argo OK

config/config_MARIA.json

@@ -1,32 +1,32 @@
 {
 "_____":"GENERAL PARAMETERS",
 
-"date_ini":"2015-03-20",
-"date_end":"2015-03-29",
-"outdir":"/home/fontana/MODEL/OUTPUTS",
-"diagdir":"/home/fontana/MODEL/DIAGS",
+"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",
 
 
 "_____":"RIVER PARAMETERS",
 
 "french_list":["Argens","Var"],
-"italian_list":["Magra","Arno","Ombrone","Serchio"],
-"river_ini":"2019-05-01",
-"river_end":"2019-05-31",
+"italian_list":["Arno","Magra","Ombrone","Serchio"],
 
 
 "_____":"METEO PARAMETERS",
-
-"meteo_ini":"2019-04-01",
-"meteo_end":"2019-04-30",
+"meteo_ini":"2015-03-10",
+"meteo_end":"2015-03-11",
 "day_cycle":2,
-"n_cycle":30,
+"n_cycle":6,
 "imin":"500",
 "imax":"600",
 "jmin":"350",
 "jmax":"450",
 
 
+
 "_____":"DYFAMED PARAMETERS",
 
 "some_par":"some_par",
@@ -36,7 +36,7 @@
 "some_par":"some_par",
 
 
-"_____":"PLOT PARAMETERS",
+"_____":"2D PLOT PARAMETERS",
 
 "resol":"10m",                       
 "fig_proj":"ccrs.PlateCarree()",      
@@ -53,31 +53,29 @@
 
 "_____":"SECTION PLOT PARAMETERS",
 
-
 "fig_secx":"8",                         
 "fig_secy":"4",                        
 "sec_lon1":"8.5",
 "sec_lat1":"43.",
 "sec_lon2":"10.",
 "sec_lat2":"43.5",
-"sec_dep":"200",
+"sec_dep":"600",
 "sec_resH":"0.05",  
 "sec_resV":"1",
 "cb_fraction_sec":"0.025",
 "cb_pad_sec":"0.06",
 
 "_____":"PROFILE PLOT PARAMETERS",
 
-"fig_prox":"6",
-"fig_proy":"8",
+"fig_prox":"8",                         
+"fig_proy":"4",                        
 "col1":"r",
 "col2":"b",
 
 
 "_____":"INTERPOLATION PARAMETERS",
 
 "dump":"0.01",
-"itp_meth":"linear",
-"cmap":"viridis"
+"itp_meth":"nearest"
 
 }

config/config_TEMPLATE.json

@@ -25,6 +25,10 @@
 "jmin":"350",
 "jmax":"450",
 
+"_____":"ARGO",
+
+"argo_ds":"phy",
+
 
 
 "_____":"DYFAMED PARAMETERS",

datasets/ARGO/compare_argo.py

@@ -7,10 +7,18 @@
 
 from fun_gen import *
 from fun_io import *
+from fun_plot_2D import *
+from fun_plot_profile import *
 import sys,os
-import datetime as dt
 import xarray as xr
 import datetime as dt
+from datetime import datetime
+
+from mpl_toolkits.axes_grid1 import host_subplot
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.use("Agg")
+
 
 
 # Get args
@@ -23,6 +31,17 @@
 load_config(config)
 from fun_gen import *
 
+tag = ''
+if argo_ds == 'bgc':
+  tag = 'bio'
+
+# Create arborescence
+# -------------------
+os.system('mkdir -p '+diagdir)
+os.system('mkdir -p '+diagdir+'/'+config)
+savedir = diagdir+'/'+config+'/ARGO'
+os.system('mkdir -p '+savedir)
+
 # Load coordinates
 # ----------------
 lon_mod,lat_mod,lev_mod=load_coords()
@@ -31,54 +50,196 @@
 # Load data
 # ---------
 savedir = diagdir+'/'+config+'/ARGO'
-ds = xr.load_dataset(savedir+'/argo.nc')
+ds = xr.open_dataset(savedir+'/'+tag+'argo.nc')
+
+lat = np.array( ds['LATITUDE'])
+lon = np.array(ds['LONGITUDE'])
+time = np.array(ds['TIME'])
+num = np.array(ds['PLATFORM_NUMBER'])
+cyc = np.array(ds['CYCLE_NUMBER'])
+pres = np.array(ds['PRES_ADJUSTED'])
 
