Extract data from GRACE.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "from netCDF4 import Dataset, num2date\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime as dt\n",
    "import os\n",
    "from pathlib import Path\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'netCDF4._netCDF4.Dataset'>\n",
       "root group (NETCDF3_CLASSIC data model, file format NETCDF3):\n",
       "    Conventions: CF-1.5\n",
       "    Institution: JPL / GRACE-TELLUS\n",
       "    Variable: scaling factor and measurement errors\n",
       "    Platform: GRACE\n",
       "    Sensor: GRACE\n",
       "    Filter_Width_KM: 300\n",
       "    DATE_CREATED: Wed Feb 19 15:04:06 2014\n",
       "    dimensions(sizes): Longitude(360), Latitude(180)\n",
       "    variables(dimensions): float32 \u001b[4mLongitude\u001b[0m(Longitude), float32 \u001b[4mLatitude\u001b[0m(Latitude), float32 \u001b[4mSCALE_FACTOR\u001b[0m(Latitude,Longitude), float32 \u001b[4mMEASUREMENT_ERROR\u001b[0m(Latitude,Longitude), float32 \u001b[4mLEAKAGE_ERROR\u001b[0m(Latitude,Longitude)\n",
       "    groups: "
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_corr = \"DATA/GRACE/CLM4.SCALE_FACTOR.DS.G300KM.RL05.DSTvSCS1409.nc\"\n",
    "corr = Dataset(path_corr,mode='r')\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('DATA/GRACE/GRCTellus.CSR.200204_201701.LND.RL05.DSTvSCS1409.nc'),\n",
       " PosixPath('DATA/GRACE/GRCTellus.GFZ.200204_201701.LND.RL05.DSTvSCS1409.nc'),\n",
       " PosixPath('DATA/GRACE/GRCTellus.JPL.200204_201701.LND.RL05_1.DSTvSCS1411.nc')]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = Path(\"DATA/GRACE/\")\n",
    "grace_data = list(data_path.glob(\"GRCTellus*\"))\n",
    "grace_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "nc = Dataset(grace_data[0], mode=\"r\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "odict_keys(['lon', 'lat', 'time', 'lwe_thickness', 'time_bounds'])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nc.variables.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_gleam = nc.variables['lat']\n",
    "lon_gleam = nc.variables['lon']\n",
    "time_gleam = nc.variables['time']\n",
    "lwe_thickness_gleam = nc.variables['lwe_thickness']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<class 'netCDF4._netCDF4.Variable'>\n",
       " float64 lat(lat)\n",
       "     units: degrees_north\n",
       "     long_name: Latitude\n",
       " unlimited dimensions: \n",
       " current shape = (180,)\n",
       " filling off,\n",
       " array([-89.5, -88.5, -87.5, -86.5, -85.5, -84.5, -83.5, -82.5, -81.5,\n",
       "        -80.5, -79.5, -78.5, -77.5, -76.5, -75.5, -74.5, -73.5, -72.5,\n",
       "        -71.5, -70.5, -69.5, -68.5, -67.5, -66.5, -65.5, -64.5, -63.5,\n",
       "        -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5, -54.5,\n",
       "        -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5,\n",
       "        -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5,\n",
       "        -35.5, -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5,\n",
       "        -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5,\n",
       "        -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,  -9.5,\n",
       "         -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,\n",
       "          9.5,  10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,\n",
       "         18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,\n",
       "         27.5,  28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,  35.5,\n",
       "         36.5,  37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,\n",
       "         45.5,  46.5,  47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,\n",
       "         54.5,  55.5,  56.5,  57.5,  58.5,  59.5,  60.5,  61.5,  62.5,\n",
       "         63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,  70.5,  71.5,\n",
       "         72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,  80.5,\n",
       "         81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5]))"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lat_gleam, lat_gleam[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "days since 2002-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "time_units_gleam = nc.variables['time'].units\n",
    "print(time_units_gleam)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([datetime.datetime(2002, 4, 18, 12, 0),\n",
       "       datetime.datetime(2002, 5, 11, 0, 0),\n",
       "       datetime.datetime(2002, 8, 17, 0, 0),\n",
       "       datetime.datetime(2002, 9, 16, 12, 0),\n",
       "       datetime.datetime(2002, 10, 17, 0, 0)], dtype=object)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_gleam = num2date(time_gleam[:], time_units_gleam)\n",
    "date_gleam[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.pcolormesh(lon_gleam[:],lat_gleam[:], lwe_thickness_gleam[0])\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "braz_lat= -15.9474753422\n",
    "braz_long = 360 + -47.8778689831"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_nearest(array, value,n=1):\n",
    "    array = np.asarray(array)\n",
    "    diff = np.abs(array - value)\n",
    "    nearest = array[diff.argsort()][:n]\n",
    "    idxs = []\n",
    "    for i in range(n):\n",
    "        idxs.append(np.where(array == nearest[i]))\n",
    "    return np.sort(idxs)[::-1].flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_mask(src,list_x,list_y):\n",
    "    src = np.asarray(src)\n",
    "    mask = np.full_like(src,False,dtype=bool)\n",
    "\n",
    "    for x in list_x:\n",
    "        for y in list_y:\n",
    "            mask[x,y] = True\n",
    "    return mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_x = find_nearest(lat_gleam,braz_lat,2)\n",
    "list_y = find_nearest(lon_gleam,braz_long,2)\n",
    "\n",
    "mask = make_mask(lwe_thickness_gleam[0],list_x,list_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       ...,\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False],\n",
