{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "# import netCDF4 as nc\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_empty_dataframe(start_year=2000, end_year=2024, latitude_len=360, longitude_len = 720):\n",
    "    df = pd.MultiIndex.from_product([[year for year in range(start_year, end_year+1)],\n",
    "                                    [month+1 for month in range(12)],\n",
    "                                    [latitude for latitude in range(latitude_len)],\n",
    "                                    [longitude for longitude in range(longitude_len)]],\n",
    "                                    names=['year', 'month', 'latitude', 'longitude']).to_frame(index=False)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# update env data to 202503"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import netCDF4 as nc\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dic = {\n",
    "#     'sst':{\n",
    "#         'variable' : 'sst',\n",
    "#         'start_year': 1981, \n",
    "#         'start_month':9, \n",
    "#         'end_year':2025, \n",
    "#         'end_month':5\n",
    "#     },\n",
    "#     'ice':{\n",
    "#         'variable' : 'ice',\n",
    "#         'start_year': 1981, \n",
    "#         'start_month':9, \n",
    "#         'end_year':2025, \n",
    "#         'end_month':5\n",
    "#     },\n",
    "#     'chl':{\n",
    "#         'variable' : 'CHL1_mean',\n",
    "#         'start_year': 1997, \n",
    "#         'start_month':9, \n",
    "#         'end_year':2025, \n",
    "#         'end_month':5\n",
    "#     },\n",
    "#     'mld':{\n",
    "#         'variable' : 'somxl030',\n",
    "#         'start_year': 1958, \n",
    "#         'start_month':1, \n",
    "#         'end_year':2025, \n",
    "#         'end_month':4\n",
    "#     },\n",
    "    \n",
    "#     'sss':{\n",
    "#         'variable' : 'salinity',\n",
    "#         'start_year': 1959, \n",
    "#         'start_month':1, \n",
    "#         'end_year':2025, \n",
    "#         'end_month':3\n",
    "#     },\n",
    "#     'ssh':{\n",
    "#         'variable' : 'sossheig',\n",
    "#         'start_year': 1958, \n",
    "#         'start_month':1, \n",
    "#         'end_year':2025, \n",
    "#         'end_month':4\n",
    "#     },\n",
    "#     'slp':{\n",
    "#         'variable' : 'msl',\n",
    "#         'start_year': 1940, \n",
    "#         'start_month':1, \n",
    "#         'end_year':2025, \n",
    "#         'end_month':5\n",
    "#     },\n",
    "#     'wind':{\n",
    "#         'variable' : 'wind',\n",
    "#         'start_year': 1940, \n",
    "#         'start_month':1, \n",
    "#         'end_year':2025, \n",
    "#         'end_month':5\n",
    "#     },\n",
    "#     # 'co2':{\n",
    "#     #     'variable' : 'CO2',\n",
    "#     #     'start_year': 1979, \n",
    "#     #     'start_month':1, \n",
    "#     #     'end_year':2023, \n",
    "#     #     'end_month':12\n",
    "#     # },\n",
    "# }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic = {\n",
    "    'sst':'sst',\n",
    "    'ice':'ice',\n",
    "    'chl':'CHL1_mean',\n",
    "    'mld':'somxl030',\n",
    "    'sss':'salinity',\n",
    "    'ssh':'sossheig',\n",
    "    'slp':'msl',\n",
    "    'wind':'wind',\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def count_month(start_year, start_month, end_year, end_month, start=(1959, 1), end=(2023, 12)):\n",
    "    s_year, s_month  = start\n",
    "    e_year, e_month  = end\n",
    "    pre_mon = (start_year-s_year)*12 + (start_month-s_month)\n",
    "    tail_mon = (e_year-end_year)*12 + (e_month-end_month)\n",
    "    return pre_mon, tail_mon\n",
    "\n",
    "def concat_pre_end(pre_month, tail_month, data):\n",
    "    new_data = data.copy()\n",
    "    if pre_month > 0:\n",
    "        prefix = np.ones(180 * 360 * pre_month)\n",
    "        prefix[:]=np.nan\n",
    "        new_data = np.concatenate((prefix, new_data))\n",
    "    elif pre_month <0:\n",
    "        del_length = abs(180 * 360 * pre_month)\n",
    "        new_data = new_data[del_length:]\n",
    "\n",
    "    if tail_month > 0:\n",
    "        tail = np.ones(180 * 360 * tail_month)\n",
    "        tail[:]=np.nan\n",
    "        new_data = np.concatenate((new_data, tail))\n",
    "    elif tail_month < 0:\n",
    "        del_length = abs(180 * 360 * tail_month)\n",
    "        new_data = new_data[:-del_length]\n",
    "    return new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CHL.nc\n",
      "CO2.nc\n",
      "ICE.nc\n",
      "MLD.nc\n",
      "Ocean_RECCAP2_mask.nc\n",
      "SLP.nc\n",
      "SSH.nc\n",
      "SSS.nc\n",
      "SST.nc\n",
      "Wind.nc\n"
     ]
    }
   ],
   "source": [
    "# env_path = '/data/kpiyu/ANGCB/Ocean/Data/Input/Environmental_variables'  # tsinghua machine\n",
    "# env_path = '/data/angcb/home/kpiyu/data/Environmental_variables/2024/Environmental_variables' # ms machine\n",
    "env_path = '/data/angcb/home/kpiyu/data/Environmental_variables/2025/Environmental_variables'\n",
    "for i in os.listdir(env_path): \n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_time(pro_data):\n",
    "    all_vars = list(pro_data.coords)\n",
    "    time_var = 'time'\n",
    "    for var in all_vars:\n",
    "        if 'time' in var:\n",
    "            time_var = var\n",
    "            break\n",
    "\n",
    "\n",
    "    start_year = pd.to_datetime(pro_data[time_var].values[0]).year\n",
    "    start_month = pd.to_datetime(pro_data[time_var].values[0]).month\n",
    "    end_year = pd.to_datetime(pro_data[time_var].values[-1]).year\n",
    "    end_month = pd.to_datetime(pro_data[time_var].values[-1]).month\n",
    "\n",
    "    return start_year, start_month, end_year, end_month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_nc_data(key, dic, env_path= '/data/kpiyu/ANGCB/Ocean/Data/Input/Environmental_variables', start=(1959, 1), end=(2023, 12)):\n",
    "#     key = 'wind'\n",
    "    # if key == 'wind':\n",
    "    #     pro_dir = os.path.join(env_path, f'Wind.nc')\n",
    "    # else:\n",
    "\n",
    "    # pro_dir = os.path.join(env_path, f'{key.upper()}.nc')\n",
    "    pro_dir = os.path.join(env_path, f'{key}.nc')\n",
    "    variable = dic[key.lower()]\n",
    "    # pro_data = nc.Dataset(pro_dir)\n",
    "    pro_data = xr.open_dataset(pro_dir)\n",
    "    \n",
    "    data = pro_data[variable].values.flatten()\n",
    "    print(data.shape)\n",
    "    start_year, start_month, end_year, end_month = get_time(pro_data)\n",
    "    # start_year, start_month, end_year, end_month = info['start_year'],info['start_month'],info['end_year'],info['end_month']\n",
    "    print(start_year, start_month, end_year, end_month)\n",
    "    pre_mon, tail_mon = count_month(start_year, start_month, end_year, end_month, start=start, end=end)\n",
    "    print(pre_mon, tail_mon)\n",
    "    new_data = concat_pre_end(pre_mon, tail_mon, data)\n",
    "    \n",
    "    # fill_value = pro_data.variables[variable]._FillValue\n",
    "    # print('fill_value:',fill_value)\n",
    "    # new_data = np.where(new_data == fill_value, np.nan, new_data)  \n",
    "    return new_data\n",
    "    # df[key] = new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CHL\n",
      "(21902400,)\n",
      "1997 9 2025 10\n",
      "464 2\n",
      "CO2\n",
      "ICE\n",
      "(34344000,)\n",
      "1981 9 2025 10\n",
      "272 2\n",
      "MLD\n",
      "(51969600,)\n",
      "1959 1 2025 10\n",
      "0 2\n",
      "Ocean_RECCAP2_mask\n",
      "SLP\n",
      "(51969600,)\n",
      "1959 1 2025 10\n",
      "0 2\n",
      "SSH\n",
      "(51969600,)\n",
      "1959 1 2025 10\n",
      "0 2\n",
      "SSS\n",
      "(51904800,)\n",
      "1959 1 2025 9\n",
      "0 3\n",
      "SST\n",
      "(34344000,)\n",
      "1981 9 2025 10\n",
      "272 2\n",
      "Wind\n",
      "(51969600,)\n",
      "1959 1 2025 10\n",
      "0 2\n"
     ]
    }
   ],
   "source": [
    "env_end_year = 2025\n",
    "env_end_month = 12\n",
    "\n",
    "env_start_year = 1959\n",
    "env_start_month = 1\n",
    "\n",
    "add_data = generate_empty_dataframe(start_year=env_start_year, end_year=env_end_year, latitude_len=180, longitude_len = 360)\n",
    "\n",
    "for key in os.listdir(env_path): \n",
    "    key = key.split('.')[0]\n",
    "    print(key)\n",
    "    if key.lower() not in dic:\n",
    "        continue\n",
    "    new_data = get_nc_data(key, dic, env_path=env_path, start=(env_start_year, env_start_month), end=(env_end_year, env_end_month))\n",
    "    add_data[key.lower()] = new_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# adddata24_idx = add_data[add_data.year == 2024].index.values\n",
    "# data24_idx = data[data.year == 2024].index.values\n",
    "\n",
    "# # for col in [ 'sst', 'ice', 'chl', 'mld','sss', 'ssh', 'slp', 'wind']:\n",
    "# #     data.loc[data24_idx, col] = add_data.loc[adddata24_idx, col].values\n",
    "\n",
    "# # or use\n",
    "# cols = [ 'sst', 'ice', 'chl', 'mld','sss', 'ssh', 'slp', 'wind']\n",
    "# data.loc[data24_idx, cols] = add_data.loc[adddata24_idx, cols].values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.concat([data, add_data[add_data.year == 2024]], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([], dtype='int64')\n",
      "Index([], dtype='int64')\n",
      "Index([], dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "for i in ['sst', 'ice', 'chl']:\n",
    "    err_idx = add_data[add_data[i] == -999].index\n",
    "    print(err_idx)\n",
    "    if err_idx.shape[0] > 0:\n",
    "        add_data.loc[err_idx, i] = np.nan\n",
    "for i in ['mld', 'ssh']:\n",
    "    if add_data[i].max() > 1e36:\n",
    "        print(i)\n",
    "        err_idx = add_data[add_data[i] == add_data[i].max()].index\n",
    "        if err_idx.shape[0] > 0:\n",
    "            add_data.loc[err_idx, i] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>chl</th>\n",
       "      <th>ice</th>\n",
       "      <th>mld</th>\n",
       "      <th>slp</th>\n",
       "      <th>ssh</th>\n",
       "      <th>sss</th>\n",
       "      <th>sst</th>\n",
