def time_serie_sea_ice_ext(path, var, lat_min, basin, path_salinity, deg):
yr, xr, time = loading.extracting_coord_2D(path)
if deg == 'grid':
ice = loading.extracting_var(path, 'ileadfra')
else:
ice = loading.extracting_var(path, var)
#S = loading.extracting_var(path_salinity,'sal')
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
if deg == 'grid':
path_grid = '/media/fig010/LACIE SHARE/EC_data/CERFACS_cell_area.nc'
area = loading.reading_grid(path_grid) * 1E-6
else:
area = size_each_bins(xr, yr, deg)
print(np.shape(area), np.shape(ice))
ice[np.where(ice < 0.15)] = 0.
ice[np.where(ice > 0.15)] = 1.
print(np.nanmean(area))
'''nt = np.size(ice[0,0,:])
ice_extent=np.zeros_like((ice[mask]))
for i in range(0,nt):
ice_extent[:,i] = ice[mask,i]*area[mask]
'''# same as #
ice_extent = area[mask] * ice[mask].transpose(1, 0)
mean_ice_ext = np.nansum(ice_extent, axis=1)
return time, mean_ice_ext
def time_serie_Arctic(path, var):
yr, xr, time, depth = loading.extracting_coord(path)
TorS = loading.extracting_var(path, var)
make_plot.points_on_map(xr, yr, var, basin)
S = loading.extracting_var(path, 'sal')
TorS[np.where(S == 0.)] = np.nan
i_zmax = np.max(np.where(depth < simu.zmax))
print(np.shape(TorS))
mean_Arctic = np.mean(np.nanmean(TorS[:, 0:i_zmax, :], axis=0), axis=0)
return time, depth, mean_Arctic
def time_serie(path, var, lat_min, basin):
yr, xr, time = loading.extracting_coord_2D(path)
melted_ice = loading.extracting_var(path, var)
S = loading.extracting_var(path, 'sal')
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
area = size_each_bins(xr, yr)
FWF = area[mask] * melted_ice[mask].transpose(1, 0) * 1E6
mean_FWF = np.nansum(FWF, axis=1)
return time, mean_FWF
def time_serie_Arctic_2D(path, var, lat_min, basin):
yr, xr, time = loading.extracting_coord_2D(path)
ice = loading.extracting_var(path, var)
S = loading.extracting_var(path, 'sal')
ice[np.where(S[:, :, 0, :] == 0.)] = np.nan
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
#make_plot.points_on_map(xr[mask],yr[mask],var,basin)
arctic = ice[mask, :]
mean_Arctic = np.nanmean(arctic, axis=0)
return time, mean_Arctic
def time_serie_sea_ice_ext(path, var, lat_min, basin):
yr, xr, time = loading.extracting_coord_2D(path)
ice = loading.extracting_var(path, var)
S = loading.extracting_var(path, 'sal')
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
area = size_each_bins(xr, yr)
ice[np.where(ice < 0.15)] = 0.
ice[np.where(ice > 0.15)] = 1.
ice_extent = area[mask] * ice[mask].transpose(1, 0)
mean_ice_ext = np.nansum(ice_extent, axis=1)
return time, mean_ice_ext
def time_serie_Arctic(path, var, lat_min, basin):
yr, xr, time, depth = loading.extracting_coord(path)
TorS = loading.extracting_var(path, var)
print(np.shape(TorS))
S = loading.extracting_var(path, 'sal')
TorS[np.where(S == 0.)] = np.nan
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
make_plot.points_on_map(xr[mask], yr[mask], var, basin)
arctic = TorS[mask, :, :]
print(np.nanmax(arctic))
mean_Arctic = np.nanmean(arctic, axis=0)
return time, depth, mean_Arctic
def time_serie_Arctic_2D(path, var_name, lat_min, basin):
yr, xr, time = loading.extracting_coord_2D(path)
VAR = loading.extracting_var(path, var_name)
S = loading.extracting_var(path, 'sal')
VAR[np.where(S[:, :, 0, :] == 0.)] = 0 #np.nan
print(np.max(VAR), np.min(VAR))
VAR[np.where(VAR > 1E10)] = 0 #np.nan
print(np.max(VAR), np.min(VAR))
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
make_plot.points_on_map(xr[mask], yr[mask], var_name, basin)
VAR[np.where(VAR == 0)] = np.nan
arctic = VAR[mask, :]
mean_Arctic = np.nansum(arctic, axis=0)
return time, mean_Arctic
