Extract_NetCDF.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://stackoverflow.com/questions/50506347/operation-along-year-with-xarray\n",
    "\n",
    "In the below link: Organizing daily Excel data into xarray dataset  (There is a nice 3-D figure represents the N-Data array)\n",
    "\n",
    "https://stackoverflow.com/questions/41904951/organizing-daily-excel-data-into-xarray-dataset\n",
    "\n",
    "https://medium.com/pangeo/step-by-step-guide-to-building-a-big-data-portal-e262af1c2977\n",
    "\n",
    "https://stackoverflow.com/questions/40544846/read-multiple-coordinates-with-xarray\n",
    "\n",
    "http://stephanhoyer.com/2015/06/11/xray-dask-out-of-core-labeled-arrays/\n",
    "\n",
    "You might pass your data off to pandas for further analysis in tabular form: https://xray.readthedocs.io/en/stable/pandas.html\n",
    "\n",
    "http://stephanhoyer.com/2015/06/11/xray-dask-out-of-core-labeled-arrays/#fn:1\n",
    "\n",
    "For distributed computation that lets us automatically parallelize grouped operations written like:\n",
    "\n",
    "s.groupby('some variable').apply(f),  where f is a function on NumPy arrays.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import pvlib\n",
    "# from pvlib.forecast import GFS, NAM, NDFD, HRRR, RAP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 5, longitude: 9, number: 10, time: 3)\n",
       "Coordinates:\n",
       "  * longitude  (longitude) float32 141.0 141.5 142.0 142.5 ... 144.0 144.5 145.0\n",
       "  * latitude   (latitude) float32 44.0 43.5 43.0 42.5 42.0\n",
       "  * number     (number) int32 0 1 2 3 4 5 6 7 8 9\n",
       "  * time       (time) datetime64[ns] 2019-01-01 ... 2019-01-01T06:00:00\n",
       "Data variables:\n",
       "    ssrdc      (time, number, latitude, longitude) float32 ...\n",
       "    ssrd       (time, number, latitude, longitude) float32 ...\n",
       "    tp         (time, number, latitude, longitude) float32 ...\n",
       "Attributes:\n",
       "    Conventions:  CF-1.6\n",
       "    history:      2019-03-13 16:34:00 GMT by grib_to_netcdf-2.10.0: /opt/ecmw..."
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "###http://stephanhoyer.com/2015/06/11/xray-dask-out-of-core-labeled-arrays/\n",
    "\n",
    "# ds=xr.open_dataset('Hokk_Ens_Aggreg_S1_S2_2017_2018.nc')  #This data with latitude=43\n",
    "\n",
    "# ds=xr.open_dataset('Hokk_Ens_Agg_lat42_44_12vars_2016.nc')  #This data with latitude=43\n",
    "\n",
    "# ds=xr.open_dataset('Hokk_Ens_Agg_lat42_44_2017_2018.nc')  #This data with latitude ranges 42-44\n",
    "\n",
    "# ds=xr.open_dataset('Hokk_Ens_Agg_lat42_44_inclHR_2016.nc')#latitude ranges 42-44, but unfortunately no HR not exist\n",
    "\n",
    "# ds=xr.open_dataset('Hokk_Ens_Agg_lat42_44_inclHR_2017_18.nc')#atitude ranges 42-44 #latitude ranges 42-44, but unfortunately no HR not exist\n",
    "\n",
    "ds=xr.open_dataset('Hokk_Ens_Agg_lat42_44_Jan2019_3vars_exist.nc')#For Jan 2019, by March 12, 2019 from ERA data at CDS only 3 variables available\n",
    "\n",
    "\n",
    "# ds.sel(time='2017-1-1').max(['latitude', 'longitude']) \n",
    "\n",
    "# ds.sel(time='2016-1-1').max(['latitude', 'longitude'])\n",
    "\n",
    "\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Variables:\n",
    "\"10m_u_component_of_wind\", \"10m_v_component_of_wind\", \"2m_temperature\", \"snow_albedo\", \"snow_depth\", \"surface_solar_radiation_downward_clear_sky\", \"surface_solar_radiation_downwards\", \"total_cloud_cover\", \"total_column_cloud_ice_water\", \"total_column_cloud_liquid_water\", \"total_precipitation\"\n",
    "\n",
    "Data variables, they are described with their units as in: https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=overview\n",
    "\n",
    "https://cds.climate.copernicus.eu/cdsapp#!/yourrequests?tab=form\n",
    "\n",
    "https://apps.ecmwf.int/codes/grib/param-db  (to know which variables with accumulated values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vars=['u10', 'v10', 't2m', 'asn', 'sd', 'ssrdc', 'ssrd', 'tcc', 'tciw', 'tclw', 'tp']\n",
    "vars=['ssrdc', 'ssrd', 'tp']\n",
    "\n",
    "len(vars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_ens = xr.concat([ds[vars]], dim = 'number')\n",
    "aggreg_ens=var_ens.groupby('time').mean(xr.ALL_DIMS)\n",
    "aggreg_ens=aggreg_ens.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ssrdc</th>\n",
       "      <th>ssrd</th>\n",
       "      <th>tp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <td>8.019003e+05</td>\n",
       "      <td>706773.875</td>\n",
       "      <td>0.000141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:00:00</th>\n",
       "      <td>3.957017e+06</td>\n",
       "      <td>3486459.750</td>\n",
       "      <td>0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 06:00:00</th>\n",
       "      <td>3.174740e+06</td>\n",
       "      <td>2538458.750</td>\n",
       "      <td>0.000139</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                            ssrdc         ssrd        tp\n",
       "time                                                    \n",