       "       [False, False, False, ..., False, False, False]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.3159766, 5.6334686, 7.1763625, 6.353309 ], dtype=float32)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lwe_thickness_gleam[0][:].filled()[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mean_mask(matrix, mask):\n",
    "    mean = matrix[:][mask].mean()\n",
    "    return mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.369779586791992"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_mean_mask(lwe_thickness_gleam[0],mask)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[6.369779586791992,\n",
       " -0.3171350061893463,\n",
       " -16.290027618408203,\n",
       " -19.481395721435547,\n",
       " -23.3328857421875,\n",
       " -19.722694396972656,\n",
       " -19.319786071777344,\n",
       " -2.4519946575164795,\n",
       " 3.2500524520874023,\n",
       " 4.753601551055908,\n",
       " 8.16615104675293,\n",
       " 2.8747546672821045,\n",
       " -12.343305587768555,\n",
       " -21.26107406616211,\n",
       " -25.042097091674805,\n",
       " -25.410423278808594,\n",
       " -22.118423461914062,\n",
       " -17.022846221923828,\n",
       " -12.087959289550781,\n",
       " 13.138374328613281,\n",
       " 19.025304794311523,\n",
       " 19.05992889404297,\n",
       " 12.624347686767578,\n",
       " 4.322315216064453,\n",
       " -2.346790313720703,\n",
       " -7.248164176940918,\n",
       " -12.440788269042969,\n",
       " -16.736892700195312,\n",
       " -14.282514572143555,\n",
       " -6.511368751525879,\n",
       " 2.972548246383667,\n",
       " 10.705867767333984,\n",
       " 16.172374725341797,\n",
       " 14.142622947692871,\n",
       " 6.196932315826416,\n",
       " -2.038717746734619,\n",
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       " -9.825677871704102]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lista = []\n",
    "for lwe_thickness in lwe_thickness_gleam:\n",
    "    lista.append(get_mean_mask(lwe_thickness,mask))\n",
    "    \n",
    "lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(grace,lat,long,ns): \n",
    "\n",
    "    name = grace.as_posix()[21:24]\n",
    "    \n",
    "    nc = Dataset(grace, mode=\"r\")\n",
    "    \n",
    "    lat_gleam = nc.variables['lat']\n",
    "    lon_gleam = nc.variables['lon']\n",
    "    time_gleam = nc.variables['time']\n",
    "    lwe_thickness_gleam = nc.variables['lwe_thickness']\n",
    "    \n",
    "    time_units_gleam = nc.variables['time'].units\n",
    "    \n",
    "    date_gleam = num2date(time_gleam[:], time_units_gleam)\n",
    "    \n",
    "    dados = pd.DataFrame(index=date_gleam)\n",
    "    \n",
    "\n",
    "    for n in ns:\n",
    "        name_column = name+\"- %i\" % n\n",
    "        \n",
    "        list_x = find_nearest(lat_gleam,lat,n)\n",
    "        list_y = find_nearest(lon_gleam,long,n)\n",
    "\n",
    "        mask = make_mask(lwe_thickness_gleam[0],list_x,list_y)\n",
    "        column = []\n",
    "        for lwe_thickness in lwe_thickness_gleam:\n",
    "            column.append(get_mean_mask(lwe_thickness,mask))\n",
    "        \n",
    "        dados[name_column] = column\n",
    "    \n",
    "    \n",
    "    return dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
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       "      <th>2002-04-16 12:00:00</th>\n",
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       "                      CSR- 2    CSR- 3    CSR- 4    GFZ- 2    GFZ- 3  \\\n",
       "2002-04-16 12:00:00      NaN       NaN       NaN       NaN       NaN   \n",
       "2002-04-18 00:00:00      NaN       NaN       NaN  2.990696  3.248541   \n",
       "2002-04-18 12:00:00  6.36978  6.862617  7.205875       NaN       NaN   \n",
       "2002-05-10 12:00:00      NaN       NaN       NaN       NaN       NaN   \n",
       "2002-05-10 12:00:00      NaN       NaN       NaN -0.469174 -0.104986   \n",
       "\n",
       "                       GFZ- 4    JPL- 2    JPL- 3    JPL- 4  \n",
       "2002-04-16 12:00:00       NaN  2.249933  3.097115  3.173393  \n",
       "2002-04-18 00:00:00  4.077078       NaN       NaN       NaN  \n",
       "2002-04-18 12:00:00       NaN       NaN       NaN       NaN  \n",
       "2002-05-10 12:00:00       NaN -4.259185 -3.519518 -3.788323  \n",
       "2002-05-10 12:00:00  0.065377       NaN       NaN       NaN  "
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs = []\n",
    "for grace in grace_data:\n",
    "    dfs.append(read_data(grace,braz_lat,braz_long,[2,3,4]))\n",
    "df = pd.concat(dfs,sort=True)\n",
    "df.sort_index(inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.groupby(pd.Grouper(freq=\"M\")).mean().dropna(how=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
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       "      <td>-22.982873</td>\n",
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       "               CSR- 2     CSR- 3     CSR- 4     GFZ- 2     GFZ- 3     GFZ- 4  \\\n",
       "2002-04-30   6.369780   6.862617   7.205875   2.990696   3.248541   4.077078   \n",
       "2002-05-31  -0.317135  -0.245743   0.190985  -0.469174  -0.104986   0.065377   \n",
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       "\n",
       "               JPL- 2     JPL- 3     JPL- 4  \n",
       "2002-04-30   2.249933   3.097115   3.173393  \n",
       "2002-05-31  -4.259185  -3.519518  -3.788323  \n",
       "2002-08-31 -16.737858 -16.217127 -16.430960  \n",
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       "2002-10-31 -23.753513 -22.982873 -23.265060  "
      ]
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     "execution_count": 95,
     "metadata": {},
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_df(graces,station):\n",
    "    dfs = []\n",
    "    lat = station[\"lat\"]\n",