       "      <th>wind</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100396.607350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.977409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100397.439225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.990139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100398.271100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.003550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100399.380266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.017982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100400.212141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.033816</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>52099195</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099196</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099197</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099198</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099199</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52099200 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          year  month  latitude  longitude  chl  ice  mld            slp  ssh  \\\n",
       "0         1959      1         0          0  NaN  NaN  NaN  100396.607350  NaN   \n",
       "1         1959      1         0          1  NaN  NaN  NaN  100397.439225  NaN   \n",
       "2         1959      1         0          2  NaN  NaN  NaN  100398.271100  NaN   \n",
       "3         1959      1         0          3  NaN  NaN  NaN  100399.380266  NaN   \n",
       "4         1959      1         0          4  NaN  NaN  NaN  100400.212141  NaN   \n",
       "...        ...    ...       ...        ...  ...  ...  ...            ...  ...   \n",
       "52099195  2025     12       179        355  NaN  NaN  NaN            NaN  NaN   \n",
       "52099196  2025     12       179        356  NaN  NaN  NaN            NaN  NaN   \n",
       "52099197  2025     12       179        357  NaN  NaN  NaN            NaN  NaN   \n",
       "52099198  2025     12       179        358  NaN  NaN  NaN            NaN  NaN   \n",
       "52099199  2025     12       179        359  NaN  NaN  NaN            NaN  NaN   \n",
       "\n",
       "          sss  sst      wind  \n",
       "0         NaN  NaN  2.977409  \n",
       "1         NaN  NaN  2.990139  \n",
       "2         NaN  NaN  3.003550  \n",
       "3         NaN  NaN  3.017982  \n",
       "4         NaN  NaN  3.033816  \n",
       "...       ...  ...       ...  \n",
       "52099195  NaN  NaN       NaN  \n",
       "52099196  NaN  NaN       NaN  \n",
       "52099197  NaN  NaN       NaN  \n",
       "52099198  NaN  NaN       NaN  \n",
       "52099199  NaN  NaN       NaN  \n",
       "\n",
       "[52099200 rows x 12 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": []
  },
  {
   "cell_type": "code",
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   "source": [
    "# add_data.to_feather('./dataset/preprocessed_data/data2503.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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       "                 co2   sst   ice  chl        mld  sss           ssh  \\\n",
       "19245600  420.977710   NaN   NaN  NaN        NaN  NaN  9.969210e+36   \n",
       "19245601  420.977710   NaN   NaN  NaN        NaN  NaN  9.969210e+36   \n",
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       "...              ...   ...   ...  ...        ...  ...           ...   \n",
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       "19310398  420.939763 -1.71  0.92  NaN  24.242706  NaN -4.814128e-01   \n",
       "19310399  420.939763 -1.69  0.92  NaN  24.357740  NaN -4.810089e-01   \n",
       "\n",
       "                    slp      wind  \n",
       "19245600  101158.466004  4.617877  \n",
       "19245601  101161.377566  4.628388  \n",
       "19245602  101164.150481  4.639112  \n",
       "19245603  101167.062043  4.650912  \n",
       "19245604  101170.112251  4.663766  \n",
       "...                 ...       ...  \n",
       "19310395  101016.908645  2.411277  \n",
       "19310396  101017.047291  2.406625  \n",
       "19310397  101017.324583  2.403111  \n",
       "19310398  101017.463228  2.399661  \n",
       "19310399  101017.740520  2.397128  \n",
       "\n",
       "[64800 rows x 9 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data[(data['year']==2024) & (data['month']==10)][[ 'co2', 'sst', 'ice', 'chl', 'mld','sss', 'ssh', 'slp', 'wind']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Add Coast feature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import xarray as xr\n",
    "\n",
    "region_mask_path = os.path.join(env_path, 'Ocean_RECCAP2_mask.nc')\n",
    "region_mask = xr.open_dataset(region_mask_path)\n",
    "# open_ocean = arctic + indian +/...\n",
    "# open ocean = coast + 公海\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: \"(\";\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: \")\";\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: \",\";\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "  border-color: var(--xr-background-color-row-even);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 523kB\n",
       "Dimensions:     (lat: 180, lon: 360)\n",
       "Coordinates:\n",
       "  * lon         (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Data variables:\n",
       "    seamask     (lat, lon) int8 65kB ...\n",
       "    coast       (lat, lon) int8 65kB ...\n",
       "    open_ocean  (lat, lon) int8 65kB ...\n",
       "    atlantic    (lat, lon) int8 65kB ...\n",
       "    pacific     (lat, lon) int8 65kB ...\n",
       "    indian      (lat, lon) int8 65kB ...\n",
       "    arctic      (lat, lon) int8 65kB ...\n",
       "    southern    (lat, lon) int8 65kB ...\n",
       "Attributes:\n",
       "    date:         2022-06-20\n",
       "    contact:      gregorl@ethz.ch\n",
       "    description:  regional masks created for the RECCAP2 ocean chapters. The ...\n",
       "    helper_code:  The following snippet of code can be used to list the regio...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d74954b2-481e-4b00-a02e-f1d734d1f9d5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d74954b2-481e-4b00-a02e-f1d734d1f9d5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a25db83f-d6b0-4b71-b461-f0337b156290' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a25db83f-d6b0-4b71-b461-f0337b156290' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-5eb70b33-200d-4ffb-a564-89557ae6a1a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5eb70b33-200d-4ffb-a564-89557ae6a1a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfe2d462-3882-45e1-8dd4-0a2c3ae34b46' class='xr-var-data-in' type='checkbox'><label for='data-dfe2d462-3882-45e1-8dd4-0a2c3ae34b46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5], shape=(360,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-ea91d6db-b173-45a8-8157-872541de227b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea91d6db-b173-45a8-8157-872541de227b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a55f4596-9768-40c8-aa77-f49328933e72' class='xr-var-data-in' type='checkbox'><label for='data-a55f4596-9768-40c8-aa77-f49328933e72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3e3da4f7-da29-4392-b3ec-16627e7e4710' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3e3da4f7-da29-4392-b3ec-16627e7e4710' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>seamask</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-96ed3803-6a5f-47d4-9a84-f6adcbae484f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96ed3803-6a5f-47d4-9a84-f6adcbae484f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6288c917-78a7-4b1a-9744-4bd3b16037ac' class='xr-var-data-in' type='checkbox'><label for='data-6288c917-78a7-4b1a-9744-4bd3b16037ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>sea mask based on OISSTv2 coverage on 2010-01-01 where True is sea and False is land</dd></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>coast</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7295c282-9da4-4294-98f9-7c7485ab15e8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7295c282-9da4-4294-98f9-7c7485ab15e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b915a360-6e02-4303-b6af-219611a52c4b' class='xr-var-data-in' type='checkbox'><label for='data-b915a360-6e02-4303-b6af-219611a52c4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>open_ocean</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8bbdbc05-1bd3-4ee2-95cb-8db69502a6a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8bbdbc05-1bd3-4ee2-95cb-8db69502a6a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44b3f2c4-53a2-4a3e-9b77-b03e821ba14c' class='xr-var-data-in' type='checkbox'><label for='data-44b3f2c4-53a2-4a3e-9b77-b03e821ba14c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>region_name :</span></dt><dd>1.Atlantic, 2.Pacific, 3.Indian, 4.Arctic, 5.Southern</dd></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>atlantic</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-04c388de-1e54-4b1c-88b0-070450f268e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04c388de-1e54-4b1c-88b0-070450f268e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c94f701-3d19-4064-bb90-c1f2ced7aa99' class='xr-var-data-in' type='checkbox'><label for='data-7c94f701-3d19-4064-bb90-c1f2ced7aa99' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>region_name :</span></dt><dd>1.NA SPSS, 2.NA STSS, 3.NA STPS, 4.AEQU, 5.SA STPS, 6.MED (not in FM14)</dd></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pacific</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-20602561-0835-427f-a023-910c446a89a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20602561-0835-427f-a023-910c446a89a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d05ccb3b-fb64-4d89-8225-c3ee97b38531' class='xr-var-data-in' type='checkbox'><label for='data-d05ccb3b-fb64-4d89-8225-c3ee97b38531' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>region_name :</span></dt><dd>1.NP SPSS, 2.NP STSS, 3.NP STPS, 4.PEQU-W, 5.PEQU-E, 6.SP STPS</dd></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>indian</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8eed89cf-18c3-4fe7-826f-923b33b7e43c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8eed89cf-18c3-4fe7-826f-923b33b7e43c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e7f0262-a75b-4639-aa7d-08efa7ca4d5e' class='xr-var-data-in' type='checkbox'><label