diff --git a/config/config_MARIA.json b/config/config_FUMSECK.json
similarity index 57%
rename from config/config_MARIA.json
rename to config/config_FUMSECK.json
index 96db83c..2b393b1 100644
--- a/config/config_MARIA.json
+++ b/config/config_FUMSECK.json
@@ -1,31 +1,33 @@
 {
 "_____":"GENERAL PARAMETERS",
 
-"date_ini":"2021-12-11",
-"date_end":"2021-12-12",
-"outdir":"/path/to/OUTPUT_SMP",
-"diagdir":"/path/to/diags/dir",
-"river_ini":"2015-01-03",
-"river_end":"2015-05-01",
+"date_ini":"2019-05-01",
+"date_end":"2019-05-15",
+"outdir":"/DISK2TB/MODEL/FUMSECK",
+"diagdir":"/DISK2TB/MODEL/DIAGS",
 
 
 "_____":"RIVER PARAMETERS",
 
 "french_list":["Argens","Var"],
-"italian_list":["Arno","Magra","Ombrone","Serchio"],
+"italian_list":["Arno","Magra","Magra","Serchio","Ombrone"],
+"river_ini":"2019-01-01",
+"river_end":"2019-12-31",
 
 
 "_____":"METEO PARAMETERS",
-"meteo_ini":"2015-03-10",
-"meteo_end":"2015-03-11",
+
+"meteo_ini":"2015-03-31",
+"meteo_end":"2015-05-30",
 "day_cycle":2,
-"n_cycle":6,
+"n_cycle":15,
 "imin":"500",
 "imax":"600",
 "jmin":"350",
 "jmax":"450",
 
-
+"_____":"MODIS PARAMETERS",
+"modis_dir":"/DISK2TB/DATA/NWMED/MODIS",
 
 "_____":"DYFAMED PARAMETERS",
 
@@ -35,8 +37,15 @@
 
 "some_par":"some_par",
 
+"_____":"CMEMS",
+
+
+"_____":"ARGO",
 
-"_____":"2D PLOT PARAMETERS",
+"argo_ds":"phy",
+
+
+"_____":"PLOT PARAMETERS",
 
 "resol":"10m",                       
 "fig_proj":"ccrs.PlateCarree()",      
@@ -44,22 +53,25 @@
 "fig_sy":"8",                        
 "fig_fmt":"jpg",                     
 "fig_res":"300",                  
-"tight":"False",                  
+"tight":"True",                  
 "fig_tck_size":"10",
 "fig_tcklbl_size":"10",
 "fig_lbl_size":"10",
 "cb_fraction_2D":"0.02",
 "cb_pad_2D":"0.12",
+"with_currents":"False",
+"with_whales":"False",
 
 "_____":"SECTION PLOT PARAMETERS",
 
+
 "fig_secx":"8",                         
 "fig_secy":"4",                        
-"sec_lon1":"8.5",
+"sec_lon1":"9",
 "sec_lat1":"43.",
-"sec_lon2":"10.",
-"sec_lat2":"43.5",
-"sec_dep":"600",
+"sec_lon2":"9.7",
+"sec_lat2":"44",
+"sec_dep":"300",
 "sec_resH":"0.05",  
 "sec_resV":"1",
 "cb_fraction_sec":"0.025",
@@ -67,15 +79,16 @@
 
 "_____":"PROFILE PLOT PARAMETERS",
 
-"fig_prox":"8",                         
-"fig_proy":"4",                        
-"col1":"r",
-"col2":"b",
+"fig_prox":"6",
+"fig_proy":"11",
+"col_bcg":["g","b","r"],
+"col_phy":["b","r"],
 
 
 "_____":"INTERPOLATION PARAMETERS",
 
 "dump":"0.01",
-"itp_meth":"nearest"
+"itp_meth":"linear",
+"cmap":"viridis"
 
