Compare_T_U_V_Rv.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1ddd801f-b37e-4897-ad2a-56d8a5bde6dd",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "16d0739e-f834-4dfa-811f-6110d9df2ddc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from base_Imports_and_Functions import *\n",
    "\n",
    "df_mf = pd.read_pickle('T:/C2H/STAGES/LEO_BARROIS/csv_stations/mf_19.csv')\n",
    "df_net = pd.read_pickle('T:/C2H/STAGES/LEO_BARROIS/csv_stations/net_19.csv')\n",
    "\n",
    "df_mf_son = df_mf[(df_mf['lat'] >=extent_son[2]) & (df_mf['lat'] <= extent_son[3]) &\n",
    "  (df_mf['lon'] >= extent_son[0]) & (df_mf['lon'] <= extent_son[1])]\n",
    "\n",
    "df_net_son = df_net[(df_net['lat'] >=extent_son[2]) & (df_net['lat'] <= extent_son[3]) &\n",
    "  (df_net['lon'] >= extent_son[0]) & (df_net['lon'] <= extent_son[1])]\n",
    "\n",
    "Elevation_father = xr.open_dataset('T:/C2H/STAGES/LEO_BARROIS/netcds/Elevation_father.nc') \n",
    "\n",
    "Elevation_father_ = Elevation_father.where(\n",
    "    (Simu_0_23['lon'] > extent_son[0]) & (Simu_0_23['lon'] < extent_son[1]) & \n",
    "    (Simu_0_23['lat'] > extent_son[2]) & (Simu_0_23['lat'] < extent_son[3]),\n",
    "    drop = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aa68a1b-b0fc-476e-88d1-30ccbd858337",
   "metadata": {},
   "source": [
    "## Producting netcdf from simu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24f791f6-20c3-491c-9e23-5498f567fd1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "@njit()\n",
    "def arange_netcdf_uvrv(simu_array,it,n,m):\n",
    "    \n",
    "    Rv = np.ravel(simu_array[7,it,0])\n",
    "    U = np.ravel(simu_array[0,it,0])\n",
    "    V = np.ravel(simu_array[1,it,0])\n",
    "    Rh = np.ravel(simu_array[6,it,0])\n",
    "    T_pot = np.ravel(simu_array[5,it,0])\n",
    "    P = np.ravel(simu_array[4,it,0])\n",
    "    iteration = np.ravel(np.ones((n,m))*it)\n",
    "\n",
    "    return Rv,U,V,Rh,T_pot,P,iteration\n",
    "\n",
    "def to_netcdf_simu_rvuv(coords_netcdf, simu_array, jour, heure_debut, save_name): \n",
    "\n",
    "    vars,its,levels,n,m = np.shape(simu_array)\n",
    "\n",
    "    latitudes = np.ravel(coords_netcdf['latitude'].values[1:-1,1:-1])\n",
    "    longitudes = np.ravel(coords_netcdf['longitude'].values[1:-1,1:-1])\n",
    "    \n",
    "    latitudes_i = latitudes.copy()\n",
    "    longitudes_i = longitudes.copy()\n",
    "    \n",
    "    var = 7\n",
    "    Rv_i = np.ravel(simu_array[var,0,0])\n",
    "    var = 0\n",
    "    U_i= np.ravel(simu_array[var,0,0])\n",
    "    var = 1\n",
    "    V_i = np.ravel(simu_array[var,0,0])\n",
    "    var = 6\n",
    "    Rh_i = np.ravel(simu_array[var,0,0])\n",
    "    var = 5\n",
    "    T_pot_i = np.ravel(simu_array[var,0,0])\n",
    "    var = 4\n",
    "    P_i = np.ravel(simu_array[var,0,0])\n",
    "    \n",
    "    heure_i = np.ravel(np.ones((n,m))*heure_debut)\n",
    "\n",
    "    for it in range(heure_debut+1,heure_debut+its): \n",
    "\n",
    "        Rv,U,V,Rh,T_pot,P,iteration = arange_netcdf_uvrv(simu_array,it,n,m)\n",
    "\n",
    "        Rv_i = np.concatenate((Rv_i, Rv), axis=None)\n",
    "        U_i = np.concatenate((U_i, U), axis=None)\n",
    "        V_i = np.concatenate((V_i, V), axis=None)\n",
    "        Rh_i = np.concatenate((Rh_i, Rh), axis=None)\n",
    "        T_pot_i = np.concatenate((T_pot_i, T_pot), axis=None)\n",
    "        P_i = np.concatenate((P_i, P), axis=None)\n",
    "        latitudes_i = np.concatenate((latitudes_i, latitudes), axis=None)\n",
    "        longitudes_i = np.concatenate((longitudes_i, longitudes), axis=None)\n",
    "        heure_i = np.concatenate((heure_i, np.ravel(np.ones((n,m))*it)), axis=None)\n",
    "        \n",
    "        print(it, ' ok')\n",
    "        \n",
    "    num_events = len(longitudes_i)\n",
    "    num_data_points = 9\n",
    "    Simu_netcdf = xr.Dataset(\n",
    "        {\n",
    "            'Rv': (['event'], Rv_i.astype(np.float32)),\n",
    "            'U': (['event'], U_i.astype(np.float32)),\n",
    "            'V': (['event'], V_i.astype(np.float32)),\n",
    "            'Rh': (['event'], Rh_i.astype(np.float32)),\n",
    "            'T_pot': (['event'], T_pot_i.astype(np.float32)),\n",
    "            'P': (['event'], P_i.astype(np.float32)),\n",
    "            'lat': (['event'], latitudes_i.astype(np.float32)),\n",
    "            'lon': (['event'], longitudes_i.astype(np.float32)),\n",
    "            'heure': (['event'], heure_i.astype(int))\n",
    "        },\n",
    "        coords={\n",
    "            'event': np.arange(num_events)\n",
    "        },\n",
    "    )\n",
    "    \n",
    "    Simu_netcdf.to_netcdf(savenet_path+save_name + '.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7eb1d01f-8cfc-411d-bd58-14a170a8f6e1",
   "metadata": {},
   "source": [
    "## Comparison with Meteorological stations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8cb84a7f-24a1-4cbf-a85c-19afc4e80787",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Comp_rvuv(day,dlat,dlon,df_net_son,df_mf_son,Simu_0_23_son,Elevation_father_,save_name):\n",
    "\n",
    "    lon_comp_i = np.zeros((1,1))[0]\n",
    "    lat_comp_i = np.zeros((1,1))[0]\n",
    "    heure_i = np.zeros((1,1))[0]\n",
    "    type_i = np.zeros((1,1))[0] #0 si Net, 1 si MF\n",
    "    station_i = np.zeros((6,1))\n",
    "    simu_i = np.zeros((6,1))\n",
    "    bias_i = np.zeros((6,1))\n",
    "    T_sta_paspot_i = np.zeros((1,1))[0]\n",
    "    \n",
    "    hours = np.arange(0,24,1)\n",
    "    for it in tqdm(range(len(hours))):\n",
    "\n",
    "        df_net_son_it = df_net_son[(df_net_son['rawtime'] == it*60*60)]\n",
    "        df_mf_son_it = df_mf_son[(df_mf_son['rawtime'] == it*60*60)]\n",
