ['Southern ST', 'SO', 'Northern ST', 'North Atlantic', 'North Pacific']
domain_name = 'Southern ST'
idomain = 0
signal_domain = 'fresher'
ibasin = 1 ; basin_name = 'Atlantic' # Atlantic southern ST
# ibasin = 2 ; basin_name = 'Pacific' # Pacific southern ST
# use_piC = False # Over projection period, signal = RCP-average(histNat), noise = std(histNat)
use_piC = True # Over projection period, signal = RCP-average(PiControl), noise = std(PiControl)
# Choose random file to read only the basic variables and properties common to all files
file = 'cmip5.' + models[1]['name'] + '.historicalNat.ensm.an.ocn.Omon.density.ver-' + \
models[1]['file_end_histNat'] + '_zon2D.nc'
f = open_ncfile(indir_histNat + file,'r')
lat = f.variables['latitude'][:]; latN = lat.size
density = f.variables['lev'][:]; levN = density.size
timN = 240
time_label = np.arange(1860,2100)
var = varname['var_zonal_w/bowl']
basinN = 4
# Define variable properties
legVar = varname['legVar']
# ----- Initialize plot ------
if use_piC == True:
"""
import numpy as np
import matplotlib.pyplot as plt
import os, glob
from netCDF4 import Dataset as open_ncfile
import datetime
# ===
# === WORKSPACE AND PRE-REQUISITES ===
# ===
# Read EN4 file : will be our climatology
indir = '/home/ericglod/Density_bining/test/'
file = 'EN4.mon.ocean.Omon.1900_2017.density.nc' #'obs.EN4.historical.r0i0p0.mo.ocn.Omon.density.ver-1.latestX_zon2D.nc'
f = open_ncfile(indir + file, 'r')
# Read variables
lat = f.variables['latitude'][:]
density = f.variables['lev'][:]
isonvol = np.ma.average(
f.variables['isonvol'][:, :, :, :],
axis=0) * 1.e03 # Volume of isopycnals in km3, make climatology
# Dimensions
basinN = 4
latN = len(lat)
densityN = len(density)
# Z grid for calculating ocean volume per depth level
#gridz = np.arange(0,5501,5)
longName = 'historicalNat'
dir = 'histNat_std/'
else:
indir_z = indir_piC_remap
indir = indir_piC
longName = 'piControl'
dir = 'piControl_std/'
# ==== Read remapped std from file ====
listfiles = sorted(glob.glob(indir_z+'*.nc'))
# -- Loop on models
for i in range(len(listfiles)):
# i=0
file = os.path.basename(listfiles[i])
f = open_ncfile(indir_z+file,'r')
name = file.split('.')[1] # Read model name
print('Reading '+work+' for '+name)
stdvar_z = f.variables[var+'Std'][:]
lat = f.variables['latitude'][:]
pseudo_depth = f.variables['pseudo_depth'][:]
# Read bowl and take the average
file2 = glob.glob(indir+'/*'+name+'*1D.nc')[0]
f2 = open_ncfile(file2,'r')
if work == 'histNat':
bowl = f2.variables['ptopdepth'][:]
else:
bowl = f2.variables['ptopdepth'][-240:,:,:]
# varname = defVarmme('depth'); v = 'Z'
iniyear = 1860
finalyear = 2005
# ----- Variables ------
domains = [
'Southern ST', 'SO', 'Northern ST', 'North Atlantic', 'North Pacific'
]
# Choose random file to read only the basic variables and properties common to all files
file = 'cmip5.' + models[0][
'name'] + '.historicalNat.r1i1p1.an.ocn.Omon.density.ver-' + models[0][
'file_end_histNat'] + '_zon2D.nc'
f = open_ncfile(indir_histNat + file, 'r')
lat = f.variables['latitude'][:]
latN = lat.size
density = f.variables['lev'][:]
levN = density.size
time = f.variables['time'][:]
timN = time.size
basinN = 4
var = varname['var_zonal_w/bowl']
legVar = varname['legVar']
unit = varname['unit']
# ----- Compute noise and average in domains for each run, then save in output file for each model -----
# == Historical and historicalNat + PiControl in RCP8.5vshistNat boxes (for RCP8.5 ToE calculation) ==
# == Historical vs. historicalNat ==
nruns = 0 # Initialize total number of runs
nrunmax = 100
nMembers = np.ma.zeros(len(models)) # Initialize array for keeping number of members per model
# -- Initialize varToE containing ToE of all runs
varToEA = np.ma.masked_all((nrunmax, len(domains)))
varToEP = np.ma.masked_all((nrunmax, len(domains)))
varToEI = np.ma.masked_all((nrunmax, len(domains)))
# -- Loop over models
for i, model in enumerate(models):
