Exploratory Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "448417bb",
   "metadata": {},
   "source": [
    "# Drilling Activity Prediction: Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "410c8269",
   "metadata": {},
   "source": [
    "- Data Overview\n",
    "- Reindex dataframe\n",
    "- Oil Variables Feature Engineering\n",
    "- Gas Variables Feature Engineering\n",
    "- Select Features for forecasting model\n",
    "- Final Pre-processing Activities\n",
    "\n",
    "\n",
    "**Note:**\n",
    "Variable We want to Predict: **Oil Rig Count** and **Gas Rig Count** separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "015355b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load specific forecasting tools\n",
    "from statsmodels.tsa.api import VAR\n",
    "from statsmodels.tsa.stattools import adfuller, grangercausalitytests\n",
    "from statsmodels.tools.eval_measures import rmse\n",
    "\n",
    "# Ignore harmless warnings\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "02bd2ffd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Oil&Gas.csv',index_col=0,parse_dates=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52902363",
   "metadata": {},
   "source": [
    "#### Data Overview"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "66772aa8",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oil_price</th>\n",
       "      <th>gas_price</th>\n",
       "      <th>oilrig_count</th>\n",
       "      <th>gasrig_count</th>\n",
       "      <th>totalrig_count</th>\n",
       "      <th>onshore_count</th>\n",
       "      <th>offshore_count</th>\n",
       "      <th>totalwell_count</th>\n",
       "      <th>oilwell_count</th>\n",
       "      <th>gaswell_count</th>\n",
       "      <th>...</th>\n",
       "      <th>oilstorage_amount_y</th>\n",
       "      <th>oilstoragechange_amount</th>\n",
       "      <th>oildaysofsupply</th>\n",
       "      <th>gasimports_amount</th>\n",
       "      <th>gasexports_amount</th>\n",
       "      <th>gasproduction_amount</th>\n",
       "      <th>gasconsumption_amount</th>\n",
       "      <th>gasstorage_amount</th>\n",
       "      <th>uspopulation</th>\n",
       "      <th>usemissions</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-25</th>\n",
       "      <td>84.64</td>\n",
       "      <td>5.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2021-10-22</th>\n",
       "      <td>84.53</td>\n",
       "      <td>5.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
       "      <td>1044998.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-21</th>\n",
       "      <td>82.64</td>\n",
       "      <td>4.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-20</th>\n",
       "      <td>84.40</td>\n",
       "      <td>4.87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-19</th>\n",
       "      <td>83.19</td>\n",
       "      <td>4.81</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            oil_price  gas_price  oilrig_count  gasrig_count  totalrig_count  \\\n",
       "date                                                                           \n",
       "2021-10-25      84.64       5.72           NaN           NaN             NaN   \n",
       "2021-10-22      84.53       5.10           NaN           NaN             NaN   \n",
       "2021-10-21      82.64       4.94           NaN           NaN             NaN   \n",
       "2021-10-20      84.40       4.87           NaN           NaN             NaN   \n",
       "2021-10-19      83.19       4.81           NaN           NaN             NaN   \n",
       "\n",
       "            onshore_count  offshore_count  totalwell_count  oilwell_count  \\\n",
       "date                                                                        \n",
       "2021-10-25            NaN             NaN              NaN            NaN   \n",
       "2021-10-22            NaN             NaN              NaN            NaN   \n",
       "2021-10-21            NaN             NaN              NaN            NaN   \n",
       "2021-10-20            NaN             NaN              NaN            NaN   \n",
       "2021-10-19            NaN             NaN              NaN            NaN   \n",
       "\n",
       "            gaswell_count  ...  oilstorage_amount_y  oilstoragechange_amount  \\\n",
       "date                       ...                                                 \n",
       "2021-10-25            NaN  ...                  NaN                      NaN   \n",
       "2021-10-22            NaN  ...            1044998.0                      NaN   \n",
       "2021-10-21            NaN  ...                  NaN                      NaN   \n",
       "2021-10-20            NaN  ...                  NaN                      NaN   \n",
       "2021-10-19            NaN  ...                  NaN                      NaN   \n",
       "\n",
       "            oildaysofsupply  gasimports_amount  gasexports_amount  \\\n",
       "date                                                                \n",
       "2021-10-25              NaN                NaN                NaN   \n",
       "2021-10-22             28.3                NaN                NaN   \n",
       "2021-10-21              NaN                NaN                NaN   \n",
       "2021-10-20              NaN                NaN                NaN   \n",
       "2021-10-19              NaN                NaN                NaN   \n",
       "\n",
       "            gasproduction_amount  gasconsumption_amount  gasstorage_amount  \\\n",
       "date                                                                         \n",
       "2021-10-25                   NaN                    NaN                NaN   \n",
       "2021-10-22                   NaN                    NaN                NaN   \n",
       "2021-10-21                   NaN                    NaN                NaN   \n",
       "2021-10-20                   NaN                    NaN                NaN   \n",
       "2021-10-19                   NaN                    NaN                NaN   \n",
       "\n",
       "            uspopulation  usemissions  \n",
       "date                                   \n",
       "2021-10-25           NaN          NaN  \n",
       "2021-10-22           NaN          NaN  \n",
       "2021-10-21           NaN          NaN  \n",
       "2021-10-20           NaN          NaN  \n",
       "2021-10-19           NaN          NaN  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
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   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "713ee0a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>oil_price</th>\n",
       "      <td>9038.0</td>\n",
       "      <td>44.52</td>\n",
       "      <td>-36.98</td>\n",
       "      <td>145.31</td>\n",
       "      <td>28.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gas_price</th>\n",
       "      <td>6222.0</td>\n",
       "      <td>4.15</td>\n",
       "      <td>0.00</td>\n",
       "      <td>23.86</td>\n",
       "      <td>2.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilrig_count</th>\n",
       "      <td>410.0</td>\n",
       "      <td>497.42</td>\n",
       "      <td>108.00</td>\n",
       "      <td>1596.00</td>\n",
       "      <td>361.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gasrig_count</th>\n",
       "      <td>410.0</td>\n",
       "      <td>584.97</td>\n",
       "      <td>70.00</td>\n",
       "      <td>1585.00</td>\n",
       "      <td>388.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalrig_count</th>\n",
       "      <td>585.0</td>\n",
       "      <td>1389.25</td>\n",
       "      <td>250.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>746.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>onshore_count</th>\n",
       "      <td>585.0</td>\n",
       "      <td>1285.06</td>\n",
       "      <td>237.00</td>\n",
       "      <td>4238.00</td>\n",
       "      <td>705.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>offshore_count</th>\n",
       "      <td>585.0</td>\n",
       "      <td>104.22</td>\n",
       "      <td>6.00</td>\n",
       "      <td>283.00</td>\n",
       "      <td>63.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalwell_count</th>\n",
       "      <td>456.0</td>\n",
       "      <td>3482.35</td>\n",
       "      <td>1268.00</td>\n",
       "      <td>8556.00</td>\n",
       "      <td>1654.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilwell_count</th>\n",
       "      <td>456.0</td>\n",
       "      <td>1363.82</td>\n",
       "      <td>291.00</td>\n",
       "      <td>3945.00</td>\n",
       "      <td>898.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gaswell_count</th>\n",
       "      <td>456.0</td>\n",
       "      <td>1259.94</td>\n",
       "      <td>454.00</td>\n",
       "      <td>3200.00</td>\n",
       "      <td>626.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilimports_amount_x</th>\n",
       "      <td>585.0</td>\n",
       "      <td>9149.60</td>\n",
       "      <td>3689.51</td>\n",
       "      <td>14696.61</td>\n",
       "      <td>2476.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilimports_amount_y</th>\n",
       "      <td>1603.0</td>\n",
       "      <td>10420.68</td>\n",
       "      <td>5775.00</td>\n",
       "      <td>15217.00</td>\n",
       "      <td>1854.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilexports_amount_x</th>\n",
       "      <td>585.0</td>\n",
       "      <td>1954.24</td>\n",
       "      <td>155.77</td>\n",
       "      <td>9589.40</td>\n",
       "      <td>2262.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilexports_amount_y</th>\n",
       "      <td>1603.0</td>\n",
       "      <td>2552.01</td>\n",
       "      <td>723.00</td>\n",
       "      <td>10134.00</td>\n",
       "      <td>2368.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilproduction_amount_x</th>\n",
       "      <td>585.0</td>\n",
       "      <td>7604.88</td>\n",
       "      <td>3973.59</td>\n",
       "      <td>12966.12</td>\n",
       "      <td>1775.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilproduction_amount_y</th>\n",
       "      <td>2022.0</td>\n",
       "      <td>7342.01</td>\n",
       "      <td>3813.00</td>\n",
       "      <td>13100.00</td>\n",
       "      <td>1901.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilconsumption_amount_x</th>\n",
       "      <td>585.0</td>\n",
       "      <td>18319.93</td>\n",
       "      <td>14504.70</td>\n",
       "      <td>21666.06</td>\n",
       "      <td>1682.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilconsumption_amount_y</th>\n",
       "      <td>1615.0</td>\n",
       "      <td>19212.41</td>\n",
       "      <td>13797.00</td>\n",
       "      <td>22820.00</td>\n",
       "      <td>1383.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilstorage_amount_x</th>\n",
       "      <td>585.0</td>\n",
       "      <td>840.02</td>\n",
       "      <td>233.04</td>\n",
       "      <td>1230.10</td>\n",
       "      <td>261.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilstorage_amount_y</th>\n",
       "      <td>2039.0</td>\n",
       "      <td>945372.14</td>\n",
       "      <td>607781.00</td>\n",
       "      <td>1227678.00</td>\n",
       "      <td>123517.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilstoragechange_amount</th>\n",
       "      <td>487.0</td>\n",
       "      <td>1206.19</td>\n",
       "      <td>-34924.00</td>\n",
       "      <td>48553.00</td>\n",
       "      <td>11206.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oildaysofsupply</th>\n",
       "      <td>2037.0</td>\n",
       "      <td>23.42</td>\n",
       "      <td>16.10</td>\n",
       "      <td>42.00</td>\n",
       "      <td>4.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gasimports_amount</th>\n",
       "      <td>583.0</td>\n",
       "      <td>203.04</td>\n",
       "      <td>42.51</td>\n",
       "      <td>426.53</td>\n",
       "      <td>103.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gasexports_amount</th>\n",
       "      <td>583.0</td>\n",
       "      <td>71.31</td>\n",
       "      <td>1.61</td>\n",
       "      <td>595.41</td>\n",
       "      <td>113.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gasproduction_amount</th>\n",
       "      <td>583.0</td>\n",
       "      <td>91.54</td>\n",
       "      <td>59.00</td>\n",
       "      <td>244.67</td>\n",
       "      <td>40.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gasconsumption_amount</th>\n",
       "      <td>583.0</td>\n",
       "      <td>1852.22</td>\n",
       "      <td>939.93</td>\n",
       "      <td>3424.30</td>\n",
       "      <td>473.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gasstorage_amount</th>\n",
       "      <td>554.0</td>\n",
       "      <td>6494.04</td>\n",
       "      <td>4446.00</td>\n",
       "      <td>8384.09</td>\n",
       "      <td>829.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uspopulation</th>\n",
       "      <td>60.0</td>\n",
       "      <td>253787.63</td>\n",
       "      <td>180671.00</td>\n",
       "      <td>328330.00</td>\n",
       "      <td>44937.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usemissions</th>\n",
       "      <td>39.0</td>\n",
       "      <td>2269.59</td>\n",
       "      <td>1978.17</td>\n",
       "      <td>2594.01</td>\n",
       "      <td>162.14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          count       mean        min         max        std\n",
       "oil_price                9038.0      44.52     -36.98      145.31      28.73\n",
       "gas_price                6222.0       4.15       0.00       23.86       2.18\n",
       "oilrig_count              410.0     497.42     108.00     1596.00     361.41\n",
       "gasrig_count              410.0     584.97      70.00     1585.00     388.99\n",
       "totalrig_count            585.0    1389.25     250.00     4521.00     746.52\n",
       "onshore_count             585.0    1285.06     237.00     4238.00     705.22\n",
       "offshore_count            585.0     104.22       6.00      283.00      63.63\n",
       "totalwell_count           456.0    3482.35    1268.00     8556.00    1654.50\n",
       "oilwell_count             456.0    1363.82     291.00     3945.00     898.65\n",
       "gaswell_count             456.0    1259.94     454.00     3200.00     626.67\n",
       "oilimports_amount_x       585.0    9149.60    3689.51    14696.61    2476.54\n",
       "oilimports_amount_y      1603.0   10420.68    5775.00    15217.00    1854.46\n",
       "oilexports_amount_x       585.0    1954.24     155.77     9589.40    2262.03\n",
       "oilexports_amount_y      1603.0    2552.01     723.00    10134.00    2368.32\n",
       "oilproduction_amount_x    585.0    7604.88    3973.59    12966.12    1775.59\n",
       "oilproduction_amount_y   2022.0    7342.01    3813.00    13100.00    1901.09\n",
       "oilconsumption_amount_x   585.0   18319.93   14504.70    21666.06    1682.81\n",
       "oilconsumption_amount_y  1615.0   19212.41   13797.00    22820.00    1383.45\n",
       "oilstorage_amount_x       585.0     840.02     233.04     1230.10     261.02\n",
       "oilstorage_amount_y      2039.0  945372.14  607781.00  1227678.00  123517.10\n",
       "oilstoragechange_amount   487.0    1206.19  -34924.00    48553.00   11206.98\n",
       "oildaysofsupply          2037.0      23.42      16.10       42.00       4.06\n",
       "gasimports_amount         583.0     203.04      42.51      426.53     103.02\n",
