ncmake_SD.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "94f68741",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from datetime import date\n",
    "today = date.today()\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ad48b863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Platform</th>\n",
       "      <th>Date-Time</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Depth_m</th>\n",
       "      <th>T_SBE_degC</th>\n",
       "      <th>S_SBE_corr</th>\n",
       "      <th>DO_SBE_raw_umol/L</th>\n",
       "      <th>DO_SBE_corr_umol/L</th>\n",
       "      <th>DO_SBE_corr_saturation_%</th>\n",
       "      <th>Press_atm_hpa</th>\n",
       "      <th>T_air_degC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SD1053</td>\n",
       "      <td>18.10.2019 11:00</td>\n",
       "      <td>-15.330453</td>\n",
       "      <td>27.975040</td>\n",
       "      <td>0.5</td>\n",
       "      <td>22.5733</td>\n",
       "      <td>36.440</td>\n",
       "      <td>217.01</td>\n",
       "      <td>218.86</td>\n",
       "      <td>100.31</td>\n",
       "      <td>1020.91</td>\n",
       "      <td>22.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SD1053</td>\n",
       "      <td>18.10.2019 12:00</td>\n",
       "      <td>-15.293437</td>\n",
       "      <td>27.938038</td>\n",
       "      <td>0.5</td>\n",
       "      <td>22.6462</td>\n",
       "      <td>36.523</td>\n",
       "      <td>216.15</td>\n",
       "      <td>217.60</td>\n",
       "      <td>99.91</td>\n",
       "      <td>1020.32</td>\n",
       "      <td>22.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SD1053</td>\n",
       "      <td>18.10.2019 13:00</td>\n",
       "      <td>-15.268942</td>\n",
       "      <td>27.893526</td>\n",
       "      <td>0.5</td>\n",
       "      <td>22.6542</td>\n",
       "      <td>36.536</td>\n",
       "      <td>216.15</td>\n",
       "      <td>217.55</td>\n",
       "      <td>99.91</td>\n",
       "      <td>1019.68</td>\n",
       "      <td>22.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SD1053</td>\n",
       "      <td>18.10.2019 14:00</td>\n",
       "      <td>-15.304269</td>\n",
       "      <td>27.848787</td>\n",
       "      <td>0.5</td>\n",
       "      <td>22.4781</td>\n",
       "      <td>36.477</td>\n",
       "      <td>217.75</td>\n",
       "      <td>219.49</td>\n",
       "      <td>100.45</td>\n",
       "      <td>1018.92</td>\n",
       "      <td>22.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SD1053</td>\n",
       "      <td>18.10.2019 15:00</td>\n",
       "      <td>-15.329208</td>\n",
       "      <td>27.802470</td>\n",
       "      <td>0.5</td>\n",
       "      <td>22.4513</td>\n",
       "      <td>36.433</td>\n",
       "      <td>217.98</td>\n",
       "      <td>219.91</td>\n",
       "      <td>100.57</td>\n",
       "      <td>1018.06</td>\n",
       "      <td>22.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6550</th>\n",
       "      <td>SD1053</td>\n",
       "      <td>17.07.2020 09:00</td>\n",
       "      <td>13.703283</td>\n",
       "      <td>45.694890</td>\n",
       "      <td>0.5</td>\n",
       "      <td>21.5026</td>\n",
       "      <td>36.499</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>1013.58</td>\n",
       "      <td>22.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6551</th>\n",
       "      <td>SD1053</td>\n",
       "      <td>17.07.2020 10:00</td>\n",
       "      <td>13.707722</td>\n",
       "      <td>45.692394</td>\n",
       "      <td>0.5</td>\n",
       "      <td>21.5904</td>\n",
       "      <td>36.698</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>1013.73</td>\n",
       "      <td>22.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6552</th>\n",
       "      <td>SD1053</td>\n",
       "      <td>17.07.2020 11:00</td>\n",
       "      <td>13.701947</td>\n",
       "      <td>45.695325</td>\n",
       "      <td>0.5</td>\n",
       "      <td>21.6926</td>\n",
       "      <td>36.706</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>1014.33</td>\n",
       "      <td>23.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6553</th>\n",
       "      <td>SD1053</td>\n",
       "      <td>17.07.2020 12:00</td>\n",
       "      <td>13.705694</td>\n",
       "      <td>45.692259</td>\n",
       "      <td>0.5</td>\n",
       "      <td>21.6247</td>\n",
       "      <td>36.642</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>1014.12</td>\n",
       "      <td>23.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6554</th>\n",
       "      <td>SD1053</td>\n",
       "      <td>17.07.2020 13:00</td>\n",
       "      <td>13.701253</td>\n",
       "      <td>45.696090</td>\n",
       "      <td>0.5</td>\n",
       "      <td>21.7699</td>\n",
       "      <td>36.590</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>-999999.00</td>\n",
       "      <td>1014.06</td>\n",