 }
diff --git a/config/variables_mit_FUMSECK.dat b/config/variables_mit_FUMSECK.dat
new file mode 100644
index 0000000..be0ba5a
--- /dev/null
+++ b/config/variables_mit_FUMSECK.dat
@@ -0,0 +1,18 @@
+# Name tag cmap log vlim vmin vmax label units
+# ---------------------------------------------
+nppv BIOL jet False set 0. 1. lab un
+o2 BIOL jet False set  0. 1.  lab un
+ph CARB jet False set 0. 1. lab un
+talk CARB jet False set 0. 1. lab un
+spco2 CO2F jet False set 0. 1. lab un
+nh4 NUTR jet False set 0. 1. NH4 $mmol.m^{-3}$
+no3 NUTR cmc.hawaii_r False set 0.5 4. NO3 $mmol.m^{-3}$
+po4 NUTR cmc.batlow False set 0. 0.3 PO4 $mmol.m^{-3}$
+si NUTR jet False set 0. 1. SI $mmol.m^{-3}$
+chl PFTC cmc.imola True set 0.05 0.3 Chlorophyll $mg.m^{-3}$
+phyc PFTC jet False set 0. 1. Phytoplankton $mmol.m^{-3}$
+zooc PFTC cmc.hawaii_r False set 1. 3. Zooplankton $mg.m^{-3}$
+so PSAL jet False set 0. 1. Salinity none 
+uo RFVL cmc.vik False set -.3 .3 Eastward_current $m.s^{-1}$
+vo RFVL jet False set 0. 1. Northward_current $m.s^{-1}$
+thetao TEMP cmo.thermal False set 13.5 14.8 Temperature °C
diff --git a/config/variables_mit_TEMPLATE.dat b/config/variables_mit_TEMPLATE.dat
index 847a0f8..3cbff5c 100644
--- a/config/variables_mit_TEMPLATE.dat
+++ b/config/variables_mit_TEMPLATE.dat
@@ -9,10 +9,10 @@ nh4 NUTR jet False set 0. 1. NH4 $mmol.m^{-3}$
 no3 NUTR cmc.hawaii_r False set 0.5 4. NO3 $mmol.m^{-3}$
 po4 NUTR cmc.batlow False set 0. 0.3 PO4 $mmol.m^{-3}$
 si NUTR jet False set 0. 1. SI $mmol.m^{-3}$
-chl PFTC cmc.imola True set 0.05 0.25 Chlorophyll $mg.m^{-3}$
+chl PFTC cmc.imola True set 0.05 0.5 Chlorophyll $mg.m^{-3}$
 phyc PFTC jet False set 0. 1. Phytoplankton $mmol.m^{-3}$
-zooc PFTC jet False set 0. 1.2 Zooplankton $mmol.m^{-3}$
+zooc PFTC cmc.broc False set 0. 3. Zooplankton $mg.m^{-3}$
 so PSAL jet False set 0. 1. Salinity none 
-uo RFVL jet False set 0. 1. Eastward_current $m.s^{-1}$
+uo RFVL cmc.vik False set -.3 .3 Eastward_current $m.s^{-1}$
 vo RFVL jet False set 0. 1. Northward_current $m.s^{-1}$
-thetao TEMP cmo.thermal False set 13. 16.5 Temperature °C
+thetao TEMP cmo.thermal False set 13.5 14.8 Temperature °C
diff --git a/datasets/ARGO/compare_argo.py b/datasets/ARGO/compare_argo.py
index d901523..6cb27d5 100644
--- a/datasets/ARGO/compare_argo.py
+++ b/datasets/ARGO/compare_argo.py
@@ -117,8 +117,10 @@
         = load_variable(config,mod_var)
 
     # Convert date
-    dstr = str(time[idx][0]).replace('.000000000','')
-    date = dt.datetime.strptime(dstr,'%Y-%m-%dT%H:%M:%S')
+    dstr = str(time[idx][0])
+    dstr = dstr.split(':')[0]
+
+    date = dt.datetime.strptime(dstr,'%Y-%m-%dT%H')
     jd = date.toordinal()
  
     hours,mnt = date.strftime('%H'),date.strftime('%M')
@@ -138,6 +140,7 @@
                  data_lon,data_lat,data_pres)
     except Exception as e:
       print('Interpolation failed')
+      print(e)
       val = data_pres
       val[:] = np.nan
 
