Norman_Avila_ML_LR_rainfall.ipynb

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        "<a href=\"https://colab.research.google.com/github/navilaufm/ML_LR_rainfall_MGGT/blob/main/Norman_Avila_ML_LR_rainfall.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "metadata": {
        "id": "lqTyurhsJ7CR"
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      "source": [
        "import pandas as pd"
      ]
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      "source": [
        "data = pd.read_csv('https://github.com/navilaufm/ML_LR_rainfall_MGGT/raw/main/metar_data.csv')\n",
        "##noaa codes por precipitation https://www.nodc.noaa.gov/archive/arc0021/0002199/1.1/data/0-data/HTML/WMO-CODE/WMO4677.HTM"
      ],
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        "id": "BDlxheblJ_UV"
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      "outputs": []
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        "data.head()"
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              "                       dia    day_date  tmp_9am  dew_9am  hum_9am  wnd_9am  \\\n",
              "0  2001-01-01 00:00:00.000  2001-01-01     17.0     10.0       59      360   \n",
              "1  2001-01-02 00:00:00.000  2001-01-02     18.2     13.2       73       20   \n",
              "2  2001-01-03 00:00:00.000  2001-01-03     18.0     10.5       58      360   \n",
              "3  2001-01-04 00:00:00.000  2001-01-04     15.2      9.2       61      360   \n",
              "4  2001-01-05 00:00:00.000  2001-01-05     15.0     10.0       67      360   \n",
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      "source": [
        "## create a date field to get month, day, year\n",
        "data['day_date'] = pd.to_datetime(data['day_date'])\n",
        "data"
      ],
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          "height": 661
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        "id": "18SnUDO4WIPf",
        "outputId": "71db20bc-ae69-4064-9d8d-1de94b01bcff"
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              "                          dia   day_date  tmp_9am  dew_9am  hum_9am  wnd_9am  \\\n",
              "0     2001-01-01 00:00:00.000 2001-01-01     17.0     10.0       59      360   \n",
              "1     2001-01-02 00:00:00.000 2001-01-02     18.2     13.2       73       20   \n",
              "2     2001-01-03 00:00:00.000 2001-01-03     18.0     10.5       58      360   \n",
              "3     2001-01-04 00:00:00.000 2001-01-04     15.2      9.2       61      360   \n",
              "4     2001-01-05 00:00:00.000 2001-01-05     15.0     10.0       67      360   \n",
              "...                       ...        ...      ...      ...      ...      ...   \n",
              "6775  2020-12-27 00:00:00.000 2020-12-27     14.0     11.0       79       40   \n",
              "6776  2020-12-28 00:00:00.000 2020-12-28     14.0     10.0       71       30   \n",
              "6777  2020-12-29 00:00:00.000 2020-12-29     16.0     10.0       63      360   \n",
              "6778  2020-12-30 00:00:00.000 2020-12-30     17.0     13.0       76      360   \n",
              "6779  2020-12-31 00:00:00.000 2020-12-31     19.0     15.0       79      180   \n",
              "\n",
              "     wdd_9am  spd_9am  alt_9am  cig_9am  ...  dew_10am  hum_10am  wnd_10am  \\\n",
              "0          N      6.2   1026.1     6000  ...      11.0      58.0     360.0   \n",
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              "\n",
              "      wdd_10am spd_10am  alt_10am  cig_10am  pcp_hrs  pcp  pcp_yesterday  \n",
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              "...        ...      ...       ...       ...      ...  ...            ...  \n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2001-01-02 00:00:00.000</td>\n",
              "      <td>2001-01-02</td>\n",
              "      <td>18.2</td>\n",
              "      <td>13.2</td>\n",
              "      <td>73</td>\n",
              "      <td>20</td>\n",
              "      <td>N</td>\n",
              "      <td>9.3</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>22000</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>N</td>\n",
              "      <td>7.2</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2001-01-03 00:00:00.000</td>\n",
              "      <td>2001-01-03</td>\n",
              "      <td>18.0</td>\n",
              "      <td>10.5</td>\n",
              "      <td>58</td>\n",
              "      <td>360</td>\n",
              "      <td>N</td>\n",
              "      <td>11.3</td>\n",
              "      <td>1028.1</td>\n",
              "      <td>22000</td>\n",
              "      <td>...</td>\n",
              "      <td>10.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>N</td>\n",
              "      <td>9.3</td>\n",
              "      <td>1028.1</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2001-01-04 00:00:00.000</td>\n",
              "      <td>2001-01-04</td>\n",
              "      <td>15.2</td>\n",
              "      <td>9.2</td>\n",
              "      <td>61</td>\n",
              "      <td>360</td>\n",
              "      <td>N</td>\n",
              "      <td>12.9</td>\n",
              "      <td>1029.1</td>\n",
              "      <td>22000</td>\n",
              "      <td>...</td>\n",
              "      <td>9.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>N</td>\n",
              "      <td>7.2</td>\n",
              "      <td>1029.1</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2001-01-05 00:00:00.000</td>\n",
              "      <td>2001-01-05</td>\n",
              "      <td>15.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>67</td>\n",
              "      <td>360</td>\n",
              "      <td>N</td>\n",
              "      <td>9.3</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>22000</td>\n",
              "      <td>...</td>\n",
              "      <td>10.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>N</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>22000.0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>6775</th>\n",
              "      <td>2020-12-27 00:00:00.000</td>\n",
              "      <td>2020-12-27</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>79</td>\n",
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              "      <td>6.2</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>366</td>\n",
              "      <td>...</td>\n",
              "      <td>11.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>N</td>\n",
              "      <td>6.2</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6776</th>\n",
              "      <td>2020-12-28 00:00:00.000</td>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>71</td>\n",
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              "      <td>N</td>\n",
              "      <td>7.2</td>\n",
              "      <td>1028.0</td>\n",
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              "      <td>10.0</td>\n",
              "      <td>63.0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>6777</th>\n",
              "      <td>2020-12-29 00:00:00.000</td>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>16.0</td>\n",
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              "      <td>11.0</td>\n",
              "      <td>69.0</td>\n",
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              "      <td>N</td>\n",
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              "      <td>1028.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6778</th>\n",
              "      <td>2020-12-30 00:00:00.000</td>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>17.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>76</td>\n",
              "      <td>360</td>\n",
              "      <td>N</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>99999</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>-99.0</td>\n",
              "      <td>CALM</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6779</th>\n",
              "      <td>2020-12-31 00:00:00.000</td>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>19.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>79</td>\n",
              "      <td>180</td>\n",
              "      <td>S</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>2438</td>\n",
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              "      <td>15.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>180.0</td>\n",
              "      <td>S</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>549.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6780 rows × 21 columns</p>\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['year'] = data['day_date'].dt.year\n",
        "data['month'] = data['day_date'].dt.month\n",
        "data['day'] = data['day_date'].dt.day\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "Stmeo1CgXMhd",
        "outputId": "625a10ba-5bc1-405f-ca5c-760daf72855a"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          dia   day_date  tmp_9am  dew_9am  hum_9am  wnd_9am  \\\n",
              "0     2001-01-01 00:00:00.000 2001-01-01     17.0     10.0       59      360   \n",
              "1     2001-01-02 00:00:00.000 2001-01-02     18.2     13.2       73       20   \n",
              "2     2001-01-03 00:00:00.000 2001-01-03     18.0     10.5       58      360   \n",
              "3     2001-01-04 00:00:00.000 2001-01-04     15.2      9.2       61      360   \n",
              "4     2001-01-05 00:00:00.000 2001-01-05     15.0     10.0       67      360   \n",
              "...                       ...        ...      ...      ...      ...      ...   \n",
              "6775  2020-12-27 00:00:00.000 2020-12-27     14.0     11.0       79       40   \n",
              "6776  2020-12-28 00:00:00.000 2020-12-28     14.0     10.0       71       30   \n",
              "6777  2020-12-29 00:00:00.000 2020-12-29     16.0     10.0       63      360   \n",
              "6778  2020-12-30 00:00:00.000 2020-12-30     17.0     13.0       76      360   \n",
              "6779  2020-12-31 00:00:00.000 2020-12-31     19.0     15.0       79      180   \n",
              "\n",
              "     wdd_9am  spd_9am  alt_9am  cig_9am  ...  wdd_10am  spd_10am  alt_10am  \\\n",
              "0          N      6.2   1026.1     6000  ...         N       5.1    1026.1   \n",
              "1          N      9.3   1027.1    22000  ...         N       7.2    1027.1   \n",
              "2          N     11.3   1028.1    22000  ...         N       9.3    1028.1   \n",
              "3          N     12.9   1029.1    22000  ...         N       7.2    1029.1   \n",
              "4          N      9.3   1027.1    22000  ...         N       5.1    1027.1   \n",
              "...      ...      ...      ...      ...  ...       ...       ...       ...   \n",
              "6775       N      6.2   1027.0      366  ...         N       6.2    1027.0   \n",
              "6776       N      7.2   1028.0      549  ...         N       5.1    1028.0   \n",
              "6777       N      8.2   1028.0    99999  ...         N       4.1    1028.0   \n",
              "6778       N      2.1   1025.0    99999  ...      CALM       2.1    1025.0   \n",
              "6779       S      3.1   1024.0     2438  ...         S       0.0    1024.0   \n",
              "\n",
              "      cig_10am pcp_hrs  pcp  pcp_yesterday  year  month  day  \n",
              "0      22000.0       0    0              0  2001      1    1  \n",
              "1      22000.0       0    0              0  2001      1    2  \n",
              "2      22000.0       0    0              0  2001      1    3  \n",
              "3      22000.0       0    0              0  2001      1    4  \n",
              "4      22000.0       0    0              0  2001      1    5  \n",
              "...        ...     ...  ...            ...   ...    ...  ...  \n",
              "6775   99999.0       0    0              0  2020     12   27  \n",
              "6776   99999.0       0    0              1  2020     12   28  \n",
              "6777   99999.0       0    0              0  2020     12   29  \n",
              "6778   99999.0       0    0              0  2020     12   30  \n",
              "6779     549.0       4    1              0  2020     12   31  \n",
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              "      <td>0</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2001-01-03 00:00:00.000</td>\n",
              "      <td>2001-01-03</td>\n",
              "      <td>18.0</td>\n",
              "      <td>10.5</td>\n",
              "      <td>58</td>\n",
              "      <td>360</td>\n",
              "      <td>N</td>\n",
              "      <td>11.3</td>\n",
              "      <td>1028.1</td>\n",
              "      <td>22000</td>\n",
              "      <td>...</td>\n",
              "      <td>N</td>\n",
              "      <td>9.3</td>\n",
              "      <td>1028.1</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2001-01-04 00:00:00.000</td>\n",
              "      <td>2001-01-04</td>\n",
              "      <td>15.2</td>\n",
              "      <td>9.2</td>\n",
              "      <td>61</td>\n",
              "      <td>360</td>\n",
              "      <td>N</td>\n",
              "      <td>12.9</td>\n",
              "      <td>1029.1</td>\n",
              "      <td>22000</td>\n",
              "      <td>...</td>\n",
              "      <td>N</td>\n",
              "      <td>7.2</td>\n",
              "      <td>1029.1</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2001-01-05 00:00:00.000</td>\n",
              "      <td>2001-01-05</td>\n",
              "      <td>15.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>67</td>\n",
              "      <td>360</td>\n",
              "      <td>N</td>\n",
              "      <td>9.3</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>22000</td>\n",
              "      <td>...</td>\n",
              "      <td>N</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2001</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6775</th>\n",
              "      <td>2020-12-27 00:00:00.000</td>\n",
              "      <td>2020-12-27</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>79</td>\n",
              "      <td>40</td>\n",
              "      <td>N</td>\n",
              "      <td>6.2</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>366</td>\n",
              "      <td>...</td>\n",
              "      <td>N</td>\n",
              "      <td>6.2</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>12</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6776</th>\n",
              "      <td>2020-12-28 00:00:00.000</td>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>14.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>71</td>\n",
              "      <td>30</td>\n",
              "      <td>N</td>\n",
              "      <td>7.2</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>549</td>\n",
              "      <td>...</td>\n",
              "      <td>N</td>\n",
              "      <td>5.1</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>2020</td>\n",
              "      <td>12</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6777</th>\n",
              "      <td>2020-12-29 00:00:00.000</td>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>63</td>\n",
              "      <td>360</td>\n",
              "      <td>N</td>\n",
              "      <td>8.2</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>99999</td>\n",
              "      <td>...</td>\n",
              "      <td>N</td>\n",
              "      <td>4.1</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>12</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6778</th>\n",
              "      <td>2020-12-30 00:00:00.000</td>\n",
              "      <td>2020-12-30</td>\n",
              "      <td>17.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>76</td>\n",
              "      <td>360</td>\n",
              "      <td>N</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>99999</td>\n",
              "      <td>...</td>\n",
              "      <td>CALM</td>\n",
              "      <td>2.1</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>12</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6779</th>\n",
              "      <td>2020-12-31 00:00:00.000</td>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>19.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>79</td>\n",
              "      <td>180</td>\n",
              "      <td>S</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>2438</td>\n",
              "      <td>...</td>\n",
              "      <td>S</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>549.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2020</td>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6780 rows × 24 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d1b01f48-6afe-4b1c-b0f4-9249cffa28f9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d1b01f48-6afe-4b1c-b0f4-9249cffa28f9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d1b01f48-6afe-4b1c-b0f4-9249cffa28f9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##create a new field with light wind speed instead of using calm\n",
        "data['spl_9am'] = (data.spd_9am<4).astype(int)\n",
        "data['spl_10am'] = (data.spd_10am<4).astype(int)\n",
        "data.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 993
        },
        "id": "75oBvtxwr6_c",
        "outputId": "0bbda986-62a9-4c0f-d862-dfef718b74b6"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  0                        1     \\\n",
              "dia            2001-01-01 00:00:00.000  2001-01-02 00:00:00.000   \n",
              "day_date           2001-01-01 00:00:00      2001-01-02 00:00:00   \n",
              "tmp_9am                           17.0                     18.2   \n",
              "dew_9am                           10.0                     13.2   \n",
              "hum_9am                             59                       73   \n",
              "wnd_9am                            360                       20   \n",
              "wdd_9am                              N                        N   \n",
              "spd_9am                            6.2                      9.3   \n",
              "alt_9am                         1026.1                   1027.1   \n",
              "cig_9am                           6000                    22000   \n",
              "tmp_10am                          19.0                     19.0   \n",
              "dew_10am                          11.0                     13.0   \n",
              "hum_10am                          58.0                     68.0   \n",
              "wnd_10am                         360.0                    360.0   \n",
              "wdd_10am                             N                        N   \n",
              "spd_10am                           5.1                      7.2   \n",
              "alt_10am                        1026.1                   1027.1   \n",
              "cig_10am                       22000.0                  22000.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        0   \n",
              "year                              2001                     2001   \n",
              "month                                1                        1   \n",
              "day                                  1                        2   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "\n",
              "                                  2                        3     \\\n",
              "dia            2001-01-03 00:00:00.000  2001-01-04 00:00:00.000   \n",
              "day_date           2001-01-03 00:00:00      2001-01-04 00:00:00   \n",
              "tmp_9am                           18.0                     15.2   \n",
              "dew_9am                           10.5                      9.2   \n",
              "hum_9am                             58                       61   \n",
              "wnd_9am                            360                      360   \n",
              "wdd_9am                              N                        N   \n",
              "spd_9am                           11.3                     12.9   \n",
              "alt_9am                         1028.1                   1029.1   \n",
              "cig_9am                          22000                    22000   \n",
              "tmp_10am                          18.0                     18.0   \n",
              "dew_10am                          10.0                      9.0   \n",
              "hum_10am                          56.0                     50.0   \n",
              "wnd_10am                         360.0                    360.0   \n",
              "wdd_10am                             N                        N   \n",
              "spd_10am                           9.3                      7.2   \n",
              "alt_10am                        1028.1                   1029.1   \n",
              "cig_10am                       22000.0                  22000.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        0   \n",
              "year                              2001                     2001   \n",
              "month                                1                        1   \n",
              "day                                  3                        4   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "\n",
              "                                  4                        5     \\\n",
              "dia            2001-01-05 00:00:00.000  2001-01-06 00:00:00.000   \n",
              "day_date           2001-01-05 00:00:00      2001-01-06 00:00:00   \n",
              "tmp_9am                           15.0                     14.8   \n",
              "dew_9am                           10.0                     11.2   \n",
              "hum_9am                             67                       76   \n",
              "wnd_9am                            360                       30   \n",
              "wdd_9am                              N                        N   \n",
              "spd_9am                            9.3                      5.1   \n",
              "alt_9am                         1027.1                   1025.1   \n",
              "cig_9am                          22000                    22000   \n",
              "tmp_10am                          17.0                     17.0   \n",
              "dew_10am                          10.0                     12.0   \n",
              "hum_10am                          59.0                     71.0   \n",
              "wnd_10am                         360.0                    330.0   \n",
              "wdd_10am                             N                        N   \n",
              "spd_10am                           5.1                      4.1   \n",
              "alt_10am                        1027.1                   1024.0   \n",
              "cig_10am                       22000.0                  22000.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        0   \n",
              "year                              2001                     2001   \n",
              "month                                1                        1   \n",
              "day                                  5                        6   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "\n",
              "                                  6                        7     \\\n",
              "dia            2001-01-07 00:00:00.000  2001-01-08 00:00:00.000   \n",
              "day_date           2001-01-07 00:00:00      2001-01-08 00:00:00   \n",
              "tmp_9am                           16.8                     17.8   \n",
              "dew_9am                           11.0                     13.2   \n",
              "hum_9am                             65                       74   \n",
              "wnd_9am                            180                      360   \n",
              "wdd_9am                              S                        N   \n",
              "spd_9am                            2.1                      2.1   \n",
              "alt_9am                         1023.0                   1024.0   \n",
              "cig_9am                          22000                    22000   \n",
              "tmp_10am                          19.0                     20.0   \n",
              "dew_10am                          12.0                      9.0   \n",
              "hum_10am                          63.0                     45.0   \n",
              "wnd_10am                         210.0                    360.0   \n",
              "wdd_10am                             S                        N   \n",
              "spd_10am                           3.1                      1.0   \n",
              "alt_10am                        1023.0                   1024.0   \n",
              "cig_10am                       22000.0                  22000.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        0   \n",
              "year                              2001                     2001   \n",
              "month                                1                        1   \n",
              "day                                  7                        8   \n",
              "spl_9am                              1                        1   \n",
              "spl_10am                             1                        1   \n",
              "\n",
              "                                  8                        9     ...  \\\n",
              "dia            2001-01-09 00:00:00.000  2001-01-10 00:00:00.000  ...   \n",
              "day_date           2001-01-09 00:00:00      2001-01-10 00:00:00  ...   \n",
              "tmp_9am                           16.0                     16.6  ...   \n",
              "dew_9am                           11.0                      9.3  ...   \n",
              "hum_9am                             69                       56  ...   \n",
              "wnd_9am                            360                      360  ...   \n",
              "wdd_9am                              N                        N  ...   \n",
              "spd_9am                            9.3                      7.2  ...   \n",
              "alt_9am                         1026.1                   1027.1  ...   \n",
              "cig_9am                          22000                    22000  ...   \n",
              "tmp_10am                          18.0                     18.0  ...   \n",
              "dew_10am                          11.0                     10.0  ...   \n",
              "hum_10am                          61.0                     56.0  ...   \n",
              "wnd_10am                         360.0                    360.0  ...   \n",
              "wdd_10am                             N                        N  ...   \n",
              "spd_10am                           5.1                      6.2  ...   \n",
              "alt_10am                        1026.1                   1026.1  ...   \n",
              "cig_10am                       22000.0                  22000.0  ...   \n",
              "pcp_hrs                              0                        0  ...   \n",
              "pcp                                  0                        0  ...   \n",
              "pcp_yesterday                        0                        0  ...   \n",
              "year                              2001                     2001  ...   \n",
              "month                                1                        1  ...   \n",
              "day                                  9                       10  ...   \n",
              "spl_9am                              0                        0  ...   \n",
              "spl_10am                             0                        0  ...   \n",
              "\n",
              "                                  6770                     6771  \\\n",
              "dia            2020-12-22 00:00:00.000  2020-12-23 00:00:00.000   \n",
              "day_date           2020-12-22 00:00:00      2020-12-23 00:00:00   \n",
              "tmp_9am                           18.0                     19.0   \n",
              "dew_9am                           14.0                     13.0   \n",
              "hum_9am                             78                       68   \n",
              "wnd_9am                            360                      360   \n",
              "wdd_9am                              N                        N   \n",
              "spd_9am                            7.2                      7.2   \n",
              "alt_9am                         1028.0                   1028.0   \n",
              "cig_9am                          99999                    99999   \n",
              "tmp_10am                          20.0                     21.0   \n",
              "dew_10am                          13.0                     14.0   \n",
              "hum_10am                          65.0                     67.0   \n",
              "wnd_10am                         360.0                    360.0   \n",
              "wdd_10am                             N                        N   \n",
              "spd_10am                           8.2                      5.1   \n",
              "alt_10am                        1028.0                   1028.0   \n",
              "cig_10am                       99999.0                  99999.0   \n",
              "pcp_hrs                              1                        0   \n",
              "pcp                                  1                        0   \n",
              "pcp_yesterday                        0                        1   \n",
              "year                              2020                     2020   \n",
              "month                               12                       12   \n",
              "day                                 22                       23   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "\n",
              "                                  6772                     6773  \\\n",
              "dia            2020-12-24 00:00:00.000  2020-12-25 00:00:00.000   \n",
              "day_date           2020-12-24 00:00:00      2020-12-25 00:00:00   \n",
              "tmp_9am                           19.0                     19.0   \n",
              "dew_9am                           14.0                     11.0   \n",
              "hum_9am                             74                       58   \n",
              "wnd_9am                            180                       30   \n",
              "wdd_9am                              S                        N   \n",
              "spd_9am                            2.1                     14.4   \n",
              "alt_9am                         1027.0                   1028.0   \n",
              "cig_9am                          99999                    99999   \n",
              "tmp_10am                          22.0                     19.0   \n",
              "dew_10am                          18.0                     10.0   \n",
              "hum_10am                          82.0                     53.0   \n",
              "wnd_10am                         200.0                     20.0   \n",
              "wdd_10am                             S                        N   \n",
              "spd_10am                           3.1                     10.3   \n",
              "alt_10am                        1026.0                   1028.0   \n",
              "cig_10am                       99999.0                  99999.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        1   \n",
              "year                              2020                     2020   \n",