       "      <td>23.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6555 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Platform         Date-Time  Longitude   Latitude  Depth_m  T_SBE_degC  \\\n",
       "0      SD1053  18.10.2019 11:00 -15.330453  27.975040      0.5     22.5733   \n",
       "1      SD1053  18.10.2019 12:00 -15.293437  27.938038      0.5     22.6462   \n",
       "2      SD1053  18.10.2019 13:00 -15.268942  27.893526      0.5     22.6542   \n",
       "3      SD1053  18.10.2019 14:00 -15.304269  27.848787      0.5     22.4781   \n",
       "4      SD1053  18.10.2019 15:00 -15.329208  27.802470      0.5     22.4513   \n",
       "...       ...               ...        ...        ...      ...         ...   \n",
       "6550   SD1053  17.07.2020 09:00  13.703283  45.694890      0.5     21.5026   \n",
       "6551   SD1053  17.07.2020 10:00  13.707722  45.692394      0.5     21.5904   \n",
       "6552   SD1053  17.07.2020 11:00  13.701947  45.695325      0.5     21.6926   \n",
       "6553   SD1053  17.07.2020 12:00  13.705694  45.692259      0.5     21.6247   \n",
       "6554   SD1053  17.07.2020 13:00  13.701253  45.696090      0.5     21.7699   \n",
       "\n",
       "      S_SBE_corr   DO_SBE_raw_umol/L  DO_SBE_corr_umol/L  \\\n",
       "0          36.440             217.01              218.86   \n",
       "1          36.523             216.15              217.60   \n",
       "2          36.536             216.15              217.55   \n",
       "3          36.477             217.75              219.49   \n",
       "4          36.433             217.98              219.91   \n",
       "...           ...                ...                 ...   \n",
       "6550       36.499         -999999.00          -999999.00   \n",
       "6551       36.698         -999999.00          -999999.00   \n",
       "6552       36.706         -999999.00          -999999.00   \n",
       "6553       36.642         -999999.00          -999999.00   \n",
       "6554       36.590         -999999.00          -999999.00   \n",
       "\n",
       "      DO_SBE_corr_saturation_%  Press_atm_hpa  T_air_degC  \n",
       "0                       100.31        1020.91       22.27  \n",
       "1                        99.91        1020.32       22.35  \n",
       "2                        99.91        1019.68       22.40  \n",
       "3                       100.45        1018.92       22.23  \n",
       "4                       100.57        1018.06       22.25  \n",
       "...                        ...            ...         ...  \n",
       "6550                -999999.00        1013.58       22.15  \n",
       "6551                -999999.00        1013.73       22.75  \n",
       "6552                -999999.00        1014.33       23.19  \n",
       "6553                -999999.00        1014.12       23.45  \n",
       "6554                -999999.00        1014.06       23.87  \n",
       "\n",
       "[6555 rows x 12 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read the Excel file into a pandas DataFrame\n",
    "data = pd.read_csv('SD1053_T_S_DO_final2.txt',delimiter='\\t', header=0)\n",
    "data=data.fillna(-999999)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fdb10dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Iterate over each column in the DataFrame\n",
    "\n",
    "data.columns = [re.sub(r'[-%/ ]', '_', col) for col in data.columns]\n",
    "\n",
    "for column in data.columns:\n",
    "    # Convert the column to a NumPy array\n",
    "    np_array = data[column].to_numpy()\n",
    "    \n",
    "    # Convert the data type to int32 or float32\n",
    "    if np_array.dtype == 'int64':\n",
    "        np_array = np_array.astype('int32')\n",
    "    elif np_array.dtype == 'float64':\n",
    "        np_array = np_array.astype('float32')\n",
    "    \n",
    "    # Delete the old NumPy array if it exists\n",
    "    if column in locals():\n",
    "        del locals()[column]\n",
    "    \n",
    "    # Use the modified column name as the variable name\n",
    "    globals()[column] = np_array\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d0e5edf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "time_a=Date_Time\n",
    "Lat_a=Latitude\n",
    "Lon_a=Longitude\n",
    "\n",
    "Air_Temp_a= T_air_degC\n",
    "\n",
    "Platform_a=Platform\n",
    "\n",
    "Press_a= Press_atm_hpa\n",
    "\n",
    "DO_a= DO_SBE_corr_umol_L\n",
    "DOsat_a= DO_SBE_corr_saturation__\n",
    "Depth_a= Depth_m\n",
    "Salinity_a=S_SBE_corr_\n",
    "Temperature_a=T_SBE_degC\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "45d422e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the Excel file into a pandas DataFrame\n",
    "data = pd.read_csv('SD1030_T_S_DO_CO2_final2.txt',delimiter='\\t', header=0)\n",
    "data=data.fillna(-999999)\n",
    "\n",
    "# Iterate over each column in the DataFrame\n",
    "\n",
    "data.columns = [re.sub(r'[-%/ ]', '_', col) for col in data.columns]\n",
    "\n",
    "for column in data.columns:\n",