    "\n",
    "        lon_net = df_net_son_it['lon'].values\n",
    "        lon_mf = df_mf_son_it['lon'].values\n",
    "        lat_net = df_net_son_it['lat'].values\n",
    "        lat_mf = df_mf_son_it['lat'].values\n",
    "\n",
    "        press_mf = filled_press_values(df_mf_son_it['pression (Pa)'].values,df_mf_son_it['altitude_baromètre (m)'].values,df_mf_son_it['altitude_station (m)'].values)\n",
    "        \n",
    "        station_mf = np.array([df_mf_son_it['altitude_baromètre (m)'].values,\n",
    "                               press_mf,\n",
    "                               list(compute_potential_T(press_mf,df_mf_son_it['T-1.5'].values)-273.15),\n",
    "                               compute_rv_from_rh(press_mf,df_mf_son_it['T-1.5'].values,df_mf_son_it['hu'].values)*1000,\n",
    "                               df_mf_son_it['u_moy'].values,\n",
    "                               df_mf_son_it['dir_vent_moy'].values])\n",
    "        \n",
    "        station_net = np.array([df_net_son_it['alt'].values,\n",
    "                                #list(compute_press(df_net_son_it['alt'].values)),\n",
    "                                df_net_son_it['psta'].values*100,\n",
    "                                list(compute_potential_T(df_net_son_it['psta'].values*100,df_net_son_it['Temp'].values + 273.15)-273.15),\n",
    "                                compute_rv_from_rh(df_net_son_it['psta'].values*100,df_net_son_it['Temp'].values + 273.15,df_net_son_it['hu'].values)*1000,\n",
    "                               [np.nan for i in range(len(df_net_son_it['alt'].values))],\n",
    "                               [np.nan for i in range(len(df_net_son_it['alt'].values))]])\n",
    "\n",
    "        Simu_it = Simu_0_23_son.where(\n",
    "            (Simu_0_23_son['heure'] == it),\n",
    "            drop = True)\n",
    "\n",
    "        if (len(Simu_it['heure'].values) != 0) and (len(lon_net) != 0) and (len(lon_mf) != 0) :\n",
    "\n",
    "            lon_simu = Simu_it['lon'].values\n",
    "            lat_simu = Simu_it['lat'].values\n",
    "\n",
    "            Simu_array = np.array([Elevation_father_['ZS'],\n",
    "                                   Simu_it['P'],\n",
    "                                   Simu_it['T_pot'],\n",
    "                                   Simu_it['Rv'],\n",
    "                                   np.sqrt(Simu_it['U']**2 + Simu_it['V']**2),\n",
    "                                   np.arctan(Simu_it['V']/Simu_it['U'])])\n",
    "\n",
    "            lon_inf_net = lon_net-dlon\n",
    "            lon_sup_net = lon_net+dlon\n",
    "            lat_inf_net = lat_net-dlat\n",
    "            lat_sup_net = lat_net+dlat\n",
    "\n",
    "            lon_inf_mf = lon_mf-dlon\n",
    "            lon_sup_mf = lon_mf+dlon\n",
    "            lat_inf_mf = lat_mf-dlat\n",
    "            lat_sup_mf = lat_mf+dlat\n",
    "            \n",
    "            res_net,res_trace_net,res_simu_net = compare_ground_rvuv(lon_inf_net,lon_sup_net,lat_inf_net,lat_sup_net,station_net,lon_simu,lat_simu,Simu_array)\n",
    "            \n",
    "            lon_comp_i = np.concatenate((lon_comp_i,lon_net), axis = 0)\n",
    "            lat_comp_i = np.concatenate((lat_comp_i,lat_net), axis = 0)\n",
    "            bias_i = np.concatenate((bias_i,res_net), axis = 1)\n",
    "            heure_i = np.concatenate((heure_i,np.ones((1,len(lon_net)))[0]*it), axis = 0)\n",
    "            station_i = np.concatenate((station_i,res_trace_net), axis = 1)\n",
    "            simu_i = np.concatenate((simu_i,res_simu_net), axis = 1)\n",
    "            type_i = np.concatenate((type_i,np.zeros((1,len(lon_net)))[0]), axis = 0)\n",
    "            T_sta_paspot_i = np.concatenate((T_sta_paspot_i,df_net_son_it['Temp'].values), axis = 0)\n",
    "    \n",
    "            res_mf,res_trace_mf,res_simu_mf = compare_ground_rvuv(lon_inf_mf,lon_sup_mf,lat_inf_mf,lat_sup_mf,station_mf,lon_simu,lat_simu,Simu_array)\n",
    "\n",
    "            lon_comp_i = np.concatenate((lon_comp_i,lon_mf), axis = 0)\n",
    "            lat_comp_i = np.concatenate((lat_comp_i,lat_mf), axis = 0)\n",
    "            bias_i = np.concatenate((bias_i,res_mf), axis = 1)\n",
    "            heure_i = np.concatenate((heure_i,np.ones((1,len(lon_mf)))[0]*it), axis = 0)\n",
    "            station_i = np.concatenate((station_i,res_trace_mf), axis = 1)\n",
    "            simu_i = np.concatenate((simu_i,res_simu_mf), axis = 1)\n",
    "            type_i = np.concatenate((type_i,np.ones((1,len(lon_mf)))[0]), axis = 0)\n",
    "            T_sta_paspot_i = np.concatenate((T_sta_paspot_i,df_mf_son_it['T-1.5'].values-273.15), axis = 0)\n",
    "    \n",
    "    num_events = len(heure_i)-1\n",
    "    num_data_points = 23\n",
    "    Comp_simu_rvuv = xr.Dataset(\n",
    "        {\n",
    "        'lon_comp': (['event'], lon_comp_i[1:].astype(np.float32)),\n",
    "        'lat_comp': (['event'], lat_comp_i[1:].astype(np.float32)),\n",
    "            \n",
    "        'Z_sta': (['event'], station_i[0,:][1:].astype(np.float32)),\n",
    "        'Z_simu': (['event'], simu_i[0,:][1:].astype(np.float32)),\n",
    "        'Z_bias': (['event'], bias_i[0,:][1:].astype(np.float32)),\n",
    "\n",
    "        'P_sta': (['event'], station_i[1,:][1:].astype(np.float32)),\n",
    "        'P_simu': (['event'], simu_i[1,:][1:].astype(np.float32)),\n",
    "        'P_biai': (['event'], bias_i[1,:][1:].astype(np.float32)),\n",
    "            \n",
    "        'T_sta_paspot': (['event'], T_sta_paspot_i[1:].astype(np.float32)),\n",
    "        'T_sta': (['event'], station_i[2,:][1:].astype(np.float32)),\n",
    "        'T_simu': (['event'], simu_i[2,:][1:].astype(np.float32)),\n",