              "month                               12                       12   \n",
              "day                                 24                       25   \n",
              "spl_9am                              1                        0   \n",
              "spl_10am                             1                        0   \n",
              "\n",
              "                                  6774                     6775  \\\n",
              "dia            2020-12-26 00:00:00.000  2020-12-27 00:00:00.000   \n",
              "day_date           2020-12-26 00:00:00      2020-12-27 00:00:00   \n",
              "tmp_9am                           14.0                     14.0   \n",
              "dew_9am                            9.0                     11.0   \n",
              "hum_9am                             64                       79   \n",
              "wnd_9am                            360                       40   \n",
              "wdd_9am                              N                        N   \n",
              "spd_9am                            8.2                      6.2   \n",
              "alt_9am                         1028.0                   1027.0   \n",
              "cig_9am                          99999                      366   \n",
              "tmp_10am                          16.0                     16.0   \n",
              "dew_10am                           9.0                     11.0   \n",
              "hum_10am                          56.0                     69.0   \n",
              "wnd_10am                         360.0                     40.0   \n",
              "wdd_10am                             N                        N   \n",
              "spd_10am                           8.2                      6.2   \n",
              "alt_10am                        1028.0                   1027.0   \n",
              "cig_10am                       99999.0                  99999.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        0   \n",
              "year                              2020                     2020   \n",
              "month                               12                       12   \n",
              "day                                 26                       27   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "\n",
              "                                  6776                     6777  \\\n",
              "dia            2020-12-28 00:00:00.000  2020-12-29 00:00:00.000   \n",
              "day_date           2020-12-28 00:00:00      2020-12-29 00:00:00   \n",
              "tmp_9am                           14.0                     16.0   \n",
              "dew_9am                           10.0                     10.0   \n",
              "hum_9am                             71                       63   \n",
              "wnd_9am                             30                      360   \n",
              "wdd_9am                              N                        N   \n",
              "spd_9am                            7.2                      8.2   \n",
              "alt_9am                         1028.0                   1028.0   \n",
              "cig_9am                            549                    99999   \n",
              "tmp_10am                          16.0                     16.0   \n",
              "dew_10am                          10.0                     11.0   \n",
              "hum_10am                          63.0                     69.0   \n",
              "wnd_10am                          20.0                    360.0   \n",
              "wdd_10am                             N                        N   \n",
              "spd_10am                           5.1                      4.1   \n",
              "alt_10am                        1028.0                   1028.0   \n",
              "cig_10am                       99999.0                  99999.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        1                        0   \n",
              "year                              2020                     2020   \n",
              "month                               12                       12   \n",
              "day                                 28                       29   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "\n",
              "                                  6778                     6779  \n",
              "dia            2020-12-30 00:00:00.000  2020-12-31 00:00:00.000  \n",
              "day_date           2020-12-30 00:00:00      2020-12-31 00:00:00  \n",
              "tmp_9am                           17.0                     19.0  \n",
              "dew_9am                           13.0                     15.0  \n",
              "hum_9am                             76                       79  \n",
              "wnd_9am                            360                      180  \n",
              "wdd_9am                              N                        S  \n",
              "spd_9am                            2.1                      3.1  \n",
              "alt_9am                         1025.0                   1024.0  \n",
              "cig_9am                          99999                     2438  \n",
              "tmp_10am                          20.0                     19.0  \n",
              "dew_10am                          13.0                     15.0  \n",
              "hum_10am                          65.0                     79.0  \n",
              "wnd_10am                         -99.0                    180.0  \n",
              "wdd_10am                          CALM                        S  \n",
              "spd_10am                           2.1                      0.0  \n",
              "alt_10am                        1025.0                   1024.0  \n",
              "cig_10am                       99999.0                    549.0  \n",
              "pcp_hrs                              0                        4  \n",
              "pcp                                  0                        1  \n",
              "pcp_yesterday                        0                        0  \n",
              "year                              2020                     2020  \n",
              "month                               12                       12  \n",
              "day                                 30                       31  \n",
              "spl_9am                              1                        1  \n",
              "spl_10am                             1                        1  \n",
              "\n",
              "[26 rows x 6780 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a5def282-be66-491d-bbcd-914f3c9a651e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>6770</th>\n",
              "      <th>6771</th>\n",
              "      <th>6772</th>\n",
              "      <th>6773</th>\n",
              "      <th>6774</th>\n",
              "      <th>6775</th>\n",
              "      <th>6776</th>\n",
              "      <th>6777</th>\n",
              "      <th>6778</th>\n",
              "      <th>6779</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>dia</th>\n",
              "      <td>2001-01-01 00:00:00.000</td>\n",
              "      <td>2001-01-02 00:00:00.000</td>\n",
              "      <td>2001-01-03 00:00:00.000</td>\n",
              "      <td>2001-01-04 00:00:00.000</td>\n",
              "      <td>2001-01-05 00:00:00.000</td>\n",
              "      <td>2001-01-06 00:00:00.000</td>\n",
              "      <td>2001-01-07 00:00:00.000</td>\n",
              "      <td>2001-01-08 00:00:00.000</td>\n",
              "      <td>2001-01-09 00:00:00.000</td>\n",
              "      <td>2001-01-10 00:00:00.000</td>\n",
              "      <td>...</td>\n",
              "      <td>2020-12-22 00:00:00.000</td>\n",
              "      <td>2020-12-23 00:00:00.000</td>\n",
              "      <td>2020-12-24 00:00:00.000</td>\n",
              "      <td>2020-12-25 00:00:00.000</td>\n",
              "      <td>2020-12-26 00:00:00.000</td>\n",
              "      <td>2020-12-27 00:00:00.000</td>\n",
              "      <td>2020-12-28 00:00:00.000</td>\n",
              "      <td>2020-12-29 00:00:00.000</td>\n",
              "      <td>2020-12-30 00:00:00.000</td>\n",
              "      <td>2020-12-31 00:00:00.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day_date</th>\n",
              "      <td>2001-01-01 00:00:00</td>\n",
              "      <td>2001-01-02 00:00:00</td>\n",
              "      <td>2001-01-03 00:00:00</td>\n",
              "      <td>2001-01-04 00:00:00</td>\n",
              "      <td>2001-01-05 00:00:00</td>\n",
              "      <td>2001-01-06 00:00:00</td>\n",
              "      <td>2001-01-07 00:00:00</td>\n",
              "      <td>2001-01-08 00:00:00</td>\n",
              "      <td>2001-01-09 00:00:00</td>\n",
              "      <td>2001-01-10 00:00:00</td>\n",
              "      <td>...</td>\n",
              "      <td>2020-12-22 00:00:00</td>\n",
              "      <td>2020-12-23 00:00:00</td>\n",
              "      <td>2020-12-24 00:00:00</td>\n",
              "      <td>2020-12-25 00:00:00</td>\n",
              "      <td>2020-12-26 00:00:00</td>\n",
              "      <td>2020-12-27 00:00:00</td>\n",
              "      <td>2020-12-28 00:00:00</td>\n",
              "      <td>2020-12-29 00:00:00</td>\n",
              "      <td>2020-12-30 00:00:00</td>\n",
              "      <td>2020-12-31 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tmp_9am</th>\n",
              "      <td>17.0</td>\n",
              "      <td>18.2</td>\n",
              "      <td>18.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.8</td>\n",
              "      <td>16.8</td>\n",
              "      <td>17.8</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.6</td>\n",
              "      <td>...</td>\n",
              "      <td>18.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dew_9am</th>\n",
              "      <td>10.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>10.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>...</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hum_9am</th>\n",
              "      <td>59</td>\n",
              "      <td>73</td>\n",
              "      <td>58</td>\n",
              "      <td>61</td>\n",
              "      <td>67</td>\n",
              "      <td>76</td>\n",
              "      <td>65</td>\n",
              "      <td>74</td>\n",
              "      <td>69</td>\n",
              "      <td>56</td>\n",
              "      <td>...</td>\n",
              "      <td>78</td>\n",
              "      <td>68</td>\n",
              "      <td>74</td>\n",
              "      <td>58</td>\n",
              "      <td>64</td>\n",
              "      <td>79</td>\n",
              "      <td>71</td>\n",
              "      <td>63</td>\n",
              "      <td>76</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wnd_9am</th>\n",
              "      <td>360</td>\n",
              "      <td>20</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>30</td>\n",
              "      <td>180</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>...</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>180</td>\n",
              "      <td>30</td>\n",
              "      <td>360</td>\n",
              "      <td>40</td>\n",
              "      <td>30</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am</th>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spd_9am</th>\n",
              "      <td>6.2</td>\n",
              "      <td>9.3</td>\n",
              "      <td>11.3</td>\n",
              "      <td>12.9</td>\n",
              "      <td>9.3</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.1</td>\n",
              "      <td>2.1</td>\n",
              "      <td>9.3</td>\n",
              "      <td>7.2</td>\n",
              "      <td>...</td>\n",
              "      <td>7.2</td>\n",
              "      <td>7.2</td>\n",
              "      <td>2.1</td>\n",
              "      <td>14.4</td>\n",
              "      <td>8.2</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.2</td>\n",
              "      <td>8.2</td>\n",
              "      <td>2.1</td>\n",
              "      <td>3.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alt_9am</th>\n",
              "      <td>1026.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>1028.1</td>\n",
              "      <td>1029.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>1025.1</td>\n",
              "      <td>1023.0</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>1026.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>...</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>1024.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cig_9am</th>\n",
              "      <td>6000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>...</td>\n",
              "      <td>99999</td>\n",
              "      <td>99999</td>\n",
              "      <td>99999</td>\n",
              "      <td>99999</td>\n",
              "      <td>99999</td>\n",
              "      <td>366</td>\n",
              "      <td>549</td>\n",
              "      <td>99999</td>\n",
              "      <td>99999</td>\n",
              "      <td>2438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tmp_10am</th>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dew_10am</th>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hum_10am</th>\n",
              "      <td>58.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>...</td>\n",
              "      <td>65.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>79.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wnd_10am</th>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>...</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>-99.0</td>\n",
              "      <td>180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am</th>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>...</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>S</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>N</td>\n",
              "      <td>CALM</td>\n",
              "      <td>S</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spd_10am</th>\n",
              "      <td>5.1</td>\n",
              "      <td>7.2</td>\n",
              "      <td>9.3</td>\n",
              "      <td>7.2</td>\n",
              "      <td>5.1</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>6.2</td>\n",
              "      <td>...</td>\n",
              "      <td>8.2</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.1</td>\n",
              "      <td>10.3</td>\n",
              "      <td>8.2</td>\n",
              "      <td>6.2</td>\n",
              "      <td>5.1</td>\n",
              "      <td>4.1</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alt_10am</th>\n",
              "      <td>1026.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>1028.1</td>\n",
              "      <td>1029.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>1023.0</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>1026.1</td>\n",
              "      <td>1026.1</td>\n",
              "      <td>...</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1026.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>1024.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cig_10am</th>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>549.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp_hrs</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp_yesterday</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>...</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>22</td>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "      <td>25</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>28</td>\n",
              "      <td>29</td>\n",
              "      <td>30</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spl_9am</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spl_10am</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>26 rows × 6780 columns</p>\n",
              "</div>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create dummy variables of wind direction feactures features\n",
        "data = pd.get_dummies(data, columns=['wdd_9am'])\n",
        "data = pd.get_dummies(data, columns=['wdd_10am'])\n",
        "data.T"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Q0WxY9_-MM_n",
        "outputId": "4d52f99a-a3ee-4840-89e9-9ed7868bd6c8"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                  0                        1     \\\n",
              "dia            2001-01-01 00:00:00.000  2001-01-02 00:00:00.000   \n",
              "day_date           2001-01-01 00:00:00      2001-01-02 00:00:00   \n",
              "tmp_9am                           17.0                     18.2   \n",
              "dew_9am                           10.0                     13.2   \n",
              "hum_9am                             59                       73   \n",
              "wnd_9am                            360                       20   \n",
              "spd_9am                            6.2                      9.3   \n",
              "alt_9am                         1026.1                   1027.1   \n",
              "cig_9am                           6000                    22000   \n",
              "tmp_10am                          19.0                     19.0   \n",
              "dew_10am                          11.0                     13.0   \n",
              "hum_10am                          58.0                     68.0   \n",
              "wnd_10am                         360.0                    360.0   \n",
              "spd_10am                           5.1                      7.2   \n",
              "alt_10am                        1026.1                   1027.1   \n",
              "cig_10am                       22000.0                  22000.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        0   \n",
              "year                              2001                     2001   \n",
              "month                                1                        1   \n",
              "day                                  1                        2   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "wdd_9am_CALM                         0                        0   \n",
              "wdd_9am_E                            0                        0   \n",
              "wdd_9am_N                            1                        1   \n",
              "wdd_9am_S                            0                        0   \n",
              "wdd_9am_W                            0                        0   \n",
              "wdd_10am_CALM                        0                        0   \n",
              "wdd_10am_E                           0                        0   \n",
              "wdd_10am_N                           1                        1   \n",
              "wdd_10am_S                           0                        0   \n",
              "wdd_10am_W                           0                        0   \n",
              "\n",
              "                                  2                        3     \\\n",
              "dia            2001-01-03 00:00:00.000  2001-01-04 00:00:00.000   \n",
              "day_date           2001-01-03 00:00:00      2001-01-04 00:00:00   \n",
              "tmp_9am                           18.0                     15.2   \n",
              "dew_9am                           10.5                      9.2   \n",
              "hum_9am                             58                       61   \n",
              "wnd_9am                            360                      360   \n",
              "spd_9am                           11.3                     12.9   \n",
              "alt_9am                         1028.1                   1029.1   \n",
              "cig_9am                          22000                    22000   \n",
              "tmp_10am                          18.0                     18.0   \n",
              "dew_10am                          10.0                      9.0   \n",
              "hum_10am                          56.0                     50.0   \n",
              "wnd_10am                         360.0                    360.0   \n",
              "spd_10am                           9.3                      7.2   \n",
              "alt_10am                        1028.1                   1029.1   \n",
              "cig_10am                       22000.0                  22000.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        0   \n",
              "year                              2001                     2001   \n",
              "month                                1                        1   \n",
              "day                                  3                        4   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "wdd_9am_CALM                         0                        0   \n",
              "wdd_9am_E                            0                        0   \n",
              "wdd_9am_N                            1                        1   \n",
              "wdd_9am_S                            0                        0   \n",
              "wdd_9am_W                            0                        0   \n",
              "wdd_10am_CALM                        0                        0   \n",
              "wdd_10am_E                           0                        0   \n",
              "wdd_10am_N                           1                        1   \n",
              "wdd_10am_S                           0                        0   \n",
              "wdd_10am_W                           0                        0   \n",
              "\n",
              "                                  4                        5     \\\n",
              "dia            2001-01-05 00:00:00.000  2001-01-06 00:00:00.000   \n",
              "day_date           2001-01-05 00:00:00      2001-01-06 00:00:00   \n",
              "tmp_9am                           15.0                     14.8   \n",
              "dew_9am                           10.0                     11.2   \n",
              "hum_9am                             67                       76   \n",
              "wnd_9am                            360                       30   \n",
              "spd_9am                            9.3                      5.1   \n",
              "alt_9am                         1027.1                   1025.1   \n",
              "cig_9am                          22000                    22000   \n",
              "tmp_10am                          17.0                     17.0   \n",
              "dew_10am                          10.0                     12.0   \n",
              "hum_10am                          59.0                     71.0   \n",
              "wnd_10am                         360.0                    330.0   \n",
              "spd_10am                           5.1                      4.1   \n",
              "alt_10am                        1027.1                   1024.0   \n",
              "cig_10am                       22000.0                  22000.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        0   \n",
              "year                              2001                     2001   \n",
              "month                                1                        1   \n",
              "day                                  5                        6   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "wdd_9am_CALM                         0                        0   \n",
              "wdd_9am_E                            0                        0   \n",
              "wdd_9am_N                            1                        1   \n",
              "wdd_9am_S                            0                        0   \n",
              "wdd_9am_W                            0                        0   \n",
              "wdd_10am_CALM                        0                        0   \n",
              "wdd_10am_E                           0                        0   \n",
              "wdd_10am_N                           1                        1   \n",
              "wdd_10am_S                           0                        0   \n",
              "wdd_10am_W                           0                        0   \n",
              "\n",
              "                                  6                        7     \\\n",
              "dia            2001-01-07 00:00:00.000  2001-01-08 00:00:00.000   \n",
              "day_date           2001-01-07 00:00:00      2001-01-08 00:00:00   \n",
              "tmp_9am                           16.8                     17.8   \n",
              "dew_9am                           11.0                     13.2   \n",
              "hum_9am                             65                       74   \n",
              "wnd_9am                            180                      360   \n",
              "spd_9am                            2.1                      2.1   \n",
              "alt_9am                         1023.0                   1024.0   \n",
              "cig_9am                          22000                    22000   \n",
              "tmp_10am                          19.0                     20.0   \n",
              "dew_10am                          12.0                      9.0   \n",
              "hum_10am                          63.0                     45.0   \n",
              "wnd_10am                         210.0                    360.0   \n",
              "spd_10am                           3.1                      1.0   \n",
              "alt_10am                        1023.0                   1024.0   \n",
              "cig_10am                       22000.0                  22000.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        0   \n",
              "year                              2001                     2001   \n",
              "month                                1                        1   \n",
              "day                                  7                        8   \n",
              "spl_9am                              1                        1   \n",
              "spl_10am                             1                        1   \n",
              "wdd_9am_CALM                         0                        0   \n",
              "wdd_9am_E                            0                        0   \n",
              "wdd_9am_N                            0                        1   \n",
              "wdd_9am_S                            1                        0   \n",
              "wdd_9am_W                            0                        0   \n",
              "wdd_10am_CALM                        0                        0   \n",
              "wdd_10am_E                           0                        0   \n",
              "wdd_10am_N                           0                        1   \n",
              "wdd_10am_S                           1                        0   \n",
              "wdd_10am_W                           0                        0   \n",
              "\n",
              "                                  8                        9     ...  \\\n",
              "dia            2001-01-09 00:00:00.000  2001-01-10 00:00:00.000  ...   \n",
              "day_date           2001-01-09 00:00:00      2001-01-10 00:00:00  ...   \n",
              "tmp_9am                           16.0                     16.6  ...   \n",
              "dew_9am                           11.0                      9.3  ...   \n",
              "hum_9am                             69                       56  ...   \n",
              "wnd_9am                            360                      360  ...   \n",
              "spd_9am                            9.3                      7.2  ...   \n",
              "alt_9am                         1026.1                   1027.1  ...   \n",
              "cig_9am                          22000                    22000  ...   \n",
              "tmp_10am                          18.0                     18.0  ...   \n",
              "dew_10am                          11.0                     10.0  ...   \n",
              "hum_10am                          61.0                     56.0  ...   \n",
              "wnd_10am                         360.0                    360.0  ...   \n",
              "spd_10am                           5.1                      6.2  ...   \n",
              "alt_10am                        1026.1                   1026.1  ...   \n",
              "cig_10am                       22000.0                  22000.0  ...   \n",
              "pcp_hrs                              0                        0  ...   \n",
              "pcp                                  0                        0  ...   \n",
              "pcp_yesterday                        0                        0  ...   \n",
              "year                              2001                     2001  ...   \n",
              "month                                1                        1  ...   \n",
              "day                                  9                       10  ...   \n",
              "spl_9am                              0                        0  ...   \n",
              "spl_10am                             0                        0  ...   \n",
              "wdd_9am_CALM                         0                        0  ...   \n",
              "wdd_9am_E                            0                        0  ...   \n",
              "wdd_9am_N                            1                        1  ...   \n",
              "wdd_9am_S                            0                        0  ...   \n",
              "wdd_9am_W                            0                        0  ...   \n",
              "wdd_10am_CALM                        0                        0  ...   \n",
              "wdd_10am_E                           0                        0  ...   \n",
              "wdd_10am_N                           1                        1  ...   \n",
              "wdd_10am_S                           0                        0  ...   \n",
              "wdd_10am_W                           0                        0  ...   \n",
              "\n",
              "                                  6770                     6771  \\\n",
              "dia            2020-12-22 00:00:00.000  2020-12-23 00:00:00.000   \n",
              "day_date           2020-12-22 00:00:00      2020-12-23 00:00:00   \n",
              "tmp_9am                           18.0                     19.0   \n",
              "dew_9am                           14.0                     13.0   \n",
              "hum_9am                             78                       68   \n",
              "wnd_9am                            360                      360   \n",
              "spd_9am                            7.2                      7.2   \n",
              "alt_9am                         1028.0                   1028.0   \n",
              "cig_9am                          99999                    99999   \n",
              "tmp_10am                          20.0                     21.0   \n",
              "dew_10am                          13.0                     14.0   \n",
              "hum_10am                          65.0                     67.0   \n",
              "wnd_10am                         360.0                    360.0   \n",
              "spd_10am                           8.2                      5.1   \n",
              "alt_10am                        1028.0                   1028.0   \n",
              "cig_10am                       99999.0                  99999.0   \n",
              "pcp_hrs                              1                        0   \n",