for='data-3e7f0262-a75b-4639-aa7d-08efa7ca4d5e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>region_name :</span></dt><dd>1.Arabian Sea, 2.Bay of Bengal, 3.Equatorial Indian, 4.Southern Indian</dd></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>arctic</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-37801c16-f8e6-41d8-8cb3-3150255d7797' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37801c16-f8e6-41d8-8cb3-3150255d7797' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe791915-3078-441f-b6b6-59c0a95cf87d' class='xr-var-data-in' type='checkbox'><label for='data-fe791915-3078-441f-b6b6-59c0a95cf87d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Arctic_biome</dd><dt><span>units :</span></dt><dd>NaN</dd><dt><span>history :</span></dt><dd>[all_my_code.0.3.2.dev19@2022-10-25] Rename coordinates to [time, lat, lon, depth] with fuzzy matching</dd></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=int8]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>southern</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-70775466-5146-4026-9c74-b25dded74fe6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70775466-5146-4026-9c74-b25dded74fe6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86f75968-ab25-485d-9758-386459831a31' class='xr-var-data-in' type='checkbox'><label for='data-86f75968-ab25-485d-9758-386459831a31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>region_name :</span></dt><dd>1.SO STSS, 2.SO SPSS, 3.SO ICE</dd></dl></div><div class='xr-var-data'><pre>[64800 values with dtype=int8]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-15fd0084-de50-44f8-9381-153a64bde1ea' class='xr-section-summary-in' type='checkbox'  ><label for='section-15fd0084-de50-44f8-9381-153a64bde1ea' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2b58ac73-3427-4c09-99dc-280cfebe1aaf' class='xr-index-data-in' type='checkbox'/><label for='index-2b58ac73-3427-4c09-99dc-280cfebe1aaf' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,\n",
       "       -170.5,\n",
       "       ...\n",
       "        170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,\n",
       "        179.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c7115378-b43c-4856-9e5d-6baa69ccac65' class='xr-index-data-in' type='checkbox'/><label for='index-c7115378-b43c-4856-9e5d-6baa69ccac65' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       ...\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5b25247-85f6-4db4-a814-565028488a0c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d5b25247-85f6-4db4-a814-565028488a0c' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>date :</span></dt><dd>2022-06-20</dd><dt><span>contact :</span></dt><dd>gregorl@ethz.ch</dd><dt><span>description :</span></dt><dd>regional masks created for the RECCAP2 ocean chapters. The region names are given in the attributes of each varible. The sub-regional masks are named according to Fay and McKinley 2014. Find the details of the naming here: https://doi.pangaea.de/10013/epic.42948.d018. \n",
       "\n",
       "Masks were created using the script located at: https://github.com/RECCAP2-ocean/shared-resources/blob/master/regions/scripts/reccap2ocean_regions.py. This mask is thus fully reproducible as long as the source data is also present for download. </dd><dt><span>helper_code :</span></dt><dd>The following snippet of code can be used to list the region names as a dictionary:\n",
       "dict([r.strip().split(&#x27;.&#x27;) for r in region_names.split(&#x27;,&#x27;)])</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 523kB\n",
       "Dimensions:     (lat: 180, lon: 360)\n",
       "Coordinates:\n",
       "  * lon         (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "Data variables:\n",
       "    seamask     (lat, lon) int8 65kB ...\n",
       "    coast       (lat, lon) int8 65kB ...\n",
       "    open_ocean  (lat, lon) int8 65kB ...\n",
       "    atlantic    (lat, lon) int8 65kB ...\n",
       "    pacific     (lat, lon) int8 65kB ...\n",
       "    indian      (lat, lon) int8 65kB ...\n",
       "    arctic      (lat, lon) int8 65kB ...\n",
       "    southern    (lat, lon) int8 65kB ...\n",
       "Attributes:\n",
       "    date:         2022-06-20\n",
       "    contact:      gregorl@ethz.ch\n",
       "    description:  regional masks created for the RECCAP2 ocean chapters. The ...\n",
       "    helper_code:  The following snippet of code can be used to list the regio..."
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "latitude_len, longitude_len = 180, 360\n",
    "df = pd.MultiIndex.from_product([[latitude for latitude in range(latitude_len)],\n",
    "                                    [longitude for longitude in range(longitude_len)]],\n",
    "                                    names=['latitude', 'longitude']).to_frame(index=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = region_mask.variables['open_ocean']\n",
    "df['type'] = a.values.flatten()\n",
    "map_arr = ['land','Atlantic','Pacific','Indian','Arctic', 'Southern']\n",
    "df['type'] = df['type'].apply(lambda x: map_arr[x])\n",
    "df_encoded = pd.get_dummies(df, columns=['type'])  \n",
    "\n",
    "df_encoded['type_ocean'] = ~df_encoded['type_land'] \n",
    "\n",
    "coast = region_mask.variables['coast']\n",
    "df_encoded['type_coast'] = coast.values.flatten()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type_Arctic\n",
      "type_Atlantic\n",
      "type_Indian\n",
      "type_Pacific\n",
      "type_Southern\n",
      "type_land\n",
      "type_ocean\n",
      "type_coast\n"
     ]
    }
   ],
   "source": [
    "cols = ['type_Arctic', 'type_Atlantic', 'type_Indian',\n",
    "       'type_Pacific', 'type_Southern', 'type_land', 'type_ocean',\n",
    "       'type_coast']\n",
    "\n",
    "for col in cols:\n",
    "    print(col)\n",
    "    df_encoded[col] = df_encoded[col].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>type_Arctic</th>\n",
       "      <th>type_Atlantic</th>\n",
       "      <th>type_Indian</th>\n",
       "      <th>type_Pacific</th>\n",
       "      <th>type_Southern</th>\n",
       "      <th>type_land</th>\n",
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       "      <th>type_coast</th>\n",
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       "      <th>64795</th>\n",
       "      <td>179</td>\n",
       "      <td>355</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64796</th>\n",
       "      <td>179</td>\n",
       "      <td>356</td>\n",
       "      <td>1</td>\n",
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       "      <td>179</td>\n",
       "      <td>358</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
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       "      <td>179</td>\n",
       "      <td>359</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64800 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       latitude  longitude  type_Arctic  type_Atlantic  type_Indian  \\\n",
       "0             0          0            0              0            0   \n",
       "1             0          1            0              0            0   \n",
       "2             0          2            0              0            0   \n",
       "3             0          3            0              0            0   \n",
       "4             0          4            0              0            0   \n",
       "...         ...        ...          ...            ...          ...   \n",
       "64795       179        355            1              0            0   \n",
       "64796       179        356            1              0            0   \n",
       "64797       179        357            1              0            0   \n",
       "64798       179        358            1              0            0   \n",
       "64799       179        359            1              0            0   \n",
       "\n",
       "       type_Pacific  type_Southern  type_land  type_ocean  type_coast  \n",
       "0                 0              0          1           0           0  \n",
       "1                 0              0          1           0           0  \n",
       "2                 0              0          1           0           0  \n",
       "3                 0              0          1           0           0  \n",
       "4                 0              0          1           0           0  \n",
       "...             ...            ...        ...         ...         ...  \n",
       "64795             0              0          0           1           0  \n",
       "64796             0              0          0           1           0  \n",
       "64797             0              0          0           1           0  \n",
       "64798             0              0          0           1           0  \n",
       "64799             0              0          0           1           0  \n",
       "\n",
       "[64800 rows x 10 columns]"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_encoded"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>sst</th>\n",
       "      <th>ice</th>\n",
       "      <th>chl</th>\n",
       "      <th>mld</th>\n",
       "      <th>sss</th>\n",
       "      <th>ssh</th>\n",
       "      <th>slp</th>\n",
       "      <th>wind</th>\n",
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       "      <th>1</th>\n",
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       "      <td>100397.4375</td>\n",
       "      <td>2.990093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>100398.3125</td>\n",
       "      <td>3.003453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
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       "          year  month  latitude  longitude  sst  ice  chl  mld  sss  ssh  \\\n",
       "0         1959      1         0          0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "1         1959      1         0          1  NaN  NaN  NaN  NaN  NaN  NaN   \n",
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       "...        ...    ...       ...        ...  ...  ...  ...  ...  ...  ...   \n",