       "2019-01-01 00:00:00  8.019003e+05   706773.875  0.000141\n",
       "2019-01-01 03:00:00  3.957017e+06  3486459.750  0.000127\n",
       "2019-01-01 06:00:00  3.174740e+06  2538458.750  0.000139"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggreg_ens.head()\n",
    "# aggreg_ens.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ssrdc</th>\n",
       "      <th>ssrd</th>\n",
       "      <th>tp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>13.000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.896950e+06</td>\n",
       "      <td>2482851.750</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.004227e+06</td>\n",
       "      <td>851910.375</td>\n",
       "      <td>0.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.019003e+05</td>\n",
       "      <td>706773.875</td>\n",
       "      <td>0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.379459e+06</td>\n",
       "      <td>2096616.750</td>\n",
       "      <td>0.000131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.305120e+06</td>\n",
       "      <td>2696459.000</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.565878e+06</td>\n",
       "      <td>3023178.750</td>\n",
       "      <td>0.000137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.957017e+06</td>\n",
       "      <td>3486459.750</td>\n",
       "      <td>0.000141</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "              ssrdc         ssrd         tp\n",
       "count  1.300000e+01       13.000  13.000000\n",
       "mean   2.896950e+06  2482851.750   0.000134\n",
       "std    1.004227e+06   851910.375   0.000005\n",
       "min    8.019003e+05   706773.875   0.000127\n",
       "25%    2.379459e+06  2096616.750   0.000131\n",
       "50%    3.305120e+06  2696459.000   0.000134\n",
       "75%    3.565878e+06  3023178.750   0.000137\n",
       "max    3.957017e+06  3486459.750   0.000141"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_data = aggreg_ens.resample('30min').interpolate()\n",
    "resampled_data.head()\n",
    "resampled_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f0d8836400>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_plt=resampled_data.loc['2017-1-1':'2017-1-10']\n",
    "df_plt=resampled_data.loc['2019-1-1':'2019-1-10']\n",
    "\n",
    "df_plt['ssrd'].plot()\n",
    "df_plt['ssrdc'].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://apps.ecmwf.int/codes/grib/param-db  (to know which variables with accumulated values)\n",
    "\n",
    "https://confluence.ecmwf.int/pages/viewpage.action?pageId=111155330\n",
    "\n",
    "These are the variables that attributed with accumulated fields:\n",
    "\n",
    "\"snow_depth\", \"surface_solar_radiation_downward_clear_sky\", \"surface_solar_radiation_downwards\",  \"total_precipitation\"\n",
    "vars=['sd', 'ssrdc', 'ssrd', 'tp']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            ssrdc         ssrd\n",
      "time                                          \n",
      "2019-01-01 00:00:00  8.019003e+05   706773.875\n",
      "2019-01-01 00:30:00  1.327753e+06  1170054.875\n",
      "2019-01-01 01:00:00  1.853606e+06  1633335.875\n",
      "2019-01-01 01:30:00  2.379459e+06  2096616.750\n",
      "2019-01-01 02:00:00  2.905312e+06  2559897.750\n",
      "2019-01-01 02:30:00  3.431164e+06  3023178.750\n",
      "2019-01-01 03:00:00  3.957017e+06  3486459.750\n",
      "2019-01-01 03:30:00  3.826638e+06  3328459.500\n",
      "2019-01-01 04:00:00  3.696258e+06  3170459.500\n",
      "                          ssrdc        ssrd\n",
      "time                                       \n",
      "2019-01-01 00:00:00   74.250031   65.442024\n",
      "2019-01-01 00:30:00  122.940102  108.338417\n",
      "2019-01-01 01:00:00  171.630173  151.234802\n",
      "2019-01-01 01:30:00  220.320251  194.131180\n",
      "2019-01-01 02:00:00  269.010315  237.027573\n",
      "2019-01-01 02:30:00  317.700378  279.923950\n",
      "2019-01-01 03:00:00  366.390472  322.820343\n",
      "2019-01-01 03:30:00  354.318298  308.190704\n",
      "2019-01-01 04:00:00  342.246124  293.561066\n"
     ]
    }
   ],
   "source": [
    "df_acc=resampled_data[['ssrdc', 'ssrd']]\n",
    "df_avg=df_acc/(3*3600)\n",
    "# df_avg.head()\n",
    "# df_acc\n",
    "# 2017-01-01 00:00:00'\n",
    "print(df_acc.head(9))\n",
    "print(df_avg.head(9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f0d895e518>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_plt=df_avg.loc['2017-1-1':'2017-1-10']\n",
    "df_plt=df_avg.loc['2019-1-1':'2019-1-10']\n",
    "\n",
    "df_plt['ssrd'].plot()\n",