diff --git a/datasets/BOUSSOLE/data/.~lock.Boussole-pig-maj20240116_modif.csv# b/datasets/BOUSSOLE/data/.~lock.Boussole-pig-maj20240116_modif.csv#
deleted file mode 100644
index e1c68d2..0000000
--- a/datasets/BOUSSOLE/data/.~lock.Boussole-pig-maj20240116_modif.csv#
+++ /dev/null
@@ -1 +0,0 @@
-,fontana,fontana,21.03.2024 15:12,file:///home/fontana/.config/libreoffice/4;
\ No newline at end of file
diff --git a/datasets/CMEMS/get_data.py b/datasets/CMEMS/get_data.py
new file mode 100644
index 0000000..72966e0
--- /dev/null
+++ b/datasets/CMEMS/get_data.py
@@ -0,0 +1,6 @@
+import pandas as pd
+
+
+    
+
+
diff --git a/datasets/CMEMS/motu_downloader.json b/datasets/CMEMS/motu_downloader.json
deleted file mode 100644
index 90775b8..0000000
--- a/datasets/CMEMS/motu_downloader.json
+++ /dev/null
@@ -1,50 +0,0 @@
-{
-	"motu_user": "XXXXXX",
-	"motu_password": "XXXXX",
-	"domain_lat_min": 41.88515625,
-	"domain_lat_max": 44.50234375,
-	"domain_lon_min": 6.06640625,
-	"domain_lon_max": 12.18359375,
-        "domain_depth_min": 0.0,
-        "domain_depth_max": 2900.0,
-	"days_before": 0,
-    	"days_after": 2,
-	"motu_server": "http://nrt.cmems-du.eu/motu-web/Motu",
-	"services": [
-		{
-			"name": "MEDSEA_ANALYSISFORECAST_PHY_006_013",
-			"products": [
-				{
-					"name": "cmems_mod_med_phy-cur_anfc_4.2km_P1D-m",
-					"variables": ["uo", "vo"]
-				},
-				{
-					"name": "cmems_mod_med_phy-sal_anfc_4.2km_P1D-m",
-					"variables": ["so"]
-				},
-				{
-					"name": "cmems_mod_med_phy-tem_anfc_4.2km_P1D-m",
-					"variables": ["thetao"]
-				}
-			]
-		},
-		{
-			"name": "MEDSEA_ANALYSISFORECAST_BGC_006_014",
-			"products": [
-				{
-					"name": "cmems_mod_med_bgc-nut_anfc_4.2km_P1D-m",
-					"variables": ["no3", "po4","nh4", "si"]
-				},
-				{
-					"name": "cmems_mod_med_bgc-bio_anfc_4.2km_P1D-m",
-					"variables": ["o2"]
-				},
-				{
-					"name": "cmems_mod_med_bgc-car_anfc_4.2km_P1D-m",
-					"variables": ["dissic","talk"]
-				}
-			]
-		}
-	]
-}
-
diff --git a/datasets/NASA/.interp_modis.py.swp b/datasets/NASA/.interp_modis.py.swp
new file mode 100644
index 0000000..947699a
Binary files /dev/null and b/datasets/NASA/.interp_modis.py.swp differ
diff --git a/datasets/NASA/get_modis.py b/datasets/NASA/get_modis.py
new file mode 100644
index 0000000..6787775
--- /dev/null
+++ b/datasets/NASA/get_modis.py
@@ -0,0 +1,38 @@
+#############################
+# Routine to get MODIS data #
+#############################
+
+
+import numpy as np
+import os
+
+
+# Get args
+# --------
+config=sys.argv[1]  # Configuration name
+
+
+# Load parameters
+# ---------------
+load_config(config)
+from fun_gen import *
+
+os.system('mkdir -p '+modis_dir)
+
+
+# Loop on days
+for jd in range(jdini,jdend+1):
+ 
+    # Get date
+    d = dt.datetime.fromordinal(jd)
+    year = d.strftime('%Y')
+    doy = d.strftime('%j').zfill(3)
+
+    # Download
+    # !!!! TO FINISH !!!!!
+
+
+
+
+
+
diff --git a/datasets/NASA/interp_modis.py b/datasets/NASA/interp_modis.py
new file mode 100644
index 0000000..0208904
--- /dev/null
+++ b/datasets/NASA/interp_modis.py
@@ -0,0 +1,161 @@
+import warnings
+warnings.simplefilter("ignore")
+
+from fun_gen import *
+from fun_io import *
+from fun_plot_2D import *
+import sys,os
+from glob import glob
+
+import numpy as np
+from numpy import array as npa
+from scipy.interpolate import griddata
+import datetime as dt
+from netCDF4 import Dataset
+
+import cartopy.feature as cfeature
+import cartopy.crs as ccrs
+from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
+import matplotlib.colors as colors
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use("Agg")
+
+import cmocean
+import cmcrameri
+
+
+# Get args
+# --------
+config=sys.argv[1]  # Configuration name
+
+
+# Load parameters
+# ---------------
+load_config(config)
+from fun_gen import *
+
+os.system('mkdir -p '+modis_dir)
+
+
+# Initialize plot
+# ---------------
+
+# Load lon/lat
+# ------------
+lon_mod,lat_mod,levels = load_coords()
+
+# Load proj
+# ---------
+exec('proj = ' + fig_proj)
+
+# Init figure
+# -----------
+lon_min,lon_max = lon_mod.min(),lon_mod.max()
+lat_min,lat_max = lat_mod.min(),lat_mod.max()
+
+extent = [lon_min,lon_max,lat_min,lat_max]
+fig, ax = plt.subplots(1,1,figsize=(float(fig_sx), float(fig_sy)), subplot_kw={'projection': proj})
+init_fig(ax,extent,proj)
+
+# Loop on tags
+# ------------
+#for tag in ['OC','SST','KD490']: 
+for tag in ['KD490']: 
+
+  print('Processing',tag,'from',date_ini,'to',date_end)