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       "52099199  2025     12       179        359  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "                  slp      wind  \n",
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       "2         100398.3125  3.003453  \n",
       "3         100399.3125  3.018199  \n",
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       "...               ...       ...  \n",
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       "\n",
       "[52099200 rows x 12 columns]"
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     "execution_count": 119,
     "metadata": {},
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   "source": [
    "add_data"
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  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_repeated = pd.concat([df_encoded] * int(add_data.shape[0] / df_encoded.shape[0] ), ignore_index=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
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       "          latitude  longitude  type_Arctic  type_Atlantic  type_Indian  \\\n",
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  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "add_data[cols] = df_repeated[cols].values"
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  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099196</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099197</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099198</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099199</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52099200 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          year  month  latitude  longitude  chl  ice  mld            slp  ssh  \\\n",
       "0         1959      1         0          0  NaN  NaN  NaN  100396.607350  NaN   \n",
       "1         1959      1         0          1  NaN  NaN  NaN  100397.439225  NaN   \n",
       "2         1959      1         0          2  NaN  NaN  NaN  100398.271100  NaN   \n",
       "3         1959      1         0          3  NaN  NaN  NaN  100399.380266  NaN   \n",
       "4         1959      1         0          4  NaN  NaN  NaN  100400.212141  NaN   \n",
       "...        ...    ...       ...        ...  ...  ...  ...            ...  ...   \n",
       "52099195  2025     12       179        355  NaN  NaN  NaN            NaN  NaN   \n",
       "52099196  2025     12       179        356  NaN  NaN  NaN            NaN  NaN   \n",
       "52099197  2025     12       179        357  NaN  NaN  NaN            NaN  NaN   \n",
       "52099198  2025     12       179        358  NaN  NaN  NaN            NaN  NaN   \n",
       "52099199  2025     12       179        359  NaN  NaN  NaN            NaN  NaN   \n",
       "\n",
       "          sss  sst      wind  type_Arctic  type_Atlantic  type_Indian  \\\n",
       "0         NaN  NaN  2.977409            0              0            0   \n",
       "1         NaN  NaN  2.990139            0              0            0   \n",
       "2         NaN  NaN  3.003550            0              0            0   \n",
       "3         NaN  NaN  3.017982            0              0            0   \n",
       "4         NaN  NaN  3.033816            0              0            0   \n",
       "...       ...  ...       ...          ...            ...          ...   \n",
       "52099195  NaN  NaN       NaN            1              0            0   \n",
       "52099196  NaN  NaN       NaN            1              0            0   \n",
       "52099197  NaN  NaN       NaN            1              0            0   \n",
       "52099198  NaN  NaN       NaN            1              0            0   \n",
       "52099199  NaN  NaN       NaN            1              0            0   \n",
       "\n",
       "          type_Pacific  type_Southern  type_land  type_ocean  type_coast  \n",
       "0                    0              0          1           0           0  \n",
       "1                    0              0          1           0           0  \n",
       "2                    0              0          1           0           0  \n",
       "3                    0              0          1           0           0  \n",
       "4                    0              0          1           0           0  \n",
       "...                ...            ...        ...         ...         ...  \n",
       "52099195             0              0          0           1           0  \n",
       "52099196             0              0          0           1           0  \n",
       "52099197             0              0          0           1           0  \n",
       "52099198             0              0          0           1           0  \n",
       "52099199             0              0          0           1           0  \n",
       "\n",
       "[52099200 rows x 20 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data24 = data.iloc[idx]\n",
    "# df_24 = pd.concat([df_encoded]*int(data24.shape[0] / df_encoded.shape[0] ), ignore_index=True)  \n",
    "# data.loc[idx, cols] = df_24[cols].values\n",
    "\n",
    "# data.to_feather('./dataset/preprocessed_data/new24.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f21bbe29fd0>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# import matplotlib.pyplot as plt  \n",
    "# im = plt.imshow(a, cmap='coolwarm', interpolation='nearest', origin='lower') \n",
    "# plt.colorbar(im) \n",
    "# # 0.land 1.Atlantic, 2.Pacific, 3.Indian, 4.Arctic, 5.Southern"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = pd.read_feather('/data2/kpiyu/blob/home/kpiyu/ANGCB/dataset/preprocessed_data/new.feather')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>NorESM-OCv1.2</th>\n",
       "      <th>CESM2</th>\n",
       "      <th>Princeton</th>\n",
       "      <th>NEMO-PlankTOM12</th>\n",
       "      <th>MRI-ESM2-1</th>\n",
       "      <th>CESM-ETHZ</th>\n",
       "      <th>...</th>\n",
       "      <th>ocean_mask</th>\n",
       "      <th>datapro_sfco2_mean</th>\n",
       "      <th>LDEO-HPD</th>\n",
       "      <th>UOEX-Watson</th>\n",
       "      <th>MPI-SOMFFN</th>\n",
       "      <th>JMA-MLR</th>\n",
       "      <th>NIES-NN</th>\n",
       "      <th>CMEMS-LSCE-FFNN</th>\n",
       "      <th>OSETHZ-GRaCER</th>\n",
       "      <th>Jena-MLS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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>50543995</th>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50543996</th>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50543997</th>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50543998</th>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50543999</th>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50544000 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          year  month  latitude  longitude  NorESM-OCv1.2  CESM2  Princeton   \n",
       "0         1959      1         0          0            NaN    NaN        NaN  \\\n",
       "1         1959      1         0          1            NaN    NaN        NaN   \n",
       "2         1959      1         0          2            NaN    NaN        NaN   \n",
       "3         1959      1         0          3            NaN    NaN        NaN   \n",
       "4         1959      1         0          4            NaN    NaN        NaN   \n",
       "...        ...    ...       ...        ...            ...    ...        ...   \n",
       "50543995  2023     12       179        355            NaN    NaN        NaN   \n",
       "50543996  2023     12       179        356            NaN    NaN        NaN   \n",
       "50543997  2023     12       179        357            NaN    NaN        NaN   \n",
       "50543998  2023     12       179        358            NaN    NaN        NaN   \n",
       "50543999  2023     12       179        359            NaN    NaN        NaN   \n",
       "\n",
       "          NEMO-PlankTOM12  MRI-ESM2-1  CESM-ETHZ  ...  ocean_mask   \n",
       "0                     NaN         NaN        NaN  ...         1.0  \\\n",
       "1                     NaN         NaN        NaN  ...         1.0   \n",
       "2                     NaN         NaN        NaN  ...         1.0   \n",
       "3                     NaN         NaN        NaN  ...         1.0   \n",
       "4                     NaN         NaN        NaN  ...         1.0   \n",
       "...                   ...         ...        ...  ...         ...   \n",
       "50543995              NaN         NaN        NaN  ...         NaN   \n",
       "50543996              NaN         NaN        NaN  ...         NaN   \n",
       "50543997              NaN         NaN        NaN  ...         NaN   \n",
       "50543998              NaN         NaN        NaN  ...         NaN   \n",
       "50543999              NaN         NaN        NaN  ...         NaN   \n",
       "\n",
       "          datapro_sfco2_mean  LDEO-HPD  UOEX-Watson  MPI-SOMFFN  JMA-MLR   \n",
       "0                        NaN       NaN          NaN         NaN      NaN  \\\n",
       "1                        NaN       NaN          NaN         NaN      NaN   \n",
       "2                        NaN       NaN          NaN         NaN      NaN   \n",
       "3                        NaN       NaN          NaN         NaN      NaN   \n",
       "4                        NaN       NaN          NaN         NaN      NaN   \n",
       "...                      ...       ...          ...         ...      ...   \n",
       "50543995                 NaN       NaN          NaN         NaN      NaN   \n",
       "50543996                 NaN       NaN          NaN         NaN      NaN   \n",
       "50543997                 NaN       NaN          NaN         NaN      NaN   \n",
       "50543998                 NaN       NaN          NaN         NaN      NaN   \n",
       "50543999                 NaN       NaN          NaN         NaN      NaN   \n",
       "\n",
       "          NIES-NN  CMEMS-LSCE-FFNN  OSETHZ-GRaCER  Jena-MLS  \n",
       "0             NaN              NaN            NaN       NaN  \n",
       "1             NaN              NaN            NaN       NaN  \n",
       "2             NaN              NaN            NaN       NaN  \n",
       "3             NaN              NaN            NaN       NaN  \n",