-lat = ds['LATITUDE']
-lon = ds['LONGITUDE']
-time = ds['TIME']
-num = ds['PLATFORM_NUMBER']
-cyc = ds['CYCLE_NUMBER']
-pres = ds['PRES_ADJUSTED']
 
 time = np.array(time,dtype=str)
 
+
 # Define profiles position
 # ------------------------
 
 lon_uni = np.unique(lon)
 print(lon_uni.shape[0],'profiles found\n')
 
+
 # Loop on profiles
 # ----------------
+print('Processing\n')
 for plon in lon_uni:
 
   # Get all indexes
   idx = np.where( lon == plon )
 
-  for var in ['CHLA_ADJUSTED','PSAL_ADJUSTED','TEMP_ADJUSTED']:
+  # Get data lon/lat
+  data_lon = lon[idx]
+  data_lat = lat[idx]
+  data_pres = pres[idx]
+
+  pnum = str(num[idx][0])
+  pcyc = str(cyc[idx][0])
+  ptime = str(time[idx][0])
+
+
+  print('Float',pnum,'cycle',pcyc)
+  print('-----')
+
+
+  full_data = []
+  full_val = []
 
-    if var == 'CHLA_ADJUSTED':
+  varlist = ['PSAL_ADJUSTED','TEMP_ADJUSTED']
+
+  if argo_ds == 'bgc':
+     varlist = np.concatenate((['CHLA'],varlist))
+
+  for var in varlist:
+
+    if var == 'CHLA':
        mod_var = 'chl'
     elif var == 'PSAL_ADJUSTED':
        mod_var = 'so'
     elif var == 'TEMP_ADJUSTED':
        mod_var = 'thetao'
 
+    # Get data
+    data = ds[var][idx]
+
     # Get current variable parameters
     vname, ftag, cmap, islog, vmod, vmin, vmax, label, units\
         = load_variable(config,mod_var)
 
     # Convert date
-    dstr = str(time[idx][0]).split('T')[0]
-    jd = dt.datetime.strptime(dstr,'%Y-%m-%d').toordinal()
+    dstr = str(time[idx][0]).replace('.000000000','')
+    date = dt.datetime.strptime(dstr,'%Y-%m-%dT%H:%M:%S')
+    jd = date.toordinal()
+
+    hours,mnt = date.strftime('%H'),date.strftime('%M')
+    hours = int(np.round(float(hours)+float(mnt)/60.))
+    if hours == 24:
+      hours = 0
+
 