    "df_plt['ssrdc'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f0d89c0080>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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D3vFc2asN89P28vzsNJ6YlcozX2zgip5tGDs0ka6tG0S6m1WGgonEtM/X7uSfn67nqt5tuKF/u0h3R8qBmTGwQ1MGdmhK2u4jvDgnjekpGby5OIPBHZsydmgi/3VmC33JWSlpmkti1tYggePpDWrz9u1DqFNT8+mx4sDRk7y6MLSusv3AcTo0q8eYoYn8sE8bJfLMRWsmuSiYSLjMrGyu/ddXbNh5mBkThijteYw6mZ3Dhyu2MzE5jeUZB2hYpwY3DGjH6EEJuscooDUTkQI8+MEalm3ZzzM39VEgiWE14qoxolcbrujZmpRN+5g4O41/fbGB577cyOU9WjHunA50b9Mw0t2sFBRMJOa8u3QrL83bxLihiQzrrgSOElpX6ZfQhH4JTdi85ygvzE1j+sItvLN0G/0TmzBuaCIXntWSOK2r5EvTXBJT1u84xBVPzKF7mwa88qOByrsl+Tp4/CTTFmzhxbnpbN1/jPZN63LrkESu7htPvVqx8zlcaya5KJjI4cwsRjyRzIFjJ/ngznNo2UBz4lK4rOwcPl61g+eTN7Jk834a1K7O9f3bMXpwAq0b1Yl098qd1kxEwrg797y5nLTdR5gyboACiRRZ9bhqfK9HK77XoxWLNu1jUnIaz83eyPPJaQw/uxVjhybSq63uT1IwkZgweW467y/fzq8uPZPBHZXAUUqmb/vG9G3fmC17jzJ5bjrTFm7hvWXbSGrfmHHnJHJx19Njdl1F01xS5S3evI/r/jWPczs357lRSbo5TcrMoeMneT0lgxfmprFl7zHaNqnDLYMTuTYpnvq1a0S6e2VCaya5KJjEpj2HM7n88WSqxxnvTziHhnWrxn9wiS7ZOc7M1d/w/Ow0Ujbto36t6lzXry23DEkgvnHdSHevVLRmIjEvO8e5a9pS9hw5wVvjByuQSLmJq2YM696KYd1bsXTLfiYmp/HC3HQmzUnjsu6tuHVoIn3bN450N8uVzkykynrk/9by2GepPHTV2YxU3i2pYNv2H2Py3HReWbCZQ8ez6N2uEeOGduDSbi2pXokuSdc0Vy4KJrFl1tqdjHlhIVf3jeevV/fATOskEhlHMrN4Y1EGk+aksWnPUdo0qsMtgxO4rn9bGlSCdRUFk1wUTGJHxr6jXP54shI4SlTJznE+XbODiclpzE/bS72acVzbry1jBifSrmn0rqtozURiUmZWNrdPXUx2tvPMTX0VSCRqxFUzLul2Opd0O52VWw8wMTmNl+dtYvLcdC7pejpjz0kkqX3jSnsWrTMTqVL++50VTPlqM8/c1Jdh3U+PdHdECvTNgeO8NC+dqfM3c+DYSXrGN+TWoYkMP7tV1KT60TRXLgomVd87S7Zy17Sl3HZuB+4bflakuyNSZEdPZPHm4q1MSk4jbfcRWjWszejBCVzfr13Er0JUMMlFwaRqW7fjECOemMPZbRryyo8GVKqrZUROyclxZq3dycTkNOZu2EPdmnFc0zeeMUMSSWhWLyJ9UjDJRcGk6jqcmcUVTyRz8FgWH945lBbKuyVVwKptB5iUnM6MZVvJynEuOqslY4cmMiCxSYWuqyiY5KJgUjW5OxNeWcJHK7czddxABnVsGukuiZSpnQeP8/JXm5jy1Sb2HT1J9zYNGDs0ke+d3Zqa1cv/DFzBJBcFk6ppUnIaD7y/mt8M68L48ztGujsi5eb4yWzeWryVSXPSSN15mJYNajFqUAI3DmhHo7o1y+24Cia5KJhUPYs27eW6f33F+We24LlRfSvtJZUixZGT43y5fhcTk9OYvX43tWtU44d94rl1aCIdy+ErqBVMclEwqVp2H87k8seSqVm9Gu/9dCgN60T/ncQiZe3rbw4yKTmNd5Zu40RWDhd0acG4oYkM6ti0zD5cKZjkomBSdWTnOKMmzSclfR9v3T6Ybq0bRrpLIhG1+3AmU77axMvzNrHnyAm6nF6fced04Ps9W1Greulu3FUwyUXBpOr428dreWJWKn/5YQ+u7dc20t0RiRrHT2YzY+k2nk/eyLodh2levxajBrbnxoHtaVKvZOsqRQ0mhV4KYGZtzWyWma0xs1Vm9rOg/PdmttXMlgaP4WFt7jWzVDNba2aXhpUPC8pSzeyesPJEM5tvZuvNbJqZ1QzKawWvU4PtCYUdQ6q2z77ewROzUrk2KV6BRCSX2jVC+b4+vutcXh7bn26tG/D3mev45yfryv3YRcnNlQX8wt0Xm1l9YJGZzQy2PerufwuvbGZdgZFAN6A18ImZnRFsfhK4GMgAFprZDHdfDTwc7Os1M3sGGAs8Hfzc5+6dzGxkUO+6/I7h7tkl/YeQ6Ldl71F+Pm0ZXVs14IER3SPdHZGoZWac07k553RuTurOQ9StWf5pGAs9M3H37e6+OHh+CFgDtCmgyQjgNXfPdPc0IBXoHzxS3X2ju58AXgNGWGiV6ALgjaD9ZODKsH1NDp6/AVwY1M/vGFJFHT8ZSuCY487TN/Whdg0lcBQpik4t6tO6UZ1yP06x7ngJppl6A/ODoglmttzMJpnZqa8RawNsCWuWEZTlV94U2O/uWbnKv7WvYPuBoH5++8rd39vMLMXMUnbt2lWcoUqUeeD91azYeoC/X9OT9k0jk1ZCRPJX5GBiZqcBbwJ3uftBQtNQHYFewHbg76eq5tHcS1Bekn19u8D9WXdPcvek5s2b59FEKoO3FmfwyvzN/Pi8DlzSTZmARaJRkYKJmdUgFEimuvtbAO6+w92z3T0HeI7/TDNlAOEro/HAtgLKdwONzKx6rvJv7SvY3hDYW8C+pIr5+puD3Pf2CgYkNuFXl5wZ6e6ISD6KcjWXAROBNe7+SFh5q7BqPwBWBs9nACODK7ESgc7AAmAh0Dm4cqsmoQX0GR66NnkWcHXQfjTwbti+RgfPrwY+C+rndwypQg4dP8n4KYupX7sGj9/QW5mARaJYUZb4hwA3AyvMbGlQdh9wvZn1IjS9lA78GMDdV5nZdGA1oSvB7jh1lZWZTQA+BuKASe6+Ktjfb4DXzOxPwBJCwYvg58tmlkrojGRkYceQqsHd+fUby9m89yivjBtAi/rKBCwSzXTTokSl52dv5E8frOG+4V247VwlcBSJlDK7aVGkoi1M38ufP/qaS7u15EfndIh0d0SkCBRMJKrsOpTJHVMX07ZxHf56TU9lAhapJMr/tkiRIsrKzuHOV5dw4NhJXhzTnwa1lQlYpLJQMJGo8cjMdczbuIe/Xt2Drq0bRLo7IlIMmuaSqPDJ6h089fkGRvZryzVJSuAoUtkomEjEbd5zlLunL6Vb6wb8/opuke6OiJSAgolE1PGT2dz+yiIAnr6xrxI4ilRSWjORiPrDe6tYufUgz49Kol3TupHujoiUkM5MJGLeWJTBqwu2cPv5Hbmoa8tId0dESkHBRCJizfaD/PbtFQzq0JS7Lz6j8AYiEtUUTKTCHTx+kvFTFtGwTg0eu14JHEWqAq2ZSIVyd371+jK27DvGa7cNpHn9WpHukoiUAX0klAr13OyNfLxqB/de1oV+CU0i3R0RKSMKJlJh5m/cw8P/Xstl3U9n7NDESHdHRMqQgolUiJ2HjjPh1SW0a1KXv1zdQwkcRaoYrZlIucvKzuGnryzh0PGTvDy2P/WVwFGkylEwkXL3t/9bx/y0vTxybU+6nK4EjiJVkaa5pFzNXL2DZ77YwA0D2nFVn/hId0dEyomCiZSbTXuOcPf0pZzdpiH/c3nXSHdHRMqRgomUi+Mnsxk/ZTHVzHjqxj5K4ChSxWnNRMrF/e+uYvX2g0y6JYm2TZTAUaSq05mJlLnpKVuYlrKFCf/ViQu6KIGjSCxQMJEytXrbQX73zkqGdGrKz5XAUSRmKJhImTlw7CTjpy6icd2a/HNkb+Kq6cZEkVihNRMpE+7OL19fxtZ9x5j244E0O00JHEViic5MpEz868uNzFy9g/uGn0Xf9krgKBJrCg0mZtbWzGaZ2RozW2VmPwvKm5jZTDNbH/xsHJSbmT1mZqlmttzM+oTta3RQf72ZjQ4r72tmK4I2j1mQuKkkx5CK99XGPfzl31/zvR6tGDMkIdLdEZEIKMqZSRbwC3c/CxgI3GFmXYF7gE/dvTPwafAa4DKgc/C4DXgaQoEBuB8YAPQH7j8VHII6t4W1GxaUF+sYUvF2HjzOhFeWkNCsHg//UAkcRWJVocHE3be7++Lg+SFgDdAGGAFMDqpNBq4Mno8AXvKQr4BGZtYKuBSY6e573X0fMBMYFmxr4O7z3N2Bl3LtqzjHkAqUlZ3DhFeXcCQzi2du6stptbQEJxKrirVmYmYJQG9gPtDS3bdDKOAALYJqbYAtYc0ygrKCyjPyKKcEx5AK9NeP17IgbS9/vupszmhZP9LdEZEIKnIwMbPTgDeBu9z9YEFV8yjzEpQX2J2itDGz28wsxcxSdu3aVcgupTg+XvUN//pyIzcNbMeVvRXHRWJdkYKJmdUgFEimuvtbQfGOU1NLwc+dQXkG0DaseTywrZDy+DzKS3KMb3H3Z909yd2TmjdvXpShShGk7z7CL6cvo2d8Q36nBI4iQtGu5jJgIrDG3R8J2zQDOHVF1mjg3bDyUcEVVwOBA8EU1cfAJWbWOFh4vwT4ONh2yMwGBscalWtfxTmGlLPjJ7MZP3UxcXHGkzf2oVZ1JXAUkaLdtDgEuBlYYWZLg7L7gIeA6WY2FtgMXBNs+xAYDqQCR4ExAO6+18z+CCwM6j3g7nuD5+OBF4E6wEfBg+IeQ8rf795ZydffHGTSLf2Ib6wEjiISYqELqKq+pKQkT0lJiXQ3KrVpCzfzmzdXcOcFnbj7kjMj3R0RqQBmtsjdkwqrpzvgpUhWbj3A795dxTmdm/Gzi5TAUUS+TcFECnXgaCiBY9N6NfnHdb2UwFFEvkN3mUmBcnKcX7y+lG8OHGfajwfRVAkcRSQPOjORAj3z5QY+WbOT3w4/iz7tGhfeQERikoKJ5Gvuht387eO1fL9na0YPToh0d0QkiimYSJ52HDzOna8uIbFZPR666mwlcBSRAmnNRL7jZHYOE15ZzNET2bz6o4HUUwJHESmE/krId/zl31+zMH0f/xzZi85K4CgiRaBpLvmWf6/cznOz0xg1qD0jeimBo4gUjYKJ/H9pu4/wq9eX07NtI377vbMi3R0RqUQUTASAYyeyGT9lEdXjjKeUwFFEiklrJoK789/vrGTtjkO8OKY/bRrViXSXRKSS0ZmJ8NrCLby5OIM7L+jMeWfoe19EpPgUTGLciowD3B8kcLzzws6R7o6IVFIKJjFs/9ETjJ+6iGan1eSfI3srgaOIlJjWTGJUTo5z9/Rl7Dh4nNd/Mpgm9WpGuksiUonpzCRGPf3FBj77eie/u7wrvdo2inR3RKSSUzCJQXNSd/P3/1vLFT1bc/PA9pHujohUAQomMeabA6EEjh2an8aflcBRRMqI1kxiyKkEjsdOZjPtpj5K4CgiZUZ/TWLIQx99TcqmfTx+fW86tVACRxEpO5rmihEfrtjOxOQ0bhmcwPd7to50d0SkilEwiQEbdx