       "4             NaN              NaN            NaN       NaN  \n",
       "...           ...              ...            ...       ...  \n",
       "50543995      NaN              NaN            NaN       NaN  \n",
       "50543996      NaN              NaN            NaN       NaN  \n",
       "50543997      NaN              NaN            NaN       NaN  \n",
       "50543998      NaN              NaN            NaN       NaN  \n",
       "50543999      NaN              NaN            NaN       NaN  \n",
       "\n",
       "[50544000 rows x 36 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "def optimize_floats(df):\n",
    "    float64_cols = df.select_dtypes(include=['float64']).columns\n",
    "    df[float64_cols] = df[float64_cols].astype('float32')\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os  \n",
    "  \n",
    "def get_file_size(file_path):  \n",
    "    return os.path.getsize(file_path)  \n",
    "  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process GOBM and Data product(GCB2024)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "gobm_dir = '/data/angcb/home/kpiyu/data/Model/GCB2025/GCB2025/'\n",
    "dgvm_dir = '/data/angcb/home/kpiyu/data/Data_product/GCB2025/GCB2025'\n",
    "model_start_year = 2000\n",
    "model_end_year = 2020\n",
    "\n",
    "for dir in [gobm_dir, dgvm_dir]:\n",
    "    for i in os.listdir(dir):\n",
    "        with xr.open_dataset(os.path.join(dir, i)) as model:\n",
    "            year = pd.to_datetime(model.time.values[-1]).year\n",
    "            # print(i, year)\n",
    "            if year>model_end_year:\n",
    "                model_end_year = year\n",
    "                \n",
    "\n",
    "print(model_end_year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = generate_empty_dataframe(start_year=model_start_year, end_year=model_end_year, latitude_len=180, longitude_len = 360)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ACCESS.nc\n",
      "(300, 180, 360)\n",
      "CESM.nc\n",
      "(300, 180, 360)\n",
      "CNRM.nc\n",
      "(300, 180, 360)\n",
      "FESOM.nc\n",
      "(300, 180, 360)\n",
      "IPSL.nc\n",
      "(300, 180, 360)\n",
      "MPI.nc\n",
      "(300, 180, 360)\n",
      "MRI.nc\n",
      "(300, 180, 360)\n",
      "NORESM.nc\n",
      "(300, 180, 360)\n",
      "PLANKTOM.nc\n",
      "(300, 180, 360)\n",
      "PRINCETON.nc\n",
      "(300, 180, 360)\n",
      "average_GOBMs.nc\n",
      "(300, 180, 360)\n",
      "CMEMS-LSCE-FFNN.nc\n",
      "(300, 180, 360)\n",
      "CSIR-ML6.nc\n",
      "(300, 180, 360)\n",
      "JENA-MLS.nc\n",
      "(300, 180, 360)\n",
      "JMA-MLR.nc\n",
      "(300, 180, 360)\n",
      "LDEO-HPD.nc\n",
      "(300, 180, 360)\n",
      "NIES-ML3.nc\n",
      "(300, 180, 360)\n",
      "OceanSODA-ETHZv2.nc\n",
      "(300, 180, 360)\n",
      "UExP-FNN-U.nc\n",
      "(300, 180, 360)\n",
      "VLIZ-SOMFFN.nc\n",
      "(300, 180, 360)\n",
      "average_data_products.nc\n",
      "(300, 180, 360)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import xarray as xr\n",
    "for dir in [gobm_dir, dgvm_dir]:\n",
    "    for i in os.listdir(dir):\n",
    "        print(i)\n",
    "        name = i.split('.')[0]\n",
    "        with xr.open_dataset(os.path.join(dir, i)) as model:\n",
    "            # model = model.sel(time = slice(f'{start_year}-01-01',f'{end_year}-12-31'))\n",
    "            model = model.sel(time=slice(str(model_start_year), str(model_end_year)))\n",
    "            print(model['fgco2'].values.shape)\n",
    "        \n",
    "            \n",
    "            df[name] = model['fgco2'].values.flatten()\n",
    "            \n",
    "    #     break\n",
    "    # break\n",
    "\n",
    "   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ACCESS', 'CESM', 'CNRM', 'FESOM', 'IPSL', 'MPI', 'MRI', 'NORESM',\n",
       "       'PLANKTOM', 'PRINCETON', 'average_GOBMs', 'CMEMS-LSCE-FFNN', 'CSIR-ML6',\n",
       "       'JENA-MLS', 'JMA-MLR', 'LDEO-HPD', 'NIES-ML3', 'OceanSODA-ETHZv2',\n",
       "       'UExP-FNN-U', 'VLIZ-SOMFFN', 'average_data_products'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_idx = add_data[(add_data['year'] >= model_start_year) & (add_data['year'] <= model_end_year)].index\n",
    "target_cols = df.columns[4:]\n",
    "add_data.loc[target_idx, target_cols] = df[target_cols].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# feature_cols = [ 'sss', 'ssh', 'slp', 'wind', 'mld', 'sst', 'ice', 'co2', 'chl', 'type_Arctic', 'type_Atlantic',\n",
    "#        'type_Indian', 'type_Pacific', 'type_Southern', 'type_land',\n",
    "#        'type_ocean', 'type_coast']\n",
    "\n",
    "# env_index = data[data['year']>=2000].index\n",
    "# df[feature_cols] = data.loc[env_index, feature_cols].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # cocat co2 data\n",
    "# co2 = pd.read_feather('/data2/kpiyu/blob/home/kpiyu/ANGCB/dataset/preprocessed_data/new2503_0618.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "co2_path = '/data/angcb/home/kpiyu/ANGCB/dataset/co2_output/co2_full_grid_2025_8.csv'\n",
    "co2 = pd.read_csv(co2_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "co2_start_year = max(int(co2.head(1)['year'].values[0]), int(add_data.head(1)['year'].values[0]))\n",
    "# co2_start_month = int(co2.head(1)['month'].values[0])\n",
    "co2_end_year = min(int(co2.tail(1)['year'].values[0]), int(add_data.tail(1)['year'].values[0]))\n",
    "# co2_end_month = int(co2.tail(1)['month'].values[0])\n",
    "\n",
    "target_idx = co2[(co2['year'] >= co2_start_year) & (co2['year'] <= co2_end_year)].index\n",
    "src_idx = add_data[(add_data['year'] >= co2_start_year) & (add_data['year'] <= co2_end_year)].index\n",
    "target_col = 'co2'\n",
    "add_data.loc[src_idx, target_col] = co2.loc[target_idx, target_col].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>chl</th>\n",
       "      <th>ice</th>\n",
       "      <th>mld</th>\n",
       "      <th>slp</th>\n",
       "      <th>ssh</th>\n",
       "      <th>sss</th>\n",
       "      <th>...</th>\n",
       "      <th>CMEMS-LSCE-FFNN</th>\n",
       "      <th>CSIR-ML6</th>\n",
       "      <th>JENA-MLS</th>\n",
       "      <th>JMA-MLR</th>\n",
       "      <th>LDEO-HPD</th>\n",
       "      <th>NIES-ML3</th>\n",
       "      <th>OceanSODA-ETHZv2</th>\n",
       "      <th>UExP-FNN-U</th>\n",
       "      <th>VLIZ-SOMFFN</th>\n",
       "      <th>average_data_products</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100396.607350</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100397.439225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
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       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100398.271100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100399.380266</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1959</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>100400.212141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <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>52099195</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099196</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>356</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099197</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099198</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52099199</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52099200 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          year  month  latitude  longitude  chl  ice  mld            slp  ssh  \\\n",
       "0         1959      1         0          0  NaN  NaN  NaN  100396.607350  NaN   \n",
       "1         1959      1         0          1  NaN  NaN  NaN  100397.439225  NaN   \n",
       "2         1959      1         0          2  NaN  NaN  NaN  100398.271100  NaN   \n",
       "3         1959      1         0          3  NaN  NaN  NaN  100399.380266  NaN   \n",
       "4         1959      1         0          4  NaN  NaN  NaN  100400.212141  NaN   \n",
       "...        ...    ...       ...        ...  ...  ...  ...            ...  ...   \n",
       "52099195  2025     12       179        355  NaN  NaN  NaN            NaN  NaN   \n",
       "52099196  2025     12       179        356  NaN  NaN  NaN            NaN  NaN   \n",
       "52099197  2025     12       179        357  NaN  NaN  NaN            NaN  NaN   \n",
       "52099198  2025     12       179        358  NaN  NaN  NaN            NaN  NaN   \n",
       "52099199  2025     12       179        359  NaN  NaN  NaN            NaN  NaN   \n",
       "\n",
       "          sss  ...  CMEMS-LSCE-FFNN  CSIR-ML6  JENA-MLS  JMA-MLR  LDEO-HPD  \\\n",
       "0         NaN  ...              NaN       NaN       NaN      NaN       NaN   \n",
       "1         NaN  ...              NaN       NaN       NaN      NaN       NaN   \n",
       "2         NaN  ...              NaN       NaN       NaN      NaN       NaN   \n",
       "3         NaN  ...              NaN       NaN       NaN      NaN       NaN   \n",
       "4         NaN  ...              NaN       NaN       NaN      NaN       NaN   \n",
       "...       ...  ...              ...       ...       ...      ...       ...   \n",
       "52099195  NaN  ...              NaN       NaN       NaN      NaN       NaN   \n",
       "52099196  NaN  ...              NaN       NaN       NaN      NaN       NaN   \n",
       "52099197  NaN  ...              NaN       NaN       NaN      NaN       NaN   \n",
       "52099198  NaN  ...              NaN       NaN       NaN      NaN       NaN   \n",
       "52099199  NaN  ...              NaN       NaN       NaN      NaN       NaN   \n",
       "\n",
       "          NIES-ML3  OceanSODA-ETHZv2  UExP-FNN-U  VLIZ-SOMFFN  \\\n",
       "0              NaN               NaN         NaN          NaN   \n",
       "1              NaN               NaN         NaN          NaN   \n",
       "2              NaN               NaN         NaN          NaN   \n",
       "3              NaN               NaN         NaN          NaN   \n",
       "4              NaN               NaN         NaN          NaN   \n",
       "...            ...               ...         ...          ...   \n",