       "gasexports_amount         583.0      71.31       1.61      595.41     113.08\n",
       "gasproduction_amount      583.0      91.54      59.00      244.67      40.73\n",
       "gasconsumption_amount     583.0    1852.22     939.93     3424.30     473.32\n",
       "gasstorage_amount         554.0    6494.04    4446.00     8384.09     829.14\n",
       "uspopulation               60.0  253787.63  180671.00   328330.00   44937.79\n",
       "usemissions                39.0    2269.59    1978.17     2594.01     162.14"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()[['count','mean','min','max','std']].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6f00072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VSie5mZl/FhFXAzZk5oURcaOWA5MkSZJWa6Vrco8AyMyLMvPC7r7/aDMkSZIkqZ9lz+RGxC2AWwPbR8RDpr60HbB1y4Gth5ZdYSRJkmbZ2PYmbWq5ws2B+wNXBx4wdf+FwOMajWndjO0fV1pLQ20dOtRxw3DH7rgXc9xSfctOcjPzE8AnIuLOmfnVNRqTJEmS1MtKS4htDTyWsnTh8mUKmfmYpb7fEmKSJElaCxsrIbbSjWeHATsC+wLHAjtTlixIkiRJM2elk9ybZOYLgYsy873A/YA7thuWJEmStHorneRe0n0+LyJuA2wPXKfNkCRJkqR+VtoM4p0RsQPwAuCTwDbAC5uNSpIkSephU3Vynzl184Du81u6z1drMiJJkiSpp02dyd22+3xzYC/KWVwoNXNPbDUoSZIkqY9N1cl9KUBEHAfcftLSNyJeAnym+egkSZKkVVjpxrPrAr+fuv377j5JkiRp5qx049n7gBMj4mPd7QcB72kxIEmSJKmvFU1yM/MVEXEk8KfdXQdk5mnthiVJkiSt3krP5JKZpwKnNhyLJEmSVMVK1+RKkiRJg+EkV5IkSaOz4uUKm4N9d9p9vYcgDdZR557RLLvla3Oo44bhjt1xL+a4NQta/q6sh8jM6qH7bNivfugaGNs/riRJ0tht2PG7seT9az0QSZIkqTUnuZIkSRodJ7mSJEkaHSe5kiRJGh0nuZIkSRodS4hJqsISSIu1rtgy1LE77sUct1SfJcSm+GKVJEkaFkuISZIkabPhJFeSJEmj4yRXkiRJo+MkV5IkSaPjJFeSJEmj4yRXkiRJo2Od3CktawlKY2edz8Va/00Z6tgd92KOW7NgbKVUrZM7ZWz/uJIkSWNnnVxJkiRtNpzkSpIkaXSc5EqSJGl0nORKkiRpdJzkSpIkaXSc5EqSJGl0nORKkiRpdGwGIakKi9kv1rr29lDH7rgXc9xSfTaDmOKLVZIkaVhsBiFJkqTNhpNcSZIkjY6TXEmSJI2Ok1xJkiSNjpNcSZIkjY6TXEmSJI2Ok1xJkiSNzuCaQVjLVpIkSZvimVxJkiSNjpNcSZIkjY6TXEmSJI3O4NbkSppN++60e7PslmvxhzpuGO7YHfdijluqLzKzeug+G/arH9rxBSVJkqSJDTt+N5a8f60HIkmSJLXmJFeSJEmj4yRXkiRJo+MkV5IkSaNjdQVJVQx1B/dQxw3DHbvjXsxxS/VZXUGSJEmDtbHqCp7JlVTFUM8WDXXcMNyxO+7FHLdUn2tyJUmSNDouV5AkSdJg2QxCkiRJmw0nuZIkSRodJ7mSJEkaHSe5kiRJGh0nuZIkSRodJ7mSJEkaHSe5kiRJGh07nkmqYqhdlYY6bhju2B33Yo5bqs8zuZIkSRodJ7mSJEkaHSe5kiRJGp3IzOqh+2zYr35ox/U/kiRJmtiw43djyfvXeiCSJElSa05yJUmSNDpOciVJkjQ6TnIlSZI0Ok5yJUmSNDpOciVJkjQ6tvWVVMVQW4cOddww3LE77sUct1SfdXIlSZI0WNbJlSRJ0mbDSa4kSZJGx0muJEmSRsdJriRJkkbH6gqSqhjqDu6hjhuGO3bHvZjjluqzuoIkSZIGy+oKkiRJ2mw4yZUkSdLoOMmVJEnS6DjJlSRJ0ug4yZUkSdLoOMmVJEnS6DjJlSRJ0ug4yZUkSdLoOMmVJEnS6DjJlSRJ0uhsud4DkCStzr477d4su2ULdce9mOOW6ovMrB66z4b96od2fEFJkiRpYsOO340l71/rgUiSJEmtOcmVJEnS6LgmV5IGyrWWa2uoz/dQxy315ZlcSZIkjY6TXEmSJI2Ok1xJkiSNjpNcSZIkjY51ciVJkjRY1smVJEnSZsMSYpI0UJaGWltDfb6HOm6pL5crTGn5h0Aau6G+Noc67iHzOZdm01APWja2XMFJriRJkgbLNbmSJEnabDjJlSRJ0ug4yZUkSdLoOMmVJEnS6DjJlSRJ0ug4yZUkSdLoOMmVJEnS6DjJlSRJ0ug4yZUkSdLobLneA5gltoOUVm+orVqHOm4Y7tgd92KOW7NgbF1lbesrSZKkwbKtryRJkjYbTnIlSZI0Ok5yJUmSNDpOciVJkjQ6TnIlSZI0Ok5yJUmSNDrWyR0JaxVqvQ21FudQxw3DHbvjXsxxaxaMrUyrdXIlSZI0WNbJlSRJ0mbDSa4kSZJGx0muJEmSRsdJriRJkkbHSa4kSZJGx0muJEmSRsdJriRJkkbHSa4kSZJGx0muJEmSRsdJriRJkkbHSa4kSZJGJzKzeugffn6z+qFrYN+ddl/vIaiio849Y72HMHNa/o63fL4d99KGOnbHvZjj1iwY6vvmhh2/G0vev9YDkSRJklrbcr0HILXiGQZJkjZfnsmVJEnS6DjJlSRJ0ui4XGHKUBdcg5fmJUmSpnkmV5IkSaPjJFeSJEmj43IFSZLW2ZCXy0mzyjO5kiRJGh3P5EqSJA2EZ/1Xzra+kqqwLelird+Mhjp2x72Y45ZWz7a+kiRJ2my4XEFSFUM9ozPUcQ+Zz7mkteCZXEmSJI2Ok1xJkiSNjpNcSZIkjY6TXEmSJI2OG88kaaDcwCVJy8jMdf8AHm+22WbPXr7ZZps9e9lDHrvZZq9l9qwsV3i82WabPZP5Zptt9uxlt8432+xRZM/KJFeSJEmqxkmuJEmSRmdWJrnvNNtss2cy32yzzZ697Nb5Zps9iuzoFvhKkiRJozErZ3IlSZKkapzkSpIkaXSc5EqSJGl01n2SGxFXHVJ2RFx5JfdtZtlfXMl9q8w+bCX3rTL71Su5b9ayW+dHxJ8tcd+jK2W/LiJuXSNrLUXEFmv8eFeqlHPNGjlX4PFmftwRcasl7rt7peynRsQONbKWyG75mm827i6/yXtERPzJSu7rkb/fSu6bpeyW75lDtG6T3Ii4S0R8E/h2d3v3iHjrrGcDX13hfaPPjoitI+IawLUiYoeIuEb3cUPgen2yp8ybEHWTjT+ulL3PEvfdZwDZrfNfFBFvi4irRcR1I+JTwAMqZX8LeGdEfD0inhAR21fKJSIOm86LiF1qHWwB34uI1y41QeorIo7pXjOT23cATqoU/7WIODwi7hsRUSkTGO64gY9ExLOjuEpEvBl4VaXs6wInRcRHIuLelcfe8jXfZNxr8B7x5hXet1rPXeF9s5Td8j2TiLhZRBwcEZ+PiC9NPipl7xcR23b//YKI+GhE3L5P5pY1BrZK/wrsC3wSIDPPiIi7zmp2ROxIeVFeJSJuB0z+CGwH9DpjPNRs4O+ApwM7AadMZV8A/Fuf4Ih4LvA8yrgvmNwN/J6epUUi4onAk4AbR8SZU1/aFvjKrGavRX7nbsDfA6d3t1+Umf9eIzgzDwEOiYibAwcAZ0bEV4CDM/PonvHHA1+PiGdSfuefRfn/qGF34K8pY98AvBv4UGZesPyPrcirgM9FxJso474P5bmp4WbAnwGPAd4UER8B3pOZ362QPdRx3xF4NXAC5XXzAaDK2b/MfEFEvBD4c8pz8W/d2N+VmT9YTeZavOZbjLvT5D0iIu4M3AW4dvd6n9gO6H3VJSLuA9wXuF73+z2df+ksZrd8z1zgcODtwMHAZRVzAV6YmYdHxN6U1/9rgbdRXrOr06rv8Ar6En+9+3za1H1nzGo28GjgaODC7vPk45PAQzbH7KnHeGrD35NXNcjcHrgh8O/ALlMf15jl7LXI7x7jGsBHgM8B3wCeQ1dusFL+FsADgY9T3vieDXyKMmnsm703cAnwM2DHRr+TdwN+ClwEvBe4SYXMu6/BuO/Rjfs84FjgzpvjuIErUd48Twe+D/x1gzHvDryBcjXxbcBpwGtWmdX8Nd9i3Atyq75HdK/BF3e/dy+e+ngmcNNKz8OjgR91nycfDwF2mNXsLr/6e+aC/FMaZp82+X8A/mb6vtV+rFud3Ij4D+D1lKO5OwIHAXtm5l/PePZfZuYRfXPGlN3l34Xyh/jyqwOZ+b5K2dej/FGfzj6uUvYWlEt109n/PevZLfMj4rvAP2fmuyPiKpSzXntm5l0qZP8rcH/gS5SzRCdOfe07mXnzHtmPBF5IebPbjXI154DMPKPfqC9/ru9HOct1Q+AwyhnAPwVemZk365H9QuBhlF7tuwHPAP4+Mz/Tc9iTta2PAB4J/A/wLsoB7h7A4Zl5ox7ZQx33GcAngH8CrkU5K/X7zKyxHvIg4FHAL4FDgI9n5iXd2f/vZeauPfNbveabjrt7jOrvERGxS2b+qOfQlsvfKjMvGWB2y/fMlwC/AD4G/G4q//9VyP405YB2H+D2wG+BEzNz91VnruMk91rAGymnpAP4PHBQZv5qxrOvDPwli1+sL9uMsw8DdqWcGZlcvsjMfFqF7H+mXCb+5oLsv6iQ/RTgJZQ30T9MZe82y9mt8yPiBgvfOCPirpM/khFx68w8e5XZBwAfycyLlvja9sDOPbI/Djw+M3/R3b4D8I7MvN1q8hZk/5ByBeRdmXnCgq+9qc/vekS8AXhuZv62u70LcEhmLrUG84pmf5cyIT80M3+y4GvPzsxVb1wa8Lj3zMyTF9z3yMw8rPvvHTLz/1aZ/VLg3UtNvCLilsDPe2S3fM0vO+7M/FbP/CbvERFxM+AfWPy+ds8+uVP5f0J5zicTxijxeeNZzW75ntnln7PE3bWek6sC9wbOyszvRcQfAbfNzM+vOnO9JrlDFRGfA86nXGa9fD1KZr5uM87+FnCrbPDLFBHfAXbLzN9t8puvePb3gTvWOPhZy+y1yN/EY5+amb02A6xVdkRcKTN/3/33czNzVRuMImKbzPz1Ml9fdfYKHvvNmfnUVf5sLPe67JO9gsce6rhn8ve7xWs+yqawjapxdq57nCbvEd1Z+bez+H3tlEr536ZcoViYX+OEWZPslu+Za6H21Yp123gWEe+lnF09r7u9A/C6zHzMLGdTzjTdu0LOmLK/AexIWR9V2w+BrZi6LFLRjykT/xZaZq9F/nJq73Zvlj2Z4Hb2Y5W76Jeb4PbNXoFVb4pawaSiWrmlmtnrPO5Z/f1u8Zo/BZg81wvHlkDvs3OdVu8Rl2bm2ypnTjs/M48cWHbL90wiYivgicBkM/8xlCtmvZdeRMRTKcvN5l2toCyHWpX1rK6w22QSCpCZ/xdl9/+sZ58QEbfNzLMq5Y0h+1rANyPiROav0alxeeQ3wOlRSkFNZ/deCkH5Y3BMRHxmQfbrZzx7LfKX0/LyT8vsWZ28aLbM6u939dd89ljbfAW1eo/4VEQ8iQbrQztHR8RrgY8uyD91hrNbvmdC2ZC4FTApy/rI7r4DK2QfBNy85tWK9Zzkbphe+9RdNqk1npbZewN/261L+R1z62hqrLUcavZLKmRszCe7jxb+u/u4UvcxlOy1yB+jWZ28SCvR9DUfEQ+hvE8k8OXM/HjF+JdUzJr26O7zs6buq3kGelK6as8F+TXW/LbKbvmeCbBXzt8I9qVu2UgN1a9WrOck93XAVyPicMqE66HAKwaQXbOg/yiyM/PYhtnvbZj90iFmr0X+Jvx+098yk9lDPZNrttlNX/NRmiXdhFKmDOAJEbFPZj65Rn6r94jWZ6Iz8x5Dy275ntm5LCJ2za5+ckTcmHr1cqtfrVi3SW5mvi8iTmbuqOUhmfnNWc9muGeDmmVHxIVT+VeiXMq4KDO3q5B9DkuMvdJOzqM3kt37KL1lduv8iPhiZt5rY/dl5p1mNPtPMvMry9x3+Ixm75eZhy9z3xvNrpp9WGY+cpn77rXEj81Cdsu/KfcEbjlZC93ta1lVlZOltHqPiIhHLXV/1itf+aKN5NeoStQku+V7ZudZlKUWP6QctO1CvSYw1a9WrHl1hYjYLjMv2Niuzj5raVpmTz3GWZRfoAC2Bm4EfCczb73sD444e8HjBKXQ/50y8zkV8qZ72G9N2eBzjcxc8g/EFcyebnW4NaXE2qWZ+Y+znN0qPyK2pnTBO5pS5H+6O97nMvMWs5g99RiLdq7X2ilv9nizu93cZ2Vm75bNjbNb/r36NPDk7EqIRSkH92+ZWaud9/RjVXuPiNKSeWJrykHEqZn50D65U/nTHRO3ptT4/lbW2SDfJLvle+bUY1wZmNQ0/05WruQQEdvAijb8bjprHSa5n87M+y9xtNG7RlzL7GUe8/bAkzKzxqLrUWR3+adlhfqkG8k+JTOr9eJekH1iZt5haNk18qMUhH86pQXnT5mbiF5Aabvbpw1ny+xJi8+nU1p6T2wHPDj7FBJvmz1p7/kw4MMLsm/V89/S7MXZl7c9pWzOAebanmbmc2cxexOPW+VvSkQcC+wFTJqz7AWcTLc+MivVWF3wmNXfIyLi6pTOiU0qCXWTu6My8+4Dy+79nhkR98zML3VrtxfJzI/2ye8e4zaU+tiTE5W/BB6Vq6ydDuuwXKGbhAZwt6zY/al19jKPeWpErL6v8giyF/zSb6AspL+4Uvb0mZtJdpXf2wVn/DcAf0xpoTnT2a3yM/ONwBsj4qmZ+eZN/sCMZFMua21D+b3Ydur+Cyjr8Wc1+1zKROIvKKWcJi6k1M80u2J2ljrGr4qIV9WedLbMnmj8N6XaWb6ltHyPWOAiylXKVq4K7DzL2Q3fM+9G6VS51Nn9pFSJ6OudwDMz82iAiLg7cDDlRMOqrGfHs7My87YDzH7m1M0NlNZz18zMfTfj7EOnbl4K/Bfl7NwvKmQfvUT2v2TmdypkT874R5d9DvCyzDx+lrPXKL9lm+Ym2dGwxWfj7KG2Dh1kdpffsu1pk+w1eM3vCNyhe4yTMvPnNXK77CbvERHxKeau2m4B3JLSUbH3Urkuf7LMb5J/bcpzvuorT62zW75ndvk3ysxzNnXfKrPPWHh1bKn7rlDmOk5y30tZ83PSwLJfPHVz8gt0RGb2PiodarbGJdq2aW6Z3azFZ+PswbUOHXh2y1bhTVuqthIRB1LO5n6J8lzfjTLheve6DmwTIuJuUzcvBX6UC9pA98zfZUH+/2TmpbOe3dJG1stXWUIYER8DTqUsWQB4BPDHmfngVWeu4yT325SSJT+iXGKoVre1ZfbUY1RbGD307IjYGXgzc12IvkzpONf7j01EbE/pgDLprnIs5Y9v71p60bZzS7Ps1vnRtk1zy+xmLT4bZw+udejAs1u2Cm+Z3fI1/x3gLpPnN8rmpRMy8+bL/+SK81u+R1yXsoYY4MQaVxAX5O8O/Gl387jMPHOWs1u9Z0bELYBbA69hfl3i7YBnZZ0N8jsAL6XUa4bye/KS7HoerMZ61sntfZl8PbIXLoyOiF8Cj87Mb2yu2cChwAcpuzihHH0dCuxTIfvdlJaQD+tuP7LLXnLx+xXUsnNLy+zW+S3bNLfMbtnis2X2EFuHDjm7ZdvTltktX/O/oqx7nriwu6+WJu8REfEw4LWUCX8Ab46IZ2Xmf/TJnco/CHgcc+tNPxAR76yxr6Bhdqv3zJtTKkBcnfnrci+k/H/01k1ma3VmA9bxTC5cvkB60mHlK1mnVV7T7Ig4AXj+goXRr8zMVS+MHkH26Zm5x6bum8Hs6ut/1iK7dX63pmsPyk7rqm2aG2e/BPgFDVp8Ns7+Z8qavOqtQ81eMvsIYHegetvTxtktX/PvA24LfILynvlA4Mzuo3e78FZ/x7srLPtMzt5GxLWB/6z4d/ZM4M6ZeVF3+2rAVytdcW6S3fI9s8u6c2Z+tUbWVOYbMvPpC9ZYX67P+8O6ncmNUgh5P+aOYg6NiMMz8+WznA1cbTJRBMjMY7pfzhqGmv2riHgEc91yHk69swC/jYi9s9tc0a3V+22l7JadW1pmt85/SaWctc5+dPe5RYvPltlDbB065OyWbU9bZrd8zf+g+5j4RPd52yW+dzVavUdsWLA84VeUjdW1BPOf48u6+2Y5u+V7JsBpEfFkytKFrSd3Zr/6vpM1uP/SZ2BLWc81ud8Bds9u41NEXAU4vcYaoMbZ1RdGjyB7F8p6qztT3ohOAJ6WFcq4RcQewHuZK5Xzf8DfZmbvXtkRcS/KZZx5nVumDwZmMXst8iXNliG/5lu9R0TEa4HdmJs8/xWl+UatpjvPpBzgfqy760HAezLzDbOaveA9M4D/R6X3zC7/cODbwN8ALwP2pzSxOKhG/tTj7ABcv+865fWc5B5NKaZ+Xnf76sBHs15L1VbZ0wujk7Iw+qV9FkYPPXstRMR2AJl5QeXcZp1bWma3zI+2bZpbZjdr8dk4e3CtQweePSnFtTC7RuWGZtldfqvX/NE0bEPeUkT8JVMb2jLzY8t9/yryJ0sfJ/mnDSS71XvmaZl5u4g4MzN3i7Ih8svZoyX7VPYxlBrZW1I2nf6Cstz0mcv93HLWc+PZ+cDZEfEFyotrH+DEiHgT9F7D1Cy7xcLooWdHKdl20NRBxQ7A63pevphkvxJ4zYLsv8/MF1TIfjLwgcmRYkTsEBGPzcy3buJH1zW7dX5mXn6JMmKuBWff3NbZzO2whqkWn0CN+r4tsy9akH1/4FsVcs1e2vQSiMvbns56dpQWwfsyV8buzyKi93rZzj9M/fflLYMr5AIQETcCnsriEny91+Jn5hHde/2W3WNdo8Za+SnnUJ6LLUt83L7G2vBW2d1JvUfRPdflz2yddeGdSTWP86JsaP85cJ1K2dtn5gVRStq9LzNf3K1dXrX1PJP76OW+npnvndHsLwD7LZh0fSjrNFUYavZpuaA941L3Vcyu1cN+qQX6tcbdLHst8pd4vMFlR8MWn42zh9o6dJDZXX7LVuG1aoh+ltIl7CzgD5P7M/OlfbM38njV2pBH2SD2LhaP/dieuX9HuUJ5cZdbrZ5yl/9PwN9S1itPJktZ6apwk+wom8y/xuLnetXzngX5BwJHUJaJHErpCPmizHx7heyzgD+nLLd4fmaeNDljvNrMdTuTu6knPCKOyMy/nLVs4FqTiWL3WP8XEbWOYoaavSEidpgsfYjSfrLW79YWEXHlyWW5KOurr1wxO7I70uvOlFxpANlN86Ntm+a1au8JbVt8tsye+dahQ86Otq3Cm2UDO/d5s19ONG5DDlycmW+qmDfxD8BtMvOXDbKhlOHaNTN/P6Dsrftc3t+UzDyk+89jqbPxdtrLgKOA47sJ7o2B7/UJXM/lCptS+8mrlf2HiLhBdgvmoyyor3U6fKjZrwO+GmVBOpRLdK+olP0B4Isx1xbyAMpRXg2fAz4cEe/obv9dd9+sZ7fOn66BOOmO98BZz46NtPgcQPaS7T3NbpNN+Xs1MfkdfNjS3zpT2UdGxJ9n5ucr5U07hcUtgx9bMf+NUbpufp66JeF+APymZ8ZyvkGpC1u1wUTj7MMi4nHAp6lY7jDKRrmNqrFsJjMPBw6fuv1DytKZVVvXOrnLqXVJunZ2RNwbeCflKCYo3Uoen5lHVRjXILO7/FsxV97nS5n5zamvXX6Wd5XZ9wb+rLv5hYpj3gA8fjobOCQze5flaZm9FvlDFA1bfDbO3mVB9iBahw41e6gi4sHA+ylnWi9h7tJ8702brUXEqyhNCX7A3CX0Gpfmb0e5ZP51Ktcl7vL3pJRT+wb163o3ye72a7wCOI/5yyB6nTjsDlI2qsaymYh4DfBySsmzz1GWRDwjM9+/6kwnuav6+Wsxt1nma9OXSiLi1pl59uaWvYnHbflv+dXMvHOj7D7LWtYtu29+tG3B2Sy7y2/W4rNx9u4MqHXokLOjbavwltnnUK56nJWV37gjYj/gc5l5YUS8ALg98PIKZ1on+d+ntPOuemk+Ik4Ejqfd+tOzgXcskd9rLXHL7Ij4IXCHhks4mpnsNekO6O4PPJPy2l91c4+aRZNrq1VwuXp2Zv4yMz/dfSz8RTpsyR8aefYmtPy33HrT37Jqs7pkpnX+oZSC9jt1H5/q7quhWXaUFp8nUpbLPAz4ekQ8dADZB1GW5Vyn+/hARDzV7DbZlLanF1L+HR8GXEC93++W2T8GvlF7gtt5YTfB3ZtydehdlJbBtUwuzde2VWY+MzMPzcz3Tj4q5v8mM9+UmUdn5rGTjxnP/j4Nl3BExKER8e6FH5XiJ0to7wccXuXgcIbP5FZZexRLFBRuuK5pkLvQ1yB7Zs/KjzG7b34MuE0zjVp8Ns4eXOvQgWcP9ff7PZSD1yOZf3m791rImKt9+irKmeIP1nxPiFL/dDfgJOpemn8lZd3zp6jcbrvLf32X+0nqt5dukh2l8dOtgaNps4Rj+grh1sCDgXNr5Edp5/0gynKFO1AOjD6dmXdc5seWteYbzxZsKJj3Jcq6kd0o/7HqSWgsUVA4Ii4vKNxqgttpedQw1GwNS8s2zS2zW7b4bJk9xNahQ85u2fa0ZfY53ceVqFupBeCnUTax7gO8OkrJtppXepddz9nDw7vPz526L6l3pWwyyZ+u5Z3UaS/dKvvj3UcTmXnE9O2I+HfKkpEa2c/p1uWen5mXRcRF9NyYvB7VFe6/Bo+xfVYuKKxeZnbpyUiz++Y/hrJu9l+Za8F5QI1BNc7+XEQcxfwWn0cOIPtQyvKH6fae7zK7WfYTgfd262cvb3s669nZoB5uRNwoM8+hLK24N/AvmXleRPwR8Kxaj1PxEv/C3FZl/Cb59xhaduXlGitxU3o2g4iIe2bml2KqxGTEvLewj646e1aXK/QRDQoKX4HH/lpWaG83tOxuLddNM/PQ7lLuNt0fT6JnB5ooO61vmpn/GaVO7paZeWH3tdtk5jdWm72Jx225rKVZ9lrkz6po2OKzcfZQW4cOMrvLb9L2tFV293f1HymXoi/fi5A9KhRE16giIr6YmfeqMMyNPc6dKAe3t6Schd6Ceu28bwPcivnPSY1OhJP8+7H4Oa9VKq96dkTcFHgVi5+TWg0yJq3Zo/v8c+C5C8/wXsHMl3YnI5dav57Zo3vqmk9yI+L4zNw75vewB+qVQ4myU/SFlILCT4pSUPi1WWE3e8wv9j1xPqWkUK8SNwPOfjGl6PnNM/NmEbETZdH4n2ziR1eS/ThKqaxrZOau3Qv47X3+IK90ycysZa9FfvcYzVpwtsyeeoztFmRXa/HZKnuyd2BBdq2d7WbPz706U21Pp7JrrClsmf154MOUBghPAB4N/G9mPrtH5mmUuqRPpFxdmafGet/ucU4G/rp7rD0pz9HNMvO5y/7gpnNfDNydMqH7LHAfyvt+rU2hb6c0IrkHcAjwUEplld41hFtlR8TxlOUh/0qpS34AZbnVi/qNeJjW9UxuROzB/BIxZ6zbYFYoIr5GKa9yJmVicRvgbEp3mCf2XEs81OzTKeuLTp1sVKh15rzLvgPw9ansszLztj0yd1nu65n5o1nMXov87jGatOBcg+xmLT4bZw+udejAs5u1PW2cPTnrevnf1og4KTP32tTPLpN5c8pSkKcDi9qy1loiEREnZ+aeC8Z+Wvbc2NYd9O8OnJaZu0cp8/f+zNynwrAvfx+b+rwNcGRm/ukmf3idsqd+Ty5/n4zKbau7ZQV7U16bX87Mj1fKvTqVDxLXreNZRDwNeBxlrUVQunQcnJlvrpC9NaVby8LLAKs+5T3lXOCx2dWUjdIE4WWUy0gfpXR02dyyf5+ZGRGTFrNX65G10O8y8/eT9TkRsSU9N8nVmAiuR/Za5HdateBsnd2yxWfL7CG2Dh1ydsu2py2zL+k+/6y7zH0ucI1lvn+TMvM7lI1mZ2ZmrTXmS/lNRFwJOD3KxqKfUWdj228z8w8RcWl3leUXlLP/tUw2Df6mu0L5K+CPZjz7d1GaBX0vIp4C/BTYpkIuABHxVuAmzO1PeEJE7JOZT64Q/1mWOEjsYz3b+h4I3CnnSsS8GvgqZd1OX4cB3wb2pUzk9ge+VSEXyiWWy5smZOY3I+IWmfnDmL9QenPK/kiUnblX75YXPAY4uG9o59iIeB5wlYjYB3gSpVzMqi1YKjP5n798jVGfJTMts9civ/PGaNOCs3V2yxafLbOH2Dp0yNlN2p6uQfbLo2xo+3vK++R2wDMq5AKcEKWkVfUmFp1HUia1T6GM+fr0bNfaObk7+3cwpZLSrynziFo+3eW/FjiV8re21ntbq+yDKMsgngb8E2U5xKMr5E7cE7hldssAIuK9lKvCNVQ/SFy35QrdZYa9MvPi7vbWwEl9LkNPZZ+Wpebf5DLAVpRT6r03bUXEhyk7Zj/U3fVXwLUoL+Lje146Glx2lBnyzsAtKJv9AjgqM7+w2rEukX/gdDalfe34dkzOiGjUgnMNspu1+GycPbjWoQPPbtL2tHX2Ch77uZn5qlX+7BGU53qyrOKRwO6Z+ZCN/1Q9UaEDZETcENgu59fEr9bJM0pZta2nJ/7dGcze73Uts5d4rDdn5qobq0TEp4EnT64qdkvo/i0zH1BhbM+gHKhUO0hcz0nuMylHF9MlYt6TmW+okH1iZt4hIo6jnPn7OWVBd40/YlfpMie7fr8CvJWyVu+qmfnrzTC71xrZZXK3AM7OzFvUzp56jOmqENcCts2uKsQsZ7fMj0YtONcgu1mLz8bZg2sdOvDsZm1PW2av4LFnsgHMCh//tGzQjKjPc7Le+a2y++ZGxLGU9uYndnftBZxM2cje60C0xUHiui1XyMzXR2naMJl0HZD1SsS8M8rO3BdQuolsQ6m20Ftm/hZ4Xfex0KonikPOBk6NiL0y86SeOfNkKQb9nYi4QWb+d81suHxn7p7AzSln6a4EvJ+5MlEzmb0G+UO9DL1Vw/WQLbN/03CdstmLtWx72rSl6ib0WXfWsonFSrQ62zbL9cjXM7uPllUa/h64Sc2DxPVckztZh1el3MwC2zNXYP4t3edLI2KPzDx9NYER8ZHMfFhspIRT9is7NcjsKXcE9o+IHwEXQb1yVsAOwNndmbSLJnfWuGxJaUd4O7rfwcw8NyK2rZDbOrt1/tWBb0dE1Raca5B9ZEQ8njYtPltmf7lbxlG9dajZS7qIsgGqRdvTltmb0mei2LJBxnpqfal6s+tCmpnHRqliMVnieGLO7wbZR/WDxHWd5Db0x5SzXJMNSvenlM56QkQcnpmvWUXmQVNZtQ01e2LfhtlVzsBvRMuqEC2zW+e3asHZOrtli8+W2UNsHTrk7I/Tru1py+xNWfWZv+7kz+7RsEHGJszqWcsx6vVcR8TDKJvljumy3hwRz8rM/6gwtuoHiWPteHYccN/JOtMo9ec+Q2lbeEpm3mqVuVsA/5kN2vENNXvqMXZnrubxl7NizeNWR40R8Q+UloT7UDrEPAb4YNYpY9csey3yJQ1LRDwvM1+5yp89iLLs6ULKDv/bA8/JBl0Tu6WE11+wQaxJh8Zo2CW0y/9oq815fbMj4qqZueisaET8bWa+p0fuGcA+k/fhKJ34/jMzd19t5lT2klUg+ux9GOsk99vAbTPzku72lYEzMvMWfRe4R8QXgYdkvdIqY8g+iLmax1Aupb+z0mRx4VHjnwK1jhqJUpaselWI1tkt86NtC85m2V1+sxafjbMH1Tp0yNnRsO1p4+ybAW8DrpuZt4mI3YC/yMyXV8g+I0szhX0p3dReABxWa+NTt//mLyhXj0+hrMn/St917t3a4dMz86KIeARlcv7GrFhPPCLuwuLmBLVe99Wzu8xDgG0y8wbdCai/y8wn9cmdyp+30TxKTd4zstLm8yib5G+QpYZzb2NdrvAB4OsR8Ynu9gOAD3aXdL/ZM/vXwFkR8QXmrxGtseZqqNmPBe6YbWoeP59Sam7eUSPQe5IbEY+ldNp7Vt+stcxeg/x/Y4kWnLOeHRtp8Qn0fkNqnL1ke8++uWZv1KHMtT29B13b0wFkHww8i1J1gsw8MyI+CPSe5DJ3Cfu+wPsy8+yI/gXUp2yfmRdExIFd/osj4sxN/tSmvY2yzGJ3yqalQyivybtVyCYiDgN2BU4HLuvuTuq87ltl/ytlCeEnATLzjIi46/I/coV8LiKOYq4ZxF9R/ib2FhEPAP6FcgLkRlG64r6sz56NUU5yM/OfIuJI5naaPyEzT+7+e/+e8R9l7oxlbUPNDuZepHT/XesP5IYFyxN+Rb03jRsA74hSX/EU4DjKUovTZzy7eX5mfj8itsjMy4BDo/S479Vnfg2yH8pci88DumUu76+Q2zr7LjnX3vOlEfE6oFb3KbMXu0pmfjEiojvj95KIOIU6u8ZbZl81M09cMPe8tEIuwCkR8XngRsBzo2xirdJxqrNlRPwRpZPd8yvmXtrtTXggpVbru7oTALXsSSl52OKSd7PszPzxgt+Tyzb2vavIflbMtfWFctX2Y8v9zBXwEuAOlCu3ZObpEdHrKsgoJ7kA3aT25E1+4xXP7V0XcyXZS61bqpXdwKGUM+fTNY/fVSm72VFjZr4YLr888jjKWZI3UC6hz2z2GuS3asHZOrtli8+m2d3nIbUOHXJ2y7anLbN/GRG70u26j4iHUl4/NTwW2AP4YWb+JiKuyVyFohpeSmnkc3xmntRNXL5XIffCiHgu8Ajgrt1zv1WF3IlvADtS73lei+wfd0sWMkojrIOo1/F14gTKxPkPQM3SoZdk5vkLJui9DrZGO8mtLTZSgmsiK5TLWmrdUkT0Wre0FuPOUvP4WObOnFeredwdNf7lVHa1o8aIeEGXuw1wGvAPwJdnPXsN8lu14Gyd3bLFZ8vsIbYOHXJ2y7anLbOfDLwTuEVE/BQ4hzK5q+Fw4N2US+dk5q8oBxa1/Gz6vSZLO/nXV8j9K+BvgMdm5s8j4gaU35largV8M0oJy9olD1tlPwF4I3A9ykHW5ym/O1V0S05eBHyJueoKL8vMd1eIPzsi/gbYIsr69qdRJtSrNsqNZy1EaV23UTUWusdcO+IDKWdxX9xdrutTJ7f5uLvH2QK4LvMX0Fdv4FBTRJxKudz3GUqv9q9m5u+W/6n1z16L/E08du8WnK2zo2GLz8bZg2wdOtTsjTxer7anrbOj7C3ZkJkXVhoWEfFnlDO3d6JMeA+ttfGny1/UZWup+1aR++rMfPam7uuRv+Ta3qzTea9ZdksR8R3KcqJfdbevCZyQmTevkH1VynKWyzdUA/+UmRevOtNJ7uzozrr+OaV/+PO7yzq9JrlrISKeStls8T/MrcfNSme3HwK8GrhOlzvJrrUbfzvKGdG9gf2AX2Tm3sv/1Ppnr