    "    # Convert the column to a NumPy array\n",
    "    np_array = data[column].to_numpy()\n",
    "    \n",
    "    # Convert the data type to int32 or float32\n",
    "    if np_array.dtype == 'int64':\n",
    "        np_array = np_array.astype('int32')\n",
    "    elif np_array.dtype == 'float64':\n",
    "        np_array = np_array.astype('float32')\n",
    "    \n",
    "    # Delete the old NumPy array if it exists\n",
    "    if column in locals():\n",
    "        del locals()[column]\n",
    "    \n",
    "    # Use the modified column name as the variable name\n",
    "    globals()[column] = np_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6befe11d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable                     Type         Data/Info\n",
      "---------------------------------------------------\n",
      "Air_Temp_a                   ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "DO_SBE_corr_saturation__     ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "DO_SBE_corr_umol_L           ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "DO_SBE_raw_umol_L            ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "DO_a                         ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "DOsat_a                      ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Date_Time                    ndarray      6555: 6555 elems, type `object`, 52440 bytes\n",
      "Depth_a                      ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Depth_m                      ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Lat_a                        ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Latitude                     ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Lon_a                        ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Longitude                    ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Platform                     ndarray      6555: 6555 elems, type `object`, 52440 bytes\n",
      "Platform_a                   ndarray      6555: 6555 elems, type `object`, 52440 bytes\n",
      "Press_a                      ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Press_atm_LiCor_hPA          ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Press_atm_hpa                ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "S_SBE_corr_                  ndarray      6555: 6555 elems, type `object`, 52440 bytes\n",
      "S_SBE_raw                    ndarray      6555: 6555 elems, type `object`, 52440 bytes\n",
      "Salinity_a                   ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "T_SBE_degC                   ndarray      6555: 6555 elems, type `object`, 52440 bytes\n",
      "T_air_degC                   ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Temperature_a                ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Unnamed:_17                  ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Unnamed:_18                  ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "Unnamed:_19                  ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "column                       str          Unnamed:_19\n",
      "data                         DataFrame         Platform         Dat<...>n[6555 rows x 20 columns]\n",
      "date                         type         <class 'datetime.date'>\n",
      "np                           module       <module 'numpy' from '/ho<...>kages/numpy/__init__.py'>\n",
      "np_array                     ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "pCO2_sea_wet_corr_uatm       ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "pd                           module       <module 'pandas' from '/h<...>ages/pandas/__init__.py'>\n",
      "re                           module       <module 're' from '/home/<...>da3/lib/python3.9/re.py'>\n",
      "time_a                       ndarray      6555: 6555 elems, type `object`, 52440 bytes\n",
      "today                        date         2023-11-01\n",
      "xCO2_air_dry_corr_umol_mol   ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "xCO2_sea_dry_corr_umol_mol   ndarray      6555: 6555 elems, type `float32`, 26220 bytes\n",
      "xr                           module       <module 'xarray' from '/h<...>ages/xarray/__init__.py'>\n"
     ]
    }
   ],
   "source": [
    "whos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0365fec7",
   "metadata": {},
   "outputs": [],
   "source": [
    "time=Date_Time\n",
    "Lat_b=Latitude\n",
    "Lon_b=Longitude\n",
    "\n",
    "Depth_b=Depth_m\n",
    "DO_b= DO_SBE_corr_umol_L\n",
    "DOsat_b= DO_SBE_corr_saturation__\n",
    "Salinity_b=S_SBE_corr_\n",
    "Temperature_b=T_SBE_degC\n",
    "\n",
    "\n",
    "Air_Temp_b= T_air_degC\n",
    "Press_b= Press_atm_hpa\n",
    "Press_atm_LiCor_hPA=Press_atm_LiCor_hPA\n",
    "\n",
    "xCO2_air_dry_corr_umol_mol=xCO2_air_dry_corr_umol_mol\n",