       "52099195       NaN               NaN         NaN          NaN   \n",
       "52099196       NaN               NaN         NaN          NaN   \n",
       "52099197       NaN               NaN         NaN          NaN   \n",
       "52099198       NaN               NaN         NaN          NaN   \n",
       "52099199       NaN               NaN         NaN          NaN   \n",
       "\n",
       "          average_data_products  \n",
       "0                           NaN  \n",
       "1                           NaN  \n",
       "2                           NaN  \n",
       "3                           NaN  \n",
       "4                           NaN  \n",
       "...                         ...  \n",
       "52099195                    NaN  \n",
       "52099196                    NaN  \n",
       "52099197                    NaN  \n",
       "52099198                    NaN  \n",
       "52099199                    NaN  \n",
       "\n",
       "[52099200 rows x 41 columns]"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_dir = '/data/angcb/home/kpiyu/ANGCB/dataset/preprocessed_data'\n",
    "add_data.to_feather(f'{output_dir}/add_data.feather')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# process nrt data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "data  = add_data.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data[data['year']>=2000].reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20217597</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20217598</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>358</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20217599</th>\n",
       "      <td>2025</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>359</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20217600 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          year  month  latitude  longitude  sst  ice  chl  mld  sss  ssh  ...  \\\n",
       "0         2000      1         0          0  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "1         2000      1         0          1  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "2         2000      1         0          2  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "3         2000      1         0          3  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "4         2000      1         0          4  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "...        ...    ...       ...        ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "20217595  2025     12       179        355  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "20217596  2025     12       179        356  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "20217597  2025     12       179        357  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "20217598  2025     12       179        358  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "20217599  2025     12       179        359  NaN  NaN  NaN  NaN  NaN  NaN  ...   \n",
       "\n",
       "          NIES-ML3  VLIZ-SOMFFN  OceanSODA-ETHZv2  JENA-MLS  JMA-MLR  \\\n",
       "0              NaN          NaN               NaN       NaN      NaN   \n",
       "1              NaN          NaN               NaN       NaN      NaN   \n",
       "2              NaN          NaN               NaN       NaN      NaN   \n",
       "3              NaN          NaN               NaN       NaN      NaN   \n",
       "4              NaN          NaN               NaN       NaN      NaN   \n",
       "...            ...          ...               ...       ...      ...   \n",
       "20217595       NaN          NaN               NaN       NaN      NaN   \n",
       "20217596       NaN          NaN               NaN       NaN      NaN   \n",
       "20217597       NaN          NaN               NaN       NaN      NaN   \n",
       "20217598       NaN          NaN               NaN       NaN      NaN   \n",
       "20217599       NaN          NaN               NaN       NaN      NaN   \n",
       "\n",
       "          LDEO-HPD  CMEMS-LSCE-FFNN  CSIR-ML6  average_data_products  \\\n",
       "0              NaN              NaN       NaN                    NaN   \n",
       "1              NaN              NaN       NaN                    NaN   \n",
       "2              NaN              NaN       NaN                    NaN   \n",
       "3              NaN              NaN       NaN                    NaN   \n",
       "4              NaN              NaN       NaN                    NaN   \n",
       "...            ...              ...       ...                    ...   \n",
       "20217595       NaN              NaN       NaN                    NaN   \n",
       "20217596       NaN              NaN       NaN                    NaN   \n",
       "20217597       NaN              NaN       NaN                    NaN   \n",
       "20217598       NaN              NaN       NaN                    NaN   \n",
       "20217599       NaN              NaN       NaN                    NaN   \n",
       "\n",
       "                 co2  \n",
       "0         366.353665  \n",
       "1         366.353665  \n",
       "2         366.353665  \n",
       "3         366.353665  \n",
       "4         366.353665  \n",
       "...              ...  \n",
       "20217595         NaN  \n",
       "20217596         NaN  \n",
       "20217597         NaN  \n",
       "20217598         NaN  \n",
       "20217599         NaN  \n",
       "\n",
       "[20217600 rows x 42 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "dir = '/data2/kpiyu/blob/home/kpiyu/ANGCB/result/add_history_2010_iterative_2024'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "601.csv\n",
      "602.csv\n",
      "603.csv\n",
      "604.csv\n",
      "606.csv\n",
      "607.csv\n",
      "608.csv\n",
      "609.csv\n",
      "610.csv\n",
      "611.csv\n",
      "616.csv\n",
      "617.csv\n",
      "618.csv\n",
      "619.csv\n",
      "620.csv\n",
      "621.csv\n",
      "622.csv\n",
      "623.csv\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "for path in os.listdir(dir):\n",
    "    if '_' not in path:\n",
    "        print(path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "601 NorESM-OCv1.2\n",
      "602 CESM2\n",
      "603 Princeton\n",
      "604 NEMO-PlankTOM12\n",
      "606 MRI-ESM2-1\n",
      "607 CESM-ETHZ\n",
      "608 MPIOM-HAMOCC6\n",
      "609 CNRM\n",
      "610 IPSL\n",
      "611 FESOM-2.1-REcoM2\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "targets=('NorESM-OCv1.2', 'CESM2', 'Princeton', 'NEMO-PlankTOM12', 'MRI-ESM2-1', 'CESM-ETHZ', 'MPIOM-HAMOCC6', 'CNRM', 'IPSL', 'FESOM-2.1-REcoM2')  \n",
    "exp_ids=(601, 602, 603, 604, 606, 607, 608, 609, 610, 611)  \n",
    "for i in range(len(targets)):\n",
    "    target = targets[i]\n",
    "    exp_id = exp_ids[i]\n",
    "    print(exp_id, target)\n",
    "\n",
    "    history_filepath = os.path.join(dir, f'{exp_id}.csv')\n",
    "    df = pd.read_csv(history_filepath)\n",
    "\n",
    "    model_type='gobm'\n",
    "    # create an instance of Namespace  \n",
    "    model_name = target\n",
    "\n",
    "    ori_path = '../data/Model/' if model_type=='gobm'  else '../data/Data_product/'\n",
    "    model = xr.open_dataset(ori_path + model_name + '.nc')\n",
    "    model = model.sel(time = slice('2010-01-15','2021-12-15'))\n",
    "    model_variable = model.variables['sfco2']\n",
    "    last_12_months_nan = np.isnan(model.sfco2.values[-12:,:,:])\n",
    "\n",
    "    for year in [2022, 2023]:\n",
    "        value = df[df['year'] ==year]['predictions'].values\n",
    "        value[last_12_months_nan.flatten()] = np.nan\n",
    "\n",
    "        idx = data[data['year'] == year].index.values\n",
    "        data.loc[idx, target] = value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "616 LDEO-HPD\n",
      "617 CMEMS-LSCE-FFNN\n",
      "618 Jena-MLS\n",
      "619 MPI-SOMFFN\n",
      "620 UOEX-Watson\n",
      "621 JMA-MLR\n",
      "622 NIES-NN\n",
      "623 OSETHZ-GRaCER\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "\n",
    "targets=('LDEO-HPD', 'CMEMS-LSCE-FFNN', 'Jena-MLS', 'MPI-SOMFFN', 'UOEX-Watson', 'JMA-MLR', 'NIES-NN', 'OSETHZ-GRaCER')  \n",
    "exp_ids=(616, 617, 618, 619, 620, 621, 622, 623)  \n",
    "for i in range(len(targets)):\n",
    "    target = targets[i]\n",
    "    exp_id = exp_ids[i]\n",
    "    print(exp_id, target)\n",
    "\n",
    "    history_filepath = os.path.join(dir, f'{exp_id}.csv')\n",
    "    df = pd.read_csv(history_filepath)\n",
    "\n",
    "    model_type='dataproduct'\n",
    "    # create an instance of Namespace  \n",
    "    model_name = target\n",
    "\n",
    "    ori_path = '../data/Model/' if model_type=='gobm'  else '../data/Data_product/'\n",
    "    model = xr.open_dataset(ori_path + model_name + '.nc')\n",
    "    model = model.sel(time = slice('2010-01-15','2021-12-15'))\n",
    "    model_variable = model.variables['sfco2']\n",
    "    last_12_months_nan = np.isnan(model.sfco2.values[-12:,:,:])\n",
    "\n",
    "    for year in [2022, 2023]:\n",
    "        value = df[df['year'] ==year]['predictions'].values\n",
    "        value[last_12_months_nan.flatten()] = np.nan\n",
    "\n",
    "        idx = data[data['year'] == year].index.values\n",
    "        data.loc[idx, target] = value\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_feather('/data2/kpiyu/blob/home/kpiyu/ANGCB/dataset/nrt_data/new24.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Average data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ave_gobm = '/data2/kpiyu/blob/NRT_LAND/data/average_GOBMs.nc'\n",
    "ave_datapro = '/data2/kpiyu/blob/NRT_LAND/data/average_data_products.nc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_feather('/data2/kpiyu/blob/home/kpiyu/ANGCB/dataset/preprocessed_data/new24_0123.feather')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>ACCESS</th>\n",
       "      <th>CESM_ETHZ</th>\n",
       "      <th>CNRM</th>\n",
       "      <th>FESOM_REcoM</th>\n",
       "      <th>IPSL</th>\n",
       "      <th>MPIOM-HAMOCC</th>\n",
       "      <th>...</th>\n",
       "      <th>co2</th>\n",
       "      <th>chl</th>\n",
       "      <th>type_Arctic</th>\n",
       "      <th>type_Atlantic</th>\n",
       "      <th>type_Indian</th>\n",