    "    long = station[\"long\"]\n",
    "    for grace in grace_data:\n",
    "        dfs.append(read_data(grace,lat,long,[2,3,4]))\n",
    "    df = pd.concat(dfs,sort=True)\n",
    "    df = df.groupby(pd.Grouper(freq=\"M\")).mean().dropna(how=\"all\")\n",
    "    file_name = \"DATA/GRACE/STATIONS/\" + station.name + \".csv\"\n",
    "    df.to_csv(file_name,index_label=\"DATE\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-15.9474753422"
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     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "station = pd.Series({\"lat\":braz_lat,\n",
    "                    \"long\":braz_long},name=\"BRAZ\")\n",
    "station[\"lat\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "scrolled": true
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       "      <td>-16.290028</td>\n",
       "      <td>-15.536709</td>\n",
       "      <td>-15.985147</td>\n",
       "      <td>-14.353841</td>\n",
       "      <td>-13.973452</td>\n",
       "      <td>-14.237137</td>\n",
       "      <td>-16.737858</td>\n",
       "      <td>-16.217127</td>\n",
       "      <td>-16.430960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2002-09-30</td>\n",
       "      <td>-19.481396</td>\n",
       "      <td>-19.447235</td>\n",
       "      <td>-19.265459</td>\n",
       "      <td>-18.929935</td>\n",
       "      <td>-18.468894</td>\n",
       "      <td>-18.576111</td>\n",
       "      <td>-27.825027</td>\n",
       "      <td>-23.994564</td>\n",
       "      <td>-25.610378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2002-10-31</td>\n",
       "      <td>-23.332886</td>\n",
       "      <td>-22.280301</td>\n",
       "      <td>-22.933765</td>\n",
       "      <td>-23.638735</td>\n",
       "      <td>-22.075061</td>\n",
       "      <td>-22.915709</td>\n",
       "      <td>-23.753513</td>\n",
       "      <td>-22.982873</td>\n",
       "      <td>-23.265060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2002-11-30</td>\n",
       "      <td>-19.722694</td>\n",
       "      <td>-18.801866</td>\n",
       "      <td>-19.537502</td>\n",
       "      <td>-20.341890</td>\n",
       "      <td>-19.304660</td>\n",
       "      <td>-20.110733</td>\n",
       "      <td>-18.874060</td>\n",
       "      <td>-17.631137</td>\n",
       "      <td>-18.579494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2002-12-31</td>\n",
       "      <td>-19.319786</td>\n",
       "      <td>-18.601535</td>\n",
       "      <td>-18.338511</td>\n",
       "      <td>-18.110817</td>\n",
       "      <td>-17.494361</td>\n",
       "      <td>-17.541786</td>\n",
       "      <td>-21.428724</td>\n",
       "      <td>-19.742789</td>\n",
       "      <td>-20.003414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2003-01-31</td>\n",
       "      <td>-2.451995</td>\n",
       "      <td>-2.122681</td>\n",
       "      <td>-2.668819</td>\n",
       "      <td>-4.548550</td>\n",
       "      <td>-4.295708</td>\n",
       "      <td>-4.480847</td>\n",
       "      <td>-4.093900</td>\n",
       "      <td>-3.792067</td>\n",
       "      <td>-4.139344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2003-02-28</td>\n",
       "      <td>3.250052</td>\n",
       "      <td>2.994657</td>\n",
       "      <td>4.097945</td>\n",
       "      <td>2.072590</td>\n",
       "      <td>1.254594</td>\n",
       "      <td>2.824111</td>\n",
       "      <td>4.647142</td>\n",
       "      <td>4.434167</td>\n",
       "      <td>5.562096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2003-03-31</td>\n",
       "      <td>4.753602</td>\n",
       "      <td>4.324463</td>\n",
       "      <td>5.818776</td>\n",
       "      <td>5.661065</td>\n",
       "      <td>5.361503</td>\n",
       "      <td>6.813591</td>\n",
       "      <td>4.820865</td>\n",
       "      <td>4.704756</td>\n",
       "      <td>5.890776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2003-04-30</td>\n",
       "      <td>8.166151</td>\n",
       "      <td>7.227407</td>\n",
       "      <td>8.691176</td>\n",
       "      <td>9.172767</td>\n",
       "      <td>8.083807</td>\n",
       "      <td>9.703140</td>\n",
       "      <td>9.445084</td>\n",
       "      <td>8.756989</td>\n",
       "      <td>9.953403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2003-05-31</td>\n",
       "      <td>2.874755</td>\n",
       "      <td>2.226595</td>\n",
       "      <td>3.269719</td>\n",
       "      <td>3.881123</td>\n",
       "      <td>3.704666</td>\n",
       "      <td>4.275807</td>\n",
       "      <td>2.860014</td>\n",
       "      <td>2.706311</td>\n",
       "      <td>3.202332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2003-07-31</td>\n",
       "      <td>-12.343306</td>\n",
       "      <td>-12.258996</td>\n",
       "      <td>-11.762691</td>\n",
       "      <td>-13.131025</td>\n",
       "      <td>-13.121426</td>\n",
       "      <td>-12.638650</td>\n",
       "      <td>-11.115901</td>\n",
       "      <td>-11.083034</td>\n",
       "      <td>-10.620917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2003-08-31</td>\n",
       "      <td>-21.261074</td>\n",
       "      <td>-20.808085</td>\n",
       "      <td>-20.673313</td>\n",
       "      <td>-19.264833</td>\n",
       "      <td>-18.911374</td>\n",
       "      <td>-18.702770</td>\n",
       "      <td>-19.640589</td>\n",
       "      <td>-19.167141</td>\n",
       "      <td>-19.115789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2003-09-30</td>\n",
       "      <td>-25.042097</td>\n",
       "      <td>-24.559913</td>\n",
       "      <td>-24.651525</td>\n",
       "      <td>-26.238302</td>\n",
       "      <td>-25.895774</td>\n",
       "      <td>-25.790489</td>\n",
       "      <td>-25.033787</td>\n",
       "      <td>-24.541680</td>\n",
       "      <td>-24.640207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2003-10-31</td>\n",
       "      <td>-25.410423</td>\n",