+
+  if tag == 'OC':
+     var = 'chlor_a'
+     long_name = 'Chlorophyll a'
+     units = 'mg.m**-3'
+     ftag = 'OC'
+
+  elif tag == 'SST':
+     var = 'sst'
+     long_name = 'Sea Surface Temperature'
+     units = 'degC'
+     ftag = 'OC'
+
+  elif tag == 'KD490':
+     var = 'Kd_490'
+     long_name = 'Attenuation coefficient'
+     units = 'm**-1'
+     ftag = 'SST'
+
+  # Create output directory
+  os.system('mkdir -p '+modis_dir+'/'+tag)
+  os.system('mkdir -p '+modis_dir+'/'+tag+'/ITP_NC')
+
+  # Loop on days
+  # ------------
+  for jd in range(jdini,jdend+1):
+
+
+     d = dt.datetime.fromordinal(jd)   
+     dstr = d.strftime('%Y%m%d')
+
+  
+     # Search for data
+     flist = glob(modis_dir+'/'+ftag+'/*'+dstr+'*'+ftag+'*')
+
+     # Load data
+     fdata,flon,flat,fqual = [],[],[],[]
+     for f in flist:
+        ds = Dataset(f,format='NETCDF4-CLASSIC')
+ 
+        if tag == 'SST':
+          fqual = ds['geophysical_data/qual_sst'][:,:]
+
+        tmp = ds['geophysical_data/'+var][:,:]
+
+        try : 
+          tmp[np.where(fqual != 0)] = -9999
+        except:
+          pass
+
+        fdata.append(tmp)
+           
+        flat.append(ds['navigation_data/latitude'][:])
+        flon.append(ds['navigation_data/longitude'][:])
+        ds.close()
+
+
+     data = npa([])
+     lon = npa([])
+     lat = npa([])
+
+     # Concatenate data
+     for i in range(0,len(flist)):
+        data = np.concatenate((data,npa(fdata[i]).flatten()))
+        lon = np.concatenate((lon,npa(flon[i]).flatten()))
+        lat = np.concatenate((lat,npa(flat[i]).flatten()))
+
+
+     # Interpolate on model grid
+     LON,LAT = np.meshgrid(lon_mod,lat_mod)
+
+
+     data[ np.where(data < -999)] = np.nan
+     idx_lon = npa( np.where(lon > lon_min-1) and np.where(lon < lon_max+1)).squeeze()
+     idx_lat = npa( np.where(lat > lat_min-1) and np.where(lat < lat_max+1)).squeeze()
+
+     idx = np.concatenate((npa(idx_lon),idx_lat))
+     data,lon,lat = data[idx],lon[idx],lat[idx]
+
+     try:
+       idata = griddata((lon,lat),data,(LON,LAT),method='linear')
+
+       dori = dt.datetime(1900,1,1).toordinal()
+       time = np.array([(jd - dori )*24])
+
+       # Save NetCDF
+       fname = modis_dir+'/'+tag+'/ITP_NC/'+var+'_'+dstr+'.nc'
+       write_nc(fname,time,lon_mod,lat_mod,var,idata,long_name,units)
+
+     except Exception as e:
+       print('INTERP FAILED')
+       print(e)
+
+
+
diff --git a/datasets/RIVERS/.compare_rivers.py.swp b/datasets/RIVERS/.compare_rivers.py.swp
new file mode 100644
index 0000000..3fad130
Binary files /dev/null and b/datasets/RIVERS/.compare_rivers.py.swp differ
diff --git a/datasets/RIVERS/compare_rivers.py b/datasets/RIVERS/compare_rivers.py
new file mode 100644
index 0000000..a0e6f38
--- /dev/null
+++ b/datasets/RIVERS/compare_rivers.py
@@ -0,0 +1,105 @@
+####################################
+# Compare computed runoffs to data #
+####################################
+
+import warnings
+warnings.simplefilter("ignore")
+import numpy as np
+from datetime import datetime
+from fun_gen import load_config
+import sys,os
+import matplotlib.pyplot as plt
+import matplotlib
+import matplotlib.dates as mdates
+matplotlib.use("Agg")
+
+from fun_io import savefig
+
+
+
+# Get args
+# --------
+config = sys.argv[1]  # Configuration name
+
+# Load config file
+# ----------------
+load_config(config)
+from fun_gen import *
+
+
+# Convert river dates
+# -------------------
+rdini = datetime.strptime(river_ini,'%Y-%m-%d').toordinal()
+rdend = datetime.strptime(river_end,'%Y-%m-%d').toordinal()
+
+# Create arborescence
+os.system('mkdir -p '+diagdir+'/'+config)
+odir = diagdir+'/'+config+'/RIVERS'
+os.system('mkdir -p '+odir)
+
+
+# Loop on  rivers
+# ---------------
+for name in italian_list:
+
+  data = []
+  comp = []
+
+  # DATA
+  # ----
+  raw=np.loadtxt('data/'+name+'.csv',skiprows=19,dtype=str)
+  print('[FILE READ]','data/'+name+'.csv')
+
+  # Loop on data
+  for line in raw:
+
+     line = line.split(',')
+
+     jd = datetime.strptime(line[0],'%d/%m/%Y').toordinal()
+     if jd >= rdini and jd <= rdend:
+       try:
+         exec('runoff = '+line[1]+'.'+line[2])
+         data.append([jd,runoff])
+       except:
+         pass
+
+
+  # COMPUTED
+  # --------
+  raw=np.loadtxt('computed/'+name+'.txt',skiprows=1,dtype=str)
+  print('[FILE READ]','computed/'+name+'.txt')
+
+   
+  # Loop on data
+  for line in raw:
+
+     jd = datetime.strptime(line[0],'%Y%m%dT00:00:00.000Z').toordinal()