              "pcp                                  1                        0   \n",
              "pcp_yesterday                        0                        1   \n",
              "year                              2020                     2020   \n",
              "month                               12                       12   \n",
              "day                                 22                       23   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "wdd_9am_CALM                         0                        0   \n",
              "wdd_9am_E                            0                        0   \n",
              "wdd_9am_N                            1                        1   \n",
              "wdd_9am_S                            0                        0   \n",
              "wdd_9am_W                            0                        0   \n",
              "wdd_10am_CALM                        0                        0   \n",
              "wdd_10am_E                           0                        0   \n",
              "wdd_10am_N                           1                        1   \n",
              "wdd_10am_S                           0                        0   \n",
              "wdd_10am_W                           0                        0   \n",
              "\n",
              "                                  6772                     6773  \\\n",
              "dia            2020-12-24 00:00:00.000  2020-12-25 00:00:00.000   \n",
              "day_date           2020-12-24 00:00:00      2020-12-25 00:00:00   \n",
              "tmp_9am                           19.0                     19.0   \n",
              "dew_9am                           14.0                     11.0   \n",
              "hum_9am                             74                       58   \n",
              "wnd_9am                            180                       30   \n",
              "spd_9am                            2.1                     14.4   \n",
              "alt_9am                         1027.0                   1028.0   \n",
              "cig_9am                          99999                    99999   \n",
              "tmp_10am                          22.0                     19.0   \n",
              "dew_10am                          18.0                     10.0   \n",
              "hum_10am                          82.0                     53.0   \n",
              "wnd_10am                         200.0                     20.0   \n",
              "spd_10am                           3.1                     10.3   \n",
              "alt_10am                        1026.0                   1028.0   \n",
              "cig_10am                       99999.0                  99999.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        1   \n",
              "year                              2020                     2020   \n",
              "month                               12                       12   \n",
              "day                                 24                       25   \n",
              "spl_9am                              1                        0   \n",
              "spl_10am                             1                        0   \n",
              "wdd_9am_CALM                         0                        0   \n",
              "wdd_9am_E                            0                        0   \n",
              "wdd_9am_N                            0                        1   \n",
              "wdd_9am_S                            1                        0   \n",
              "wdd_9am_W                            0                        0   \n",
              "wdd_10am_CALM                        0                        0   \n",
              "wdd_10am_E                           0                        0   \n",
              "wdd_10am_N                           0                        1   \n",
              "wdd_10am_S                           1                        0   \n",
              "wdd_10am_W                           0                        0   \n",
              "\n",
              "                                  6774                     6775  \\\n",
              "dia            2020-12-26 00:00:00.000  2020-12-27 00:00:00.000   \n",
              "day_date           2020-12-26 00:00:00      2020-12-27 00:00:00   \n",
              "tmp_9am                           14.0                     14.0   \n",
              "dew_9am                            9.0                     11.0   \n",
              "hum_9am                             64                       79   \n",
              "wnd_9am                            360                       40   \n",
              "spd_9am                            8.2                      6.2   \n",
              "alt_9am                         1028.0                   1027.0   \n",
              "cig_9am                          99999                      366   \n",
              "tmp_10am                          16.0                     16.0   \n",
              "dew_10am                           9.0                     11.0   \n",
              "hum_10am                          56.0                     69.0   \n",
              "wnd_10am                         360.0                     40.0   \n",
              "spd_10am                           8.2                      6.2   \n",
              "alt_10am                        1028.0                   1027.0   \n",
              "cig_10am                       99999.0                  99999.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        0                        0   \n",
              "year                              2020                     2020   \n",
              "month                               12                       12   \n",
              "day                                 26                       27   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "wdd_9am_CALM                         0                        0   \n",
              "wdd_9am_E                            0                        0   \n",
              "wdd_9am_N                            1                        1   \n",
              "wdd_9am_S                            0                        0   \n",
              "wdd_9am_W                            0                        0   \n",
              "wdd_10am_CALM                        0                        0   \n",
              "wdd_10am_E                           0                        0   \n",
              "wdd_10am_N                           1                        1   \n",
              "wdd_10am_S                           0                        0   \n",
              "wdd_10am_W                           0                        0   \n",
              "\n",
              "                                  6776                     6777  \\\n",
              "dia            2020-12-28 00:00:00.000  2020-12-29 00:00:00.000   \n",
              "day_date           2020-12-28 00:00:00      2020-12-29 00:00:00   \n",
              "tmp_9am                           14.0                     16.0   \n",
              "dew_9am                           10.0                     10.0   \n",
              "hum_9am                             71                       63   \n",
              "wnd_9am                             30                      360   \n",
              "spd_9am                            7.2                      8.2   \n",
              "alt_9am                         1028.0                   1028.0   \n",
              "cig_9am                            549                    99999   \n",
              "tmp_10am                          16.0                     16.0   \n",
              "dew_10am                          10.0                     11.0   \n",
              "hum_10am                          63.0                     69.0   \n",
              "wnd_10am                          20.0                    360.0   \n",
              "spd_10am                           5.1                      4.1   \n",
              "alt_10am                        1028.0                   1028.0   \n",
              "cig_10am                       99999.0                  99999.0   \n",
              "pcp_hrs                              0                        0   \n",
              "pcp                                  0                        0   \n",
              "pcp_yesterday                        1                        0   \n",
              "year                              2020                     2020   \n",
              "month                               12                       12   \n",
              "day                                 28                       29   \n",
              "spl_9am                              0                        0   \n",
              "spl_10am                             0                        0   \n",
              "wdd_9am_CALM                         0                        0   \n",
              "wdd_9am_E                            0                        0   \n",
              "wdd_9am_N                            1                        1   \n",
              "wdd_9am_S                            0                        0   \n",
              "wdd_9am_W                            0                        0   \n",
              "wdd_10am_CALM                        0                        0   \n",
              "wdd_10am_E                           0                        0   \n",
              "wdd_10am_N                           1                        1   \n",
              "wdd_10am_S                           0                        0   \n",
              "wdd_10am_W                           0                        0   \n",
              "\n",
              "                                  6778                     6779  \n",
              "dia            2020-12-30 00:00:00.000  2020-12-31 00:00:00.000  \n",
              "day_date           2020-12-30 00:00:00      2020-12-31 00:00:00  \n",
              "tmp_9am                           17.0                     19.0  \n",
              "dew_9am                           13.0                     15.0  \n",
              "hum_9am                             76                       79  \n",
              "wnd_9am                            360                      180  \n",
              "spd_9am                            2.1                      3.1  \n",
              "alt_9am                         1025.0                   1024.0  \n",
              "cig_9am                          99999                     2438  \n",
              "tmp_10am                          20.0                     19.0  \n",
              "dew_10am                          13.0                     15.0  \n",
              "hum_10am                          65.0                     79.0  \n",
              "wnd_10am                         -99.0                    180.0  \n",
              "spd_10am                           2.1                      0.0  \n",
              "alt_10am                        1025.0                   1024.0  \n",
              "cig_10am                       99999.0                    549.0  \n",
              "pcp_hrs                              0                        4  \n",
              "pcp                                  0                        1  \n",
              "pcp_yesterday                        0                        0  \n",
              "year                              2020                     2020  \n",
              "month                               12                       12  \n",
              "day                                 30                       31  \n",
              "spl_9am                              1                        1  \n",
              "spl_10am                             1                        1  \n",
              "wdd_9am_CALM                         0                        0  \n",
              "wdd_9am_E                            0                        0  \n",
              "wdd_9am_N                            1                        0  \n",
              "wdd_9am_S                            0                        1  \n",
              "wdd_9am_W                            0                        0  \n",
              "wdd_10am_CALM                        1                        0  \n",
              "wdd_10am_E                           0                        0  \n",
              "wdd_10am_N                           0                        0  \n",
              "wdd_10am_S                           0                        1  \n",
              "wdd_10am_W                           0                        0  \n",
              "\n",
              "[34 rows x 6780 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6b4acbb9-4335-492f-9af2-6837d3ec022a\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
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              "      <th>6770</th>\n",
              "      <th>6771</th>\n",
              "      <th>6772</th>\n",
              "      <th>6773</th>\n",
              "      <th>6774</th>\n",
              "      <th>6775</th>\n",
              "      <th>6776</th>\n",
              "      <th>6777</th>\n",
              "      <th>6778</th>\n",
              "      <th>6779</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>dia</th>\n",
              "      <td>2001-01-01 00:00:00.000</td>\n",
              "      <td>2001-01-02 00:00:00.000</td>\n",
              "      <td>2001-01-03 00:00:00.000</td>\n",
              "      <td>2001-01-04 00:00:00.000</td>\n",
              "      <td>2001-01-05 00:00:00.000</td>\n",
              "      <td>2001-01-06 00:00:00.000</td>\n",
              "      <td>2001-01-07 00:00:00.000</td>\n",
              "      <td>2001-01-08 00:00:00.000</td>\n",
              "      <td>2001-01-09 00:00:00.000</td>\n",
              "      <td>2001-01-10 00:00:00.000</td>\n",
              "      <td>...</td>\n",
              "      <td>2020-12-22 00:00:00.000</td>\n",
              "      <td>2020-12-23 00:00:00.000</td>\n",
              "      <td>2020-12-24 00:00:00.000</td>\n",
              "      <td>2020-12-25 00:00:00.000</td>\n",
              "      <td>2020-12-26 00:00:00.000</td>\n",
              "      <td>2020-12-27 00:00:00.000</td>\n",
              "      <td>2020-12-28 00:00:00.000</td>\n",
              "      <td>2020-12-29 00:00:00.000</td>\n",
              "      <td>2020-12-30 00:00:00.000</td>\n",
              "      <td>2020-12-31 00:00:00.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day_date</th>\n",
              "      <td>2001-01-01 00:00:00</td>\n",
              "      <td>2001-01-02 00:00:00</td>\n",
              "      <td>2001-01-03 00:00:00</td>\n",
              "      <td>2001-01-04 00:00:00</td>\n",
              "      <td>2001-01-05 00:00:00</td>\n",
              "      <td>2001-01-06 00:00:00</td>\n",
              "      <td>2001-01-07 00:00:00</td>\n",
              "      <td>2001-01-08 00:00:00</td>\n",
              "      <td>2001-01-09 00:00:00</td>\n",
              "      <td>2001-01-10 00:00:00</td>\n",
              "      <td>...</td>\n",
              "      <td>2020-12-22 00:00:00</td>\n",
              "      <td>2020-12-23 00:00:00</td>\n",
              "      <td>2020-12-24 00:00:00</td>\n",
              "      <td>2020-12-25 00:00:00</td>\n",
              "      <td>2020-12-26 00:00:00</td>\n",
              "      <td>2020-12-27 00:00:00</td>\n",
              "      <td>2020-12-28 00:00:00</td>\n",
              "      <td>2020-12-29 00:00:00</td>\n",
              "      <td>2020-12-30 00:00:00</td>\n",
              "      <td>2020-12-31 00:00:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tmp_9am</th>\n",
              "      <td>17.0</td>\n",
              "      <td>18.2</td>\n",
              "      <td>18.0</td>\n",
              "      <td>15.2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>14.8</td>\n",
              "      <td>16.8</td>\n",
              "      <td>17.8</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.6</td>\n",
              "      <td>...</td>\n",
              "      <td>18.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dew_9am</th>\n",
              "      <td>10.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>10.5</td>\n",
              "      <td>9.2</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>13.2</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.3</td>\n",
              "      <td>...</td>\n",
              "      <td>14.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hum_9am</th>\n",
              "      <td>59</td>\n",
              "      <td>73</td>\n",
              "      <td>58</td>\n",
              "      <td>61</td>\n",
              "      <td>67</td>\n",
              "      <td>76</td>\n",
              "      <td>65</td>\n",
              "      <td>74</td>\n",
              "      <td>69</td>\n",
              "      <td>56</td>\n",
              "      <td>...</td>\n",
              "      <td>78</td>\n",
              "      <td>68</td>\n",
              "      <td>74</td>\n",
              "      <td>58</td>\n",
              "      <td>64</td>\n",
              "      <td>79</td>\n",
              "      <td>71</td>\n",
              "      <td>63</td>\n",
              "      <td>76</td>\n",
              "      <td>79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wnd_9am</th>\n",
              "      <td>360</td>\n",
              "      <td>20</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>30</td>\n",
              "      <td>180</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>...</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>180</td>\n",
              "      <td>30</td>\n",
              "      <td>360</td>\n",
              "      <td>40</td>\n",
              "      <td>30</td>\n",
              "      <td>360</td>\n",
              "      <td>360</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spd_9am</th>\n",
              "      <td>6.2</td>\n",
              "      <td>9.3</td>\n",
              "      <td>11.3</td>\n",
              "      <td>12.9</td>\n",
              "      <td>9.3</td>\n",
              "      <td>5.1</td>\n",
              "      <td>2.1</td>\n",
              "      <td>2.1</td>\n",
              "      <td>9.3</td>\n",
              "      <td>7.2</td>\n",
              "      <td>...</td>\n",
              "      <td>7.2</td>\n",
              "      <td>7.2</td>\n",
              "      <td>2.1</td>\n",
              "      <td>14.4</td>\n",
              "      <td>8.2</td>\n",
              "      <td>6.2</td>\n",
              "      <td>7.2</td>\n",
              "      <td>8.2</td>\n",
              "      <td>2.1</td>\n",
              "      <td>3.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alt_9am</th>\n",
              "      <td>1026.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>1028.1</td>\n",
              "      <td>1029.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>1025.1</td>\n",
              "      <td>1023.0</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>1026.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>...</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>1024.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cig_9am</th>\n",
              "      <td>6000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>22000</td>\n",
              "      <td>...</td>\n",
              "      <td>99999</td>\n",
              "      <td>99999</td>\n",
              "      <td>99999</td>\n",
              "      <td>99999</td>\n",
              "      <td>99999</td>\n",
              "      <td>366</td>\n",
              "      <td>549</td>\n",
              "      <td>99999</td>\n",
              "      <td>99999</td>\n",
              "      <td>2438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tmp_10am</th>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>...</td>\n",
              "      <td>20.0</td>\n",
              "      <td>21.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>19.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dew_10am</th>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>12.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>...</td>\n",
              "      <td>13.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hum_10am</th>\n",
              "      <td>58.0</td>\n",
              "      <td>68.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>59.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>45.0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>...</td>\n",
              "      <td>65.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>82.0</td>\n",
              "      <td>53.0</td>\n",
              "      <td>56.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>63.0</td>\n",
              "      <td>69.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>79.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wnd_10am</th>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>330.0</td>\n",
              "      <td>210.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>...</td>\n",
              "      <td>360.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>360.0</td>\n",
              "      <td>-99.0</td>\n",
              "      <td>180.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spd_10am</th>\n",
              "      <td>5.1</td>\n",
              "      <td>7.2</td>\n",
              "      <td>9.3</td>\n",
              "      <td>7.2</td>\n",
              "      <td>5.1</td>\n",
              "      <td>4.1</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5.1</td>\n",
              "      <td>6.2</td>\n",
              "      <td>...</td>\n",
              "      <td>8.2</td>\n",
              "      <td>5.1</td>\n",
              "      <td>3.1</td>\n",
              "      <td>10.3</td>\n",
              "      <td>8.2</td>\n",
              "      <td>6.2</td>\n",
              "      <td>5.1</td>\n",
              "      <td>4.1</td>\n",
              "      <td>2.1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alt_10am</th>\n",
              "      <td>1026.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>1028.1</td>\n",
              "      <td>1029.1</td>\n",
              "      <td>1027.1</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>1023.0</td>\n",
              "      <td>1024.0</td>\n",
              "      <td>1026.1</td>\n",
              "      <td>1026.1</td>\n",
              "      <td>...</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1026.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1027.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1028.0</td>\n",
              "      <td>1025.0</td>\n",
              "      <td>1024.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cig_10am</th>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>22000.0</td>\n",
              "      <td>...</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>99999.0</td>\n",
              "      <td>549.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp_hrs</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp_yesterday</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>2001</td>\n",
              "      <td>...</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "      <td>2020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>6</td>\n",
              "      <td>7</td>\n",
              "      <td>8</td>\n",
              "      <td>9</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>22</td>\n",
              "      <td>23</td>\n",
              "      <td>24</td>\n",
              "      <td>25</td>\n",
              "      <td>26</td>\n",
              "      <td>27</td>\n",
              "      <td>28</td>\n",
              "      <td>29</td>\n",
              "      <td>30</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spl_9am</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spl_10am</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_CALM</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_E</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_N</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_S</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_W</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_CALM</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_E</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_N</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_S</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_W</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>34 rows × 6780 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b4acbb9-4335-492f-9af2-6837d3ec022a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6b4acbb9-4335-492f-9af2-6837d3ec022a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6b4acbb9-4335-492f-9af2-6837d3ec022a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1wdQa4cEW-ah",
        "outputId": "01bf28ae-6c04-4206-c456-720404df5264"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 6780 entries, 0 to 6779\n",
            "Data columns (total 34 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   dia            6780 non-null   object        \n",
            " 1   day_date       6780 non-null   datetime64[ns]\n",
            " 2   tmp_9am        6780 non-null   float64       \n",
            " 3   dew_9am        6780 non-null   float64       \n",
            " 4   hum_9am        6780 non-null   int64         \n",
            " 5   wnd_9am        6780 non-null   int64         \n",
            " 6   spd_9am        6779 non-null   float64       \n",
            " 7   alt_9am        6752 non-null   float64       \n",
            " 8   cig_9am        6780 non-null   int64         \n",
            " 9   tmp_10am       6549 non-null   float64       \n",
            " 10  dew_10am       6545 non-null   float64       \n",
            " 11  hum_10am       6541 non-null   float64       \n",
            " 12  wnd_10am       6606 non-null   float64       \n",
            " 13  spd_10am       6379 non-null   float64       \n",
            " 14  alt_10am       6535 non-null   float64       \n",
            " 15  cig_10am       6606 non-null   float64       \n",
            " 16  pcp_hrs        6780 non-null   int64         \n",
            " 17  pcp            6780 non-null   int64         \n",
            " 18  pcp_yesterday  6780 non-null   int64         \n",
            " 19  year           6780 non-null   int64         \n",
            " 20  month          6780 non-null   int64         \n",
            " 21  day            6780 non-null   int64         \n",
            " 22  spl_9am        6780 non-null   int64         \n",
            " 23  spl_10am       6780 non-null   int64         \n",
            " 24  wdd_9am_CALM   6780 non-null   uint8         \n",
            " 25  wdd_9am_E      6780 non-null   uint8         \n",
            " 26  wdd_9am_N      6780 non-null   uint8         \n",
            " 27  wdd_9am_S      6780 non-null   uint8         \n",
            " 28  wdd_9am_W      6780 non-null   uint8         \n",
            " 29  wdd_10am_CALM  6780 non-null   uint8         \n",
            " 30  wdd_10am_E     6780 non-null   uint8         \n",
            " 31  wdd_10am_N     6780 non-null   uint8         \n",
            " 32  wdd_10am_S     6780 non-null   uint8         \n",
            " 33  wdd_10am_W     6780 non-null   uint8         \n",
            "dtypes: datetime64[ns](1), float64(11), int64(11), object(1), uint8(10)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['cig_9am'].unique() ##todo check if this values can be dummied."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ri7kEpytMi9v",
        "outputId": "76cd140b-709b-44a9-bc5b-7fafedc946f6"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 6000, 22000,   420,   510,   180,   360,  2100,   480,   390,\n",
              "         450,   600,  4500,  2400,   540, 10500,  2700,  3000,   900,\n",
              "         120,   240,   750,   660, 99999,   300,  3600,   720,  1800,\n",
              "          90,    60,  6096,   427,   396,   305,   488,  2438,  3048,\n",
              "         457,   549,  2743,   610,   518,   366,  2134,  1829,   671,\n",
              "          61,   183,  1981,   122,  3658,   244])"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#data = pd.get_dummies(data, columns=['cig_9am'])\n",
        "#data\n",
        "\n",
        "## no relevant variable and correlation after dummies. so reverted"
      ],
      "metadata": {
        "id": "QfhZAs3qaOAN"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"pcp\"].hist() ##this is the value wanted to predict if will rain in afternoon"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "oZl0gY0xP27c",
        "outputId": "d2af9584-62ea-402a-920e-32abdff55ff4"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd76ee02890>"
            ]
          },
          "metadata": {},
          "execution_count": 83
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['spl_10am'].hist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "MCTG6tdYA2gf",
        "outputId": "9ed7a042-9924-43af-e4fa-5e23a3a705b4"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd76e73fcd0>"
            ]
          },
          "metadata": {},
          "execution_count": 91
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MIwKEUikSEpV",
        "outputId": "4f229be8-d2d6-4e72-f012-9e099ffe9204"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['dia', 'day_date', 'tmp_9am', 'dew_9am', 'hum_9am', 'wnd_9am',\n",
              "       'spd_9am', 'alt_9am', 'cig_9am', 'tmp_10am', 'dew_10am', 'hum_10am',\n",
              "       'wnd_10am', 'spd_10am', 'alt_10am', 'cig_10am', 'pcp_hrs', 'pcp',\n",
              "       'pcp_yesterday', 'year', 'month', 'day', 'spl_9am', 'spl_10am',\n",
              "       'wdd_9am_CALM', 'wdd_9am_E', 'wdd_9am_N', 'wdd_9am_S', 'wdd_9am_W',\n",
              "       'wdd_10am_CALM', 'wdd_10am_E', 'wdd_10am_N', 'wdd_10am_S',\n",
              "       'wdd_10am_W'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "zhkZWV3r82QD"
      },
      "execution_count": 93,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "##sns.pairplot(data[\n",
        "##        ['tmp_9am', 'dew_9am', 'hum_9am', 'wnd_9am',\n",
        "##       'spd_9am', 'alt_9am', 'cig_9am', 'pcp','pcp_yesterday']        \n",
        "##])"
      ],
      "metadata": {
        "id": "DNKzO2RjMi-4"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.pcp.isnull().sum() ##no null values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9x1O0rygUjoI",
        "outputId": "88ed695f-aaab-499d-ad45-494632f83020"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##cleanup\n",
        "#data=data.drop(data[data.hum_10am.isnull()].index)  \n",
        "#data[data.hum_10am.isnull()==False]\n",
        "\n",
        "data= data[data.hum_9am.isnull()==False]\n",
        "data= data[data.hum_10am.isnull()==False]\n",
        "data= data[data.alt_9am.isnull()==False]\n",
        "data= data[data.alt_10am.isnull()==False]\n",
        "data= data[data.spd_9am.isnull()==False]\n",
        "data= data[data.spd_10am.isnull()==False]\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "IhtcLgXv2oR3",
        "outputId": "549dc875-46b8-4dff-992c-eff260f76916"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          dia   day_date  tmp_9am  dew_9am  hum_9am  wnd_9am  \\\n",
              "0     2001-01-01 00:00:00.000 2001-01-01     17.0     10.0       59      360   \n",
              "1     2001-01-02 00:00:00.000 2001-01-02     18.2     13.2       73       20   \n",
              "2     2001-01-03 00:00:00.000 2001-01-03     18.0     10.5       58      360   \n",
              "3     2001-01-04 00:00:00.000 2001-01-04     15.2      9.2       61      360   \n",
              "4     2001-01-05 00:00:00.000 2001-01-05     15.0     10.0       67      360   \n",
              "...                       ...        ...      ...      ...      ...      ...   \n",
              "6775  2020-12-27 00:00:00.000 2020-12-27     14.0     11.0       79       40   \n",