       "      <td>-24.777045</td>\n",
       "      <td>-25.053099</td>\n",
       "      <td>-22.707054</td>\n",
       "      <td>-22.165483</td>\n",
       "      <td>-22.326506</td>\n",
       "      <td>-22.305223</td>\n",
       "      <td>-21.587609</td>\n",
       "      <td>-21.965542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2003-11-30</td>\n",
       "      <td>-22.118423</td>\n",
       "      <td>-21.318322</td>\n",
       "      <td>-21.821232</td>\n",
       "      <td>-23.491257</td>\n",
       "      <td>-22.589079</td>\n",
       "      <td>-23.202274</td>\n",
       "      <td>-19.419596</td>\n",
       "      <td>-18.853739</td>\n",
       "      <td>-19.179361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2003-12-31</td>\n",
       "      <td>-17.022846</td>\n",
       "      <td>-16.888436</td>\n",
       "      <td>-16.779102</td>\n",
       "      <td>-15.920524</td>\n",
       "      <td>-15.826875</td>\n",
       "      <td>-15.688605</td>\n",
       "      <td>-15.187391</td>\n",
       "      <td>-15.212182</td>\n",
       "      <td>-14.987539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2004-01-31</td>\n",
       "      <td>-12.087959</td>\n",
       "      <td>-11.972403</td>\n",
       "      <td>-11.771278</td>\n",
       "      <td>-17.662470</td>\n",
       "      <td>-17.544790</td>\n",
       "      <td>-17.373096</td>\n",
       "      <td>-13.066944</td>\n",
       "      <td>-13.013412</td>\n",
       "      <td>-12.748642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2004-02-29</td>\n",
       "      <td>13.138374</td>\n",
       "      <td>13.633330</td>\n",
       "      <td>13.025967</td>\n",
       "      <td>15.370454</td>\n",
       "      <td>16.094228</td>\n",
       "      <td>15.351665</td>\n",
       "      <td>13.409797</td>\n",
       "      <td>14.343638</td>\n",
       "      <td>13.397733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2004-03-31</td>\n",
       "      <td>19.025305</td>\n",
       "      <td>19.573788</td>\n",
       "      <td>18.854607</td>\n",
       "      <td>19.985523</td>\n",
       "      <td>20.608226</td>\n",
       "      <td>19.935713</td>\n",
       "      <td>19.852787</td>\n",
       "      <td>20.436761</td>\n",
       "      <td>19.759512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2004-04-30</td>\n",
       "      <td>19.059929</td>\n",
       "      <td>19.969254</td>\n",
       "      <td>18.911015</td>\n",
       "      <td>21.543365</td>\n",
       "      <td>22.293242</td>\n",
       "      <td>21.474270</td>\n",
       "      <td>20.423214</td>\n",
       "      <td>21.412837</td>\n",
       "      <td>20.221178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2004-05-31</td>\n",
       "      <td>12.624348</td>\n",
       "      <td>12.940631</td>\n",
       "      <td>12.457984</td>\n",
       "      <td>12.742107</td>\n",
       "      <td>12.869291</td>\n",
       "      <td>12.543344</td>\n",
       "      <td>15.086021</td>\n",
       "      <td>15.586907</td>\n",
       "      <td>14.867765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2004-06-30</td>\n",
       "      <td>4.322315</td>\n",
       "      <td>4.540306</td>\n",
       "      <td>4.419805</td>\n",
       "      <td>6.587780</td>\n",
       "      <td>6.649169</td>\n",
       "      <td>6.607500</td>\n",
       "      <td>4.459651</td>\n",
       "      <td>4.328069</td>\n",
       "      <td>4.125405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2004-07-31</td>\n",
       "      <td>-2.346790</td>\n",
       "      <td>-2.129722</td>\n",
       "      <td>-2.161875</td>\n",
       "      <td>-1.292259</td>\n",
       "      <td>-1.052427</td>\n",
       "      <td>-1.191553</td>\n",
       "      <td>-0.482072</td>\n",
       "      <td>-0.157299</td>\n",
       "      <td>-0.504699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2004-08-31</td>\n",
       "      <td>-7.248164</td>\n",
       "      <td>-6.917360</td>\n",
       "      <td>-7.173278</td>\n",
       "      <td>-6.695548</td>\n",
       "      <td>-6.410896</td>\n",
       "      <td>-6.593496</td>\n",
       "      <td>-6.082361</td>\n",
       "      <td>-6.019300</td>\n",
       "      <td>-6.211475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2004-09-30</td>\n",
       "      <td>-12.440788</td>\n",
       "      <td>-12.073799</td>\n",
       "      <td>-12.430477</td>\n",
       "      <td>-14.524635</td>\n",
       "      <td>-13.582054</td>\n",
       "      <td>-14.533315</td>\n",
       "      <td>-13.844083</td>\n",
       "      <td>-13.198644</td>\n",
       "      <td>-13.968958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2004-10-31</td>\n",
       "      <td>-16.736893</td>\n",
       "      <td>-16.017285</td>\n",
       "      <td>-16.529253</td>\n",
       "      <td>-19.176521</td>\n",
       "      <td>-18.384983</td>\n",
       "      <td>-18.965782</td>\n",
       "      <td>-16.783075</td>\n",
       "      <td>-15.794747</td>\n",
       "      <td>-16.581902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2004-11-30</td>\n",
       "      <td>-14.282515</td>\n",
       "      <td>-13.477036</td>\n",
       "      <td>-14.189545</td>\n",
       "      <td>-16.921297</td>\n",
       "      <td>-15.845495</td>\n",
       "      <td>-16.642067</td>\n",
       "      <td>-13.098007</td>\n",
       "      <td>-12.246674</td>\n",
       "      <td>-12.922140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2004-12-31</td>\n",
       "      <td>-6.511369</td>\n",
       "      <td>-6.048361</td>\n",
       "      <td>-6.564404</td>\n",
       "      <td>-6.252694</td>\n",
       "      <td>-5.572017</td>\n",
       "      <td>-6.281512</td>\n",
       "      <td>-5.244415</td>\n",
       "      <td>-4.822237</td>\n",
       "      <td>-5.214270</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>2013-10-31</td>\n",
       "      <td>-19.883970</td>\n",