0X+Mo97WjZowbkG2YNrwWm22VM/u9QVvfMpZTFP7zWwucfYnlIT+vnAjylnz9+fXaWiVeTdGbgL8HTKhqiJ7YAHZ8+yUxuZPFd9z4x2jQ+aZEfEUp0CzwdOzsxPLPG1K5p/AnD37Fqzd8vPjsnMu/TNXvA4WwBXy541m12usEIRcXxm7h0RFzL/8n/NSdfLaLNuqXXppoOAm0+O7Cp7DfCAzKy9pmhSEupPKTtx96T8Ua+1XKFZ9lrkb8JQu/wMtQWn2WubPav2pH6To8t1Z+QeQVlSdBqlStHelOUWd19l7JUoS6q2BKY7Ml5A2cy52rE+EXgSsGvMr9KwLfCV1eYu8TjNGh80zN4auAXljDyU5WDnUKpQ3CMzn94z//vMVa9K4IHAmZODsMxc9TKUKNVCnkA5WXYSsF1EvDEzV70ExUnuCk3OkGXmtlHKWkwaHxyXlRofZObhzP1ikpk/pN56xZZloX5MOVJs4X9aTHA7/0yZGL4JOGm1ZyvWIXuSf1zD/DEa6uTc7LXNnlU7A7fPuSZHL6YsV7orZZ34qie5UTYN3xw4jHJSYbIZ6sMRseoN3FlawB4P7JaZL11tzhI+SKm28SrgOVP3X5h1Wm1PNCth2TB7N+BPslSvISLeRnkv2hs4q2c2wA+6j4nJ2eEabeVvlaXU3P6Uf9/nUH63neSulYh4GnONDwI4LCIOzjqND7am7HJdWAD9MX2zu5yqpZumLp/9EDgmIj7D/AX0fY7oJp1eTo6ID1NaZU5n9y6HlpnNWh23zF6YHxE7RES1Shwr4Bk6jdms/n5fh6m/gcAllMYQv42Ivuvx35SZRy/1hczcs09wZl4WpUJGNVl24P8auF2tfSUb0bKEZavsHShnzycnnq4GXKP7d+i9b2P6YCVKNYtt+i4pmLJVlIoQD6KUhLskepZrdpJ7xR0I3CnbND44DPg2pZDzyyg1fWudxWxRumly5Pbf3ceVuo8aHjD137+hrFWeSHrU/I2NV5zovZa4ZfaCxzmGxZU4TsjMZ9TIn3qcpUrZ1drU0Sx7I34/0Oz/Mrt+dmyk7SllZ/osZjdrcpSZR0fDjn6U951PUq4kTjcjWvXf8W7S9p2IuEG22+TcrIRlw+zXUJ7vYyjvO3cFXtn9nvxn3/AWSwqmvJ2ytOJM4LgoG+d7XSV249kV1E1i9prs9uvOvp6UFVraxVx1hTOzFELfilLgv3fv7e6X5X8ok9BnANsDb8nMHyz7g1f8cWof2VUXDStOtMxe8DiT35VqlTimso+hQQvONchu1uKzZXaXP6jWoUPOjoZtT1tmd/l7UTZyQXndVKkFHxvp6JeZq143uyD/0CXuzr5XKSPiOOB2lG5405PnGiW+Jo8xXcLyy1mv8UGz7CiNN+7Q3TwpM8+tkdtln56Ze3RLCm5Pt6Sg0nvPi6duJuVE3BaZ+cLVZnom94pr2fhgsq7yvO6o+ueUS1Q1PCgz3whcTCnMTUQcRJ2zFs2O7Lqzzi+nFIf/HGW90TMyc9Xdplpe3mp86Wxaqw5C0K4FZ+vsli0+m2XHMFuHDjabtm1PW7dUPZVS+3RLgIpnMVt29CMzazaWmLbqyc9KZeYRwBEDy76YcrV2a+AmEXGTzDyuUnb1JQVTfj3131tTDrb6Xc3OTD+u4Afl6OVp3cftKuYeSFlPc1fKOtdfUM4C1Mg+dYn7TquUfXr3eX/gdZSOM2dWzn4w5WBie+CMStl3okzKf0253HwZcEHPzOO7zxdSdhBPPi7sm73gcfajXNJ5a3f7xsARlbLPonSX+jzlqgUV/z1bZp/afX4RpTj8kr/3M5j9LbqrarU/zF4y++vd59Om7qv1N6Vl9lOBXwJnd6/9syq+dk7sPp9CKe8VwLcrPuc7Ax/r3tN+QZnY7Vwp+7qUShP3B65TKbPZ3/HW7xGUecRZwP8BR1NOEH2p4r/lU4GfUM74B7AL5Sx0lfwFj3VlSnmyVWd4JncVMvNUyhF1bdsz10rxLd3nSyNij1xlHcSIeDilI8yNujVRE9sBtXahLnVkV2sdzKRF4/2Aw3Nxy78+qlecyDWowtHlt6zE0aoFZ+vsli0+W2YPsXXokLNbtj1tmd2yVGPLjn5QroB+kHJwDuV1dCiwT5/QaFSGa/rveJ+ctc7uHESpvfu1zLxHRNwCeGXF/Gsw133whZQlBcdUzJ92VcoB0qo5yZ0tf0z9OognUN4orkU5yzpxYZddwzsoG0HOYG6xeK01uZ+KiG9TjkafGKXMyqq7nyyUlStOTETDKhxdfstKHK1acLbObtnis2X2EFuHDjm7ZdvTltnNSjXm3Jrht0fE51jQ0a+Ca2fm9Lrc90TE0yvktizxRUQclpmP3NR9M5Z9cWZeHBFExJUz89sR0bsb2ZT6Swo6MX/j9hbAtSmb8Fef2Z0S1gzoFtHfN+fqIG5DqYN4b8rC7lv1zG/WuWWJx9oyMy/t8fP7ZebhEXEjyh/287Pspr0asG1m/rzCGI8D/oyyvvLnlIOBv82eXXi67DOBO+dcFY6rUVrv1qqucDilEsffMFWJIzMPqpDdpAXnGmQ3a/HZOHuQrUOHmj1UEfEuSi3bmqUal33ddVcte4uIL1LO3E4qCTwcOCAz79Uz96yc2vTdXWE5IytsBO/y5v1tiogtKUtEer0Xt8zu9gsdQOkyd0/KsoWtMvO+fXKXebwrA0dl5t0rZE1v3L6UUid/1fMI8EzurGlWBzEi9gP+hTadW65MuVR+Q+b/TvU5Ansu5XL8EdN/CLpJ40Ub/akr5pGUSy1PoVScuD7wkGV/YuWCuQ0zMNfuuJabZOZ+EfHAzHxvt/mvV8ezmGvBee2Y30J0O8pR9UxmT9mHxSXI7rPEfTOVnaVgfpMDULMXi4ZtT1tm06ZU4+Tq3taUq4hnUP5O7QacDNy50uM8hlJm81+7219hbmleH03KcHVLk54HXCUiJlclg7J3452VsyfvC72zATLzwd1/viQijqYsg/xc39xl9F5SMJENNm47yZ0tzeogAi+g3WWdT9D1UGf+JL2P/xcRnwduvGAtMVDtsuWDslHFCdpW4YA2lTiatOBsnR0NW3y2zJ56jCG2Dh1sNm3bnjbLzrodwyaZ9wCIiI9Suqmd1d2+DfCSio/zI0rpwKoy81lRGgft3d31zqxQhiszXwW8KiJelZm9l6+tVXZEbAGcnZm36B6r+pWPFksKWnK5woyJiD2Zq5tXsw5is8s6EfGNzLxN35wFmVeiVLE4jLJbdJ5Kl0SXunR+Wmberm92l3V75v74fjkzT6uR22UfSNmhfFvgPZQJ5Asz8x09c7cAPpKZtTaxNc+OiO0pVUmqt/hsmT31GGcA+yw8AK20bMbsxdlfY37b0y2Zanva53Jx4+xrA//I4nX491xt5lT22Zl5603d1yP/xpSTB3eiTJC+SikF+cMK2TsCdwT+QKkJ23sp21T2gymVCc7vbl8duHtmfrxCdlCqBu1NeU6+XCn3E8BTs1GDjBZLClryTO6M6Sa1VSa2CxzZ4rJO54SIuO3kLEAl78rMR0bZrFX1aDTWpuJEyyoc0KASB7Rpwdk6Oxu2+GyZPWWIrUOHnN2y7WnL7A8AH6ZsSH4C8Gjgf3tmTpwZEYcwVxt3f+ptTIZSWeEtlEkdlKo2/06ZnK5ad7D/IuBLzJ3xf1lmvrtP7pQXT58ZzszzojQs+HiF7LcAN2HuPfkJEbFPZvbdqLgDcHaUTZvVG2Q0/DvYhJPczUdSqiBcflmHclRdw97AARHxQ8pyhRotbP+4mxDtHxEHs2A9a8+zaGtRcaK1FpU4Jk6Pyi04W2dnwxafLbM7Q2wdOuTslm1PW2ZfMzPfFREHdQf+x0bEST0zJw4AnkgpPwVwHKUBSi1XzczDpm6/PyKeVSH3WZQD0F8BRMQ1KX/fa01ylzqwqjVvuidwy+wup0fEeyk1kPtq3iBjSFyusJnYyKX5Wm1gd6EcPV5eExY4r88RX5QSXE+kNDn46fSXKBPoG682e8HjrFnFiZqiYSWOaNSCcw2ym7X4bJnd5Q+udejAs1u2PW2SHRFfy8w7dZP/NwHnAv+RmbvWyG8pIl5N2eX/IcoJl7+ivGe8FlZ/0iIiTqAsH/h9d/tKlOYBd1n+J1ec/27gPOaulj2Zcmb+bytkfxp48uR9snsf/bfMfECF7EG+r7XgJHfkpjbO3Bj4wdSXtqWs+X1Ehcc4iLJudlIT9kFAlZqwEfE24O2UMyJQsalCLK448adArc0tTUWpHXzbzLyku31lyhrrW9RcVzwkYUkrrVBE7ADclPlrW6u0PW2VHRH3p6zvvT6lUsF2wEszc9HG3CuQOb2JaJEaJ0G6xzlnmS+v+qRFRLyPsi/hE5T/jwdSrmid2QX3qsHdnYF/IaXUJMAXgJdnVxqyZ/axlInoid1de1GWKp4Pqz+AjsWbNgfzvtaCk9yRW6ONM81qwjaeQDfb3NJaRLyQsr5tuhLHJynLL96Zmfv3yN6Z8iZ6+Vk04KDM/MnqR9w+u8tvdgajdnZEHJ+Ze0fEhcyfaEyuVmxndr3sqcc4kHJZfmfgdMqyra9mnQ1czbJbWLCJaJFZX3/ZrY/dqGxQkaKWjR04T6z2AHrI72stOMlVb93ZgL0y8+Lu9taUy3Q1Kje0nEA3LSTeWrSrxPEFykaRyRq6RwD7Z2avFpxrkN3sDIZnR8Zj8veK0vZ0j+janmZm7xrZjbOvTemieEOm1oXWWOrTWpTKKvdj8dj7nmndevK+M3XftTLzl31yp7KOZokz3TUPWiJiO+Y/J30rwgz6fa02N56phpY1YVs2VWhZcaK5bFeJo1ULztbZLVt8NsuOYbYOHWw2bduetsz+BOXKx38y/2/iqq3FmfPOpyj1yM+ilPqq5cSIeHxmfg0uX8f9KuBmlfL/Yeq/t6bUPa5SLisiHk+pL3sx5TkJyr9B3/0mS23aPLJn5mA5yVVvmfn6bjfxpHLDAVmvJmzLCXTLihND9quIeATzW3D+agDZQy1ptbA+6ZaU6hlmt8n+SZR6px8HvhAR/wfUuizfMvuqWaGN9LTM3Lv7vG1E7MHU5uFaex86O9da37vA/sC7u/efnYBrUqoWVJGZpyy46ytRSnPV8CzgNrXOOk9kowYZQ+VyBc28aNRUIRpWnBiybp3em5lr6fkV4GlZoXxW4+zXUtqRTp/BOLPGxKBFdky19wR+M7mbrr1n9uiGtMbZ89qSzmr2Rh7vbnRtT7PboT+r2RHxcuCEzKx+tSlKNZvH0WDvQ5f/auCLmfn5GnkLsh9EWf50IXDXzPx+xexrTN3cQDnQelNm9j47HxGfAx6Smb/Z5DdfsdxXL/y7tNR9mwsnudrsxBpUnND6WHAGo3bZqSbZ0aC9p9kbzZ3X9nQI2VNLCYLSXOJ3lLbeNTfjNdv70OU9mNJoYgMVxx4R7wJ2pdT5vRmlq9qbM/Mty/7gyvPPYe65v5TSovllmXl8hezb0V2ppPybApCZT+uZ68mbKS5X0Obog5Q1Ss0qTgxZtG3B2Sy7cwJlveIfgFqF8ltnnxgR22eD1qGNs583NfGv1pa0ZXYOsGlIZm5bK2sZLfc+ALyecvXmrKx7Zu0s4MAu85yIuGP3WFVk5o1qZS3hHZRObVXWKU+fvOkOWia2pVwx2yx5JlfSPBHxNUrx88ml+b+m9ELv1YJzDbIXtvi8G+WsS+/uR42zT8/MPRbcd1pWqHXcOPutzG9L+lfAD7J/W9LW2YNsGtKdDf1SiwOWiHgmpU3w9N6H92TmG/pmd/nHUcZac9PZJHsX4KaZ+Z8RcRVgy8y8sGfmstUwskL3x1qvw6m85uVCh8hJrqR5lrq0FRFnZIU6i42zvwPcJRe0+Ky0fq5l9lLPybwyQDOa/W3mtyXdQLlcf8sZzx5k05CWByxdVpO9D132eyjLw45k/qX5viXEHgc8ntKFbNeIuCnw9sy8V8/cSQWY6wB3oRzcAtyD8rq/f5/87jFeCfwXpfLE9HPSt4TYrsBPMvN3EXF3yl6C92XmeX1yh8rlCpIWOjIinsP8FpyfnWzC6PlHuGX2ryibTyYupGJViIbZJ0fE65nfOnThru5ZzP4+cAPmqgdcv7tvprMz89ho1DSkZTZLV/Oo9h6emacCp9bKW+Cc7uNK3UctT6a0UP46QGZ+LyKu0zc0Mw8AiIjPA7fKzJ91t/8IeE/f/M7Du8/Ta89rlBA7AtgzIm5CqRj0CcoSvfv2zB0kz+RKmicateBcg+xmLT4bZ7dsHTq4tqRrkD3IpiER8W7gPOYfsFwjM/+2b/ZQRcTXM/OOkzPaUUrNnVpxw9y3pq8e1Lyi0Mpk41lE/CPw28x8c+2lEUPimVxJ87TcbNF4I8cPmF8tY9LyuMbGnWbZ3YTzOZv8xhnLpqxRbqVl9iCbhgBPpRywfJhyoPUFykR3ZkXEGzLz6RHxKZbuHNZ3rfKxEfE84CoRsQ9l49WnemZO+2IsbqzwnzWCI+JRS92fme/rGX1JRDwceBSl3TvAVj0zB8szuZLmiUYtONcgu1mLz8bZzVqHtsyeeoyqbUlbZy9ckxwV2562zJ7KvFqNM/FrISL+ODNPabVWuXt+Hwv8OeXM+VHAITUrOHQb/u7a3Twu65UOnK5BvDVwL8pZ6If2zL0V8ARKCbh/j4gbAQ/LzFf3yR0qz+RKWqhVC87W2S1bfLbMbtY6tGV2tGtL2jSbtm1Pm2VHxF2AQ4BtgBtExO7A32Xmk2rkt5Bdx7DpyWxE7ABcPzPP3OgPrjz/D8DBwMHduv6da05wOydQXjPJ3PKZ3jLzqdO3u2oZH6qQ+03gaV3mDsC2m+sEFzyTK2mBpXbkDyT7tsC7KeshJy0+D8zMn8xy9kYe78TMvMMsZ0fE9ygNBKq2JW2d3eUPsWnI14GHAp+crK+MiG9k5m1q5LcUpe3uX1BOrJ0C/ILSeOeZDXJPyMxn9Mmdym+2xnqJx9oK+Ebfii2tnuuh8kyupIWOjIg/zwYtOFtmZ+ZZEfEK5rf4rDIJbZkdS7cO3X7WsylrlKu2JF2L7JhrcfrRJe6b2WyAzPxxxLweDZdt7HtnzPaZeUGUetPvy8wXx/yGBbOWO9FsjfWCdcobgFsBH+mbS/vnZFCc5Epa6GvAx7r1blXbh7bMjrkWn7tRlhF8OiKqtPhsmU0527KwdehjK+S2zn4ucEJ3hrFaW9I1yN4HWDjpvM8S981a9o+7JQvZnfU7CPhWhdy1sGVXfuthlInjrOdObFhQAu5XLF3KbTX+Zeq/LwV+VOnAufVzMihOciUt1KoFZ+vsli0+m2UPuJpF1bakrbOjYdvTltlTnkBpiX094KfA55nx6gpTXkbZFHZ8Zp4Upb3392Y4d2KpNdafrZR9MqXE1x8i4mbA7SPifzLzkp65rZ+TQXFNrqR5om0LzmbZXX71Fp+tsqNh69CW2VOP0az2ZovsaNj2tGW21keUdSE7U2o0t1hjfQplje8OlAOhk4DfZ+b+NfJVOMmVNE80asG5BtlNWny2yo6GrUNbZk89RpO2pGuQ3aztaePsNy1x9/nAyZn5iSW+NjMiYmvKMplbU6p8AJCZj5nF3Kn8Ki2wN5I9adrwVOAqmfmaWKJ18ypymz4nQ1NrbYmk8TgH+CKl/ea2Ux+znv1k4E+AC6C0+KRM8mYyOzMPyNI+dCtK69C/zMy/pLw59Sre3jJ7ysPp1s5S1v6eQrkEO+vZRwCXxVzb0+tT2p7OevbWwB6US8/fo0ygdwYeGxFvqPQYrRwG7AjsCxxLGXeNKyytcidOjYi9Nv1tqxIRcWdgf+Az3X1bVMht/ZwMimtyJc2TmS8dYjbwu8z8/WT3eZQWn7UuVbXMvn5m/mzq9v8AN5j17AGvJf5DZl7aLel4c3ZtTweQvRvwJ5l5GUBEvA34MuVS+lmVHqOVm2TmfhHxwMx8b0R8kDL2Wc2duCPwiIj4L+Ai5jbK1iiDeBDlQO5jmXl2t3b26Aq5rZ+TQXGSKwlo24KzZfaUY6Ndi8+W2c1ah7bMjnZtSZtm07btacvsHSiNIM7vbl+Nsnzmsoj43cZ/bCZMNlOdFxG3AX5OnassrXIn9q2YNU9mHgccN3X7h3RNHHpq/ZwMipNcSROHdZ//Zdnvmr3siedQ1qKdBfwdZRf0IbOenZlPifmtQ99Za3NLy2zKhpyJy9uSAjUmoi2zD6BUKnhFZp4Tpe3pYZv4mVnIfg1wepRi/0H5N31lRFyNegdFrbwzSvetFwCfpEzWXzjDuQBk5o8i4vaUs+VJaapwao3srqLCP7C4xXnflttNn5OhceOZJK2ziLgucAe61qELanPObPaCx7k68KHMvPeQsoekq3866VZ3Umaeu57jWamI+HvmruBMulmcB5ySmafPWu5U/ouA/Zhr7vEg4PDMfHmF7DOAt1PWm1/e1CO7Vsg9cps+J0PjJFcSUHYSs8w60z7r0Mxe9jGatQ5tmb3EY1VpS9oqe+i/J93j7ADclPm75o/b+E/Mhm5d6J7MLfG5P3Am5Szm4Zn5mlnKncr/DrB7Zl7c3b4KcHqN3/GIOCUz/7hvzhK5TZ+ToXGSKwm4vA7sRmXmj8yulz31GGcA++SC1qGZufuMZy/ZljQzn7Pxn1q/7BH8nhxI2ay0M3A6cCfgqxUubzcXpT72fTPz193tbSgVBe5NOcN4q1nKnco/GnjwpARcd0XhozWe84h4CfAL4GNULJPX+jkZGtfkSgLqvBGbvSotW4cOsS1pk+wR/J4cRFmr/LXMvEdE3AJ45Ro8bg3XYWoiR9kcdd3M/G3PTXOtcifOB86OiC9QDrr2AU6c1CzOfm2mH919ftbUfUmpI95H6+dkUJzkSgIgIo7PzL0j4kLmX3qdlM3Zzux62VNatg4dYlvSJtkj+D25ODMvjggi4sqZ+e2IqL40pJEPAF+PiEnTigcAH+w2zX1zBnMnPtZ9TBxTIRNoWiav9XMyKC5XkLRIROxBWb8JcFxmnmF2/eyIdq1DW2Z3+c3akrbM7vL3YEC/J13uxyjVG54O3BP4P2CrzLxvjfzWImJPSkMVKFUKqjT3aJW7xOPsQKk7fWalvK2AJzJX+eQY4B01DhLX6jkZAie5kuaJiKcBj6PsKA7KjuKDM/PNZjfJbtk6dHBtSdcge5C/Jwse527A9sDnMvP3NbM1pyvX9heUq96nUNbQfiUzn1kh+xBKDeX3dnc9ErgsMw/sm605TnIlzRMRZwJ3zsyLuttXo2xwqVFJwOzF2e8F/i0zT+qbtcbZp1GaYvwr8NgsXZuqTKobZw/u9yQitgDOzsxb9B2jVi4iTsvM23Wb/q6fmS+OiDMr/a6csXAD6FL3qZ9aGxAkjUcwVbex++/YyPea3d8dga9FxA8i4syIOKubLM16dqu2pK2zB/d7kqWV73ciola7Z63MllFqEz8M+HTl7MsiYtfJje53/LJlvl+r4MYzSQsdStm4MFm7+SDgXWY3y27WOrRldrZrS9o0m+H+nuxA2el/InDR5M6s0xJbS3sZcBRwfGae1E1Ev1cp+1nA0RHxQ8qB0C6UNdeqyOUKkhaJuVaWUDYrnWb2mmRXbR3aMjvatSVtmt3lD+73pFuHu0hmHlsjX2svIq4MTCpkfCczN7sSX605yZWkdRRtW4cOri1p6+whi9Kiea/uZrMWzSoiYmvgscCtmd9l7jEVsp8MfCDnGk3sADw8M9/aN1tznORK0jqKtq1DB9eWtHX2UMUatmhWERGHA98G/oaydGF/4FuZeVCF7EXVQiYb3fpma45rciVpfZ1LOUt0cXf7ysBPB5D9qYh4EpXbkq5B9lA9H9grF7RoBpzktnOTzNwvIh6Yme+NiA8CX66UvUVERHZnGrsKGleqlK2Ok1xJWl8tW4cOsS1p6+yhatmiWUubNGY4LyJuA/yc0ja3hs8BH46Id3S3/667TxW5XEGS1lFEPHq5r2fme5f7+npla21FxGuB3ZjfovmszPzH9RvVuHX1cY8Abgu8B9gGeGFmvmO5n1th9gbg8cCfdXd9ATikKxenSpzkSpKusMZtSZtlD1lEPIQGLZq1tIj4e8oVBJird3wecEpmnl7xca4B7FyrZbDmOMmVpHUQEWcx9wa6SJ+uSi2zpx6jWVtSW54uFhGvzsxnb+o+1dOtwd0T+FR31/2BMyml7Q7PzNf0yD6GxS2DT8jMZ/QYshZwkitJ6yAidlnu65n5o1nMnnqMZm1JbXm6WEScmpm3X3BflRazWlpEHAfcNzN/3d3eBvgMcG/K2dxb9chu1jJYc9x4JknroMZEcz2yp1wWEbtm5g+gelvSltmDEhFPBJ4E3HhBS+Ztga+sz6g2G9dhqroHZSPadTPztxHRt3HDdMvg5/fM0kY4yZWkdRARx2fm3hFxIfOXFgSQmbndLGZPadmW1Jancz4IHAm8CnjO1P0XbuYl1dbCByhtmj/R3X4A8MGIuBrwzZ7ZLVsGq+NyBUlaZxGxB6W4P8BxmXnGQLKbtSW15el8EbEr8JPM/F1E3J1SaeF9k45ZaiMi9gT+pLv5lcw8eT3HoyvGSa4kraOIeBrwOErr3aC03j04M98849nN2pLa8nSxiDidsgnqhsBngU8At87M+67jsLRKLVsGa46TXElaR906yztn5kXd7asBX61UAaFldrO2pLY8XWyy8Swi/hH4bWa+eXN/ToasZctgzbFbiiStr2D+pqrLmKvJOcvZW0TE5VmV25K2zB6qSyLi4cCjgE939221juNRPzfJzBcCF3VNWe4H3HGdxzQ6bjyTpPV1KGVzy6Sw/4OAdw0gu2VbUlueLnYA8ATgFZl5TkTcCDhsncek1WvZMlgdlytI0jqLiNszv5PVabOe3bUl/TvgXt1d1dqStsyWZkHLlsGa4yRXkqQZtRbd67T21qpl8ObOSa4kacWG3o54aNaie53WXsuWwZrjJFeStGJDb0cszYKWLYM1x41nkqQVG0E74kFZo+51WnstWwar4yRXkrRiI2hHPCiZuXf3eduW3eu05lq2DFbH5QqSpFUZajviIWrZvU7rw5bB7TnJlSRdYUNtRzxULbvXSWPlJFeSdIUNtR3xUHWVJ/bKzIu721sDJ2Xmbdd3ZNLsck2uJGk1htqOeKhadq+TRslJriRpNYbajniQMvP1EXEMc93rDqjZGU8aI5crSJJWZYjtiCVtPpzkSpIkaXQ2rPcAJEmSpNqc5EqSJGl0nORKkiRpdJzkSpIkaXSc5EqSJGl0/j/7h0qFtgt0kgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "There is a lot of missing data in this dataframe. Yellow means missing, purple means data.\n",
    "\"\"\"\n",
    "\n",
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = plt.axes()\n",
    "ax.set_title('Check for Missing Data')\n",
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis',ax=ax);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c918ec5d",
   "metadata": {},
   "source": [
    "#### Reindex dataframe\n",
    "- Make sure index is in datetime format after importing csv file\n",
    "- Make sure dates are day by day all the way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd026bcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.indexes.datetimes.DatetimeIndex"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "106443db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date Range:\n",
      "Minimum: 1960-01-01 00:00:00\n",
      "Maximum: 2021-10-25 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"Date Range:\n",
    "Minimum: {df.index.min()}\n",
    "Maximum: {df.index.max()}\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "638955c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "idx = pd.date_range(start='1960-01-01 00:00:00',end='2021-10-25 00:00:00',freq='d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "700b2717",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.reindex(idx, fill_value=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "de483dc0",
   "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>oil_price</th>\n",
       "      <th>gas_price</th>\n",
       "      <th>oilrig_count</th>\n",
       "      <th>gasrig_count</th>\n",
       "      <th>totalrig_count</th>\n",
       "      <th>onshore_count</th>\n",
       "      <th>offshore_count</th>\n",
       "      <th>totalwell_count</th>\n",
       "      <th>oilwell_count</th>\n",
       "      <th>gaswell_count</th>\n",
       "      <th>...</th>\n",
       "      <th>oilstorage_amount_y</th>\n",
       "      <th>oilstoragechange_amount</th>\n",
       "      <th>oildaysofsupply</th>\n",
       "      <th>gasimports_amount</th>\n",
       "      <th>gasexports_amount</th>\n",
       "      <th>gasproduction_amount</th>\n",
       "      <th>gasconsumption_amount</th>\n",
       "      <th>gasstorage_amount</th>\n",
       "      <th>uspopulation</th>\n",
       "      <th>usemissions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-25</th>\n",
       "      <td>84.64</td>\n",
       "      <td>5.72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-22</th>\n",
       "      <td>84.53</td>\n",
       "      <td>5.10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>1044998.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-21</th>\n",
       "      <td>82.64</td>\n",
       "      <td>4.94</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            oil_price  gas_price  oilrig_count  gasrig_count  totalrig_count  \\\n",
       "2021-10-25      84.64       5.72           NaN           NaN             NaN   \n",
       "2021-10-24        NaN        NaN           NaN           NaN             NaN   \n",
       "2021-10-23        NaN        NaN           NaN           NaN             NaN   \n",
       "2021-10-22      84.53       5.10           NaN           NaN             NaN   \n",
       "2021-10-21      82.64       4.94           NaN           NaN             NaN   \n",
       "\n",
       "            onshore_count  offshore_count  totalwell_count  oilwell_count  \\\n",
       "2021-10-25            NaN             NaN              NaN            NaN   \n",
       "2021-10-24            NaN             NaN              NaN            NaN   \n",
       "2021-10-23            NaN             NaN              NaN            NaN   \n",
       "2021-10-22            NaN             NaN              NaN            NaN   \n",
       "2021-10-21            NaN             NaN              NaN            NaN   \n",
       "\n",
       "            gaswell_count  ...  oilstorage_amount_y  oilstoragechange_amount  \\\n",
       "2021-10-25            NaN  ...                  NaN                      NaN   \n",
       "2021-10-24            NaN  ...                  NaN                      NaN   \n",
       "2021-10-23            NaN  ...                  NaN                      NaN   \n",
       "2021-10-22            NaN  ...            1044998.0                      NaN   \n",
       "2021-10-21            NaN  ...                  NaN                      NaN   \n",