def time_serie_Arctic_2D(path, var_name, lat_min, basin):
yr, xr, time = loading.extracting_coord_2D(path)
VAR = loading.extracting_var(path, var_name)
S = loading.extracting_var(path, 'sal')
VAR[np.where(S[:, :, 0, :] == 0.)] = 0 #np.nan
VAR[np.where(VAR > 1E10)] = 0 #np.nan
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
VAR[np.where(VAR == 0)] = np.nan
area = size_each_bins(xr, yr)
FWF = area[mask] * VAR[mask, :].transpose(1, 0) * 1E6
mean_FWF = np.nansum(FWF, axis=1)
return time, mean_FWF
period) + '/vita.nc'
elif var == 'ice_velocity' or var == 'wind_stress':
self.path = '/media/fig010/LACIE SHARE/EC_Earth/EC_data/100velocity/' + str(
period) + '/tau_Coupled.nc'
nb = 10
while y1 <= first_year + 100 - 10:
y2 = y1 + 10
simu = From1950to2100(nb, specificity, var, y1, y2, period)
yr, xr, time = loading.extracting_coord_2D(simu.path)
if var == 'ice_velocity':
U = loading.extracting_var(simu.path, 'iicevelu')
V = loading.extracting_var(simu.path, 'iicevelv')
elif var == 'wind_stress':
U = loading.extracting_var(simu.path, 'iocestru')
V = loading.extracting_var(simu.path, 'iocestrv')
elif var == 'svelocity':
U = loading.extracting_var(simu.path, 'sovitua')
V = loading.extracting_var(simu.path, 'sovitva')
index_y1 = np.min(
np.where(first_year + time[:] / (3600 * 24 * 364.5) > simu.y1))
index_y2 = np.min(
np.where(first_year + time[:] / (3600 * 24 * 364.5) > simu.y2))
mask = (xr > 85) | (xr < -105)
U[mask, :] = -U[mask, :]
else:
self.vmin = 0
self.vmax = 0
self.vminC = 0
self.vmaxC = 0
return #//
#//////////////////////////////////////////////////////////////////////////////////////////
simu = From1950to2100(option, var, y1, y2)
yrE, xrE, time, depth, XE = loading.extracting_coord_1D(simu.pathE)
yrW, xrW, time, depth, XW = loading.extracting_coord_1D(simu.pathW)
VARE = loading.extracting_var(simu.pathE, var)
VARW = loading.extracting_var(simu.pathW, var)
time = simu.y1 + time[:] / (3600 * 24 * 364.5)
index_y1h = np.min(np.where(time > simu.y1h))
index_y1f = np.min(np.where(time > simu.y1f))
print(index_y1h, index_y1f)
if var == 'sal':
VARE[np.where(VARE < 30.)] = np.nan
VARW[np.where(VARW < 30.)] = np.nan
elif var == 'density':
VARE[np.where(VARE < 24)] = np.nan
VARW[np.where(VARW < 24)] = np.nan
+str(scenario)+str(option)+'_'+str(lat_min)+'N'
else:
output_file = str(var)+str(filtre)+str(ttype)+'_' \
+str(scenario)+str(option)+'_'+str(basin)
#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# STARTING ANALYSIS \\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\
yr, xr, time = loading.extracting_coord_2D(path)
time = first_year+time[:]/(3600*24*364.5)
index_y1h = np.min(np.where(time>y1h))
index_y1f = np.min(np.where(time>y1f))
IceF1 = loading.extracting_var(path, var)
IceF1[np.where(IceF1>1E10)]=0
if filtre == 'Positif':
IceF1[np.where(IceF1<0)] = 0
else:
IceF1[np.where(IceF1<-1E10)] = 0
if var == 'Icef':
IceF = 1E2*IceF1 # in cm
elif var == 'salt':
path_grid = '/media/fig010/LACIE SHARE/EC_data/CERFACS_cell_area.nc'
area = loading.reading_grid(path_grid)
IceF = area*IceF1.transpose(2, 0,1)
IceF = IceF.transpose(1,2,0)
#IceF = IceF1*np.mean(area)
'''
self.output_file = str(var) + '_' + str(month) + str(
self.y1) + '_' + str(sufix)
self.output_file2 = str(var) + '_' + str(month) + str(
self.y1) + '_' + str(self.y2) + '_' + str(sufix)
#//
return #//
#//
#//////////////////////////////////////////////////////////////////////////////////////////
simu = yearly_LongPeriod(option, var, y1, y2, basin, lat_min, month)
yr, xr, time, depth = loading.extracting_coord(simu.path)
VarArray_simuRho = loading.extracting_var(simu.path,