    "        'T_biai': (['event'], bias_i[2,:][1:].astype(np.float32)),\n",
    "\n",
    "        'Rv_sta': (['event'], station_i[3,:][1:].astype(np.float32)),\n",
    "        'Rv_simu': (['event'], simu_i[3,:][1:].astype(np.float32)),\n",
    "        'Rv_biai': (['event'], bias_i[3,:][1:].astype(np.float32)),\n",
    "\n",
    "        'U_moy_sta': (['event'], station_i[4,:][1:].astype(np.float32)),\n",
    "        'U_moy_simu': (['event'], simu_i[4,:][1:].astype(np.float32)),\n",
    "        'U_moy_biai': (['event'], bias_i[4,:][1:].astype(np.float32)),\n",
    "\n",
    "        'Dir_moy_sta': (['event'], station_i[5,:][1:].astype(np.float32)),\n",
    "        'Dir_moy_simu': (['event'], simu_i[5,:][1:].astype(np.float32)),\n",
    "        'Dir_moy_biai': (['event'], bias_i[5,:][1:].astype(np.float32)),\n",
    "        \n",
    "        'heure': (['event'], heure_i[1:].astype(int)),\n",
    "        'type': (['event'], type_i[1:].astype(int))\n",
    "    },\n",
    "    coords={\n",
    "        'event': np.arange(num_events)\n",
    "    },\n",
    "    )\n",
    "    \n",
    "    Comp_simu_rvuv.to_netcdf(savenet_path+save_name+'.nc')   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "796f742f-9d27-4f4d-a952-45837ae791df",
   "metadata": {},
   "outputs": [],
   "source": [
    "type = ['AROME','father','son']\n",
    "for i in range(2) :\n",
    "    for j in type:\n",
    "        Simu_0_23 = xr.open_dataset(savenet_path+'j'+'_'+str(19+i)+'_RvUV' + '.nc')\n",
    "        Comp_rvuv(i+19,0.01,0.01,df_net_son,df_mf_son,Simu_0_23,Elevation_father_,'comp_'+j+'_'+str(19+i)+'_RvUV.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c979890d-8590-4c60-8c6e-491d15b85311",
   "metadata": {},
   "source": [
    "## Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4286f024-859c-431c-8fda-fd4e7b90e61f",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'T:\\\\C2H\\\\STAGES\\\\Wiki_glide\\\\Netcdf\\\\comp_AROME_19_RvUV.nc'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\site-packages\\xarray\\backends\\file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 211\u001b[0m     file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m    212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\site-packages\\xarray\\backends\\lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[1;32m---> 56\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[0;32m     57\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n",
      "\u001b[1;31mKeyError\u001b[0m: [<class 'netCDF4._netCDF4.Dataset'>, ('T:\\\\C2H\\\\STAGES\\\\Wiki_glide\\\\Netcdf\\\\comp_AROME_19_RvUV.nc',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), '0866aa3b-7109-4fd6-850e-686595822d35']",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[4], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m### AROME\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m Comp_simu_rvuv_19_AROME \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43msavenet_path\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcomp_AROME_19_RvUV.nc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m \n\u001b[0;32m      3\u001b[0m Comp_simu_rvuv_20_AROME \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mopen_dataset(savenet_path\u001b[38;5;241m+\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcomp_AROME_20_RvUV.nc\u001b[39m\u001b[38;5;124m'\u001b[39m) \n\u001b[0;32m      5\u001b[0m \u001b[38;5;66;03m### Father\u001b[39;00m\n",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\site-packages\\xarray\\backends\\api.py:572\u001b[0m, in \u001b[0;36mopen_dataset\u001b[1;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[0;32m    560\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[0;32m    561\u001b[0m     decode_cf,\n\u001b[0;32m    562\u001b[0m     open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    568\u001b[0m     decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[0;32m    569\u001b[0m )\n\u001b[0;32m    571\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m--> 572\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    573\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    574\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    575\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    576\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    577\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    578\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[0;32m    579\u001b[0m     backend_ds,\n\u001b[0;32m    580\u001b[0m     filename_or_obj,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    590\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[0;32m    591\u001b[0m )\n\u001b[0;32m    592\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:644\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[1;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[0;32m    623\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m(  \u001b[38;5;66;03m# type: ignore[override]  # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[0;32m    624\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    625\u001b[0m     filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    641\u001b[0m     autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m    642\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[0;32m    643\u001b[0m     filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[1;32m--> 644\u001b[0m     store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    645\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    646\u001b[0m \u001b[43m        \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    647\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    648\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    