       "\n",
       "            oildaysofsupply  gasimports_amount  gasexports_amount  \\\n",
       "2021-10-25              NaN                NaN                NaN   \n",
       "2021-10-24              NaN                NaN                NaN   \n",
       "2021-10-23              NaN                NaN                NaN   \n",
       "2021-10-22             28.3                NaN                NaN   \n",
       "2021-10-21              NaN                NaN                NaN   \n",
       "\n",
       "            gasproduction_amount  gasconsumption_amount  gasstorage_amount  \\\n",
       "2021-10-25                   NaN                    NaN                NaN   \n",
       "2021-10-24                   NaN                    NaN                NaN   \n",
       "2021-10-23                   NaN                    NaN                NaN   \n",
       "2021-10-22                   NaN                    NaN                NaN   \n",
       "2021-10-21                   NaN                    NaN                NaN   \n",
       "\n",
       "            uspopulation  usemissions  \n",
       "2021-10-25           NaN          NaN  \n",
       "2021-10-24           NaN          NaN  \n",
       "2021-10-23           NaN          NaN  \n",
       "2021-10-22           NaN          NaN  \n",
       "2021-10-21           NaN          NaN  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail().sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "856c9432",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(22579, 29)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb72ab65",
   "metadata": {},
   "source": [
    "#### Oil Variables Feature Engineering\n",
    "- Get count of remedial rigs aka rigs that are not for oil or gas.\n",
    "- Merge monthly data columns with weekly data columns to increase data density\n",
    "- Re-scale Oil Storage, merge data from Oil Storage Features.\n",
    "- Interpolate features\n",
    "- Drop extra features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f146cc2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['totalrig_count','oilrig_count','gasrig_count']].resample(rule='M').mean().fillna(0).plot.line(figsize=(12,6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b9baae95",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['remrig_count'] = df['totalrig_count'] - df['oilrig_count'] - df['gasrig_count']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d2fb9885",
   "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>totalrig_count</th>\n",
       "      <th>oilrig_count</th>\n",
       "      <th>gasrig_count</th>\n",
       "      <th>remrig_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1987-08-01</th>\n",
       "      <td>1003.0</td>\n",
       "      <td>626.0</td>\n",
       "      <td>352.0</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-09-01</th>\n",
       "      <td>1101.0</td>\n",
       "      <td>713.0</td>\n",
       "      <td>364.0</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-10-01</th>\n",
       "      <td>1124.0</td>\n",
       "      <td>727.0</td>\n",
       "      <td>377.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-11-01</th>\n",
       "      <td>1152.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>386.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1987-12-01</th>\n",
       "      <td>1162.0</td>\n",
       "      <td>746.0</td>\n",
       "      <td>403.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-05-01</th>\n",
       "      <td>453.0</td>\n",
       "      <td>353.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-06-01</th>\n",
       "      <td>464.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-07-01</th>\n",
       "      <td>483.0</td>\n",
       "      <td>381.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-08-01</th>\n",
       "      <td>501.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-09-01</th>\n",
       "      <td>508.0</td>\n",
       "      <td>407.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>410 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            totalrig_count  oilrig_count  gasrig_count  remrig_count\n",
       "1987-08-01          1003.0         626.0         352.0          25.0\n",
       "1987-09-01          1101.0         713.0         364.0          24.0\n",
       "1987-10-01          1124.0         727.0         377.0          20.0\n",
       "1987-11-01          1152.0         746.0         386.0          20.0\n",
       "1987-12-01          1162.0         746.0         403.0          13.0\n",
       "...                    ...           ...           ...           ...\n",
       "2021-05-01           453.0         353.0         100.0           0.0\n",
       "2021-06-01           464.0         367.0          97.0           0.0\n",
       "2021-07-01           483.0         381.0         102.0           0.0\n",
       "2021-08-01           501.0         400.0         100.0           1.0\n",
       "2021-09-01           508.0         407.0         101.0           0.0\n",
       "\n",
       "[410 rows x 4 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['totalrig_count','oilrig_count','gasrig_count','remrig_count']].dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f980289f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_list = ['oilimports_amount_y','oilimports_amount_x',\n",
    "             'oilexports_amount_x','oilexports_amount_y',\n",
    "             'oilconsumption_amount_x','oilconsumption_amount_y',\n",
    "            'oilproduction_amount_x','oilproduction_amount_y']\n",
    "\n",
    "df[plot_list].resample('M').mean().fillna(0).plot(figsize=(12,6),title='Oil Variables');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5e847819",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function will append two columns that contain same data at different times. \n",
    "Also, it will include data that is not in one feature but it is available in the other one.\n",
    "\"\"\"\n",
    "def mergecolumns(column1,column2,newcolumn='newcolumn'):\n",
    "    date1 = df[column2].dropna().index.min() - pd.to_timedelta('1 day')\n",
    "    date2 = df[column2].dropna().index.min()\n",
    "    df[newcolumn] = df[column1][:date1].append(df[column2][date2:])\n",
    "    filtered_data = df[[newcolumn,column1]].loc[(-df[column1].isna()) & (df[newcolumn].isna()),column1]\n",
    "    df[newcolumn].loc[filtered_data.index,] = filtered_data.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "08e32a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mergecolumns('oilimports_amount_x','oilimports_amount_y','oilimports')\n",
    "mergecolumns('oilexports_amount_x','oilexports_amount_y','oilexports')\n",
    "mergecolumns('oilconsumption_amount_x','oilconsumption_amount_y','oilconsumption')\n",
    "mergecolumns('oilproduction_amount_x','oilproduction_amount_y','oilproduction')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b4699ff4",
   "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>oilimports</th>\n",
       "      <th>oilimports_amount_x</th>\n",
       "      <th>oilimports_amount_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2136.000000</td>\n",
       "      <td>585.000000</td>\n",
       "      <td>1603.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10071.030379</td>\n",
       "      <td>9149.604550</td>\n",
       "      <td>10420.676232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2120.527310</td>\n",
       "      <td>2476.540502</td>\n",
       "      <td>1854.460564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3689.513000</td>\n",
       "      <td>3689.513000</td>\n",
       "      <td>5775.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8651.250000</td>\n",
       "      <td>7296.887000</td>\n",
       "      <td>9112.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10011.500000</td>\n",
       "      <td>9092.258000</td>\n",
       "      <td>10227.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11555.250000</td>\n",
       "      <td>11002.517000</td>\n",
       "      <td>11717.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15217.000000</td>\n",
       "      <td>14696.613000</td>\n",
       "      <td>15217.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         oilimports  oilimports_amount_x  oilimports_amount_y\n",
       "count   2136.000000           585.000000          1603.000000\n",
       "mean   10071.030379          9149.604550         10420.676232\n",
       "std     2120.527310          2476.540502          1854.460564\n",
       "min     3689.513000          3689.513000          5775.000000\n",
       "25%     8651.250000          7296.887000          9112.500000\n",
       "50%    10011.500000          9092.258000         10227.000000\n",
       "75%    11555.250000         11002.517000         11717.000000\n",
       "max    15217.000000         14696.613000         15217.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['oilimports','oilimports_amount_x','oilimports_amount_y']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "3d3595b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Compare Imports curves with Merged curve'}>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['oilimports_amount_x','oilimports_amount_y','oilimports']].resample('M').mean().fillna(0).plot(figsize=(12,6),title='Compare Imports curves with Merged curve')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1df21f88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Oil Storage'}>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Oil Storage Y is in Thousand Barrels\n",
    "Oil Storage X is in Million Barrels\n",
    "\n",
    "We will convert Oil Storage X in Thousand Barrels\n",
    "\"\"\"\n",
    "\n",
    "df['oilstorage_amount_y'].resample('M').mean().fillna(0).plot(figsize=(12,6),title='Oil',legend=True)\n",
    "(df['oilstorage_amount_x'].apply(lambda x: x*1000)).resample('M').mean().fillna(0).plot(figsize=(12,6),title='Oil Storage',legend=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "11f9b8a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['oilstorage_amount_x'] = df['oilstorage_amount_x'].apply(lambda x: x*1000)\n",
    "mergecolumns('oilstorage_amount_x','oilstorage_amount_y','oilstorage')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f38d94e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Interpolate Features\n",
    "\"\"\"\n",
    "oil_list = ['oil_price','oilrig_count', 'gasrig_count',\n",
    "       'totalrig_count', 'totalwell_count',\n",
    "       'oilwell_count','oilstoragechange_amount',\n",
    "       'oildaysofsupply', 'uspopulation', 'usemissions', 'remrig_count', 'oilimports',\n",
    "       'oilexports', 'oilconsumption', 'oilproduction', 'oilstorage']\n",
    "\n",
    "for feature in oil_list:\n",
    "    df[feature] = df[feature].interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d6f5ccad",
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>oil_price</th>\n",
       "      <td>13081.0</td>\n",
       "      <td>44.56</td>\n",
       "      <td>-36.98</td>\n",
       "      <td>145.31</td>\n",
       "      <td>28.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilrig_count</th>\n",
       "      <td>12505.0</td>\n",
       "      <td>497.03</td>\n",
       "      <td>108.00</td>\n",
       "      <td>1596.00</td>\n",
       "      <td>360.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gasrig_count</th>\n",
       "      <td>12505.0</td>\n",
       "      <td>583.81</td>\n",
       "      <td>70.00</td>\n",
       "      <td>1585.00</td>\n",
       "      <td>388.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalrig_count</th>\n",
       "      <td>17830.0</td>\n",
       "      <td>1387.64</td>\n",
       "      <td>250.00</td>\n",
       "      <td>4521.00</td>\n",
       "      <td>745.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>totalwell_count</th>\n",
       "      <td>17830.0</td>\n",
       "      <td>3412.86</td>\n",
       "      <td>1268.00</td>\n",
       "      <td>8556.00</td>\n",
       "      <td>1458.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilwell_count</th>\n",
       "      <td>17830.0</td>\n",
       "      <td>1367.11</td>\n",
       "      <td>291.00</td>\n",
       "      <td>3945.00</td>\n",
       "      <td>789.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilstoragechange_amount</th>\n",
       "      <td>14908.0</td>\n",
       "      <td>1108.49</td>\n",
       "      <td>-34924.00</td>\n",
       "      <td>48553.00</td>\n",
       "      <td>9725.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oildaysofsupply</th>\n",
       "      <td>14277.0</td>\n",
       "      <td>23.43</td>\n",
       "      <td>16.10</td>\n",
       "      <td>42.00</td>\n",
       "      <td>4.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uspopulation</th>\n",
       "      <td>22579.0</td>\n",
       "      <td>257168.32</td>\n",
       "      <td>180671.00</td>\n",
       "      <td>328330.00</td>\n",
       "      <td>45607.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usemissions</th>\n",
       "      <td>15274.0</td>\n",
       "      <td>2278.29</td>\n",
       "      <td>1978.17</td>\n",
       "      <td>2594.01</td>\n",
       "      <td>155.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>remrig_count</th>\n",
       "      <td>12505.0</td>\n",
       "      <td>7.22</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>42.00</td>\n",
       "      <td>8.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilimports</th>\n",
       "      <td>17830.0</td>\n",
       "      <td>9067.31</td>\n",
       "      <td>3689.51</td>\n",
       "      <td>15217.00</td>\n",
       "      <td>2428.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilexports</th>\n",
       "      <td>17830.0</td>\n",
       "      <td>1834.90</td>\n",
       "      <td>155.77</td>\n",
       "      <td>10134.00</td>\n",
       "      <td>2129.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilconsumption</th>\n",
       "      <td>17830.0</td>\n",
       "      <td>18346.59</td>\n",
       "      <td>13797.00</td>\n",
       "      <td>22820.00</td>\n",
       "      <td>1736.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilproduction</th>\n",
       "      <td>17830.0</td>\n",
       "      <td>7599.39</td>\n",
       "      <td>3813.00</td>\n",
       "      <td>13100.00</td>\n",
       "      <td>1771.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oilstorage</th>\n",
       "      <td>17830.0</td>\n",
       "      <td>832444.45</td>\n",
       "      <td>233035.00</td>\n",
       "      <td>1230105.00</td>\n",
       "      <td>259070.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count       mean        min         max        std\n",
       "oil_price                13081.0      44.56     -36.98      145.31      28.74\n",
       "oilrig_count             12505.0     497.03     108.00     1596.00     360.41\n",
       "gasrig_count             12505.0     583.81      70.00     1585.00     388.89\n",
       "totalrig_count           17830.0    1387.64     250.00     4521.00     745.98\n",
       "totalwell_count          17830.0    3412.86    1268.00     8556.00    1458.55\n",
       "oilwell_count            17830.0    1367.11     291.00     3945.00     789.73\n",
       "oilstoragechange_amount  14908.0    1108.49  -34924.00    48553.00    9725.76\n",
       "oildaysofsupply          14277.0      23.43      16.10       42.00       4.05\n",
       "uspopulation             22579.0  257168.32  180671.00   328330.00   45607.99\n",