    "xCO2_sea_dry_corr_umol_mol=xCO2_sea_dry_corr_umol_mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ce3868d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds=xr.Dataset(\n",
    "    data_vars=dict(\n",
    "        \n",
    "       \n",
    "        Oxygen_1053=([\"time\"], DO_a,{\"units\": \"umol/L\",\n",
    "                                            \"standard_name\": \"mole_concentration_of_dissolved_molecular_oxygen_in_sea_water\",\n",
    "                                            \"short_name\": \"DO\",\n",
    "                                            \"long_name\": \"mole_concentration_of_dissolved_molecular_oxygen_in_sea_water\",\n",
    "                                            \"description\": \"Molarity of dissolved Oxygen in sea water measured by SBE37-SMP-ODO \",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"400\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        DO_sat_1053=([\"time\"], DOsat_a,{\"units\": \"uatm\",\n",
    "                                            \"standard_name\": \"dissolved_molecular_oxygen_saturation_in_sea_water\",\n",
    "                                            \"short_name\": \"DOsat\",\n",
    "                                            \"long_name\": \"dissolved_molecular_oxygen_saturation_in_sea_water\",\n",
    "                                            \"description\": \"Dissolved Oxygen saturation in sea water measured by SBE37-SMP-ODO.\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"400\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        \n",
    "        \n",
    "        Depth_1053=([\"time\"], Depth_a,{\"units\": \"m\",\n",
    "                                            \"standard_name\": \"depth\",\n",
    "                                            \"short_name\": \"depth\",\n",
    "                                            \"long_name\": \"depth\",\n",
    "                                            \"description\": \"depth from where the water sample is collected (m)\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"10000\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "    \n",
    "        air_temperature_1053=([\"time\"], Air_Temp_a,{\"units\": \"K\",\n",
    "                                            \"standard_name\": \"air_temperature\",\n",
    "                                            \"short_name\": \"Temp\",\n",
    "                                            \"long_name\": \"Temperature\",\n",
    "                                            \"description\": \"Air temperature is the bulk temperature of the air\",\n",
    "                                            \"valid_min\": \"-273\",\n",
    "                                            \"valid_max\": \"500\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        Press_1053=([\"time\"], Press_a,{\"units\": \"hPa\",\n",
    "                                            \"standard_name\": \"atmospheric_pressure\",\n",
    "                                            \"short_name\": \"atm_press\",\n",
    "                                            \"long_name\": \"Atmospheric Pressure\",\n",
    "                                            \"description\": \"Atmospheric Pressure\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"10000\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        \n",
    "\n",
    "        Salinity_1053=([\"time\"], Salinity_a,{\"units\": \"psu\",\n",
    "                                            \"standard_name\": \"sea_water_salinity\",\n",
    "                                            \"short_name\": \"so\",\n",
    "                                            \"long_name\": \"Sea Water Salinity\",\n",
    "                                            \"description\": \"Sea water salinity is the salt content of sea water on the Practical Salinity Scale, measured by SBE37 deployed from the research vessel\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"100\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"uncorrected\"}),\n",
    "\n",
    "        Temperature_1053=([\"time\"], Temperature_a,{\"units\": \"degree Celcius\",\n",
    "                                            \"standard_name\": \"sea_water_temperature\",\n",
    "                                            \"short_name\": \"SW_Temperature\",\n",
    "                                            \"long_name\": \"Sea Water Temperature\",\n",
    "                                            \"description\": \"Sea water temperature is the in situ temperature of the sea water measured by the SBE37-SMP-ODO\",\n",
    "                                            \"valid_min\": \"-10\",\n",
    "                                            \"valid_max\": \"100\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "     \n",
    "  #Here it starts the 1030\n",
    "        \n",
    "        Oxygen_1030=([\"time\"], DO_b,{\"units\": \"umol/L\",\n",
    "                                            \"standard_name\": \"mole_concentration_of_dissolved_molecular_oxygen_in_sea_water\",\n",