+     comp.append([jd,line[1]])
+
+
+  data = np.array(data,dtype=float)
+  comp = np.array(comp,dtype=float)
+
+
+  fig, ax = plt.subplots(1,1,figsize=(6,6))
+
+  plt.plot(data[:,0],data[:,1],'ro',markersize=2,label='Data')
+  plt.plot(comp[:,0],comp[:,1],'b-',linewidth=0.5,label='Computed')
+  plt.plot(comp[:,0],comp[:,1],'bo',markersize=.55)
+
+  plt.legend()
+
+  loc = mdates.AutoDateLocator()
+  ax.xaxis.set_major_locator(loc)
+  ax.xaxis.set_major_formatter(mdates.ConciseDateFormatter(loc))
+
+  fname = odir +'/'+name+'.jpg'
+  savefig(fname)
+
+  plt.close()
+
+
+
+
+
+
diff --git a/datasets/RIVERS/computed/Argens.txt b/datasets/RIVERS/computed/Argens.txt
new file mode 100644
index 0000000..6087482
--- /dev/null
+++ b/datasets/RIVERS/computed/Argens.txt
@@ -0,0 +1,365 @@
+20190102T00:00:00.000Z 0.12695
+20190103T00:00:00.000Z 0.11572
+20190104T00:00:00.000Z 0.10889
+20190105T00:00:00.000Z 0.104
+20190106T00:00:00.000Z 0.10254
+20190107T00:00:00.000Z 0.10254
+20190108T00:00:00.000Z 0.098633
+20190109T00:00:00.000Z 0.095215
+20190110T00:00:00.000Z 0.091797
+20190111T00:00:00.000Z 0.09082
+20190112T00:00:00.000Z 0.091797
+20190113T00:00:00.000Z 0.090332
+20190114T00:00:00.000Z 0.087891
+20190115T00:00:00.000Z 0.080811
+20190116T00:00:00.000Z 0.081787
+20190117T00:00:00.000Z 0.086182
+20190118T00:00:00.000Z 0.17554
+20190119T00:00:00.000Z 19.2555
+20190120T00:00:00.000Z 10.0543
+20190121T00:00:00.000Z 16.5886
+20190122T00:00:00.000Z 9.1172
+20190123T00:00:00.000Z 4.4384
+20190124T00:00:00.000Z 3.0645
+20190125T00:00:00.000Z 9.014
+20190126T00:00:00.000Z 4.9855
+20190127T00:00:00.000Z 2.4226
+20190128T00:00:00.000Z 1.0906
+20190129T00:00:00.000Z 0.63135
+20190130T00:00:00.000Z 0.63916
+20190131T00:00:00.000Z 0.52832
+20190201T00:00:00.000Z 8.6044
+20190202T00:00:00.000Z 63.9486
+20190203T00:00:00.000Z 204.3887
+20190204T00:00:00.000Z 148.8101
+20190205T00:00:00.000Z 84.1565
+20190206T00:00:00.000Z 46.5206
+20190207T00:00:00.000Z 25.3022
+20190208T00:00:00.000Z 13.1921
+20190209T00:00:00.000Z 5.9994
+20190210T00:00:00.000Z 2.3003
+20190211T00:00:00.000Z 0.85547
+20190212T00:00:00.000Z 0.58154
+20190213T00:00:00.000Z 1.687
+20190214T00:00:00.000Z 0.85059
+20190215T00:00:00.000Z 0.35791
+20190216T00:00:00.000Z 0.16406
+20190217T00:00:00.000Z 0.12158
+20190218T00:00:00.000Z 0.10645
+20190219T00:00:00.000Z 0.099121
+20190220T00:00:00.000Z 0.095703
+20190221T00:00:00.000Z 0.09668
+20190222T00:00:00.000Z 0.097656
+20190223T00:00:00.000Z 0.1001
+20190224T00:00:00.000Z 0.097656
+20190225T00:00:00.000Z 0.095215
+20190226T00:00:00.000Z 0.092285
+20190227T00:00:00.000Z 0.090332
+20190228T00:00:00.000Z 0.09082
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diff --git a/datasets/RIVERS/computed/Arno.txt b/datasets/RIVERS/computed/Arno.txt
new file mode 100644
index 0000000..574cf00
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diff --git a/datasets/RIVERS/computed/Magra.txt b/datasets/RIVERS/computed/Magra.txt
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diff --git a/datasets/RIVERS/computed/Ombrone.txt b/datasets/RIVERS/computed/Ombrone.txt
new file mode 100644
index 0000000..ad5a2f0
--- /dev/null
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diff --git a/datasets/RIVERS/computed/Roia.txt b/datasets/RIVERS/computed/Roia.txt
new file mode 100644
index 0000000..5d9d0f2
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diff --git a/datasets/RIVERS/computed/Serchio.txt b/datasets/RIVERS/computed/Serchio.txt
new file mode 100644
index 0000000..787b790
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diff --git a/datasets/RIVERS/computed/Var.txt b/datasets/RIVERS/computed/Var.txt
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diff --git a/datasets/RIVERS/test.txt b/datasets/RIVERS/test.txt
new file mode 100644
index 0000000..320124c
--- /dev/null
+++ b/datasets/RIVERS/test.txt
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diff --git a/modules/__pycache__/fun_io.cpython-310.pyc b/modules/__pycache__/fun_io.cpython-310.pyc
index 4d59a8f..266ccad 100644
Binary files a/modules/__pycache__/fun_io.cpython-310.pyc and b/modules/__pycache__/fun_io.cpython-310.pyc differ
diff --git a/modules/fun_io.py b/modules/fun_io.py
index 32015ec..403edbd 100644
--- a/modules/fun_io.py
+++ b/modules/fun_io.py
@@ -61,11 +61,11 @@ def load_coords():
 # ----------------- #
 # Load 2D variables #
 # ----------------- #
-def get_var_2D(fname,var,lev):
+def get_var_2D(fname,var,hour,lev):
 