       "usemissions              15274.0    2278.29    1978.17     2594.01     155.42\n",
       "remrig_count             12505.0       7.22      -1.00       42.00       8.15\n",
       "oilimports               17830.0    9067.31    3689.51    15217.00    2428.28\n",
       "oilexports               17830.0    1834.90     155.77    10134.00    2129.83\n",
       "oilconsumption           17830.0   18346.59   13797.00    22820.00    1736.62\n",
       "oilproduction            17830.0    7599.39    3813.00    13100.00    1771.79\n",
       "oilstorage               17830.0  832444.45  233035.00  1230105.00  259070.25"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[oil_list].describe().transpose()[['count','mean','min','max','std']].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "b397c5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_list = ['oilimports','oilexports','oilconsumption','oilproduction']\n",
    "df[plot_list].resample('D').mean().fillna(0).plot(figsize=(12,6),title='Oil Final Variables overview');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "91ef35ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['oilimports_amount_x',\n",
    "       'oilimports_amount_y', 'oilexports_amount_x', 'oilexports_amount_y',\n",
    "       'oilproduction_amount_x', 'oilproduction_amount_y',\n",
    "       'oilconsumption_amount_x', 'oilconsumption_amount_y',\n",
    "       'oilstorage_amount_x', 'oilstorage_amount_y'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "908594f1",
   "metadata": {},
   "source": [
    "#### Gas Variables Feature Engineering\n",
    "- Interpolate Variables\n",
    "- Evaluate Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2c1c803f",
   "metadata": {},
   "outputs": [],
   "source": [
    "gas_list = ['gas_price','gasrig_count',\n",
    "       'onshore_count', 'offshore_count', 'gaswell_count', \n",
    "       'gasimports_amount', 'gasexports_amount',\n",
    "       'gasproduction_amount', 'gasconsumption_amount', 'gasstorage_amount']\n",
    "\n",
    "for feature in gas_list:\n",
    "    df[feature] = df[feature].interpolate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "06d2e67a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_list = ['gasimports_amount','gasexports_amount','gasconsumption_amount','gasproduction_amount']\n",
    "df[plot_list].resample('D').mean().fillna(0).plot(figsize=(12,6),title='Gas Final Variables overview');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9712b1d3",
   "metadata": {},
   "source": [
    "--- Pending backcasting to fill missing data in the past --- For now, will just dropna values to continue with project"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2e74ce86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = plt.axes()\n",
    "ax.set_title('Check for Missing Data')\n",
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis',ax=ax);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "28b9fd6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9058, 25)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.dropna()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20bb97f9",
   "metadata": {},
   "source": [
    "#### Select Features for forecasting model\n",
    "- Correlation Plot\n",
    "- Granger Causality Test\n",
    "- Select features for Oil Counts\n",
    "- Select features for Gas Rig Count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "735c378a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "oilrig_count               1.000000\n",
       "oil_price                  0.668226\n",
       "oilwell_count              0.661305\n",
       "onshore_count              0.576134\n",
       "uspopulation               0.566449\n",
       "totalrig_count             0.534911\n",
       "oilstorage                 0.434567\n",
       "oilexports                 0.361687\n",
       "oilproduction              0.331508\n",
       "gasproduction_amount       0.302828\n",
       "gasconsumption_amount      0.272281\n",
       "gasexports_amount          0.237308\n",
       "gasstorage_amount          0.217094\n",
       "oildaysofsupply            0.178339\n",
       "totalwell_count            0.150101\n",
       "remrig_count               0.090129\n",
       "oilstoragechange_amount   -0.001197\n",
       "oilconsumption            -0.159903\n",
       "gas_price                 -0.197755\n",
       "gaswell_count             -0.201369\n",
       "gasrig_count              -0.364592\n",
       "oilimports                -0.379046\n",
       "offshore_count            -0.477353\n",
       "gasimports_amount         -0.525591\n",
       "usemissions               -0.696405\n",
       "Name: oilrig_count, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['oilrig_count'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "84827109",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "gasrig_count               1.000000\n",
       "oilimports                 0.882810\n",
       "gasimports_amount          0.838126\n",
       "gaswell_count              0.738234\n",
       "gas_price                  0.732487\n",
       "totalrig_count             0.591709\n",
       "onshore_count              0.547746\n",
       "remrig_count               0.494310\n",
       "offshore_count             0.471420\n",
       "totalwell_count            0.471094\n",
       "usemissions                0.386313\n",
       "oilconsumption             0.241871\n",
       "oil_price                  0.169827\n",
       "oilstoragechange_amount    0.064229\n",
       "gasstorage_amount         -0.198323\n",
       "oilwell_count             -0.280672\n",
       "oilrig_count              -0.364592\n",
       "gasconsumption_amount     -0.441030\n",
       "oilstorage                -0.457413\n",
       "uspopulation              -0.506626\n",
       "gasexports_amount         -0.622342\n",
       "oildaysofsupply           -0.677238\n",
       "oilexports                -0.733829\n",
       "gasproduction_amount      -0.759580\n",
       "oilproduction             -0.826225\n",
       "Name: gasrig_count, dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()['gasrig_count'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "aac0aac1",
   "metadata": {},
   "outputs": [],
   "source": [
    "oil_list = ['oil_price','oilrig_count',\n",
    "       'totalrig_count', 'oilwell_count',\n",
    "       'onshore_count','uspopulation', 'usemissions','oilimports',\n",
    "       'oilexports','oilproduction', 'oilstorage', 'oilconsumption']\n",
    "\n",
    "\n",
    "gas_list = ['gas_price','gasrig_count', 'remrig_count', 'totalrig_count',\n",
    "       'onshore_count', 'offshore_count', 'gaswell_count', 'totalwell_count',\n",
    "       'gasimports_amount', 'oilimports', 'gasexports_amount',\n",
    "       'gasproduction_amount', 'gasconsumption_amount', 'oilproduction',\n",
    "       'uspopulation', 'usemissions']\n",
    "\n",
    "df_oil = df[oil_list]\n",
    "df_gas = df[gas_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a3b87bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "sns.heatmap(df_oil.corr(),cmap=['red','gray','gray','red'],annot=True,linecolor='white',linewidth=2,center=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fb76987f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,8))\n",
    "sns.heatmap(df_gas.corr(),cmap=['red','gray','gray','red'],annot=True,linecolor='white',linewidth=2,center=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "40645dec",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "***Test Oil Rig Count Versus oil_price***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=22.6380 , p=0.0000  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=22.8690 , p=0.0000  , df=1\n",
      "likelihood ratio test: chi2=22.0313 , p=0.0000  , df=1\n",
      "parameter F test:         F=22.6380 , p=0.0000  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=21.5183 , p=0.0000  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=43.7760 , p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=40.8264 , p=0.0000  , df=2\n",
      "parameter F test:         F=21.5183 , p=0.0000  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=14.5934 , p=0.0000  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=44.8443 , p=0.0000  , df=3\n",
      "likelihood ratio test: chi2=41.7461 , p=0.0000  , df=3\n",
      "parameter F test:         F=14.5934 , p=0.0000  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=10.9579 , p=0.0000  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=45.2158 , p=0.0000  , df=4\n",
      "likelihood ratio test: chi2=42.0587 , p=0.0000  , df=4\n",
      "parameter F test:         F=10.9579 , p=0.0000  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=8.7243  , p=0.0000  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=45.3232 , p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=42.1419 , p=0.0000  , df=5\n",
      "parameter F test:         F=8.7243  , p=0.0000  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus oilrig_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.0000  , p=1.0000  , df_denom=295, df_num=1\n",
      "ssr based chi2 test:   chi2=0.0000  , p=1.0000  , df=1\n",
      "likelihood ratio test: chi2=-0.0000 , p=1.0000  , df=1\n",
      "parameter F test:         F=40465.4294, p=0.0000  , df_denom=295, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.0000  , p=1.0000  , df_denom=293, df_num=2\n",
      "ssr based chi2 test:   chi2=0.0000  , p=1.0000  , df=2\n",
      "likelihood ratio test: chi2=-0.0000 , p=1.0000  , df=2\n",
      "parameter F test:         F=71939.7886, p=0.0000  , df_denom=293, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=0.0000  , p=1.0000  , df_denom=291, df_num=3\n",
      "ssr based chi2 test:   chi2=0.0000  , p=1.0000  , df=3\n",
      "likelihood ratio test: chi2=0.0000  , p=1.0000  , df=3\n",
      "parameter F test:         F=65769.1483, p=0.0000  , df_denom=291, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=-0.0000 , p=1.0000  , df_denom=289, df_num=4\n",
      "ssr based chi2 test:   chi2=-0.0000 , p=1.0000  , df=4\n",
      "likelihood ratio test: chi2=-0.0000 , p=1.0000  , df=4\n",
      "parameter F test:         F=59184.8463, p=0.0000  , df_denom=289, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.0000  , p=1.0000  , df_denom=287, df_num=5\n",
      "ssr based chi2 test:   chi2=0.0000  , p=1.0000  , df=5\n",
      "likelihood ratio test: chi2=0.0000  , p=1.0000  , df=5\n",
      "parameter F test:         F=53364.1871, p=0.0000  , df_denom=287, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus totalrig_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=5.8811  , p=0.0159  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=5.9411  , p=0.0148  , df=1\n",
      "likelihood ratio test: chi2=5.8824  , p=0.0153  , df=1\n",
      "parameter F test:         F=5.8811  , p=0.0159  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=1.2813  , p=0.2792  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=2.6067  , p=0.2716  , df=2\n",
      "likelihood ratio test: chi2=2.5953  , p=0.2732  , df=2\n",
      "parameter F test:         F=1.2813  , p=0.2792  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.6783  , p=0.1718  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=5.1571  , p=0.1606  , df=3\n",
      "likelihood ratio test: chi2=5.1126  , p=0.1637  , df=3\n",
      "parameter F test:         F=1.6783  , p=0.1718  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.7933  , p=0.1302  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=7.3998  , p=0.1162  , df=4\n",
      "likelihood ratio test: chi2=7.3082  , p=0.1205  , df=4\n",
      "parameter F test:         F=1.7933  , p=0.1302  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=2.4883  , p=0.0317  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=12.9270 , p=0.0241  , df=5\n",
      "likelihood ratio test: chi2=12.6500 , p=0.0269  , df=5\n",
      "parameter F test:         F=2.4883  , p=0.0317  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus oilwell_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=1.8294  , p=0.1772  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=1.8481  , p=0.1740  , df=1\n",
      "likelihood ratio test: chi2=1.8424  , p=0.1747  , df=1\n",
      "parameter F test:         F=1.8294  , p=0.1772  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=2.9025  , p=0.0565  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=5.9048  , p=0.0522  , df=2\n",
      "likelihood ratio test: chi2=5.8467  , p=0.0538  , df=2\n",
      "parameter F test:         F=2.9025  , p=0.0565  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=2.0954  , p=0.1010  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=6.4389  , p=0.0921  , df=3\n",
      "likelihood ratio test: chi2=6.3696  , p=0.0949  , df=3\n",
      "parameter F test:         F=2.0954  , p=0.1010  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.5926  , p=0.1764  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=6.5714  , p=0.1603  , df=4\n",
      "likelihood ratio test: chi2=6.4990  , p=0.1649  , df=4\n",
      "parameter F test:         F=1.5926  , p=0.1764  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=1.0738  , p=0.3750  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=5.5786  , p=0.3494  , df=5\n",
      "likelihood ratio test: chi2=5.5261  , p=0.3551  , df=5\n",
      "parameter F test:         F=1.0738  , p=0.3750  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus onshore_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=7.0100  , p=0.0085  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=7.0815  , p=0.0078  , df=1\n",
      "likelihood ratio test: chi2=6.9984  , p=0.0082  , df=1\n",
      "parameter F test:         F=7.0100  , p=0.0085  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=1.1244  , p=0.3262  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=2.2875  , p=0.3186  , df=2\n",
      "likelihood ratio test: chi2=2.2787  , p=0.3200  , df=2\n",
      "parameter F test:         F=1.1244  , p=0.3262  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.9232  , p=0.1259  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=5.9099  , p=0.1161  , df=3\n",
      "likelihood ratio test: chi2=5.8514  , p=0.1191  , df=3\n",
      "parameter F test:         F=1.9232  , p=0.1259  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=2.0358  , p=0.0895  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=8.4005  , p=0.0780  , df=4\n",
      "likelihood ratio test: chi2=8.2827  , p=0.0818  , df=4\n",
      "parameter F test:         F=2.0358  , p=0.0895  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=2.4373  , p=0.0349  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=12.6618 , p=0.0268  , df=5\n",
      "likelihood ratio test: chi2=12.3958 , p=0.0297  , df=5\n",
      "parameter F test:         F=2.4373  , p=0.0349  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus uspopulation***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=53.4335 , p=0.0000  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=53.9788 , p=0.0000  , df=1\n",
      "likelihood ratio test: chi2=49.5971 , p=0.0000  , df=1\n",
      "parameter F test:         F=53.4335 , p=0.0000  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.2268  , p=0.7972  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=0.4613  , p=0.7940  , df=2\n",
      "likelihood ratio test: chi2=0.4610  , p=0.7941  , df=2\n",
      "parameter F test:         F=0.2268  , p=0.7972  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=0.5338  , p=0.6595  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=1.6402  , p=0.6503  , df=3\n",