'rho') # Practical Salinity
index_y1 = np.min(
np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > simu.y1))
index_y2 = np.min(
np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > y2))
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
make_plot.points_on_map(xr[mask], yr[mask], var, basin)
VarArray_simuRho[np.where(VarArray_simuRho == 0)] = np.nan
region = VarArray_simuRho[mask, :, :]
mean_region = np.nanmean(region, axis=0)
elif var == 'density': #//
self.vmin = 20. #//
self.vmax = 27.5 #//
elif var == 'Uorth': #//
self.vmin = -2.4 #//
self.vmax = 2.4 #//
return #//
#//////////////////////////////////////////////////////////////////////////////////////////
simu = From1950to2100(option, var, y1, y2, comparison, lat_min, basin)
yr, xr, time, depth, X = loading.extracting_coord_1D(simu.path)
VAR = loading.extracting_var(simu.path, var)
if var == 'sal':
VAR[np.where(VAR < 32.)] = np.nan
elif var == 'density':
VAR[np.where(VAR < 18)] = np.nan
else:
S = loading.extracting_var(simu.path, 'sal')
VAR[S == 0] = np.nan
print(np.nanmin(VAR))
make_plot.points_on_map(xr, yr, var, basin)
index_y1 = np.min(
np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > simu.y1))
index_y2 = np.min(
for i in range(1, 2):
y1 = y1 + 1
ax = plt.subplot(3, 4, 1)
ax.set_title(str(y1), color='r')
title_plot = str(specificity) + ' ' + str(y1) + ' to ' + str(y1 + 1)
simu = From1950to2100(option, specificity, var, y1, y2, comparison)
yr, xr, time, depth = loading.extracting_coord(simu.path)
x, y = m(xr, yr)
m.drawcoastlines(linewidth=0.5)
m.fillcontinents(color='0.8')
U = loading.extracting_var(simu.path, 'sovitua')
'''V = loading.extracting_var(simu.path, 'sovitva')
index_y1 = np.min(np.where(simu.first_year+time[:]/(3600*24*364.5)>simu.y1))
yy = np.arange(0, y.shape[0], 4)
xx = np.arange(0, x.shape[1], 4)
u = U[:,:,0,index_y1]
v = V[:,:,0,index_y1]
points = np.meshgrid(yy, xx)
speed = np.sqrt(u*u + v**2)
cs = m.quiver(x[points], y[points],\
u[points], v[points], speed[points],\
cmap=plt.cm.autumn, scale=1)
'''
plt.savefig(str(var) + '/' + str(simu.output_file.replace(".", "")) + '.png')
self.vmax = 1E-4 #//
return #//
#//////////////////////////////////////////////////////////////////////////////////////////
simu = From1950to2100(option,specificity,var,y1,y2,comparison)
if var == 'ML' or var == 'runoff':
yr, xr, time = loading.extracting_coord_2D(simu.path)
else:
yr, xr, time, depth = loading.extracting_coord(simu.path)
VarArray_simu = loading.extracting_var(simu.path, var)
#index_y1 = np.min(np.where(simu.first_year+time[:]/(3600*24*364.5)>simu.y1))
#index_y2 = np.min(np.where(simu.first_year+time[:]/(3600*24*364.5)>simu.y2))
if comparison == 'no':
array_to_plot = VarArray_simu[:,:,0]
array_to_plot[np.where(array_to_plot>1E20)]=0#np.nan
array_to_plot[array_to_plot==0] = np.nan
array_to_plot = np.ma.masked_invalid(array_to_plot)
make_plot.plot_map(xr,yr,array_to_plot ,var,title_plot,simu.output_file,simu.vmin,simu.vmax)
elif comparison == 'yes':
if control == 'Uncoupled':
path2 = '/media/fig010/LACIE SHARE/EC_Earth/EC_data/DR8n01_2090_jan.nc'
plt.gca().invert_yaxis()
# rotate and align the tick labels so they look better
fig.autofmt_xdate()
#plt.xticks(tick_locs,tick_lbls)
cbar = plt.colorbar()
cbar.ax.get_yaxis().labelpad = 15
cbar.set_label(r'Meridional transport', fontsize=18)
plt.title('')
plt.savefig(str(variable_name) + '/' + str(option) + '.png')
plt.close(fig)
return
simu = From1950to2100(option, var, y1, y2, comparison)
yr, xr, time, depth = loading.extracting_coord(simu.path, 'v')
v = loading.extracting_var(simu.path, 'v')
print(yr)
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