file_toe = 'cmip5.' + model['name'] + '.toe_histNat_method2_' + method_noise_hn + '.nc'
ftoe = open_ncfile(indir_hhn + method_noise_hn + '/' + file_toe, 'r')
# Read ToE (members, basin, domain)
toe2read = ftoe.variables[var + 'ToE2'][:]
if runs == 'all':
nMembers[i] = toe2read.shape[0]
print('- Reading ToE of',model['name'], 'with', nMembers[i], 'members')
nruns1 = nruns + nMembers[i]
# Save ToE
varToEA[nruns:nruns1,:] = toe2read[:,1,:]
varToEP[nruns:nruns1,:] = toe2read[:,2,:]
varToEI[nruns:nruns1,:] = toe2read[:,3,:]
nruns = nruns1
unit = varname['unit'] iniyear = 1860 finalyear = 2005 plotName = 'cmip5_remap_test' + varname['var'] # ------------------------------------------------------------------------------- # file inits os.chdir(inDir) file='cmip5.multimodel_All.historical.ensm.an.ocn.Omon.density_zon2D.nc' var = varname['var'] # find dimensions fi = open_ncfile(inDir+'/'+file) print inDir+'/'+file fieldr = fi.variables[var][:] depthr = fi.variables['isondepth'][:] volumr = fi.variables['isonvol'][:] lat = fi.variables['latitude'][:] valmask = 1.e20 # Target grid targetz = [0.,5.,15.,25.,35.,45.,55.,65.,75.,85.,95.,105.,116.,128.,142.,158.,181.,216.,272.,364.,511.,732.,1033.,1405.,1830.,2289.,2768.,3257.,3752.,4350.,4749.,5250.] # Remap fieldz = remapToZ(fieldr.data,depthr.data,volumr.data, valmask, targetz)
multStd = 2. # detect ToE at multStd std dev of histNat (or PiControl)
# In fact now we save both 1 std and multStd=2 std
# use_piC = False # Over projection period, signal = RCP-average(histNat), noise = std(histNat)
use_piC = True # Over projection period, signal = RCP-average(PiControl), noise = std(PiControl)
iniyear = 1860
finalyear = 2100
deltay = 10.
# Choose random file to read only the basic variables and properties common to all files
file = 'cmip5.' + models[1]['name'] + '.historicalNat.ensm.an.ocn.Omon.density.ver-' + \
models[1]['file_end_histNat'] + '_zon2D.nc'
f = open_ncfile(indir_histNat + file,'r')
lat = f.variables['latitude'][:]; latN = lat.size
density = f.variables['lev'][:]; levN = density.size
timN = 240
var = varname['var_zonal_w/bowl']
basinN = 4
# Define variable properties
legVar = varname['legVar']
# ----- Average signal and noise and compute ToE for each simulation ------
nMembers = np.ma.zeros(len(models)) # Initialize array for keeping nb of members per model
for i, model in enumerate(models):
# use cdo yseasmean to compute seasonal climatology run1 = 'CM61-LR-hist-03.2110' file1 = run1+'_1950_2009_seasmean_transf_north40.nc' run2 = 'CM6-pace-TSTr8fgT' file2 = run2+'_1950_2009_seasmean_transf_north40.nc' file2c = run2+'_1950_2009_seasmean_transf_north40_corr.nc' run3 = 'CM6-pace-TSTr8vg' file3 = run3+'_1950_1999_seasmean_transf_north40.nc' file3c = run3+'_1950_1999_seasmean_transf_north40_corr.nc' run4 = 'CM6-pace-TSTr8vgS0' file4 = run4+'_1950_2009_seasmean_transf_north40.nc' file4c = run4+'_1950_2009_seasmean_transf_north40_corr.nc' # # -- Open netcdf files nc1 = open_ncfile(inDir + run1 + '/' + file1) nc2 = open_ncfile(inDir + run2 + '/' + file2) nc3 = open_ncfile(inDir + run3 + '/' + file3) nc4 = open_ncfile(inDir + run4 + '/' + file4) nc2c = open_ncfile(inDir + run2 + '/' + file2c) nc3c = open_ncfile(inDir + run3 + '/' + file3c) nc4c = open_ncfile(inDir + run4 + '/' + file4c) # -- Read variables for North Atl (anual mean from cdo yearmean on monthly data) trfatltot1 = nc1.variables['trsftotAtl'][0,:].squeeze() trfatlhef1 = nc1.variables['trsfhefAtl'][0,:].squeeze() trfatlwfo1 = nc1.variables['trsfwfoAtl'][0,:].squeeze() trfatltot2 = nc2.variables['trsftotAtl'][0,:].squeeze() trfatlhef2 = nc2.variables['trsfhefAtl'][0,:].squeeze()
# density domain
rhomin = 21
rhomid = 26
rhomax = 28
domrho = [rhomin, rhomid, rhomax]
# ----- Variables ------
var = varname['var_zonal_w/bowl']
legVar = varname['legVar']
unit = varname['unit']