       "      <td>-19.831055</td>\n",
       "      <td>-19.776855</td>\n",
       "      <td>-19.746614</td>\n",
       "      <td>-19.515842</td>\n",
       "      <td>-19.541958</td>\n",
       "      <td>-21.860270</td>\n",
       "      <td>-21.861596</td>\n",
       "      <td>-21.750874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>2013-11-30</td>\n",
       "      <td>-17.951689</td>\n",
       "      <td>-17.678472</td>\n",
       "      <td>-17.878567</td>\n",
       "      <td>-17.545496</td>\n",
       "      <td>-17.324193</td>\n",
       "      <td>-17.466167</td>\n",
       "      <td>-21.597095</td>\n",
       "      <td>-21.361330</td>\n",
       "      <td>-21.350090</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>2013-12-31</td>\n",
       "      <td>-7.114188</td>\n",
       "      <td>-6.506821</td>\n",
       "      <td>-7.291891</td>\n",
       "      <td>-4.772312</td>\n",
       "      <td>-4.177369</td>\n",
       "      <td>-4.743716</td>\n",
       "      <td>-8.713623</td>\n",
       "      <td>-8.302150</td>\n",
       "      <td>-8.948681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>2014-01-31</td>\n",
       "      <td>4.752780</td>\n",
       "      <td>6.180957</td>\n",
       "      <td>4.409497</td>\n",
       "      <td>0.928747</td>\n",
       "      <td>2.493935</td>\n",
       "      <td>0.775488</td>\n",
       "      <td>1.142642</td>\n",
       "      <td>2.509270</td>\n",
       "      <td>0.858418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>2014-03-31</td>\n",
       "      <td>5.896018</td>\n",
       "      <td>6.871508</td>\n",
       "      <td>5.914853</td>\n",
       "      <td>5.364076</td>\n",
       "      <td>6.400744</td>\n",
       "      <td>5.356269</td>\n",
       "      <td>5.612525</td>\n",
       "      <td>6.720335</td>\n",
       "      <td>5.619271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2014-04-30</td>\n",
       "      <td>5.547657</td>\n",
       "      <td>6.259777</td>\n",
       "      <td>5.287659</td>\n",
       "      <td>6.754492</td>\n",
       "      <td>7.519115</td>\n",
       "      <td>6.388005</td>\n",
       "      <td>4.932554</td>\n",
       "      <td>5.534496</td>\n",
       "      <td>4.630267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>2014-05-31</td>\n",
       "      <td>-0.297667</td>\n",
       "      <td>0.452662</td>\n",
       "      <td>-0.445184</td>\n",
       "      <td>-0.084369</td>\n",
       "      <td>0.717795</td>\n",
       "      <td>-0.252695</td>\n",
       "      <td>0.787866</td>\n",
       "      <td>1.460755</td>\n",
       "      <td>0.653013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>2014-06-30</td>\n",
       "      <td>-6.055377</td>\n",
       "      <td>-5.231532</td>\n",
       "      <td>-6.151235</td>\n",
       "      <td>-5.232599</td>\n",
       "      <td>-4.410575</td>\n",
       "      <td>-5.360711</td>\n",
       "      <td>-5.552900</td>\n",
       "      <td>-4.777907</td>\n",
       "      <td>-5.671156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>2014-08-31</td>\n",
       "      <td>-16.852480</td>\n",
       "      <td>-16.279816</td>\n",
       "      <td>-16.946657</td>\n",
       "      <td>-17.782343</td>\n",
       "      <td>-17.109928</td>\n",
       "      <td>-17.865229</td>\n",
       "      <td>-18.524042</td>\n",
       "      <td>-17.876799</td>\n",
       "      <td>-18.594212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>2014-09-30</td>\n",
       "      <td>-22.443039</td>\n",
       "      <td>-21.365404</td>\n",
       "      <td>-22.631187</td>\n",
       "      <td>-22.167606</td>\n",
       "      <td>-21.175571</td>\n",
       "      <td>-22.368719</td>\n",
       "      <td>-24.127289</td>\n",
       "      <td>-23.237596</td>\n",
       "      <td>-24.353397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>2014-10-31</td>\n",
       "      <td>-26.024818</td>\n",
       "      <td>-24.932782</td>\n",
       "      <td>-26.112677</td>\n",
       "      <td>-25.700319</td>\n",
       "      <td>-24.572549</td>\n",
       "      <td>-25.707317</td>\n",
       "      <td>-25.843990</td>\n",
       "      <td>-24.781655</td>\n",
       "      <td>-25.920692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>2014-11-30</td>\n",
       "      <td>-22.624952</td>\n",
       "      <td>-20.924166</td>\n",
       "      <td>-22.648527</td>\n",
       "      <td>-21.934071</td>\n",
       "      <td>-20.195606</td>\n",
       "      <td>-21.908251</td>\n",
       "      <td>-22.033186</td>\n",
       "      <td>-20.456134</td>\n",
       "      <td>-22.080547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>2015-01-31</td>\n",
       "      <td>-12.104342</td>\n",
       "      <td>-11.210520</td>\n",
       "      <td>-12.211308</td>\n",
       "      <td>-9.621220</td>\n",
       "      <td>-8.399601</td>\n",
       "      <td>-9.410498</td>\n",
       "      <td>-16.373779</td>\n",
       "      <td>-15.343823</td>\n",
       "      <td>-16.360918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>2015-02-28</td>\n",
       "      <td>-5.946175</td>\n",
       "      <td>-4.775208</td>\n",
       "      <td>-5.603221</td>\n",
       "      <td>-6.920004</td>\n",
       "      <td>-5.975946</td>\n",
       "      <td>-6.492736</td>\n",
       "      <td>-11.618717</td>\n",
       "      <td>-11.073487</td>\n",
       "      <td>-11.840231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>2015-03-31</td>\n",
       "      <td>-3.874774</td>\n",
       "      <td>-3.630413</td>\n",
       "      <td>-4.040146</td>\n",
       "      <td>-1.680863</td>\n",
       "      <td>-1.474264</td>\n",
       "      <td>-1.742041</td>\n",
       "      <td>-3.856456</td>\n",
       "      <td>-3.581561</td>\n",
       "      <td>-3.954263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>2015-04-30</td>\n",
       "      <td>1.010579</td>\n",
       "      <td>1.188364</td>\n",