   try :
     ds = xr.open_dataset(fname)
-    arr = ds[var][23,int(lev),:,:].squeeze()
+    arr = ds[var][hour,int(lev),:,:].squeeze()
     ds.close()
 
   except Exception as e :
@@ -78,22 +78,26 @@ def get_var_2D(fname,var,lev):
 # ----------------- #
 def get_var_3D(fname,hours,var,**kargs):
 
-  dmn = kargs['domain']
+  try :
+
+    dmn = kargs['domain']
 
-  idz = np.array(dmn[0][0]) 
-  idy = np.array(dmn[1][0]) 
-  idx = np.array(dmn[2][0]) 
+    idz = np.array(dmn[0][0]) 
+    idy = np.array(dmn[1][0]) 
+    idx = np.array(dmn[2][0]) 
+  except:
+    pass
 
   try:
 
     ds = xr.open_dataset(fname)
 
     # Reduce domain
-    if dmn:
+    try:
       arr = ds[var][hours,idz,idy,idx]
       arr = arr.squeeze()
 
-    else:
+    except:
       arr = ds[var][hours,:,:,:].squeeze()
 
     ds.close()
@@ -217,6 +221,49 @@ def load_moose(year,var):
 
 
   return jdlist,depth,lon,lat,data
+
+
+
+def write_nc(fname,time,lon,lat,var,data,long_name,units):
+
+
+  # Open file
+  dataset = Dataset(fname,'w',format='NETCDF4_CLASSIC')
+
+  # Create dimension
+  dlon = dataset.createDimension('longitude',lon.shape[0])
+  dlat = dataset.createDimension('latitude',lat.shape[0])
+  dlat = dataset.createDimension('time',1)
+
+  # Create variables & set attributes
+  vlon = dataset.createVariable('longitude',np.float32,('longitude'))
+  vlon.units = "degrees east"
+  vlon.long_name = "longitude"
+  vlon[:] = lon
+
+  vlat = dataset.createVariable('latitude',np.float32,('latitude'))
+  vlat.units = "degrees north"
+  vlat.long_name = "latitude"
+  vlat[:] = lat
+
+  vtime = dataset.createVariable('time',np.float32,('time'))
+  vtime.units = "hours since 1900-01-01 00:00:00"
+  vtime.calendar = "gregorian"
+
+  vtime[:] = time
+
+
+  # Write variable to NetCDF
+  # ------------------------
+  vdata = dataset.createVariable(var,np.float32,('time','latitude','longitude'),fill_value=-9999)
+  vdata.units = units
+  vdata.long_name = long_name
+  vdata[:] = data
+
+  print('')
+  print('[FILE SAVED] '+fname+'\n')
+  dataset.close()
+
   
 
 
diff --git a/plot/make_plot_2D.ksh b/plot/make_plot_2D.ksh
index c1e5f5c..668604f 100755
--- a/plot/make_plot_2D.ksh
+++ b/plot/make_plot_2D.ksh
@@ -7,8 +7,8 @@ config=$1
 #varlist={'so','thetao'}
 #levlist={'0','1'}
 
-varlist='chl'
-levlist='0'
+varlist={'chl','thetao','no3','po4'}
+levlist='8'
 
 
 for var in $varlist; do
diff --git a/plot/make_plot_2D.py b/plot/make_plot_2D.py
index 873e36b..cb9f871 100644
--- a/plot/make_plot_2D.py
+++ b/plot/make_plot_2D.py
@@ -1,6 +1,9 @@
 # --------------------- #
 #  2D plotting function #
 # --------------------- #
+import warnings
+warnings.simplefilter("ignore")
+
 from fun_gen import *
 from fun_io import *
 from fun_plot_2D import *
@@ -8,6 +11,7 @@
 from glob import glob
 
 import numpy as np
+from numpy import array as npa
 import xarray as xr
 import datetime as dt
 
@@ -16,6 +20,9 @@
 from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
 import matplotlib.colors as colors
 import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use("Agg")
+
 import cmocean
 import cmcrameri
 
@@ -33,10 +40,43 @@
 load_config(config)
 from fun_gen import *
 
+# Load whales data
+# ----------------
+if with_whales:
+
+  # Get whale position
+  dtrack=np.loadtxt('/home/fontana/SONG/SICILIA/87780.dat')
+
+  mon=dtrack[:,5].squeeze()
+  day=dtrack[:,4].squeeze()
+  year=dtrack[:,6].squeeze()
+
+  mon=np.array(mon,dtype=int)
+  day=np.array(day,dtype=int)
+  year=np.array(year,dtype=int)
+
+  # Compute julian day
+  jd_w = []
+  for i in range(0,year.shape[0]):
+     d = dt.datetime(year[i],mon[i],day[i])
+     jd_w.append(d.toordinal())
+
+  # Compute mean position
+  tlon = []
+  tlat = []
+  tjd = []
+
+  for jd in np.unique(jd_w):
+     
+     idx = np.where( jd_w == jd)
+     tlon.append(np.mean(dtrack[idx,1]))
+     tlat.append(np.mean(dtrack[idx,0]))
+     tjd.append(jd)
+
+tlon = np.array(tlon)
+tlat = np.array(tlat)
+tjd = np.array(tjd)
 
-# Get current variable parameters
-vname, ftag, cmap, islog, vmod, vmin, vmax, label, units\
-        = load_variable(config,var)
 
 # Create diagnostic arborescence
 vdir = create_arbo(config,var,'PLOT_2D')
@@ -54,6 +94,7 @@
 # Load proj
 # ---------
 exec('proj = ' + fig_proj)
+cb_done = 'False'
 
 # Init figure
 # -----------
@@ -67,11 +108,19 @@
 print('Processing')
 for jd in range(jdini,jdend+1):
 
+ #for hour in range(12,13):
+ for hour in range(0,24):
+   # Get current variable parameters
+   vname, ftag, cmap, islog, vmod, vmin, vmax, label, units\
+        = load_variable(config,var)
+
    # Get filename
    fname,dtag = get_filename(jd,ftag)
 
    # Get 2D variable
-   var2d = get_var_2D(fname,var,lev)
+   var2d = get_var_2D(fname,var,hour,lev)
+   var2d = np.array(var2d)
+   var2d[np.where(var2d > 999) ] = np.nan
 
    # Plot
    if vmod == 'auto' :
@@ -85,9 +134,56 @@
       else:
         p = plt.pcolor(lon,lat,var2d,cmap=cmap,norm=colors.LogNorm(vmin=vmin,vmax=vmax),zorder=1)
 