649\u001b[0m \u001b[43m        \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    650\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    651\u001b[0m \u001b[43m        \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    652\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    653\u001b[0m \u001b[43m        \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    654\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    656\u001b[0m     store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[0;32m    657\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:407\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[1;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[0;32m    401\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m    402\u001b[0m     clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[0;32m    403\u001b[0m )\n\u001b[0;32m    404\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[0;32m    405\u001b[0m     netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[0;32m    406\u001b[0m )\n\u001b[1;32m--> 407\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:354\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[1;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[0;32m    352\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[0;32m    353\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[1;32m--> 354\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mformat \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mds\u001b[49m\u001b[38;5;241m.\u001b[39mdata_model\n\u001b[0;32m    355\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mds\u001b[38;5;241m.\u001b[39mfilepath()\n\u001b[0;32m    356\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mis_remote \u001b[38;5;241m=\u001b[39m is_remote_uri(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_filename)\n",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:416\u001b[0m, in \u001b[0;36mNetCDF4DataStore.ds\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    414\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m    415\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m--> 416\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\site-packages\\xarray\\backends\\netCDF4_.py:410\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    409\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m--> 410\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire_context\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m    411\u001b[0m \u001b[43m        \u001b[49m\u001b[43mds\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m_nc4_require_group\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_group\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    412\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m ds\n",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    135\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[0;32m    136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 137\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[0;32m    139\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\site-packages\\xarray\\backends\\file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[0;32m    197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m    198\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 199\u001b[0m     file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    200\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m    201\u001b[0m         \u001b[38;5;28;01myield\u001b[39;00m file\n",
      "File \u001b[1;32m~\\.conda\\envs\\barbapapa\\Lib\\site-packages\\xarray\\backends\\file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[1;34m(self, needs_lock)\u001b[0m\n\u001b[0;32m    215\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[0;32m    216\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[1;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[0;32m    219\u001b[0m     \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[0;32m    220\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
      "File \u001b[1;32msrc\\\\netCDF4\\\\_netCDF4.pyx:2469\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32msrc\\\\netCDF4\\\\_netCDF4.pyx:2028\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'T:\\\\C2H\\\\STAGES\\\\Wiki_glide\\\\Netcdf\\\\comp_AROME_19_RvUV.nc'"
     ]
    }
   ],
   "source": [
    "### AROME\n",
    "Comp_simu_rvuv_19_AROME = xr.open_dataset(savenet_path+'comp_AROME_19_RvUV.nc') \n",
    "Comp_simu_rvuv_20_AROME = xr.open_dataset(savenet_path+'comp_AROME_20_RvUV.nc') \n",
    "\n",
    "Simu_AROME_19 = xr.open_dataset(savenet_path+'AROME_19_RvUV.nc')\n",
    "\n",
    "reduced_AROME_19_14 = Simu_AROME_19.where(\n",
    "    (Simu_AROME_19['heure'] == 14),\n",
    "    drop = True)\n",
    "reduced_comp_19_AROME_14 = Comp_simu_rvuv_19_AROME.where(\n",
    "    (Comp_simu_rvuv_19_AROME['heure'] == 14),\n",
    "    drop = True)\n",
    "reduced_comp_net_19_AROME_14 = reduced_comp_19_AROME_14.where(\n",
    "    (reduced_comp_19_AROME_14['type'] == 0),\n",
    "        drop = True)\n",
    "reduced_comp_mf_19_AROME_14 = reduced_comp_19_AROME_14.where(\n",
    "    (reduced_comp_19_AROME_14['type'] == 1),\n",
    "        drop = True)\n",
    "\n",
    "reduced_comp_19_AROME_4 = Comp_simu_rvuv_19_AROME.where(\n",
    "    (Comp_simu_rvuv_19_AROME['heure'] == 4),\n",
    "    drop = True)\n",
    "reduced_comp_net_19_AROME_4 = reduced_comp_19_AROME_4.where(\n",
    "    (reduced_comp_19_AROME_4['type'] == 0),\n",
    "        drop = True)\n",
    "reduced_comp_mf_19_AROME_4 = reduced_comp_19_AROME_4.where(\n",
    "    (reduced_comp_19_AROME_4['type'] == 1),\n",
    "        drop = True)\n",
    "\n",
    "### Father\n",