              "6776  2020-12-28 00:00:00.000 2020-12-28     14.0     10.0       71       30   \n",
              "6777  2020-12-29 00:00:00.000 2020-12-29     16.0     10.0       63      360   \n",
              "6778  2020-12-30 00:00:00.000 2020-12-30     17.0     13.0       76      360   \n",
              "6779  2020-12-31 00:00:00.000 2020-12-31     19.0     15.0       79      180   \n",
              "\n",
              "      spd_9am  alt_9am  cig_9am  tmp_10am  ...  wdd_9am_CALM  wdd_9am_E  \\\n",
              "0         6.2   1026.1     6000      19.0  ...             0          0   \n",
              "1         9.3   1027.1    22000      19.0  ...             0          0   \n",
              "2        11.3   1028.1    22000      18.0  ...             0          0   \n",
              "3        12.9   1029.1    22000      18.0  ...             0          0   \n",
              "4         9.3   1027.1    22000      17.0  ...             0          0   \n",
              "...       ...      ...      ...       ...  ...           ...        ...   \n",
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              "\n",
              "      wdd_9am_N  wdd_9am_S  wdd_9am_W  wdd_10am_CALM  wdd_10am_E  wdd_10am_N  \\\n",
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          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(data.corr())"
      ],
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      },
      "execution_count": 97,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fd76e8df950>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_corr = data.corr()\n",
        "data_corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jMZGYv3NZKoN",
        "outputId": "ed0c155f-ffd7-4a5b-ba7c-31ccf66a7475"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                tmp_9am   dew_9am   hum_9am   wnd_9am   spd_9am   alt_9am  \\\n",
              "tmp_9am        1.000000  0.597908  0.039722 -0.168097 -0.345001 -0.022346   \n",
              "dew_9am        0.597908  1.000000  0.818762 -0.199035 -0.475398 -0.021305   \n",
              "hum_9am        0.039722  0.818762  1.000000 -0.128296 -0.347316 -0.011486   \n",
              "wnd_9am       -0.168097 -0.199035 -0.128296  1.000000  0.499257  0.028778   \n",
              "spd_9am       -0.345001 -0.475398 -0.347316  0.499257  1.000000  0.063028   \n",
              "alt_9am       -0.022346 -0.021305 -0.011486  0.028778  0.063028  1.000000   \n",
              "cig_9am        0.004755 -0.269960 -0.335489 -0.050645  0.004127 -0.010595   \n",
              "tmp_10am       0.890675  0.520614  0.023372 -0.217859 -0.417572 -0.023941   \n",
              "dew_10am       0.566511  0.922867  0.744487 -0.176864 -0.439030 -0.018353   \n",
              "hum_10am       0.119284  0.735668  0.834018 -0.073136 -0.252759 -0.006968   \n",
              "wnd_10am      -0.158107 -0.176751 -0.106756  0.432792  0.393178  0.032867   \n",
              "spd_10am      -0.428311 -0.501841 -0.320100  0.350680  0.683168  0.035770   \n",
              "alt_10am      -0.015172 -0.020732 -0.014641  0.007034  0.004548 -0.001210   \n",
              "cig_10am      -0.030429 -0.244382 -0.279113 -0.046606 -0.006766 -0.020531   \n",
              "pcp_hrs        0.189067  0.367960  0.312704 -0.100336 -0.260422 -0.015611   \n",
              "pcp            0.259782  0.435098  0.344160 -0.121131 -0.291609 -0.013918   \n",
              "pcp_yesterday  0.175899  0.483836  0.461585 -0.076377 -0.212637 -0.012869   \n",
              "year           0.081000  0.112598  0.079499 -0.148197 -0.112165 -0.075087   \n",
              "month          0.109625  0.214679  0.180395  0.041548 -0.004554 -0.012161   \n",
              "day            0.000315  0.001120  0.000411 -0.023530 -0.039530 -0.022971   \n",
              "spl_9am        0.219577  0.312391  0.227479 -0.412612 -0.735907 -0.012810   \n",
              "spl_10am       0.349347  0.419727  0.268430 -0.323031 -0.580620 -0.017256   \n",
              "wdd_9am_CALM   0.081155  0.130866  0.104588 -0.528749 -0.496234 -0.000809   \n",
              "wdd_9am_E      0.089621  0.065143  0.015298 -0.129170 -0.109920 -0.006651   \n",
              "wdd_9am_N     -0.191277 -0.246736 -0.173811  0.446666  0.566207  0.008269   \n",
              "wdd_9am_S      0.132283  0.171610  0.124037 -0.133136 -0.284219 -0.014798   \n",
              "wdd_9am_W      0.043732  0.049508  0.029449  0.009086 -0.077864  0.038308   \n",
              "wdd_10am_CALM  0.049573  0.090687  0.078395 -0.206987 -0.282072 -0.010572   \n",
              "wdd_10am_E     0.060053  0.059199  0.028922 -0.068067 -0.104052 -0.007586   \n",
              "wdd_10am_N    -0.204098 -0.246277 -0.164005  0.388667  0.573266  0.011512   \n",
              "wdd_10am_S     0.161213  0.196784  0.134707 -0.283218 -0.419906 -0.010206   \n",
              "wdd_10am_W     0.047345  0.043023  0.020281 -0.047336 -0.084734  0.038326   \n",
              "\n",
              "                cig_9am  tmp_10am  dew_10am  hum_10am  ...  wdd_9am_CALM  \\\n",
              "tmp_9am        0.004755  0.890675  0.566511  0.119284  ...      0.081155   \n",
              "dew_9am       -0.269960  0.520614  0.922867  0.735668  ...      0.130866   \n",
              "hum_9am       -0.335489  0.023372  0.744487  0.834018  ...      0.104588   \n",
              "wnd_9am       -0.050645 -0.217859 -0.176864 -0.073136  ...     -0.528749   \n",
              "spd_9am        0.004127 -0.417572 -0.439030 -0.252759  ...     -0.496234   \n",
              "alt_9am       -0.010595 -0.023941 -0.018353 -0.006968  ...     -0.000809   \n",
              "cig_9am        1.000000  0.070760 -0.278871 -0.354090  ...      0.014912   \n",
              "tmp_10am       0.070760  1.000000  0.462619 -0.064607  ...      0.122676   \n",
              "dew_10am      -0.278871  0.462619  1.000000  0.849051  ...      0.115456   \n",
              "hum_10am      -0.354090 -0.064607  0.849051  1.000000  ...      0.056942   \n",
              "wnd_10am      -0.035934 -0.196201 -0.167122 -0.074458  ...     -0.224021   \n",
              "spd_10am       0.034187 -0.461368 -0.466077 -0.256343  ...     -0.250225   \n",
              "alt_10am       0.026609 -0.004340 -0.020991 -0.019741  ...     -0.007489   \n",
              "cig_10am       0.610568  0.063545 -0.255833 -0.326316  ...      0.006607   \n",
              "pcp_hrs       -0.143299  0.121451  0.383362  0.352884  ...      0.078342   \n",
              "pcp           -0.165485  0.186548  0.450448  0.388228  ...      0.087574   \n",
              "pcp_yesterday -0.212040  0.082149  0.484636  0.488482  ...      0.059304   \n",
              "year           0.200313  0.077347  0.124460  0.090447  ...      0.023739   \n",
              "month         -0.085026  0.027318  0.249480  0.254872  ...     -0.006914   \n",
              "day            0.020031  0.002697  0.006112  0.004784  ...      0.030484   \n",
              "spl_9am       -0.012254  0.283380  0.279767  0.147370  ...      0.410845   \n",
              "spl_10am      -0.043585  0.377870  0.383307  0.208710  ...      0.229078   \n",
              "wdd_9am_CALM   0.014912  0.122676  0.115456  0.056942  ...      1.000000   \n",
              "wdd_9am_E      0.009807  0.096760  0.049593 -0.000607  ...     -0.030914   \n",
              "wdd_9am_N     -0.001583 -0.229794 -0.218753 -0.117966  ...     -0.461524   \n",
              "wdd_9am_S     -0.009917  0.148818  0.155023  0.095538  ...     -0.106768   \n",
              "wdd_9am_W     -0.006960  0.038044  0.045088  0.028161  ...     -0.018632   \n",
              "wdd_10am_CALM  0.013619  0.090279  0.087752  0.047519  ...      0.346433   \n",
              "wdd_10am_E    -0.009862  0.076174  0.045959  0.005957  ...     -0.000238   \n",
              "wdd_10am_N    -0.010473 -0.251249 -0.222406 -0.108891  ...     -0.356187   \n",
              "wdd_10am_S     0.001918  0.178258  0.175094  0.099541  ...      0.196034   \n",
              "wdd_10am_W    -0.007555  0.051170  0.034210  0.010756  ...      0.046981   \n",
              "\n",
              "               wdd_9am_E  wdd_9am_N  wdd_9am_S  wdd_9am_W  wdd_10am_CALM  \\\n",
              "tmp_9am         0.089621  -0.191277   0.132283   0.043732       0.049573   \n",
              "dew_9am         0.065143  -0.246736   0.171610   0.049508       0.090687   \n",
              "hum_9am         0.015298  -0.173811   0.124037   0.029449       0.078395   \n",
              "wnd_9am        -0.129170   0.446666  -0.133136   0.009086      -0.206987   \n",
              "spd_9am        -0.109920   0.566207  -0.284219  -0.077864      -0.282072   \n",
              "alt_9am        -0.006651   0.008269  -0.014798   0.038308      -0.010572   \n",
              "cig_9am         0.009807  -0.001583  -0.009917  -0.006960       0.013619   \n",
              "tmp_10am        0.096760  -0.229794   0.148818   0.038044       0.090279   \n",
              "dew_10am        0.049593  -0.218753   0.155023   0.045088       0.087752   \n",
              "hum_10am       -0.000607  -0.117966   0.095538   0.028161       0.047519   \n",
              "wnd_10am       -0.084771   0.290496  -0.160392  -0.021414      -0.410498   \n",
              "spd_10am       -0.074405   0.557935  -0.448413  -0.085065      -0.164473   \n",
              "alt_10am       -0.003564   0.016907  -0.013257  -0.002347      -0.005662   \n",
              "cig_10am       -0.009223  -0.000063   0.002034  -0.015900       0.001071   \n",
              "pcp_hrs         0.040385  -0.111212   0.057867   0.036581       0.085356   \n",
              "pcp             0.051983  -0.134089   0.069559   0.062108       0.085551   \n",
              "pcp_yesterday   0.051183  -0.098296   0.053933   0.025548       0.059673   \n",
              "year           -0.014438  -0.005391   0.000335  -0.024011       0.005807   \n",
              "month          -0.002944   0.089267  -0.099047  -0.002438       0.006453   \n",
              "day             0.002604  -0.013132  -0.009793   0.020186       0.040709   \n",
              "spl_9am         0.143476  -0.552148   0.306671   0.109441       0.295897   \n",
              "spl_10am        0.079772  -0.514751   0.411214   0.078163       0.165642   \n",
              "wdd_9am_CALM   -0.030914  -0.461524  -0.106768  -0.018632       0.346433   \n",
              "wdd_9am_E       1.000000  -0.222600  -0.051496  -0.008986       0.055953   \n",
              "wdd_9am_N      -0.222600   1.000000  -0.768788  -0.134159      -0.197228   \n",
              "wdd_9am_S      -0.051496  -0.768788   1.000000  -0.031036      -0.018676   \n",
              "wdd_9am_W      -0.008986  -0.134159  -0.031036   1.000000       0.009511   \n",
              "wdd_10am_CALM   0.055953  -0.197228  -0.018676   0.009511       1.000000   \n",
              "wdd_10am_E      0.161388  -0.016791  -0.038861   0.022318      -0.027121   \n",
              "wdd_10am_N     -0.082077   0.838620  -0.703222  -0.098582      -0.327453   \n",
              "wdd_10am_S     -0.005155  -0.815684   0.817995   0.068989      -0.091985   \n",
              "wdd_10am_W      0.046621  -0.086560   0.018793   0.177387      -0.014304   \n",
              "\n",
              "               wdd_10am_E  wdd_10am_N  wdd_10am_S  wdd_10am_W  \n",
              "tmp_9am          0.060053   -0.204098    0.161213    0.047345  \n",
              "dew_9am          0.059199   -0.246277    0.196784    0.043023  \n",
              "hum_9am          0.028922   -0.164005    0.134707    0.020281  \n",
              "wnd_9am         -0.068067    0.388667   -0.283218   -0.047336  \n",
              "spd_9am         -0.104052    0.573266   -0.419906   -0.084734  \n",
              "alt_9am         -0.007586    0.011512   -0.010206    0.038326  \n",
              "cig_9am         -0.009862   -0.010473    0.001918   -0.007555  \n",
              "tmp_10am         0.076174   -0.251249    0.178258    0.051170  \n",
              "dew_10am         0.045959   -0.222406    0.175094    0.034210  \n",
              "hum_10am         0.005957   -0.108891    0.099541    0.010756  \n",
              "wnd_10am        -0.149595    0.400839   -0.158383    0.011644  \n",
              "spd_10am        -0.078867    0.563102   -0.493599   -0.080243  \n",
              "alt_10am         0.042654    0.003520   -0.014709   -0.002298  \n",
              "cig_10am        -0.007483   -0.014588    0.009089   -0.013192  \n",
              "pcp_hrs          0.065184   -0.125769    0.066881   -0.000708  \n",
              "pcp              0.070229   -0.147670    0.086883    0.007793  \n",
              "pcp_yesterday    0.045128   -0.093256    0.055580    0.011921  \n",
              "year            -0.045017   -0.000902    0.009617   -0.042913  \n",
              "month            0.010948    0.097910   -0.101888   -0.006289  \n",
              "day              0.008389   -0.025569    0.001510    0.024764  \n",
              "spl_9am          0.106276   -0.585182    0.421093    0.094461  \n",
              "spl_10am         0.089477   -0.522855    0.453314    0.069182  \n",
              "wdd_9am_CALM    -0.000238   -0.356187    0.196034    0.046981  \n",
              "wdd_9am_E        0.161388   -0.082077   -0.005155    0.046621  \n",
              "wdd_9am_N       -0.016791    0.838620   -0.815684   -0.086560  \n",
              "wdd_9am_S       -0.038861   -0.703222    0.817995    0.018793  \n",
              "wdd_9am_W        0.022318   -0.098582    0.068989    0.177387  \n",
              "wdd_10am_CALM   -0.027121   -0.327453   -0.091985   -0.014304  \n",
              "wdd_10am_E       1.000000   -0.235085   -0.066038   -0.010269  \n",
              "wdd_10am_N      -0.235085    1.000000   -0.797321   -0.123986  \n",
              "wdd_10am_S      -0.066038   -0.797321    1.000000   -0.034829  \n",
              "wdd_10am_W      -0.010269   -0.123986   -0.034829    1.000000  \n",
              "\n",
              "[32 rows x 32 columns]"
            ],
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              "    <div class=\"colab-df-container\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>tmp_9am</th>\n",
              "      <th>dew_9am</th>\n",
              "      <th>hum_9am</th>\n",
              "      <th>wnd_9am</th>\n",
              "      <th>spd_9am</th>\n",
              "      <th>alt_9am</th>\n",
              "      <th>cig_9am</th>\n",
              "      <th>tmp_10am</th>\n",
              "      <th>dew_10am</th>\n",
              "      <th>hum_10am</th>\n",
              "      <th>...</th>\n",
              "      <th>wdd_9am_CALM</th>\n",
              "      <th>wdd_9am_E</th>\n",
              "      <th>wdd_9am_N</th>\n",
              "      <th>wdd_9am_S</th>\n",
              "      <th>wdd_9am_W</th>\n",
              "      <th>wdd_10am_CALM</th>\n",
              "      <th>wdd_10am_E</th>\n",
              "      <th>wdd_10am_N</th>\n",
              "      <th>wdd_10am_S</th>\n",
              "      <th>wdd_10am_W</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>tmp_9am</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.597908</td>\n",
              "      <td>0.039722</td>\n",
              "      <td>-0.168097</td>\n",
              "      <td>-0.345001</td>\n",
              "      <td>-0.022346</td>\n",
              "      <td>0.004755</td>\n",
              "      <td>0.890675</td>\n",
              "      <td>0.566511</td>\n",
              "      <td>0.119284</td>\n",
              "      <td>...</td>\n",
              "      <td>0.081155</td>\n",
              "      <td>0.089621</td>\n",
              "      <td>-0.191277</td>\n",
              "      <td>0.132283</td>\n",
              "      <td>0.043732</td>\n",
              "      <td>0.049573</td>\n",
              "      <td>0.060053</td>\n",
              "      <td>-0.204098</td>\n",
              "      <td>0.161213</td>\n",
              "      <td>0.047345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dew_9am</th>\n",
              "      <td>0.597908</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.818762</td>\n",
              "      <td>-0.199035</td>\n",
              "      <td>-0.475398</td>\n",
              "      <td>-0.021305</td>\n",
              "      <td>-0.269960</td>\n",
              "      <td>0.520614</td>\n",
              "      <td>0.922867</td>\n",
              "      <td>0.735668</td>\n",
              "      <td>...</td>\n",
              "      <td>0.130866</td>\n",
              "      <td>0.065143</td>\n",
              "      <td>-0.246736</td>\n",
              "      <td>0.171610</td>\n",
              "      <td>0.049508</td>\n",
              "      <td>0.090687</td>\n",
              "      <td>0.059199</td>\n",
              "      <td>-0.246277</td>\n",
              "      <td>0.196784</td>\n",
              "      <td>0.043023</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hum_9am</th>\n",
              "      <td>0.039722</td>\n",
              "      <td>0.818762</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.128296</td>\n",
              "      <td>-0.347316</td>\n",
              "      <td>-0.011486</td>\n",
              "      <td>-0.335489</td>\n",
              "      <td>0.023372</td>\n",
              "      <td>0.744487</td>\n",
              "      <td>0.834018</td>\n",
              "      <td>...</td>\n",
              "      <td>0.104588</td>\n",
              "      <td>0.015298</td>\n",
              "      <td>-0.173811</td>\n",
              "      <td>0.124037</td>\n",
              "      <td>0.029449</td>\n",
              "      <td>0.078395</td>\n",
              "      <td>0.028922</td>\n",
              "      <td>-0.164005</td>\n",
              "      <td>0.134707</td>\n",
              "      <td>0.020281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wnd_9am</th>\n",
              "      <td>-0.168097</td>\n",
              "      <td>-0.199035</td>\n",
              "      <td>-0.128296</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.499257</td>\n",
              "      <td>0.028778</td>\n",
              "      <td>-0.050645</td>\n",
              "      <td>-0.217859</td>\n",
              "      <td>-0.176864</td>\n",
              "      <td>-0.073136</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.528749</td>\n",
              "      <td>-0.129170</td>\n",
              "      <td>0.446666</td>\n",
              "      <td>-0.133136</td>\n",
              "      <td>0.009086</td>\n",
              "      <td>-0.206987</td>\n",
              "      <td>-0.068067</td>\n",
              "      <td>0.388667</td>\n",
              "      <td>-0.283218</td>\n",
              "      <td>-0.047336</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spd_9am</th>\n",
              "      <td>-0.345001</td>\n",
              "      <td>-0.475398</td>\n",
              "      <td>-0.347316</td>\n",
              "      <td>0.499257</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.063028</td>\n",
              "      <td>0.004127</td>\n",
              "      <td>-0.417572</td>\n",
              "      <td>-0.439030</td>\n",
              "      <td>-0.252759</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.496234</td>\n",
              "      <td>-0.109920</td>\n",
              "      <td>0.566207</td>\n",
              "      <td>-0.284219</td>\n",
              "      <td>-0.077864</td>\n",
              "      <td>-0.282072</td>\n",
              "      <td>-0.104052</td>\n",
              "      <td>0.573266</td>\n",
              "      <td>-0.419906</td>\n",
              "      <td>-0.084734</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alt_9am</th>\n",
              "      <td>-0.022346</td>\n",
              "      <td>-0.021305</td>\n",
              "      <td>-0.011486</td>\n",
              "      <td>0.028778</td>\n",
              "      <td>0.063028</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.010595</td>\n",
              "      <td>-0.023941</td>\n",
              "      <td>-0.018353</td>\n",
              "      <td>-0.006968</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>-0.006651</td>\n",
              "      <td>0.008269</td>\n",
              "      <td>-0.014798</td>\n",
              "      <td>0.038308</td>\n",
              "      <td>-0.010572</td>\n",
              "      <td>-0.007586</td>\n",
              "      <td>0.011512</td>\n",
              "      <td>-0.010206</td>\n",
              "      <td>0.038326</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cig_9am</th>\n",
              "      <td>0.004755</td>\n",
              "      <td>-0.269960</td>\n",
              "      <td>-0.335489</td>\n",
              "      <td>-0.050645</td>\n",
              "      <td>0.004127</td>\n",
              "      <td>-0.010595</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.070760</td>\n",
              "      <td>-0.278871</td>\n",
              "      <td>-0.354090</td>\n",
              "      <td>...</td>\n",
              "      <td>0.014912</td>\n",
              "      <td>0.009807</td>\n",
              "      <td>-0.001583</td>\n",
              "      <td>-0.009917</td>\n",
              "      <td>-0.006960</td>\n",
              "      <td>0.013619</td>\n",
              "      <td>-0.009862</td>\n",
              "      <td>-0.010473</td>\n",
              "      <td>0.001918</td>\n",
              "      <td>-0.007555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tmp_10am</th>\n",
              "      <td>0.890675</td>\n",
              "      <td>0.520614</td>\n",
              "      <td>0.023372</td>\n",
              "      <td>-0.217859</td>\n",
              "      <td>-0.417572</td>\n",
              "      <td>-0.023941</td>\n",
              "      <td>0.070760</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.462619</td>\n",
              "      <td>-0.064607</td>\n",
              "      <td>...</td>\n",
              "      <td>0.122676</td>\n",
              "      <td>0.096760</td>\n",
              "      <td>-0.229794</td>\n",
              "      <td>0.148818</td>\n",
              "      <td>0.038044</td>\n",
              "      <td>0.090279</td>\n",
              "      <td>0.076174</td>\n",
              "      <td>-0.251249</td>\n",
              "      <td>0.178258</td>\n",
              "      <td>0.051170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dew_10am</th>\n",
              "      <td>0.566511</td>\n",
              "      <td>0.922867</td>\n",
              "      <td>0.744487</td>\n",
              "      <td>-0.176864</td>\n",
              "      <td>-0.439030</td>\n",
              "      <td>-0.018353</td>\n",
              "      <td>-0.278871</td>\n",
              "      <td>0.462619</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.849051</td>\n",
              "      <td>...</td>\n",
              "      <td>0.115456</td>\n",
              "      <td>0.049593</td>\n",
              "      <td>-0.218753</td>\n",
              "      <td>0.155023</td>\n",
              "      <td>0.045088</td>\n",
              "      <td>0.087752</td>\n",
              "      <td>0.045959</td>\n",
              "      <td>-0.222406</td>\n",
              "      <td>0.175094</td>\n",
              "      <td>0.034210</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hum_10am</th>\n",
              "      <td>0.119284</td>\n",
              "      <td>0.735668</td>\n",
              "      <td>0.834018</td>\n",
              "      <td>-0.073136</td>\n",
              "      <td>-0.252759</td>\n",
              "      <td>-0.006968</td>\n",
              "      <td>-0.354090</td>\n",
              "      <td>-0.064607</td>\n",
              "      <td>0.849051</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>0.056942</td>\n",
              "      <td>-0.000607</td>\n",
              "      <td>-0.117966</td>\n",
              "      <td>0.095538</td>\n",
              "      <td>0.028161</td>\n",
              "      <td>0.047519</td>\n",
              "      <td>0.005957</td>\n",
              "      <td>-0.108891</td>\n",
              "      <td>0.099541</td>\n",
              "      <td>0.010756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wnd_10am</th>\n",
              "      <td>-0.158107</td>\n",
              "      <td>-0.176751</td>\n",
              "      <td>-0.106756</td>\n",
              "      <td>0.432792</td>\n",
              "      <td>0.393178</td>\n",
              "      <td>0.032867</td>\n",
              "      <td>-0.035934</td>\n",
              "      <td>-0.196201</td>\n",
              "      <td>-0.167122</td>\n",
              "      <td>-0.074458</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.224021</td>\n",
              "      <td>-0.084771</td>\n",
              "      <td>0.290496</td>\n",
              "      <td>-0.160392</td>\n",
              "      <td>-0.021414</td>\n",
              "      <td>-0.410498</td>\n",
              "      <td>-0.149595</td>\n",
              "      <td>0.400839</td>\n",
              "      <td>-0.158383</td>\n",
              "      <td>0.011644</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spd_10am</th>\n",
              "      <td>-0.428311</td>\n",
              "      <td>-0.501841</td>\n",
              "      <td>-0.320100</td>\n",
              "      <td>0.350680</td>\n",
              "      <td>0.683168</td>\n",
              "      <td>0.035770</td>\n",
              "      <td>0.034187</td>\n",
              "      <td>-0.461368</td>\n",
              "      <td>-0.466077</td>\n",
              "      <td>-0.256343</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.250225</td>\n",
              "      <td>-0.074405</td>\n",
              "      <td>0.557935</td>\n",
              "      <td>-0.448413</td>\n",
              "      <td>-0.085065</td>\n",
              "      <td>-0.164473</td>\n",
              "      <td>-0.078867</td>\n",
              "      <td>0.563102</td>\n",
              "      <td>-0.493599</td>\n",
              "      <td>-0.080243</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alt_10am</th>\n",
              "      <td>-0.015172</td>\n",
              "      <td>-0.020732</td>\n",
              "      <td>-0.014641</td>\n",
              "      <td>0.007034</td>\n",
              "      <td>0.004548</td>\n",
              "      <td>-0.001210</td>\n",
              "      <td>0.026609</td>\n",
              "      <td>-0.004340</td>\n",
              "      <td>-0.020991</td>\n",
              "      <td>-0.019741</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.007489</td>\n",
              "      <td>-0.003564</td>\n",
              "      <td>0.016907</td>\n",
              "      <td>-0.013257</td>\n",
              "      <td>-0.002347</td>\n",
              "      <td>-0.005662</td>\n",
              "      <td>0.042654</td>\n",
              "      <td>0.003520</td>\n",
              "      <td>-0.014709</td>\n",
              "      <td>-0.002298</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cig_10am</th>\n",
              "      <td>-0.030429</td>\n",
              "      <td>-0.244382</td>\n",
              "      <td>-0.279113</td>\n",
              "      <td>-0.046606</td>\n",
              "      <td>-0.006766</td>\n",
              "      <td>-0.020531</td>\n",
              "      <td>0.610568</td>\n",
              "      <td>0.063545</td>\n",
              "      <td>-0.255833</td>\n",
              "      <td>-0.326316</td>\n",
              "      <td>...</td>\n",
              "      <td>0.006607</td>\n",
              "      <td>-0.009223</td>\n",
              "      <td>-0.000063</td>\n",
              "      <td>0.002034</td>\n",
              "      <td>-0.015900</td>\n",
              "      <td>0.001071</td>\n",
              "      <td>-0.007483</td>\n",
              "      <td>-0.014588</td>\n",
              "      <td>0.009089</td>\n",
              "      <td>-0.013192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp_hrs</th>\n",
              "      <td>0.189067</td>\n",
              "      <td>0.367960</td>\n",
              "      <td>0.312704</td>\n",
              "      <td>-0.100336</td>\n",
              "      <td>-0.260422</td>\n",
              "      <td>-0.015611</td>\n",
              "      <td>-0.143299</td>\n",
              "      <td>0.121451</td>\n",
              "      <td>0.383362</td>\n",
              "      <td>0.352884</td>\n",
              "      <td>...</td>\n",
              "      <td>0.078342</td>\n",
              "      <td>0.040385</td>\n",
              "      <td>-0.111212</td>\n",
              "      <td>0.057867</td>\n",
              "      <td>0.036581</td>\n",
              "      <td>0.085356</td>\n",
              "      <td>0.065184</td>\n",
              "      <td>-0.125769</td>\n",
              "      <td>0.066881</td>\n",
              "      <td>-0.000708</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp</th>\n",
              "      <td>0.259782</td>\n",
              "      <td>0.435098</td>\n",
              "      <td>0.344160</td>\n",
              "      <td>-0.121131</td>\n",
              "      <td>-0.291609</td>\n",
              "      <td>-0.013918</td>\n",
              "      <td>-0.165485</td>\n",
              "      <td>0.186548</td>\n",
              "      <td>0.450448</td>\n",
              "      <td>0.388228</td>\n",
              "      <td>...</td>\n",
              "      <td>0.087574</td>\n",
              "      <td>0.051983</td>\n",
              "      <td>-0.134089</td>\n",
              "      <td>0.069559</td>\n",
              "      <td>0.062108</td>\n",
              "      <td>0.085551</td>\n",
              "      <td>0.070229</td>\n",
              "      <td>-0.147670</td>\n",
              "      <td>0.086883</td>\n",
              "      <td>0.007793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp_yesterday</th>\n",
              "      <td>0.175899</td>\n",
              "      <td>0.483836</td>\n",
              "      <td>0.461585</td>\n",
              "      <td>-0.076377</td>\n",
              "      <td>-0.212637</td>\n",
              "      <td>-0.012869</td>\n",
              "      <td>-0.212040</td>\n",
              "      <td>0.082149</td>\n",
              "      <td>0.484636</td>\n",
              "      <td>0.488482</td>\n",
              "      <td>...</td>\n",
              "      <td>0.059304</td>\n",
              "      <td>0.051183</td>\n",
              "      <td>-0.098296</td>\n",
              "      <td>0.053933</td>\n",
              "      <td>0.025548</td>\n",
              "      <td>0.059673</td>\n",
              "      <td>0.045128</td>\n",
              "      <td>-0.093256</td>\n",
              "      <td>0.055580</td>\n",
              "      <td>0.011921</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>0.081000</td>\n",
              "      <td>0.112598</td>\n",
              "      <td>0.079499</td>\n",
              "      <td>-0.148197</td>\n",
              "      <td>-0.112165</td>\n",
              "      <td>-0.075087</td>\n",
              "      <td>0.200313</td>\n",
              "      <td>0.077347</td>\n",
              "      <td>0.124460</td>\n",
              "      <td>0.090447</td>\n",
              "      <td>...</td>\n",
              "      <td>0.023739</td>\n",
              "      <td>-0.014438</td>\n",
              "      <td>-0.005391</td>\n",
              "      <td>0.000335</td>\n",
              "      <td>-0.024011</td>\n",
              "      <td>0.005807</td>\n",
              "      <td>-0.045017</td>\n",
              "      <td>-0.000902</td>\n",
              "      <td>0.009617</td>\n",
              "      <td>-0.042913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>0.109625</td>\n",
              "      <td>0.214679</td>\n",
              "      <td>0.180395</td>\n",
              "      <td>0.041548</td>\n",
              "      <td>-0.004554</td>\n",
              "      <td>-0.012161</td>\n",
              "      <td>-0.085026</td>\n",
              "      <td>0.027318</td>\n",
              "      <td>0.249480</td>\n",