make_plot.points_on_map(xr, yr, var, basin)
'''
index_y = np.min(np.where(1950+time[:]/(3600*24*364.5)>year))
ny = np.size(yr[0,:])
nz = np.size(depth)
VT = np.zeros((nz,ny))
for j in range(0,ny):
elif var == 'ML':
self.vmin = 0
self.vmax = 300
self.output_file = str(var) + '_ymeans_' + str(option) #//
#//
return #//
#//
#//////////////////////////////////////////////////////////////////////////////////////////
if comparison == 'no':
simu = yearly_LongPeriod(option, 'ML')
elif comparison == 'yes':
simu = yearlyAnomaly_LongPeriod(option, var)
VarArray_ref = loading.extracting_var(simu.path_ref, var)
yr, xr, time, depth = loading.extracting_coord(simu.path)
VarArray_simuT = loading.extracting_var(simu.path,
'temp') # Practical Salinity
VarArray_simuS = loading.extracting_var(simu.path,
'sal') # In situ temperature
ni = np.size(VarArray_simuT[:, 0, 0, 0])
nj = np.size(VarArray_simuT[0, :, 0, 0])
nz = np.size(VarArray_simuT[0, 0, :, 0])
SA = np.zeros((ni, nj, nz))
CT = np.zeros((ni, nj, nz))
CT_freezing = np.zeros((ni, nj, nz))
# '2100/icef'+str(month)+'_'+str(y1)+'-2100.nc'
self.output_fileH = str(var)+'_'+str(self.y1h)+'-'+str(self.y2h) #//
self.output_fileF = str(var)+'_'+str(self.y1f)+'-'+str(self.y2f) #//
self.output_fileC = str(var)+'Anomaly_'+str(self.y1h)+'-'+str(self.y1f) #//
return #//
#//////////////////////////////////////////////////////////////////////////////////////////
simu = From1950to2100(option,var,y1p,y2p)
yr, xr, time,depth = loading.extracting_coord_moc(simu.path)
time = simu.first_year+time[:]/(3600*24*364.5)
moc = loading.extracting_var(simu.path, var)
moc[np.where(moc==0.000)]=np.nan
index_y1h = np.min(np.where(time>simu.y1h))
index_y1f = np.min(np.where(time>simu.y1f))
'''
array_to_plotH = np.nanmean(moc[:,:,0,index_y1h:index_y1h+10],axis=2)
array_to_plotH[array_to_plotH==0] = np.nan
array_to_plotH = np.ma.masked_invalid(array_to_plotH)
array_to_plotF = np.nanmean(moc[:,:,0,index_y1f:index_y1f+10],axis=2)
array_to_plotF[array_to_plotF==0] = np.nan
array_to_plotF = np.ma.masked_invalid(array_to_plotF)
print(np.nanmin(array_to_plotH),np.nanmax(array_to_plotF))
for i in range(1,13): y1 = y1+1 ax = plt.subplot(3, 4, i) ax.set_title(str(y1),color='r') title_plot = str(specificity)+' '+str(y1)+' to '+str(y1+1) simu = From1950to2100(option,specificity,var,y1,y2,comparison,month) yr, xr, time = loading.extracting_coord_2D(simu.path) x,y = m(xr, yr) m.drawcoastlines(linewidth=0.5) m.fillcontinents(color='0.8') VarArray_simu = loading.extracting_var(simu.path, var) index_y1 = np.min(np.where(simu.first_year+time[:]/(3600*24*364.5)>simu.y1)) index_y1c = np.min(np.where(simu.first_year+time[:]/(3600*24*364.5)>simu.y1_compa)) array_to_plot = VarArray_simu[:,:,index_y1c]-VarArray_simu[:,:,index_y1] array_to_plot[array_to_plot==0] = np.nan array_to_plot = np.ma.masked_invalid(array_to_plot) #make_plot.plot_map_with_ice_extent(xr,yr,array_to_plot ,var,VarArray_simu[:,:,index_y1],y1,VarArray_simu[:,:,index_y1c],simu.y1_compa,title_plot,simu.output_file,simu.vmin,simu.vmax) cs = m.pcolor(x,y,array_to_plot,vmin=simu.vmin,vmax=simu.vmax) cs2=m.contour(x, y, VarArray_simu[:,:,index_y1], [0.15],colors='r') cs3=m.contour(x, y, VarArray_simu[:,:,index_y1c], [0.15],colors='b') plt.savefig(str(var)+'/'+str(output_file)+'.png',format='png')
self.vmax = 35 #//
elif var == 'density': #//
self.vmin = 20. #//
self.vmax = 27.5 #//
elif var == 'Uorth': #//
self.vmin = -7 #//
self.vmax = 7 #//
return #//
#//////////////////////////////////////////////////////////////////////////////////////////
simuW = From1950to2100(option,var,y1,y2,comparison,lat_min,period,'W')