    "Comp_simu_rvuv_19_father = xr.open_dataset(savenet_path+'comp_father_19_RvUV.nc') \n",
    "Comp_simu_rvuv_20_father = xr.open_dataset(savenet_path+'comp_father_20_RvUV.nc') \n",
    "\n",
    "Simu_father_19 = xr.open_dataset(savenet_path+'father_19_RvUV.nc')\n",
    "\n",
    "reduced_father_19_14 = Simu_father_19.where(\n",
    "    (Simu_father_19['heure'] == 14),\n",
    "    drop = True)\n",
    "reduced_comp_19_father_14 = Comp_simu_rvuv_19_father.where(\n",
    "    (Comp_simu_rvuv_19_father['heure'] == 14),\n",
    "    drop = True)\n",
    "reduced_comp_net_19_father_14 = reduced_comp_19_father_14.where(\n",
    "    (reduced_comp_19_father_14['type'] == 0),\n",
    "        drop = True)\n",
    "reduced_comp_mf_19_father_14 = reduced_comp_19_father_14.where(\n",
    "    (reduced_comp_19_father_14['type'] == 1),\n",
    "        drop = True)\n",
    "\n",
    "reduced_comp_19_father_4 = Comp_simu_rvuv_19_father.where(\n",
    "    (Comp_simu_rvuv_19_father['heure'] == 4),\n",
    "    drop = True)\n",
    "reduced_comp_net_19_father_4 = reduced_comp_19_father_4.where(\n",
    "    (reduced_comp_19_father_4['type'] == 0),\n",
    "        drop = True)\n",
    "reduced_comp_mf_19_father_4 = reduced_comp_19_father_4.where(\n",
    "    (reduced_comp_19_father_4['type'] == 1),\n",
    "        drop = True)\n",
    "\n",
    "### Son \n",
    "Comp_simu_rvuv_19_son = xr.open_dataset(savenet_path+'comp_son_19_RvUV.nc') \n",
    "Comp_simu_rvuv_20_son = xr.open_dataset(savenet_path+'comp_son_20_RvUV.nc') \n",
    "\n",
    "Simu_son_19 = xr.open_dataset(savenet_path+'son_19_RvUV.nc')\n",
    "\n",
    "reduced_son_19_14 = Simu_son_19.where(\n",
    "    (Simu_son_19['heure'] == 14),\n",
    "    drop = True)\n",
    "reduced_comp_19_son_14 = Comp_simu_rvuv_19_son.where(\n",
    "    (Comp_simu_rvuv_19_son['heure'] == 14),\n",
    "    drop = True)\n",
    "reduced_comp_net_19_son_14 = reduced_comp_19_son_14.where(\n",
    "    (reduced_comp_19_son_14['type'] == 0),\n",
    "        drop = True)\n",
    "reduced_comp_mf_19_son_14 = reduced_comp_19_son_14.where(\n",
    "    (reduced_comp_19_son_14['type'] == 1),\n",
    "        drop = True)\n",
    "\n",
    "reduced_comp_19_son_4 = Comp_simu_rvuv_19_son.where(\n",
    "    (Comp_simu_rvuv_19_son['heure'] == 4),\n",
    "    drop = True)\n",
    "reduced_comp_net_19_son_4 = reduced_comp_19_son_4.where(\n",
    "    (reduced_comp_19_son_4['type'] == 0),\n",
    "        drop = True)\n",
    "reduced_comp_mf_19_son_4 = reduced_comp_19_son_4.where(\n",
    "    (reduced_comp_19_son_4['type'] == 1),\n",
    "        drop = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a66c56e-e2c5-4a34-83c8-d4c76b93dd6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotting_time_series_quantils_rvuv(netcdf,vars): \n",
    "    medians = np.zeros((8,24))\n",
    "    lows = np.zeros((8,24))\n",
    "    highs = np.zeros((8,24))\n",
    "    for i in range(4) :\n",
    "        medians[2*i,:] = compute_diurn_cycle(var[2*i], var[2*i+1],50,netcdf)[0]\n",
    "        medians[2*i+1,:] = compute_diurn_cycle(var[2*i], var[2*i+1],50,netcdf)[1]\n",
    "    \n",
    "        highs[2*i,:] = compute_diurn_cycle(var[2*i], var[2*i+1],75,netcdf)[0]\n",
    "        highs[2*i+1,:] = compute_diurn_cycle(var[2*i], var[2*i+1],75,netcdf)[1]\n",
    "    \n",
    "        lows[2*i,:] = compute_diurn_cycle(var[2*i], var[2*i+1],25,netcdf)[0]\n",
    "        lows[2*i+1,:] = compute_diurn_cycle(var[2*i], var[2*i+1],25,netcdf)[1]\n",
    "    return lows,medians,highs\n",
    "\n",
    "def plotting(reduced_father_19,reduced_comp_net_19,reduced_comp_mf_19,reduced_comp_net_19_6,reduced_comp_mf_19_6,Comp_simu_rvuv_19,day,type,save_name,color_plot):\n",
    "    \n",
    "    fig, axs = plt.subplots(nrows = 3, ncols = 4, figsize=(15,12),layout=\"constrained\")\n",
    "    \n",
    "    var = ['T_simu','T_sta','U_moy_simu','U_moy_sta','Rv_simu','Rv_sta','P_simu','P_sta']\n",
    "    var_simu = ['T_pot','U', 'Rv','P']\n",
    "    cmaps = ['RdYlBu_r','inferno','YlGnBu_r','viridis']\n",
    "    scatter_labels = ['$\\\\theta$ simulation (°C)','$\\\\theta$ station (°C)','Wind norm simu ($m.s^{-1}$)','Wind norm station ($m.s^{-1}$)',\n",
    "                      'Rv simulation (g/kg)','Rv station (g/kg)','Pression simulation (Bar)','Pression station (Bar)']\n",
    "    diurn_labels = ['$\\\\theta$ (°C)', 'Wind norm ($m.s^{-1}$)','Rv (g/kg)','Pression (Bar)']\n",
    "    \n",
    "    min_l = [35,0,5,0.6]\n",
    "    max_l = [45,7,20,1.1]\n",
    "    pas = [1,1,3,0.1]\n",
    "    extends = ['both','max','both','both']\n",
    "    clabels = ['$\\\\theta$ (°C)','||U|| ($m.s^{-1}$)','Rv (g/kg)','Pression (Bar)']\n",
    "    P0 = 100000\n",
    "    laterals = [4,2,3,0.05]\n",
    "    ymin = [20,0,5,0.85]\n",
    "    ymax = [45,6,14,1.005]\n",
    "\n",
    "    filled_marker_style = dict(markeredgecolor='k')\n",
    "    \n",
    "    for i in range(3):\n",
    "        for j in range(4) :\n",
    "        \n",
    "            ax = axs[i,j]\n",
    "            ax.tick_params('x', labelsize=size)\n",
    "            ax.tick_params('y', labelsize=size)\n",
    "            ax.text(0, 1.1, string.ascii_lowercase[i*4+j], transform=ax.transAxes, \n",
    "                size=20, weight='bold')\n",
    "    \n",
    "            if j == 0 :\n",
    "                if var_simu[i] == 'U' :\n",
    "                    c = np.sqrt(reduced_father_19['U'].values**2 + reduced_father_19['V'].values**2)\n",
    "                    cax = ax.scatter(reduced_father_19['lon'],reduced_father_19['lat'], s = 1,c = c,cmap = cmaps[i],vmin = min_l[i], vmax = max_l[i])\n",
    "                    ax.scatter(reduced_comp_net_19['lon_comp'],reduced_comp_net_19['lat_comp'],c = reduced_comp_net_19[var[2*i+1]],cmap = cmaps[i],marker = 'X', edgecolors='k',vmin = min_l[i], vmax = max_l[i])\n",