              "      <td>0.254872</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.006914</td>\n",
              "      <td>-0.002944</td>\n",
              "      <td>0.089267</td>\n",
              "      <td>-0.099047</td>\n",
              "      <td>-0.002438</td>\n",
              "      <td>0.006453</td>\n",
              "      <td>0.010948</td>\n",
              "      <td>0.097910</td>\n",
              "      <td>-0.101888</td>\n",
              "      <td>-0.006289</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>0.000315</td>\n",
              "      <td>0.001120</td>\n",
              "      <td>0.000411</td>\n",
              "      <td>-0.023530</td>\n",
              "      <td>-0.039530</td>\n",
              "      <td>-0.022971</td>\n",
              "      <td>0.020031</td>\n",
              "      <td>0.002697</td>\n",
              "      <td>0.006112</td>\n",
              "      <td>0.004784</td>\n",
              "      <td>...</td>\n",
              "      <td>0.030484</td>\n",
              "      <td>0.002604</td>\n",
              "      <td>-0.013132</td>\n",
              "      <td>-0.009793</td>\n",
              "      <td>0.020186</td>\n",
              "      <td>0.040709</td>\n",
              "      <td>0.008389</td>\n",
              "      <td>-0.025569</td>\n",
              "      <td>0.001510</td>\n",
              "      <td>0.024764</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spl_9am</th>\n",
              "      <td>0.219577</td>\n",
              "      <td>0.312391</td>\n",
              "      <td>0.227479</td>\n",
              "      <td>-0.412612</td>\n",
              "      <td>-0.735907</td>\n",
              "      <td>-0.012810</td>\n",
              "      <td>-0.012254</td>\n",
              "      <td>0.283380</td>\n",
              "      <td>0.279767</td>\n",
              "      <td>0.147370</td>\n",
              "      <td>...</td>\n",
              "      <td>0.410845</td>\n",
              "      <td>0.143476</td>\n",
              "      <td>-0.552148</td>\n",
              "      <td>0.306671</td>\n",
              "      <td>0.109441</td>\n",
              "      <td>0.295897</td>\n",
              "      <td>0.106276</td>\n",
              "      <td>-0.585182</td>\n",
              "      <td>0.421093</td>\n",
              "      <td>0.094461</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spl_10am</th>\n",
              "      <td>0.349347</td>\n",
              "      <td>0.419727</td>\n",
              "      <td>0.268430</td>\n",
              "      <td>-0.323031</td>\n",
              "      <td>-0.580620</td>\n",
              "      <td>-0.017256</td>\n",
              "      <td>-0.043585</td>\n",
              "      <td>0.377870</td>\n",
              "      <td>0.383307</td>\n",
              "      <td>0.208710</td>\n",
              "      <td>...</td>\n",
              "      <td>0.229078</td>\n",
              "      <td>0.079772</td>\n",
              "      <td>-0.514751</td>\n",
              "      <td>0.411214</td>\n",
              "      <td>0.078163</td>\n",
              "      <td>0.165642</td>\n",
              "      <td>0.089477</td>\n",
              "      <td>-0.522855</td>\n",
              "      <td>0.453314</td>\n",
              "      <td>0.069182</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_CALM</th>\n",
              "      <td>0.081155</td>\n",
              "      <td>0.130866</td>\n",
              "      <td>0.104588</td>\n",
              "      <td>-0.528749</td>\n",
              "      <td>-0.496234</td>\n",
              "      <td>-0.000809</td>\n",
              "      <td>0.014912</td>\n",
              "      <td>0.122676</td>\n",
              "      <td>0.115456</td>\n",
              "      <td>0.056942</td>\n",
              "      <td>...</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.030914</td>\n",
              "      <td>-0.461524</td>\n",
              "      <td>-0.106768</td>\n",
              "      <td>-0.018632</td>\n",
              "      <td>0.346433</td>\n",
              "      <td>-0.000238</td>\n",
              "      <td>-0.356187</td>\n",
              "      <td>0.196034</td>\n",
              "      <td>0.046981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_E</th>\n",
              "      <td>0.089621</td>\n",
              "      <td>0.065143</td>\n",
              "      <td>0.015298</td>\n",
              "      <td>-0.129170</td>\n",
              "      <td>-0.109920</td>\n",
              "      <td>-0.006651</td>\n",
              "      <td>0.009807</td>\n",
              "      <td>0.096760</td>\n",
              "      <td>0.049593</td>\n",
              "      <td>-0.000607</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.030914</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.222600</td>\n",
              "      <td>-0.051496</td>\n",
              "      <td>-0.008986</td>\n",
              "      <td>0.055953</td>\n",
              "      <td>0.161388</td>\n",
              "      <td>-0.082077</td>\n",
              "      <td>-0.005155</td>\n",
              "      <td>0.046621</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_N</th>\n",
              "      <td>-0.191277</td>\n",
              "      <td>-0.246736</td>\n",
              "      <td>-0.173811</td>\n",
              "      <td>0.446666</td>\n",
              "      <td>0.566207</td>\n",
              "      <td>0.008269</td>\n",
              "      <td>-0.001583</td>\n",
              "      <td>-0.229794</td>\n",
              "      <td>-0.218753</td>\n",
              "      <td>-0.117966</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.461524</td>\n",
              "      <td>-0.222600</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.768788</td>\n",
              "      <td>-0.134159</td>\n",
              "      <td>-0.197228</td>\n",
              "      <td>-0.016791</td>\n",
              "      <td>0.838620</td>\n",
              "      <td>-0.815684</td>\n",
              "      <td>-0.086560</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_S</th>\n",
              "      <td>0.132283</td>\n",
              "      <td>0.171610</td>\n",
              "      <td>0.124037</td>\n",
              "      <td>-0.133136</td>\n",
              "      <td>-0.284219</td>\n",
              "      <td>-0.014798</td>\n",
              "      <td>-0.009917</td>\n",
              "      <td>0.148818</td>\n",
              "      <td>0.155023</td>\n",
              "      <td>0.095538</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.106768</td>\n",
              "      <td>-0.051496</td>\n",
              "      <td>-0.768788</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.031036</td>\n",
              "      <td>-0.018676</td>\n",
              "      <td>-0.038861</td>\n",
              "      <td>-0.703222</td>\n",
              "      <td>0.817995</td>\n",
              "      <td>0.018793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_W</th>\n",
              "      <td>0.043732</td>\n",
              "      <td>0.049508</td>\n",
              "      <td>0.029449</td>\n",
              "      <td>0.009086</td>\n",
              "      <td>-0.077864</td>\n",
              "      <td>0.038308</td>\n",
              "      <td>-0.006960</td>\n",
              "      <td>0.038044</td>\n",
              "      <td>0.045088</td>\n",
              "      <td>0.028161</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.018632</td>\n",
              "      <td>-0.008986</td>\n",
              "      <td>-0.134159</td>\n",
              "      <td>-0.031036</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.009511</td>\n",
              "      <td>0.022318</td>\n",
              "      <td>-0.098582</td>\n",
              "      <td>0.068989</td>\n",
              "      <td>0.177387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_CALM</th>\n",
              "      <td>0.049573</td>\n",
              "      <td>0.090687</td>\n",
              "      <td>0.078395</td>\n",
              "      <td>-0.206987</td>\n",
              "      <td>-0.282072</td>\n",
              "      <td>-0.010572</td>\n",
              "      <td>0.013619</td>\n",
              "      <td>0.090279</td>\n",
              "      <td>0.087752</td>\n",
              "      <td>0.047519</td>\n",
              "      <td>...</td>\n",
              "      <td>0.346433</td>\n",
              "      <td>0.055953</td>\n",
              "      <td>-0.197228</td>\n",
              "      <td>-0.018676</td>\n",
              "      <td>0.009511</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.027121</td>\n",
              "      <td>-0.327453</td>\n",
              "      <td>-0.091985</td>\n",
              "      <td>-0.014304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_E</th>\n",
              "      <td>0.060053</td>\n",
              "      <td>0.059199</td>\n",
              "      <td>0.028922</td>\n",
              "      <td>-0.068067</td>\n",
              "      <td>-0.104052</td>\n",
              "      <td>-0.007586</td>\n",
              "      <td>-0.009862</td>\n",
              "      <td>0.076174</td>\n",
              "      <td>0.045959</td>\n",
              "      <td>0.005957</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.000238</td>\n",
              "      <td>0.161388</td>\n",
              "      <td>-0.016791</td>\n",
              "      <td>-0.038861</td>\n",
              "      <td>0.022318</td>\n",
              "      <td>-0.027121</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.235085</td>\n",
              "      <td>-0.066038</td>\n",
              "      <td>-0.010269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_N</th>\n",
              "      <td>-0.204098</td>\n",
              "      <td>-0.246277</td>\n",
              "      <td>-0.164005</td>\n",
              "      <td>0.388667</td>\n",
              "      <td>0.573266</td>\n",
              "      <td>0.011512</td>\n",
              "      <td>-0.010473</td>\n",
              "      <td>-0.251249</td>\n",
              "      <td>-0.222406</td>\n",
              "      <td>-0.108891</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.356187</td>\n",
              "      <td>-0.082077</td>\n",
              "      <td>0.838620</td>\n",
              "      <td>-0.703222</td>\n",
              "      <td>-0.098582</td>\n",
              "      <td>-0.327453</td>\n",
              "      <td>-0.235085</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.797321</td>\n",
              "      <td>-0.123986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_S</th>\n",
              "      <td>0.161213</td>\n",
              "      <td>0.196784</td>\n",
              "      <td>0.134707</td>\n",
              "      <td>-0.283218</td>\n",
              "      <td>-0.419906</td>\n",
              "      <td>-0.010206</td>\n",
              "      <td>0.001918</td>\n",
              "      <td>0.178258</td>\n",
              "      <td>0.175094</td>\n",
              "      <td>0.099541</td>\n",
              "      <td>...</td>\n",
              "      <td>0.196034</td>\n",
              "      <td>-0.005155</td>\n",
              "      <td>-0.815684</td>\n",
              "      <td>0.817995</td>\n",
              "      <td>0.068989</td>\n",
              "      <td>-0.091985</td>\n",
              "      <td>-0.066038</td>\n",
              "      <td>-0.797321</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.034829</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_W</th>\n",
              "      <td>0.047345</td>\n",
              "      <td>0.043023</td>\n",
              "      <td>0.020281</td>\n",
              "      <td>-0.047336</td>\n",
              "      <td>-0.084734</td>\n",
              "      <td>0.038326</td>\n",
              "      <td>-0.007555</td>\n",
              "      <td>0.051170</td>\n",
              "      <td>0.034210</td>\n",
              "      <td>0.010756</td>\n",
              "      <td>...</td>\n",
              "      <td>0.046981</td>\n",
              "      <td>0.046621</td>\n",
              "      <td>-0.086560</td>\n",
              "      <td>0.018793</td>\n",
              "      <td>0.177387</td>\n",
              "      <td>-0.014304</td>\n",
              "      <td>-0.010269</td>\n",
              "      <td>-0.123986</td>\n",
              "      <td>-0.034829</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>32 rows × 32 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8e0867c8-4aff-4e1c-b7e2-0ef965f93b96')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8e0867c8-4aff-4e1c-b7e2-0ef965f93b96 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8e0867c8-4aff-4e1c-b7e2-0ef965f93b96');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(10,10))\n",
        "sns.heatmap(data_corr[['pcp']].sort_values(by=['pcp'],ascending=False),\n",
        "            vmin=-1,\n",
        "            cmap='coolwarm',\n",
        "            annot=True).set(title='Correlation to pcp');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "TQb_uYCUZd3q",
        "outputId": "f47b83ff-1b99-4d7e-bda9-30f826cc467c"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ],
            "image/png": 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RPZPvNtcBjsuotyYiegD3As8Dl5D+Ukvf5KsrZpYlc5asoHXDekXbrRrWZc7S5cXqTPpmPrOXLOfwTu02On7WomWc3v9FLnxoMGOmz670eM3MqlKudCsfBjwbESsAJL0A1Aa+AzyZNNgB1Ep+vgMcDuwK3Aj8DHgbGFXBcX1X0lVAXaApMAF4Mdn3QvJzHDAhIr5JYp8CtAeKfXpPUj+gH8Cd55/Ihd/rVcGhmllZUhHc+toorjth4wnoW9Svw+DLT6Fx3dpM/GY+v37iTZ6+6ETq16pZBZGaWUXJxbGA2ZITLYdlyAMWRcR+GUvnZN8w0gllL+BloDHQm3TSuKVmkU7mCrUDZkmqDdwN/Dgi9gHuI52wFlqd/ExlrBdub5SUR0T/iOgRET2cGJpVrJYN6/Ltkg0thbOXrKBlgw0tictXr+XLOYv46X8Gc8w/nmLczLn86vE3mfD1PGoW5NO4bvpXu8tOzWjXpAFfzV+S9XswM8uWXEkOhwEnSaojqQFwPLACmCrpVIBkjGG3pP5I0q2KqYhYBXwM/Dw5z5YaBXSStKukmqS7twtbLgHmSaoP/Hgr783MKlnXNs2ZvmAJsxYuZe369QyZMJUj9tjQfdygdk2GXnEGr1z+Y165/Mfs064Ft5/+Pbq2ac6C5atYn0oBMHPhUqYvWEK7Jg2q6lbMrIIoLy9rS67JiW7liBgj6XFgLDCHDd3DZwP3SLoGqEH6ZZGxEbFa0gxgRFLvHeBM0l28pZLUGhgNNARSkn4FdImIJZIuBYYA+cADETEhOeY+YDzwLRXfZW1mFaQgL4/f9TmQix95nVSkOLFbJ3Zv2YS7h35El52a0XvPncs8dsz02dw99CMK8vPIk7jm2INpVKdWmfXNzHKdIqKqY7ASVg78m/9RzMysWqlzzh+yOghw3HHfzdrf2n1eeiunBjjmXlunmZmZmVWanOhWrkiSzgd+WaJ4eERcUhXxmJmZmW1Pql1yGBEPAg9WdRxmZmZWdXJxcupsqXbJYS5Qvr/OYGZmZlXDyaGZmZlVO54Eu2x+IcXMzMzMirjl0MzMzKqdXJycOlv8ZMzMzMysyA6VHEr6s6QrKvicTSQ9K+kTSSMl7V2R5zez7Bg+eSYn3PkUx93xJPe/M7bMeq9PnEa3Pz/AhFnzipV/s2gZB93wHx4aXuaHlswshyhPWVtyzQ6VHFaSPwAfR8S+wLnAHVUcj5ltofWpFH97+X3uPvsonr3kRwweP4Uv5yzcqN7y1Wt5eMQE9mnbYqN9tw4ZyaGd2m1Ubma2o8n55FDSHyV9LuldYM+krKOkwZI+lPSOpL0k5UuaqrTGktZLOjypP0xSpzIu0QV4EyAiPgU6SGqVHPdcco0JkvplxLRM0i1J+euSekkaKmmKpBMq83mY2cbGz5pH+6YNade0ITUK8umz924M/Wz6RvXuevNDzj90X2oVFJ9O6s1JX9G2SX06tmicrZDNrJK55bBsOZ0cSuoOnAHsBxwL9Ex29Qcui4juwBXA3RGxHviMdLJ3KDAGOExSLaB9REwu4zJjgR8l1+sF7AIUNh9ckFyjB3C5pGZJeT3gzYjoCiwFrgeOBE4GrquIezez8puzZDmtG9Yr2m7ZsB6zl6woVmfS1/P4dslyDt+jfbHyFavX8uDwT7joiP2zEquZWVXL6eQQOAx4NiJWRMQS4AWgNvAd4ElJHwP/AnZK6r8DHJ4sN5JOEnsCozZxjZuAxsm5LgM+AtYn+y6XNBYYAbQHClsf1wCDk/VxwNsRsTZZ71DaRST1kzRa0uj73/ig/E/AzLZZKhXcOmQkvz2q10b77hn6Eecc1JW6tWpUQWRmVlnccli2HXEqmzxgUUTsV8q+YcDFQBvgWuBKoDfppLFUSdJ5PoAkAVOBKZJ6Az8ADo6IFZKGkk5MAdZGRCTrKWB1cq6UpFKfeUT0J93iyapHb47S6pjZ1mnZsB7fLlletD1nyXJaNaxbtL18zVq+mLOQnw54BYB5y1byy0df444zj2TcrLm8PnEat782mqWr1iBBzYJ8zjywS9bvw8wsG3I9ORwGDJB0I+l7OZ50S+FUSadGxJNJQrdvRIwFRgL/BaZExKqkNfDnwHFlXUBSY2BFRKwBfgoMi4glkhoBC5PEcC/goMq8UTPbel3bNGf6/MXMXLiUVg3qMnj8FG48pXfR/ga1a/L21WcXbV/44Mv85qhedG3bnAEX/LCo/J63xlC3Zg0nhmY7AM9zWLacTg4jYoykx0mPC5zDhu7hs4F7JF0D1AAeA8ZGxGpJM0h3A0O6xfBM0t29ZekMPCQpgAnAhUn5YOAiSZNIj2UcUcbxZlbFCvLz+P2xB3Pxf4eQiuCk/Tuxe8sm3PXmGLq2aU7vvXau6hDNzLYb2tD7adsLdyubmVl1U/vMq7M6OO/Lc3+Ytb+1Hf8zKKcGHrpN1czMzKwKSeoj6TNJX0j6XSn7b5P0cbJ8LmlRxr71GfteqIh4crpbuSJJOh/4ZYni4RFxSVXEY2ZmZjs+SfnAXaSnvJsJjJL0QkRMLKwTEb/OqH8ZkDm31soyXsLdak4OExHxIPBgVcdhZmZmlW87mmKmF/BFREwBkPQYcCIwsYz6ZwJ/qsyAnBxujwr8z2JmZrajSL6i1i+jqH8yhR1AW2BGxr6ZwIFlnGcXYFeSL7claksaDawDboqI57Y1XmchZmZmVu1kcyqbzLmMt9EZwFPJV98K7RIRsyTtBrwpaVxEfLktF/ELKWZmZmZVZxbpr6wVapeUleYM4NHMgoiYlfycAgyl+HjEreLk0MzMzKqd7ejzeaOATpJ2lVSTdAK40VvHyQc3mgDvZ5Q1kVQrWW8OHELZYxXLzd3KZmZmZlUkItZJuhQYAuQDD0TEBEnXAaMjojBRPAN4LIpPUN0Z+JekFOkGv5sy33LeWtU6OZT0K9KDQldswzl6A1dERJmf4DOzqjf88xnc/PJ7pFLByd334sIjSp/54fUJU/jto6/zyMUn07VtCxatWMVvH32NCbPmcsL+e/CH4w/NcuRmVhm2o7eViYiXgZdLlF1bYvvPpRz3HrBPRcdT3buVfwXU3ZIDkvmIzCyHrE+l+NuL73L3ucfw7OWnMnjcF3w5Z+FG9ZavXsPD741nn3Yti8pqFuRzyfd78ps+/ny6mVUPlZocSuog6VNJD0uaJOkpSXUl9ZT0nqSxkkZKaiCpr6TnJQ2VNFlSmXP4SLouafUr3L5B0i+T9SsljZL0iaS/JGX1JA1Krjde0umSLgfaAG9Jeiupd5Sk9yWNkfSkpPpJ+TRJN0saA5yazGT+abL9o4w4eiXHf5Tc355J+TBJ+2XUe1dSt4p81mZWtvEz59K+WSPaNW1IjYJ8+uzTkaGTpm1U767XR3P+4ftRq2DD/wPWrVmDAzq0LlZmZrlPeXlZW3JNNiLeE7g7IjoDS4BLgceBX0ZEN+AHwMqkbi/gFGBf0klYjzLO+QBwLoCkPNL98AMlHQV0Ss6zH9Bd0uFAH+DriOgWEXsDgyPiH8DXwHcj4rvJQM5rgB9ExAHAaOA3Gdecn5Q/B9wHHA90B1pn1PkUOCwi9geuBf6WlN8P9E3i3QOoHRFjy/sAzWzbzFmynNaN6hVtt2xYj9lLlherM+nreXy7eBmH77lztsMzM9uuZCM5nBERw5P1gcDRwDcRMQogIpZExLpk/2sRMT8iVgLPAKUO7omIacB8SfsDRwEfRcT8ZP0o4CNgDLAX6WRxHHBk0vp3WEQsLuW0BwFdgOGSPgbOA3bJ2P948nMvYGpETE4GhQ7MqNMIeFLSeOA2oGtS/iRwnKQawAXAgJIXl9RP0mhJo+9/fURpt21mlSSVCm59+X1+e8zBVR2KmWXJdvS28nYnGy+kRIntJUDtctYtuZ3p36Rb41qTbkkEEHBjRPyrZGVJBwDHAtdLeiMiritZhXRyemYZ11teRnmmvwJvRcTJkjqQnm+IiFgh6TXSn8M5jXSLYzGZE2SuevLvm7pvM9tCLRvW49vFG36F5yxZTquGG1oSl69ZyxdzFvDT+18EYN6ylfxy4BDuOOdourZtkfV4zcyqUjZaDneWVPi/42cBI4CdJPUESMYbFiapR0pqKqkOcBIwfOPTFXmWdHdxT9Kvf5P8vCBjrGBbSS0ltQFWRMRA4BbggKT+UqBBsj4COETS7smx9ZIu4JI+BTpI6phsZyaTjdgwcWXfEsf9G/gHMCoiNh4Jb2aVpmvbFkyfv5iZC5awdt16Bo/7kiP22tAx0KB2Td7+w3m8csVZvHLFWezbrqUTQ7MdnMccli0bLYefAZdIeoD0xIx3kv4m4J1JEriS9LhDgJHA06RnBx8YEaPLOmlErEleJFlU+BmZiHhVUmfgfUkAy4BzgN2BW5J5gNYCFyen6Q8MlvR1Mu6wL/Bo4YSSpMcgfl7iuquSbyQOkrQCeIcNCeb/Ag9JugYYVOK4DyUtAR4s11MzswpTkJ/H7487hIsfeoVUKsVJ3fdk91ZNuev10XRt25zenTts8vhjbn2EZavXsnb9et6a9BX39j2Wji2bZCd4M7MsU/G5FCv45Omu1ZeSl0A2V7cv0CMiLi3nufNIjys8NSImb0OYWZG0Xg4F9oqI1KbqulvZzMyqm9qn/jarg/NmXnZa1v7WtrvziZwaeJh7bZ2ApC7AF8AbOZIYngt8APxxc4mhmZmZWVWq1G7l5K3izbYaJnUHUOItXknNgDdKqf79iNhtG8PLmoj4D/Cfqo7DzMzMbHO268/nJdPTlP6NKzMzM7OtlItTzGTLdp0cVls1alR1BGZmZlZNOTk0MzOzaicXp5jJFj8ZMzMzMyvilkMzMzOrdjzmsGxuOTQzMzOzItt1y+GWTKJdAdc6Ffgz0Bnolfl1Fkm/By4E1gOXR8SQUk9iZtut4Z99xc0vvEsqUpzcswsXfrf4J86fGDGex98fR75EnVo1ufZHvenYqilr163numeGMnHWHPIkrjr+MHp2bFtFd2FmFcVjDsu2XSeHWTYe+BHwr8zCZMLtM4CuQBvgdUl7FH6yz8y2f+tTKf723DD+9dMTaNWoPmf980l6d9mVjq2aFtU5dr89OO2g9P+HDp04lVtfGs49Fx7P0yMnAvD0r89k/rIVXPLASzxy6ankuUvKzHZQuZA250u6T9IESa9KqiNpqKQeAJKaS5qWrPeV9Jyk1yRNk3SppN9I+kjSCElNy7pIREyKiM9K2XUi8FhErI6IqaS/zNIrud5zkj5MYutXeICkZZJuScpfl9QriXmKpBMq8NmYWTmMnzGH9s0a0a5ZI2oU5NOnWyeGTpxarE792jWL1leuWUth6jdlzgJ67d4OgGb169Kgdk0mzJqTrdDNrJIoT1lbck0uJIedgLsioiuwCDhlM/X3Jt0C2BO4AVgREfsD7wPnbsX12wIzMrZnJmUAF0REd6AHcHnyRReAesCbScxLgeuBI4GTgeu2IgYz2wZzFi+jdeP6RdstG9Vn9uLlG9V77L1x/PDm/3Lby+9z9YmHAbDHTs15e+JU1q1PMXPBEibNmsvsRcuyFruZWbblQnI4NSI+TtY/BDpspv5bEbE0IuYCi4EXk/Jx5Th2S10uaSwwAmhPOpEFWAMMzrju2xGxdlMxSOonabSk0fe/+l4Fh2lm5XHGd/Zh0NU/4VfHHMx9b6SHHZ/Uo3O6K/rOJ7jlxXfotktrdymb7QDccli2XBhzuDpjfT1QB1jHhsS29ibqpzK2U2zd/c4infgVagfMktQb+AFwcESskDQ0I5a1ERElY4iIlKRSY4iI/kB/gFXP/SNKq2NmW6dlo/p8m9HaN2fxMlo1qldm/T7dOnHDs28DUJCfx5XHH1q079y7nmaX5o0rL1gzsyqWCy2HpZkGFL5q+ONKvtYLwBmSaknalXTr4EigEbAwSQz3Ag6q5DjMbCt1bdeS6fMXM3PBEtauW8/gsZM5onOHYnW+mreoaH3Yp9PYuXkjID3+cMWatQC8//kM8vNV7EUWM8tReXnZW3JMLrQcluZW4InkJZBBFXFCSScDdwItgEGSPo6IoyNigqQngImkWywviYj1kgYDF0maBHxGumvZzLZDBfl5/P7Ew7j4/hdIpYKTenZm99bNuOvVD2OdgmoAACAASURBVOjariW9u+zKY++NY8TkGdTIz6NBndr89bTvA7Bg2Uouvv9F8iRaNqrHDaf/oIrvxsyscmlD76dtL9ytbGZm1U3tky7P6uC8udecn7W/tS2ufzCnBh7mXlunmZmZmVWaXO1W3mqS7gIOKVF8R0Q8WBXxmJmZWfb5Cyllq3bJYURcUtUxmJmZmW2vql1ymBNqlpydx8zMzCw7nByamZlZtZOLk1NnizvczczMzKyIWw7NzMys+vELKWXykzEzMzOzIjnbciipA/BSROydhWt1A+4F6pP+dN/ZEbGksq9rZhVn+KSp3PzsW6QiOPnAvbnwBwcW2//E8LE8Pvxj8iXq1KrBtacdRcfWzYr2f7NwCSffNICL+xzMed/tme3wzayCecxh2dxyWD7/Bn4XEfsAzwJXVnE8ZrYF1qdS/O3pN7i734949uq+DP7oM778dn6xOsd234unrzqPJ648l/O/15Nbnx9abP+tzw3l0M67ZjFqM7OqkevJYb6k+yRNkPSqpDqShkrqASCpuaRpyXpfSc9Jek3SNEmXSvqNpI8kjZDUdBPX2QMYlqy/BpySnLODpHckjUmW7yTlvSW9Lel5SVMk3STpbEkjJY2T1LHSnoiZbWT89G9p37wx7Zo3pkZBPn3235Oh478oVqd+7VpF6yvXrCWzTeHNcZNp26xRsZZEM8ttUl7WllyTexEX1wm4KyK6AotIkrZN2Bv4EdATuAFYERH7A+8D527iuAnAicn6qUD7ZH0OcGREHACcDvwj45huwEVAZ+AnwB4R0Yt0K+Rl5bo7M6sQcxYto3XjBkXbLRs1YPbiZRvVe+zdj/jh9f/mtheHcfWPvgfAitVrePCNUVx09MFZi9fMrCrlenI4NSI+TtY/BDpspv5bEbE0IuYCi4EXk/Jxmzn2AuAXkj4EGgBrkvIawH2SxgFPAl0yjhkVEd9ExGrgS+DVTV1LUj9JoyWNvv+VYSV3m1kWnHHo/gy65qf86rjDue/VEQDcM/g9zjmiO3Vr1azi6MysQuUpe0uOydkXUhKrM9bXA3WAdWxIekt+aiSzfipjO8UmnkVEfAocBSBpD+CHya5fA7NJtxLmAau29loR0R/oD7Dq5f5RVixmtuVaNq7Pt4uWFm3PWbyUVo3ql1m/z/57ccNTrwMw7qtveX3sZG5/cRhLV65GeaJmQQFnHrZ/pcdtZlYVcj05LM00oDswEvhxRZxQUsuImKP0wIFrSL+5DNAImBkRKUnnAfkVcT0zq1hd27dm+txFzJy/mFaN6jP4o8+48Zxji9X5au5CdmnRBIBhE6ewc/P0+oDLzyiqc8/g96hbq4YTQ7MdgDzPYZl2xOTwVuAJSf2AQRV0zjMlXZKsPwM8mKzfDTwt6VxgMLC8gq5nZhWoID+P35/yPS7+19OkUilOOnBvdt+pOXe9Mpyu7VvRe+/deeydjxjx+XRq5OfRoG5t/npWn6oO28ysSijCPZjbG3crm5lZdVP72H5ZHZy3+JbLsva3ttGVd+bUwEO3qZqZmZlZkR2xW3mrSboLOKRE8R0R8WBp9c3MzCxH5eD8g9ni5DBDRFyy+VpmZmZmOy4nh9uhVP3GVR2CmZmZVVNODs3MzKzaUQ5OTp0t7nA3MzMzsyJuOTQzM7Pqx5Ngl8lPxszMzMyKVJuWQ0l/BpZFxK1l7D8V+DPQGegVEaMz9v0euJD095svj4ghlR6wmVWo4eMnc8tjg0ilgpMO684FxxxebP9/Xx3Os+9+SEFeHk0a1ONPfU+mTbPGjPp0Crc+/kpRvWnfzuOmfqfy3f27ZPsWzKwCSR5zWJZqkxyWw3jgR8C/MgsldQHOALoCbYDXJe0REeuzH6KZbY31qRQ3PfIi9/y6L62aNOTsG+7liG570bFNy6I6e+28Ew//8SLq1KrJE0NHcsdTQ7j556fTc6/dePxP6VmuFi9fwQl/uJ2DuuxeVbdiZlbpcrpbWVI9SYMkjZU0XtLpkqZJ+l9J4ySNlFSu/4pHxKSI+KyUXScCj0XE6oiYCnwB9Equ/5ykDyVNSL7lXBjXMkm3JOWvS+olaaikKZJOqIh7N7PyGz91Ju1bNKNdi6bUKCjg6J77MPTjScXq9NxrN+rUqgnAvru1Y/bCJRud5/UPJ3DI3p2K6plZDsvLy96SY3Iv4uL6AF9HRLeI2BsYnJQvjoh9gH8Ct2/jNdoCMzK2ZyZlABdERHegB3C5pGZJeT3gzYjoCiwFrgeOBE4GrtvGeMxsC81ZtIRWTRsVbbdq0oi5i5aWWf+5d8dwyN6dNiofMnIcfXrtWykxmpltL3I9ORwHHCnpZkmHRcTipPzRjJ8HV+L1L5c0FhgBtAcK/5qsYUOiOg54OyLWJusdSjuRpH6SRksa/cALr1diyGa2KYNGfMzEabM47+hDi5XPXbSUybNmc3BXdymb7QiUp6wtuSanxxxGxOeSDgCOBa6X9Ebhrsxq23iZWaQTv0LtgFmSegM/AA6OiBWShgK1kzprI6LwuilgdRJvSlKpzzwi+gP9AVYMe2JbYzazDC0bN2T2gsVF27MXLqZF4wYb1Rsx8UvuH/Q2/77yQmrWKP6r+tro8Xxv/y7UKMiv9HjNzKpSTrccSmoDrIiIgcAtwAHJrtMzfr6/jZd5AThDUi1Ju5JuHRwJNAIWJonhXsBB23gdM6skXTu0Zfqc+cyau5C169YxZNQ4enfbq1idT6d/zQ0Dn+e2S8+hacP6G51j8MhP6NNrn2yFbGaVTXnZW3JMTrccAvsAt0hKAWuBi4GngCaSPiHdYndmeU4k6WTgTqAFMEjSxxFxdERMkPQEMBFYB1wSEeslDQYukjQJ+Ix017KZbYcK8vO5+qzj+MXtD5GKFCcecgAd27bi7uffoMsubei9X2due2oIK1at4ap7HwOgdbNG3HHpOQB8PW8h3y5cTPc9OlThXZiZZYc29H7uGCRNA3pExLyqjmVruVvZzMyqm7qHn5bVwXnL7v191v7W1r/oxpwaeJh7bZ1mZmZmOxBJfSR9JukLSb8rZX9fSXMlfZwsP83Yd56kyclyXkXEk+vdyhuJiA6b2i/pLuCQEsV3RMSDlRaUmZmZbVe0nYwFlJQP3EV6yruZwChJL0TExBJVH4+IS0sc2xT4E+kp9QL4MDl24bbEtMMlh5sTEZdUdQxmZmZmiV7AFxExBUDSY6Q/wFEyOSzN0cBrEbEgOfY10nNAP7rJozaj2iWHOcHfezQzM9thJF9R65dR1D+Zwg5K/9jGgaWc5hRJhwOfA7+OiBllHNu2lGO3iJNDMzMzq36yODl15lzGW+lF4NGIWC3p58BDwPcqJLhSbB8d7mZmZmbVU6kf28isEBHzI2J1svlvoHt5j90aTg7NzMys2lFeXtaWzRgFdJK0q6SawBmkP8CxIVZpp4zNE4BJyfoQ4ChJTSQ1AY5KyraJu5XNzMzMqkhErJN0KemkLh94IPkAx3XA6Ih4Abhc0gmkP8axAOibHLtA0l9JJ5gA1xW+nLItcj45lNQYOCsi7q6k89cE/kX6NfEU8MuIGFoZ1zKzyjN8/Ofc8ujLpFIpTjqsOxcce0Sx/f99dTjPvjOagrw8mjSox5/OP5k2zZoA8M38RVz30LPMXrAEBP/85bm0ad6kKm7DzCrKdvTyZ0S8DLxcouzajPXfA78v49gHgAcqMp6cTw6BxsAvgEpJDoGfAUTEPpJaAq9I6hkRqUq6nplVsPWpFDc9/CL3/OZ8WjVpyNnX38sR+3WmY5uWRXX22nknHr7mYurUqskTb33AHU8O4eaLzgDgf+5/ip/+sDcHdd2dFatWo+3oj4qZWUXbEcYc3gR0TGYMHyXpbUnPS5oi6SZJZ0saKWmcpI4AkgZIulfSaEmfSzpuE+fvArwJEBFzgEWkWxGRdE9yjgmS/lJ4gKRpkm5MYhot6QBJQyR9KemiSnsSZlaq8VNn0r5lM9q1aEqNggKO7rUPQz+eVKxOz712o06tmgDs27E9sxcuAeDLr+ewPpXioK67A1C3dq2iemaWw/LysrfkmNyLeGO/A76MiP2AK4FuwEVAZ+AnwB4R0Yv02z2XZRzXgfTEkz8E7pVUu4zzjwVOkFQgaVfSbwgVvhn0x4joAewLHCFp34zjpicxvQMMAH4MHAT8BTPLqjkLl9CqSaOi7VZNGjI3Sf5K89w7H3LIPp0AmD57Hg3q1uG3dz3CGX+5i9ueHMz6lDsOzGzHtSMkhyWNiohvkle+vwReTcrHkU4ICz0REamImAxMAfYq43wPkJ5UcjRwO/AesD7Zd5qkMcBHQFfSrYyFCt80Ggd8EBFLI2IusDoZJ1mMpH5JK+PoB154fcvu2MwqzKD3P2biV7M47+jDAFi3PsVHk6fx69P6MPCai5g5dwEvDB9TxVGa2TaTsrfkmB1hzGFJqzPWUxnbKYrfb5Q4ruR2ujBiHfDrwm1J7wGfJ62IVwA9I2KhpAFAZutj5nVLxrTRc8+cIHPFO0+WGouZbZ2WTRoye+Hiou3ZC5fQoknDjeqNmPgF9w96m39fdSE1a6R/TVs1acQe7XeiXYumAHx3/86MmzIzO4GbmVWBHaHlcCnQYCuOO1VSXjIOcTfgs9IqSaorqV6yfiSwLvkYdkNgObBYUivgmK2K3swqXdcObZk+ez6z5i5g7bp1DBk5jt7dincWfDr9a2747/PcdtnZNG1Yf8Oxu7Zl6YpVLFi6HIBRk6aw204tshq/mVW87Wiew+1OzrccRsR8ScMljQdWArPLeeh0YCTpJO+iiFhVRr2WwBBJKdKzjv8kue5YSR8Bn5L+ruHwbbgNM6tEBfn5XH3Wcfzi9odIpVKceEh3OrZtxd3PvU6XDm3pvV9nbntyMCtWreGqex8DoHXTxtxx2Tnk5+Xxm1P7cNGtDxBA513a8KPDe1TtDZmZVSJFVL8ezKQL+KWIeKqqYymNu5XNzKy6qXvYqVkdnLdy4N+y9re2zjl/yKmBh7nX1mlmZmZmlSbnu5W3RkT0LVkm6Wjg5hLFUyPi5KwEZWZmZtmTl1ONeVlVLZPD0kTEECrgY9VmZmZmuczJ4XYo8vOrOgQzMzOrppwcmpmZWbUj+bWLsvjJmJmZmVkRtxyamZlZ9eMXUsrklkMzMzMzK1LtWw4l/RlYFhG3lrG/G3AvUB+YBpwdEUuyFqCZVYjh4z7j1kdeYn0qxcmH9+T8H/Yutn/gkHd4dtho8vPyaNKgHn+64BTaNG8CQI8L/sDu7VoD0LpZY27/5bnZDt/MKprHHJap2ieH5fBv4IqIeFvSBcCVwP9UcUxmtgXWp1Lc/N8XuPuKC2nVtCHnXHcXR+zXmd3atiqqs+fObRh47SXUqVWTJ98cwR1PvMLNvzgLgFo1a/DYdZdXVfhmZlm1Q6bNkupJGiRprKTxkk6XNE3S/0oaJ2mkpN3Lebo9gGHJ+mvAKck1Okh6R9KYZPlOUt5b0tuSnpc0RdJNks5OrjlOUseKv2Mz25TxU2bQrmUz2rVsSo2CAo7u1Y2hH00qVqdn547UqVUTgH067syche4gMNuhSdlbcswOmRwCfYCvI6JbROwNDE7KF0fEPsA/gdvLea4JwInJ+qlA+2R9DnBkRBwAnA78I+OYbsBFQGfgJ8AeEdGLdCvkZVt3S2a2teYuXELrpo2Ktls2bcichYvLrP/csFEcss8eRdtr1q7j7L/8k3P/ejdvjZlQqbGamVW1HTU5HAccKelmSYdFROFfgUczfh5cznNdAPxC0odAA2BNUl4DuE/SOOBJoEvGMaMi4puIWA18CbyaEVeH0i4iqZ+k0ZJGP/D8q6VVMbMsGPTeR0ycNotzjzl8Q9mtV/Hwny7lbz8/g1sfeYkZc+ZXYYRmViHy8rK35JgdcsxhRHwu6QDgWOB6SW8U7sqsVs5zfQocBSBpD+CHya5fA7NJtxLmAasyDludsZ7K2E5RxjOPiP5Af4Dl7z1TrtjMrHxaNGnItws2tBTOWbCElk0abVTvgwlfcP9Lb/Hv3/WjZo0Nv6qFddu1bEqPvXbjs6++pn3LZpUfuJlZFci9dLYcJLUBVkTEQOAW4IBk1+kZP98v57laJj/zgGtIv7kM0Aj4JiJSpLuO/c07s+1U113bMWPOPGbNXcDadesYMnIsR+zfuVidT7/6mhseepbbLz+Xpg3rF5UvWb6SNWvXAbBw6XI+nvwVu7VpmdX4zawSKC97S47ZIVsOgX2AWySlgLXAxcBTQBNJn5BuyTuznOc6U9IlyfozwIPJ+t3A05LOJT2mcXlFBW9mFasgP5+rzz6BS/7+AKlUcMJhPejYthX3PPsaXTq05Yj9u3D7Ey+zYvUarrr7EWDDlDVTv57DDQ89i/JEpILzf3hEsbeczcx2NIqoHj2YkqYBPSJiXlXHsjnuVjYzs+qm3nd+lNXXelc994+s/a2tfdLlOfXKcu61dZqZmZlZpdlRu5U3EhEdNrVf0l3AISWK74iIB0urb2ZmZjksB8cCZku1SQ43JyIu2XwtMzMzsx2bk8Pt0PraDao6BDMzM6umnByamZlZ9ZODn7XLFne4m5mZmVkRtxyamZlZ9ZODn7XLFj8ZMzMzMyuSE8mhpMaSflGJ528m6S1JyyT9s8S+7pLGSfpC0j8kD1Iwy0XvfTyRU35zHSf/6s8MeP7VjfaPmfQF5/z+Jg46+3Le+OCjYvvufOQ5Tr/yBk6/8gZeff/DbIVsZpVJyt6SY3IiOQQaA5WWHAKrgP8Brihl3z3Az4BOydKnEuMws0qwPpXifx98gjuu/gVP3HoNr773IVNmflOsTuvmTfjTRT/h6EN6FCt/d8x4Pp06g4dv+h0D/noFA196g2UrVmYzfDOzrMqV5PAmoKOkjyWNkvS2pOclTZF0k6SzJY1MWvg6AkgaIOleSaMlfS7puLJOHhHLI+Jd0kliEUk7AQ0jYkSkvzP4H+CkZN/PkljGSnpaUt2M694jaUQSX29JD0iaJGlA5TweM9uUCV9Mo33r5rRr1ZwaBQUcefABvD36k2J12rRoRqdd2lKyc2DqrG/Zv/PuFOTnU6d2LTrt3Jb3x07KZvhmVhmUl70lx+RKxL8DvoyI/YArgW7ARUBn4CfAHhHRC/g3cFnGcR2AXsAPgXsl1d7C67YFZmZsz0zKAJ6JiJ4R0Q2YBFyYUa8JcDDwa+AF4DagK7CPpP22MAYz20ZzFy6mVbMmRdutmjVh7sLF5Tq20y7pZHDV6jUsWrKM0RM/Z/b8hZUVqplZlcuV5LCkURHxTUSsBr4ECgcQjSOdEBZ6IiJSETEZmALsVYEx7C3pHUnjgLNJJ3+FXkxaGscBsyNiXESkgAkl4isiqV/Syjn6wWcGVWCYZrYtDtq3M4fs14UL/vR3/njng+zTaVfy/JajWe7Ly8vekmNydSqb1RnrqYztFMXvKUocV3J7c2YB7TK22yVlAAOAkyJirKS+QO9S4suMrbT4NgQW0R/oD7BkzGtbGqeZbUKLJo2KtfbNnr+QFk0alfv4C07uwwUnp4cbX3Png+yyU8sKj9HMbHuRK+nsUmBrvil3qqS8ZBzibsBnW3JwRHwDLJF0UPKW8rnA88nuBsA3kmqQbjk0s+1Ul467MP3bucyaM4+169bx2vtjOLz7vuU6dn0qxaKlywCY/NUsJk//mgP3rchOCDOrEn5buUw50XIYEfMlDZc0HlgJzC7nodOBkUBD4KKIWFVWRUnTkno1JZ0EHBURE0m/JT0AqAO8kiyQfrv5A2Bu8tMfRDbbThXk53NV39O4/Ma7WJ8KTuh9EB3b78S9T75E51135oge+zLhy6+46v/uY8nyFbw7Zhz/enIQT9x6DevWraffX24HoF6d2lx3yXkU5OdX8R2ZmVUepYfG7XiSN4NfioinqjqWLeVuZTMzq24aHnBkVpvYVr36YNb+1tY+6vycaj7MlW5lMzMzM8uCnOhW3hoR0bdkmaSjgZtLFE+NiJOzEpSZmZltH3JwLGC27LDJYWkiYggwpKrjMDMzM9teVavkMFesz69Z1SGYmZlZNeXk0MzMzKqfHJycOlv8ZMzMzMysiFsOzczMrNoJv5BSJrccmpmZmVkRtxxWMEmNgbMi4u5kuzdwRUQcV6WBmVVz7388ntsefJxUKsUJ3z+Uc086ptj+jyZ+zm0PPc6XX83ir7/6Gd87qHvRvu+c/nM67twWgFbNm3Lr1ZdmNXYzqwRy+1hZnBxWvMakP7l3d1UHYmZp61Mpbr3/Ef5xza9p2awJ5//+bxzWoxu7tmtTVKdV86b8zy/O55EXX93o+Fo1a/LfW67NZshmZlWmWqfNkjpI+lTSAEmfS3pY0g+S7zhPltRLUlNJz0n6RNIISfsmx/5Z0gOShkqaIuny5LQ3AR0lfSzplqSsvqSnkms9LHmgg1k2TfxiKu1at6RtqxbUKCjgyO/0ZNioscXqtGnZnE67tMO/nmbVhPKyt+QYtxzC7sCpwAXAKOAs4FDgBOAPwAzgo4g4SdL3gP8A+yXH7gV8F2gAfCbpHuB3wN4RsR8UdSvvD3QFvgaGA4cA72bj5swM5i5YRMtmTYu2WzZrzITJU8t9/Jq1a+n7uxvIz8/j3BP7cESv/SsjTDOz7YKTw/Tn88YBSJoAvBERIWkc0AHYBTgFICLelNRMUsPk2EERsRpYLWkO0KqMa4yMiJnJNT5OzlssOZTUD+gH8H/X/Ja+Pz6+Am/RzLbFs3ffSMumTZg1ey6XXPd/dNy5Le1at6zqsMxsG/ht5bI5OYTVGeupjO0U6eeztpzHrqfs57nZehHRH+gP/D97dx5WZZk+cPx7e0RNQY1FXFNzaVPTxBVxtyxbrLSpadOa1LJsGc36NVNWOmWaS2mppU1ZqTVNy8w0WWoqIi5oKZaFa42KC5i54+Fw//54X/CAICpwALk/13Uu3uV53ud5IePmWflt3RI9fZWNMWcjIrQ6e1P3Z53vTT1AROiFZ5y/hpu2TmQEV13elKTt/7Pg0Bhz3ip9HeGBFwvcCVldxCmqevA06Q/hdDMbY0qIyxo14H/Je9m1NwVvejrfLF9NTNSVZ5T34OEjnPA6fyMeOHiI9T9voWHdWkVZXWNMINiYwzxZy2H+RgGzRGQ9cBS493SJVTXVndCyAfgv8J+ir6Ix5nTKezwMv+8OHh0ziYyMDK7vFs3F9WozY97nXNqoPp2jWvLj5u2MHP8Gh44cZdma9bz10RfMmfA823fuZuyM2Ui5cmhGBvf07Z1tlrMxxpxvRNV6MEsa61Y2xhhT1lx4ZZeADgI8GvtxwH7XVo7pX6oGOJa+tk5jjDHGmPOIiPQWkZ9FZLOIPJXL/SdE5Ed3Wb2FIlLf757PXT7vexH5ojDqY93KxhhjjCl7ypWM9jER8QBTgV7ADmC1iHyhqj/6JfsOiFLVoyLyIPAK8Af33rHM5fMKS8n4zhhjjDHGlE1tgc2qulVVTwBzgZv8E6jqt6p61D1dAdQtygpZy2EJdKJCcHFXwRhjjDGFxH8tY9cMdwk7gDo4G25k2gG0O83j7seZ8JqpkogkAOnAy6r6WUHra8GhMcYYY8qcQC6C7b+WcUGIyF1AFNDF73J9Vd0pIhcDi0QkUVW3FKQc61Y2xhhjjCk+O4F6fud13WvZiEhP4BngRnd3NgBUdaf7dSuwGGfL3gKx4NAYY4wxZU/JWQR7NdBERBqKSAXgdiDbrGMRaQVMxwkM9/pdv1BEKrrH4UA04D+R5ZxYt7IxxhhjTDFR1XQReRiYD3iAWar6g4i8ACSo6hfAOCAY+Fic7vBfVfVG4DJguohk4DT4vZxjlvM5CUhwKCIDcKZgP5zLvcOqmucMDBEZC/RxT19U1XlFU0sQkZrAJKANcADYAzymqknu/ceAl4FIVf3dvdYVGK6q1+d41mLgYpyxAOpe+wzoebr3NcYUnpVr1/Ha2++RkZFBn17duOvWG7PdP+H1MmbSmyRt2UbVkGBGDR9GrcgI0tPTGTv1LZK2bMeX4aN31xju6udMHvzoiy/59zffIiJcXL8eTz0ymIoVKhTH6xljCkBL0LZ2qvol8GWOa8/6HffMI99yoHlh16fkfGdyISJ9gKuAljgzd4aLSNUiKkuAT4HFqtpIVVsDTwORfsnuwGn+veUMH3sAp4kXEakO2IasxgSIz5fBxOnvMO7ZJ3nv9XEsjF3O9v/tyJbmP98sJiS4CnOmTeS2G69l2ntzAPg2biVer5d3XxvL26+O4Yv5C0nes499qfv5x7/n89b4Mbz72itk+DJYFBtfHK9njDFF5pyDQxEZISLD3OOJIrLIPe4uIh+IyEARSRKRVbgBknu/oYjEi0iiiIzOp5jLgaWqmq6qR4D1QG/3Oc+KyGoR2SAiM9zgDhFZ7NYnQUQ2ikgbEfmniGzKp7xugFdVp2VeUNV1qhrrPrcRTpPuX3CCxDMxF2fsADgB5T/PMJ8xpoA2btpMnVqR1K4ZSVBQeXp06sCylWuypVm2KoHe3WIA6NKxHWvXb0BVERGOH08j3ecjLe0E5YPKU6XyBQD4fD7STpwg3efj+IkThIVeGPB3M8YUApHAfUqZgrQcxgIx7nEUECwiQe61JOB5nKCwE06Ql2ky8KaqNgeS8yljHdBbRCq7Ay27cXJGzxRVbaOqzYALAP9u3ROqGgVMAz4HhgLNgAEiEpZHWc2ANXncAyfIm+u+9yUiEnmatJkWAp3d1c9vB4qsS9wYk13K/t+oEX7yn3tEWCj79u/PM015j4cqlSvz+6FDdO3YlkqVKnLzwIfo/8Awbr+pD1VDgokIC+X2vn3o/8Aj3DzwIapUvoC2rVoE9L2MMaaoFSQ4XAO0drt504B4nCAxBvDidM/uc1f79g+KooE57vHs0xWgql/j9MEvd/PEAz73djcRWSkiiUB34Aq/rJmzfBKBH1Q12Z32vZXs08XPxh3AXFXNAD4B+p9BVgrkNwAAIABJREFUHh+wDCcwvEBVt+eVUEQGua2dCbM/sgZGY4rTxk1bKFeuHJ/Omsq86ZOY9/mX7Nq9h0OHD7Ns1RrmTZ/Mp7Omcvx4Gl8vXlbc1TXGnAOVcgH7lDbnXGNV9QLbgAE4wVssTsteY2BjftnPopwxqtpSVXsBAiSJSCXgDaCf2wL5FlDJL1vm+j8ZfseZ53lNwvkBaJ3bDRFpDjQBvhGR7TjB3tl0Lb8GfHS6RKo6Q1WjVDXq7tvOdEijMSYv4aEXsjclNet8X+p+IkJD80yT7vNx5OhRqoWE8M3S5bRrdSXly5fnwurVaH5ZU37avI2EdRuoVaMG1atVpXz58nTu0IYNPyUF9L2MMaaoFTScjQWGA0vd4yE4m0OvALqISJjb1ezfyhbHyXF4d57u4SLiyewGFpEWQAvga04GgikiEgz0K+B7ACwCKrpb3GSW30JEYnACwVGq2sD91AZqi0j9M3huLPASJ1tLjTEBcGmTRuxI3s2uPXvxetNZuCye6LbZ//6Lbtuar76NBWDJ8pVc1fwKRITIiDDWJv4AwLHjx/nh583Ur1ubyIhwfkzaxPG0NFSVNet/oH7dOgF/N2NMIbAxh3kq6FI2sTirdcer6hEROQ7EqmqyiIzC6QY+AHzvl+dR4EMRGYkzHvB0goBYd67JQeAuVU0HDojIW8AGYDfODOICUVUVkZuBSW7djgPbgcdwgtnrcmT51L2+EughIv7TILOCYXcZm/EFrZ8x5uyU93h47IEBDH/+ZTJ8GVzXsysNL6rLzA8/5pLGF9OpbWv69OzKmElvcMeQxwkJqcKoPz8CwM3XXs3Lr0/jnkdGoArX9ehMowYXAdC1Yzv+9MT/4fF4aNKwATdc070Y39IYYwqfuEvwmRJkz8Y19kMxxhhTpkRe1jqgTWyHEr4K2O/akKjepar5sPSNkjTGGGOMMUWmRGyf5074yDlzOU1V2xVBWWE4S8zk1ENVU3O5bowxxpjzjJbCsYCBUiKCQ1VNxNkFJRBlpQaqLGOMMcaY0sa6lY0xxhhjTJYS0XJojDHGGBNQpXBx6kCx74wxxhhjjMliLYfGGGOMKXMUm5CSF2s5NMYYY4wxWUp8y6GIDACiVPXhXO4dVtXg0+T9CmgPLFPV6/2uN8TZ8zgMWAPcraonCrvublnXAy/iBOJBwGRVnV4UZRljslu5dh2vvf0eGRkZ9OnVjbtuvTHb/RNeL2MmvUnSlm1UDQlm1PBh1IqMID09nbFT3yJpy3Z8GT56d43hrn438evOXYwa93pW/l179nLfHf247cZrA/1qxpgCUhtzmKfz/TszDrg7l+tjgYmq2hj4Dbi/KAp395WeAdygqlcCrYDFRVGWMSY7ny+DidPfYdyzT/Le6+NYGLuc7f/bkS3Nf75ZTEhwFeZMm8htN17LtPecLdC/jVuJ1+vl3dfG8varY/hi/kKS9+zjojq1mTXpJWZNeom3Xh1DpYoV6Nw+qjhezxhjikyxBIciMkJEhrnHE0VkkXvcXUQ+EJGBIpIkIquAaL98DUUkXkQSRWR0fuWo6kLgUI6yBegO/MO99C7Q1713g4isFJHvRGSBiES610eJyLsiEisiv4jILSLyiluPr9wgMDchOK2zqW590lT15zP+RhljztnGTZupUyuS2jUjCQoqT49OHVi2ck22NMtWJdC7WwwAXTq2Y+36DagqIsLx42mk+3ykpZ2gfFB5qlS+IFveNes3ULtmJDVrRATsnYwxhUjKBe5TyhRXjWOBGPc4Cgh2A6wYIAl4Hico7ARc7pdvMvCmqjYHks+x7DDggKqmu+c7gDru8TKgvaq2wul2ftIvXyOcoPJG4H3gW7cex4A+uRWkqvuBL4BfRGSOiNwpUgr/KzGmFErZ/xs1wsOyziPCQtm3f3+eacp7PFSpXJnfDx2ia8e2VKpUkZsHPkT/B4Zx+019qBqSfQTLomXx9IjpUPQvYowxAVZcgcoaoLWIVAXSgHicIDEG8AKLVXWfOw5wnl++aGCOe5xzu73CUBeYLyKJwAjgCr97/1VVL5AIeICv3OuJQIO8HqiqfwJ6AKuA4cCs3NKJyCARSRCRhNkf/bOg72GMKYCNm7ZQrlw5Pp01lXnTJzHv8y/ZtXtP1n2vN524VWvoFt2+GGtpjCkIFQnYp7QpluDQDbK2AQOA5Tgtid2AxsDG/LIXsPhUoLqIZE7GqQvsdI9fB6a4LYKDgUp++dLcumcAXlXNrEcG+UzsUdVEVZ0I9AJuzSPNDFWNUtWou2+75RxeyxjjLzz0QvamnNwufV/qfiJCQ/NMk+7zceToUaqFhPDN0uW0a3Ul5cuX58Lq1Wh+WVN+2rwtK9+Ktd/T5OKGhFavFpiXMcaYACrOLs5YnJa0pe7xEOA7YAXQRUTC3K7m/n554oDb3eM7z6VQN6j7FujnXroX+Nw9rsbJQPHec3m+PxEJFpGufpdaAr8U9LnGmPxd2qQRO5J3s2vPXrzedBYuiye6betsaaLbtuarb2MBWLJ8JVc1vwIRITIijLWJPwBw7Phxfvh5M/Xr1s7KtzB2OT07W5eyMaWZSrmAfUqb4g4OawHxqroHOA7EqmoyMAqnqzmO7C2JjwJD3W7fOuRDRGKBj4EeIrJDRK5xb40EnhCRzThjEGe610cBH4vIGiClYK/nVAF4UkR+FpHvccZSDiiE5xpj8lHe4+GxBwYw/PmXufvh4XSLbk/Di+oy88OPWbbKmZjSp2dXDh46xB1DHmfeF18y+B7nb8+br72aY8ePc88jIxg0/K9c16MzjRpcBDjBYsK6DXRu37bY3s0YY4qSnOwdNSXFno1r7IdijDGmTIm8rHVAB+ftT1wWsN+1oc07laqBh6WvrdMYY4wxxhSZEr9DSn5EpDmnzlxOU9V2Aa7Hp0DDHJdHqur8QNbDGGOMMfkrjWMBA6XUB4eqmogz0aO463FzcdfBGGOMMaagSn1weD6yv2aMMcYYU1wsODTGGGNMmaOUqjkiAWVNVMYYY4wxJou1HBpjjDGmzLEhXHmz74wxxhhjjMlSqC2HIjIAiFLVh3O5d1hVg0+T9yugPbBMVa/3u94QmIuzk8ka4G5VPVGY9fYrKwh4EWf/40M4+ym/oKr/de+3xNni71pV/cov3ynvJiKjgOeAJqq62b32GDARaKOqCUXxDsaYk1au/Z7X33qXjIwM+vTqzp39bsp2/4TXy98mTiVpyzaqhgTz3IhHqRVZg/T0dF6ZMoOkrdvw+Xxc060zd/XrC8Chw0cYN2U6237dAQIjHxlCs0ubFsfrGWMKQmzMYV5KUsvhOODuXK6PBSaqamPgN+D+IqzDizhb+jVT1auAvkCI3/07gGXu1zORyMm9oMHZJ/qHQqinMSYfPl8Gk6bP4pXnnuLdKa+yMDaO7b/uyJbmP998S0hwMB9On0z/G/sw/d0PAfg2bgVer5e/vzaOtya8xL/mLyB5z14AXn/7Xdpe1ZLZb0xg1qRXqF833508jTGmVMk3OBSRESIyzD2eKCKL3OPuIvKBiAwUkSQRWQVE++VrKCLxIpIoIqPzK0dVF+K01vmXLUB34B/upXdxAjZE5AYRWSki34nIAhGJdK+PEpF3RSRWRH4RkVtE5BW3Hl+5rYO5vWdl4AHgEVVNc+u0R1U/8qtLf5y9kXuJSKX83gn4DLjJzd8I+J3C2bPZGJOPjZs2U6dmTWrXjCQoqDzdYzqybFX2Bvu4lQlc070zAF2i27F2/Q+oKiLCsbQ00n0+0tJOUL58eapUrszhI0dZ98NG+vTqBkBQUHlCgqsE/N2MMQWnlAvYp7Q5kxrHAjHucRQQ7AZYMUAS8DxOUNgJuNwv32TgTVVtDiSfY/3CgAOqmu6e7wAy/0xfBrRX1VY43c5P+uVrhBNU3gi8D3zr1uMY0CePshoDv6rqwTzudwS2qeoWYPFpnuPvIPA/EWmG04I47wzyGGMKQUrqfmqEh2WdR4SFkpK6P3ua/SfTlPd4qFLlAn4/dIiuHdtxQcWK3DJgCLf96WH+0Pd6qoYEk7xnL9WrVeXl197k/see4pXXp3Ps+PGAvpcxxhS1MwkO1wCtRaQqzhi8eJwgMQbwAotVdZ87DtA/+IkG5rjHObe3Kwx1gfkikgiMAK7wu/dfVfXidOt6gMzxgYlAg3Ms7w6cIBT365l2Lc/FCQz7Ap/mlUhEBolIgogkzP7ok3OsojGmMGzctIVy5crxz3feZO6M1/jos/+wa/cefD4fm7Zs46bevZg56WUqVarIh598XtzVNcacAxUJ2Ke0yTc4dIOsbTjdqctxWhK74bS0bcwvewHrlwpUF5HMiTN1gZ3u8evAFLdFcDDg382b2S2cAXhVNbMeGeQ9CWczcJEbBGcjIh6cSSrPish2t+zeIhKSM20u/o0zlvJ0rZKo6gxVjVLVqLtvu/UMHmuMOZ3wsFD2pqRmne9L3U94WGj2NKEn06T7fBw5coxqISEsWBJH26uupHz58lxYvRrNLruEnzZvJSI8jIjwUC6/pAkAXTq2I2nL9oC9kzHGBMKZdoTHAsOBpe7xEJxZuyuALiIS5nY19/fLE8fJyRh3nkvl3KDuW6Cfe+leIPPP9GqcDBTvPZfn5yjrKDATmCwiFQBEJEJE+gM9gPWqWk9VG6hqfeATIN/9lN3njgTGFLSOxpgzd2mTRuxI3k3ynr14veksil1OdNvW2dJEt23N/EVLAVgSt5JWLa5ARIiMCGPtemfu2LHjx/nx503Ur1ubsAurExEexq87dgGwdv0GGtSzCSnGlEYq5QL2KW3OJjisBcSr6h7gOBCrqsnAKJyu5jiytyQ+Cgx1u33z/b+niMQCHwM9RGSHiFzj3hoJPCEim3HGIM50r48CPhaRNRTeJI+/APuAH0VkA06r30GcLuScXcKfcLJrubJb58zPE/4JVXWuqq4tpDoaY85AeY+HxwYNZPiov3HPw0/QLboDDS+qx8wPPiJupTMx5bpe3fj90CH+OPhRPvriPwy+x/kn3fe6azh2/Dj3PjycwX9+hmt7dKVRg/oAPPrAQEZPmMLAYU+yedsv3NW/b7G9ozHGFAU52eNqSordP31nPxRjjDFlSs1LWwV0cF7yT98H7HdtrUtblqqBh6WvrdMYY4wxxhSZgO6tLCLNOXXmcpqqtgtwPT4FGua4PFJV5weyHsYYY4wpHqVxLGCgBDQ4VNVEoGUgy8yjHvlOJDHGGGOMKYsCGhyaM5MhnuKugjHGGGPKKAsOjTHGGFPmlMbFqQPFOtyNMcYYY0wWazk0xhhjTJmjWMthXqzl0BhjjDHGZCnxLYciMgCIUtWHc7l3WFWDT5P3K6A9sExVr/e73hCYi7PjyhrgblU9Udh1d8tajLO7zDH30mZV7Zd3DmNMUVi15jumvP0OPl8Gfa7uwR/7ZV+04ITXy0sTXydp81aqVg3huRGPUzOyBt8sjmXep59npdu6/VdmTBxL44tzroZljClNbCmbvJ3v35lxwN25XB8LTFTVxsBvwP1FXI87VbWl+7HA0JgA8/l8TJ4+k5efe4a/T53IwqVxbP/1f9nSfPnNIkKCg/lgxhT633g90999H4BeXWN4e/J43p48nv97/BFqRdawwNAYc14rluBQREaIyDD3eKKILHKPu4vIByIyUESSRGQVEO2Xr6GIxItIooiMzq8cVV0IHMpRtgDdgX+4l94F+rr3bhCRlSLynYgsEJFI9/ooEXlXRGJF5BcRuUVEXnHr8ZWIBBX8u2KMKSo/bdpM7Vo1qV0zkqCgILrHRGftr5wpbuVqruneBYAu0e1Zu24DObcXXbg0jm4xHQNWb2NM0VEkYJ/SprhaDmOBGPc4Cgh2A6wYIAl4Hico7ARc7pdvMvCmqjYHks+x7DDggKqmu+c7gDru8TKgvaq2wul2ftIvXyOcoPJG4H3gW7cex4A++ZT5gYh8737GnWO9jTHnKCV1PzXCw7LOI8JDSUlNzSVNOAAej4fgKpU5eCjb35YsXracHp07FX2FjTGmGBVXcLgGaC0iVYE0IB4nSIwBvMBiVd3njgOc55cvGpjjHufchq8w1AXmi0giMAK4wu/ef1XVCyQCHuAr93oi0CCf5/p3K4/ILYGIDBKRBBFJeH/eP3JLYowpRj/+vImKFSvQsP5FxV0VY0whUCkXsE9pUyw1doOsbcAAYDlOS2I3oDGwMb/sBSw+FaguIpmTceoCO93j14EpbovgYKCSX740t+4ZgFdP9jdlUAgTe1R1hqpGqWrUXX+wYYnGFKbwsFD2ppxsKdyXsp/wsLBc0qQAzhjFw0eOUjUkJOv+t7FxdI+xVkNjzPmvOMPZWGA4sNQ9HgJ8B6wAuohImNvV3N8vTxxwu3t857kU6gZ13wKZEdi9QOZUxGqcDBTvPZfnG2NKnkubNGbnrmSSd+/B6/WyKDaOju2isqXp2DaK+YuWALAkbgWtWjRD3B0UMjIyWLxsOd07R5/ybGNM6WRjDvNW3MFhLSBeVfcAx4FYVU0GRuF0NceRvSXxUWCo2+1bh3yISCzwMdBDRHaIyDXurZHAEyKyGWcM4kz3+ijgYxFZA6QU7PWy8R9zuKAQn2uMOQMej4dhg+/nyVFjGDD0cbp16kDDi+ox64O5xK1cDUCfXt05ePAQdw56mI8//xeD7j359+f6HzYSER5O7ZqRxfUKxhgTMJJzNp4pfrt+Xm8/FGOMMWVK7UtaBLSJbduWzQH7XduwUeNS1XxY+kZJGmOMMcacR0Skt4j8LCKbReSpXO5XFJF57v2VItLA797T7vWf/XpIC6TE75CSHxFpzqkzl9NUtV2A6/EpkHNl3JGqOj+Q9TDGGGNM/krKWEAR8QBTgV44y+utFpEvVPVHv2T3A7+pamMRuR1nM48/iMjlOHMxrgBqAwtEpKmq+gpSp1IfHKpqItCyBNTj5vxTGWOMMcZk0xZna92tACIyF7gJ8A8Ob8KZFwHOJh5T3E09bgLmqmoasM2dS9EWZ97GObNuZWOMMcaUOSoSsI//WsbuZ5BfVeoA/vt5+m/OcUoadxOP33Em1J5J3rNW6lsOjTHGGGNKMlWdAcwo7nqcKWs5NMYYY4wpPjuBen7n/ptznJLG3cSjGs6mHmeS96xZcGiMMcaYMkdVAvbJx2qgiYg0FJEKOBNMvsiR5gtObs7RD1jkburxBXC7O5u5IdAEWFXQ7411KxtjyoRVa75jytvv4PNl0OfqHvyxX/Y5ZCe8Xl6a+DpJm7dStWoIz414nJqRNfhmcSzzPv08K93W7b8yY+JYGl+cc3ECY4w5e6qaLiIPA/MBDzBLVX8QkReABFX9AmezjtnuhJP9uLvFuek+wpm8kg4MLehMZSjFi2CLyAAgSlUfzuXeYVUNPk3esUAf9/RFVZ1XNLUEEXkG+CPgw9mHebCqrjxdHlsE25jC5fP5uOfBRxn3wl+JCAtlyJ+f5q/DH6XBRSd7Yz77cj5bt//CEw8NYtHSOGJXrOS5J5/I9pyt23/hr38bxwczpgT6FYw57wV6EexNW34J2O/aJo3ql4x1c85QmetWFpE+wFU4y9+0A4aLSNUiKqsDcD1wlaq2AHqSfVaRMSYAftq0mdq1alK7ZiRBQUF0j4kmbmVCtjRxK1dzTfcuAHSJbs/adRvI+cfzwqVxdIvpGLB6G2NMcShRwaGIjBCRYe7xRBFZ5B53F5EPRGSgiCSJyCog2i9fQxGJF5FEERmdTzGXA0tVNV1VjwDrgd7uc54VkdUiskFEZrhrCCEii936JIjIRhFpIyL/FJFN+ZRXC0hx1x9CVVNUddc5fnuMMecoJXU/NcLDss4jwkNJSU3NJU044OzFHFylMgcPHcqWZvGy5fTo3KnoK2yMKXKKBOxT2pSo4BCIBWLc4yggWESC3GtJwPM4QWEnnCAv02TgTVVtDiTnU8Y6oLeIVBaRcKAbJ2f6TFHVNqraDLgAp9Uv0wlVjQKmAZ8DQ4FmwAARCSN3XwP13ID2DRHpklel/NdAen/eP/J5BWNMoP348yYqVqxAw/oXFXdVjDGmSJW04HAN0Nrt5k3DWeE7Cic49AKLVXWfqp4A/McJRgNz3OOcW+llo6pfA18Cy9088TjjAQG6uXsWJgLdcbajyZQ5cygR+EFVk90Wwa1kn0buX9ZhoDUwCNgHzHPHSuaWdoaqRqlq1F1/6He6VzDGnKXwsFD2ppxsKdyXsp/wsLBc0qQAzhjFw0eOUjUkJOv+t7FxdI+xVkNjzhfWcpi3EhUcqqoX2AYMwAneYnFa9hoDG/PLfhbljFHVlqraCxAgSUQqAW8A/dwWyLeASn7Z0tyvGX7Hmed5zvpWVZ+qLlbV54CHgVvPtJ7GmMJxaZPG7NyVTPLuPXi9XhbFxtGxXVS2NB3bRjF/0RIAlsStoFWLZrgjS8jIyGDxsuV07xx9yrONMeZ8U6KCQ1csMBxY6h4PAb4DVgBdRCTM7Wru75cnDndaN3Dn6R4uIp7MbmARaQG0wOn+zQwEU0QkGGcdoQIRkUtEpInfpZbALwV9rjHm7Hg8HoYNvp8nR41hwNDH6dapAw0vqsesD+YSt3I1AH16defgwUPcOehhPv78Xwy69+T/Stb/sJGI8HBq14wsrlcwxhQyaznMW0lc5zAWeAaIV9UjInIciFXVZBEZhdMNfAD43i/Po8CHIjISZzzg6QQBsW6LwEHgLnefwgMi8hawAdiNsyhlQQUDr4tIdZz1hzbjdDEbYwKsfdRVtI+6Ktu1++68Peu4QoUKjHrqz7nmbdn8Ct4Y/7cirZ8xxpQUpXadw/OZrXNojDGmrAn0Oocbt+wM2O/ayxrVKVXNhyWxW9kYY4wxxhSTktitXChEpDmnzlxOU9V2RVBWGLAwl1s9VDU1l+vGGGOMKUZnsOdxmXXeBoeqmogzASQQZaUWZlkq9h+sMcYYY4rHeRscGmOMMcbkpTTOIg4UG3NojDHGGGOyWHBojDHGGGOyWLeyMcYYY8oc61bOmwWHxpjz1qo13zHlrXfIyMjgul49+GP/m7PdP+H18vKE10naspWqIcE8++QT1IysAcCWbduZOHUGR44epVy5crw54WUyMpTnx77KruTdlCtXjg5toxg04K7ieDVjjCkypSo4FJEBQJSqPpzLvcOqGnyavGOBPu7pi6o6r4jqOBH4RVUnuefzgf+p6p/c81eBnao6oSjKN8Y4fD4fk6e9zbgXnyUiLJQHn3iKju2iaHBRvaw0//16ISHBVXh/xhQWLV3GjL+/z7Mjn8Dn8/HShNd4+olhNGrYgN8PHsLj8ZCRkc5tN99IqxbN8Hq9DP/L86xMWEu7HDuvGGNKPms5zFuZGHMoIn2Aq3CWm2kHDBeRqkVUXBzQ0S23HBAOXOF3vyOwvIjKNsa4ftq0mTq1alK7ZiRBQUF07xzN8pXZd8WMW7maq3t0BaBLdAfWrktEVVn93ToublCfRg0bAFCtaggej4dKlSrSqkUzAIKCgmjS6GL2pdpSpsaY80uxB4ciMkJEhrnHE0VkkXvcXUQ+EJGBIpIkIquAaL98DUUkXkQSRWR0PsVcDixV1XRVPQKsB3q7z3lWRFaLyAYRmSHupssistitT4KIbBSRNiLyTxHZlE95y4EO7vEVOHs1HxKRC0WkInAZsPZsv0/GmLOTkrqfGuHhWefhYWHsS92fZxqPx0OVKpU5ePAQO3buAoQnn32RQY+OYO4nn53y/MOHjxC/KoGrrmxRpO9hjCkaqhKwT2lT7MEhEAvEuMdRQLCIBLnXkoDncYLCTjhBXqbJwJuq2hxIzqeMdUBvEaksIuFANyCzb2mKqrZR1WbABcD1fvlOqGoUMA34HBgKNAMGuLuinEJVdwHpInIRTithPLASJ2CMAhJV9UTOfCIyyA1EE96f9498XscYU5R8Ph8bfvyJZ/78KK+NHc2y+FWsXbc+2/3R4yZy8w3XUbtmZDHW1BhjCl9JCA7XAK3dbt40nGAqCic49AKLVXWfG1D5jxOMBua4xzm3yctGVb8GvsRp1ZvjluFzb3cTkZUikgh0J3sX8Bfu10TgB1VNVtU0YCsng8vcLMcJDDODw3i/87g86jhDVaNUNequP/Q73esYY85AeFgoe1NSss5TUlOJCAvNM43P5+PIkaNUrRpCRHgYLZpdRrVqValUqSLtolqRtGVbVr5Xp0yjTu1a9LvpeowxpVMGErBPaVPswaGqeoFtwACcoCoWp2WvMbAxv+xnUc4YVW2pqr0AAZJEpBLwBtDPbYF8C6jkly3N/Zrhd5x5frrJPJnjDpvjdCuvwGk5tPGGxgTIpU0as3NXMsm79+D1elm0NI4ObdtkS9OxXRRfL1wMwJK4eFq1aIaI0Oaqlmzd/ivHj6fh8/lYt+FHGtSrC8DM2XM4cuQoQx8YGOhXMsaYgCgps5VjgeHAfTitdBNwWhRXAJPcLtyDQH+cLmJwArDbgfeBO0/3cBHxANVVNVVEWgAtgK+BzNnNKSISDPQDCqNPd7n7PltV1QfsF5HqOK2SDxTC840x+fB4PDwy5E+MfG40vowMru3ZnYb16/HO+3Np2qQR0e3acF2vHvxtwmvcNehhQoKD+euTjwMQEhxM/7438OATIxER2kVdRfs2rdmXksoHH33CRXXrMPixJwHo26c3fa7pWZyvaow5BzZbOW8lKTh8BohX1SMichyIVdVkERmF0y17APjeL8+jwIciMhJnPODpBAGx7lyTg8BdqpoOHBCRt3Ba93YDq/N+xFlJxJml/GGOa8GqmpJ7FmNMYWsfdRXtcywzM/Cu27OOK1SowKinhueat1e3zvTq1jnbtYjwMBb9y8YEG2POb6J6xj2zJkB2JiXaD8UYY0yZUqdp84A25a1NSg3Y79qrmoaVqmbKYh9zaIwxxhhjSo6S0q1cKESkOafOXE5T1XZFUFYYsDCXWz1U1VbFNcYYY0qUejZNAAAgAElEQVQwG3OYt/MqOFTVRJxdUAJRVmpRlWX/wRpjjDGmuJxXwaExxhhjzJkojTuXBIqNOTTGGGOMMVksODTGGGOMMVmsW9kYY4wxZY6N78+bBYfGmPPWqjXfMfWtWWRkZHBdrx7c0f+WbPdPeL2MnfAaSVu2UjUkhL8++QQ1I2uwe89eBj70KPXq1Abgskua8vjQwRw/nsYLY8ezK3k35cqVo0PbKB4YcHdxvJoxxhSZUhscisgAIEpVH87l3mFVDT41V9b9sUAf9/RFVZ1XNLUEEfHh7I6Saa6qvlxU5RljHD6fj9emvcUrLz5LRFgYDz0xkg7t2tDgonpZaf779UKCg4OZPWMqi5Yu462/z+avI/8MQO2akcx47dVTntv/5htp1aI5Xq+X4X95npUJa2mXYxcWY0zJZxNS8lbmxhyKSB/gKpxlaNoBw0WkahEWeUxVW/p9LDA0JgB+2rSZOrVqUrtmTYKCgujWuRPLV2bfIXP5ylVc3aMrAF2iO7B2XSKn2zWqUqWKtGrRHICgoCCaNGpISqota2qMOb+UqOBQREaIyDD3eKKILHKPu4vIByIyUESSRGQVEO2Xr6GIxItIooiMzqeYy4GlqpquqkeA9UBv9znPishqEdkgIjPE3YxZRBa79UkQkY0i0kZE/ikim86gPGNMMUhJ3U9EeHjWeURY6CmBXErqfmq4aTweD1WqVObgwUMA7N6zl8GPDufxp/7K+h9+POX5hw8fYcWqBFpd2bwI38IYU1QyAvgpbUpUcAjEAjHucRQQLCJB7rUk4HmcoLATTpCXaTLwpqo2B5LzKWMd0FtEKotIONANyOxnmqKqbVS1GXABcL1fvhOqGgVMAz4HhgLNgAHubil5uUBEvvf7/CG3RCIyyA0+Ez6Y93E+r2CMKUqhoRfy4azpTJ88ngf/NIC/jZ/EkaNHs+77fD5Gj5vIzTf0oXbNmsVYU2OMKXwlbczhGqC1282bBqzFCRJjgEXAYlXdByAi84Cmbr5o4Fb3eDYwNq8CVPVrEWkDLAf2AfGAz73dTUSeBCoDocAPwL/ce1+4XxOBH1Q12a3HVpzgMq++pWOqmu9OKqo6A5gBsCNpQ8A2AzfmfBUeFsq+lJSs832p+wkPCzslzd6UFCLCw/D5fBw5cpSqVUMQESoEBQHQtHEjatesyY6du7ikSWMAJkyZRt3atbj1pusxxpRONuYwbyWq5VBVvcA2YABO8BaL07LXGNiYX/azKGeMO/6vFyBAkohUAt4A+rktkG8BlfyypblfM/yOM89LWpBtTJl3aZPG7NyVTPLuPXi9Xr5duoyObaOypenQrg1fL1wMwJK4eFq1aIaIcOD33/H5nL8Zd+3ezY5dydSqGQnArNkfcuTIER56YGBA38cYYwKlJAY1scBw4D6cVroJOC2KK4BJbhfuQaA/ThcxQBxwO/A+cOfpHi4iHqC6qqaKSAugBfA1kDm7OUVEgoF+wD8K8b2MMQHk8Xh4ZMifGPnci2RkZHBtz+40qH8R77w/h0uaNKZjuzZc16sHL014jbsHDSUkOJi/PPk4AOs3/MjfP5hL+fLlEREeGzqIqiEh7EtJ5YOPPuGiunUY8tgIAG7qcy19rulZnK9qjDkHts5h3uR0M/OKg4j0AL7CCeCOiEgSME1VJ4jIQOBp4ADwPc44wIdFpCHwIU6A9znwWF5L2bgthGvd04PAEFX93r03GrgD2I0zxvEXVR0lIouB4aqaICJd3ePr3TxZ9/IoL+dSNl+p6lOn+x5Yt7Ixxpiypm7TZgGN1pZvPBSw37UdLwspVZFoiQsOjQWHxhhjyp5AB4dxPx4O2O/a6MuDS1VwWKLGHBpjjDHGmOJVEsccFgoRaY4zc9lfmqq2K4KywoCFudzqoaq2Qq4xxhhTwtiYw7ydt8Ghqibi7IISiLJSC7Msr6dS/omMMcYYY4rAeRscGmOMMcbkJcNG9+fJxhwaY4wxxpgsFhwaY4wxxpgs1q1sjDHGmDLHJqTkzYLDAhKRUcBhVR1f3HUxxmSXkJDAm9NnkJGRQe9rruYPt92W7f4Jr5fx419l0+bNVA0J4emnn6JmZCQHDx5k9N/+RlLSJnr17MnQhx485dnPPf88u3fvYfqbbwTqdYwxJiCsW9kYc17y+XxMfeNNRr/wPDOmvcniJUv55ddfs6WZP38+wcHBvDPzbW6+uS+zZr0DQIUKFbjn7rt54P77c332srg4Lqh0QZG/gzGm6KhKwD6ljQWH50BEnhGRJBFZBlziXntARFaLyDoR+UREKotIiIhsE5EgN01V/3NjTNH5OSmJWrVrU6tWLYKCgujSuTPx8SuypYlfsZKePXsAENOpE9+vW4eqUqlSJZpdcQVBFU79p3rs2DH++eln3HHH7QF5D2OMCTQLDs+SiLQGbsdZ1/A6oI1765+q2kZVrwQ2Aver6iFgMdDHTXO7m84b2FobU/akpqYSER6edR4eHk5qauqpaSIiAPB4PFSpXJmDBw+e9rnvzZ7NrbfcTMWKFQu/0saYgFEN3Ke0seDw7MUAn6rqUVU9CHzhXm8mIrEikgjcCVzhXn8bGOgeDwTeye2hIjJIRBJEJGHO3LlFWH1jzLnasmULu5KTie7YsbirYowxRcYmpBSevwN9VXWdiAwAugKoapyINBCRroBHVTfklllVZwAzALZt2VwK/84wpmQJCwtjX0pK1nlKSgphYWGnptm3j4jwcHw+H0eOHqVq1ap5PnPjTz+xadNm7hkwkAyfjwO//86IkU8xbuzLRfYexpiikWGzlfNkLYdnbynQV0QuEJEQ4Ab3egiQ7I4nvDNHnveAD8mj1dAYU/guadqUXbt2snv3brxeL0uWLqV9++xbq7dv144FC5xt0WOXLePKFi0QyfsXxvV9+vDh+7N57+/vMH78OOrUqWOBoTHmvGMth2dJVdeKyDxgHbAXWO3e+iuwEtjnfg3xy/YBMBqYE8CqGlOmeTweHnrwQZ75y1/JyMjg6qt70aB+fd6bPZsmTZrQoX17el9zNa+MH8/A+/9ESEgIT498Miv/PQMGcvToUdLT04mPj2fMmNHUv+iiYnwjY0xhKo2ziANFtDSOlCxlRKQfcJOq3n0m6a1b2RhjTFnTsFHjgEZrC9anBex3bc8WFUtVJGoth0VMRF4HrsWZ2WyMMcaYEsDaxvJmYw6LmKo+oqqNVTWpuOtijDHGmNJFREJF5BsR2eR+vTCXNC1FJF5EfhCR9SLyB797f3fXWP7e/bTMr0wLDo0xxhhT5igSsE8BPQUsVNUmwEL3PKejwD2qegXQG5gkItX97o9Q1Zbu5/v8CrRu5RLINgM3xhhjjOsm3OXxgHdxNtcY6Z/Av3dSVXeJyF4gAjhwLgVay6ExxhhjypwMDdzHf6ML9zPoLKoaqarJ7vFuIPJ0iUWkLVAB2OJ3eYzb3TxRRPLd3slaDo0xxhhjipD/Rhe5EZEFQM1cbj2T4zkqInlOpRGRWsBs4F5VzXAvP40TVFZw6zASeOF09bXg0BhjjDGmGKlqz7zuicgeEamlqslu8Lc3j3RVgf8Az6jqCr9nZ7Y6ponIO8Dw/Opj3crGGGOMKXNUJWCfAvoCuNc9vhf4PGcCEakAfAq8p6r/yHGvlvtVgL5Artv4+rOWQ2PMeSUhIYFp06eTkZFB72uu4bbbbst2/4TXy6vjx7Np82aqhoTw9NNPExnpDOGZN28e87/+mnLlyvHgkCG0bt2aHTt28NLLJ7fIS05O5u677+bmvn3ZsmULr0+ZgtfrxVOuHEOHDuWSSy4J6PsaY857LwMficj9wC/AbQAiEgUMUdU/udc6A2EiMsDNN8CdmfyBiEQAAnwPDMmvwBLZcigiA0RkSh73DueT9ysROSAi/85xvaGIrBSRzSIyz42yC52I3CQin/mdPy0im/3ObxCRL4qibGPKOp/Px9Q33uDFF15g+rRpLF6yhF9+/TVbmq/nzyc4OJhZM2fS9+abmTVrFgC//PorS5YuZdq0aYx+8UWmTJ2Kz+ejbt26TJ0yhalTpvDa5MlUqlSJjh06ADBz1izu/OMfmTplCnfdfTcz3WcZY0o+1cB9ClZPTVXVHqraRFV7qup+93qCGxiiqu+rapDfcjVZS9aoandVba6qzVT1LlU9bRwFJTQ4LKBxQG7b1I0FJqpqY+A34P4iKn850N7vvANwUERquOcd3TTGmEKWlJRE7dq1qVWrFkFBQXTp3JkV8fHZ0sSvWEHPns7wnphOnfh+3TpUlRXx8XTp3JkKQUHUrFmT2rVrk5SUfe3679eto1bNmlktjSLC0aNHATh65AhhoaEBeEtjjClaAQsORWSEiAxzjyeKyCL3uLuIfCAiA0UkSURWAdF++Rq6q34nisjo/MpR1YXAoRxlC9AdyOyHfxen3z2zJW+liHwnIgtEJNK9PkpE3hWRWBH5RURuEZFX3Hp8JSJBeZS/DycYbOxeqgN8ghMU4n6Ny/87Zow5WympqUSEh2edh4eHk5qami1Namoq4RERAHg8HipXrszBgwdJTU0lwr2emTclR94lS5bQpWvXrPPBgwYxc9Ys7r7nHt6eOZMBAwYU/ksZY4pEBhKwT2kTyJbDWCDGPY4Cgt0AKwZIAp7HCQo7AZf75ZsMvKmqzYFkzk0YcEBV093zHThBG8AyoL2qtgLmAk/65WuEE1TeCLwPfOvW4xjQ5zTlxQEdReQSYBOwwj0vD1wJrM6ZwX8NpDlz557jaxpjiorX62XlypXEdOqUde0/X37JoAceYPZ77zHogQeYNHlyMdbQGGMKRyCDwzVAa3eqdRoQjxMkxgBeYLGq7lPVE8A8v3zRwBz3eHYR1KsuMF9EEoERwBV+9/6rql4gEfAAX7nXE4EGp3nmcpwWwo4477kKaAe0An5S1eM5M6jqDFWNUtWoO26/vWBvZEwZFR4Wxr6UlKzzlJQUwsLCsqUJCwsjZd8+wBmjePToUapWrUpYWBj73OuZecP98iYkJNCoUSMuvPDktqYLFiwgOtrp6IiJieHnn38ukvcyxhS+0jLmsDgELDh0g6xtwACc4CkW6AY0Bjbml72AxacC1d2WO3ACwp3u8evAFLdFcDBQyS9fmlv3DMCrmvUjzuD0M73j8AsOVfWQ+9yu2HhDY4pM06ZN2bVrF7t378br9bJk6VLat2+fLU37du1YsGABALHLlnFlixaICO3bt2fJ0qWc8HrZvXs3u3btomnTpln5Fi9ZQtcuXbI9KywsjMTERMAZj1inTh2MMaa0C/RSNrE4iy/eh9P6NgGnRXEFzibRYcBBoD+wzs0TB9yO061757kU6q4o/i3QD6fr2H+doGqcDBTvzSX7udgI1MbpIn/IvZY5ffzJvDIZYwrG4/Hw4IMP8pe//AVfRgZXX3019evX573Zs2napAnt27fnmmuuYdz48dx3//2EhITw1Ehni9L69esTExPD4MGD8Xg8PPTgg3g8HgCOHz/Od999x7BHHslW3rBhw5g+fTo+n48KQUGn3DfGlFyFsP7geUs0gO2dItIDp2u2uqoeEZEkYJqqThCRgThbvBzACaROqOrDItIQ+BAIxgnoHlPV4NOUEQtc6qZPBe5X1fkicjFOYBgKfAfcpappInITMBFnBvMioI2qdhWRUcBhVR3vPvdwZrk57+VRj/8A1VS1k3s+AHgHqO23Wnmutm7ZUgoboY0xxphzd3GjRgGN1r5I8AXsd+2NUZ5SFYkGNDg0Z8aCQ2OMMWVNoIPDz1YHLjjs26Z0BYfn4zqHxhhjjDHmHJXK7fNEpDmnzlxOU9V2Aa7Hp0DDHJdHqur8QNbDGGOMMWfHOk7zViqDQ1VNBFqWgHrcXCTPlVLV+myMMcaY80ipDA6NMcYYYwpCS+HOJYFiYw6NMcYYY0wWCw6NMcYYY0wW61Y2xhhjTJmTYRNS8mTBoTHmvKKqTJ82jdWrV1OxYkWe+POfady48SnpNm3axIQJEziRlkabNm0YPGQIIsKhQ4d46aWX2LtnDzUiI3n66acJCQnhf//7HxMnTGDz5s3ce++93NqvHwD79u3j1fHj+e233xARel97LX379g30axtjTKGxbuViICKe4q6DMeerhNWr2blrF2/PnMmwYcOYMmVKrummTpnCo8OG8fbMmezctYuEhAQAPvroI1q2bMnbM2fSsmVLPv7oIwBCQkIYMmQIt956a7bneDwe/vTAA0yfMYMJEyfy73//m19/+aVoX9IYU2CqgfuUNhYc5kNEXhCRx/zOx4jIoyIyQkRWi8h6EXne7/5nIrJGRH4QkUF+1w+LyKsisg7oEODXMKbMWLFiBT169EBEuPSyyzhy+DD79+/Plmb//v0cPXqUSy+7DBGhR48erIiPd/LHx9OzZ08AevbsSbx7vXr16jS95BI85bN3uISGhma1TFauXJmL6tUjJTW1qF/TGGOKjAWH+ZsF3AMgIuWA24HdQBOgLc56i61FpLOb/j5VbQ1EAcNEJMy9XgVYqapXquqyQL6AMWVJSmoqEeHhWefh4eGkpKRkT5OSQnjONG5Ad+DAAUJDQwG48MILOXDgwBmXvWfPHrZs2cKll1xSkFcwxgSAtRzmzYLDfKjqdiBVRFoBVwPfAW38jtcCl+IEi+AEhOuAFUA9v+s+4JO8yhGRQSKSICIJc+fMKYpXMcacJRFBznBR+mPHjjFm9GgGDR5M5SpVirhmxhhTdGxCypl5GxgA1MRpSewBvKSq0/0TiUhXoCfQQVWPishioJJ7+7iq+vIqQFVnADMAtmzdWgr/zjCm+PzrX/9i/ldfAdCkaVP2+bUU5mwlhFNbE1NSUggPcxr5q1evzv79+wkNDWX//v1Uq1Yt3/LT09MZM3o0Xbt1Izo6ujBeyRhTxDLUFsHOi7UcnplPgd44LYbz3c99IhIMICJ1RKQGUA34zQ0MLwXaF1eFjSlLbrjhBqZMncqUqVPp0KEDCxcuRFX5aeNGqlSpktVNnCk0NJTKlSvz08aNqCoLFy6kfXvnn2v79u1ZsGABAAsWLKB9h9MPEVZVJk2aRL169bjllluK5gWNMSaArOXwDKjqCRH5Fjjgtv59LSKXAfFul9Nh4C7gK2CIiGwEfsbpWjbGBFCbNm1YvXo19993HxUrVeLxxx/Puvfw0KFMmToVgIeGDmXihAmkpaUR1aYNUW3aAND/ttt46W9/4+v586lRowZP/9//Ac4klkeHDePo0aOUK1eOzz77jOnTp7Nt2zYWLVxIgwYNeHjoUADuvfde2rRtG+A3N8acjdI4FjBQRO27ky93IspaoL+qbirq8qxb2RhjTFnT6OKLA9rPOycucAHQHdFnOHi5hLBu5XyIyOXAZmBhIAJDY4wxxhQ9m62cN+tWzoeq/ghcXNz1MMb8f3t3Hl9Vcf9//PUGRUAUCChatVVERQtIBURFq+JSK+77LoLFtVitu7XVX903XFAQtLii4I6ogLIUBJRFQEQUcftaV0hAIEhc8vn9cQ/xErKwJDe5yfv5eNxHzp2ZMzPnKsnnzpwzY2ZmmeDg0MzMzGod761cOk8rm5mZmVkRjxyamZlZrRNe57BUHjk0MzMzsyIODs3MzMysiKeVzczMrNbJxiVmMsUjh2ZWo0QE/fv1o2ePHpx/3nnMnz+/xHIfffQR5513Hj179KB/v36s3BBg6dKlXH311ZzdsydXX301S5cuXeW8eR9+yGHduvHmhAmrpC/Pz+f0007jgQceqJwLMzPLkBodHEr6TFJzSU0knb8G5UdIWixpeLH07SS9LWm+pCGS6lVer81sfUybOpUvv/qKhx5+mN69e9O3b98Sy93fty8X9e7NQw8/zJdffcW0adMAGDp0KO3bt+ehhx+mffv2PDN0aNE5v/zyC/8ZNIjddttttfoee/xx2rRtWzkXZWYVrjAy98o2NTo4TNMEKDc4BG4HTi8h/VagT0S0AhYBPSuwb2ZWgd566y0OOOAAJNF6553JX7aMvLy8Vcrk5eWxfPlyWu+8M5I44IADeGvy5NT5kydz4IEHAnDggQcyOUkHeHnYMLp06UKTJk1Wqe+jjz5i8aJFJQaNZmbZpsYEh5JelDRd0hxJvYpl3wJsL2mmpNtLqyMiRgOrzCFJEtAVeDZJehQ4Ksk7PBlRnCHpDUktkvTrJD0qaYKkzyUdI+k2SbOT0ckNK+iyzayYhbm5bNa8edH75s2bs3DhwlXLLFxI8+JlcnMBWLx4MTk5OQA0bdqUxYsXF50zadIkunXrtkpdhYWFPDRwIGeffXalXI+ZVQ5vn1e6GhMcAj0iogPQEegtqVla3pXAxxHRPiIuW8t6mwGLI+Ln5P3/gK2S4zeBPSLiD8DTwOVp521PKqg8AngCGBsRbYEfgFX/ugCSekmaJmna0089tZZdNLPKIInU90MY8OCD9OjRgzp1Vv21+crw4XTs1Inmm21WFV00M6twNelp5d6Sjk6OtwF2yECbWwNDJG0J1AM+Tct7LSJ+kjQbqAuMSNJnA9sWrygiBgADAD7+5JMs/J5hVnVefvllRo5I/RPbYccdWZA2Ulh8lBBWH01cuHAhzZulvk82adKEvLw8cnJyyMvLo3HjxkBq6viWW24BYMmSJUydOpU6desyd+5c5syZwyvDh7NixQp++uknGtSvz1k9elTqNZvZ+snGEb1MqRHBoaT9gAOBPSNiuaRxQP0Kqj4XaCJpg2T0cGvgyyTvPuCuiBiW9OG6tPMKACKiUNJPEUX/GxZSQz53s+ri8MMP5/DDDwdgypQpvPzyy+y77758+MEHbLzxxkXTxCvl5OTQsGFDPpg7l51at2b06NEckZy/xx578MYbb3DCCSfwxhtvsMeeewIw6JFHis6/68472X333dlrr73Ya6+9itJff/11PvroIweGZpbVasq0cmNgURIYtgb2KJa/FNhkXSpOgrqxwHFJ0pnAS2ntfpmWbmZVrFOnTmyxxRb07NGDe+69l/MvuKAo78K04/MvuIB77rmHnj16sOWWW9KxUycAjj/hBGa88w5n9+zJzBkzOOGEEzJ+DWZW+fy0cukUNWBcVdJGwIukpms/JPV08nXAI0DHiFgoaTDQjtR0b4n3HUqaALQGGpEaMewZESMltSR1T2EOMAM4LSIKJB0J9CH1BPMYoFNE7CfpOmBZRNyR1LssIholx6vklcTTymZmVtts37JlRjc7fmg0Gftbe/YBZNVGzjUiOKxpHByamVltk+ngcOAbmQsO/3JgdgWHNWVa2czMzMwqQK17MEJSW+DxYskFEdG5KvpjZmZmmVdYWNU9qL5qXXAYEbOB9lXdj7JEZNXos5mZmdUgtS44NDMzM/MjF6XzPYdmZmZmVsTBoZmZmZkV8bSymZmZ1TqeVi6dRw7NrMaKCPr3f4Cze57FBeefy/z5H5VY7qOPPuL8887l7J5n0b//A6xc/3XChPGcd24vDuv2Zz6aN2+187777juOPeYonnvu2Uq9DjOzTKqVwaGkzyQ1l9RE0vlrUP5WSe8lrxMz0UczW3/Tpk3lqy+/YuBD/+GvvS/i/r59Syz3wP330fuiixj40H/46suvmD5tGgC/+922XPOPa2nTpk2J5z00cAAdOnastP6bWeXx9nmlq5XBYZomQJnBoaRuwG6klr/pDFwqadMM9M3M1tNbb02m6wEHIInWrXcmP38ZeXm5q5TJy8tl+fLltG69M5LoesABTH5rEgC//e1v2XrrbUqse/KkSbTYogW/++3vKv06zMwyqcYHh5JelDRd0hxJvYpl3wJsL2mmpNtLqWIXYHxE/BwR+cC7wCFJ3f+UNDUZURwgSUn6OEl9JE2TNFdSJ0nPS/pI0g2Vda1mtqrchblsttlmRe+bN9+M3IW5q5Vp1rx5mWWK++GHH3j22aGccsppFdthM8uYiMjYK9vU+OAQ6BERHYCOQG9JzdLyrgQ+joj2EXFZKefPAg6R1FBSc2B/YOVQQt+I6BQRbYAGwGFp5/0YER2B/sBLwAVAG6B7sT4AIKlXEkxOe/rpp9bjcs2ssj355BMcddQxNGjQoKq7YmZW4WrD08q9JR2dHG8D7LA2J0fEKEmdgEnAAmAy8EuSvb+ky4GGQA4wB3g5yRuW/JwNzImIrwEkfZL0Y5WhiYgYAAwAmP/xp9n3NcOsmhj+8jBGjBwBwI477MiCBQuK8hYuXECz5qt+N2vWvBm5CxeWWaa4eR9+wMQ3J/Cf/zxEfn4+kqhXrx6HH35EBV6JmVWmLBzQy5gaHRxK2g84ENgzIpZLGgfUX9t6IuJG4MakzsHAPEn1gQeAjhHxhaTritVdkPwsTDte+b5Gf+5mVemww4/gsCRImzLlbYa//DL77rsfH374ARtvvDE5OasGfjk5zWjYsCEffDCXnXZqzZjRozn8iLKDvNtuv7Po+MknHqd+gwYODM2sxqjpQUpjYFESGLYG9iiWvxTYpKwKJNUFmkRErqR2QDtgFNAoKbJQUiPgOMDrWZhVI5067c60qVM5u2cPNtpoIy6++JKivAsvPJ++fR8A4PzzL6RPnzspKPiRjh070rFjJwAmTZpI/379+P7777nuun/SsmVL/n3DTVVyLWZWsQoLq7oH1VdNDw5HAOdKmgt8CLyVnpkEfBMlvQe8Vsp9hxsCE5JnTZYAp0XEz8BiSQOB94BvgKmVeB1mtg4kcf4FF5aYtzIwBNhhxx15oN+Dq5XZa68u7LVXlzLbOPW009evk2Zm1Yyy8Smams73HJqZWW3TavvtlMn27h6WuQDob0coo9e2vmrD08pmZmZmWUlSjqTXk+XwXpfUtJRyvyRL882UNCwtfTtJb0uaL2mIpHrltengMCGpbdqHuvL1dlX3y8zMzCpeFu2QciUwOiJ2AEYn70vyQ7I0X/uISH9C7lagT0S0AhYBPctrsKbfc7jGImI2qV1Qqtz3vzSu6i6YmSReSXEAACAASURBVJlZ9XAksF9y/CgwDrhiTU5MNufoCpySdv51QL+yzvPIoZmZmdU6EZl7pW90kbyK79hWlhYr10om9QBsi1LK1U/qfkvSUUlaM2Bx8iAtwP+Arcpr0COHZmZmZpUofaOLkkh6A9iihKxritUTkkqbqP5dRHwpqSUwRtJs4Pt16a+DQzMzM7MqFBEHlpYn6VtJW0bE15K2BL4rpY4vk5+fJJt+/AF4DmgiaYNk9HBr4Mvy+uNpZTMzM6t1ojAy9lpPw4Azk+MzgZeKF5DUVNJGyXFzoAvwfqTWKxxLaqOOUs8vziOHZlYrRASPDejDzOmTqLdRfc696Fq2a7XTauWGPNafCWNfI3/ZUgY9M6Yo/ZUXn2LcqGHUqVuXTTdtQq+LrmGzzbfM5CWYWe10CzBUUk/gc+AEAEkdgXMj4mxgZ+BBSYWkBv5uiYj3k/OvAJ6WdAMwA3i4vAZrzcihpGXl5I+QtFjS8GLpa70+kJlVPzOnT+abr77grgef4ewLruQ//W4rsdxuu+/Nv+9c/Xfnti135Ia7BnHrfU+we5euPDXo/srusplVomxZyiYiciPigIjYISIOjIi8JH1aEhgSEZMiom1E7Jr8fDjt/E8iYveIaBURx0dEQXlt1prgcA3cDpS0D9Zarw9kZtXP9LfGs0/XPyOJHVq3YXn+MhblLVyt3A6t29A0p/lq6b9v14GN6tdPldnp9+Tllnjbj5lZ1qv2waGkyyT1To77SBqTHHeV9KSkZZJulDQreXy7RZK/naTJkmYnQ6lliojRwNJiba9cH+jZJOlR4Kgk7/BkRHGGpDfS2r1O0qOSJkj6XNIxkm5L+jFC0oYV9NGY2VpYlLuAnOa/rgCR02wzFuUuWKe6xr7+Mrt22LOiumZmVSCTS9lkm2ofHAITgH2S445AoyTA2gcYD2wMvBURuybv/5KUvQfoFxFtga9ZN2WtD/QmsEdE/AF4Grg87bztSQWVRwBPAGOTfvwAdCupofQ1kJ4f8ug6dtfMKtubY0fw6fwPOOyYU6u6K2ZmlSIbHkiZDnSQtClQALxDKkjcB+gN/AgMTyt7UHLcBTg2OX6c1PRwRdoaGJI8Vl4P+DQt77WI+ClZY6guMCJJnw1sW1Jl6WsgTZ+Xl4XfM8yqn1GvPMvYkaktRlvusDN5C78tysvLXUDTZputVX2zZ07hxaGPcO3ND7Dhhr792CybFVbAvnY1VbUPDpMg61OgOzAJeBfYH2gFzAV+Sh7VBviFVa9pff/L51L6+kD3AXdFxDBJ+5HajmalgqTvhZLS+1dIFnzmZjXFwd2O4+BuqRUcZkydyKjhz7LnHw9i/odzaNBw4xLvLSzNZx9/yMP338YV1/ehcZOcyuqymVmVy4ZpZUhNLV9Katp4AnAuMCMt6CrJROCk5Hid5n/KWR+oMb8GimdiZtVa+457sfkWv+HiXsfzUN+b6XHeZUV5V/U+o+h48KC+XNj9CH4sWMGF3Y/g2cEPAfDkoL6sWLGce2+5hqt6n8Ed/75stTbMLHv4nsPSqez4qnqQdACpqdkmEZEvaR7QPyLukrQsIhol5Y4DDouI7pK2AwYDjUgFdH9bWa6UNiYArZPyuUDPiBiZbEPzNJBDan2g0yKiQNKRQB9STzCPATpFxH6SrgOWRcQdSb3p/VslrzSeVjYzs9qmw445ymR7Nw35JWN/a68+sW5Gr219ZUVwWNs4ODQzs9om08HhjU9nLji85qTsCg6zZVrZzMzMzDKgVj0cIaktqSeX0xVEROeq6I+ZmZlVjULPnJaqVgWHETEbaF/V/TAzMzOrrmpVcGhmZmYGEIVV3YPqy/ccmpmZmVkRB4dmZmZmVsTTymZmZlbreCm/0jk4NLNaISJ4bEAfZk6fRL2N6nPuRdeyXaudVis35LH+TBj7GvnLljLomTFF6a+8+BTjRg2jTt26bLppE3pddA2bbb5lJi/BzCwjPK1MaheTcvJvlfRe8joxU/0ys4ozc/pkvvnqC+568BnOvuBK/tPvthLL7bb73vz7zodXS9+25Y7ccNcgbr3vCXbv0pWnBt1f2V02s0pUWJi5V7ZxcFgOSd2A3UgtgdMZuFTSplXbKzNbW9PfGs8+Xf+MJHZo3Ybl+ctYlLdwtXI7tG5D05zmq6X/vl0HNqpfP1Vmp9+Tl/tdpffZzKwqZHVwKOkySb2T4z6SxiTHXSU9KWmZpBslzZL0lqQWSf52kiZLmi3phnKa2QUYHxE/R0Q+8C5wSFLPPyVNTUYUB0hSkj4u6c80SXMldZL0vKSP1qA9M6sEi3IXkNO8RdH7nGabsSh3wTrVNfb1l9m1w54V1TUzqwIRkbFXtsnq4BCYAOyTHHcEGknaMEkbD2wMvBURuybv/5KUvQfoFxFtga/LaWMWcIikhpKaA/sD2yR5fSOiU0S0ARoAh6Wd92NEdAT6Ay8BFwBtgO6SmhVvRFKvJJic9vyQR9fiIzCzTHpz7Ag+nf8Bhx1zalV3xcysUmT7AynTgQ7JNG8B8A6pIHEfoDfwIzA8rexByXEX4Njk+HHg1tIaiIhRkjoBk4AFwGTglyR7f0mXAw2BHGAO8HKSNyz5ORuYExFfA0j6hFRwmVusnQHAAIDp8/Ky72uGWTU06pVnGTsy9U+x5Q47k7fw26K8vNwFNG222VrVN3vmFF4c+gjX3vwAG25Yr0L7amaZVei/tKXK6uAwIn6S9CnQnVTw9i6pkb1WwFzgp/h1PPcXVr3eNf7fIiJuBG4EkDQYmCepPvAA0DEivpB0HVA/7bSC5Gdh2vHK91n9uZtli4O7HcfB3Y4DYMbUiYwa/ix7/vEg5n84hwYNNy7x3sLSfPbxhzx8/21ccX0fGjfJqawum5lVuWyfVobU1PKlpKaNJwDnAjOi7En+icBJyXGZc0OS6q6cBpbUDmgHjOLXQHChpEbAcet8BWZW6dp33IvNt/gNF/c6nof63kyP8y4ryruq9xlFx4MH9eXC7kfwY8EKLux+BM8OfgiAJwf1ZcWK5dx7yzVc1fsM7vj3Zau1YWbZIwojY69sUxNGsCYA1wCTIyJf0ookrSwXAYMlXUHqfsCybAhMSJ41WQKcFhE/A4slDQTeA74Bpq7HNZhZJZPEWeeVHNDdfO9jRcennHUhp5x14Wplrrnhvkrrm5lZdaJsfIqmpvM9h2ZmVtt02DFHmWzv6ocLMva39qaeG2X02tZXTZhWNjMzM7MKUhOmlSuEpLaknlxOVxARnauiP2ZmZlZ5CrPwXsBMcXCYiIjZpHZBqXKF4QFdMzMzqxoODs3MzKzW8TMXpfMQlZmZmZkV8cihmZmZ1TpRWNU9qL48cmhmZmZmRTxyaGa1QkTw+MC7mDltEhttVJ9ef7uW7bZvvVq5oY/3482xr5K/bCkPDx1XlD76ted5/dVnqVOnDvXrN6DnBVex1W9bZvAKzMwyIytHDiV1l9S3lLxl5Zx7q6T3kteJldPDorZC0p1p7y9N9mA2swybNX0S33z1BXc++Cw9L7iSR/rdVmK53TrtzfV3DFotfc99D+aW+wZz0z1P0O2Y03ni4Xsqu8tmVokKIzL2yjZZGRyuK0ndgN1ILVnTGbhU0qaV2GQBcIyk5pXYhpmtgelvj2fv/f+MJFq1bkt+/lIW5S1crVyr1m1pmrP6P9mGDRsVHRes+IFkS00zsxqn2gSHki6T1Ds57iNpTHLcVdKTks6SNE/SFKBL2nnbSZosabakG8ppZhdgfET8HBH5wLvAIUk9/5Q0NRlRHKDkN7+kcUl/pkmaK6mTpOclfbQG7f0MDAAuXpfPxMwqzqLcBTTbrEXR+5xmm7Mod8Fa1fH6K89wSa9jePrRvpzR65KK7qKZZVBEZOyVbapNcAhMAPZJjjsCjSRtmKTNA64nFRTuTSrIW+keoF9EtAW+LqeNWcAhkhomo3n7A9skeX0jolNEtAEaAIelnfdjRHQE+gMvARcAbYDukpqV0+b9wKmSGpdVSFKvJACd9sKQR8qp0syqwkHdjueuAc9z0pkX8uKQ1aeezcxqgur0QMp0oEMyzVsAvEMqSNwHGAOMi4gFAJKGADsm53UBjk2OHwduLa2BiBglqRMwCVgATAZ+SbL3l3Q50BDIAeYALyd5w5Kfs4E5EfF10o9PSAWXuWW0uUTSY0Bv4Icyyg0gNcrI1A8XZ9/XDLNq6PVXnmHsqJcAaLnDLuQu+LYoLy/3O5o222yd6t1jn4MY1K/UXzVmlgW8fV7pqs3IYUT8BHwKdCcVvE0gNbLXCphb3ulr0c6NEdE+Ig4CBMyTVB94ADguGYEcCNRPO60g+VmYdrzy/ZoE2HcDPYGN17SfZrb+Dup2PDfd8wQ33fMEHTr/kTfHvkZEMP+D2TRs2KjEewtL881X/1d0PHPaRLb4zTZllDYzy17VaeQQUgHhpUAPUqN0d5EaUXwLuDuZwl0CHE9qihhgInAS8ARwalmVS6oLNImIXEntgHbAKGDlneYLJTUCjgOeraiLiog8SUNJBYj/qah6zWzNte/YhVnTJ/H3c46l3kb16dX72qK8qy86jZvueQKApwbdx6TxI/mxYAV/Pesw9jvoSI495S+MeuUZ5sycSt0NNmDjRptwzt/+VVWXYmYVIAtvBcyY6hgcXgNMjoh8SSuACRHxdbIEzGRgMTAz7ZyLgMGSriB1P2BZNgQmJM+aLAFOi4ifgcWSBgLvAd8AUyvwmla6E7iwEuo1szUgie7nXl5i3srAEODks/7KyWf9dbUyZ/zl75XWNzOz6kTZ+BRNTed7Ds3MrLbptFOTjK4PddE9SzP2t/aeizbJqrWvqs09h2ZmZmZW9arbtHKFkNSW1JPL6QoionMltNUMGF1C1gERUepTzGZmZlZ1snHnkkypkcFhRMwmtQtKJtrKrei2IrJq9NnMzMxqkBoZHJqZmZmVJbzOYal8z6GZmZmZFfHIoZmZmdU6HjksnUcOzczMzKyIRw7NrFaICB4feCezpk9io43q0+uif7Lt9q1XK/fM4w/w5thXyc9fykND/luUPn70cJ5+5N6i/ZgPOvR49jv4qIz138wsU6r1yKGk7pL6lpK3rJxzR0haLGl4sfTtJL0tab6kIZLqVWSfi7U1TtK0tPcdJY2rrPbMrHSzpk/i26+/4I7+z9HjgqsY1O/WEsv9Yfd9uP6OR0rM67z3Qdx495PcePeTDgzNslxhZO6Vbap1cLiebgdOLyH9VqBPRLQCFpHa77gybS7pz5XchpmV450p49l7/0ORRKud2rI8fymL8xauVq7VTm1pktO8CnpoZlY9ZDw4lHSZpN7JcR9JY5LjrpKelHSWpHmSpgBd0s7bTtJkSbMl3VBeOxExGlharG0BXYFnk6RHgaOSvMOTEcUZkt6Q1CJJv07So5ImSPpc0jGSbkv6MULShuV05XZS+0WbWRValPsdOc1bFL3Pab45ebnfrVUdUyeP4erep3DvLVeSu+Dbiu6imWVQFEbGXtmmKkYOJwD7JMcdgUZJgLUPMA+4nlRQuDewS9p59wD9IqIt8PU6tt0MWBwRPyfv/wdslRy/CewREX8AngYuTztve1JB5RHAE8DYpB8/AN3KaXMy8KOk/csqJKmXpGmSpr0w9JG1uCQzy4Q/dNqbPgNf4qZ7B9Om/e48eM91Vd0lM7NKURUPpEwHOkjaFCgA3iEVJO4DjAHGRcQCAElDgB2T87oAxybHj5OaHq5IWwNDJG0J1AM+Tct7LSJ+kjQbqAuMSNJnA9uuQd03AP8AriitQEQMAAYATPng++z7mmFWDb3+yjOMe/1FAFq22oW8hb+O9uUt/I6cZpuvcV2bbNqk6Hi/g47k6Ufvq7iOmlnGhbfPK1XGRw4j4idSgVd3YBKpkcT9gVbA3PJOX8/mc4EmklYGxVsDXybH9wF9kxHBc4D6aecVJH0vBH6KX/+PKmQNAuyIGAM0APZYz/6b2Vo4qNvxRQ+QdNhjX94c+yoRwfwPZ9Nw40ZrdW9h+v2J70wZz2+23q4yumxmVuWqaimbCcClQA9So293kRpRfAu4W1IzYAlwPDArOWcicBKpad1T16XRiAhJY4HjSE0dnwm8lGQ35tdA8cx1qb8cNwD9gU8qoW4zK8euHbowc9okLj33GOptVJ+//PXaorxr/nYqN979JABPPXIvk8eP4seCFfTucRj7HXQEx5zci5HDhzBjynjq1K1Lo0aN6XXRP6vqUsysAhRm4b2AmaKqGFaVdACpqdkmEZEvaR7QPyLuknQWcBWwGJgJ/BgRF0raDhgMNCIV0P0tIhqV0cYEoHVSPhfoGREjJbUkFRjmADOA0yKiQNKRQB9STzCPATpFxH6SrgOWRcQdSb3LVrZbPK+EPowDLo2Iacn76cDSiNivrM/H08pmZlbb7N66sTLZ3l9uys3Y39qBVzfL6LWtryoJDq1sDg7NzKy2yXRwePaNCzP2t/aha5pnVXBYk9c5NDMzM8tqknIkvS7po+Rn0xLK7C9pZtprhaSVS/U9IunTtLz25bWZ1dvnSWpL6snldAUR0TnD/XgBKH53+hURMTKT/TAzM7M1k0XrD14JjI6IWyRdmbxfZfWTiBgLtIdUMAnMB0alFbksIp5lDWV1cBgRs0k+jCrux9EVWd/P4QFdMzMzA+BIYL/k+FFgHGUsjUfqodvXImL5ujboKMTMzMxqnUzukJK+0UXy6rUWXW0RESs3//gGaFFWYVIruzxVLO1GSe8mO9NtVF6DWT1yaGZmZlbdpW90URJJbwBblJC1yva7yZJ8pc6HJxt5tAXSb2u7ilRQWS/pwxXA/yurvw4OzczMrNYprEartUTEgaXlSfpW0pYR8XUS/JW1KfwJwAvJhiMr61456lggaRCpdabL5GllMzMzs+prGL9uzpG+eUdJTqbYlHISUCJJwFHAe+U16JFDM6sVIoLBD93Bu9MnUm+j+vTsfR3bbt96tXLPPXE/E8e+yvL8JfR/ekJR+lMP38nc2dMB+PHHFSxZnMcDg8dlqPdmVovdAgyV1BP4nNToIJI6AudGxNnJ+22BbYD/Fjv/SUmbASK1uci55TVYa4JDSecCyyPisbU8rx7wINCR1F7KF0XEuIrvoZlVpnenT+Tbr7/gln4v8Mm893i8/81ce/ujq5Vr3+mPHHDoiVx5/qqLEJzc8+9Fx28Mf5rPP/2w0vtsZpUnW5ayiYhc4IAS0qcBZ6e9/wzYqoRyXde2zVoTHEZE/3U89S/J+W0lbQ68JqlTRBRWXO/MrLLNmPJf9trvUCSx/U5tWZ6/lMV5C2mS03yVctvv1Lbcut6aMIqjT16bhw3NzLJHjb3nUNIZyWPbsyQ9Luk6SZcmeZ2SvJmSbpdU1vz7LqT2WiYiviO153PHpJ5+ySPpcyRdn9b2Z5JuTuqfJmk3SSMlfZyMYJpZhi3OW0BO818fBmzarAWL8sq6r7tkC7/7moXffcnObTtVZPfMLMMiImOvbFMjg0NJvwf+AXSNiF2Bi4oVGQScExHtgV/KqW4WcISkDSRtB3QgNacPcE1EdATaAftKapd23v8l9U8AHiG1KOUewPWUIH0NpJeGDlrTSzWzDHv7zZF03PMA6tStW9VdMTOrFDV1Wrkr8ExELASIiLzUQzogqQmwSURMTsoOBg4ro67/ADsD00jdCDqJXwPKE5KFLDcAtiQ1yvhukjcs+TkbaBQRS4GlkgokNYmIxemNpK+BNGnu0uz7mmFWDY1+dSj/HfUiANvtsAt5C78pyluU+y1NczZf6zqnTBjFaeeUtTmBmWWDwiy557Aq1NTgsMJExM/AxSvfS5oEzEtGES8FOkXEIkmPAPXTTi1IfhamHa9878/dLAMOOPQEDjj0BABmTXuT0a8OpfM+f+KTee/RYONGq91vWJ6v//cZ+cuW0mqnduUXNjPLUjVyWpnUPYLHS2oGRZtQA5CM2C2V1DlJOqmsiiQ1lLRxcnwQ8HNEvA9sCuQD30tqAfy54i/DzCpKuw5d2KzFVlxx7lEMuv8GTj/nyqK8f/7tlKLjoY/cwyU9D+XHghVc0vNQXnzqwaK8tyeMpPM+B7NyJsLMslcmt8/LNjVyBCsi5ki6EfivpF+AGcBnaUV6AgMlFZJaD+j7MqrbHBiZlP0SOD1pY5akGcAHwBfAxAq/EDOrMJI4vZTp4P939+Ci4xO6X8QJ3Yvfppxy1MnnVErfzMyqkxoZHAJExKPA6ouYpcyJiHYAkq4kdT9hafV8BuxUSl73UtK3TTt+hNQDKavlmZmZWdXIxqeIM6XGBofl6CbpKlLX/znQvWq7Y2ZmZlY91MrgMCKGAEPS0yT9Cbi1WNFPI+JozMzMrEaJQu9lUZpaGRyWJCJGAiOruh8AhVFTnxMyMzOz6s7BoZmZmdU6XuewdB6iMjMzM7MiHjk0MzOzWsdPK5fOI4dmZmZmVsQjh2ZWK0QETz18O7Onv0m9jerT46/X87vtd16t3PNP9GXSuFdYnr+EB576dW373AVf8/C9/2J5/lKi8BeOPb037TrsnclLMDPLiBo3cijpXElnrMN5zSSNlbRMUt9ieR0kzZY0X9K98t5ZZlln9jsT+far/+OmB17ijPP+weMP3lxiuV07/ZF/3PbYaunDn3mITl0O4rq7nuKcv9/CE6Wcb2bZwdvnla7GBYcR0T8iVv/NXr4VwLXApSXk9QP+AuyQvA5Z9x6aWVWYOWUce+1/GJLYfqd2LM9fyuK8BauV236ndjTJ2Wy1dEn8sDwfgOX5S0ssY2ZWE2T9tHIySngpEMC7wMfAsoi4Q1In4GGgEHgd+HNEtCmpnojIB96U1KpY/VsCm0bEW8n7x4CjgNck/QXoBdQD5gOnR8RySY8APwB/ILU3cw/gDGBP4O3Stt0zs8qzKPc7cpq1KHrftNnmLM5bsMZB3hEnnsNd11/AmFefpmDFD/z9+n6V1VUzy4BsHNHLlKweOZT0e+AfQNeI2BW4qFiRQcA5EdEe+GUdm9kK+F/a+/8laQDPR0SnpO25QM+0ck1JBYMXA8OAPsDvgbaS2pdwLb0kTZM0bdjQ/6xjV82ssrw9YSRduh7OHQ+N4KJ/3MtDd19LoXdYMLMaKNtHDrsCz0TEQoCIyFt5O6CkJsAmETE5KTsYOKyC228j6QagCdCIVXdYeTkiQtJs4NuImJ30aw6wLTAzvaKIGAAMAHjz/Xx/nTGrAGNeHcL4118AYNtWvycv99uivEW5363V1PCbo1/k4n+mbkdu1XpXfvrpR5YtWcymTXIqttNmlhGF4S93pcn24DATvgS2Tnu/dZIG8AhwVETMktQd2C+tXEHyszDteOV7f+5mGdD10BPpeuiJAMyaNoExrw5h973/xCfzZtOwYaO1Cg5zmm/B++9OYe+uR/DVF5/w048FbNK4aWV13cysymT1tDIwBjheUjMASUVf4SNiMbBUUuck6aR1aSAivgaWSNojeUr5DOClJHsT4GtJGwKnruM1mFkGtOuwN5u12IqrzjuSRx+4gdPOuaoo77qLf/318Myjd3Pp2YfwY8EKLj37EF56uj8AJ551CRNef4F/XXwiA+66mh69r8cLF5hlLz+tXDpl+wrhks4ELiN1T+EM4DN+fSClMzCQ1Gjdf4GOEdGljLo+AzYl9YDJYuDgiHhfUkdSo4QNgNeAvyZTxucBlwMLgLdJTWN3Tx5IGR4Rz0raNjluk7RRlFdaPzytbGZmtc3eu2yc0W9bR1/4Ucb+1r7Qd4es+iaZ9cFhWSQ1iohlyfGVwJYRUfyhlWrHwaGZmdU2mQ4Ojzp/Xsb+1r74wI5ZFRzW9Hvfukm6itR1fg50r9rumJmZmVVvNTo4jIghwJD0NEl/Am4tVvTTiDg6Yx0zMzOzKlWTZ07XV40ODksSESNZdcmZaidvef2q7oKZmZnVUrUuODQzMzPzIvaly/albMzMzMysAnnk0MzMzGqdbFx/MFM8cmhmZmZmRTxyaGa1QkTw0mM38cGs8WxYrwEnnnMTW2+3yyplfiz4gcfvvZjcb7+gTp067LLb/hx60iUALFrwJUMH/oNlSxbRsFFjTj7vVpo026IqLsXMrFJ55BCQtKyc/FslvZe8TsxUv8ys4nwwazwLv/mcK+4cwXE9r+f5QdeXWG7fQ8/i8jte4W83Pcdn897hg5njARg++HY67H0kf7/lRQ46+jxeG9Ink903swoWUZixV7ZxcFgOSd2A3YD2QGfgUkmbVm2vzGxtzZk+hg77HIkkfrfDrqxYvpQlixasUqbeRg1o9fvUduwbbFCPrbbdhe/zvgXg2y8/LsrbfpfOzJk+JrMXYGaWITUmOJS0saRXJM1aOcIn6TNJt0maLWmKpFZJ2e0kTU7Sbyin6l2A8RHxc0TkA+8ChyT1/FPS1KS9AZKUpI+T1EfSNElzJXWS9Lykj9agPTOrBEvyvltlGrhxTgu+X/RtqeV/yF/C+++Mo1WbPQDY8retmT31DQDem/YGBSvyyV+6uHI7bWaVJgojY69sU2OCQ1IB21cRsWtEtAFGJOnfR0RboC9wd5J2D9AvSf+6nHpnAYdIaiipObA/sE2S1zciOiXtNQAOSzvvx4joCPQHXgIuANoA3SU1K96IpF5JMDlt5PMD1/LSzawi/fLLzzzZ91L2/tNpNNs89c/9sFMv45O5U+lz9TF8MncqjZu2oE6dmvQr1MwspSY9kDIbuFPSrcDwiJiQDOQ9leQ/Bay8SagLcGxy/Dirb6dXJCJGSeoETAIWAJOBX5Ls/SVdDjQEcoA5wMtJ3rC0fs2JiK8BJH1CKrjMLdbOAGAAwLBpv2Tf1wyzamjiqMG8PfYZALZp2ZbFud8U5X2f9y2Nm7Yo8bznHv4Xzbf4Hfv8+YyitMZNN+fMi+8FoGBFPrOnvE6DjX2HiVm2ysYRvUypMcFhRMyTtBtwKHCDpNErs9KLlXJcXt03AjcCIjIe4AAACT5JREFUSBoMzJNUH3gA6BgRX0i6Dkjf964g+VmYdrzyfY353M2qsy4Hn0KXg08BYO6M/zJx1JO03/NQ/m/+u9RvsAmbNt1stXNGDL2HH5Yv47iz/71Kev7SRTTYuDF16tRhzLCBdNrvmIxcg5lZptWYIEXSb4C8iHhC0mLg7CTrROCW5OfkJG0icBLwBHBqOfXWBZpERK6kdkA7YBTQKCmyUFIj4Djg2Qq8JDOrQK3b/5G5M8dzyyWHUK9efU4458aivLuuOppLbn6BxbnfMPqlB9n8Ny25+5rU5EKXg0+l8/7H8fH7U1JPKEu0bN2Ro7tfW1WXYmYVoDALnyLOlBoTHAJtgdslFQI/AeeRCtaaSnqX1OjdyUnZi4DBkq4gdT9gWTYEVk5RLwFOi4ifgcWSBgLvAd8AUyv4esysAknimLNKDuguufkFAJo024Lbn3y/xDLtOv+Jdp3/VGn9MzOrLhRRc+fcJX1Gatp3YVX3ZW34nkMzM6ttjuhYV5ls7+DTZ2Tsb+2ox/+Q0WtbX37UzszMzMyK1KRp5dVExLZrWlZSW1JPLqcriIjOFdopMzMzq3JR6HsOS1Ojg8O1ERGzSe2CYmZmZlZrOTg0MzOzWsfrHJbO9xyamZmZWRGPHJqZmVmtE17nsFQeOTQzMzOzIh45NLNaISJ46bGb+GDWeDas14ATz7mJrbfbZZUyPxb8wOP3Xkzut19Qp04ddtltfw496RIAFi34kqED/8GyJYto2KgxJ593K02abVEVl2JmVqlq1cihpGXl5I+QtFjS8GLp20l6W9J8SUMk1avcnppZRftg1ngWfvM5V9w5guN6Xs/zg64vsdy+h57F5Xe8wt9ueo7P5r3DBzPHAzB88O102PtI/n7Lixx09HmprfTMLGsVFkbGXtmmVgWHa+B24PQS0m8F+kREK2AR0DOjvTKz9TZn+hg67HMkkvjdDruyYvlSlixasEqZehs1oNXvU0ubbrBBPbbadhe+z/sWgG+//Lgob/tdOjNn+pjMXoCZWYZkXXAoaWNJr0iaJek9SSdK+kzSbZJmS5oiqVVSdjtJk5P0G8qrOyJGA0uLtSegK6l9mgEeBY5K8g5PRhRnSHpDUosk/TpJj0qaIOlzScek9W+EpA0r8CMxszWwJO+7VaaBG+e04PtF35Za/of8Jbz/zjhatdkDgC1/25rZU98A4L1pb1CwIp/8pYsrt9NmVmmisDBjr2yTdcEhcAjwVUTsGhFtgBFJ+vcR0RboC9ydpN0D9EvSv17H9poBiyPi5+T9/4CtkuM3gT0i4g/A08DlaedtTyqoPAJ4Ahib9OMHoFvxRiT1kjRN0rSRzw9cx66aWUX45ZefebLvpez9p9Notvk2ABx26mV8Mncqfa4+hk/mTqVx0xbUqZONv0LNzMqWjQ+kzAbulHQrMDwiJqQG93gqyX8KWHkzUBfg2OT4cVLTwxVpa2CIpC2BesCnaXmvRcRPkmYDdfk1iJ0NbFu8oogYAAwAGDbtl+y7QcGsGpo4ajBvj30GgG1atmVx7jdFed/nfUvjpi1KPO+5h/9F8y1+xz5/PqMorXHTzTnz4nsBKFiRz+wpr9Ng400rsfdmVpm8CHbpsi44jIh5knYDDgVukDR6ZVZ6sVKO10Uu0ETSBsno4dbAl0nefcBdETFM0n7AdWnnFST9LZT0U0Ss7EchWfi5m2WjLgefQpeDTwFg7oz/MnHUk7Tf81D+b/671G+wCZs23Wy1c0YMvYcfli/juLP/vUp6/tJFNNi4MXXq1GHMsIF02u+YjFyDmVmmZV2QIuk3QF5EPCFpMXB2knUicEvyc3KSNhE4idS07qnr0l5EhKSxwHGkpo7PBF5Kshvza6B45rrUb2aZ0br9H5k7czy3XHII9erV54RzbizKu+uqo7nk5hdYnPsNo196kM1/05K7r0lNOnQ5+FQ6738cH78/JfWEskTL1h05uvu1VXUpZlYBvAh26fTrgFZ2kPQnUk8VFwI/AeeRelhkCPBnUiN2J0fEfEnbAYOBRqQCur9FRKMy6p4AtE7K5wI9I2KkpJakAsMcYAZwWkQUSDqS1BT2ImAM0Cki9pN0HbAsIu5I6l22st3ieSXxtLKZmdU2R3Ssq0y298ej38zY39rxL+yd0WtbX1kXHJZE0mdAx4hYWNV9qQgODs3MrLbJdHC4z5ETMva3dsJL+2RVcOhH7czMzMyqKUnHS5ojqVBSxzLKHSLpw2TDjivT0td6I48aERxGxLZrOmooqa2kmcVeb1d2H83MzKz6yKJ1Dt8DjgHGl1ZAUl3gflK31+0CnCxp5f6ga72RR9Y9kLK+ImI20L6q+2FmZmZWnoiYC5As21ea3YH5EfFJUvZp4EhJc0mtuXxKUu5RUiur9CursloXHGaDTN93YWa/ktQrWXfUzGqwN1/eN2N/ayX1AnqlJQ2o4N8zWwFfpL3/H9CZsjfyKJWDQzOzVfUiWZDezKwipG90URJJbwBblJB1TUS8VEJ6pXJwaGZmZlaFIuLA9aziS2CbtPcrN+woayOPUtWIB1LMzMzMarGpwA7Jk8n1SG0AMizZnW3lRh6w6kYepXJwaGa2Kk8pm1m1IeloSf8D9gRekTQySf+NpFcBklHBC4GRwFxgaETMSaq4ArhE0nxS9yA+XG6bNWERbDMzMzOrGB45NDMzM7MiDg7NzMzMrIiDQzMzMzMr4uDQzMzMzIo4ODSzWkPStpI+kPSkpLmSnpXUUFInSZMkzZI0RdImkrpLeknSOEkfSfpXVfffzCwTHByaWW2zE/BAROwMLCG1/MMQ4KKI2BU4EPghKbs7cCzQDjheUscq6K+ZWUY5ODSz2uaLiJiYHD8B/An4OiKmAkTEkrR9SF+PiNyI+AF4Htg78901M8ssB4dmVtsUX9x1yVqU9cKwZlbjOTg0s9rmt5L2TI5PAd4CtpTUCSC533DlvvMHScqR1AA4Cpi4enVmZjWLg0Mzq20+BC6QNBdoCtwHnAjcJ2kW8DpQPyk7BXgOeBd4LiKmVUF/zcwyaoPyi5iZ1Sg/R8RpxdKmAnukJ0gC+F9EHJWpjpmZVQceOTQzMzOzIorw/dVmZmZmluKRQzMzMzMr4uDQzMzMzIo4ODQzMzOzIg4OzczMzKyIg0MzMzMzK/L/AV1+AClW+5TKAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_corr[['pcp']].sort_values(by=['pcp'],ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "DgMo4QkI_O0t",