    "                                            \"short_name\": \"DO\",\n",
    "                                            \"long_name\": \"mole_concentration_of_dissolved_molecular_oxygen_in_sea_water\",\n",
    "                                            \"description\": \"Molarity of dissolved Oxygen in sea water measured by SBE37-SMP-ODO \",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"400\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        DO_sat_1030=([\"time\"], DOsat_b,{\"units\": \"uatm\",\n",
    "                                            \"standard_name\": \"dissolved_molecular_oxygen_saturation_in_sea_water\",\n",
    "                                            \"short_name\": \"DOsat\",\n",
    "                                            \"long_name\": \"dissolved_molecular_oxygen_saturation_in_sea_water\",\n",
    "                                            \"description\": \"Dissolved Oxygen saturation in sea water measured by SBE37-SMP-ODO.\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"400\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        \n",
    "        \n",
    "        Depth_1030=([\"time\"], Depth_b,{\"units\": \"m\",\n",
    "                                            \"standard_name\": \"depth\",\n",
    "                                            \"short_name\": \"depth\",\n",
    "                                            \"long_name\": \"depth\",\n",
    "                                            \"description\": \"depth from where the water sample is collected (m)\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"10000\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "    \n",
    "        air_temperature_1030=([\"time\"], Air_Temp_b,{\"units\": \"K\",\n",
    "                                            \"standard_name\": \"air_temperature\",\n",
    "                                            \"short_name\": \"Temp\",\n",
    "                                            \"long_name\": \"Temperature\",\n",
    "                                            \"description\": \"Air temperature is the bulk temperature of the air\",\n",
    "                                            \"valid_min\": \"-273\",\n",
    "                                            \"valid_max\": \"500\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        Press_1030=([\"time\"], Press_b,{\"units\": \"hPa\",\n",
    "                                            \"standard_name\": \"atmospheric_pressure\",\n",
    "                                            \"short_name\": \"atm_press\",\n",
    "                                            \"long_name\": \"Atmospheric Pressure\",\n",
    "                                            \"description\": \"Atmospheric Pressure\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"10000\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        Press_LICOR_1030=([\"time\"], Press_atm_LiCor_hPA,{\"units\": \"hPa\",\n",
    "                                            \"standard_name\": \"atmospheric_pressure\",\n",
    "                                            \"short_name\": \"atm_press\",\n",
    "                                            \"long_name\": \"Atmospheric Pressure\",\n",
    "                                            \"description\": \"Atmospheric Pressure\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"10000\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        \n",
    "\n",
    "        Salinity_1030=([\"time\"], Salinity_b,{\"units\": \"psu\",\n",
    "                                            \"standard_name\": \"sea_water_salinity\",\n",
    "                                            \"short_name\": \"so\",\n",
    "                                            \"long_name\": \"Sea Water Salinity\",\n",
    "                                            \"description\": \"Sea water salinity is the salt content of sea water on the Practical Salinity Scale, measured by SBE37 deployed from the research vessel\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"100\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"uncorrected\"}),\n",
    "\n",
    "        Temperature_1030=([\"time\"], Temperature_b,{\"units\": \"degree Celcius\",\n",
    "                                            \"standard_name\": \"sea_water_temperature\",\n",
    "                                            \"short_name\": \"SW_Temperature\",\n",
    "                                            \"long_name\": \"Sea Water Temperature\",\n",
    "                                            \"description\": \"Sea water temperature is the in situ temperature of the sea water measured by the SBE37-SMP-ODO\",\n",
    "                                            \"valid_min\": \"-10\",\n",
    "                                            \"valid_max\": \"100\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "                \n",
    "        xCO2_air=([\"time\"], xCO2_air_dry_corr_umol_mol,{\"units\": \"umol mol-1\",\n",