      "likelihood ratio test: chi2=1.6356  , p=0.6513  , df=3\n",
      "parameter F test:         F=0.5338  , p=0.6595  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.5023  , p=0.7341  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=2.0725  , p=0.7224  , df=4\n",
      "likelihood ratio test: chi2=2.0652  , p=0.7238  , df=4\n",
      "parameter F test:         F=0.5023  , p=0.7341  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.4913  , p=0.7827  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=2.5523  , p=0.7686  , df=5\n",
      "likelihood ratio test: chi2=2.5413  , p=0.7703  , df=5\n",
      "parameter F test:         F=0.4913  , p=0.7827  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus usemissions***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=3.6236  , p=0.0579  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=3.6605  , p=0.0557  , df=1\n",
      "likelihood ratio test: chi2=3.6382  , p=0.0565  , df=1\n",
      "parameter F test:         F=3.6236  , p=0.0579  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=2.3002  , p=0.1021  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=4.6795  , p=0.0964  , df=2\n",
      "likelihood ratio test: chi2=4.6429  , p=0.0981  , df=2\n",
      "parameter F test:         F=2.3002  , p=0.1021  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=0.7674  , p=0.5131  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=2.3583  , p=0.5015  , df=3\n",
      "likelihood ratio test: chi2=2.3489  , p=0.5032  , df=3\n",
      "parameter F test:         F=0.7674  , p=0.5131  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.8412  , p=0.5000  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=3.4710  , p=0.4823  , df=4\n",
      "likelihood ratio test: chi2=3.4506  , p=0.4854  , df=4\n",
      "parameter F test:         F=0.8412  , p=0.5000  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.4969  , p=0.7785  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=2.5816  , p=0.7642  , df=5\n",
      "likelihood ratio test: chi2=2.5703  , p=0.7659  , df=5\n",
      "parameter F test:         F=0.4969  , p=0.7785  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus oilimports***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.0026  , p=0.9595  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=0.0026  , p=0.9593  , df=1\n",
      "likelihood ratio test: chi2=0.0026  , p=0.9593  , df=1\n",
      "parameter F test:         F=0.0026  , p=0.9595  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.1358  , p=0.8731  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=0.2763  , p=0.8710  , df=2\n",
      "likelihood ratio test: chi2=0.2761  , p=0.8710  , df=2\n",
      "parameter F test:         F=0.1358  , p=0.8731  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.4871  , p=0.2182  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=4.5698  , p=0.2062  , df=3\n",
      "likelihood ratio test: chi2=4.5347  , p=0.2092  , df=3\n",
      "parameter F test:         F=1.4871  , p=0.2182  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.5787  , p=0.1800  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=6.5143  , p=0.1639  , df=4\n",
      "likelihood ratio test: chi2=6.4431  , p=0.1684  , df=4\n",
      "parameter F test:         F=1.5787  , p=0.1800  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=2.0039  , p=0.0782  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=10.4106 , p=0.0644  , df=5\n",
      "likelihood ratio test: chi2=10.2299 , p=0.0690  , df=5\n",
      "parameter F test:         F=2.0039  , p=0.0782  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus oilexports***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.5424  , p=0.4620  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=0.5479  , p=0.4592  , df=1\n",
      "likelihood ratio test: chi2=0.5474  , p=0.4594  , df=1\n",
      "parameter F test:         F=0.5424  , p=0.4620  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=2.0830  , p=0.1264  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=4.2375  , p=0.1202  , df=2\n",
      "likelihood ratio test: chi2=4.2074  , p=0.1220  , df=2\n",
      "parameter F test:         F=2.0830  , p=0.1264  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=2.1744  , p=0.0912  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=6.6816  , p=0.0828  , df=3\n",
      "likelihood ratio test: chi2=6.6071  , p=0.0855  , df=3\n",
      "parameter F test:         F=2.1744  , p=0.0912  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.7396  , p=0.1413  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=7.1780  , p=0.1268  , df=4\n",
      "likelihood ratio test: chi2=7.0917  , p=0.1311  , df=4\n",
      "parameter F test:         F=1.7396  , p=0.1413  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=2.0197  , p=0.0760  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=10.4923 , p=0.0624  , df=5\n",
      "likelihood ratio test: chi2=10.3088 , p=0.0669  , df=5\n",
      "parameter F test:         F=2.0197  , p=0.0760  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus oilproduction***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=13.9354 , p=0.0002  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=14.0776 , p=0.0002  , df=1\n",
      "likelihood ratio test: chi2=13.7541 , p=0.0002  , df=1\n",
      "parameter F test:         F=13.9354 , p=0.0002  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=8.3902  , p=0.0003  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=17.0687 , p=0.0002  , df=2\n",
      "likelihood ratio test: chi2=16.5947 , p=0.0002  , df=2\n",
      "parameter F test:         F=8.3902  , p=0.0003  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=5.1623  , p=0.0017  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=15.8634 , p=0.0012  , df=3\n",
      "likelihood ratio test: chi2=15.4516 , p=0.0015  , df=3\n",
      "parameter F test:         F=5.1623  , p=0.0017  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=4.5107  , p=0.0015  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=18.6126 , p=0.0009  , df=4\n",
      "likelihood ratio test: chi2=18.0472 , p=0.0012  , df=4\n",
      "parameter F test:         F=4.5107  , p=0.0015  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=3.5341  , p=0.0041  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=18.3596 , p=0.0025  , df=5\n",
      "likelihood ratio test: chi2=17.8073 , p=0.0032  , df=5\n",
      "parameter F test:         F=3.5341  , p=0.0041  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus oilstorage***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.5598  , p=0.4549  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=0.5656  , p=0.4520  , df=1\n",
      "likelihood ratio test: chi2=0.5650  , p=0.4522  , df=1\n",
      "parameter F test:         F=0.5598  , p=0.4549  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=4.1128  , p=0.0173  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=8.3669  , p=0.0152  , df=2\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "likelihood ratio test: chi2=8.2508  , p=0.0162  , df=2\n",
      "parameter F test:         F=4.1128  , p=0.0173  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=4.7675  , p=0.0029  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=14.6501 , p=0.0021  , df=3\n",
      "likelihood ratio test: chi2=14.2980 , p=0.0025  , df=3\n",
      "parameter F test:         F=4.7675  , p=0.0029  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=3.7597  , p=0.0054  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=15.5136 , p=0.0037  , df=4\n",
      "likelihood ratio test: chi2=15.1182 , p=0.0045  , df=4\n",
      "parameter F test:         F=3.7597  , p=0.0054  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=3.1383  , p=0.0090  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=16.3038 , p=0.0060  , df=5\n",
      "likelihood ratio test: chi2=15.8664 , p=0.0072  , df=5\n",
      "parameter F test:         F=3.1383  , p=0.0090  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Oil Rig Count Versus oilconsumption***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.7395  , p=0.3905  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=0.7470  , p=0.3874  , df=1\n",
      "likelihood ratio test: chi2=0.7461  , p=0.3877  , df=1\n",
      "parameter F test:         F=0.7395  , p=0.3905  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=4.5317  , p=0.0115  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=9.2190  , p=0.0100  , df=2\n",
      "likelihood ratio test: chi2=9.0784  , p=0.0107  , df=2\n",
      "parameter F test:         F=4.5317  , p=0.0115  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=4.7174  , p=0.0031  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=14.4962 , p=0.0023  , df=3\n",
      "likelihood ratio test: chi2=14.1513 , p=0.0027  , df=3\n",
      "parameter F test:         F=4.7174  , p=0.0031  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=4.4911  , p=0.0016  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=18.5319 , p=0.0010  , df=4\n",
      "likelihood ratio test: chi2=17.9712 , p=0.0013  , df=4\n",
      "parameter F test:         F=4.4911  , p=0.0016  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=3.6685  , p=0.0031  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=19.0581 , p=0.0019  , df=5\n",
      "likelihood ratio test: chi2=18.4640 , p=0.0024  , df=5\n",
      "parameter F test:         F=3.6685  , p=0.0031  , df_denom=282, df_num=5\n"
     ]
    }
   ],
   "source": [
    "\"\"\"\n",
    "Granger Causality Test:\n",
    "\n",
    "The Granger causality test is a a hypothesis test to determine if one time series is useful in forecasting another.\n",
    "While it is fairly easy to measure correlations between series - when one goes up the other goes up, and vice versa - \n",
    "it's another thing to observe changes in one series correlated to changes in another after a consistent amount of time. \n",
    "This may indicate the presence of causality, that changes in the first series influenced the behavior of the second. \n",
    "However, it may also be that both series are affected by some third factor, just at different rates. \n",
    "Still, it can be useful if changes in one series can predict upcoming changes in another, whether there is causality or not. \n",
    "In this case we say that one series \"Granger-causes\" another.\n",
    "\n",
    "In the case of two series,  𝑦  and  𝑥 , the null hypothesis is that lagged values of  𝑥  do not explain variations in  𝑦 .\n",
    "In other words, it assumes that  𝑥𝑡  doesn’t Granger-cause  𝑦𝑡 .\n",
    "\n",
    "The stattools grangercausalitytests function offers four tests for granger non-causality of 2 timeseries\n",
    "\n",
    "Null Hupothesis: There is no causality between the variables\n",
    "if p value < 0.05 --> We reject the null hypothesis. We could say there is no sufficient proof to say there is no causality\n",
    "if p value > 0.05 --> We take the null hypothesis. There is sufficient evidence to say there is no causality\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "for value in oil_list:\n",
    "    print(f' \\n***Test Oil Rig Count Versus {value}***')\n",
    "    grangercausalitytests(df_oil.resample('MS').mean().sort_index(ascending=False)[[value,'oilrig_count']],maxlag=5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "3b3fa934",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n",
      "***Test Gas Rig Count Versus gas_price***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=23.7709 , p=0.0000  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=24.0135 , p=0.0000  , df=1\n",
      "likelihood ratio test: chi2=23.0920 , p=0.0000  , df=1\n",
      "parameter F test:         F=23.7709 , p=0.0000  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=12.3670 , p=0.0000  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=25.1590 , p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=24.1467 , p=0.0000  , df=2\n",
      "parameter F test:         F=12.3670 , p=0.0000  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=8.5169  , p=0.0000  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=26.1718 , p=0.0000  , df=3\n",
      "likelihood ratio test: chi2=25.0752 , p=0.0000  , df=3\n",
      "parameter F test:         F=8.5169  , p=0.0000  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=6.9613  , p=0.0000  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=28.7244 , p=0.0000  , df=4\n",
      "likelihood ratio test: chi2=27.4063 , p=0.0000  , df=4\n",
      "parameter F test:         F=6.9613  , p=0.0000  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=8.3460  , p=0.0000  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=43.3578 , p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=40.4348 , p=0.0000  , df=5\n",
      "parameter F test:         F=8.3460  , p=0.0000  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus gasrig_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.0000  , p=1.0000  , df_denom=295, df_num=1\n",
      "ssr based chi2 test:   chi2=0.0000  , p=1.0000  , df=1\n",
      "likelihood ratio test: chi2=-0.0000 , p=1.0000  , df=1\n",
      "parameter F test:         F=64383.5093, p=0.0000  , df_denom=295, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=-0.0000 , p=1.0000  , df_denom=293, df_num=2\n",
      "ssr based chi2 test:   chi2=-0.0000 , p=1.0000  , df=2\n",
      "likelihood ratio test: chi2=-0.0000 , p=1.0000  , df=2\n",
      "parameter F test:         F=129235.6330, p=0.0000  , df_denom=293, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=0.0000  , p=1.0000  , df_denom=291, df_num=3\n",
      "ssr based chi2 test:   chi2=0.0000  , p=1.0000  , df=3\n",
      "likelihood ratio test: chi2=-0.0000 , p=1.0000  , df=3\n",
      "parameter F test:         F=102958.7137, p=0.0000  , df_denom=291, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.0000  , p=1.0000  , df_denom=289, df_num=4\n",
      "ssr based chi2 test:   chi2=0.0000  , p=1.0000  , df=4\n",
      "likelihood ratio test: chi2=0.0000  , p=1.0000  , df=4\n",
      "parameter F test:         F=79187.4921, p=0.0000  , df_denom=289, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.0000  , p=1.0000  , df_denom=287, df_num=5\n",
      "ssr based chi2 test:   chi2=0.0000  , p=1.0000  , df=5\n",
      "likelihood ratio test: chi2=0.0000  , p=1.0000  , df=5\n",
      "parameter F test:         F=74667.7419, p=0.0000  , df_denom=287, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus remrig_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.0553  , p=0.8142  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=0.0559  , p=0.8131  , df=1\n",
      "likelihood ratio test: chi2=0.0559  , p=0.8131  , df=1\n",
      "parameter F test:         F=0.0553  , p=0.8142  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=1.2985  , p=0.2745  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=2.6417  , p=0.2669  , df=2\n",
      "likelihood ratio test: chi2=2.6300  , p=0.2685  , df=2\n",
      "parameter F test:         F=1.2985  , p=0.2745  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.0925  , p=0.3526  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=3.3571  , p=0.3398  , df=3\n",
      "likelihood ratio test: chi2=3.3381  , p=0.3424  , df=3\n",
      "parameter F test:         F=1.0925  , p=0.3526  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.7398  , p=0.5655  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=3.0525  , p=0.5491  , df=4\n",
      "likelihood ratio test: chi2=3.0368  , p=0.5517  , df=4\n",