-      if jd == jdini: # plot cb only once
+      if cb_done == 'False': # plot cb only once
         cb = plt.colorbar(extend='both',fraction=float(cb_fraction_2D),pad=float(cb_pad_2D),\
                 label=label+' ('+units+')')
+        cb_done = 'True'
+
+
+   # Plot currents
+   if with_currents == 'True':
+
+   #if 1 == 2:
+     vname, ftag, cmap, islog, vmod, vmin, vmax, label, units\
+           = load_variable(config,'uo')
+
+     # Get filename
+     fname,dtag = get_filename(jd,ftag)
+
+     # Get 2D variable
+     u = get_var_2D(fname,'uo',hour,0)
+     v = get_var_2D(fname,'vo',hour,0)
+
+     sub = 10
+
+     lons,lats = lon[::sub],lat[::sub]
+     us,vs = npa(u[::sub,::sub]),npa(v[::sub,::sub])
+
+     norm = np.sqrt(np.square(us)+np.square(vs))
+
+     us[np.where(norm < 0.1)] = np.nan
+     vs[np.where(norm < 0.1)] = np.nan
+
+     q = ax.quiver(lons,lats,us,vs,color='k',edgecolor='k',linewidth=.2,scale=18,zorder=4,width=0.002)
+
+   # Plot whales
+   if with_whales == 'True':
+     # Plot today position
+     idx = np.where( tjd == jd )
+     tw1 = plt.scatter(tlon[idx],tlat[idx],marker='o',color='r',s=56,transform=ccrs.PlateCarree(),zorder=9)
+
+     # Plot last days position
+     idx = np.where( tjd-jd >= -3 ) and np.where(tjd-jd <= 0)
+     tw2 = ax.plot(tlon[idx],tlat[idx],'r-')
+
+
+
+   # Title
+   date = dt.datetime.fromordinal(jd)
+   y,m,d = date.strftime('%Y'),date.strftime('%m'),date.strftime('%d')
+
+   title = y+'-'+m+'-'+d+' '+str(hour).zfill(2)+'h'
+   plt.title(title)
 
    # Save file
    # ---------
@@ -95,11 +191,19 @@
 
    plt.draw()
 
-   fout = savedir+'/'+dtag+'_'+var+'.'+fig_fmt
+   fout = savedir+'/'+dtag+str(hour).zfill(2)+'_'+var+'.'+fig_fmt
    savefig(fout)
 
    p.remove()
 
+   if with_currents == 'True':
+     q.remove()
+   
+   if with_whales == 'True':
+     ax.lines[0].remove()
+     tw1.remove()
+
+
 
 plt.close()
 
diff --git a/plot/make_plot_section.py b/plot/make_plot_section.py
index 32a88be..d6e2171 100644
--- a/plot/make_plot_section.py
+++ b/plot/make_plot_section.py
@@ -19,6 +19,9 @@
 from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
 import matplotlib.colors as colors
 import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use("Agg")
+
 from cmcrameri import cm
 import cmocean
 
@@ -125,47 +128,55 @@
 # Loop on files
 # -------------
 print('\nProcessing')
-for jd in range(jdini+1,jdend+1):
+for jd in range(jdini,jdend+1):
 
-  # Get var
-  fname,dtag = get_filename(jd,ftag)
-  var3d = get_var_3D(fname,var)
+  for hour in range(0,1):  
+    # Get var
+    fname,dtag = get_filename(jd,ftag)
+    var3d = get_var_3D(fname,hour,var)
 
-  if ori == "horizontal":
-    sec = var3d[:,idfix,idmin:idmax]
-  else:
-    sec = var3d[:,idmin:idmax,idfix]
+    if ori == "horizontal":
+      sec = var3d[:,idfix,idmin:idmax]
+    else:
+      sec = var3d[:,idmin:idmax,idfix]
 
-  fig, ax = plt.subplots(1,1,figsize=(float(fig_secx), float(fig_secy)))
-  sec = np.flip(sec)
-  sec = np.fliplr(sec)
+    fig, ax = plt.subplots(1,1,figsize=(float(fig_secx), float(fig_secy)))
+    sec = np.flip(sec)
+    sec = np.fliplr(sec)
 
-  # Interpolate data
-  X,Y = np.meshgrid(coord[idmin:idmax],levels)  
-  X,Y = X.flatten(),Y.flatten()
-  sec = sec.flatten()
+    # Interpolate data
+    X,Y = np.meshgrid(coord[idmin:idmax],levels)  
+    X,Y = X.flatten(),Y.flatten()
+    sec = sec.flatten()
 
-  sec[sec > 10000]=np.nan
+    sec[sec > 10000]=np.nan
 
-  XI,YI = np.meshgrid(icoord,ilev)
-  isec = gd((X,Y),sec,(XI,YI))
+    XI,YI = np.meshgrid(icoord,ilev)
+    isec = gd((X,Y),sec,(XI,YI))
 
 
-  p = plt.pcolor(XI,YI,isec,cmap=cmap,vmin=vmin,vmax=vmax)
-  cb= plt.colorbar(p,extend='both',fraction=float(cb_fraction_sec),\
+    p = plt.pcolor(XI,YI,isec,cmap=cmap,vmin=vmin,vmax=vmax)
+    cb= plt.colorbar(p,extend='both',fraction=float(cb_fraction_sec),\
           pad=float(cb_pad_sec),label=label+' ('+units+')')
 