     # Get filename
     fname,dtag = get_filename(int(np.floor(jd)),ftag)
 
-    print(fname,dtag)
+
+    # Interpolate model on data
+    try:
+      val  = get_model_val_3d(fname,hours,vname,\
+               lon_mod,lat_mod,lev_mod,\
+                 data_lon,data_lat,data_pres)
+    except Exception as e:
+      print('Interpolation failed')
+      val = data_pres
+      val[:] = np.nan
+
+    full_val.append(val)
+    full_data.append(data)
+
+
+  full_val = np.array(full_val)
+  full_data = np.array(full_data)
+
+  # Plot profile
+  # ------------
+
+  os.system('mkdir -p '+savedir+'/'+tag+str(pnum))
+
+  fout = savedir+'/'+tag+str(pnum)+'/profile_'+pcyc+'.'+fig_fmt
+  ptime = ptime.replace('.000000000','')
+
+  if argo_ds == 'bgc':
+    mchl,mpsal,mtemp = full_val[0],full_val[1],full_val[2]
+    dchl,dpsal,dtemp = full_data[0],full_data[1],full_data[2]
+
+    plot_profiles(fout,ptime,-data_pres,\
+               mchl,dchl,'Chlorophyll (mg.m$^{-3}$)',\
+               mpsal,dpsal,'Salinity',\
+               mtemp,dtemp,'Temperature (°C)')
+  else:
+    mpsal,mtemp = full_val[0],full_val[1]
+    dpsal,dtemp = full_data[0],full_data[1]
+
+    plot_profiles(fout,ptime,-data_pres,\
+               mpsal,dpsal,'Salinity',\
+               mtemp,dtemp,'Temperature (°C)')
+
+
+
+
+# Plot trajectories
+# -----------------
+
+print('Plot trajectories ...')
+
+# Load proj
+exec('proj = ' + fig_proj)
+
+pos = []
+prof = []
+
+# Loop on Argo float
+for n in np.unique(num):
+
+   print(n)
+
+
+   # Initialize map
+   extent = [lon_mod.min(),lon_mod.max(),lat_mod.min(),lat_mod.max()]
+   fig, ax = plt.subplots(1,1,figsize=(float(fig_sx), float(fig_sy)), subplot_kw={'projection': proj})
+   init_fig(ax,extent,proj)
+
+   # Get all indexes
+   idx = np.where( num == n )
+
+   plon = np.unique( lon[idx])
+   plat = np.unique( lat[idx])
+   txt = np.unique(cyc[idx])
+
+   pos.append([plon,plat,n])
+
+
+   plt.plot(plon,plat,marker='+',linestyle='-')
+
+   for i in [0,plon.shape[0]-1]:
+     plt.text(plon[i],plat[i],str(txt[i]),fontsize=8)
+
+   # Save figure
+   fout = savedir+'/'+tag+str(n)+'/map'+'.'+fig_fmt
+   savefig(fout)
+
+   plt.close()
+
+
+# Figure for all trajectories
+fig, ax = plt.subplots(1,1,figsize=(float(fig_sx), float(fig_sy)), subplot_kw={'projection': proj})
+init_fig(ax,extent,proj)
+
+
+for p in pos:
+
+   lon = np.array(p[0])
+   lat = np.array(p[1])
+
+   plt.plot(lon,lat,marker='+',linestyle='none')
+   plt.text(lon[0],lat[0],str(p[2]))
+
+# Save figure
+fout = savedir+'/map'+tag+'.'+fig_fmt
+savefig(fout)
+
+plt.close()
+
 
 
-    exit()
 
 
 

datasets/ARGO/get_argo.py

@@ -1,6 +1,8 @@
 # ------------------------- #
 # Routine to get Argo data  #
 # ------------------------- #
+import warnings
+warnings.simplefilter("ignore")
 
 import numpy as np
 
@@ -52,11 +54,14 @@
 # -------------
 box = [lon_min, lon_max, lat_min, lat_max, 0, 300, date_ini, date_end]
 
-f = DataFetcher(ds='bgc', mode='expert', params='all',
+f = DataFetcher(ds=argo_ds, mode='expert', params='all',
                 parallel=True, progress=True, cache=False,
                 chunks_maxsize={'time': 30},
                )
 
+
+
+
 f = f.region(box).load()
 
 ds = f.data
@@ -67,6 +72,13 @@
 
 # Save data
 # ---------
-ds.to_netcdf(path=savedir+'/argo.nc', mode='w', format=None, group=None, engine=None, encoding=None, unlimited_dims=None, compute=True, invalid_netcdf=False)
+tag=''
+if argo_ds == 'bgc':
+  tag = 'bio'
+
+fname = savedir+'/'+tag+'argo.nc'
+ds.to_netcdf(path=fname, mode='w', format=None, group=None, engine=None, encoding=None, unlimited_dims=None, compute=True, invalid_netcdf=False)
+
+print('[SAVED]',fname)