        "outputId": "bd78c194-81fa-461a-e0c3-b4825e605574"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    pcp\n",
              "pcp            1.000000\n",
              "pcp_hrs        0.768175\n",
              "dew_10am       0.450448\n",
              "dew_9am        0.435098\n",
              "pcp_yesterday  0.411585\n",
              "hum_10am       0.388228\n",
              "hum_9am        0.344160\n",
              "spl_10am       0.272583\n",
              "tmp_9am        0.259782\n",
              "spl_9am        0.245715\n",
              "tmp_10am       0.186548\n",
              "month          0.150475\n",
              "wdd_9am_CALM   0.087574\n",
              "wdd_10am_S     0.086883\n",
              "wdd_10am_CALM  0.085551\n",
              "wdd_10am_E     0.070229\n",
              "wdd_9am_S      0.069559\n",
              "wdd_9am_W      0.062108\n",
              "wdd_9am_E      0.051983\n",
              "day            0.013794\n",
              "wdd_10am_W     0.007793\n",
              "year          -0.001191\n",
              "alt_10am      -0.004447\n",
              "alt_9am       -0.013918\n",
              "wnd_10am      -0.119167\n",
              "wnd_9am       -0.121131\n",
              "wdd_9am_N     -0.134089\n",
              "wdd_10am_N    -0.147670\n",
              "cig_9am       -0.165485\n",
              "cig_10am      -0.183111\n",
              "spd_9am       -0.291609\n",
              "spd_10am      -0.292488"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e08a4540-da14-4501-b26f-413401c05463\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>pcp</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>pcp</th>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp_hrs</th>\n",
              "      <td>0.768175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dew_10am</th>\n",
              "      <td>0.450448</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dew_9am</th>\n",
              "      <td>0.435098</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pcp_yesterday</th>\n",
              "      <td>0.411585</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hum_10am</th>\n",
              "      <td>0.388228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>hum_9am</th>\n",
              "      <td>0.344160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spl_10am</th>\n",
              "      <td>0.272583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tmp_9am</th>\n",
              "      <td>0.259782</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spl_9am</th>\n",
              "      <td>0.245715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tmp_10am</th>\n",
              "      <td>0.186548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>month</th>\n",
              "      <td>0.150475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_CALM</th>\n",
              "      <td>0.087574</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_S</th>\n",
              "      <td>0.086883</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_CALM</th>\n",
              "      <td>0.085551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_E</th>\n",
              "      <td>0.070229</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_S</th>\n",
              "      <td>0.069559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_W</th>\n",
              "      <td>0.062108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_E</th>\n",
              "      <td>0.051983</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>day</th>\n",
              "      <td>0.013794</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_W</th>\n",
              "      <td>0.007793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>-0.001191</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alt_10am</th>\n",
              "      <td>-0.004447</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>alt_9am</th>\n",
              "      <td>-0.013918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wnd_10am</th>\n",
              "      <td>-0.119167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wnd_9am</th>\n",
              "      <td>-0.121131</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_9am_N</th>\n",
              "      <td>-0.134089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>wdd_10am_N</th>\n",
              "      <td>-0.147670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cig_9am</th>\n",
              "      <td>-0.165485</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cig_10am</th>\n",
              "      <td>-0.183111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spd_9am</th>\n",
              "      <td>-0.291609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>spd_10am</th>\n",
              "      <td>-0.292488</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e08a4540-da14-4501-b26f-413401c05463')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e08a4540-da14-4501-b26f-413401c05463 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e08a4540-da14-4501-b26f-413401c05463');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "pisW9CLC9uaz"
      },
      "execution_count": 101,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data to predict"
      ],
      "metadata": {
        "id": "5CE4R72VAB1g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#removing pcp and storing in variable y this will not change..\n",
        "y = data.pop('pcp')\n",
        "y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Z2nu30S9dYE",
        "outputId": "1be6073b-f389-4bf7-cc27-cc776624c0b9"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       0\n",
              "1       0\n",
              "2       0\n",
              "3       0\n",
              "4       0\n",
              "       ..\n",
              "6775    0\n",
              "6776    0\n",
              "6777    0\n",
              "6778    0\n",
              "6779    1\n",
              "Name: pcp, Length: 6126, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_corr[['pcp']].sort_values(by=['pcp'],ascending=False).index\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-FVKdSIK4QW_",
        "outputId": "53a1074e-ecf6-44e5-a79b-6d6c400b17e8"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['pcp', 'pcp_hrs', 'dew_10am', 'dew_9am', 'pcp_yesterday', 'hum_10am',\n",
              "       'hum_9am', 'spl_10am', 'tmp_9am', 'spl_9am', 'tmp_10am', 'month',\n",
              "       'wdd_9am_CALM', 'wdd_10am_S', 'wdd_10am_CALM', 'wdd_10am_E',\n",
              "       'wdd_9am_S', 'wdd_9am_W', 'wdd_9am_E', 'day', 'wdd_10am_W', 'year',\n",
              "       'alt_10am', 'alt_9am', 'wnd_10am', 'wnd_9am', 'wdd_9am_N', 'wdd_10am_N',\n",
              "       'cig_9am', 'cig_10am', 'spd_9am', 'spd_10am'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#X = data[['tmp_9am','dew_9am', 'tmp_10am','dew_10am','month', 'wdd_9am_CALM','pcp_yesterday']] ##features with most correlation\n",
        "\n",
        "X = data[['tmp_10am','hum_10am','month', 'wdd_9am_CALM','pcp_yesterday']] ##features with most correlation         \n",
        "#question \n",
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "qP6qEbHa9545",
        "outputId": "e23b77fa-8fff-4060-ade7-8aabc07e8460"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      tmp_10am  hum_10am  month  wdd_9am_CALM  pcp_yesterday\n",
              "0         19.0      58.0      1             0              0\n",
              "1         19.0      68.0      1             0              0\n",
              "2         18.0      56.0      1             0              0\n",
              "3         18.0      50.0      1             0              0\n",
              "4         17.0      59.0      1             0              0\n",
              "...        ...       ...    ...           ...            ...\n",
              "6775      16.0      69.0     12             0              0\n",
              "6776      16.0      63.0     12             0              1\n",
              "6777      16.0      69.0     12             0              0\n",
              "6778      20.0      65.0     12             0              0\n",
              "6779      19.0      79.0     12             0              0\n",
              "\n",
              "[6126 rows x 5 columns]"
            ],
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              "      <th>pcp_yesterday</th>\n",
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              "      <th>0</th>\n",
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              "      <th>1</th>\n",
              "      <td>19.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>18.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <th>6775</th>\n",
              "      <td>16.0</td>\n",
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              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>6776</th>\n",
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              "      <td>1</td>\n",
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              "      <th>6777</th>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6779</th>\n",
              "      <td>19.0</td>\n",
              "      <td>79.0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6126 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c968a7b8-666f-42d9-b0b7-0a2abc602431')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c968a7b8-666f-42d9-b0b7-0a2abc602431 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c968a7b8-666f-42d9-b0b7-0a2abc602431');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#ML Libraries\n",
        "from sklearn.preprocessing import StandardScaler, LabelEncoder\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import roc_curve,confusion_matrix,accuracy_score,classification_report\n",
        "from sklearn.metrics import roc_auc_score\n",
        "\n",
        "import statsmodels.api as sm"
      ],
      "metadata": {
        "id": "QfAg3smo-Ys9"
      },
      "execution_count": 106,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# splitting data\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)\n",
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "8rO5x8r_-nV1",
        "outputId": "5e93b8b8-a935-4ff7-d750-5a32f45a86f0"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      tmp_10am  hum_10am  month  wdd_9am_CALM  pcp_yesterday\n",
              "5411      20.0      55.0      2             0              0\n",
              "3395      24.0      50.0      4             0              0\n",
              "3359      18.0      67.0      3             0              0\n",
              "5362      20.0      75.0     12             0              1\n",
              "5688      22.0      64.0     11             0              0\n",
              "...        ...       ...    ...           ...            ...\n",
              "6556      25.0      52.0      5             0              0\n",
              "2154      21.0      81.0      9             0              1\n",
              "6002      23.0      65.0     10             0              1\n",
              "3221      21.0      67.0     10             0              0\n",
              "3401      22.0      64.0      4             1              1\n",
              "\n",
              "[4594 rows x 5 columns]"
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              "\n",
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              "\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>tmp_10am</th>\n",
              "      <th>hum_10am</th>\n",
              "      <th>month</th>\n",
              "      <th>wdd_9am_CALM</th>\n",
              "      <th>pcp_yesterday</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5411</th>\n",
              "      <td>20.0</td>\n",
              "      <td>55.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3395</th>\n",
              "      <td>24.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3359</th>\n",
              "      <td>18.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5362</th>\n",
              "      <td>20.0</td>\n",
              "      <td>75.0</td>\n",
              "      <td>12</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5688</th>\n",
              "      <td>22.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>11</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6556</th>\n",
              "      <td>25.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2154</th>\n",
              "      <td>21.0</td>\n",
              "      <td>81.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6002</th>\n",
              "      <td>23.0</td>\n",
              "      <td>65.0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3221</th>\n",
              "      <td>21.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3401</th>\n",
              "      <td>22.0</td>\n",
              "      <td>64.0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4594 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aedd2c40-f26a-49c8-9876-60ffbe86f5ab')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-aedd2c40-f26a-49c8-9876-60ffbe86f5ab button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-aedd2c40-f26a-49c8-9876-60ffbe86f5ab');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit a model\n",
        "clf = LogisticRegression(penalty='l2').fit(X_train, y_train)\n",
        "# predict probabilities\n",
        "predictions = clf.predict_proba(X_test)[:, 1]\n",
        "predictionsb = clf.predict(X_test)\n",
        "print(predictions)\n",
        "print(predictionsb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUpQDz_Z-x7H",
        "outputId": "73b2d1ca-3096-4f83-8e75-92408fe67737"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.14256004 0.10874851 0.19395537 ... 0.54767    0.08534164 0.1933752 ]\n",
            "[0 0 0 ... 1 0 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('ConfMatrix\\n', confusion_matrix(y_test,predictionsb))\n",
        "print('\\n\\nAccuracy:',accuracy_score(y_test,predictionsb))\n",
        "print('\\n\\nClassification Report\\n\\n',classification_report(y_test,predictionsb))\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IbbU2XPhDtn6",
        "outputId": "22e87a8c-289b-4e32-910b-4e4fe2f35d37"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConfMatrix\n",
            " [[931 173]\n",
            " [189 239]]\n",
            "\n",
            "\n",
            "Accuracy: 0.7637075718015666\n",
            "\n",
            "\n",
            "Classification Report\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.83      0.84      0.84      1104\n",
            "           1       0.58      0.56      0.57       428\n",
            "\n",
            "    accuracy                           0.76      1532\n",
            "   macro avg       0.71      0.70      0.70      1532\n",
            "weighted avg       0.76      0.76      0.76      1532\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## evaluating Model\n",
        "# calculate scores\n",
        "auc = roc_auc_score(y_test, predictions)\n",
        "\n",
        "# calculate roc curves\n",
        "fpr, tpr, _ = roc_curve(y_test, predictions)\n",
        "\n",
        "plt.figure(figsize=(10, 8))\n",
        "# plot horizontal line \n",
        "plt.plot([0, 1], [0, 1], linestyle='--')\n",
        "# plot the roc curve for the model\n",
        "plt.plot(fpr, tpr, label='ROC curve (AUC = %0.2f)' % auc)\n",
        "# axis labels\n",
        "plt.xlabel('FPR')\n",
        "plt.ylabel('TPR')\n",
        "# show the legend\n",
        "plt.legend(loc='lower right')\n",
        "# show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 497
        },
        "id": "o2gOUl0h-6QD",
        "outputId": "1a2aeeec-c8af-4408-c201-5b5eb95663b7"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## feature importance\n",
        "\n",
        "## TOP Correlations. - see above\n",
        "#-------------------------------#\n",
        "#dew_9am\t0.436143\n",
        "#pcp_yesterday\t0.413329\n",
        "#hum_9am\t0.347378\n",
        "#tmp_9am\t0.260427\n",
        "#month\t0.147885\n",
        "\n",
        "feature_importance = abs(clf.coef_[0])\n",
        "feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
        "sorted_idx = np.argsort(feature_importance)\n",
        "pos = np.arange(sorted_idx.shape[0]) + .5\n",
        "\n",
        "featfig = plt.figure()\n",
        "featax = featfig.add_subplot(1, 1, 1)\n",
        "featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
        "featax.set_yticks(pos)\n",
        "featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=8)\n",
        "\n",
        "plt.title('Feature Importance')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "Rsi_cD6q_Bq5",
        "outputId": "5c33f50a-67c3-40a1-cf8d-5723f6705150"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = sm.OLS(y, X)\n",
        "results = model.fit()\n",
        "print(results.summary())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_u1K7lcQEEvf",
        "outputId": "d0b949b8-8778-4d4c-9df9-a12afe104893"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                 OLS Regression Results                                \n",
            "=======================================================================================\n",
            "Dep. Variable:                    pcp   R-squared (uncentered):                   0.425\n",
            "Model:                            OLS   Adj. R-squared (uncentered):              0.424\n",
            "Method:                 Least Squares   F-statistic:                              903.1\n",
            "Date:                Tue, 17 May 2022   Prob (F-statistic):                        0.00\n",
            "Time:                        19:30:14   Log-Likelihood:                         -3160.4\n",
            "No. Observations:                6126   AIC:                                      6331.\n",
            "Df Residuals:                    6121   BIC:                                      6364.\n",
            "Df Model:                           5                                                  \n",
            "Covariance Type:            nonrobust                                                  \n",
            "=================================================================================\n",
            "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "tmp_10am         -0.0066      0.001     -4.931      0.000      -0.009      -0.004\n",
            "hum_10am          0.0040      0.000      8.720      0.000       0.003       0.005\n",
            "month             0.0047      0.002      3.028      0.002       0.002       0.008\n",
            "wdd_9am_CALM      0.1162      0.022      5.312      0.000       0.073       0.159\n",
            "pcp_yesterday     0.3158      0.012     27.097      0.000       0.293       0.339\n",
            "==============================================================================\n",
            "Omnibus:                      581.624   Durbin-Watson:                   1.917\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              568.151\n",
            "Skew:                           0.686   Prob(JB):                    4.24e-124\n",
            "Kurtosis:                       2.414   Cond. No.                         300.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##prediction testing\n",
        "\n",
        "## selection: 'tmp_9am', 'dew_9am', 'hum_9am', 'pcp_yesterday', 'month','wdd_9am_CALM'\n",
        "\n",
        "##with data of 10am also combined with 10am rain\n",
        "## 'tmp_9am','dew_9am', 'tmp_10am','dew_10am', 'month', 'wdd_9am_CALM','pcp_yesterday'\n",
        "prediction = results.get_prediction([23.3,23.3*.65,5,0,1])\n",
        "prediction.summary_frame()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "5lE2Ya1Q_5We",
        "outputId": "2a3f6fbc-01ef-40e3-f416-222e81c020c0"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       mean   mean_se  mean_ci_lower  mean_ci_upper  obs_ci_lower  \\\n",
              "0  0.246961  0.029492       0.189146       0.304775     -0.550068   \n",
              "\n",
              "   obs_ci_upper  \n",
              "0      1.043989  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ca503ac7-df9e-4636-b6cb-f302b293ad52\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>mean</th>\n",
              "      <th>mean_se</th>\n",
              "      <th>mean_ci_lower</th>\n",
              "      <th>mean_ci_upper</th>\n",
              "      <th>obs_ci_lower</th>\n",
              "      <th>obs_ci_upper</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.246961</td>\n",
              "      <td>0.029492</td>\n",
              "      <td>0.189146</td>\n",
              "      <td>0.304775</td>\n",
              "      <td>-0.550068</td>\n",
              "      <td>1.043989</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca503ac7-df9e-4636-b6cb-f302b293ad52')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ca503ac7-df9e-4636-b6cb-f302b293ad52 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ca503ac7-df9e-4636-b6cb-f302b293ad52');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[(data['wdd_9am_CALM']==1) & (data['pcp_hrs']>0)].hist('month',bins=12) ##when was wind calm and rained"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "EbMMs6yDJJWo",
        "outputId": "58a05a64-6037-4854-a1be-63113b964fbb"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd77197ca90>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[(data['pcp_hrs']>5)].hist('month',bins=12) ##rainy afternoon / night"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "lUg_9m3nLmxH",
        "outputId": "2f40f4f2-c0e5-4f54-a795-556d12c59bd0"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7fd77199d590>]],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def make_prediction(predictors,data,inputs=[]):\n",
        "  X = data[predictors]\n",
        "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)\n",
        "\n",
        "  ##model\n",
        "  clf = LogisticRegression(penalty='l2',max_iter=200).fit(X_train, y_train)\n",
        "\n",
        "  # predict probabilities\n",
        "  predictions = clf.predict_proba(X_test)[:, 1]\n",
        "  predictionsb = clf.predict(X_test)\n",
        "  #print(predictions)\n",
        "  #print(predictionsb)\n",
        "  accuracy = accuracy_score(y_test,predictionsb)\n",
        "  print('ConfMatrix\\n', confusion_matrix(y_test,predictionsb))\n",
        "  print('\\n\\nAccuracy:',accuracy)\n",
        "  print('\\n\\nClassification Report\\n\\n',classification_report(y_test,predictionsb))\n",
        "\n",
        "  \n",
        "  # calculate scores\n",
        "  auc = roc_auc_score(y_test, predictions)\n",
        "\n",
        "  # calculate roc curves\n",
        "  fpr, tpr, _ = roc_curve(y_test, predictions)\n",
        "\n",
        "  plt.figure(figsize=(10, 8))\n",
        "  # plot horizontal line \n",
        "  plt.plot([0, 1], [0, 1], linestyle='--')\n",
        "  # plot the roc curve for the model\n",
        "  plt.plot(fpr, tpr, label='ROC curve (AUC = %0.2f)' % auc)\n",
        "  # axis labels\n",
        "  plt.xlabel('FPR')\n",
        "  plt.ylabel('TPR')\n",
        "  # show the legend\n",
        "  plt.legend(loc='lower right')\n",
        "  # show the plot\n",
        "  plt.show()\n",
        "\n",
        "  ## feature importance\n",
        "\n",
        "  ## TOP Correlations. - see above\n",
        "  #-------------------------------#\n",
        "  #dew_9am\t0.436143\n",
        "  #pcp_yesterday\t0.413329\n",
        "  #hum_9am\t0.347378\n",
        "  #tmp_9am\t0.260427\n",
        "  #month\t0.147885\n",
        "\n",
        "  feature_importance = abs(clf.coef_[0])\n",
        "  feature_importance = 100.0 * (feature_importance / feature_importance.max())\n",
        "  sorted_idx = np.argsort(feature_importance)\n",
        "  pos = np.arange(sorted_idx.shape[0]) + .5\n",
        "\n",
        "  featfig = plt.figure()\n",
        "  featax = featfig.add_subplot(1, 1, 1)\n",
        "  featax.barh(pos, feature_importance[sorted_idx], align='center')\n",
        "  featax.set_yticks(pos)\n",
        "  featax.set_yticklabels(np.array(X.columns)[sorted_idx], fontsize=8)\n",
        "\n",
        "  plt.title('Feature Importance')\n",
        "  plt.show()\n",
        "\n",
        "  model = sm.OLS(y, X)\n",
        "  results = model.fit()\n",
        "  print(results.summary())\n",
        "  if len(inputs)>0:\n",
        "    st1 =\" \"\n",
        "    prediction = results.get_prediction(inputs)\n",
        "    print('\\n\\n====================================================\\nPrediction for: '+st1.join([str(x) for x in inputs])+'\\n\\n',prediction.summary_frame())\n",
        "    print('\\n\\nAccuracy:',accuracy)\n",
        "    print('\\n\\nAUC:',auc)\n"
      ],
      "metadata": {
        "id": "3YMIyhzfNjS8"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "make_prediction(['tmp_9am','hum_9am', 'spl_9am','pcp_yesterday'],data,[22,68,1,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "HlSE9wS5K4tO",
        "outputId": "eb701e4e-0eba-4415-fb2e-4819cd0ea604"
      },
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConfMatrix\n",
            " [[970 122]\n",
            " [209 231]]\n",
            "\n",
            "\n",
            "Accuracy: 0.7839425587467362\n",
            "\n",
            "\n",
            "Classification Report\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.89      0.85      1092\n",
            "           1       0.65      0.53      0.58       440\n",
            "\n",
            "    accuracy                           0.78      1532\n",
            "   macro avg       0.74      0.71      0.72      1532\n",
            "weighted avg       0.77      0.78      0.78      1532\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                 OLS Regression Results                                \n",
            "=======================================================================================\n",
            "Dep. Variable:                    pcp   R-squared (uncentered):                   0.434\n",
            "Model:                            OLS   Adj. R-squared (uncentered):              0.434\n",
            "Method:                 Least Squares   F-statistic:                              1174.\n",
            "Date:                Tue, 17 May 2022   Prob (F-statistic):                        0.00\n",
            "Time:                        20:34:03   Log-Likelihood:                         -3109.3\n",
            "No. Observations:                6126   AIC:                                      6227.\n",
            "Df Residuals:                    6122   BIC:                                      6254.\n",
            "Df Model:                           4                                                  \n",
            "Covariance Type:            nonrobust                                                  \n",
            "=================================================================================\n",
            "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "tmp_9am           0.0008      0.002      0.500      0.617      -0.002       0.004\n",
            "hum_9am           0.0014      0.000      3.062      0.002       0.000       0.002\n",
            "spl_9am           0.1672      0.012     14.169      0.000       0.144       0.190\n",
            "pcp_yesterday     0.3261      0.011     28.835      0.000       0.304       0.348\n",
            "==============================================================================\n",
            "Omnibus:                      514.071   Durbin-Watson:                   1.947\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              546.539\n",
            "Skew:                           0.688   Prob(JB):                    2.09e-119\n",
            "Kurtosis:                       2.505   Cond. No.                         183.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "\n",
            "\n",
            "====================================================\n",
            "Prediction for: 22 68 1 1\n",
            "\n",
            "        mean   mean_se  mean_ci_lower  mean_ci_upper  obs_ci_lower  \\\n",
            "0  0.602861  0.013439       0.576515       0.629207     -0.185842   \n",
            "\n",
            "   obs_ci_upper  \n",
            "0      1.391565  \n",
            "\n",
            "\n",
            "Accuracy: 0.7839425587467362\n",
            "\n",
            "\n",
            "AUC: 0.8355779637029637\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "make_prediction(['tmp_10am','hum_10am','month', 'spl_10am','pcp_yesterday'],data,[24,15/24*100,5,1,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pKxzpXYrJJdn",
        "outputId": "66866e20-9ff4-4659-be6d-00fee8ad701e"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConfMatrix\n",
            " [[926 135]\n",
            " [219 252]]\n",
            "\n",
            "\n",
            "Accuracy: 0.7689295039164491\n",
            "\n",
            "\n",
            "Classification Report\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.81      0.87      0.84      1061\n",
            "           1       0.65      0.54      0.59       471\n",
            "\n",
            "    accuracy                           0.77      1532\n",
            "   macro avg       0.73      0.70      0.71      1532\n",
            "weighted avg       0.76      0.77      0.76      1532\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                 OLS Regression Results                                \n",
            "=======================================================================================\n",
            "Dep. Variable:                    pcp   R-squared (uncentered):                   0.444\n",
            "Model:                            OLS   Adj. R-squared (uncentered):              0.444\n",
            "Method:                 Least Squares   F-statistic:                              979.2\n",
            "Date:                Tue, 17 May 2022   Prob (F-statistic):                        0.00\n",
            "Time:                        20:33:41   Log-Likelihood:                         -3052.7\n",
            "No. Observations:                6126   AIC:                                      6115.\n",
            "Df Residuals:                    6121   BIC:                                      6149.\n",