       "      <th>type_Pacific</th>\n",
       "      <th>type_Southern</th>\n",
       "      <th>type_land</th>\n",
       "      <th>type_ocean</th>\n",
       "      <th>type_coast</th>\n",
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       "  </thead>\n",
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       "      <th>1</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>366.353665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19439998</th>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19439999</th>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>179</td>\n",
       "      <td>359</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>19440000 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          year  month  latitude  longitude  ACCESS  CESM_ETHZ  CNRM  \\\n",
       "0         2000      1         0          0     NaN        NaN   NaN   \n",
       "1         2000      1         0          1     NaN        NaN   NaN   \n",
       "2         2000      1         0          2     NaN        NaN   NaN   \n",
       "3         2000      1         0          3     NaN        NaN   NaN   \n",
       "4         2000      1         0          4     NaN        NaN   NaN   \n",
       "...        ...    ...       ...        ...     ...        ...   ...   \n",
       "19439995  2024     12       179        355     NaN        NaN   NaN   \n",
       "19439996  2024     12       179        356     NaN        NaN   NaN   \n",
       "19439997  2024     12       179        357     NaN        NaN   NaN   \n",
       "19439998  2024     12       179        358     NaN        NaN   NaN   \n",
       "19439999  2024     12       179        359     NaN        NaN   NaN   \n",
       "\n",
       "          FESOM_REcoM  IPSL  MPIOM-HAMOCC  ...         co2  chl  type_Arctic  \\\n",
       "0                 NaN   NaN           NaN  ...  366.353665  NaN          0.0   \n",
       "1                 NaN   NaN           NaN  ...  366.353665  NaN          0.0   \n",
       "2                 NaN   NaN           NaN  ...  366.353665  NaN          0.0   \n",
       "3                 NaN   NaN           NaN  ...  366.353665  NaN          0.0   \n",
       "4                 NaN   NaN           NaN  ...  366.353665  NaN          0.0   \n",
       "...               ...   ...           ...  ...         ...  ...          ...   \n",
       "19439995          NaN   NaN           NaN  ...         NaN  NaN          1.0   \n",
       "19439996          NaN   NaN           NaN  ...         NaN  NaN          1.0   \n",
       "19439997          NaN   NaN           NaN  ...         NaN  NaN          1.0   \n",
       "19439998          NaN   NaN           NaN  ...         NaN  NaN          1.0   \n",
       "19439999          NaN   NaN           NaN  ...         NaN  NaN          1.0   \n",
       "\n",
       "          type_Atlantic  type_Indian  type_Pacific  type_Southern  type_land  \\\n",
       "0                   0.0          0.0           0.0            0.0        1.0   \n",
       "1                   0.0          0.0           0.0            0.0        1.0   \n",
       "2                   0.0          0.0           0.0            0.0        1.0   \n",
       "3                   0.0          0.0           0.0            0.0        1.0   \n",
       "4                   0.0          0.0           0.0            0.0        1.0   \n",
       "...                 ...          ...           ...            ...        ...   \n",
       "19439995            0.0          0.0           0.0            0.0        0.0   \n",
       "19439996            0.0          0.0           0.0            0.0        0.0   \n",
       "19439997            0.0          0.0           0.0            0.0        0.0   \n",
       "19439998            0.0          0.0           0.0            0.0        0.0   \n",
       "19439999            0.0          0.0           0.0            0.0        0.0   \n",
       "\n",
       "          type_ocean  type_coast  \n",
       "0                0.0         0.0  \n",
       "1                0.0         0.0  \n",
       "2                0.0         0.0  \n",
       "3                0.0         0.0  \n",
       "4                0.0         0.0  \n",
       "...              ...         ...  \n",
       "19439995         1.0         0.0  \n",
       "19439996         1.0         0.0  \n",
       "19439997         1.0         0.0  \n",
       "19439998         1.0         0.0  \n",
       "19439999         1.0         0.0  \n",
       "\n",
       "[19440000 rows x 40 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_23 = data[(data['year'] <=2023)].index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "  * lon         (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "Data variables:\n",
       "    sfco2       (time, lat, lon) float32 75MB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f4c05dc8-55b3-4361-aefd-c6b108c9c60e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f4c05dc8-55b3-4361-aefd-c6b108c9c60e' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 288</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-add46f21-eee7-4a58-8b18-f01b08d25e3b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-add46f21-eee7-4a58-8b18-f01b08d25e3b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-15 ... 2023-12-15</div><input id='attrs-2df79ed0-c95e-4889-a0c9-6caa40dbfd58' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2df79ed0-c95e-4889-a0c9-6caa40dbfd58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0522a609-a515-4871-83f1-df084491b3cd' class='xr-var-data-in' type='checkbox'><label for='data-0522a609-a515-4871-83f1-df084491b3cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-15T00:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-03-15T00:00:00.000000000&#x27;, ..., &#x27;2023-10-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-11-15T00:00:00.000000000&#x27;, &#x27;2023-12-15T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-4b828c54-0294-467c-a1ce-a679a025f875' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b828c54-0294-467c-a1ce-a679a025f875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e29c914-056d-48cb-a233-8ae3fdb05bee' class='xr-var-data-in' type='checkbox'><label for='data-9e29c914-056d-48cb-a233-8ae3fdb05bee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>simulation</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-004ecf5b-94b5-49b9-8cf6-308790274a07' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-004ecf5b-94b5-49b9-8cf6-308790274a07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e625048f-a9a4-450f-ae1e-423050cf3294' class='xr-var-data-in' type='checkbox'><label for='data-e625048f-a9a4-450f-ae1e-423050cf3294' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=&lt;U1]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-898fc578-d930-4cf7-9adc-35ffa208f5a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-898fc578-d930-4cf7-9adc-35ffa208f5a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e11a4a9a-2631-4146-bc44-87f04d4fbe78' class='xr-var-data-in' type='checkbox'><label for='data-e11a4a9a-2631-4146-bc44-87f04d4fbe78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56703300-fe5f-498a-b3be-1293b50df4f1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-56703300-fe5f-498a-b3be-1293b50df4f1' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sfco2</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a7e0dc25-7f32-48ae-914c-25bb08d5453a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a7e0dc25-7f32-48ae-914c-25bb08d5453a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc8693fe-44d5-40f8-900d-ce676e97df1b' class='xr-var-data-in' type='checkbox'><label for='data-dc8693fe-44d5-40f8-900d-ce676e97df1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[18662400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-94a9247a-ffe4-43f5-b639-93179d8aae56' class='xr-section-summary-in' type='checkbox'  ><label for='section-94a9247a-ffe4-43f5-b639-93179d8aae56' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-421161bf-2c80-4e33-8ecc-407f25aa0233' class='xr-index-data-in' type='checkbox'/><label for='index-421161bf-2c80-4e33-8ecc-407f25aa0233' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-15&#x27;, &#x27;2000-02-15&#x27;, &#x27;2000-03-15&#x27;, &#x27;2000-04-15&#x27;,\n",
       "               &#x27;2000-05-15&#x27;, &#x27;2000-06-15&#x27;, &#x27;2000-07-15&#x27;, &#x27;2000-08-15&#x27;,\n",
       "               &#x27;2000-09-15&#x27;, &#x27;2000-10-15&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-03-15&#x27;, &#x27;2023-04-15&#x27;, &#x27;2023-05-15&#x27;, &#x27;2023-06-15&#x27;,\n",
       "               &#x27;2023-07-15&#x27;, &#x27;2023-08-15&#x27;, &#x27;2023-09-15&#x27;, &#x27;2023-10-15&#x27;,\n",
       "               &#x27;2023-11-15&#x27;, &#x27;2023-12-15&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=288, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7f43dd63-9513-4102-952a-354f134055a5' class='xr-index-data-in' type='checkbox'/><label for='index-7f43dd63-9513-4102-952a-354f134055a5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       ...\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-79ef3b45-4bee-4cd5-9157-b608653f0c2c' class='xr-index-data-in' type='checkbox'/><label for='index-79ef3b45-4bee-4cd5-9157-b608653f0c2c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,\n",
       "       -170.5,\n",
       "       ...\n",
       "        170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,\n",
       "        179.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8664ec65-0f9b-4399-bc60-5daad53e73f1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8664ec65-0f9b-4399-bc60-5daad53e73f1' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 75MB\n",
       "Dimensions:     (time: 288, lat: 180, lon: 360)\n",
       "Coordinates:\n",
       "  * time        (time) datetime64[ns] 2kB 2000-01-15 2000-02-15 ... 2023-12-15\n",
       "  * lat         (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 87.5 88.5 89.5\n",
       "    simulation  <U1 4B ...\n",
       "  * lon         (lon) float64 3kB -179.5 -178.5 -177.5 ... 177.5 178.5 179.5\n",
       "Data variables:\n",
       "    sfco2       (time, lat, lon) float32 75MB ..."