# Read latitude and density from original file
fileh_2d = '/data/ericglod/Density_binning/Prod_density_april15/mme_hist/' \
'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon2D.nc'
fh2d = open_ncfile(fileh_2d, 'r')
lat = fh2d.variables['latitude'][:]
latN = len(lat)
density = fh2d.variables['lev'][:]
levN = len(density)
basinN = 4
# ------------------------------------
# ----- Read ToE for each model ------
# ------------------------------------
# == Historical + RCP8.5 vs. historicalNat or vs. PiControl ==
nruns = 0 # Initialize total number of runs
nrunmax = 100
nMembers = np.ma.zeros(
"""
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset as open_ncfile
from maps_matplot_lib import proj_map, proj_map_zonal_changes, custom_div_cmap, defVarDurack
# ----- Workspace ------
indir = '/data/ericglod/Density_binning/Obs_Prod_density_april16/'
file = 'DurackandWijffels_GlobalOceanChanges-NeutralDensity_1950-2000_120209_11_46_11_beta.nc'
name = 'Durack & Wijffels'
data = indir + file
f = open_ncfile(data, 'r')
# ----- Variables ------
# Read variables
density = f.variables['density'][:]
lat = f.variables['latitude'][:]
lon = f.variables['longitude'][:]
varname = defVarDurack('temp')
v = 'T'
#varname = defVarDurack('salinity'); v='S'
# -- Look at zonal differences or not
zonal_change = 'No'
#zonal_change = 'global' # Zonal mean is global
if name == 'mme_hist_histNat':
indirh = '/data/ericglod/Density_binning/Prod_density_april15/mme_hist/'
fileh_2d = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon2D.nc'
# fileh_2d = 'cmip5.CCSM4.historical.ensm.an.ocn.Omon.density.ver-v20121128_zon2D.nc'
fileh_1d = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon1D.nc'
# fileh_1d = 'cmip5.CCSM4.historical.ensm.an.ocn.Omon.density.ver-v20121128_zon1D.nc'
datah_2d = indirh + fileh_2d
datah_1d = indirh + fileh_1d
indirhn = '/data/ericglod/Density_binning/Prod_density_april15/mme_histNat/'
filehn_2d = 'cmip5.multimodel_Nat.historicalNat.ensm.an.ocn.Omon.density_zon2D.nc'
# filehn_2d = 'cmip5.CCSM4.historicalNat.ensm.an.ocn.Omon.density.ver-v20121128_zon2D.nc'
filehn_1d = 'cmip5.multimodel_Nat.historicalNat.ensm.an.ocn.Omon.density_zon1D.nc'
# filehn_1d = 'cmip5.CCSM4.historicalNat.ensm.an.ocn.Omon.density.ver-v20121128_zon1D.nc'
datahn_2d = indirhn + filehn_2d
datahn_1d = indirhn + filehn_1d
fh2d = open_ncfile(datah_2d, 'r')
fh1d = open_ncfile(datah_1d, 'r')
fhn2d = open_ncfile(datahn_2d, 'r')
fhn1d = open_ncfile(datahn_1d, 'r')
# File for remapped density
file = 'cmip5.multimodel_Nat.historical.remaptoz_density.zon2D.nc'
fsigma = open_ncfile(indir_density + file, 'r')
if name == 'mme_hist':
indir = '/data/ericglod/Density_binning/Prod_density_april15/mme_hist/'
file_2d = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon2D.nc'
file_1d = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon1D.nc'
data_2d = indir + file_2d
data_1d = indir + file_1d
fh2d = open_ncfile(data_2d, 'r')
"""
import numpy as np
import matplotlib.pyplot as plt
from netCDF4 import Dataset as open_ncfile
from maps_matplot_lib import zonal_2D, defVarmme, averageDom
from modelsDef import defModels, defModelsCO2piC
from libToE import ToEdomainhistvshistNat
# ----- Workspace ------
indirh = '/data/ericglod/Density_binning/Prod_density_april15/mme_hist/'
fileh = 'cmip5.multimodel_Nat.historical.ensm.an.ocn.Omon.density_zon2D.nc'
indirhn = '/data/ericglod/Density_binning/Prod_density_april15/mme_histNat/'
filehn = 'cmip5.multimodel_All.historicalNat.ensm.an.ocn.Omon.density_zon2D.nc'
fh = open_ncfile(indirh + fileh,'r')
fhn = open_ncfile(indirhn + filehn,'r')
# ----- Work/Variables ------
domains = ['Southern ST', 'SO', 'Northern ST', 'North Atlantic', 'North Pacific']
multStd = 2. # detect ToE at multStd std dev of histNat
varname = defVarmme('salinity'); v = 'S'
#varname = defVarmme('temp'); v = 'T'
#varname= defVarmme('depth'); v = 'Z'
var = varname['var_zonal']
legVar = varname['legVar']