      "parameter F test:         F=0.7398  , p=0.5655  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=1.1894  , p=0.3145  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=6.1789  , p=0.2892  , df=5\n",
      "likelihood ratio test: chi2=6.1146  , p=0.2952  , df=5\n",
      "parameter F test:         F=1.1894  , p=0.3145  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus totalrig_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=5.8939  , p=0.0158  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=5.9541  , p=0.0147  , df=1\n",
      "likelihood ratio test: chi2=5.8952  , p=0.0152  , df=1\n",
      "parameter F test:         F=5.8939  , p=0.0158  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=1.3652  , p=0.2570  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=2.7772  , p=0.2494  , df=2\n",
      "likelihood ratio test: chi2=2.7643  , p=0.2510  , df=2\n",
      "parameter F test:         F=1.3652  , p=0.2570  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.6611  , p=0.1756  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=5.1043  , p=0.1643  , df=3\n",
      "likelihood ratio test: chi2=5.0607  , p=0.1674  , df=3\n",
      "parameter F test:         F=1.6611  , p=0.1756  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.7390  , p=0.1415  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=7.1757  , p=0.1269  , df=4\n",
      "likelihood ratio test: chi2=7.0895  , p=0.1312  , df=4\n",
      "parameter F test:         F=1.7390  , p=0.1415  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=2.3844  , p=0.0386  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=12.3870 , p=0.0299  , df=5\n",
      "likelihood ratio test: chi2=12.1323 , p=0.0330  , df=5\n",
      "parameter F test:         F=2.3844  , p=0.0386  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus onshore_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=6.9952  , p=0.0086  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=7.0666  , p=0.0079  , df=1\n",
      "likelihood ratio test: chi2=6.9838  , p=0.0082  , df=1\n",
      "parameter F test:         F=6.9952  , p=0.0086  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=1.7902  , p=0.1688  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=3.6420  , p=0.1619  , df=2\n",
      "likelihood ratio test: chi2=3.6197  , p=0.1637  , df=2\n",
      "parameter F test:         F=1.7902  , p=0.1688  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=1.9632  , p=0.1196  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=6.0329  , p=0.1100  , df=3\n",
      "likelihood ratio test: chi2=5.9720  , p=0.1130  , df=3\n",
      "parameter F test:         F=1.9632  , p=0.1196  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.6140  , p=0.1708  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=6.6599  , p=0.1550  , df=4\n",
      "likelihood ratio test: chi2=6.5856  , p=0.1595  , df=4\n",
      "parameter F test:         F=1.6140  , p=0.1708  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=2.0671  , p=0.0697  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=10.7384 , p=0.0568  , df=5\n",
      "likelihood ratio test: chi2=10.5463 , p=0.0612  , df=5\n",
      "parameter F test:         F=2.0671  , p=0.0697  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus offshore_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=1.8938  , p=0.1698  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=1.9131  , p=0.1666  , df=1\n",
      "likelihood ratio test: chi2=1.9070  , p=0.1673  , df=1\n",
      "parameter F test:         F=1.8938  , p=0.1698  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=1.0507  , p=0.3510  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=2.1375  , p=0.3434  , df=2\n",
      "likelihood ratio test: chi2=2.1299  , p=0.3448  , df=2\n",
      "parameter F test:         F=1.0507  , p=0.3510  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=2.7043  , p=0.0457  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=8.3100  , p=0.0400  , df=3\n",
      "likelihood ratio test: chi2=8.1951  , p=0.0421  , df=3\n",
      "parameter F test:         F=2.7043  , p=0.0457  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.2460  , p=0.2916  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=5.1414  , p=0.2731  , df=4\n",
      "likelihood ratio test: chi2=5.0969  , p=0.2775  , df=4\n",
      "parameter F test:         F=1.2460  , p=0.2916  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=1.6413  , p=0.1491  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=8.5268  , p=0.1295  , df=5\n",
      "likelihood ratio test: chi2=8.4051  , p=0.1353  , df=5\n",
      "parameter F test:         F=1.6413  , p=0.1491  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus gaswell_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.1412  , p=0.7074  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=0.1426  , p=0.7057  , df=1\n",
      "likelihood ratio test: chi2=0.1426  , p=0.7057  , df=1\n",
      "parameter F test:         F=0.1412  , p=0.7074  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=18.4046 , p=0.0000  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=37.4417 , p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=35.2562 , p=0.0000  , df=2\n",
      "parameter F test:         F=18.4046 , p=0.0000  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=15.1087 , p=0.0000  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=46.4276 , p=0.0000  , df=3\n",
      "likelihood ratio test: chi2=43.1173 , p=0.0000  , df=3\n",
      "parameter F test:         F=15.1087 , p=0.0000  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=13.8764 , p=0.0000  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=57.2585 , p=0.0000  , df=4\n",
      "likelihood ratio test: chi2=52.3151 , p=0.0000  , df=4\n",
      "parameter F test:         F=13.8764 , p=0.0000  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=13.0330 , p=0.0000  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=67.7069 , p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=60.9127 , p=0.0000  , df=5\n",
      "parameter F test:         F=13.0330 , p=0.0000  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus totalwell_count***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.0213  , p=0.8840  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=0.0215  , p=0.8833  , df=1\n",
      "likelihood ratio test: chi2=0.0215  , p=0.8833  , df=1\n",
      "parameter F test:         F=0.0213  , p=0.8840  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=26.2433 , p=0.0000  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=53.3884 , p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=49.0841 , p=0.0000  , df=2\n",
      "parameter F test:         F=26.2433 , p=0.0000  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=17.9219 , p=0.0000  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=55.0726 , p=0.0000  , df=3\n",
      "likelihood ratio test: chi2=50.4937 , p=0.0000  , df=3\n",
      "parameter F test:         F=17.9219 , p=0.0000  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=15.5114 , p=0.0000  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=64.0051 , p=0.0000  , df=4\n",
      "likelihood ratio test: chi2=57.9084 , p=0.0000  , df=4\n",
      "parameter F test:         F=15.5114 , p=0.0000  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=13.5927 , p=0.0000  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=70.6145 , p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=63.2650 , p=0.0000  , df=5\n",
      "parameter F test:         F=13.5927 , p=0.0000  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus gasimports_amount***\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=21.1051 , p=0.0000  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=21.3204 , p=0.0000  , df=1\n",
      "likelihood ratio test: chi2=20.5899 , p=0.0000  , df=1\n",
      "parameter F test:         F=21.1051 , p=0.0000  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=23.2890 , p=0.0000  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=47.3784 , p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=43.9482 , p=0.0000  , df=2\n",
      "parameter F test:         F=23.2890 , p=0.0000  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=10.8877 , p=0.0000  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=33.4571 , p=0.0000  , df=3\n",
      "likelihood ratio test: chi2=31.6921 , p=0.0000  , df=3\n",
      "parameter F test:         F=10.8877 , p=0.0000  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=9.8007  , p=0.0000  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=40.4407 , p=0.0000  , df=4\n",
      "likelihood ratio test: chi2=37.8907 , p=0.0000  , df=4\n",
      "parameter F test:         F=9.8007  , p=0.0000  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=6.7622  , p=0.0000  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=35.1299 , p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=33.1784 , p=0.0000  , df=5\n",
      "parameter F test:         F=6.7622  , p=0.0000  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus oilimports***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=30.0654 , p=0.0000  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=30.3722 , p=0.0000  , df=1\n",
      "likelihood ratio test: chi2=28.9176 , p=0.0000  , df=1\n",
      "parameter F test:         F=30.0654 , p=0.0000  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=10.7055 , p=0.0000  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=21.7789 , p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=21.0150 , p=0.0000  , df=2\n",
      "parameter F test:         F=10.7055 , p=0.0000  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=6.7254  , p=0.0002  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=20.6666 , p=0.0001  , df=3\n",
      "likelihood ratio test: chi2=19.9748 , p=0.0002  , df=3\n",
      "parameter F test:         F=6.7254  , p=0.0002  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=5.0128  , p=0.0006  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=20.6843 , p=0.0004  , df=4\n",
      "likelihood ratio test: chi2=19.9891 , p=0.0005  , df=4\n",
      "parameter F test:         F=5.0128  , p=0.0006  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=4.4241  , p=0.0007  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=22.9834 , p=0.0003  , df=5\n",
      "likelihood ratio test: chi2=22.1265 , p=0.0005  , df=5\n",
      "parameter F test:         F=4.4241  , p=0.0007  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus gasexports_amount***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.1852  , p=0.6673  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=0.1871  , p=0.6654  , df=1\n",
      "likelihood ratio test: chi2=0.1870  , p=0.6654  , df=1\n",
      "parameter F test:         F=0.1852  , p=0.6673  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.5690  , p=0.5667  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=1.1576  , p=0.5606  , df=2\n",
      "likelihood ratio test: chi2=1.1553  , p=0.5612  , df=2\n",
      "parameter F test:         F=0.5690  , p=0.5667  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=0.2528  , p=0.8593  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=0.7768  , p=0.8550  , df=3\n",
      "likelihood ratio test: chi2=0.7757  , p=0.8553  , df=3\n",
      "parameter F test:         F=0.2528  , p=0.8593  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.3841  , p=0.8199  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=1.5851  , p=0.8115  , df=4\n",
      "likelihood ratio test: chi2=1.5808  , p=0.8122  , df=4\n",
      "parameter F test:         F=0.3841  , p=0.8199  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.3211  , p=0.9001  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=1.6681  , p=0.8929  , df=5\n",
      "likelihood ratio test: chi2=1.6634  , p=0.8935  , df=5\n",
      "parameter F test:         F=0.3211  , p=0.9001  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus gasproduction_amount***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.4411  , p=0.5071  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=0.4456  , p=0.5044  , df=1\n",
      "likelihood ratio test: chi2=0.4453  , p=0.5046  , df=1\n",
      "parameter F test:         F=0.4411  , p=0.5071  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.3723  , p=0.6895  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=0.7575  , p=0.6847  , df=2\n",
      "likelihood ratio test: chi2=0.7565  , p=0.6851  , df=2\n",
      "parameter F test:         F=0.3723  , p=0.6895  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=0.3500  , p=0.7892  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=1.0755  , p=0.7830  , df=3\n",
      "likelihood ratio test: chi2=1.0735  , p=0.7835  , df=3\n",
      "parameter F test:         F=0.3500  , p=0.7892  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.5232  , p=0.7188  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=2.1587  , p=0.7066  , df=4\n",
      "likelihood ratio test: chi2=2.1508  , p=0.7080  , df=4\n",
      "parameter F test:         F=0.5232  , p=0.7188  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.4992  , p=0.7768  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=2.5932  , p=0.7624  , df=5\n",
      "likelihood ratio test: chi2=2.5818  , p=0.7641  , df=5\n",
      "parameter F test:         F=0.4992  , p=0.7768  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus gasconsumption_amount***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=8.0111  , p=0.0050  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=8.0929  , p=0.0044  , df=1\n",
      "likelihood ratio test: chi2=7.9846  , p=0.0047  , df=1\n",
      "parameter F test:         F=8.0111  , p=0.0050  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=24.4240 , p=0.0000  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=49.6873 , p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=45.9319 , p=0.0000  , df=2\n",
      "parameter F test:         F=24.4240 , p=0.0000  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=6.6730  , p=0.0002  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=20.5056 , p=0.0001  , df=3\n",
      "likelihood ratio test: chi2=19.8243 , p=0.0002  , df=3\n",
      "parameter F test:         F=6.6730  , p=0.0002  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=7.6309  , p=0.0000  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=31.4874 , p=0.0000  , df=4\n",
      "likelihood ratio test: chi2=29.9128 , p=0.0000  , df=4\n",
      "parameter F test:         F=7.6309  , p=0.0000  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=7.0607  , p=0.0000  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=36.6803 , p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=34.5596 , p=0.0000  , df=5\n",
      "parameter F test:         F=7.0607  , p=0.0000  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus oilproduction***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=0.1874  , p=0.6654  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=0.1893  , p=0.6635  , df=1\n",
      "likelihood ratio test: chi2=0.1893  , p=0.6635  , df=1\n",
      "parameter F test:         F=0.1874  , p=0.6654  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=0.4484  , p=0.6391  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=0.9122  , p=0.6337  , df=2\n",
      "likelihood ratio test: chi2=0.9108  , p=0.6342  , df=2\n",
      "parameter F test:         F=0.4484  , p=0.6391  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=0.2888  , p=0.8335  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=0.8873  , p=0.8285  , df=3\n",
      "likelihood ratio test: chi2=0.8860  , p=0.8288  , df=3\n",