    "                                            \"standard_name\": \"surface_partial_pressure_of_carbon_dioxide_in_air\",\n",
    "                                            \"short_name\": \"pCO2\",\n",
    "                                            \"long_name\": \"Surface partial pressure of carbon dioxide in air\",\n",
    "                                            \"description\": \"pCO2 in air\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"400\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        xCO2_sea=([\"time\"], xCO2_sea_dry_corr_umol_mol,{\"units\": \"umol mol-1\",\n",
    "                                            \"standard_name\": \"surface_partial_pressure_of_carbon_dioxide_in_sea_water\",\n",
    "                                            \"short_name\": \"pCO2\",\n",
    "                                            \"long_name\": \"Surface partial pressure of carbon dioxide in sea water\",\n",
    "                                            \"description\": \"pCO2 in water\",\n",
    "                                            \"valid_min\": \"0\",\n",
    "                                            \"valid_max\": \"400\",\n",
    "                                            \"missing_value\": \"-9999999\",\n",
    "                                            \"axis\": \"time\",\n",
    "                                            \"history\": \"\"}),\n",
    "        \n",
    "\n",
    "    ),\n",
    "    coords=dict(\n",
    "    lon_1053=([\"lon_1053\"], Lon_a,{\"units\": \"degrees East\",\n",
    "                                        \"standard_name\": \"longitude\",\n",
    "                                        \"short_name\": \"lon\",\n",
    "                                        \"long_name\": \"Longitude\",\n",
    "                                        \"description\": \"The Longitude represents the degrees east\",\n",
    "                                        \"valid_min\": \"0\",\n",
    "                                        \"valid_max\": \"360\",\n",
    "                                        \"missing_value\": \"-9999999\",\n",
    "                                        \"history\": \"\"}),\n",
    "                       \n",
    "    lat_1053=([\"lat_1053\"], Lat_a,{\"units\": \"degrees North\",\n",
    "                                        \"standard_name\": \"latitude\",\n",
    "                                        \"short_name\": \"lat\",\n",
    "                                        \"long_name\": \"Latitude\",\n",
    "                                        \"description\": \"The latitude represents the degrees north\",\n",
    "                                        \"valid_min\": \"-90\",\n",
    "                                        \"valid_max\": \"90\",\n",
    "                                        \"missing_value\": \"-9999999\",\n",
    "                                        \"history\": \"\"}),\n",
    "    lon_1030=([\"lon_1030\"], Lon_b,{\"units\": \"degrees East\",\n",
    "                                        \"standard_name\": \"longitude\",\n",
    "                                        \"short_name\": \"lon\",\n",
    "                                        \"long_name\": \"Longitude\",\n",
    "                                        \"description\": \"The Longitude represents the degrees east\",\n",
    "                                        \"valid_min\": \"0\",\n",
    "                                        \"valid_max\": \"360\",\n",
    "                                        \"missing_value\": \"-9999999\",\n",
    "                                        \"history\": \"\"}),\n",
    "                       \n",
    "    lat_1030=([\"lat_1030\"], Lat_b,{\"units\": \"degrees North\",\n",
    "                                        \"standard_name\": \"latitude\",\n",
    "                                        \"short_name\": \"lat\",\n",
    "                                        \"long_name\": \"Latitude\",\n",
    "                                        \"description\": \"The latitude represents the degrees north\",\n",
    "                                        \"valid_min\": \"-90\",\n",
    "                                        \"valid_max\": \"90\",\n",
    "                                        \"missing_value\": \"-9999999\",\n",
    "                                        \"history\": \"\"}),\n",
    "    time=time,\n",
    "    reference_time=time[0],\n",
    ")\n",
    ")\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "81129138",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "ds.attrs['id']='DOI needed'\n",
    "\n",
    "ds.attrs['naming_authority']='NORCE Norwegian Research Centre (Norway),Istituto Nazionale di Oceanografia e Geofisica Sperimentale (Italy),Consiglio Nazionale delle Ricerche - Istituto di scienze marine (Italy),Consiglio Nazionale delle Ricerche - Istituto per lo studio degli impatti Antropici e Sostenibilità in ambiente marino (Italy),Laboratoire Océanographique de Villefranche/French National Centre for Scientific Research (France),GEOMAR Helmholtz Centre for Ocean Research Kiel (Germany) '\n",