       "      <td>0.936853</td>\n",
       "      <td>0.864134</td>\n",
       "      <td>0.966298</td>\n",
       "      <td>0.749147</td>\n",
       "      <td>0.455997</td>\n",
       "      <td>0.561297</td>\n",
       "      <td>0.390720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>2015-07-31</td>\n",
       "      <td>-14.089824</td>\n",
       "      <td>-13.806882</td>\n",
       "      <td>-14.058526</td>\n",
       "      <td>-13.828341</td>\n",
       "      <td>-13.733795</td>\n",
       "      <td>-13.637571</td>\n",
       "      <td>-16.701876</td>\n",
       "      <td>-16.486915</td>\n",
       "      <td>-16.594398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>2015-08-31</td>\n",
       "      <td>-21.602730</td>\n",
       "      <td>-21.351010</td>\n",
       "      <td>-21.561066</td>\n",
       "      <td>-21.622824</td>\n",
       "      <td>-21.376651</td>\n",
       "      <td>-21.565872</td>\n",
       "      <td>-22.271687</td>\n",
       "      <td>-21.922797</td>\n",
       "      <td>-22.196960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>2015-09-30</td>\n",
       "      <td>-28.950508</td>\n",
       "      <td>-28.907613</td>\n",
       "      <td>-28.745937</td>\n",
       "      <td>-24.930195</td>\n",
       "      <td>-24.790843</td>\n",
       "      <td>-24.772343</td>\n",
       "      <td>-25.788952</td>\n",
       "      <td>-25.540149</td>\n",
       "      <td>-25.746639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>2015-12-31</td>\n",
       "      <td>-24.896338</td>\n",
       "      <td>-25.209305</td>\n",
       "      <td>-24.565445</td>\n",
       "      <td>-22.426184</td>\n",
       "      <td>-23.282383</td>\n",
       "      <td>-22.163950</td>\n",
       "      <td>-26.206217</td>\n",
       "      <td>-26.501695</td>\n",
       "      <td>-25.833229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>2016-01-31</td>\n",
       "      <td>-13.355442</td>\n",
       "      <td>-14.063146</td>\n",
       "      <td>-12.818172</td>\n",
       "      <td>-8.524130</td>\n",
       "      <td>-9.109433</td>\n",
       "      <td>-8.489168</td>\n",
       "      <td>-13.464023</td>\n",
       "      <td>-13.945262</td>\n",
       "      <td>-12.912786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>2016-02-29</td>\n",
       "      <td>-0.106278</td>\n",
       "      <td>-0.445094</td>\n",
       "      <td>0.205529</td>\n",
       "      <td>0.220877</td>\n",
       "      <td>-0.625012</td>\n",
       "      <td>0.087549</td>\n",
       "      <td>-1.166986</td>\n",
       "      <td>-1.365643</td>\n",
       "      <td>-0.803569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2016-03-31</td>\n",
       "      <td>-2.563282</td>\n",
       "      <td>-3.510505</td>\n",
       "      <td>-2.118033</td>\n",
       "      <td>-0.079829</td>\n",
       "      <td>-1.196480</td>\n",
       "      <td>0.184099</td>\n",
       "      <td>-3.337182</td>\n",
       "      <td>-4.195184</td>\n",
       "      <td>-2.912051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>2016-05-31</td>\n",
       "      <td>-19.132475</td>\n",
       "      <td>-20.234131</td>\n",
       "      <td>-18.581387</td>\n",
       "      <td>-15.842950</td>\n",
       "      <td>-16.963555</td>\n",
       "      <td>-15.244143</td>\n",
       "      <td>-23.828522</td>\n",
       "      <td>-24.814628</td>\n",
       "      <td>-23.163651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>2016-06-30</td>\n",
       "      <td>-22.521250</td>\n",
       "      <td>-23.058244</td>\n",
       "      <td>-21.720552</td>\n",
       "      <td>-21.439398</td>\n",
       "      <td>-22.268017</td>\n",
       "      <td>-20.680305</td>\n",
       "      <td>-23.285225</td>\n",
       "      <td>-24.127994</td>\n",
       "      <td>-22.579285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>2016-07-31</td>\n",
       "      <td>-27.991007</td>\n",
       "      <td>-28.453803</td>\n",
       "      <td>-27.216616</td>\n",
       "      <td>-23.679438</td>\n",
       "      <td>-24.860733</td>\n",
       "      <td>-23.174431</td>\n",
       "      <td>-26.361423</td>\n",
       "      <td>-26.646625</td>\n",
       "      <td>-25.671631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>2016-08-31</td>\n",
       "      <td>-32.642998</td>\n",
       "      <td>-32.671241</td>\n",
       "      <td>-31.931881</td>\n",
       "      <td>-27.529375</td>\n",
       "      <td>-28.229670</td>\n",
       "      <td>-27.090008</td>\n",
       "      <td>-31.193026</td>\n",
       "      <td>-31.303996</td>\n",
       "      <td>-30.496208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>2016-11-30</td>\n",
       "      <td>-18.232407</td>\n",
       "      <td>-18.099577</td>\n",
       "      <td>-18.000624</td>\n",
       "      <td>-22.651611</td>\n",
       "      <td>-22.635139</td>\n",
       "      <td>-22.318184</td>\n",
       "      <td>-23.536852</td>\n",
       "      <td>-24.080977</td>\n",
       "      <td>-23.366688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>2016-12-31</td>\n",
       "      <td>-22.596676</td>\n",
       "      <td>-22.036780</td>\n",
       "      <td>-22.087778</td>\n",
       "      <td>-14.549780</td>\n",
       "      <td>-14.943197</td>\n",
       "      <td>-14.200380</td>\n",
       "      <td>-21.597067</td>\n",
       "      <td>-21.512031</td>\n",
       "      <td>-21.015167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>2017-01-31</td>\n",
       "      <td>-9.825678</td>\n",
       "      <td>-10.412693</td>\n",
       "      <td>-9.302179</td>\n",
       "      <td>0.796209</td>\n",
       "      <td>-0.291784</td>\n",
       "      <td>1.029503</td>\n",
       "      <td>-14.763456</td>\n",
       "      <td>-15.308451</td>\n",
       "      <td>-14.291983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>158 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           DATE     CSR- 2     CSR- 3     CSR- 4     GFZ- 2     GFZ- 3  \\\n",