    "                    ax.scatter(reduced_comp_mf_19['lon_comp'],reduced_comp_mf_19['lat_comp'],c = reduced_comp_mf_19[var[2*i+1]],cmap = cmaps[i],marker = 'P', edgecolors='k',vmin = min_l[i], vmax = max_l[i])\n",
    "    \n",
    "                elif var_simu[i] == 'T_pot' :\n",
    "                    c = reduced_father_19[var_simu[i]]-273.15\n",
    "                    cax = ax.scatter(reduced_father_19['lon'],reduced_father_19['lat'], s = 1,c = c,cmap = cmaps[i],vmin = min_l[i], vmax = max_l[i])\n",
    "                    ax.scatter(reduced_comp_net_19['lon_comp'],reduced_comp_net_19['lat_comp'],c = reduced_comp_net_19[var[2*i+1]],cmap = cmaps[i],marker = 'X', edgecolors='k',vmin = min_l[i], vmax = max_l[i])\n",
    "                    ax.scatter(reduced_comp_mf_19['lon_comp'],reduced_comp_mf_19['lat_comp'],c = reduced_comp_mf_19[var[2*i+1]],cmap = cmaps[i],marker = 'P', edgecolors='k',vmin = min_l[i], vmax = max_l[i])\n",
    "                    \n",
    "                elif var_simu[i] == 'P' :\n",
    "                    c = reduced_father_19[var_simu[i]]/P0\n",
    "                    cax = ax.scatter(reduced_father_19['lon'],reduced_father_19['lat'], s = 1,c = c,cmap = cmaps[i],vmin = min_l[i], vmax = max_l[i])\n",
    "                    ax.scatter(reduced_comp_net_19['lon_comp'],reduced_comp_net_19['lat_comp'],c = reduced_comp_net_19[var[2*i+1]]/P0,cmap = cmaps[i],marker = 'X', edgecolors='k',vmin = min_l[i], vmax = max_l[i])\n",
    "                    ax.scatter(reduced_comp_mf_19['lon_comp'],reduced_comp_mf_19['lat_comp'],c = reduced_comp_mf_19[var[2*i+1]]/P0,cmap = cmaps[i],marker = 'P', edgecolors='k',vmin = min_l[i], vmax = max_l[i])\n",
    "                else :\n",
    "                    c = reduced_father_19[var_simu[i]]\n",
    "                    cax = ax.scatter(reduced_father_19['lon'],reduced_father_19['lat'], s = 1,c = c,cmap = cmaps[i],vmin = min_l[i], vmax = max_l[i])\n",
    "                    ax.scatter(reduced_comp_net_19['lon_comp'],reduced_comp_net_19['lat_comp'],c = reduced_comp_net_19[var[2*i+1]],cmap = cmaps[i],marker = 'X', edgecolors='k',vmin = min_l[i], vmax = max_l[i])\n",
    "                    ax.scatter(reduced_comp_mf_19['lon_comp'],reduced_comp_mf_19['lat_comp'],c = reduced_comp_mf_19[var[2*i+1]],cmap = cmaps[i],marker = 'P', edgecolors='k',vmin = min_l[i], vmax = max_l[i])\n",
    "                \n",
    "                ax.set_xlabel('°E',fontsize = size)\n",
    "                ax.set_ylabel('°N',fontsize = size)\n",
    "            \n",
    "                ticks = np.arange(min_l[i],max_l[i]+pas[i],pas[i])\n",
    "\n",
    "                if var_simu[i] == 'P' : \n",
    "                    ticks = [0.6+0.1*i for i in range(6)]\n",
    "                \n",
    "                cbar = fig.colorbar(cax,\n",
    "                            ax = ax,\n",
    "                            ticks=ticks,\n",
    "                            format=mticker.FixedFormatter(ticks),\n",
    "                            location = 'left',\n",
    "                            extend=extends[i],\n",
    "                            orientation='vertical',\n",
    "                            fraction = 0.028,\n",
    "                            aspect = 55,\n",
    "                            boundaries = ticks\n",
    "                            )\n",
    "            \n",
    "                cbar.ax.tick_params(labelsize=size) \n",
    "                cbar.ax.set_title(label = clabels[i], fontsize=size)\n",
    "                ax.set_xlim((5.499792,7.032229))\n",
    "                ax.set_ylim((44.104446,45.18888))\n",
    "    \n",
    "                ax.scatter([-10],[-10], marker = '+', color = 'k', label = 'RADOME')\n",
    "                ax.scatter([-10],[-10], marker = 'x', color = 'k', label = 'Net')\n",
    "\n",
    "                ax.set_title(type +' 14h '+str(day)+'/08',fontsize = size)\n",
    "    \n",
    "                ax.legend()\n",
    "                \n",
    "            if j == 2 :\n",
    "    \n",
    "                if var_simu[i] == 'T_pot' :\n",
    "                    cax = ax.scatter(reduced_comp_net_19[var[2*i+1]],reduced_comp_net_19[var[2*i]]-273.15,c = reduced_comp_net_19['Z_sta'],cmap = 'terrain',marker = 'o',vmin = 0, vmax = 4000)\n",
    "                    ax.scatter(reduced_comp_mf_19[var[2*i+1]],reduced_comp_mf_19[var[2*i]]-273.15,c = reduced_comp_mf_19['Z_sta'],cmap = 'terrain',marker = 'o', edgecolor = 'k',vmin = 0, vmax = 4000)\n",
    "                    ax.plot(np.linspace(20,50,10000),np.linspace(20,50,10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(20,50,10000)+laterals[i],np.linspace(20,50,10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(20,50,10000),np.linspace(20,50,10000)+laterals[i],color = 'k', linestyle = 'dashed')\n",
    "    \n",
    "                elif var_simu[i] == 'P' :\n",
    "                    cax = ax.scatter(reduced_comp_net_19[var[2*i+1]]/P0,reduced_comp_net_19[var[2*i]]/P0,c = reduced_comp_net_19['Z_sta'],cmap = 'terrain',marker = 'o',vmin = 0, vmax = 4000)\n",
    "                    ax.scatter(reduced_comp_mf_19[var[2*i+1]]/P0,reduced_comp_mf_19[var[2*i]]/P0,c = reduced_comp_mf_19['Z_sta'],cmap = 'terrain',marker = 'o',edgecolor = 'k',vmin = 0, vmax = 4000)\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000),np.linspace(min_l[i],max_l[i],10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000)+laterals[i],np.linspace(min_l[i],max_l[i],10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000),np.linspace(min_l[i],max_l[i],10000)+laterals[i],color = 'k', linestyle = 'dashed')\n",
    "    \n",