yr, xr, time, depthW, XW = loading.extracting_coord_1D(simuW.path)
VARW = loading.extracting_var(simuW.path, var)
S = loading.extracting_var(simuW.path,'sal')
VARW[ S==0 ] = np.nan
simuE = From1950to2100(option,var,y1,y2,comparison,lat_min,period,'E')
yr, xr, time, depthE, XE = loading.extracting_coord_1D(simuE.path)
VARE = loading.extracting_var(simuE.path, var)
S = loading.extracting_var(simuE.path,'sal')
VARE[ S==0 ] = np.nan
#make_plot.points_on_map(xr,yr,var,area)
index_y1 = np.min(np.where(simuW.first_year+time[:]/(3600*24*364.5)>simuW.y1))
self.output_fileH = str(var) + '_' + str(month) + '_' + str(
self.y1h) + '-' + str(self.y2h) + '_' + str(colorbar) #//
self.output_fileF = str(var) + '_' + str(month) + '_' + str(
self.y1f) + '-' + str(self.y2f) + '_' + str(colorbar) #//
return #//
#//////////////////////////////////////////////////////////////////////////////////////////
simu = From1950to2100(option, var, y1p, y2p, colorbar, basin, month)
yr, xr, time = loading.extracting_coord_2D(simu.path)
time = simu.first_year + time[:] / (3600 * 24 * 364.5)
IceExt = loading.extracting_var(simu.path, var)
#IceExt[np.where(IceExt>1E10)]=np.nan
#IceExt[np.where(IceExt<0)] = np.nan
if basin == 'undefined':
mask = loading.latitudinal_band_mask(yr, lat_min, 90)
else:
mask = loading.Ocean_mask(xr, yr, basin)
index_y1h = np.min(np.where(time > simu.y1h))
index_y1f = np.min(np.where(time > simu.y1f))
#array_to_plotH = np.nanmean(IceExt[:,:,index_y1h:index_y1h+10],axis=2)
#array_to_plotH[array_to_plotH==np.nan] = 0
#array_to_plotF = np.nanmean(IceExt[:,:,index_y1f:index_y1f+10],axis=2)
self.output_fileH = str(var) + str(month) + '_' + str(
self.y1h) + '-' + str(self.y2h) #//
self.output_fileF = str(var) + str(month) + '_' + str(
self.y1f) + '-' + str(self.y2f) #//
return #//
#//////////////////////////////////////////////////////////////////////////////////////////
simu = From1950to2100(option, var, y1p, y2p, month)
yr, xr, time = loading.extracting_coord_2D(simu.path)
time = simu.first_year + time[:] / (3600 * 24 * 364.5)
IceExt = loading.extracting_var(simu.path, var)
index_y1h = np.min(np.where(time > simu.y1h))
index_y1f = np.min(np.where(time > simu.y1f))
array_to_plotH = np.nanmean(IceExt[:, :, index_y1h:index_y1h + 10], axis=2)
array_to_plotH[array_to_plotH == 0] = np.nan
array_to_plotH = np.ma.masked_invalid(array_to_plotH)
array_to_plotF = np.nanmean(IceExt[:, :, index_y1f:index_y1f + 10], axis=2)
array_to_plotF[array_to_plotF == 0] = np.nan
array_to_plotF = np.ma.masked_invalid(array_to_plotF)
IceThick = loading.extracting_var(simu.path_thickness, 'iicethic')
IceThick_to_ploth = np.nanmean(IceThick[:, :, index_y1h:index_y1h + 10],
[box.x0 * 1.2, box.y0 + box.height * 0.3, box.width, box.height * 0.7])
h, l = ax.get_legend_handles_labels()
ax.legend(h, l, bbox_to_anchor=(-.5, -.05, 2, -0.35), loc=9, ncol=1)
plt.savefig(
str(variable_name) + '/' + str(name_file.replace(".", "")) + '.png')
plt.close(fig)
return
simu = yearly_LongPeriod(option, var, y1, y2, basin, lat_min)
yr, xr, time, depth = loading.extracting_coord(simu.path)
VarArray_simuRho = loading.extracting_var(simu.path, var) # Practical Salinity
if var == 'temp':
S = loading.extracting_var(simu.path, 'sal') # Practical Salinity
VarArray_simuRho[np.where(S == 0)] = np.nan
else:
VarArray_simuRho[np.where(VarArray_simuRho == 0)] = np.nan
index_y1 = np.min(
np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > simu.y1h))
index_y2 = np.min(
np.where(simu.first_year + time[:] / (3600 * 24 * 364.5) > simu.y1f))
b1 = 'arctic_ocean'
b2 = 'Siberian_seas'
b3 = 'BS_and_KS'
'''