       "0    2002-04-30   6.369780   6.862617   7.205875   2.990696   3.248541   \n",
       "1    2002-05-31  -0.317135  -0.245743   0.190985  -0.469174  -0.104986   \n",
       "2    2002-08-31 -16.290028 -15.536709 -15.985147 -14.353841 -13.973452   \n",
       "3    2002-09-30 -19.481396 -19.447235 -19.265459 -18.929935 -18.468894   \n",
       "4    2002-10-31 -23.332886 -22.280301 -22.933765 -23.638735 -22.075061   \n",
       "5    2002-11-30 -19.722694 -18.801866 -19.537502 -20.341890 -19.304660   \n",
       "6    2002-12-31 -19.319786 -18.601535 -18.338511 -18.110817 -17.494361   \n",
       "7    2003-01-31  -2.451995  -2.122681  -2.668819  -4.548550  -4.295708   \n",
       "8    2003-02-28   3.250052   2.994657   4.097945   2.072590   1.254594   \n",
       "9    2003-03-31   4.753602   4.324463   5.818776   5.661065   5.361503   \n",
       "10   2003-04-30   8.166151   7.227407   8.691176   9.172767   8.083807   \n",
       "11   2003-05-31   2.874755   2.226595   3.269719   3.881123   3.704666   \n",
       "12   2003-07-31 -12.343306 -12.258996 -11.762691 -13.131025 -13.121426   \n",
       "13   2003-08-31 -21.261074 -20.808085 -20.673313 -19.264833 -18.911374   \n",
       "14   2003-09-30 -25.042097 -24.559913 -24.651525 -26.238302 -25.895774   \n",
       "15   2003-10-31 -25.410423 -24.777045 -25.053099 -22.707054 -22.165483   \n",
       "16   2003-11-30 -22.118423 -21.318322 -21.821232 -23.491257 -22.589079   \n",
       "17   2003-12-31 -17.022846 -16.888436 -16.779102 -15.920524 -15.826875   \n",
       "18   2004-01-31 -12.087959 -11.972403 -11.771278 -17.662470 -17.544790   \n",
       "19   2004-02-29  13.138374  13.633330  13.025967  15.370454  16.094228   \n",
       "20   2004-03-31  19.025305  19.573788  18.854607  19.985523  20.608226   \n",
       "21   2004-04-30  19.059929  19.969254  18.911015  21.543365  22.293242   \n",
       "22   2004-05-31  12.624348  12.940631  12.457984  12.742107  12.869291   \n",
       "23   2004-06-30   4.322315   4.540306   4.419805   6.587780   6.649169   \n",
       "24   2004-07-31  -2.346790  -2.129722  -2.161875  -1.292259  -1.052427   \n",
       "25   2004-08-31  -7.248164  -6.917360  -7.173278  -6.695548  -6.410896   \n",
       "26   2004-09-30 -12.440788 -12.073799 -12.430477 -14.524635 -13.582054   \n",
       "27   2004-10-31 -16.736893 -16.017285 -16.529253 -19.176521 -18.384983   \n",
       "28   2004-11-30 -14.282515 -13.477036 -14.189545 -16.921297 -15.845495   \n",
       "29   2004-12-31  -6.511369  -6.048361  -6.564404  -6.252694  -5.572017   \n",
       "..          ...        ...        ...        ...        ...        ...   \n",
       "128  2013-10-31 -19.883970 -19.831055 -19.776855 -19.746614 -19.515842   \n",
       "129  2013-11-30 -17.951689 -17.678472 -17.878567 -17.545496 -17.324193   \n",
       "130  2013-12-31  -7.114188  -6.506821  -7.291891  -4.772312  -4.177369   \n",
       "131  2014-01-31   4.752780   6.180957   4.409497   0.928747   2.493935   \n",
       "132  2014-03-31   5.896018   6.871508   5.914853   5.364076   6.400744   \n",
       "133  2014-04-30   5.547657   6.259777   5.287659   6.754492   7.519115   \n",
       "134  2014-05-31  -0.297667   0.452662  -0.445184  -0.084369   0.717795   \n",
       "135  2014-06-30  -6.055377  -5.231532  -6.151235  -5.232599  -4.410575   \n",
       "136  2014-08-31 -16.852480 -16.279816 -16.946657 -17.782343 -17.109928   \n",
       "137  2014-09-30 -22.443039 -21.365404 -22.631187 -22.167606 -21.175571   \n",
       "138  2014-10-31 -26.024818 -24.932782 -26.112677 -25.700319 -24.572549   \n",
       "139  2014-11-30 -22.624952 -20.924166 -22.648527 -21.934071 -20.195606   \n",
       "140  2015-01-31 -12.104342 -11.210520 -12.211308  -9.621220  -8.399601   \n",
       "141  2015-02-28  -5.946175  -4.775208  -5.603221  -6.920004  -5.975946   \n",
       "142  2015-03-31  -3.874774  -3.630413  -4.040146  -1.680863  -1.474264   \n",
       "143  2015-04-30   1.010579   1.188364   0.936853   0.864134   0.966298   \n",
       "144  2015-07-31 -14.089824 -13.806882 -14.058526 -13.828341 -13.733795   \n",
       "145  2015-08-31 -21.602730 -21.351010 -21.561066 -21.622824 -21.376651   \n",
       "146  2015-09-30 -28.950508 -28.907613 -28.745937 -24.930195 -24.790843   \n",
       "147  2015-12-31 -24.896338 -25.209305 -24.565445 -22.426184 -23.282383   \n",
       "148  2016-01-31 -13.355442 -14.063146 -12.818172  -8.524130  -9.109433   \n",
       "149  2016-02-29  -0.106278  -0.445094   0.205529   0.220877  -0.625012   \n",
       "150  2016-03-31  -2.563282  -3.510505  -2.118033  -0.079829  -1.196480   \n",
       "151  2016-05-31 -19.132475 -20.234131 -18.581387 -15.842950 -16.963555   \n",
       "152  2016-06-30 -22.521250 -23.058244 -21.720552 -21.439398 -22.268017   \n",
       "153  2016-07-31 -27.991007 -28.453803 -27.216616 -23.679438 -24.860733   \n",
       "154  2016-08-31 -32.642998 -32.671241 -31.931881 -27.529375 -28.229670   \n",
       "155  2016-11-30 -18.232407 -18.099577 -18.000624 -22.651611 -22.635139   \n",
       "156  2016-12-31 -22.596676 -22.036780 -22.087778 -14.549780 -14.943197   \n",
       "157  2017-01-31  -9.825678 -10.412693  -9.302179   0.796209  -0.291784   \n",
       "\n",
       "        GFZ- 4     JPL- 2     JPL- 3     JPL- 4  \n",
       "0     4.077078   2.249933   3.097115   3.173393  \n",
       "1     0.065377  -4.259185  -3.519518  -3.788323  \n",
       "2   -14.237137 -16.737858 -16.217127 -16.430960  \n",
       "3   -18.576111 -27.825027 -23.994564 -25.610378  \n",
       "4   -22.915709 -23.753513 -22.982873 -23.265060  \n",
       "5   -20.110733 -18.874060 -17.631137 -18.579494  \n",
       "6   -17.541786 -21.428724 -19.742789 -20.003414  \n",
       "7    -4.480847  -4.093900  -3.792067  -4.139344  \n",
       "8     2.824111   4.647142   4.434167   5.562096  \n",
       "9     6.813591   4.820865   4.704756   5.890776  \n",