    "\n",
    "ds.attrs['title'] = \"SD1053\"\n",
    "\n",
    "ds.attrs['summary']='SD1053 is an Autonomous Surface Vehicle (ASV) provided by the US company Saildrone. The SD1053 took part in the ATL2MED demonstration experiment from October 2019 to July 2020. During this time, the SD1053 equipped with scientific instruments sailed from the North-East Atlantic Ocean to the Gulf of Trieste in the Mediterranean Sea. A number of fixed ocean stations also took part in the experiment, e.g., DYFAMED, W1M3A, E2M3A, PALOMA, and MIRAMARE. A second ASV, SD1030, also took part in the demonstration experiment.'\n",
    "\n",
    "ds.attrs['keywords']='Meteorology, Hydrography'\n",
    "ds.attrs['keywords_vocabulary']='Temperature, Air pressure, Relative Humidity, Wind speed, Wind Direction'\n",
    "\n",
    "ds.attrs['geospatial_lat_min']=np.min(Lat_a)\n",
    "ds.attrs['geospatial_lat_max']=np.max(Lat_a)\n",
    "ds.attrs['geospatial_lon_miN']=np.min(Lon_a)\n",
    "ds.attrs['geospatial_lon_max']=np.max(Lon_a)\n",
    "\n",
    "ds.attrs['time_coverage_start']=str(np.min(time_a))\n",
    "ds.attrs['time_coverage_end']=str(np.max(time_a))\n",
    "\n",
    "ds.attrs['Conventions']='CF 1.14'\n",
    "\n",
    "ds.attrs['processing_level']='LEVEL 2'\n",
    "\n",
    "ds.attrs['date_created']=str(today)\n",
    "\n",
    "ds.attrs['creator_type']='Group'\n",
    "ds.attrs['creator_institution']='Geophysical Institute'\n",
    "ds.attrs['creator_name']='Joan Mateu Horrach Pou'\n",
    "ds.attrs['creator_email']='joan.pou@uib.no'\n",
    "ds.attrs['creator_url']=''\n",
    "\n",
    "ds.attrs['institution']='Geophysical Institute'\n",
    "\n",
    "ds.attrs['publisher_name']='Joan Mateu Horrach Pou'\n",
    "ds.attrs['publisher_email']='joan.pou@uib.no'\n",
    "ds.attrs['publisher_url']='I need the url'\n",
    "\n",
    "ds.attrs['project']='ICOS'\n",
    "\n",
    "ds.attrs['platform']='SD1053'\n",
    "ds.attrs['platform_vocabulary']='Saildrone Autonomous Surface Vehicle'\n",
    "\n",
    "ds.attrs['instrument']='Saildrone, SBE37-SMP-ODO'\n",
    "\n",
    "ds.attrs['iso_topic_category']='Needed'\n",
    "ds.attrs['activity_type']='in situ observation'\n",
    "ds.attrs['operational_status']='active'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8789be90",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "expected bytes, int found",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mSD_1053.nc\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/xarray/core/dataset.py:1957\u001b[0m, in \u001b[0;36mDataset.to_netcdf\u001b[0;34m(self, path, mode, format, group, engine, encoding, unlimited_dims, compute, invalid_netcdf)\u001b[0m\n\u001b[1;32m   1954\u001b[0m     encoding \u001b[38;5;241m=\u001b[39m {}\n\u001b[1;32m   1955\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mxarray\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbackends\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m to_netcdf\n\u001b[0;32m-> 1957\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mto_netcdf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# type: ignore  # mypy cannot resolve the overloads:(\u001b[39;49;00m\n\u001b[1;32m   1958\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1959\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1960\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[1;32m   1961\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[1;32m   1962\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[1;32m   1963\u001b[0m \u001b[43m    \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mengine\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1964\u001b[0m \u001b[43m    \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1965\u001b[0m \u001b[43m    \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munlimited_dims\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1966\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcompute\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcompute\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1967\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmultifile\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   1968\u001b[0m \u001b[43m    \u001b[49m\u001b[43minvalid_netcdf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minvalid_netcdf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1969\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/xarray/backends/api.py:1272\u001b[0m, in \u001b[0;36mto_netcdf\u001b[0;34m(dataset, path_or_file, mode, format, group, engine, encoding, unlimited_dims, compute, multifile, invalid_netcdf)\u001b[0m\n\u001b[1;32m   1267\u001b[0m \u001b[38;5;66;03m# TODO: figure out how to refactor this logic (here and in save_mfdataset)\u001b[39;00m\n\u001b[1;32m   1268\u001b[0m \u001b[38;5;66;03m# to avoid this mess of