 
-  plt.ylabel("Depth (m)")
+    plt.ylabel("Depth (m)")
 
-  if ori == 'horizontal':
-    plt.xlabel("Longitude (°)")
-  else:
-    plt.xlabel("Latitude (°)")
+    if ori == 'horizontal':
+      plt.xlabel("Longitude (°)")
+    else:
+      plt.xlabel("Latitude (°)")
 
-  savefig( odir+'/'+dtag+'_'+var+'.'+fig_fmt)
-  #plt.show()
-  plt.close()
+    # Title
+    date = dt.datetime.fromordinal(jd)
+    y,m,d = date.strftime('%Y'),date.strftime('%m'),date.strftime('%d')
+
+    title = y+'-'+m+'-'+d+' '+str(hour).zfill(2)+'h'
+    plt.title(title)
+
+    savefig( odir+'/'+dtag+str(hour).zfill(2)+'_'+var+'.'+fig_fmt)
+    #plt.show()
+    plt.close()
 
 
 
diff --git a/tools/compute_okubo.py b/tools/compute_okubo.py
new file mode 100644
index 0000000..eaeffe5
--- /dev/null
+++ b/tools/compute_okubo.py
@@ -0,0 +1,128 @@
+###########################################
+# Routine to compute Okubo Weis parameter #
+###########################################
+#   ^
+#  /!\ THIS ROUTINE DOES NOT USE fun_io FUNCTIONS
+# / ! \  PROBLEM OF COMPATIBILITY, NEED A VIRTUAL ENV
+# -----
+
+from matplotlib import pyplot as plt
+from numpy import arange, ma, where, array
+from fun_gen import *
+from fun_plot_2D import *
+#from fun_io import *
+from netCDF4 import Dataset
+
+from py_eddy_tracker import data
+from py_eddy_tracker.dataset.grid import RegularGridDataset
+from py_eddy_tracker.observations.observation import EddiesObservations
+
+import datetime as dt
+from glob import glob
+import sys
+
+
+# Get args
+# --------
+config = sys.argv[1]  # Configuration name
+
+
+# Load config file
+# ----------------
+load_config(config)
+from fun_gen import *
+
+
+# Create arborescence
+# -------------------
+os.system('mkdir -p '+diagdir)
+os.system('mkdir -p '+diagdir+'/'+config)
+savedir = diagdir+'/'+config+'/OKUBO'
+os.system('mkdir -p '+savedir)
+
+
+for jd in range(jdini,jdend+1):
+
+   for hour in range(0,1):
+
+    try:
+      d = dt.datetime.fromordinal(jd)
+      dtag = d.strftime('%Y%m%d')
+
+
+      fname = glob(outdir+'/'+dtag+'*RFVL*')[0]
+
+      g = RegularGridDataset(fname,"longitude","latitude")
+
+      dataset = Dataset(fname,format='NETCDF4-CLASSIC')
+ 
+      u = dataset['uo'][hour,0,:,:].squeeze()
+      v = dataset['vo'][hour,0,:,:].squeeze()
+      lon = dataset['longitude']
+      lat = dataset['latitude']
+
+      u[np.where(u>9999)] = np.nan
+      v[np.where(v>9999)] = np.nan
+
+      u_x = g.compute_stencil(u)
+      u_y = g.compute_stencil(u, vertical=True)
+
+
+      v_x = g.compute_stencil(v)
+      v_y = g.compute_stencil(v, vertical=True)
+
+      ow = g.vars["ow"] = (u_x - v_y) ** 2 + (v_x + u_y) ** 2 - (v_x - u_y) ** 2
+
+
+
+      exec('proj = ' + fig_proj)
+      extent = [np.amin(lon),np.amax(lon),np.amin(lat),np.amax(lat)]
+      fig, ax = plt.subplots(1,1,figsize=(float(fig_sx), float(fig_sy)), subplot_kw={'projection': proj})
+      init_fig(ax,extent,proj)
+
+      p = plt.pcolor(lon,lat,ow,cmap='bwr',vmin=-3e-9,vmax=3e-9,zorder=1)
+      #plt.colorbar()
+
+      sub = 8
+
+      lons,lats = lon[::sub],lat[::sub]
+      us,vs = array(u[::sub,::sub]),array(v[::sub,::sub])
+
+      norm = np.sqrt(np.square(us)+np.square(vs))
+
+      us[np.where(norm < 0.1)] = np.nan
+      vs[np.where(norm < 0.1)] = np.nan
+
+      q = ax.quiver(lons,lats,us,vs,color='k',edgecolor='k',linewidth=.1,scale=22,zorder=4,width=0.001)
+
+      # Tune 
+      cb = plt.colorbar(extend='both',fraction=float(cb_fraction_2D),pad=float(cb_pad_2D),\
+                label='Okubo Weis')
+
+      plt.title(dtag+' '+str(hour).zfill(2)+'h')
+
+      # Save
+      fout = fname.replace("RFVL","OKWSh"+str(hour).zfill(2))
+      print(fout)
+      fout = fout.replace("nc",fig_fmt)
+      fout = os.path.basename(fout)
+      fout = savedir +'/'+ fout
+
+      if tight == "True" :
+        plt.savefig(fout,bbox_inches='tight',dpi=int(fig_res))
+      else:
+        plt.savefig(fout,dpi=int(fig_res))
+
+      print('[SAVED FIG] '+fout)
+
+      plt.close()
+
+    except:
+      pass
+
+
+
+
+
+
+