            "Df Model:                           5                                                  \n",
            "Covariance Type:            nonrobust                                                  \n",
            "=================================================================================\n",
            "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "tmp_10am         -0.0104      0.001     -7.752      0.000      -0.013      -0.008\n",
            "hum_10am          0.0042      0.000      9.319      0.000       0.003       0.005\n",
            "month             0.0064      0.002      4.184      0.000       0.003       0.009\n",
            "spl_10am          0.1708      0.011     15.755      0.000       0.150       0.192\n",
            "pcp_yesterday     0.2800      0.012     23.922      0.000       0.257       0.303\n",
            "==============================================================================\n",
            "Omnibus:                      470.003   Durbin-Watson:                   1.952\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              493.973\n",
            "Skew:                           0.653   Prob(JB):                    5.43e-108\n",
            "Kurtosis:                       2.521   Cond. No.                         174.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "\n",
            "\n",
            "====================================================\n",
            "Prediction for: 24 62.5 5 1 1\n",
            "\n",
            "        mean   mean_se  mean_ci_lower  mean_ci_upper  obs_ci_lower  \\\n",
            "0  0.498723  0.011897         0.4754       0.522046     -0.282703   \n",
            "\n",
            "   obs_ci_upper  \n",
            "0       1.28015  \n",
            "\n",
            "\n",
            "Accuracy: 0.7689295039164491\n",
            "\n",
            "\n",
            "AUC: 0.8369983050881373\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "make_prediction(['tmp_9am','hum_9am','tmp_10am','hum_10am','wdd_9am_CALM','wdd_10am_CALM','pcp_yesterday'],data,[22,15/22*100,24,15/24*100,1,0,1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IlqOO2HtJdo0",
        "outputId": "8658a625-e48a-48b4-c67b-85bbf3fe1436"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConfMatrix\n",
            " [[957 131]\n",
            " [207 237]]\n",
            "\n",
            "\n",
            "Accuracy: 0.779373368146214\n",
            "\n",
            "\n",
            "Classification Report\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.88      0.85      1088\n",
            "           1       0.64      0.53      0.58       444\n",
            "\n",
            "    accuracy                           0.78      1532\n",
            "   macro avg       0.73      0.71      0.72      1532\n",
            "weighted avg       0.77      0.78      0.77      1532\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                 OLS Regression Results                                \n",
            "=======================================================================================\n",
            "Dep. Variable:                    pcp   R-squared (uncentered):                   0.429\n",
            "Model:                            OLS   Adj. R-squared (uncentered):              0.429\n",
            "Method:                 Least Squares   F-statistic:                              657.5\n",
            "Date:                Tue, 17 May 2022   Prob (F-statistic):                        0.00\n",
            "Time:                        19:49:16   Log-Likelihood:                         -3135.1\n",
            "No. Observations:                6126   AIC:                                      6284.\n",
            "Df Residuals:                    6119   BIC:                                      6331.\n",
            "Df Model:                           7                                                  \n",
            "Covariance Type:            nonrobust                                                  \n",
            "=================================================================================\n",
            "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "tmp_9am           0.0324      0.008      4.238      0.000       0.017       0.047\n",
            "hum_9am          -0.0022      0.001     -1.981      0.048      -0.004   -2.35e-05\n",
            "tmp_10am         -0.0319      0.007     -4.507      0.000      -0.046      -0.018\n",
            "hum_10am          0.0054      0.001      4.685      0.000       0.003       0.008\n",
            "wdd_9am_CALM      0.0960      0.023      4.135      0.000       0.051       0.142\n",
            "wdd_10am_CALM     0.1166      0.029      3.955      0.000       0.059       0.174\n",
            "pcp_yesterday     0.3123      0.012     26.679      0.000       0.289       0.335\n",
            "==============================================================================\n",
            "Omnibus:                      567.688   Durbin-Watson:                   1.928\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              537.605\n",
            "Skew:                           0.662   Prob(JB):                    1.82e-117\n",
            "Kurtosis:                       2.404   Cond. No.                         633.\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "\n",
            "\n",
            "====================================================\n",
            "Prediction for: 22 68.18181818181817 24 62.5 1 0 1\n",
            "\n",
            "        mean   mean_se  mean_ci_lower  mean_ci_upper  obs_ci_lower  \\\n",
            "0  0.543538  0.024871       0.494783       0.592293     -0.249741   \n",
            "\n",
            "   obs_ci_upper  \n",
            "0      1.336817  \n",
            "\n",
            "\n",
            "Accuracy: 0.779373368146214\n",
            "\n",
            "\n",
            "AUC: 0.8414532409247484\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[data.spd_9am.isnull()==True]"
      ],
      "metadata": {
        "colab": {
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          "height": 143
        },
        "id": "uNVt5zbvPPIG",
        "outputId": "7f4a5c0d-33f5-4a79-955b-990b42f9f0ee"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [dia, day_date, tmp_9am, dew_9am, hum_9am, wnd_9am, spd_9am, alt_9am, cig_9am, tmp_10am, dew_10am, hum_10am, wnd_10am, spd_10am, alt_10am, cig_10am, pcp_hrs, pcp_yesterday, year, month, day, wdd_9am_CALM, wdd_9am_E, wdd_9am_N, wdd_9am_S, wdd_9am_W, wdd_10am_CALM, wdd_10am_E, wdd_10am_N, wdd_10am_S, wdd_10am_W]\n",
              "Index: []\n",
              "\n",
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              "      <th>tmp_10am</th>\n",
              "      <th>...</th>\n",
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              "      <th>wdd_10am_E</th>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "make_prediction([ 'tmp_9am', 'dew_9am', 'hum_9am', 'spd_9am', 'alt_9am', 'tmp_10am', 'dew_10am', 'hum_10am', 'spd_10am', 'alt_10am',\n",
        "                 'pcp_yesterday',  'month', 'wdd_9am_E', 'wdd_9am_N', 'wdd_9am_S',\n",
        "                 'wdd_9am_W', 'wdd_10am_CALM', 'wdd_10am_E', 'wdd_10am_N', 'wdd_10am_S', 'wdd_10am_W'],data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "UkMDWLnPJuPE",
        "outputId": "a555a944-2a78-4401-8473-3ab56cc0c159"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ConfMatrix\n",
            " [[998 115]\n",
            " [191 228]]\n",
            "\n",
            "\n",
            "Accuracy: 0.8002610966057441\n",
            "\n",
            "\n",
            "Classification Report\n",
            "\n",
            "               precision    recall  f1-score   support\n",
            "\n",
            "           0       0.84      0.90      0.87      1113\n",
            "           1       0.66      0.54      0.60       419\n",
            "\n",
            "    accuracy                           0.80      1532\n",
            "   macro avg       0.75      0.72      0.73      1532\n",
            "weighted avg       0.79      0.80      0.79      1532\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ],
            "image/png": 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C9Z24/disPwUMgFe8t3CrHvxgudomxGr8jd2V0qCW05HgozxWwqy1xcaYcZK+lOSS9Ja1dqUx5glJGdbaGZLulfQPY8zdKh3SH2GtZaUL1VO2/SiVli+2HwF4UZ8WDTS8V7IevjRVUeHcfBunZvyt86Snp9uMjAynY8CXTbisdP6L7UcAXrJie44+WLRd/zeoPcdO4DjGmExrbfrJ3nN6MB+oWWUHsLL9CMBL3s/YpiGvzdPnK3Zo18ECp+PAj3DvSASWsjkwth8BeFhBcYn+8MkqTf5hq/q2rK8Xh3ZVg5gIp2PBj1DCEHhYBQPgBWMmLtJ/1+zWmAEtde/ANgp1sbmEyqGEIXCU34oEAA/7Tf/mui69qS7umOh0FPgpShgCB1uRADzI7bZ6bfZGWWs17rzW6tuqgdOR4OcoYfBf5c8CkzgPDIDH5OQV6d5pS/T16t26sktjWWu5ChLVRgmD/yq7EXdiWulzzgMD4AGrdxzU6ImZ2r4/T48PTtWv+6ZQwFAjKGHwb9yIG4AH5Rwp0nWvz1dUmEtTR/VWeko9pyMhgFDC4F9OvB1R2SoYANQgt9sqJMQoLjpMzw7ppO4pddUwNtLpWAgwXE8L/1F2O6Itc0qfs/0IwAN25OTpmvHz9OXKnZKkS9IaUcDgEayEwX+UrYBxOyIAHjJvwx7dPmWx8otK5Gd39YMfooTBv3D1IwAPsNbq9e826dkv1qhFfIzG39hdrRrGOB0LAY7tSPiHsoNYAcADvlu/R09/vkaXpDXSx7f1o4DBK1gJg3/gIFYAHpBXWKKocJfObt1A79zcU2e3bsDxE/AaVsLgP9iKBFCDPl6yXf2f+a/W7DwoY4zOaRNPAYNXUcLg+9iKBFCDCovdenzGSt05dYlaxNdSvehwpyMhSLEdCd/HViSAGrLrYL5um7RIGVv26+Z+zfXQpe0U5mI9As6ghMF3lR3Myj0hAdSQt+dt1qodB/XS0K4a3Lmx03EQ5Chh8E1lB7NKpQWMVTAAVWStVfahAjWMjdTdF7TRtd2T1CKeqx/hPEoYfBMHswKoAYcKivXA9KValpWjz+48S7Ujwyhg8BmUMPgutiABVMOG3bm69d1M/bTnsB68pJ1iI/gjD76FX5HwLeXnwLg5N4AqmrnsZz0wfZmiw12a+Jte6tuygdORgF+ghMG3lC9gzIEBqAJrrSYt2Kp2ibF6dXh3JcZx8234JkoYfE9imjTyU6dTAPAzu3PzFWKMGsREaPyN3RUV7lJ4KMdPwHfxqxMA4PcWbt6nQS/O0f3vL5UkxUWHUcDg8/gVCt+QMUGacFnpViQAVJC1Vm/N+UlD31ig6HCXfndJO6cjARXGdiS8r2z4vryy2xJxJhiACjpcUKzf/XuZZi7boYGpCXruus6qHRnmdCygwihh8L6TXf1YVr44kgJABRWVuLV8e47uv6itxpzTUiEh3Hwb/oUSBmcwfA+giuas36OezeupTnS4vrzrbEWGuZyOBFQJM2HwHua+AFRDcYlbf/5stW588wdNmPuTJFHA4NdYCYPnlc2AMfcFoIqycwt0+5RFWrBpn4b3StaIfilORwKqjRIGzyubAWPuC0AVLNl2QLe+m6EDR4r03LWdNaR7ktORgBpBCYN3MAMGoIqiw12qGx2uCSN6KrVxbafjADWGmTB4VsaE/21DAkAF5RWW6L2FW2WtVZuEWH12x1kUMAQcVsLgWWXngTEDBqCCftpzWGMmZmrtrlx1aBynjk3iOH4CAYkSBs8oG8YvmwVjDgxABXy1cqfunbZULpfR2yN7qmOTOKcjAR5DCYNnlD+QlVUwABXw8n/X669frVNakzi9OrybmtaLdjoS4FGUMNS8sjmwZv0ZxgdQYW0SYjW0Z1M9NrgD538hKFDCUPOYAwNQQUu2HdC6Xbm6Lr2pLuyQqAs7JDodCfAaro6EZzAHBuA0rLWauGCLrh0/T6/N2qiC4hKnIwFeRwlDzeJICgBnkFdYonvfX6pHPlqhvi0b6MOxfRURyvYjgg/bkahZbEUCOI3CYreufX2eVv58UHee31p3nt+a4ycQtChhqHlsRQI4hfDQEF3ZpYnuHdhW57Zr6HQcwFGUMACAR5W4rV74ep16Nq+v/q0b6DdntXA6EuATmAkDAHjM/sOFGjHhR7343w2avW6303EAn8JKGADAI5ZlHdCYiYuUnVugP1+dpht6NHU6EuBTWAlDzeHKSABHrdl5UNe8Nl+SNH1MHw3tmSxjGMAHymMlDDUjY4I0867Sx1wZCQS9tgmxumtga93QI1n1aoU7HQfwSayEoWaUHU0x6HmujASC1LZ9RzT8nwu0Ze9hGWM0dkArChhwGqyEofrK3yuSAgYEpW/X7tZdU5fIba22789Ts/q1nI4E+DxKGKomY8L/Vr/K5sDYhgSCjttt9eJ/1+uFb9arbUKsxt/YXSkNKGBARVDCUDXLp0s7l0uJaaUrYGnXsAoGBKG35v6k579er6u7NdGfrkxTVDi3HwIqihKGyilbASsrYCM/dToRAAeUuK1cIUbDezVTfGyELu/cmKsfgUpiMB+VU76Asf0IBKVpGdt0+ctzdKigWFHhLl3RpQkFDKgCVsJQceUH8FkBA4JOflGJ/vDJKk35cav6tqyvwmK3FOF0KsB/UcJQMZwDBgS1rP1HNHbSIi3LytGYAS1178A2CnWxmQJUByUMFcM5YEBQe+SjFfop+7Bev6m7LuqQ6HQcICBQwnB65QfxOQcMCCput1V+cYmiw0P11FVpKih2qznHTwA1hhKG02MQHwhKOXlFunfaElkr/eNX6WpcJ8rpSEDAoYTh1BjEB4LSqp8PasykTG3fn6f/G5QqLnwEPIMShlMrmwNjBQwIGv/OzNLDHy1XXFSY3ru1t7o3q+d0JCBgUcLwP+VvRSQxBwYEmdz8Ij39xRp1Tqqjl4d1U3ws508AnkQJw/+Un/+SmAMDgsTug/mqHxOh2MgwvX9rHyXVjeL4CcALKGE4HrciAoLK3A17dPuUxbqxdzPdM7ANN98GvIi/6gBAELLW6rVZG3XTmz+oXq1wXd65sdORgKDDShgABJmD+UW6b9pSfbVqlwZ1aqRnhnRSrQj+OAC8jf/rACDIbNlzRHM27NGjg1I1sl8KN98GHEIJA4AgsWJ7jjo2iVNaUpzm/O481asV7nQkIKgxExbsMiZIEy4r/bFzudNpAHhAYbFbj89YqUEvzdHsddmSRAEDfAArYcEsY4I0867Sx836cyQFEIB25uTrtsmLlLllv27p31x9W9Z3OhKAozxawowxF0t6QZJL0j+ttU+f5DPXSXpckpW01Fo7zJOZUE7ZwayDnudAViAALdi0V+MmL9KRwhK9PKyrBnXiCkjAl3ishBljXJJekTRQUpakhcaYGdbaVeU+01rSQ5L6WWv3G2MaeioPToET8YGAtX1/nuKiwjTlt73VOiHW6TgATuDJmbCekjZYazdZawslTZV0xQmf+a2kV6y1+yXJWrvbg3lQXtnNuQEElNz8Is3bsEeSNKR7kj678ywKGOCjPFnCmkjaVu551tHXymsjqY0xZq4xZsHR7Ut4AzfnBgLO+l25uuKVufrtvzK0/3ChJCki1OVwKgCn4vRgfqik1pIGSEqS9J0xJs1ae6D8h4wxoySNkqTk5GRvZwxcbEUCAWPmsp/1wPRlig536Z+/7qG6XP0I+DxProRtl9S03POko6+VlyVphrW2yFr7k6R1Ki1lx7HWvmGtTbfWpsfHx3sscNBgKxIIGNZaPTlzlcZNXqx2ibGaeftZ6sMVkIBf8GQJWyiptTGmuTEmXNINkmac8JmPVLoKJmNMA5VuT27yYCZIbEUCAcQYoxAjjeiboqmj+igxLtLpSAAqyGPbkdbaYmPMOElfqvSIirestSuNMU9IyrDWzjj63oXGmFWSSiTdb63d66lMKIetSMCv/fjTPoW5jLom19XvL23PrYcAP+TRmTBr7WeSPjvhtUfLPbaS7jn6AwBwBtZavTV3s576bLV6ptTTlFG9KWCAn3J6MB8AUEGHC4r1u38v08xlO3RhaoL+el1npyMBqAZKGAD4gezcAg37xwJtzD6k313cTqPPacEKGODnuIF3sOHKSMAv1asVro5N4vTuLb00ZkBLChgQAChhwYYrIwG/UVzi1t//s047c/LlCjH6+/Vd1K9VA6djAaghbEcGI66MBHxedm6Bbp+ySAs27VNcVJhu7t/c6UgAahglDAB8TOaWfRo7aZFy8or0t+s66+puSU5HAuABbEcGE+bBAJ/37Zrduv71BYoMc+nDsf0oYEAAYyUsmDAPBvi87il1NbRnsu67qK3iosKcjgPAg1gJCzbMgwE+Z1P2Id01dbHyi0pUOzJMT17ZkQIGBAFWwgDAQV+u3Kn7pi1VqMvopz2H1b5RbacjAfASShgAOKC4xK3n/rNOr83aqE5JcXp1eDcl1Y12OhYAL6KEAYADHv9kpSYu2KqhPZP12OBURYa5nI4EwMsoYQDggJv7NVfnpDq6Nr2p01EAOITB/GCQMUGacJm0c7nTSYCgZa3Vuwu26L73l8paqxbxMRQwIMhRwoLB8umlBSwxjeMpAAfkFZbo3veX6v8+WqG9hwpUUOx2OhIAH8B2ZLBITJNGfup0CiDobNl7WLe+m6m1u3J19wVtdPt5rRQSws23AVDCAl/ZKfnN+judBAg6xSVu3fTmj8rJK9JbI3ro3LYNnY4EwIdQwgIdp+QDXlfitgoxUqgrRH+5ppMa14lS03ocPwHgeMyEBbLyq2Cckg94xb7DhRox4Ue9/t0mSVKvFvUpYABOihIWyFgFA7xqWdYBDX5pjn74aZ/qRnPbIQCnx3ZkoGMVDPA4a62mLtymxz5eqfjYCE0f3Uedkuo4HQuAj6OEAUA1bcw+rEc+WqF+rRroheu7qG6tcKcjAfADlDAAqKLc/CLFRoapVcMYTR3VW92S68rF8RMAKoiZMACogm/X7tbZz36rb9fsliT1SKlHAQNQKayEAUAluN1WL3yzXi/+d73aJdZWi/haTkcC4KcoYQBQQQeOFOrOqUs0e122hnRL0h+v7KiocJfTsQD4qUpvRxpjQowxwz0RBgB82Vcrd2n+xr3601Ud9ddrO1HAAFTLKVfCjDG1Jd0mqYmkGZL+I2mcpHslLZU0yRsBUUXcrgioMdsP5KlJnShdm56kns3rKaUBW5AAqu90K2HvSmorabmk30j6VtI1kq601l7hhWyoDg5qBaotv6hED32wTBf+bba27j0iYwwFDECNOd1MWAtrbZokGWP+KWmHpGRrbb5XkqFqMiaUFrCdyzmoFaiGrP1HNGbiIi3fnqOxA1qqSd0opyMBCDCnK2FFZQ+stSXGmCwKmB8oK2CJaayCAVX03bps3TF1sUpKrN64qbsu7JDodCQAAeh0JayzMeagpLKDb6LKPbfW2toeT4fKKT8HNvJTp9MAfuuLlTuVWDtSr93YXc3ZfgTgIacsYdZaLvvxN8yBAVWWc6RIew8XqEV8jB4dlCq3tYoO5xQfAJ5zuqsjIyWNltRK0jJJb1lri70VDFXEHBhQaat+PqjREzMVERqiL+46W5Fh/B0UgOed7urIdySlq/TqyEslPeeVRKiasq1IAJXy78wsXfXqXBUUl+jpIZ249RAArzndWntquasj35T0o3ciodIyJkgz7yp9zFYkUCGFxW49MXOlJi7Yqt4t6umlod0UHxvhdCwAQaSiV0cWG8PfDn1W2SzYoOfZigQqyBhp7c5c3XpOC91/YVuFuip9AxEAqJbTlbAuR6+GlEqviOTqSF/GLBhQIfM27lG7xNqqVytck37TW+GhlC8Azjjd7z5LrbW1j/6ItdaGlntMAfMVzIIBFeJ2W73y7Qbd+M8f9Lf/rJUkChgAR51uJcx6LQWqjmMpgDPKySvSvdOW6uvVuzSoUyM9dEl7pyMBwGlLWENjzD2netNa+zcP5EFVsBUJnNLG7EO65e2Fytqfp0cHpWpkvxQx4wrAF5yuhLkkxeh/J+YDgN+pExWmuOhw/eXazuqRUs/pOABwzOlK2A5r7RNeSwIANaSw2K13F2zRr/o0U/2YCH00ti+rXwB8zulKGL9jATqyngsAACAASURBVPA7O3PyNXZSphZtPaDketEamJpAAQPgk053adD5XkuBquHKSOA48zbu0aCXvteanbl6eVhXDUxNcDoSAJzS6W7gvc+bQVAFXBkJHPPewq166IPlat6glqaO6q1WDWOdjgQAp3W67Uj4A66MBCRJHZvE6YouTfTklR0VE8FvbQB8HycVAvBb63fl6uX/rpckdWgcp79f34UCBsBvUMIA+KVPlv6sK16Zq7fnbdGeQwVOxwGASuOvjAD8SlGJW3/+bI3emvuTujerq1eHd1ODmAinYwFApVHCAPgNa61++68MzVqbrRF9U/T7S9tz/0cAfosSBsBvGGN0fXpTXdW1ia7o0sTpOABQLZQwf1V2Rliz/k4nATzKWqs35/yk6PBQDeuVrEvSGjkdCQBqBOv4/oozwhAEDhUUa9zkxfrjp6s1f9NeWWudjgQANYaVMH9UfhWMM8IQoDbsztXoiYu0KfuQHrqknUad3YLbDwEIKJQwf8QqGALc3kMFuuqVeQoPDdHEW3qpb6sGTkcCgBpHCfNXrIIhAFlrZYxR/ZgIPTKovc5uE69GcVFOxwIAj2AmDIBP2J2brxvf/EE/bNorSbq+RzIFDEBAYyUMgOMyNu/T2EmLdDC/SHsOFTodBwC8gpUwf1M2lA8EAGutJsz9STe8sUDR4S59OLafLuvEERQAggMrYf6GoXwEkC9X7tIfPlmlC9on6LnrOisuKszpSADgNZQwf8RQPvxcUYlbYa4QXZiaoJeHddWlHRspJITjJwAEF7Yj/UXGBGnCZdLO5U4nAarlixU7dd5zs7Rt3xGFhBgN6tSYAgYgKFHC/MXy6aUFLDGNrUj4peISt57+fI1GT8xUvehwuSheAIIc25H+JDFNGvmp0ymASttzqEC3T16s+Zv2alivZD02OFURoS6nYwGAoyhhADzupW/Wa9HW/frrtZ11Tfckp+MAgE+ghPmD8veKBPyEtVYH84sVFxWmBy5upxt6Jqt9o9pOxwIAn8FMmD/gWAr4mbzCEt07bamuGz9feYUlqhURSgEDgBOwEuYvOJYCfmLznsMaPTFTa3fl6u4L2igilL/rAcDJUMIA1JivV+3S3dOWKMQYTRjRQwPaNnQ6EgD4LI/+FdUYc7ExZq0xZoMx5sHTfG6IMcYaY9I9mQeA55S4rZ7/Zp2a1Y/WzNv7U8AA4Aw8thJmjHFJekXSQElZkhYaY2ZYa1ed8LlYSXdK+sFTWQB4zr7DhQpzGcVGhunNX/dQXFSYIsM4fgIAzsSTK2E9JW2w1m6y1hZKmirpipN87klJz0jK92AW/8UNu+HDlmw7oEEvfq+HP1whSUqoHUkBA4AK8mQJayJpW7nnWUdfO8YY001SU2stJ5CeCldGwgdZazX5h626bvx8GWP027NaOB0JAPyOY4P5xpgQSX+TNKICnx0laZQkJScnezaYL+LKSPiQ/KIS/d9HK/R+ZpbObhOvF67vorq1wp2OBQB+x5MrYdslNS33POnoa2ViJXWUNMsYs1lSb0kzTjacb619w1qbbq1Nj4+P92BkAGdy4EiRvl2brTvOb60JI3pQwACgijy5ErZQUmtjTHOVlq8bJA0re9NamyOpQdlzY8wsSfdZazM8mAlAFWVu2a+uTesoMS5S39x7juKiwpyOBAB+zWMrYdbaYknjJH0pabWkadbalcaYJ4wxl3vq+wKoWSVuq7/9Z52GvDZPk37YIkkUMACoAR6dCbPWfibpsxNee/QUnx3gySwAKm//4ULd+d4SfbcuW0O6Jena9KZn/kkAgArhxHwAJ7Vie45ufTdT2bkF+tNVHTWsZ7KMMU7HAoCAwU3dfBlnhMFB+UUlCnUZvT+6j4b3akYBA4AaRgnzZZwRBi/LLyrR58t3SJLSU+rp63vOUeemdRxOBQCBiRLm6zgjDF6ybd8RXTt+vsZOXqT1u3IlSWEufosAAE/hd1hfxVYkvGj2umwNfnmONu89rDduSlfrhFinIwFAwGMw31exFQkveW3WRj375Rq1TYjV+Bu7K6VBLacjAUBQoIT5orJVMLYi4QV1o8N0ZZcmeuqqNEWFc/NtAPAWSpgvYhUMHrby5xxl7c/TRR0SdUPPZF3foylXPwKAl1HCfA2rYPCw6ZlZevjD5WpSJ0rntWuoMFcIBQwAHEAJ8yUZE6SZd5U+ZhUMNayguERPfLJKk37Yqj4t6uulYV25+hEAHEQJ8yVl25CDnmcVDDUqv6hE17+xQEu3HdDoc1rqvgvbKJQCBgCOooT5GrYh4QGRYS71a1lfY85poYs7NnI6DgBAlDDfkDGhdBVs53IpMc3pNAgQbrfV+O826qxW8UpLitMDF7dzOhIAoBxKmC8oX8CYBUMNyMkr0r3Tlurr1bu0r3+h0pLinI4EADgBJcxXJKZJIz91OgUCwOodBzVmYqay9ufpscGpGtE3xelIAICToIQBAWTF9hxdM36eakeGaeqo3kpPqed0JADAKVDCgADSLjFWv+6TolvOaq6GsZFOxwEAnAbXqAN+bkdOnka/m6ndufkKdYXooUvbU8AAwA+wEgb4sXkb9+j2yYuVX1Si9bsOUb4AwI9QwgA/ZK3V699t0rNfrFGL+BiNv7GbWjWMdToWAKASKGGAHxo/e5Oe+WKNLktrpGeu6aSYCP5XBgB/w+/cTit/w27gDKy1MsZoaM+mqh0VqmE9k7n5NgD4KQbznVZ2v0gOacUZzFj6s2588wcVFJeoTnS4hvdqRgEDAD9GCfMF3C8Sp1FU4tYfPlmpO6YsVkGRW4cLSpyOBACoAWxHAj5s18F83TZpkTK27NfIfin6/aXtFebi704AEAgoYYAPu2PKYq3acVAvDu2qyzs3djoOAKAGUcKcxFA+TsJaq6ISq/DQEP3pqo5yW6lNAsdPAECgoYQ5iaF8nOBQQbEemL5U0eGh+ss1nTj7CwACGMMlTmMoH0dt2J2rK16eoy9W7FSrhjFOxwEAeBgrYU5hKxLlfLpshx6YvlSRYS5NvKWX+rZq4HQkAICHUcKcwlYkjjpwpFAPfrBMbRJj9erwbmoUF+V0JACAF1DCnMRWZFA7cKRQcVFhqhMdrqmjeqt1w1iFhzIhAADBgt/xAQcs3LxPF/79O02Yu1mS1KFxHAUMAIIMv+s7oWweDEHHWqu35vykoW8sUHS4S31a1nc6EgDAIWxHOoF5sKB0uKBYD36wXJ8s/VkDUxP012s7Ky4qzOlYAACHUMKcwjxY0Fm+PUdfrNih+y9qqzHntFRICDffBoBgRgkDPOynPYfVvEEt9W5RX7PuP1dN6nD1IwCAmTDvypggTbhM2rnc6STwguISt/78+Wpd8LfZWrh5nyRRwAAAx7AS5i0ZE6SZd5U+btafebAAl51boDumLNb8TXs1vFeyOiXFOR0JAOBjKGHeUjaMP+h5ZsECXOaW/Ro7KVMHjhTpuWs7a0j3JKcjAQB8ECXMmxjGDwqLt+5XRKhLH4ztoQ6NWQEDAJwcJQyoAXmFJVq3K1edm9bRLf2b64aeyYqJ4H8vAMCpMZgPVNPmPYd11atz9au3flRufpGMMRQwAMAZ8ScFUA3/WbVL90xbIleI0YtDuyo2ksNXAQAVQwkDqsDttvrrV2v16qyNSmsSp1eHd1PTetFOxwIA+BG2I72Be0UGHGOkXQcLNLRnU70/ug8FDABQaayEeQP3igwYS7YdUEyES60axuqZIWkKdfH3GABA1fAniLdwPIVfs9Zq4oItum78fD05c7UkUcAAANXCShhwBnmFJXrkoxX696IsndMmXi/c0MXpSACAAEAJA05j98F8/XrCQq3ZeVB3nt9ad57fWiEhxulYAIAAQAkDTiMuOkwJtSP0wEU9dG67hk7HAQAEEIZagBOUuK3+8d0m5RwpUkSoS2+P7EkBAwDUOFbCgHL2Hy7Une8t0XfrshUeGqJf901xOhIAIEBRwoCjlmflaPTETGXnFujPV6fphh5NnY4EAAhgbEd6Gge1+oVvVu/SkPHzJEnvj+6joT2TZQwD+AAAz2ElzNM4qNUvpCXF6bK0Rvq/QamqVyvc6TgAgCDASpg3cFCrT9q274gen7FSxSVuNYyN1N+v70IBAwB4DSXMk9iK9Fmz1u7W4Jfn6N+LsrRpz2Gn4wAAghDbkZ7EVqTPcbutXvrvBj3/zTq1TYjV+Bu7K6VBLadjAQCCECXM09iK9CkPf7RCU37cqqu7NtGfrkpTVLjL6UgAgCBFCfOUsq3IZv2dToJybujRVKmNYnVj72Zc/QgAcBQlzFPYivQZ72ds04bsQ3rokvbq3LSOOjet43QkAAAoYR5RfhWMrUjHFBSX6PEZqzTlx63q27K+CovdCg/lWhQAgG+ghHkCq2CO234gT2MnZmppVo7GDGipewe2UaiLAgYA8B2UME9hFcwxBcUlum78fB3MK9LrN3XXRR0SnY4EAMAvUMIQMKy1MsYoItSlxy/voJbxtdQiPsbpWAAAnBT7MzWNA1odkZNXpN/+K0PTFm6TJA1MTaCAAQB8GiWspjEP5nWrdxzU5S/P0ay12SoocTsdBwCACmE70hOYB/OaDxdn6aEPlisuKkzv3dpb3ZvVczoSAAAVwkpYTWIr0qtW/pyju99bqs5JdfTJ7f0pYAAAv+LREmaMudgYs9YYs8EY8+BJ3r/HGLPKGLPMGPONMaaZJ/N4HFuRXpFfVCJJ6tA4Tm+NSNek3/RSw9hIh1MBAFA5HithxhiXpFckXSIpVdJQY0zqCR9bLCndWttJ0nRJz3oqj9ewFelR8zbs0YC/zFLmln2SpPPaJXD+FwDAL3nyT6+ekjZYazdZawslTZV0RfkPWGu/tdYeOfp0gaQkD+aBH7PW6rVZG3Xjmz8oJjJUcVHhTkcCAKBaPDmY30TStnLPsyT1Os3nb5H0uQfzwE8dzC/S/e8v1Zcrd+myTo307JBOqhXBNSUAAP/mE3+SGWNulJQu6ZxTvD9K0ihJSk5O9mIy+IL3M7L09erdeuSy9rqlf3MZY5yOBABAtXmyhG2X1LTc86Sjrx3HGHOBpIclnWOtLTjZF7LWviHpDUlKT0+3NR+1BpS/aTdqxL7DhapXK1wj+6aod4t66tA4zulIAADUGE/OhC2U1NoY09wYEy7pBkkzyn/AGNNV0uuSLrfW7vZgFs/jysgaU1js1uMzVurCv8/WroP5CgkxFDAAQMDx2EqYtbbYGDNO0peSXJLestauNMY8ISnDWjtD0l8kxUh6/+gW01Zr7eWeyuRxXBlZbbsO5mvspEXK3LJft/Rvrnq1GMAHAAQmj86EWWs/k/TZCa89Wu7xBZ78/vAvCzbt1bjJi3WksFgvD+uqQZ0aOx0JAACP8YnBfECS3p2/RbWjQjXlt73UOiHW6TgAAHgUJQyOOlRQrNz8IjWKi9LTQ9IkSbGRYQ6nAgDA8yhhcMyG3bm69d1MxUSE6sOx/ShfAICgwv1e4IhPl+3QFS/PVU5ekR68pL1CQjj7CwAQXFgJg1cVlbj19Odr9Oacn9QtuY5eHd5diXHcfBsAEHxYCasJZQe14oyKStyas36PRvRN0dRRfShgAICgxUpYTeCg1jNatHW/2ibEqlZEqD4Y25d7PwIAgh4rYTWFg1pPylqrt+b8pOvGz9fzX6+TJAoYAABiJQwedLigWL/79zLNXLZDA1MTdPv5rZ2OBACAz6CEwSN+2nNYo/6VoY3Zh/TAxW01+uyWXAEJAEA5lLDqKhvKb9bf6SQ+JTTEyG2t3r2ll/q1auB0HAAAfA4zYdXFUP4xxSVuvZ+xTW63VdN60frq7nMoYAAAnAIrYdVRfhUsyIfys3MLdPuURVqwaZ8a1o7UOW3i5WL7EQCAU6KEVQerYJKkzC37NHbSIh04UqTnru2sc9rEOx0JAACfRwmrriBfBZuWsU2//2C5GteJ0odjeyq1cW2nIwEA4BcoYaiWpnWjdV67hvrLNZ0VF80NuAEAqChKGCrtpz2HNXfDHt3Yu5n6tKyvPi3rOx0JAAC/QwlDpXy1cqfunbZUYaEhGtypMatfAABUESUMFVJc4tZz/1mn12ZtVKekOL06vBsFDACAaqCE4YystfrNvzI0a222hvZM1mODUxUZ5nI6FgAAfo0ShjMyxujC1ERd2rGRruvR1Ok4AAAEBEoYTspaq0k/bFW9WuG6NK2RhvVKdjoSAAABhdsWVVXZafkBKK+wRPe+v1SPfLRCny7b4XQcAAACEithVRWgp+Vv2XtYoycu0pqdB3Xn+a115/mtnY4EAEBAooRVR4Cdlr8zJ1+DX5ojY4zeGtFD57Zt6HQkAAACFiUMxyTGRWrsua10WVojNa0X7XQcAAACGjNhQW7f4UL99l8ZWvXzQUnS6HNaUsAAAPACSlgQW5Z1QINfmqPZa7O1MfuQ03EAAAgqbEcGqak/btWjH69UfGyEpo/po05JdZyOBABAUKGEBaFPlv6sBz9YrrNaN9ALN3RVvVrhTkcCACDoUMKCiNttFRJidHHHRD11VZqu79FUrhDjdCwAAIISM2FB4tu1u3XZS3O073ChwlwhGtYrmQIGAICDKGFV4Uen5bvdVn//zzrd/PZCSdLhgmKHEwEAAIntyKrxk9PyDxwp1J1Tl2j2umxd3a2J/nRlmqLCXU7HAgAAooRVnR+clv/HT1dr3sY9+uOVHTW8V7KMYfsRAABfQQkLQPlFJYoMc+mhS9rpxt7N1KUpx08AAOBrmAkLIPlFJXrog2W66c0fVFTiVv2YCAoYAAA+ihIWILL2H9F1r8/XlB+3KT2lnth4BADAt7EdGQC+W5etO6YuVkmJ1Rs3ddeFHRKdjgQAAM6AEubnikrcenzGSiXERmr8Td3VvEEtpyMBAIAKoIRVVtkZYc36OxojJ69IkWEhigh1acLIHoqPjVB0OP85AQDwF8yEVZYPnBG26ueDGvzSHD05c5UkqVn9WhQwAAD8DCWsMsqvgjl0Rti/M7N01atzVVBcoqu6NnEkAwAAqD6WTyoqY4I0867Sxw6sghUUl+jJmas0ccFW9W5RTy8N7ab42Aiv5wAAADWDElZRZduQg553ZBXs5wP5+mjxz7r17Ba6/6K2CnWxiAkAgD+jhFWGA9uQa3fmqk1CjJo3qKX/3neOGsZGevX7AwAAz2A5xUdZa/XqrA265IXv9OHi7ZJEAQMAIICwEuaDDuYX6b5pS/XVql0a1KmRLuLwVQAAAg4lzMes3Zmr0RMztW3fET06KFUj+6XIGG5CBABAoGE7siLKjqbwgu0HjuhIYbGmjOqtm/s3p4ABABCgWAmrCA8f0FpY7FbG5n3q26qBzmuXoFn3NVBUuMsj3wsAAPgGVsIqykNXRu7MydcNb8zXTW/9qK17j0gSBQwAgCDASpiD5m/cq9unLNKRwhK9cEMXJdePdjoSAADwEkrY6WRMKN2K3LlcSkyr0S/9z+836c+fr1Gz+tGa8tveap0QW6NfHwAA+DZK2OmUL2A1PA9WUOzWhakJevaaToqNDKvRrw0AAHwfJexMEtOkkZ/WyJdavytX2YcK1LdlA40d0FKSuPoRAIAgxWC+l3yy9Gdd8cpcPfrxSpW4rYwxFDAAAIIYK2EeVlTi1p8/W6O35v6k7s3q6pVh3eQKoXwBABDsKGEedLigWCMm/KiFm/drRN8U/f7S9goPZfERAABQwk6t7JT8Zv2r/CWiw11q0SBGN/Zupiu6NKnBcAAAwN9Rwk6liqfkW2v19rzNOqdNvFrEx+iZazp5IBwAAPB37I2dTiVPyT9cUKxxUxbrD5+s0nsLt3kwGAAA8HeshNWQDbsPafTETG3KPqQHL2mnW89u4XQkAADgwyhhNWDJtgMa/o8FigxzaeItvdS3VQOnIwEAAB9HCasB7RJjNbhzY91xfms1rhPldBwAAOAHmAmrouzcAt33/lIdzC9SZJhLTw/pRAEDAAAVRgmrgswt+zTope81c9nPWrE9x+k4AADAD7EdWQnWWr0zb7P++OlqNakbpbdH9lT7RrWdjgUAAPwQJawSXvl2g/761Tpd0L6hnruui+KiwpyOBAAA/BQl7GROcVr+Nd2bKjw0RL/p30Ih3P8RAABUg0dnwowxFxtj1hpjNhhjHjzJ+xHGmPeOvv+DMSbFk3kqrNxp+V+u3KlxkxfJ7bZKjIvUqLNbUsAAAEC1eWwlzBjjkvSKpIGSsiQtNMbMsNauKvexWyTtt9a2MsbcIOkZSdd7KlNl2Gb99Ex2H42fnanOSXHKyStS3VrhTscCAFRQUVGRsrKylJ+f73QUBIHIyEglJSUpLKzio0qe3I7sKWmDtXaTJBljpkq6QlL5EnaFpMePPp4u6WVjjLHWWg/mOr2jW5Grwztp/NqNGtYrWY8NTlVEqMuxSACAysvKylJsbKxSUlJkDDsY8Bxrrfbu3ausrCw1b968wj/Pk9uRTSSVv4Fi1tHXTvoZa22xpBxJ9T2Y6Yzs8vclSVPyeuov13TSU1elUcAAwA/l5+erfv36FDB4nDFG9evXr/Sqq18M5htjRkkaJUnJycme/V6JnbS7Vhtd3+9RdWwS59HvBQDwLAoYvKUqv9Y8WcK2S2pa7nnS0ddO9pksY0yopDhJe0/8QtbaNyS9IUnp6eme3aq85Gk1lNTQo98EAAAEO09uRy6U1NoY09wYEy7pBkkzTvjMDEm/Pvr4Gkn/dXQeDACAGuRyudSlSxd17NhRgwcP1oEDB469t3LlSp133nlq27atWrdurSeffFLl/wj8/PPPlZ6ertTUVHXt2lX33nuvE/8Ip7V48WLdcsstx7125ZVXqnfv3se9NmLECE2fPv2412JiYo49XrdunS699FK1bt1a3bp103XXXaddu3ZVK9u+ffs0cOBAtW7dWgMHDtT+/ftP+rkHHnhAHTp0UPv27XXHHXcc+28wYMAAtW3bVl26dFGXLl20e/duSdLLL7+st956q1rZynishB2d8Ron6UtJqyVNs9auNMY8YYy5/OjH3pRU3xizQdI9kn5xjAUAAP4qKipKS5Ys0YoVK1SvXj298sorkqS8vDxdfvnlevDBB7V27VotXbpU8+bN06uvvipJWrFihcaNG6eJEydq1apVysjIUKtWrWo0W3FxcbW/xlNPPaU77rjj2PMDBw4oMzNTOTk52rRpU4W+Rn5+vi677DKNGTNG69ev16JFizR27FhlZ2dXK9vTTz+t888/X+vXr9f555+vp59++hefmTdvnubOnatly5ZpxYoVWrhwoWbPnn3s/UmTJmnJkiVasmSJGjYs3SO7+eab9dJLL1UrWxmPzoRZaz+T9NkJrz1a7nG+pGs9mQEAAH3+oLRzec1+zcQ06ZJf/sF+Kn369NGyZcskSZMnT1a/fv104YUXSpKio6P18ssva8CAAbrtttv07LPP6uGHH1a7du0kla6ojRkz5hdf89ChQ7r99tuVkZEhY4wee+wxDRkyRDExMTp06JAkafr06Zo5c6befvttjRgxQpGRkVq8eLH69eunDz74QEuWLFGdOnUkSa1bt9acOXMUEhKi0aNHa+vWrZKk559/Xv369Tvue+fm5mrZsmXq3Lnzsdc++OADDR48WAkJCZo6dap+//vfn/Hfy+TJk9WnTx8NHjz42GsDBgyo6L/WU/r44481a9YsSdKvf/1rDRgwQM8888xxnzHGKD8/X4WFhbLWqqioSAkJCaf9utHR0UpJSdGPP/6onj17ViujXwzmAwDgz0pKSvTNN98c27pbuXKlunfvftxnWrZsqUOHDungwYNasWJFhbYfn3zyScXFxWn58tKCeaott/KysrI0b948uVwulZSU6MMPP9TIkSP1ww8/qFmzZkpISNCwYcN09913q3///tq6dasuuugirV69+rivk5GRoY4dOx732pQpU/Too48qISFBQ4YMqVAJW7FixS/+XZxMbm6uzjrrrJO+N3nyZKWmph732q5du9SoUSNJUmJi4km3N/v06aNzzz1XjRo1krVW48aNU/v27Y+9P3LkSLlcLg0ZMkSPPPLIseH79PR0ff/995QwAADOqBIrVjUpLy9PXbp00fbt29W+fXsNHDiwRr/+119/ralTpx57Xrdu3TP+nGuvvVYuV+nRS9dff72eeOIJjRw5UlOnTtX1119/7OuuWvW/Yz0PHjyoQ4cOHTfHtWPHDsXHxx97vmvXLq1fv179+/eXMUZhYWFasWKFOnbseNIrByt7NWFsbKyWLFlSqZ9T/nud7Ptt2LBBq1evVlZWliRp4MCB+v7773XWWWdp0qRJatKkiXJzczVkyBC9++67+tWvfiVJatiwodasWVOlLOV59LZFAAAEs7KZsC1btshae2wmLDU1VZmZmcd9dtOmTYqJiVHt2rXVoUOHX7xfGeULx4lnV9WqVevY4z59+mjDhg3Kzs7WRx99pKuvvlqS5Ha7tWDBgmPzUNu3bz+ugJX9s5X/2tOmTdP+/fvVvHlzpaSkaPPmzZoyZYokqX79+set0u3bt08NGjSQpAr/s+bm5h4bkj/xR/nCWCYhIUE7duyQVFoYy2a6yvvwww/Vu3dvxcTEKCYmRpdcconmz58vSWrSpPRo09jYWA0bNkw//vjjsZ+Xn5+vqKioM2Y+E0oYAAAeFh0drRdffFHPPfeciouLNXz4cM2ZM0dff/21pNIVszvuuEMPPPCAJOn+++/XU089pXXr1kkqLUXjx4//xdcdOHDgsWIn/W87MiEhQatXr5bb7daHH354ylzGGF111VW655571L59e9WvX3pe+oUXXnjc8PnJVqDat2+vDRs2HHs+ZcoUffHFF9q8ebM2b96szMzMY6t0AwYM0HvvvafCwkJJ0ttvv61zzz1XkjRs2DDNmzdPn3766bGv9d1332nFihXHfb+ylbCT/ThxK1KSLr/8cr3zzjuSpHfeeUdXXHHFpLd5fQAAB75JREFULz6TnJys2bNnq7i4WEVFRZo9e7bat2+v4uJi7dmzR1Lp7a9mzpx53NbrunXrfrEVWxWUMAAAvKBr167q1KmTpkyZoqioKH388cf64x//qLZt2yotLU09evTQuHHjJEmdOnXS888/r6FDh6p9+/bq2LHjSa82fOSRR7R//3517NhRnTt3/v/27j60rruO4/j741p7g8ZO1n+kmWvoMmpck8wGmZVqYWK3kbZ/WOwGq07aCgXFJ8SIwsT+M59aaVmdimPa4kMdTGKnjFYrHcEW70gddlUpWzvjA1vjDMXN6ravf5xjSLM0Oam553eT83lB4N5zTu79wDf35nt/v9+5h6NHjwLZmYF9fX2sXr16bF3U5WzevJkDBw6MTUUC7Nmzh3q9TldXF52dnZM2gCtWrGB0dJQLFy5w9uxZzp07d8lXU7S3t7N48WJOnDhBX18fa9asYdWqVfT09DA4ODi2SL6lpYVDhw6xd+9eOjo66OzsZN++fZdMdV6J/v5+Dh8+TEdHB0eOHKG/P/sChnq9zrZt2wDYtGkTy5cvZ+XKlXR3d9Pd3c369eu5ePEi69ato6uri56eHpYuXcr27dvHHntwcHBWppY1176Wq7e3N+r1euoYZmbW5E6fPn3JImubfbt376a1tXWsqamCoaEhdu3axf79+1+1b7K/OUmPR0TvZI/lkTAzMzO7Ijt27GDRokWpY5Tq/Pnz7Ny5c1Yey2dHmpmZ2RWp1Wps2bIldYxSzeYZrh4JMzOzeWuuLbmxuetK/tbchJmZ2bxUq9UYGRlxI2YNFxGMjIxQq9Vm9HuejjQzs3mpra2N4eHh//sahGZF1Go12traZvQ7bsLMzGxeWrhwIe3t7aljmF2WpyPNzMzMEnATZmZmZpaAmzAzMzOzBObcN+ZLeg441+CnWQKcb/Bz2My5Ls3HNWlOrkvzcU2aUxl1uS4iJr0G05xrwsogqX65SwxYOq5L83FNmpPr0nxck+aUui6ejjQzMzNLwE2YmZmZWQJuwib3rdQBbFKuS/NxTZqT69J8XJPmlLQuXhNmZmZmloBHwszMzMwSqHQTJulWSX+QdEZS/yT7F0n6Ub7/hKRl5aesngJ1+aSkJyU9IekXkq5LkbNKpqvJuOPeJykk+SywBitSE0nvz18rpyR9v+yMVVTg/evNko5KGsrfw25PkbNKJD0g6VlJv7vMfknak9fsCUlvKytbZZswSVcB9wG3AZ3AnZI6Jxy2FXg+Iq4HdgNfKjdl9RSsyxDQGxFdwEPAl8tNWS0Fa4KkVuBjwIlyE1ZPkZpI6gA+C7wzIt4KfLz0oBVT8LXyeeBgRNwE3AHsKzdlJT0I3DrF/tuAjvznw8A3SsgEVLgJA94OnImIpyLi38APgY0TjtkIfDe//RBwiySVmLGKpq1LRByNiBfyu8eBmV223maqyGsFYCfZB5V/lRmuoorUZDtwX0Q8DxARz5acsYqK1CWAN+S3FwN/KTFfJUXEMeDvUxyyEfheZI4DV0t6UxnZqtyELQX+NO7+cL5t0mMi4iVgFLimlHTVVaQu420Fft7QRDZtTfLh+2sj4pEyg1VYkdfJDcANkgYlHZc01UiAzY4idfkCcJekYeBnwEfLiWZTmOn/nVmzoIwnMWsESXcBvcC7U2epMkmvAXYBdyeOYpdaQDa9spZstPiYpJUR8Y+kqexO4MGI+JqkdwD7Jd0YEa+kDmblq/JI2J+Ba8fdb8u3TXqMpAVkQ8cjpaSrriJ1QdJ7gM8BGyLiYknZqmq6mrQCNwK/knQWuBkY8OL8hiryOhkGBiLiPxHxNPBHsqbMGqdIXbYCBwEi4tdAjez6hZZOof87jVDlJuw3QIekdkmvJVsgOTDhmAHgg/ntTcAvw1+s1mjT1kXSTcA3yRowr3NpvClrEhGjEbEkIpZFxDKydXobIqKeJm4lFHn/+gnZKBiSlpBNTz5VZsgKKlKXZ4BbACS9hawJe67UlDbRAPCB/CzJm4HRiPhrGU9c2enIiHhJ0keAR4GrgAci4pSkLwL1iBgAvkM2VHyGbFHfHekSV0PBunwFeD3w4/w8iWciYkOy0PNcwZpYiQrW5FHgvZKeBF4GPh0RHslvoIJ1+RTwbUmfIFukf7c/3DeWpB+QfSBZkq/FuwdYCBAR95OtzbsdOAO8AHyotGyuvZmZmVn5qjwdaWZmZpaMmzAzMzOzBNyEmZmZmSXgJszMzMwsATdhZmZmZgm4CTOzSpD0sqST436WSVoraTS/f1rSPfmx47f/XtJXU+c3s/mnst8TZmaV82JE9IzfIGkZ8FhE9El6HXBS0k/z3f/b3gIMSXo4IgbLjWxm85lHwszMgIj4J/A4cP2E7S8CJynpgr5mVh1uwsysKlrGTUU+PHGnpGvIrnt5asL2N5Jdc/FYOTHNrCo8HWlmVfGq6cjcGklDwCvAvfllZtbm239L1oB9PSL+VmJWM6sAN2FmVnWPRUTf5bZLageOSzoYESfLDmdm85enI83MphARTwP3Ap9JncXM5hc3YWZm07sfeFd+NqWZ2axQRKTOYGZmZlY5HgkzMzMzS8BNmJmZmVkCbsLMzMzMEnATZmZmZpaAmzAzMzOzBNyEmZmZmSXgJszMzMwsATdhZmZmZgn8F21+iQwoH4jsAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                 OLS Regression Results                                \n",
            "=======================================================================================\n",
            "Dep. Variable:                    pcp   R-squared (uncentered):                   0.483\n",
            "Model:                            OLS   Adj. R-squared (uncentered):              0.481\n",
            "Method:                 Least Squares   F-statistic:                              271.8\n",
            "Date:                Tue, 17 May 2022   Prob (F-statistic):                        0.00\n",
            "Time:                        19:30:19   Log-Likelihood:                         -2831.0\n",
            "No. Observations:                6126   AIC:                                      5704.\n",
            "Df Residuals:                    6105   BIC:                                      5845.\n",
            "Df Model:                          21                                                  \n",
            "Covariance Type:            nonrobust                                                  \n",
            "=================================================================================\n",
            "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
            "---------------------------------------------------------------------------------\n",
            "tmp_9am           0.0162      0.021      0.764      0.445      -0.025       0.058\n",
            "dew_9am           0.0313      0.027      1.145      0.252      -0.022       0.085\n",
            "hum_9am          -0.0054      0.005     -1.015      0.310      -0.016       0.005\n",
            "spd_9am          -0.0188      0.003     -6.134      0.000      -0.025      -0.013\n",
            "alt_9am         4.87e-06   1.05e-05      0.466      0.641   -1.56e-05    2.54e-05\n",
            "tmp_10am         -0.0314      0.019     -1.636      0.102      -0.069       0.006\n",
            "dew_10am          0.0383      0.027      1.417      0.157      -0.015       0.091\n",
            "hum_10am          0.0005      0.006      0.085      0.932      -0.011       0.012\n",
            "spd_10am         -0.0140      0.003     -4.192      0.000      -0.020      -0.007\n",
            "alt_10am       1.666e-05   2.14e-05      0.777      0.437   -2.54e-05    5.87e-05\n",
            "pcp_yesterday     0.2167      0.012     18.229      0.000       0.193       0.240\n",
            "month             0.0044      0.001      2.928      0.003       0.001       0.007\n",
            "wdd_9am_E         0.0740      0.048      1.548      0.122      -0.020       0.168\n",
            "wdd_9am_N         0.0622      0.031      1.995      0.046       0.001       0.123\n",
            "wdd_9am_S         0.0286      0.029      0.994      0.320      -0.028       0.085\n",
            "wdd_9am_W         0.2586      0.072      3.613      0.000       0.118       0.399\n",
            "wdd_10am_CALM     0.0156      0.046      0.335      0.737      -0.076       0.107\n",
            "wdd_10am_E        0.0585      0.053      1.108      0.268      -0.045       0.162\n",
            "wdd_10am_N       -0.0391      0.040     -0.967      0.333      -0.118       0.040\n",
            "wdd_10am_S       -0.0566      0.044     -1.291      0.197      -0.142       0.029\n",
            "wdd_10am_W       -0.1735      0.078     -2.211      0.027      -0.327      -0.020\n",
            "==============================================================================\n",
            "Omnibus:                      441.582   Durbin-Watson:                   1.939\n",
            "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              303.814\n",
            "Skew:                           0.435   Prob(JB):                     1.07e-66\n",
            "Kurtosis:                       2.341   Cond. No.                     3.00e+04\n",
            "==============================================================================\n",
            "\n",
            "Warnings:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
            "[2] The condition number is large,  3e+04. This might indicate that there are\n",
            "strong multicollinearity or other numerical problems.\n"
          ]
        }
      ]
    }
  ]
}