3m128sp3e7Rtw3XAkcRaTsKZhUcUdPZDF+ymJqVq/Gkzf0oWZ1/cpFpOxpzaQKc3d++/ZK1u08xEu39qe1EjiKSDnRx9QqbOr8zby9ZCt3XXgG53RWAkcRKT8KJlXU8oz9PPDeas4/szk/vaBTpLsjIlVcocHEzCaZ2U4zWxlW9nsz22pmS4PH8LBt95pZqpmtNbNLw8qHBWWpZnZPWHmimc03s/VmNs3MagbltYLXqcH2hMKOISH7jpxg/JTFNK9fi0ev7UU1JXAUkXJWlDOTF4FheZQ/6u69gseHAGbWFRgJdAvaPGVmcWYWBzwJXAZ0Ba4P6gI8HOyrM7APGBuUjwX2uXsn4NGgXr7HKN6wq66cHOfn05ey61AmT93Yh8ZK4CgiFaDQYOLuXwJ7i7i/EcBr7p7p7mlAKtA/eKS6+0Z3PwG8BoywUC6PC4A3gvaTgSvD9jU5eP4GcGFQP79jCPDkrFQ+X7uL332/Kz2VwFFEKkhp1kwmmNnyYBqscVDWBtgSVicjKMuvvCmw392zcpV/a1/B9gNB/fz29R1mdpuZpZhZyq5du0o2ykokef1uHvlkHVf2as1NA9pFujva0BuFAAARU0lEQVQiEkNKGkyeBjoCvYDtwN+D8rwm570E5SXZ13cL3Z919yR3T2revGpfzbT9wDHufG0JnVucxv8qgaOIVLASBRN33+Hu2e6eAzzHf6aZMoC2YVXjgW0FlO8GGplZ9Vzl39pXsL0hoem2/PYVs05k5XDH1MVknszm6Zv6Urembh8SkYpVomBiZq3CXv4AOHWl1wxgZHAlViLQGVgALAQ6B1du1SS0gD7D3R2YBVwdtB8NvBu2r9HB86uBz4L6+R0jZv35ozUs3ryfv1zdk47NT4t0d0QkBhX6EdbMXgXOB5qZWQZwP3C+mfUiNL2UDvwYwN1Xmdl0YDWQBdzh7tnBfiYAHwNxwCR3XxUc4jfAa2b2J2AJMDEonwi8bGaphM5IRhZ2jFj0/vJtvDAnnTFDEvhej1aFNxARKQcW+rBf9SUlJXlKSkqku1GmUnceZsQTyZx5en1eu22Q8m6JSJkzs0XunlRYPf31qaSOZGYxfsoiatWI48kblcBRRCJLf4EqIXfnvrdXsGHXYR6/vjetGiqBo4hEloJJJTTlq028u3Qbd198BkM6NYt0d0REFEwqm6Vb9vPA+6u5oEsLbj9fCRxFJDoomFQi+46c4I6pi2nZoDaPXNtTCRxFJGro7rZKIifHuWtaKIHjG+MH0aiuEjiKSPTQmUkl8fhnqXyxbhf3X9GVHvFK4Cgi0UXBpBL4ct0u/vHpOq7q3YYb+iuBo4hEHwWTKLdt/zF+9toSzmhRnwd/oASOIhKdFEyi2ImsHG6fupiT2c7TN/WhTk19B5iIRCctwEex//1wDUu37OepG/vQQQkcRSSK6cwkSs1Yto0X56Yzdmgiw89WAkcRiW4KJlEodech7nlzOUntG3PPZV0i3R0RkUIpmESZI5lZ/GTKYurWjOOJG/pQI06/IhGJfloziSLuzj1vrWDjrsNMGTeA0xvWjnSXRESKRB97o8hL8zbx3rJt/OKSMxncUQkcRaTyUDCJEos37+NPH6zmwi4tGH9ex0h3R0SkWBRMosDeIyeYMHUxpzeszSPX9lICRxGpdLRmEmHZOc7PXlvC7iMneGv8YBrWrRHpLomIFJvOTCLssU/XM3v9bv5wRTe6t2kY6e6IiJSIgkkEfb52J499tp4f9olnZL+2ke6OiEiJKZhEyNb9x7hr2lLObFmfP13ZXQkcRaRSUzCJgMysbG6fupjsbOfpm/oqgaOIVHpagI+ABz9Yw7It+3nmpj4kNqsX6e6IiJSazkwq2LtLt/LSvE386JxEhnVXAkcRqRoUTCrQuh2HuOfNFfRLaMyvhymBo4hUHYUGEzObZGY7zWxlWFkTM5tpZuuDn42DcjOzx8ws1cyWm1mfsDajg/rrzWx0WHlfM1sRtHnMgpXokhwjmh3OzOInUxZRr1Z1JXAUkSqnKH/RXgSG5Sq7B/jU3TsDnwavAS4DOgeP24CnIRQYgPuBAUB/4P5TwSGoc1tYu2ElOUY0c3d+8+Zy0ncf4fHre9OygRI4ikjVUmgwcfcvgb25ikcAk4Pnk4Erw8pf8pCvgEZm1gq4FJjp7nvdfR8wExgWbGvg7vPc3YGXcu2rOMeIWi/OTeeD5dv51aVdGNSxaaS7IyJS5ko619LS3bcDBD9bBOVtgC1h9TKCsoLKM/IoL8kxotKiTft48IM1XHRWS35yXodId0dEpFyU9cR9XnfeeQnKS3KM71Y0u83MUswsZdeuXYXstuztOZzJhFcW07pRHf5+bU/dmCgiVVZJg8mOU1NLwc+dQXkGEJ4XJB7YVkh5fB7lJTnGd7j7s+6e5O5JzZs3L9YASyuUwHEpe46c4Kkb+9CwjhI4ikjVVdJgMgM4dUXWaODdsPJRwRVXA4EDwRTVx8AlZtY4WHi/BPg42HbIzAYGV3GNyrWv4hwjqvzzk3Ukp+7mjyOUwFFEqr5C74A3s1eB84FmZpZB6Kqsh4DpZjYW2AxcE1T/EBgOpAJHgTEA7r7XzP4ILAzqPeDupxb1xxO6YqwO8FHwoLjHiCaz1u7ksc9SuaZvPNf1axfp7oiIlDsLXURV9SUlJXlKSkq5Hydj31EufzyZVg3r8Pbtg6ldQ3m3RKTyMrNF7p5UWD3dOVeGvpXA8cY+CiQiEjOU6LEMPfDeapZnHOBfN/clQQkcRSSG6MykjLy9JIOp8zfz43M7cGm30yPdHRGRCqVgUgbWfnOIe99aQf/EJvzq0jMj3R0RkQqnYFJKh46fZPyURZxWqwZPXN+b6krgKCIxSGsmpXAqgeOmvUeZOm4ALZTAUURilD5Gl8KkOel8uOIbfnXpmQzsoASOIhK7FExKKCV9L3/+cA0Xd23Jj89VAkcRiW0KJiWw+3Amd7yymDaN6/C3a5TAUUREaybFFErguIT9R0/y1u39lMBRRAQFk2J7dOY65qTu4S9X96BbayVwFBEBTXMVy2df7+CJWalcl9SWa5PaFt5ARCRGKJgU0Za9R/n5tGV0bdWAP4zoFunuiIhEFQWTIjh+MpvxUxeR484zN/VVAkcRkVy0ZlIEf3hvNSu3HuS5UUm0a1o30t0REYk6OjMpxIxl23h1wWZ+cl5HLu7aMtLdERGJSjozKcTQTs24/fyO3H3xGZHuiohI1FIwKUSTejX59bAuke6GiEhU0zSXiIiUmoKJiIiUmoKJiIiUmoKJiIiUmoKJiIiUmoKJiIiUmoKJiIiUmoKJiIiUmrl7pPtQIczsELC2hM3bAZvLsDuR1BA4EOlOlBGNJfpUlXGAxnLKme5ev7BKsXQH/Fp3TypJQzPbVdK20cbMnnX32yLdj7KgsUSfqjIO0FjC2qYUpZ6muYpmf6Q7UIbei3QHypDGEn2qyjhAYymWWJrmSinFmUmJ24qIVGZF/fsXS2cmz0aorYhIZVakv38xc2YiIiLlJ5bOTKokMxtmZmvNLNXM7gnKJgSv3cyaFdB2tJmtDx6jw8r7mtmKYB+PmZlFaBxTg7KVZjbJzGpE+zgKGMtEM1tmZsvN7A0zOy2ftvcG7daa2aUF7TNSYwnb9riZHS6gbdSMJZ/fyYtmlmZmS4NHr3zaVob3l5nZg2a2zszWmNmdFT4Wd4+5BzCM0GXCqcA9QVkiMB9YD0wDaubT9t6g3Vrg0oL2WQHjiAM2AB2AmsAyoCvQG0gA0oFm+bRtAmwMfjYOnjcOti0ABgEGfARcFqFxDA/6YMCrwPhoHkchY2kQVueRvN4jQb1lQK3g/bgh2F+e+4zUWIJtScDLwOF82kbNWAr4nbwIXF1I28ry/hoDvARUC+q1qOixxNyZiZnFAU8ClxH6JVxvZl2Bh4FH3b0zsA8Ym0fbrsBIoBuh4PGUmcUVsM/y1h9IdfeN7n4CeA0Y4e5L3D29kLaXAjPdfa+77wNmAsPMrBWhP3zzPPQuewm4shzHAPmP40MPEHqzx0f5OAoay0EIfYIE6gB5zS+PAF5z90x3TyP0waR/fvuM1FiC9/tfgV8X0DaaxlKaY1aK9xcwHnjA3XMA3H1nRY8l5oIJ+f8yLgDeCOpMJu9/zGj6DwLQBtgS9jojKMuTmSWZ2fOFtG0TPC/SPstIgeMIprduBv4dvI7WcRTUH8zsBeAboAvweFB2hZk9UEjbYv2ey1B+x50AzHD37eGVo3gsBR3zwWDq8VEzqwWV9v3VEbjOzFLM7CMz6wwVO5ZYDCb5/YPud/esXGXR/B8EQqekueV7RYW7p7j7uELaFmufZaSwYz4FfOnusyGqx0FBx3X3MUBrYA1wXVA2w93/p5C20TSWWsA1BMEwXBSPJb9j3ksosPcjNPXzG6i0769awHEPXcL7HDAJKnYssRhM8vqHi8uj7NQfgGj9DwKhoNU27HU8sK2UbTP49nRScfZZUvmOw8zuB5oDdxezbSTGUVB/AHD3bEJrcj8sRtvS/J5LI6/jpgOdgFQzSwfqmllqEdtGaix5HtPdtwezqJnAC4RmGIrUluh7f2UAbwZlbwM9itm29GMp7wWjaHsQWmT6OOz1vcFjN1A9rzq564a9/jiom+c+K2As1QktoiXyn8W4bmHb0yl4AT6N0EJc4+B5k2DbQmAg/1mMGx6JcQDjgLlAnQLaRs04ChlLp2C7AX8D/pZH2258e9F6I6EPOgX+niP1/grq5LcAHzVjKeB30irsd/IP4KFK/P56CLg1qHM+sLCix1KuA4/GRwG/jNeBkUGdZ4Db82gbNf9Bwvo0HFhH6AqP3wZldxL6tJFF6BPG80F50qnnwetbCa37pAJjwsqTgJXBPp8guB8pAuPICl4vDR7/E+3jyGsshGYA5gArgv5MJbi6C7iC0MLpqba/DdqtJeyKmrz+fSL1/sq1/XDY86gdSz7vr8/CfidTgNMq4/srKGsEfBCMZx7Qs6LHEpM3LZrZcEKfROKASe7+oJl1ILRw3gRYAtzk7plmdgWQ5MFUl5n9ltAvJAu4y90/ym+fFT0uEZFIiclgIiIiZSsWF+BFRKSMKZiIiEipKZiIiEipxVQwySdBWlGTIn5uZvpOExGRPMRMMCkgf9Yc4CJgUwS7JyJSqcVMMKF0SRG/xcyeDnLgrDKzP4SVp5vZH8xscZDOuUvZDkFEJDrFUjApy/xZv/VQDpwewHlmFp66YLe79wGeBn5Zwv2LiFQqsRRMyjJ/1rVmtpjQzY3dCE2bnfJW8HMRoe8UERGp8mIpmBQrwZyZfRx8+9rzucoTCZ1xXOjuPQilMKgdViUz+JlNKM2KiEiVF0t/7BYCnYNgsJXQl1zdkF9ld780n00NgCPAATNrSWhB//Oy7aqISOUSM2cmHvqukgmEMv2uAaa7+yozu9PMTqVgXp77TCRMdSDT3ZcRmt5aReg7A+aUf+9FRKKbcnMVQfANbKlAd3c/EOn+iIhEm5g5Mymp4EbFpcBTCiQiInnTmYmIiJRaTJ6ZmFlbM5tlZmuCGw9/FpQ3MbOZZrY++Nk4KO9iZvPMLNPMfplrXz8zs5XBfu6KxHhERCItJoMJoS+2+oW7n0XoqyrvCFKr3AN86u6dgU+D1wB7CX174d/Cd2Jm3YEfEbq7vidwuZl1rpghiIhEj5gMJu6+3d0XB88PEbq6qw0wApgcVJsMXBnU2enuC4GTuXZ1FvCVux8Nrhb7AvhBBQxBRCSqxGQwCWdmCUBvYD7Q0t23QyjgAC0Kab4SONfMmppZXULfzdy2kDYiIlVOLN20+B1mdhrwJqHvcj9ollfGlfy5+xozexiYCRwGlhGaQhMRiSkxe2ZiZjUIBZKp7n4qn9YOM2sVbG8F7CxsP+4+0d37uPu5hNZW1pdXn0VEolVMBhMLnYJMBNa4+yNhm2YAo4Pno4F3i7CvFsHPdsBVwKtl21sRkegXk/eZmNlQYDawAsgJiu8jtG4yHWgHbAaucfe9ZnY6kEIoL1cOoSmtrsHU2GygKaHF+bvd/dMKHYyISBSIyWAiIiJlKyanuUREpGwpmIiISKkpmIiISKkpmIiISKkpmIiISKkpmIiUEzNrZGa3B89bm9kbke6TSHnRpcEi5STI+/a+u3ePcFdEyl1M5+YSKWcPAR3NbCmhNDtnuXt3M7uFUEbqOKA78HegJnAzkAkMD26W7Qg8CTQHjgI/cvevK34YIoXTNJdI+bkH2ODuvYBf5drWHbiB0HfhPAgcdffewDxgVFDnWeCn7t4X+CXwVIX0WqQEdGYiEhmzgu/SOWRmB4D3gvIVQI8go/Vg4PWwbNa1Kr6bIkWjYCISGZlhz3PCXucQ+n9ZDdgfnNWIRD1Nc4mUn0NA/ZI0dPeDQJqZXQOhTNdm1rMsOydSlhRMRMqJu+8B5pjZSuCvJdjFjcBYM1sGrCL0tdIiUUmXBouISKnpzEREREpNwUREREpNwUREREpNwUREREpNwUREREpNwUREREpNwUREREpNwURERErt/wHekWewlRr7oQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_plt=resampled_data['2017-1-1':'2018-12-31']\n",
    "df_plt=resampled_data['2019-1-1':'2019-12-31']\n",
    "\n",
    "df_plt['ssrd'].plot()\n",
    "# df_plt['ssrdc'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>ssrdc</th>\n",
       "      <th>ssrd</th>\n",
       "      <th>tp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <td>74.250031</td>\n",
       "      <td>65.442024</td>\n",
       "      <td>0.000141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:30:00</th>\n",
       "      <td>122.940102</td>\n",
       "      <td>108.338417</td>\n",
       "      <td>0.000139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:00:00</th>\n",
       "      <td>171.630173</td>\n",
       "      <td>151.234802</td>\n",
       "      <td>0.000136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:30:00</th>\n",
       "      <td>220.320251</td>\n",
       "      <td>194.131180</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:00:00</th>\n",
       "      <td>269.010315</td>\n",
       "      <td>237.027573</td>\n",
       "      <td>0.000131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:30:00</th>\n",
       "      <td>317.700378</td>\n",
       "      <td>279.923950</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:00:00</th>\n",
       "      <td>366.390472</td>\n",
       "      <td>322.820343</td>\n",
       "      <td>0.000127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 03:30:00</th>\n",
       "      <td>354.318298</td>\n",
       "      <td>308.190704</td>\n",
       "      <td>0.000129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 04:00:00</th>\n",
       "      <td>342.246124</td>\n",
       "      <td>293.561066</td>\n",
       "      <td>0.000131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          ssrdc        ssrd        tp\n",
       "time                                                 \n",
       "2019-01-01 00:00:00   74.250031   65.442024  0.000141\n",
       "2019-01-01 00:30:00  122.940102  108.338417  0.000139\n",
       "2019-01-01 01:00:00  171.630173  151.234802  0.000136\n",
       "2019-01-01 01:30:00  220.320251  194.131180  0.000134\n",
       "2019-01-01 02:00:00  269.010315  237.027573  0.000131\n",
       "2019-01-01 02:30:00  317.700378  279.923950  0.000129\n",
       "2019-01-01 03:00:00  366.390472  322.820343  0.000127\n",
       "2019-01-01 03:30:00  354.318298  308.190704  0.000129\n",
       "2019-01-01 04:00:00  342.246124  293.561066  0.000131"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_data[['ssrdc', 'ssrd']]=df_avg\n",
    "resampled_data.head(9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f0d8a0fa90>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DOJiBvifwGhAD1AGmOuf+aGZnUnR55RJgpHPuqH855utAHyAHGOGcW1faayjQ13L7tnrJ19iGMO5jaNCiyrsMRdVrOHHOkb5xN1PTMnn/m60cPJbPGa0aMrxfPMP6xtOheYNQd7F2OrgzIJn7kZfMrcIN7VUwJZEh7xi8NsS7bvnH86Bd1c+8C6teB3ZpxcujQlP1Gk4OHctj9rJtTE3L5Kv1OZjBhWe1JjkpgSu6tdPNz0Pl2EEv2Dfv5F2XXwkK9BIZZv4S0l6G4a9Aj2FV3l24Vb2Gm027DpG6OItp6Vls3nOYpvXrcl3vDiT3S6BnfDMN7UQYBXoJf0vegHfvhYH3wRV/qvLuvss+wPAwrnoNJwUFjvnrdjE1LZP/LN/G0bwCzm7XmOR+CQzt01E/uwihQC/hbfNimHAVdBoAI6dXOhlVaPu+Iwx7/kuO5uWT+pPwr3oNJ3sP5zLzmy2kpGWxNHMPdesYg85pS3JSPJd2bUuspl0IWwr0Er4OZHvTDpvBuE+hUasq7S6w6nXKuAs4L75ZcPpZC2Xs2E9KehbTF28me/9RWjWqx9A+HUlOiqfraU1D3T05iQK9hKf8PHh9KGQuhDFzoEOfKu0umqpew0lefgGfrc1m6qIs5q3aTm6+47yOzUhOiue6Xh1o3rBeqLsoKNBLuJrzCMx/Foa+AL1vqdKu8gsc909awvvLtvL3m3oztE/HIHVSAuUcPOZNu5Cexcqt+6gXU4cfdm9Hcr94LkpsQ0wtu3Q1nAR1CgSRoFiW6gX5/mOrHOSdc/zxvRW8v2wrjww+V0G+GrVsVI87L+zMnRd2ZsWWomkX3v9mK6c1rc+wvh0Z3i+eM9s0DnVXpQQ6o5easW05vHS5d73wqPegbtX+9X/u4wwen7OasRd15pFrakfVazg5mpfPRyu9GTU/Wb2DAgdJp7cgOSmea3p20GWtNURDNxI+DuXAiz/wZqa86zNo0q5Ku6vtVa/hZvu+I0xfvJmU9EzWZR+kQWwMV593Gsn9Evhe55