      "parameter F test:         F=0.2888  , p=0.8335  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=0.2495  , p=0.9099  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=1.0294  , p=0.9053  , df=4\n",
      "likelihood ratio test: chi2=1.0276  , p=0.9056  , df=4\n",
      "parameter F test:         F=0.2495  , p=0.9099  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=0.2072  , p=0.9593  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=1.0763  , p=0.9562  , df=5\n",
      "likelihood ratio test: chi2=1.0743  , p=0.9563  , df=5\n",
      "parameter F test:         F=0.2072  , p=0.9593  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus uspopulation***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=53.5722 , p=0.0000  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=54.1189 , p=0.0000  , df=1\n",
      "likelihood ratio test: chi2=49.7157 , p=0.0000  , df=1\n",
      "parameter F test:         F=53.5722 , p=0.0000  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=1.0217  , p=0.3613  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=2.0784  , p=0.3537  , df=2\n",
      "likelihood ratio test: chi2=2.0712  , p=0.3550  , df=2\n",
      "parameter F test:         F=1.0217  , p=0.3613  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=0.7181  , p=0.5419  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=2.2067  , p=0.5306  , df=3\n",
      "likelihood ratio test: chi2=2.1985  , p=0.5323  , df=3\n",
      "parameter F test:         F=0.7181  , p=0.5419  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=1.2123  , p=0.3057  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=5.0024  , p=0.2871  , df=4\n",
      "likelihood ratio test: chi2=4.9603  , p=0.2914  , df=4\n",
      "parameter F test:         F=1.2123  , p=0.3057  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=1.1497  , p=0.3343  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=5.9726  , p=0.3089  , df=5\n",
      "likelihood ratio test: chi2=5.9126  , p=0.3148  , df=5\n",
      "parameter F test:         F=1.1497  , p=0.3343  , df_denom=282, df_num=5\n",
      " \n",
      "***Test Gas Rig Count Versus usemissions***\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 1\n",
      "ssr based F test:         F=212.7394, p=0.0000  , df_denom=294, df_num=1\n",
      "ssr based chi2 test:   chi2=214.9102, p=0.0000  , df=1\n",
      "likelihood ratio test: chi2=161.6919, p=0.0000  , df=1\n",
      "parameter F test:         F=212.7394, p=0.0000  , df_denom=294, df_num=1\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 2\n",
      "ssr based F test:         F=27.3545 , p=0.0000  , df_denom=291, df_num=2\n",
      "ssr based chi2 test:   chi2=55.6491 , p=0.0000  , df=2\n",
      "likelihood ratio test: chi2=50.9932 , p=0.0000  , df=2\n",
      "parameter F test:         F=27.3545 , p=0.0000  , df_denom=291, df_num=2\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 3\n",
      "ssr based F test:         F=9.7052  , p=0.0000  , df_denom=288, df_num=3\n",
      "ssr based chi2 test:   chi2=29.8232 , p=0.0000  , df=3\n",
      "likelihood ratio test: chi2=28.4101 , p=0.0000  , df=3\n",
      "parameter F test:         F=9.7052  , p=0.0000  , df_denom=288, df_num=3\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 4\n",
      "ssr based F test:         F=9.2960  , p=0.0000  , df_denom=285, df_num=4\n",
      "ssr based chi2 test:   chi2=38.3582 , p=0.0000  , df=4\n",
      "likelihood ratio test: chi2=36.0542 , p=0.0000  , df=4\n",
      "parameter F test:         F=9.2960  , p=0.0000  , df_denom=285, df_num=4\n",
      "\n",
      "Granger Causality\n",
      "number of lags (no zero) 5\n",
      "ssr based F test:         F=7.1664  , p=0.0000  , df_denom=282, df_num=5\n",
      "ssr based chi2 test:   chi2=37.2300 , p=0.0000  , df=5\n",
      "likelihood ratio test: chi2=35.0477 , p=0.0000  , df=5\n",
      "parameter F test:         F=7.1664  , p=0.0000  , df_denom=282, df_num=5\n"
     ]
    }
   ],
   "source": [
    "for value in gas_list:\n",
    "    print(f' \\n***Test Gas Rig Count Versus {value}***')\n",
    "    grangercausalitytests(df_gas.resample('MS').mean().sort_index(ascending=False)[[value,'gasrig_count']],maxlag=5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "cebb5b9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "oil_list = ['oilrig_count',\n",
    "           'oil_price','oilconsumption','oilproduction', 'oilstorage']\n",
    "\n",
    "\n",
    "gas_list = ['gasrig_count', \n",
    "            'gas_price', 'gaswell_count',\n",
    "       'gasimports_amount', 'gasconsumption_amount']\n",
    "\n",
    "df_oil = df[oil_list]\n",
    "df_gas = df[gas_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d912be29",
   "metadata": {},
   "source": [
    "#### Final Pre-processing Activities\n",
    "- Stationarity Test (Augmented Dicky Fuller Test)\n",
    "- Perform Transformations (Apply differencing to features to make sure data is stationary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b274d5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "A stationary time series is one whose properties do not depend on the time at which the series is observed.\n",
    "Thus, time series with trends, or with seasonality, are not stationary — \n",
    "the trend and seasonality will affect the value of the time series at different times. \n",
    "On the other hand, a white noise series is stationary — it does not matter when you observe it, \n",
    "it should look much the same at any point in time.\n",
    "\n",
    "Forecasting models like VAR and VARMA assume stationarity. Therefore, we need to transform data to stationary. ADF test will \n",
    "help us identify whether features are stationary or not.\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "def adf_test(series,title=''):\n",
    "    \"\"\"\n",
    "    Pass in a time series and an optional title, returns an ADF report\n",
    "    \"\"\"\n",
    "    print(f'Augmented Dickey-Fuller Test: {title}')\n",
    "    result = adfuller(series.dropna(),autolag='AIC') # .dropna() handles differenced data\n",
    "    \n",
    "    labels = ['ADF test statistic','p-value','# lags used','# observations']\n",
    "    out = pd.Series(result[0:4],index=labels)\n",
    "\n",
    "    for key,val in result[4].items():\n",
    "        out[f'critical value ({key})']=val\n",
    "        \n",
    "    print(out.to_string())          # .to_string() removes the line \"dtype: float64\"\n",
    "    \n",
    "    if result[1] <= 0.05:\n",
    "        print(\"Strong evidence against the null hypothesis\")\n",
    "        print(\"Reject the null hypothesis\")\n",
    "        print(\"Data has no unit root and is stationary\")\n",
    "    else:\n",
    "        print(\"Weak evidence against the null hypothesis\")\n",
    "        print(\"Fail to reject the null hypothesis\")\n",
    "        print(\"Data has a unit root and is non-stationary\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7833b0fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: oilrig_count\n",
      "ADF test statistic        -2.232796\n",
      "p-value                    0.194559\n",
      "# lags used                1.000000\n",
      "# observations          9056.000000\n",
      "critical value (1%)       -3.431072\n",
      "critical value (5%)       -2.861859\n",
      "critical value (10%)      -2.566940\n",
      "Weak evidence against the null hypothesis\n",
      "Fail to reject the null hypothesis\n",
      "Data has a unit root and is non-stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: oil_price\n",
      "ADF test statistic        -2.213077\n",
      "p-value                    0.201533\n",
      "# lags used               38.000000\n",
      "# observations          9019.000000\n",
      "critical value (1%)       -3.431075\n",
      "critical value (5%)       -2.861861\n",
      "critical value (10%)      -2.566941\n",
      "Weak evidence against the null hypothesis\n",
      "Fail to reject the null hypothesis\n",
      "Data has a unit root and is non-stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: oilconsumption\n",
      "ADF test statistic        -5.155186\n",
      "p-value                    0.000011\n",
      "# lags used               34.000000\n",
      "# observations          9023.000000\n",
      "critical value (1%)       -3.431075\n",
      "critical value (5%)       -2.861860\n",
      "critical value (10%)      -2.566941\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: oilproduction\n",
      "ADF test statistic         0.351383\n",
      "p-value                    0.979583\n",
      "# lags used               35.000000\n",
      "# observations          9022.000000\n",
      "critical value (1%)       -3.431075\n",
      "critical value (5%)       -2.861860\n",
      "critical value (10%)      -2.566941\n",
      "Weak evidence against the null hypothesis\n",
      "Fail to reject the null hypothesis\n",
      "Data has a unit root and is non-stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: oilstorage\n",
      "ADF test statistic        -1.795874\n",
      "p-value                    0.382512\n",
      "# lags used               35.000000\n",
      "# observations          9022.000000\n",
      "critical value (1%)       -3.431075\n",
      "critical value (5%)       -2.861860\n",
      "critical value (10%)      -2.566941\n",
      "Weak evidence against the null hypothesis\n",
      "Fail to reject the null hypothesis\n",
      "Data has a unit root and is non-stationary\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for feature in oil_list:\n",
    "    adf_test(df_oil[feature],title=str(feature))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b5af58f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Differencing: compute the differences between consecutive observations.\n",
    "\"\"\"\n",
    "dfoil_transformed = df_oil.diff(periods=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "98e3eb2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfoil_transformed = dfoil_transformed.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "192c321e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: oilrig_count\n",
      "ADF test statistic     -7.221717e+00\n",
      "p-value                 2.102962e-10\n",
      "# lags used             3.800000e+01\n",
      "# observations          9.017000e+03\n",
      "critical value (1%)    -3.431075e+00\n",
      "critical value (5%)    -2.861861e+00\n",
      "critical value (10%)   -2.566941e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: oil_price\n",
      "ADF test statistic     -1.323356e+01\n",
      "p-value                 9.477736e-25\n",
      "# lags used             3.800000e+01\n",
      "# observations          9.017000e+03\n",
      "critical value (1%)    -3.431075e+00\n",
      "critical value (5%)    -2.861861e+00\n",
      "critical value (10%)   -2.566941e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: oilconsumption\n",
      "ADF test statistic       -20.940280\n",
      "p-value                    0.000000\n",
      "# lags used               37.000000\n",
      "# observations          9018.000000\n",
      "critical value (1%)       -3.431075\n",
      "critical value (5%)       -2.861861\n",
      "critical value (10%)      -2.566941\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: oilproduction\n",
      "ADF test statistic     -1.665408e+01\n",
      "p-value                 1.594798e-29\n",
      "# lags used             3.800000e+01\n",
      "# observations          9.017000e+03\n",
      "critical value (1%)    -3.431075e+00\n",
      "critical value (5%)    -2.861861e+00\n",
      "critical value (10%)   -2.566941e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: oilstorage\n",
      "ADF test statistic     -1.043038e+01\n",
      "p-value                 1.616536e-18\n",
      "# lags used             3.800000e+01\n",
      "# observations          9.017000e+03\n",
      "critical value (1%)    -3.431075e+00\n",
      "critical value (5%)    -2.861861e+00\n",
      "critical value (10%)   -2.566941e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for feature in oil_list:\n",
    "    adf_test(dfoil_transformed[feature],title=str(feature))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2dd15d24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9056, 5)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfoil_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a9af26e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfgas_transformed = df_gas.diff(periods=2)\n",
    "dfgas_transformed = dfgas_transformed.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "558e8af6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Augmented Dickey-Fuller Test: gasrig_count\n",
      "ADF test statistic     -5.861351e+00\n",
      "p-value                 3.404547e-07\n",
      "# lags used             3.800000e+01\n",
      "# observations          9.017000e+03\n",
      "critical value (1%)    -3.431075e+00\n",
      "critical value (5%)    -2.861861e+00\n",
      "critical value (10%)   -2.566941e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: gas_price\n",
      "ADF test statistic     -1.782734e+01\n",
      "p-value                 3.143436e-30\n",
      "# lags used             3.800000e+01\n",
      "# observations          9.017000e+03\n",
      "critical value (1%)    -3.431075e+00\n",
      "critical value (5%)    -2.861861e+00\n",
      "critical value (10%)   -2.566941e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: gaswell_count\n",
      "ADF test statistic     -9.404007e+00\n",
      "p-value                 6.079949e-16\n",
      "# lags used             3.700000e+01\n",
      "# observations          9.018000e+03\n",
      "critical value (1%)    -3.431075e+00\n",
      "critical value (5%)    -2.861861e+00\n",
      "critical value (10%)   -2.566941e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: gasimports_amount\n",
      "ADF test statistic     -1.226054e+01\n",
      "p-value                 9.100547e-23\n",
      "# lags used             3.700000e+01\n",
      "# observations          9.018000e+03\n",
      "critical value (1%)    -3.431075e+00\n",
      "critical value (5%)    -2.861861e+00\n",
      "critical value (10%)   -2.566941e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n",
      "Augmented Dickey-Fuller Test: gasconsumption_amount\n",
      "ADF test statistic     -1.032762e+01\n",
      "p-value                 2.897983e-18\n",
      "# lags used             3.800000e+01\n",
      "# observations          9.017000e+03\n",
      "critical value (1%)    -3.431075e+00\n",
      "critical value (5%)    -2.861861e+00\n",
      "critical value (10%)   -2.566941e+00\n",
      "Strong evidence against the null hypothesis\n",
      "Reject the null hypothesis\n",
      "Data has no unit root and is stationary\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for feature in gas_list:\n",
    "    adf_test(dfgas_transformed[feature],title=str(feature))\n",
    "    print('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "64f266be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9056, 5)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfgas_transformed.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7810f7f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_oil.to_csv('dfoil.csv')\n",
    "dfoil_transformed.to_csv('dfoil_transformed.csv')\n",
    "\n",
    "df_gas.to_csv('dfgas.csv')\n",
    "dfgas_transformed.to_csv('dfgas_transformed.csv')"
   ]
  }
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