    "                else :\n",
    "                    cax = ax.scatter(reduced_comp_net_19[var[2*i+1]],reduced_comp_net_19[var[2*i]],c = reduced_comp_net_19['Z_sta'],cmap = 'terrain',marker = 'o',vmin = 0, vmax = 4000)\n",
    "                    ax.scatter(reduced_comp_mf_19[var[2*i+1]],reduced_comp_mf_19[var[2*i]],c = reduced_comp_mf_19['Z_sta'],cmap = 'terrain',marker = 'o',edgecolor = 'k',vmin = 0, vmax = 4000)\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000),np.linspace(min_l[i],max_l[i],10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000)+laterals[i],np.linspace(min_l[i],max_l[i],10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000),np.linspace(min_l[i],max_l[i],10000)+laterals[i],color = 'k', linestyle = 'dashed')\n",
    "\n",
    "\n",
    "                ax.set_xlabel(scatter_labels[2*i+1],fontsize = size)\n",
    "                ax.set_ylabel(scatter_labels[2*i],fontsize = size)\n",
    "    \n",
    "                if i == 2 :\n",
    "    \n",
    "                    ticks = np.arange(0,5000,1000)\n",
    "                    \n",
    "                    cbar = fig.colorbar(cax,\n",
    "                                ax = axs[2,1:-1],\n",
    "                                ticks=ticks,\n",
    "                                format=mticker.FixedFormatter(ticks),\n",
    "                                location = 'bottom',\n",
    "                                extend='max',\n",
    "                                orientation='horizontal',\n",
    "                                fraction = 0.028,\n",
    "                                aspect = 55,\n",
    "                                boundaries = ticks\n",
    "                                )\n",
    "                \n",
    "                    cbar.ax.tick_params(labelsize=size) \n",
    "                    cbar.ax.set_title(label = 'Elevation (m asl)',fontsize = size)\n",
    "    \n",
    "                ax.scatter([-10],[-10], marker = 'o', color = 'y', edgecolor ='k', label = 'RADOME')\n",
    "                ax.scatter([-10],[-10], marker = 'o', color = 'y',  label = 'Net')\n",
    "                if var_simu[i] == 'T_pot' :\n",
    "                    ax.set_xlim((20,50))\n",
    "                    ax.set_ylim((20,50))\n",
    "                else :\n",
    "                    ax.set_xlim((min_l[i],max_l[i]))\n",
    "                    ax.set_ylim((min_l[i],max_l[i]))\n",
    "                ax.legend()\n",
    "                ax.set_title('14h '+str(day)+'/08',fontsize = size)\n",
    "\n",
    "            if j == 1 :\n",
    "    \n",
    "                if var_simu[i] == 'T_pot' :\n",
    "                    cax = ax.scatter(reduced_comp_net_19_6[var[2*i+1]],reduced_comp_net_19_6[var[2*i]]-273.15,c = reduced_comp_net_19_6['Z_sta'],cmap = 'terrain',marker = 'o',vmin = 0, vmax = 4000)\n",
    "                    ax.scatter(reduced_comp_mf_19_6[var[2*i+1]],reduced_comp_mf_19_6[var[2*i]]-273.15,c = reduced_comp_mf_19_6['Z_sta'],cmap = 'terrain',marker = 'o', edgecolor = 'k',vmin = 0, vmax = 4000)\n",
    "                    ax.plot(np.linspace(15,50,10000),np.linspace(15,50,10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(15,50,10000)+laterals[i],np.linspace(15,50,10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(15,50,10000),np.linspace(15,50,10000)+laterals[i],color = 'k', linestyle = 'dashed')\n",
    "    \n",
    "                elif var_simu[i] == 'P' :\n",
    "                    cax = ax.scatter(reduced_comp_net_19_6[var[2*i+1]]/P0,reduced_comp_net_19_6[var[2*i]]/P0,c = reduced_comp_net_19_6['Z_sta'],cmap = 'terrain',marker = 'o',vmin = 0, vmax = 4000)\n",
    "                    ax.scatter(reduced_comp_mf_19_6[var[2*i+1]]/P0,reduced_comp_mf_19_6[var[2*i]]/P0,c = reduced_comp_mf_19_6['Z_sta'],cmap = 'terrain',marker = 'o', edgecolor = 'k',vmin = 0, vmax = 4000)\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000),np.linspace(min_l[i],max_l[i],10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000)+laterals[i],np.linspace(min_l[i],max_l[i],10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000),np.linspace(min_l[i],max_l[i],10000)+laterals[i],color = 'k', linestyle = 'dashed')\n",
    "    \n",
    "                else :\n",
    "                    cax = ax.scatter(reduced_comp_net_19_6[var[2*i+1]],reduced_comp_net_19_6[var[2*i]],c = reduced_comp_net_19_6['Z_sta'],cmap = 'terrain',marker = 'o',vmin = 0, vmax = 4000)\n",
    "                    ax.scatter(reduced_comp_mf_19_6[var[2*i+1]],reduced_comp_mf_19_6[var[2*i]],c = reduced_comp_mf_19_6['Z_sta'],cmap = 'terrain',marker = 'o', edgecolor = 'k',vmin = 0, vmax = 4000)\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000),np.linspace(min_l[i],max_l[i],10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000)+laterals[i],np.linspace(min_l[i],max_l[i],10000),color = 'k', linestyle = 'dashed')\n",
    "                    ax.plot(np.linspace(min_l[i],max_l[i],10000),np.linspace(min_l[i],max_l[i],10000)+laterals[i],color = 'k', linestyle = 'dashed')\n",
    "                        \n",
    "\n",
    "                ax.set_xlabel(scatter_labels[2*i+1],fontsize = size)\n",
    "                ax.set_ylabel(scatter_labels[2*i],fontsize = size)\n",
    "    \n",
    "                ax.scatter([-10],[-10], marker = 'o', color = 'y', edgecolor = 'k', label = 'RADOME')\n",
    "                ax.scatter([-10],[-10], marker = 'o', color = 'y', label = 'Net')\n",
    "                if var_simu[i] == 'T_pot' :\n",
    "                    ax.set_xlim((15,50))\n",
    "                    ax.set_ylim((15,50))\n",
    "                else :\n",
    "                    ax.set_xlim((min_l[i],max_l[i]))\n",
    "                    ax.set_ylim((min_l[i],max_l[i]))\n",
    "                ax.legend()\n",
    "                ax.set_title('4h '+str(day)+'/08',fontsize = size)\n",
    "    \n",
    "            if j == 3 :\n",
    "    \n",
    "                ticks_time = np.array([i for i in range(0,24,4)])\n",
    "                labels_ticks_time = [str(i)+'h' for i in ticks_time]\n",
    "                \n",
    "                ax.grid(visible=True, axis='both', color = '0.80')\n",
    "                ax.set_xticks(ticks_time, labels_ticks_time,fontsize = size)\n",
    "\n",
    "                lows,medians,highs = fill_traces(Comp_simu_rvuv_19)\n",
    "    \n",
    "                if i == 0 :\n",
    "                    ax.fill_between(np.arange(0,24,1), lows[2*i,:], highs[2*i,:],color = 'k',alpha = 0.3)\n",
    "                    ax.fill_between(np.arange(0,24,1), lows[2*i+1,:]-273.15, highs[2*i+1,:]-273.15,color = color_plot,alpha = 0.3)\n",
    "                    ax.plot(np.arange(0,24,1),medians[2*i,:],'k',label ='Station')\n",
    "                    ax.plot(np.arange(0,24,1),medians[2*i+1,:]-273.15,color_plot,label = 'simulation')\n",
    "\n",
    "                elif i == 3 :\n",
    "                    P0 = 100000\n",
    "                    ax.fill_between(np.arange(0,24,1), lows[2*i,:]/P0, highs[2*i,:]/P0,color = 'k',alpha = 0.3)\n",
    "                    ax.fill_between(np.arange(0,24,1), lows[2*i+1,:]/P0, highs[2*i+1,:]/P0,color = color_plot,alpha = 0.3)\n",
    "                    ax.plot(np.arange(0,24,1),medians[2*i,:]/P0,'k',label ='Station')\n",
    "                    ax.plot(np.arange(0,24,1),medians[2*i+1,:]/P0,color_plot,label = 'simulation')\n",
    "\n",
    "                else :\n",
    "                    ax.fill_between(np.arange(0,24,1), lows[2*i,:], highs[2*i,:],color = 'k',alpha = 0.3)\n",
    "                    ax.fill_between(np.arange(0,24,1), lows[2*i+1,:], highs[2*i+1,:],color = color_plot,alpha = 0.3)\n",
    "                    ax.plot(np.arange(0,24,1),medians[2*i,:],'k',label ='Station')\n",
    "                    ax.plot(np.arange(0,24,1),medians[2*i+1,:],color_plot,label = 'simulation')\n",
    "\n",
    "                ax.vlines(14, ymin = ymin[i], ymax = ymax[i], linestyle = 'dashed', color  ='r')\n",
    "                ax.vlines(4, ymin = ymin[i], ymax = ymax[i], linestyle = 'dashed', color  ='r')\n",
    "                    \n",
    "                ax.set_ylabel(diurn_labels[i],fontsize = size)\n",
    "                ax.legend()\n",
    "                ax.set_title(type + ' ' + str(day) +'/08',fontsize = size)\n",
    "    \n",
    "    plt.savefig(savefig_path+save_name+'.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "17740c8f-e880-494b-becd-b1badb737465",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Comp_simu_rvuv_19_son' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[9], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28mvars\u001b[39m \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mT_simu\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mT_sta\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mU_moy_simu\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mU_moy_sta\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRv_simu\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRv_sta\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mP_simu\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mP_sta\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m----> 3\u001b[0m lows,medians,highs \u001b[38;5;241m=\u001b[39m plotting_time_series_quantils_rvuv(\u001b[43mComp_simu_rvuv_19_son\u001b[49m,\u001b[38;5;28mvars\u001b[39m)\n\u001b[0;32m      5\u001b[0m day \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m19\u001b[39m\n\u001b[0;32m      6\u001b[0m size \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m15\u001b[39m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Comp_simu_rvuv_19_son' is not defined"
     ]
    }
   ],
   "source": [
    "vars = ['T_simu','T_sta','U_moy_simu','U_moy_sta','Rv_simu','Rv_sta','P_simu','P_sta']\n",
    "\n",
    "lows,medians,highs = plotting_time_series_quantils_rvuv(Comp_simu_rvuv_19_son,vars)\n",
    "\n",
    "day = 19\n",
    "size = 15\n",
    "type = 'LES200'\n",
    "plotting(reduced_son_19_14,reduced_comp_net_19_son_14,reduced_comp_mf_19_son_14,reduced_comp_net_19_son_4,reduced_comp_mf_19_son_4,Comp_simu_rvuv_19_son,day,type,'RvUV_19_son','b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b20da68-7237-469e-828e-ad0ea94fb7c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "vars = ['T_simu','T_sta','U_moy_simu','U_moy_sta','Rv_simu','Rv_sta','P_simu','P_sta']\n",
    "\n",
    "lows,medians,highs = plotting_time_series_quantils_rvuv(Comp_simu_rvuv_19_father,vars)\n",
    "\n",
    "day = 19\n",
    "size = 15\n",
    "type = 'LES600'\n",
    "plotting(reduced_father_19_14,reduced_comp_net_19_father_14,reduced_comp_mf_19_father_14,reduced_comp_net_19_father_4,reduced_comp_mf_19_father_4,Comp_simu_rvuv_19_father,day,type,'RvUV_19_father','b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13b6edbb-12b3-40b5-8ae9-5947ab793381",
   "metadata": {},
   "outputs": [],
   "source": [
    "vars = ['T_simu','T_sta','U_moy_simu','U_moy_sta','Rv_simu','Rv_sta','P_simu','P_sta']\n",
    "\n",
    "lows,medians,highs = plotting_time_series_quantils_rvuv(Comp_simu_rvuv_19_AROME,vars)\n",
    "\n",
    "day = 19\n",
    "size = 15\n",
    "type = 'AROME'\n",
    "plotting(reduced_AROME_19_14,reduced_comp_net_19_AROME_14,reduced_comp_mf_19_AROME_14,reduced_comp_net_19_AROME_4,reduced_comp_mf_19_AROME_4,Comp_simu_rvuv_19_AROME,day,type,'RvUV_19_AROME','b')"
   ]
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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