b6/VQjBXoJDwX58NaNsO5TGD0LEs6v0u5U9Rq+nHMsydxDSloWM7/ewv6jeSS0bMDwvgnc0K8j8S2Cc1N3KaJAL+Fh3v/C50/ANU9B/zFV2lVh1etZbRszadwADQ+EscPH8pmzYhsp6Zn8N2MXZjCwSyuS+yVwZffTaFBP0y4EgwK9hN7KmTDlVugzEq57tkrTshZWvTZtEEvqT1T1Gkmydh9iWvpmUhdnkplzmCZxdRnSqwPJSfH0SWiuaReqQIFeQit7Dbx4qXfzkNGzIbZ+pXcVWPU67e6BuqlGhCoocHy1PoeU9ExmL9vG4dx8urRpRHJSAsP6dKRt08q/R2orBXoJnSP7vCB/eDfc9Sk0i6/0rvYdyeXGf80nM+cQk1X1GjX2H8ll1rKtpKRlkbZxNzF1jEvObkNyv3guO7edci/lpOvoJTQKCuCduyFnHYyaUaUgfyQ3n3ET08jYcYBXRvdXkI8iTerHclP/TtzUvxPrsg+Qmp7FtMVZfLRqBy0axnJ9b2/ahe4d9DsPBp3RS3B99jh89Ce48i9wwT2V3k1+geO+SYuZtWybql5rifwCx+drs0lJz2Luiu0cyy+gW/umJCfFc33vjrRspGkXTqahG6l5a+fCm8lw3nAY9mKlk6/OOX4/YwWvzd/I7645lx9fFJ33epWS7Tl0jBlfezNqLtu8l9gY4/Jz25GcFM/FiW2oqxk1AQV6qWk567wZKZt1gjEfQL3KXzOtqlcJtGrbPlLSsnhnyWZ2HTxG2yZx/KhvR5L7JXBW29o97YICvdScYwfhpR/Cvs0w7hNo2bnSuyqseh3auwNPqepVAhzLK+Dj1TtIScvi49U7yC9w9OnUnOR+CQzp1Z6m9WND3cUap0AvNcM5mDYGlk+Hkalw1uWV3tVHq7YzdmK6ql6lTNn7j/ozamayZvsB4urW4eoep5GclMAFZ7aqNScIuupGasb852D5NLjs0SoF+SWbdnPPm4vp1r4pL4zspyAvpWrTJI6xF5/Jjy/qzDdZe0lJz2TG0i28s3QLHZs34IZ+8ST3iyehpaZdAJ3RS1Ws/wwmDoWug+HG1yudfFXVqwTDkdx8Pvh2OylpmXyRsRPnYMCZLRneL4HB551Gw3rRd16roRupXnsyYfwl0LA1jJ0HcU0qtRtVvUp12LLnMNMXZ5GansWGXYdoVC+GIT29aRf6nd4iaqZdUKCX6pN7BF65CnZ9B2M/8qY5qARVvUp1c86xaMNuUtIyeX/ZVg4dy+fM1o24oV88N/SN57RmkT3tQtACvZklABOB04ACYLxz7h9m9ntgLJDtb/pb59wsv83DwBggH7jfOTentNdQoI8gzsG7P4Wlb8CISd6wTSUcyfXu9bp4k3ev14sSda9XqV4Hj+Z50y6kZ7FwfQ51DC5M9KZd+GG3dtSPjbwZNYOZjM0DHnDOLTazJkC6mc311z3tnHvipBfuBowAugMdgA/N7GznXH7FDkHCUtrLXpC/+MFKB/n8Ascvpy7lq/U5/GNEbwV5qRGN4uqSnJRAclICG3cd9KZdSM/ivklLaNYgluv8GTXP69gsaoZ2CpUZ6J1zW4Gt/uP9ZrYSKK0e/XpgsnPuKLDezDKA84H5QeivhNKmr2D2Q5B4BQx6uFK7cM7xh/dWMGvZNn53zblc31tTG0jNO71VIx644hx+fvnZfPndTlLSspialsnrCzZyTrsmJCfFM7RPR1o3jo4LAyqUhjazM4A+wFfA94GfmtntQBreWf9uvA+BBQHNsijmg8HMxgHjADp16lSJrkuN2r8Npt7mTVI2bDzUqdzlj89/8h0T529k3MVnamoDCbmYOsZFiW24KLENew/n8t7XW0hJz+JP76/kr7NX8YOubUnuF88PurYlNoKnXSh3oDezxsA04OfOuX1m9gLwv4Dzvz8J3AkU9z/PKYkA59x4YDx4Y/QV77rUmLxjMPV2OLofbnsHGrSo1G6mpmXy+JzVDO3dgYeu6hrkTopUTbMGsYwccDojB5zOmu37SU3PYvrizcz9djutG9djaO+OJCclcM5plbvCLJTKFejNLBYvyL/pnJsO4JzbHrD+RWCm/zQLSAhoHg9sCUpvJTT+8xBkfgXDX4F2lZt75qNV23l4+jIuSmzN/xveq9ZULkpkOrtdE347+Fx+feU5fLo6m5T0TF79cgMvfbGenvHNSO4Xz3W9OtKsYWRMu1Ceq24MeA3Icc79PGB5e3/8HjP7BfA959wIM+sOvIU3Lt8BmAcklpaM1VU3YWzJG/DuvTDwPrjiT5XaxeJNu7nlxQUktm2ie71KxNp14CjvLN1CSlomq7btp17dOlzRrR3JSQlceFZrYkJw8hLMyysvBD4HluFdXgnwW+BmoDfesMwG4K6AwP8I3jBOHt5Qz+zSXkOBPkxtXgwTroJOA2DkdIipeIDO2HGA4f/6kmYNYpl298CoSW5J7eWcY8WWfaSkZfLu11vYcyiX9s3qM6xvR4b3S6Bz65or+lPBlFTNgWxv2mEzGPcpNGpV4V2o6lWi3dG8fD78dgcp6Zl8tiabAgf9z2hBcr8EBvdsX+3/vSrQS+Xl58HrQyFzIYyZAx36VHgXew/nctO/varXKXddQI+OqnqV6LZt7xGmL8kiNS2LdTsP0rBeDFf3aE9yUjzf69yyWq7N1+yVUnkfPgYbPoehL1QqyBfe6/W77ANMuKO/grzUCqc1q889g87i7ku6sHjTblLTs3jv661MW5xFp5YNGd4vnhv6xdOxeYMa75vO6OVEy1K9+eX7j4Vrnih7+5ME3uv1HyN6qyBKarXDx/L5z4qtTF2Uxfx1uzCD73dpTXJSPFd2P63K0y5o6EYqbttyeOlyaN8LRr0HdSt2M2bnHI/NWMFE3etV5BSZOYdITfdm1Ny85zBN6tfl2l4duOX8TpX+r1dDN1Ixh3Jgyq1QvxncOLHCQR5U9SpSmoSWDfnFD8/mZ5clsmD9LlLTspi+OIuWDetV+/CmAr1AQT5MHwt7N8PoWdCkXYV3UVj1+qM+HVX1KlKKOnWMgV1aM7BLa/5wfXfyC6p/VEWBXuDj/4OMD+GapyDh/Ao3n7eyqOr1bzf0VNWrSDk1qaEbmkfuLD0SHCtnwudPQJ+RkHRnhZsv3rSbe9/SvV5Fwpn+Kmuz7DXw9k+gQ18Y/GSF7/maseMAd766iHZN6/PK6P6a2kAkTCnQ11ZH9sHkW6BuHNz0OsRW7JZq2/cdYdSEhdStY0y883xNbSASxnQKVhsVFMA7d0POOrj9XW+O+QrYeziXURMWsufQMabcdYGmNhAJcwr0tdEXT8KqmXDl/0HniyrUNLDq9ZU7zlfVq0gEUKCvbdbOhY/+DOclw4B7KtT05Hu9XpjYupo6KSLBpDH62iRnnTe9QbsecO0zFUq+6l6vIpFLgb62OHYQJo8EzEu+1mtYoeaFVa93qepVJOJo6KY2cA5m3Ac7voWRqdCyc4WaT11UVPX6G1W9ikQcndHXBvOfg+XT4LL/gbMur1DTeSu38/DbqnoViWQK9NFu/Wcw91E491q48JcVapq+0at67d6hKf9S1atIxNJfbjTbkwkpd0Crs7ybiFQg+Zqx4wBjXlvEaU3rM+GO/jRS1atIxFKgj1a5R2DqbZCfCyPehLgm5W66bW9g1ev3VPUqEuF0mhaNnIP3H4AtS2DEJGidWO6mew/ncscrRVWvnVpV7OocEQk/CvTRKO1lWPoGXPwgdB1c7maqehWJTgr00WbTVzD7IUi8AgY9XO5m+QWOX0zxql6fubmPql5FoojG6KPJ/m3euHyzeBg2HuqU79dbWPU6e/k2/mdIN67r1aGaOyoiNanMSGBmCWb2sZmtNLMVZvYzf3lLM5trZmv97y385WZmz5hZhpl9Y2Z9q/sgBMg7BlNvh6P7YcRb0KBFuZs+93HG8arXMRdWrJhKRMJfeU758oAHnHPnAgOAe82sG/AQMM85lwjM858DXA0k+l/jgBeC3ms51ZyHIfMruP45aNet3M2mLsrkiQ/WqOpVJIqVGeidc1udc4v9x/uBlUBH4HrgNX+z14Ch/uPrgYnOswBobmbtg95zKbLkTVj0Egy8D3oMK3ezwqrXi89uw/8brqpXkWhVoTF6MzsD6AN8BbRzzm0F78MAaOtv1hHIDGiW5S87eV/jzCzNzNKys7Mr3nPxbF4MM38BnS+By35f7maBVa8v3NqX2Bila0SiVbn/us2sMTAN+Llzbl9pmxazzJ2ywLnxzrkk51xSmzZtytsNCXRwJ0y5DRq3heGvQEz5LqLK2LFfVa8itUi5Ar2ZxeIF+Tedc9P9xdsLh2T87zv85VlAQkDzeGBLcLorx+XnQepoOJjtTTvcqFW5mnlVr4uoW6eOql5FaonyXHVjwMvASufcUwGrZgCj/MejgHcDlt/uX30zANhbOMQjQfThY96EZdf+HTr0KVeTwqrXvYdzeXV0f1W9itQS5fmf/fvAbcAyM1vqL/st8FdgqpmNATYByf66WcBgIAM4BIwOao8FlqXC/Geh/1jofUu5mhzJzWesql5FaqUyA71z7guKH3cHuKyY7R1wbxX7JSXZtty7iUjCAO/m3uVQWPW6UFWvIrWSLrWIJId3w5RbIa4p3DgR6tYrs4lzjt/PUNWrSG2myy0iRUE+TBsLezfD6FnQpF25mj33cQavL9jIXZeo6lWktlKgjxSf/AUy5sI1T0HC+eVqMmXRJp74YA3D+nTkN1eq6lWkttLQTSRYORM+exz6jISkO8vV5MNvt/PwdK/q9W+qehWp1RTow132Gnj7J9ChLwx+sly3A0zfuJufTlpMj47NVPUqIgr0Ye3IPi/5WjfOK4qKrV9mE1W9isjJFAXCVUEBvHM37PoObn/Xm2O+DNv2HuH2lxeq6lVETqBAH66+eApWzfSule98UZmb7z2cy6gJC9l3JI/J4wao6lVEjtPQTTha+yF89Cc4LxkG3FPm5oVVr+t2HuDft/VT1auInEBn9OEmZz1MGwPtusO1z5SZfM0vcPx8clHV6/fPUtWriJxIZ/Th5NhBmDLSe3zTG1Cv9OGXwqrX/6zYxqOqehWREuiMPlw4BzMFtJS2AAAUtElEQVTuh+0r4NZUaFl2FeuzHxVVvd6pqlcRKYHO6MPFgudheSpc+jtIvLzMzScv3MSTc1X1KiJlU6APB+s/gw/+B7oOgYseKHPzD7/dzm/fVtWriJSPAn2o7c2ClNHQ6iz40b/KTL6mb8zh3rcWc56qXkWknBQlQin3iJd8zT8GI96EuCalbu5VvabRvpmqXkWk/BQpQsU5mPUAbFkCIyZB68RSNz+56rWVql5FpJx0Rh8qaRNgyRtw8YPQdXCpmwZWveperyJSUQr0oZC5EGb/BhKvgEEPl7ppYNXreFW9ikglaOimpu3fBlNu8yYpGzYe6pT8WRtY9frPm/swUFWvIlIJCvQ1Ke8YTB0FR/fBbW9DgxYlbuqc47EZy49XvV6rqlcRqSQF+po057eQuQCGvwLtupW66bMfZfDGgk2qehWRKtMYfU1Z8iYsehEG3gc9hpW66fGq174deegqVb2KSNUo0NeEzYth5i+g8yVw2e9L3XSuX/V6ydlt+NsNPbFy3DpQRKQ0ZQZ6M5tgZjvMbHnAst+b2WYzW+p/DQ5Y97CZZZjZajO7sro6HjEO7vSSr43bekM2MSWPlqVvzOGnftXr86p6FZEgKU8keRW4qpjlTzvnevtfswDMrBswAujut3nezGKC1dmIk58HqaPhYLZ3z9dGrUrcNGPHfu58NY0OzRuo6lVEgqrMQO+c+wzIKef+rgcmO+eOOufWAxnA+VXoX2T78DFvwrJr/w4d+pS4WWHVa2xMHSbeeb6qXkUkqKoyNvBTM/vGH9opvE6wI5AZsE2Wv+wUZjbOzNLMLC07O7sK3QhTy1Jh/rPQfyz0vqXEzfYeOrHqNaGlql5FJLgqG+hfALoAvYGtwJP+8uIyh664HTjnxjvnkpxzSW3atKlkN8LUtuUw4z5IGODd3LsEqnoVkZpQqUDvnNvunMt3zhUAL1I0PJMFJARsGg9sqVoXI8zh3TDlVohrCjdOhLr1it3seNXrhhyeurG3ql5FpNpUKtCbWfuApz8CCq/ImQGMMLM4M+sMJAILq9bFCFKQD9PGwt7NXvK1SbtiN3PO8ei7qnoVkZpR5qUdZjYJGAS0NrMs4DFgkJn1xhuW2QDcBeCcW2FmU4FvgTzgXudcfvV0PQx98hfImAvXPAUJJeeg//lRBm9+tYmfXNJFVa8iUu3KDPTOuZuLWfxyKdv/GfhzVToVkVbOhM8ehz4jIenOEjebvHATT/lVr7+56pwa7KCI1FaqyAmG7DXw9k+gQ18Y/GSJtwNU1auIhIICfVUd2eclX+vGeePysfWL3UxVryISKiq/rIqCAnjnbtj1Hdz+rjfHfDHWblfVq4iEjiJOVXzxFKya6V0r3/miYjfZuvcwoyYspF5dVb2KSGho/KCy1n4IH/0JzkuGAfcUu4mqXkUkHCjQV0bOepg2Btp1h2ufKTb5Wlj1un7nQcbf1o/uHVT1KiKhoaGbijp2EKaM9B7f9AbUO/UsPb/A8bPJS1i0MYdnRuheryISWgr0FeEczLgftq+AW1Oh5anFTs45/ufd5cxZsZ3HrlXVq4iEnoZuKmLB87A8FS79HSReXuwmz8zL4C2/6nX091X1KiKhp0BfXus/gw/+B7oOgYseKHaTSQs38fSHqnoVkfCiQF8ee7MgZTS06gJDXyg2+frBim088vYyBp2jqlcRCS8K9GXJPeLd8zXvKIx4C+o3PWWTtA053DdpiapeRSQsKRlbGudg1gOwZbEX5FsnnrLJmu37GfNaUdVrw3r6kYpIeNGpZ2nSJsCSN+DiB6HrNaesVtWriEQCBfqSZC6E2b+BxCtg0MOnrC6set2vqlcRCXMK9MXZv80bl28WD8PGQ50Tf0xHcvP58cRFbNh5SFWvIhL2NKB8srxjMHUUHN0Ht70NDVqcuDq/gPsnLSFt427+ebOqXkUk/CnQn2zOw5C5AIa/Au26nbDKq3pdwQffelWvQ3qq6lVEwp+GbgIteRMWvQQD74Mew05Z/Y95a5m0cBN3D1LVq4hEDgX6QpsXw8xfQOdL4LLfn7L6ra828fcP13JD33gevFJVryISORToAQ7u9JKvjdt6QzYxJ45ofbBiG797x6t6/esN56nqVUQiisbo8/MgdTQczIYxc6BRqxNWH696jW+uqlcRiUgK9B8+5k1YNvQF6NDnhFVrtu/nzlcXeVWvo5JU9SoiEal2n54uS4X5z0L/sdD7lhNWbdnjVb3Gxcao6lVEIlqZgd7MJpjZDjNbHrCspZnNNbO1/vcW/nIzs2fMLMPMvjGzvtXZ+SrZthxm3AcJA7ybewcorHo9cCSP10afr6pXEYlo5TmjfxW46qRlDwHznHOJwDz/OcDVQKL/NQ54ITjdDLLDu2HKrRDXFG6cCHXrHV9VWPW6cdch/n17P7p1OHW2ShGRSFJmoHfOfQbknLT4euA1//FrwNCA5ROdZwHQ3MzaB6uzQVGQD9PGwt7NcNPr0KTd8VV5+QXc51e9PnVTLwZ2UdWriES+yo7Rt3PObQXwv7f1l3cEMgO2y/KXncLMxplZmpmlZWdnV7IblfDJXyBjLlz9N0g4//jiwqrXud9u57EhqnoVkegR7GRscReYu+I2dM6Nd84lOeeS2rRpE+RulGDlTPjscegzEpLuPGFVYNXrHap6FZEoUtlAv71wSMb/vsNfngUkBGwXD2ypfPeCKHsNvP0T6NAXBj95wu0AVfUqItGssoF+BjDKfzwKeDdg+e3+1TcDgL2FQzwhdWSfl3ytG+eNy8fWP75qjl/1+gNVvYpIlCqzAsjMJgGDgNZmlgU8BvwVmGpmY4BNQLK/+SxgMJABHAJGV0OfK6agAN65G3Z9B7e/680x71u0IYf7/arX51T1KiJRqsxA75y7uYRVlxWzrQPurWqnguqLp2DVTO9a+c4XHV+8Zvt+xry6iI7NG/CK7vUqIlEsuk9h186Fj/4E5yXDgHuOLw6sen3tzvNp2aheKTsREYls0Rvoc9bBtDHQrjtc+8zx5KuqXkWktonO8YpjB2HySMDgpjegnhfMA6teX72zv6peRaRWiL5A75w3h82Ob+HWVGjpXRMfWPX6z5v7qOpVRGqN6Bu6mf8cLJ8Gl/4OEi8HVPUqIrVbdAX69Z/B3Eeh6xC46IHjiwurXu9R1auI1ELRE+j3ZELKHdCqi3cTET/5Wlj1OrxfPL9W1auI1ELREehzj8DU2yDvGIx4C+p7SdbAqte/DFPVq4jUTpGfjHUO3n8AtiyBEZOgdSJQVPXaU1WvIlLLRX70S3sZlr4BFz8IXQcDJ1a9TlDVq4jUcpEd6Dd9BbMfgsQrYNDDQFHVa31VvYqIAJEe6OvGwekDYdh4qFOHPYeOHa96fVVVryIiQKSP0XfoDaNmAH7V62tpbNx1iNfuPF9VryIivsgO9L7Cqtf0Tbt59ua+XNClVai7JCISNiJ76IZTq16v6Rle9yIXEQm1iA/0qnoVESldRAf6d5duVtWriEgZInqM/pKz23DPoC784odnq+pVRKQEER3omzesx4NXdQ11N0REwlpED92IiEjZFOhFRKKcAr2ISJRToBcRiXJVSsaa2QZgP5AP5DnnksysJTAFOAPYANzonNtdtW6KiEhlBeOM/gfOud7OuST/+UPAPOdcIjDPfy4iIiFSHUM31wOv+Y9fA4ZWw2uIiEg5VTXQO+ADM0s3s3H+snbOua0A/ve2VXwNERGpgqoWTH3fObfFzNoCc81sVXkb+h8MhR8OR81seSX70AnYVMm24aYZsDfUnQgSHUv4iZbjAB1LoXLN/WLOuUru/6Qdmf0eOACMBQY557aaWXvgE+dcqZ0xs7SAMf6Kvm62c65NZdqGGzMb75wbV/aW4U/HEn6i5ThAxxLQtlyxs9JDN2bWyMyaFD4GrgCWAzOAUf5mo4B3K/sa5bSnmvdfk94LdQeCSMcSfqLlOEDHUiGVPqM3szOBt/2ndYG3nHN/NrNWwFSKhlSSnXM5ZeyrKmf0lW4rIhLJyhv/Kj1G75xbB/QqZvku4LIK7m58ZftRxbYiIpGsXPEvaGP0IiISnjQFQjUys6vMbLWZZZjZQ/6yn/rPnZm1LqXtKDNb63+NCljez8yW+ft4xmpgIv4SjuNNf9lyM5tgZrHhfhylHMvLZva1mX1jZqlm1riEtg/77Vab2ZWl7TNUxxKw7p9mdqCUtmFzLCX8Tl41s/VmttT/6l1C20h4f5mZ/dnM1pjZSjO7v8aPxTkXVl/AVcBqIAN4yF/WGfgKWIs3vUK9Eto+7LdbDVxZ2j5r4DhigO+AM4F6wNdAN6APRdNDtC6hbUtgnf+9hf+4hb9uIXABYMBs4OoQHcdgvw8GTALuDufjKONYmgZs81Rx7xF/u6+BOP/9+J2/v2L3Gapj8dclAa8DB0poGzbHUsrv5FVgeBltI+X9NRqYCNTxt2tb08cSVmf0ZhYDPAdcjfcDutnMugF/A5523rQKu4ExxbTtBowAuuMF9ufNLKaUfVa384EM59w659wxYDJwvXNuiXNuQxltrwTmOudynDdP0FzgKvMuV23qnJvvvHfARKq/8rik45jlfHhvxPgwP47SjmUfeGdeQAO8QsCTXQ9Mds4ddc6txztpOL+kfYbqWPz3++PAg6W0DadjqcprRsT7C7gb+KNzrgDAObejpo8lrAI9Jf+gLgVS/W1KmlYhnN68AB2BzIDnWf6yYplZkpm9VEbbjv7jcu0zSEo9Dn/I5jbgP/7zcD2O0vqDmb0CbAO6Av/0l11nZn8so22Ffs9BVNLr/hSY4fzq9EJhfCylveaf/eG0p80sDiL2/dUFuMnM0sxstpklQs0eS7gF+pIOdo9zLu+kZeH85gXv36yTlZj5ds6lOed+XEbbCu0zSMp6zeeBz5xzn0NYHwelva5zbjTQAVgJ3OQvm+Gce7SMtuF0LHFAMv4HVaAwPpaSXvNhvA/d/njDGb+BiH1/xQFHnHcZ5IvABKjZYwm3QF/cQcUUs6zwjzNc37zgfaAkBDyPB7ZUsW0WJw6RVGSflVXicZjZY0Ab4JcVbBuK4yitPwA45/LxckA3VKBtVX7PVVHc624AzgIyzJtCvKGZZZSzbaiOpdjXdM5t9UcGjwKv4P1nXq62hN/7KwuY5i97G+hZwbZVP5bqTlBU5Asv4TAn4PnD/tdOoG5x25y8bcDzOf62xe6zBo6lLl5CpTNFiZnuAes3UHoydj1eUqaF