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pro_data = xr.open_dataset(ave_gobm)\n",
    "pro_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[index_23, 'average_gobm'] = pro_data['sfco2'].values.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 75MB\n",
       "Dimensions:  (time: 288, lat: 180, lon: 360)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2kB 2000-01-01 2000-02-01 ... 2023-12-01\n",
       "  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "  * lon      (lon) float64 3kB -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5\n",
       "Data variables:\n",
       "    sfco2    (time, lat, lon) float32 75MB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4289e453-ebb5-46b0-80ae-0709a1b9b3bc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4289e453-ebb5-46b0-80ae-0709a1b9b3bc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 288</li><li><span class='xr-has-index'>lat</span>: 180</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9f70b956-5e69-4479-b2e8-2df2af45b545' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9f70b956-5e69-4479-b2e8-2df2af45b545' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-01 ... 2023-12-01</div><input id='attrs-8bcafb17-fe66-4999-8672-9809c4cf1c9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8bcafb17-fe66-4999-8672-9809c4cf1c9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bedf06b5-2a4b-4234-9785-17e4cd4bb27b' class='xr-var-data-in' type='checkbox'><label for='data-bedf06b5-2a4b-4234-9785-17e4cd4bb27b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-01T00:00:00.000000000&#x27;, &#x27;2000-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2000-03-01T00:00:00.000000000&#x27;, ..., &#x27;2023-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2023-11-01T00:00:00.000000000&#x27;, &#x27;2023-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.5 -88.5 -87.5 ... 88.5 89.5</div><input id='attrs-e82bc1d9-a244-4d26-8f7b-cdc5529576b4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e82bc1d9-a244-4d26-8f7b-cdc5529576b4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1983075b-05f8-4f1f-afc6-baf6ef5fffa6' class='xr-var-data-in' type='checkbox'><label for='data-1983075b-05f8-4f1f-afc6-baf6ef5fffa6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5, -71.5, -70.5,\n",
       "       -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5, -62.5, -61.5, -60.5,\n",
       "       -59.5, -58.5, -57.5, -56.5, -55.5, -54.5, -53.5, -52.5, -51.5, -50.5,\n",
       "       -49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5,\n",
       "         0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.5 -178.5 ... 178.5 179.5</div><input id='attrs-ba781002-8cc7-4dc4-95a4-72a6cfd6f97a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba781002-8cc7-4dc4-95a4-72a6cfd6f97a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e8f19c6-7113-4cab-ada3-a70b6876058a' class='xr-var-data-in' type='checkbox'><label for='data-3e8f19c6-7113-4cab-ada3-a70b6876058a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-179.5, -178.5, -177.5, ...,  177.5,  178.5,  179.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db86482b-d501-422c-8fbb-83b79bc55883' class='xr-section-summary-in' type='checkbox'  checked><label for='section-db86482b-d501-422c-8fbb-83b79bc55883' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>sfco2</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e05604fb-34fc-4fe6-845b-646463ea70c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e05604fb-34fc-4fe6-845b-646463ea70c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea679da-22c0-488a-87dc-9dc11f823932' class='xr-var-data-in' type='checkbox'><label for='data-5ea679da-22c0-488a-87dc-9dc11f823932' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[18662400 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-313e0d0d-82e3-4a3f-833a-4150aec6a700' class='xr-section-summary-in' type='checkbox'  ><label for='section-313e0d0d-82e3-4a3f-833a-4150aec6a700' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c9c7a94d-c8df-4bee-92ea-487cdd23d4fa' class='xr-index-data-in' type='checkbox'/><label for='index-c9c7a94d-c8df-4bee-92ea-487cdd23d4fa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2000-01-01&#x27;, &#x27;2000-02-01&#x27;, &#x27;2000-03-01&#x27;, &#x27;2000-04-01&#x27;,\n",
       "               &#x27;2000-05-01&#x27;, &#x27;2000-06-01&#x27;, &#x27;2000-07-01&#x27;, &#x27;2000-08-01&#x27;,\n",
       "               &#x27;2000-09-01&#x27;, &#x27;2000-10-01&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-03-01&#x27;, &#x27;2023-04-01&#x27;, &#x27;2023-05-01&#x27;, &#x27;2023-06-01&#x27;,\n",
       "               &#x27;2023-07-01&#x27;, &#x27;2023-08-01&#x27;, &#x27;2023-09-01&#x27;, &#x27;2023-10-01&#x27;,\n",
       "               &#x27;2023-11-01&#x27;, &#x27;2023-12-01&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=288, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8e5a0384-98a6-4db6-95c2-1463b25c2b13' class='xr-index-data-in' type='checkbox'/><label for='index-8e5a0384-98a6-4db6-95c2-1463b25c2b13' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5, -80.5,\n",
       "       ...\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=180))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-13a9ecda-9ba1-4b20-9fa4-a8d8b0c50ed6' class='xr-index-data-in' type='checkbox'/><label for='index-13a9ecda-9ba1-4b20-9fa4-a8d8b0c50ed6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-179.5, -178.5, -177.5, -176.5, -175.5, -174.5, -173.5, -172.5, -171.5,\n",
       "       -170.5,\n",
       "       ...\n",
       "        170.5,  171.5,  172.5,  173.5,  174.5,  175.5,  176.5,  177.5,  178.5,\n",
       "        179.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-58385945-f858-4d8e-9ffb-78112bfbe30e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-58385945-f858-4d8e-9ffb-78112bfbe30e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 75MB\n",
       "Dimensions:  (time: 288, lat: 180, lon: 360)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2kB 2000-01-01 2000-02-01 ... 2023-12-01\n",
       "  * lat      (lat) float64 1kB -89.5 -88.5 -87.5 -86.5 ... 86.5 87.5 88.5 89.5\n",
       "  * lon      (lon) float64 3kB -179.5 -178.5 -177.5 -176.5 ... 177.5 178.5 179.5\n",
       "Data variables:\n",
       "    sfco2    (time, lat, lon) float32 75MB ..."
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pro_data = xr.open_dataset(ave_datapro)\n",
    "pro_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.loc[index_23, 'average_datapro'] = pro_data['sfco2'].values.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_feather('/data2/kpiyu/blob/home/kpiyu/ANGCB/dataset/preprocessed_data/new24_0123.feather')"
   ]
  }
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