       "10    9.703140   9.445084   8.756989   9.953403  \n",
       "11    4.275807   2.860014   2.706311   3.202332  \n",
       "12  -12.638650 -11.115901 -11.083034 -10.620917  \n",
       "13  -18.702770 -19.640589 -19.167141 -19.115789  \n",
       "14  -25.790489 -25.033787 -24.541680 -24.640207  \n",
       "15  -22.326506 -22.305223 -21.587609 -21.965542  \n",
       "16  -23.202274 -19.419596 -18.853739 -19.179361  \n",
       "17  -15.688605 -15.187391 -15.212182 -14.987539  \n",
       "18  -17.373096 -13.066944 -13.013412 -12.748642  \n",
       "19   15.351665  13.409797  14.343638  13.397733  \n",
       "20   19.935713  19.852787  20.436761  19.759512  \n",
       "21   21.474270  20.423214  21.412837  20.221178  \n",
       "22   12.543344  15.086021  15.586907  14.867765  \n",
       "23    6.607500   4.459651   4.328069   4.125405  \n",
       "24   -1.191553  -0.482072  -0.157299  -0.504699  \n",
       "25   -6.593496  -6.082361  -6.019300  -6.211475  \n",
       "26  -14.533315 -13.844083 -13.198644 -13.968958  \n",
       "27  -18.965782 -16.783075 -15.794747 -16.581902  \n",
       "28  -16.642067 -13.098007 -12.246674 -12.922140  \n",
       "29   -6.281512  -5.244415  -4.822237  -5.214270  \n",
       "..         ...        ...        ...        ...  \n",
       "128 -19.541958 -21.860270 -21.861596 -21.750874  \n",
       "129 -17.466167 -21.597095 -21.361330 -21.350090  \n",
       "130  -4.743716  -8.713623  -8.302150  -8.948681  \n",
       "131   0.775488   1.142642   2.509270   0.858418  \n",
       "132   5.356269   5.612525   6.720335   5.619271  \n",
       "133   6.388005   4.932554   5.534496   4.630267  \n",
       "134  -0.252695   0.787866   1.460755   0.653013  \n",
       "135  -5.360711  -5.552900  -4.777907  -5.671156  \n",
       "136 -17.865229 -18.524042 -17.876799 -18.594212  \n",
       "137 -22.368719 -24.127289 -23.237596 -24.353397  \n",
       "138 -25.707317 -25.843990 -24.781655 -25.920692  \n",
       "139 -21.908251 -22.033186 -20.456134 -22.080547  \n",
       "140  -9.410498 -16.373779 -15.343823 -16.360918  \n",
       "141  -6.492736 -11.618717 -11.073487 -11.840231  \n",
       "142  -1.742041  -3.856456  -3.581561  -3.954263  \n",
       "143   0.749147   0.455997   0.561297   0.390720  \n",
       "144 -13.637571 -16.701876 -16.486915 -16.594398  \n",
       "145 -21.565872 -22.271687 -21.922797 -22.196960  \n",
       "146 -24.772343 -25.788952 -25.540149 -25.746639  \n",
       "147 -22.163950 -26.206217 -26.501695 -25.833229  \n",
       "148  -8.489168 -13.464023 -13.945262 -12.912786  \n",
       "149   0.087549  -1.166986  -1.365643  -0.803569  \n",
       "150   0.184099  -3.337182  -4.195184  -2.912051  \n",
       "151 -15.244143 -23.828522 -24.814628 -23.163651  \n",
       "152 -20.680305 -23.285225 -24.127994 -22.579285  \n",
       "153 -23.174431 -26.361423 -26.646625 -25.671631  \n",
       "154 -27.090008 -31.193026 -31.303996 -30.496208  \n",
       "155 -22.318184 -23.536852 -24.080977 -23.366688  \n",
       "156 -14.200380 -21.597067 -21.512031 -21.015167  \n",
       "157   1.029503 -14.763456 -15.308451 -14.291983  \n",
       "\n",
       "[158 rows x 10 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_df(grace_data,station)\n",
    "pd.read_csv(\"DATA/GRACE/STATIONS/BRAZ.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "stations = pd.read_csv(\"DATA/RBMC_2017.csv\",index_col=\"Name\")\n",
    "stations.long = 360 + stations.long \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ALAR\n",
      "ALMA\n",
      "AMBC\n",
      "AMCO\n",
      "AMHA\n",
      "AMMU\n",
      "AMPR\n",
      "AMTA\n",
      "AMTE\n",
      "AMUA\n",
      "APLJ\n",
      "APS1\n",
      "BABJ\n",
      "BABR\n",
      "BAIL\n",
      "BAIR\n",
      "BAIT\n",
      "BATF\n",
      "BAVC\n",
      "BELE\n",
      "BEPA\n",
      "BOAV\n",
      "BRAZ\n",
      "BRFT\n",
      "CEEU\n",
      "CEFE\n",
      "CEFT\n",
      "CESB\n",
      "CHPI\n",
      "COAM\n",
      "CORU\n",
      "CRAT\n",
      "CRUZ\n",
      "CUIB\n",
      "EESC\n",
      "GOGY\n",
      "GOJA\n",
      "GOUR\n",
      "GVA1\n",
      "IFSC\n",
      "ILHA\n",
      "IMBT\n",
      "IMPZ\n",
      "ITAI\n",
      "ITAM\n",
      "JAMG\n",
      "MABA\n",
      "MABB\n",
      "MABS\n",
      "MCL1\n",
      "MGBH\n",
      "MGIN\n",
      "MGMC\n",
      "MGMT\n",
      "MGRP\n",
      "MGUB\n",
      "MGV1\n",
      "MSAQ\n",
      "MSCB\n",
      "MSCG\n",
      "MSDR\n",
      "MSNV\n",
      "MSPP\n",
      "MTCN\n",
      "MTCO\n",
      "MTGA\n",
      "MTJI\n",
      "MTLA\n",
      "MTLE\n",
      "MTNX\n",
      "MTSF\n",
      "MTSR\n",
      "NAUS\n",
      "NEIA\n",
      "ONRJ\n",
      "PAAR\n",
      "PAIT\n",
      "PASM\n",
      "PBCG\n",
      "PBJP\n",
      "PEAF\n",
      "PEPE\n",
      "PICR\n",
      "PIFL\n",
      "PISR\n",
      "PITN\n",
      "POAL\n",
      "POLI\n",
      "POVE\n",
      "PPTE\n",
      "PRCV\n",
      "PRGU\n",
      "PRMA\n",
      "RECF\n",
      "RIOB\n",
      "RIOD\n",
      "RJCG\n",
      "RNMO\n",
      "RNNA\n",
      "RNPF\n",
      "ROCD\n",
      "ROGM\n",
      "ROJI\n",
      "ROSA\n",
      "RSAL\n",
      "RSCL\n",
      "RSPE\n",
      "RSPF\n",
      "SAGA\n",
      "SALU\n",
      "SAVO\n",
      "SCAQ\n",
      "SCCH\n",
      "SCFL\n",
      "SCLA\n",
      "SEAJ\n",
      "SJRP\n",
      "SJSP\n",
      "SMAR\n",
      "SPAR\n",
      "SPBO\n",
      "SPBP\n",
      "SPC1\n",
      "SPDR\n",
      "SPFE\n",
      "SPFR\n",
      "SPJA\n",
      "SPLI\n",
      "SPPI\n",
      "SPS1\n",
      "SPTU\n",
      "SSA1\n",
      "TOGU\n",
      "TOPL\n",
      "UBA1\n",
      "UBE1\n",
      "UFPR\n",
      "VICO\n"
     ]
    }
   ],
   "source": [
    "for station in stations.index:\n",
    "    #print(station)\n",
    "    get_df(grace_data,stations.loc[station])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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