/7ilv24RMICixMzgUBwH8GPgS6BBKW3D5jjKOJaz/PUGPAE8UUzb7pyYwFyHdxJS6u85VO8vf5uSkrFhcyyl/E7aB/xO/g78NYLfX38F7vS3GQQsquljqdYDD+IPKgUY4W/zL+CecH7zBvRpMLAGLxP/iL/sfrxP6Ty8T+aX/OVJhY/953fi5RkygNEBy5Pwppr4DngWvxYiBMeR5z9f6n89Gu7HUdyx4P1X+19gmd+fN/GvwgGuw0uiFbZ9xG+3moArH4r7+YTq/XXS+gMBj8P2WEp4f30U8Dt5A2gcie8vf1lz4H3/eOYDvWr6WMKuYMrMBuN9gscAE5w3rcKZeEnUlsASYKRz7qiZXQckOX/4xswewfth5QE/d87NLmmfNX1cIiKhEnaBXkREgivckrEiIhJkCvQiIlFOgV5EJMqFTaAvYTKg8k4A9omZaU56EZFihEWgL2U+mv8ClwMbQ9g9EZGIFhaBnqpNAHYCM3vBn1NihZn9IWD5BjP7g5kt9qf87BrcQxARCU/hEuiDOR/NI86bU6IncImZBZYb73TO9QVeAH5Vyf2LiESUcAn0wZyP5kYzW4xXWNUdbyio0HT/ezrenPAiIlEvXAJ9hSZTMrM5/l1nXjppeWe8M/XLnHM98cqO6wdsctT/nk8V7pcrIhJJwiXYLQIS/UC9Ge8GIreUtLFz7soSVjUFDgJ7zawdXnL3k+B2VUQksoTFGb3z5pr/Kd6MkyuBqc65FWZ2v5kVTtP5zcln8AHqAkedc1/jDdmswJvz+b/V33sRkfAW8XPd+HeeyQB6OOf2hro/IiLhJizO6CvLL5JaCjyvIC8iUryIP6MXEZHShd0ZvZklmNnHZrbSL3r6mb+8pZnNNbO1/vcW/vKuZjbfzI6a2a9O2tfPzGy5v5+fh+J4RERCLewCPd5NQx5wzp2Ld/use/3pEB4C5jnnEoF5/nOAHLy7Nj0RuBMz6wGMxau67QUMKbz7uohIbRJ2gd55NwVe7D/ej3cVTkfgeuA1f7PXgKH+Njucc4uA3JN2dS6wwDl3yL+q51PgRzVwCCIiYSXsAn0gMzsD6AN8BbRzzm0F78MAaFtG8+XAxWbWyswa4t3LMaGMNiIiUSdcCqZOYWaNgWl4937dZ1bcLAklc86tNLO/AXOBA3g3Bc8LekdFRMJcWJ7Rm1ksXpB/0zlXOD/NdjNr769vD+woaz/OuZedc32dcxfjjeWvra4+i4iEq7AL9Oadur8MrHTOPRWwagYwyn88Cni3HPtq63/vBAwDJgW3tyIi4S/srqM3swuBz4FlQIG/+Ld44/RTgU7AJiDZOZdjZqcBaXjz3BTgDdN084d7Pgda4SVqf+mcm1ejByMiEgbCLtCLiEhwhd3QjYiIBJcCvYhIlFOgFxGJcgr0IiJRToFeRCTKKdBLrWRmzc3sHv9xBzNLDXWfRKqLLq+UWsmfR2mmc65HiLsiUu3Cdq4bkWr2V6CLmS3FmxrjXOdcDzO7A29m1BigB/AkUA+4DTgKDPYL9boAzwFtgEPAWOfcqpo/DJGyaehGaquHgO+cc72BX5+0rgdwC969DP4MHHLO9QHmA7f724wH7nPO9QN+BTxfI70WqQSd0Yuc6mP/Xgj7zWwv8J6/fBnQ059ZdSCQEjCralzNd1OkfBToRU51NOBxQcDzAry/mTrAHv+/AZGwp6Ebqa32A00q09A5tw9Yb2bJ4M24ama9gtk5kWBSoJdayTm3C/ivmS0HHq/ELm4FxpjZ18AKvFtdioQlXV4pIhLldEYvIhLlFOhFRKKcAr2ISJRToBcRiXIK9CIiUU6BXkQkyinQi4hEOQV6EZEo9/8BknzXvjTJqQsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# df_plt=df_avg.loc['2017-1-1':'2017-1-10']\n",
    "df_plt=resampled_data.loc['2019-1-1':'2019-1-10']\n",
    "\n",
    "df_plt['ssrd'].plot()\n",
    "df_plt['ssrdc'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# resampled_data.to_csv('Hokk_Ens_Aggreg_S1_S2_2017_2018.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "res_dx=pd.to_datetime(resampled_data.index.strftime('%Y-%m-%d %H:%M:%S'))\n",
    "res_dx_utc=res_dx.tz_localize('UTC')\n",
    "res_jp=res_dx_utc.tz_convert('Japan')\n",
    "form_res_jp=res_jp.strftime('%Y-%m-%d %H:%M:%S')\n",
    "# print(res_dx)\n",
    "# print(res_dx_utc)\n",
    "# print(res_jp)\n",
    "# print(form_res_jp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>DATE</th>\n",
       "      <th>TIME</th>\n",
       "      <th>ssrdc</th>\n",
       "      <th>ssrd</th>\n",
       "      <th>tp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>09:00:00</td>\n",
       "      <td>74.250031</td>\n",
       "      <td>65.442024</td>\n",
       "      <td>0.000141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 00:30:00</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>09:30:00</td>\n",
       "      <td>122.940102</td>\n",
       "      <td>108.338417</td>\n",
       "      <td>0.000139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:00:00</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>10:00:00</td>\n",
       "      <td>171.630173</td>\n",
       "      <td>151.234802</td>\n",
       "      <td>0.000136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 01:30:00</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>10:30:00</td>\n",
       "      <td>220.320251</td>\n",
       "      <td>194.131180</td>\n",
       "      <td>0.000134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-01 02:00:00</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>11:00:00</td>\n",
       "      <td>269.010315</td>\n",
       "      <td>237.027573</td>\n",
       "      <td>0.000131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           DATE      TIME       ssrdc        ssrd        tp\n",
       "time                                                                       \n",
       "2019-01-01 00:00:00  2019-01-01  09:00:00   74.250031   65.442024  0.000141\n",
       "2019-01-01 00:30:00  2019-01-01  09:30:00  122.940102  108.338417  0.000139\n",
       "2019-01-01 01:00:00  2019-01-01  10:00:00  171.630173  151.234802  0.000136\n",
       "2019-01-01 01:30:00  2019-01-01  10:30:00  220.320251  194.131180  0.000134\n",
       "2019-01-01 02:00:00  2019-01-01  11:00:00  269.010315  237.027573  0.000131"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resampled_data.insert(0, 'DATE', res_jp.date)\n",
    "resampled_data.insert(1, 'TIME', res_jp.time)\n",
    "\n",
    "resampled_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "### resampled_data.to_csv('Hokk_Ens_Agg_Res_Jp_lat43_2017_18.csv')  #just 1 latitude 43\n",
    "### resampled_data.to_csv('Hokk_Ens_Agg_Res_Jp_lat42_44_2017_18.csv') #here latitude ranges from 42-44\n",
    "# resampled_data.to_csv('Hokk_Ens_Agg_Res_Jp_lat42_44_Jan_2019.csv')  #just 1 latitude 43\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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