conditionals\u001b[39;00m\n\u001b[1;32m   1269\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1270\u001b[0m     \u001b[38;5;66;03m# TODO: allow this work (setting up the file for writing array data)\u001b[39;00m\n\u001b[1;32m   1271\u001b[0m     \u001b[38;5;66;03m# to be parallelized with dask\u001b[39;00m\n\u001b[0;32m-> 1272\u001b[0m     \u001b[43mdump_to_store\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1273\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdataset\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstore\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mencoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munlimited_dims\u001b[49m\n\u001b[1;32m   1274\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1275\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m autoclose:\n\u001b[1;32m   1276\u001b[0m         store\u001b[38;5;241m.\u001b[39mclose()\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/xarray/backends/api.py:1319\u001b[0m, in \u001b[0;36mdump_to_store\u001b[0;34m(dataset, store, writer, encoder, encoding, unlimited_dims)\u001b[0m\n\u001b[1;32m   1316\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m encoder:\n\u001b[1;32m   1317\u001b[0m     variables, attrs \u001b[38;5;241m=\u001b[39m encoder(variables, attrs)\n\u001b[0;32m-> 1319\u001b[0m \u001b[43mstore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstore\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_encoding\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munlimited_dims\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/xarray/backends/common.py:278\u001b[0m, in \u001b[0;36mAbstractWritableDataStore.store\u001b[0;34m(self, variables, attributes, check_encoding_set, writer, unlimited_dims)\u001b[0m\n\u001b[1;32m    276\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_attributes(attributes)\n\u001b[1;32m    277\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_dimensions(variables, unlimited_dims\u001b[38;5;241m=\u001b[39munlimited_dims)\n\u001b[0;32m--> 278\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_variables\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    279\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcheck_encoding_set\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwriter\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43munlimited_dims\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munlimited_dims\u001b[49m\n\u001b[1;32m    280\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/xarray/backends/common.py:320\u001b[0m, in \u001b[0;36mAbstractWritableDataStore.set_variables\u001b[0;34m(self, variables, check_encoding_set, writer, unlimited_dims)\u001b[0m\n\u001b[1;32m    315\u001b[0m check \u001b[38;5;241m=\u001b[39m vn \u001b[38;5;129;01min\u001b[39;00m check_encoding_set\n\u001b[1;32m    316\u001b[0m target, source \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprepare_variable(\n\u001b[1;32m    317\u001b[0m     name, v, check, unlimited_dims\u001b[38;5;241m=\u001b[39munlimited_dims\n\u001b[1;32m    318\u001b[0m )\n\u001b[0;32m--> 320\u001b[0m \u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/xarray/backends/common.py:165\u001b[0m, in \u001b[0;36mArrayWriter.add\u001b[0;34m(self, source, target, region)\u001b[0m\n\u001b[1;32m    163\u001b[0m     target[region] \u001b[38;5;241m=\u001b[39m source\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 165\u001b[0m     target[\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;241m.\u001b[39m] \u001b[38;5;241m=\u001b[39m source\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.9/site-packages/xarray/backends/netCDF4_.py:79\u001b[0m, in \u001b[0;36mBaseNetCDF4Array.__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m     77\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatastore\u001b[38;5;241m.\u001b[39mlock:\n\u001b[1;32m     78\u001b[0m     data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_array(needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[0;32m---> 79\u001b[0m     data[key] \u001b[38;5;241m=\u001b[39m value\n\u001b[1;32m     80\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatastore\u001b[38;5;241m.\u001b[39mautoclose:\n\u001b[1;32m     81\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdatastore\u001b[38;5;241m.\u001b[39mclose(needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:5505\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Variable.__setitem__\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:5807\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Variable._put\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: expected bytes, int found"
     ]
    }
   ],
   "source": [
    "comp = dict(zlib=True, complevel=6)  # Adjust complevel as needed\n",
    "encoding = {var: comp for var in ds.data_vars}\n",
    "ds.to_netcdf('SD_1053.nc', encoding=encoding)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d8858b1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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