Belief_in_supernatural_evil_and_guns.ipynb

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    "## Belief in Supernatural Evil and Guns\n",
    "\n",
    "---\n",
    "\n",
    "> <h4>Konstantina Saketou</h4><br />\n",
    "> Assignment in Applied Machine Learning <br />\n",
    "> Management Science and Technology Department <br />\n",
    "> Athens University of Economics and Business\n",
    "\n",
    "#### The assignment is based on the below study:  \n",
    "Christopher G. Ellison, Benjamin Dowd-Arrow, Amy M. Burdette, Pablo E. Gonzalez, Margaret S. Kelley, Paul Froese,\n",
    "\"Peace through superior firepower: Belief in supernatural evil and attitudes toward gun policy in the United States\", Social Science Research, Volume 99, 2021, https://doi.org/10.1016/j.ssresearch.2021.102595.\n",
    "\n",
    "#### Dataset used:\n",
    "* Froese, P. (2020, March 3). Baylor Religion Survey, Wave IV (2014), DOI: 10.17605/OSF.IO/3ZPC2  \n",
    "[Available here](https://www.thearda.com/Archive/Files/Descriptions/BRS2014.asp)\n",
    "\n",
    "#### Other scientific papers studied:\n",
    "* Baker, J. (2008). Who Believes in Religious Evil? An Investigation of Sociological Patterns of Belief in Satan, Hell, and Demons. Review of Religious Research, 50(2), 206–220. http://www.jstor.org/stable/20447562  \n",
    "* The Economist, 2021, \"Belief in supernatural evil is a strong predictor of pro-gun beliefs\" (United States section), available at https://www.economist.com/united-states/2021/11/06/belief-in-supernatural-evil-is-a-strong-predictor-of-pro-gun-beliefs \n",
    "* Sperandei, S. (2014). Understanding logistic regression analysis. Biochemia Medica, 12–18. http://dx.doi.org/10.11613/BM.2014.003"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "758b9f83",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import statsmodels.formula.api as smf\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fa35add",
   "metadata": {},
   "source": [
    "* First of all, we will read the data file and import the data  \n",
    "* From the data, we keep only the columns that refer to answers to the questions asked and that also contain information about the respondents"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c8fc3a84",
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       "      <td>2.0</td>\n",
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       "        Q1  Q2_DK   Q3  Q3_1   Q4   Q7   Q8  Q25  Q10  Q11  ...  STATE  \\\n",
       "0     20.0    NaN  4.0   4.0  8.0  5.0  1.0  3.0  5.0  8.0  ...     TX   \n",
       "1      NaN    NaN  4.0   4.0  1.0  1.0  8.0  3.0  3.0  5.0  ...     TX   \n",
       "2     31.0    NaN  3.0   4.0  7.0  5.0  5.0  3.0  5.0  1.0  ...     ND   \n",
       "3     33.0    NaN  3.0   4.0  6.0  4.0  2.0  3.0  4.0  6.0  ...     TX   \n",
       "4     14.0    NaN  4.0   4.0  8.0  5.0  2.0  2.0  5.0  8.0  ...     IA   \n",
       "...    ...    ...  ...   ...  ...  ...  ...  ...  ...  ...  ...    ...   \n",
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       "1568  16.0    NaN  8.0   3.0  0.0  NaN  8.0  2.0  3.0  1.0  ...     CA   \n",
       "1569   1.0    NaN  4.0   3.0  7.0  5.0  2.0  3.0  4.0  8.0  ...     CA   \n",
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       "\n",
       "            PROJWT    WEIGHT                         Q2  RELTRAD  I_GENDER  \\\n",
       "0      20086.69661  0.270000           Church of Christ      1.0       2.0   \n",
       "1      26218.57317  0.352423    Church of God In Christ      3.0       2.0   \n",
       "2      41408.88989  0.556608                       ELCA      2.0       2.0   \n",
       "3      50023.02736  0.672397      Independent Methodist      2.0       2.0   \n",
       "4     230734.42840  3.101474   Don't have specific name      1.0       1.0   \n",
       "...            ...       ...                        ...      ...       ...   \n",
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       "1569  153453.64200  2.062685                        NaN      6.0       2.0   \n",
       "1570   33324.20001  0.447935  Catolica Romana - Latinos      4.0       1.0   \n",
       "1571  117172.71330  1.575006                        NaN      4.0       2.0   \n",
       "\n",
       "      I_EDUC  I_MARITAL  I_RELIGION  I_ATTEND  \n",
       "0        5.0        3.0         1.0       6.0  \n",
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       "3        4.0        3.0         2.0       5.0  \n",
       "4        2.0        2.0         1.0       6.0  \n",
       "...      ...        ...         ...       ...  \n",
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       "1570     5.0        2.0         4.0       3.0  \n",
       "1571     1.0        NaN         4.0       4.0  \n",
       "\n",
       "[1572 rows x 265 columns]"
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     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "initial_data = pd.read_csv(\"brs_data.csv\")\n",
    "initial_data = initial_data.loc[:,'Q1':'I_ATTEND'] # keep only columns needed\n",
    "initial_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e66e00f5",
   "metadata": {},
   "source": [
    "### Q1: Belief in Supernatural Evil Metric"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9dfbcc25",
   "metadata": {},
   "source": [
    "* Three questions seem to relate more with the degree in which someone belives in supernatural evil  \n",
    "* These are the belief in devil, hell and demons  \n",
    "* We gather the answers of these three questions in the dataframe `sup_evil_data`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "c9dd02f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "sup_evil_data = initial_data.loc[:,('Q23A','Q23C','Q23G')].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e2dfe8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "sup_evil_data.columns = ['devil', 'hell', 'demons']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8a4646b4",
   "metadata": {},
   "outputs": [
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       "      <th>1567</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1571</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1572 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      devil  hell  demons\n",
       "0       4.0   4.0     4.0\n",
       "1       4.0   4.0     4.0\n",
       "2       3.0   2.0     2.0\n",
       "3       4.0   4.0     4.0\n",
       "4       4.0   4.0     4.0\n",
       "...     ...   ...     ...\n",
       "1567    4.0   2.0     4.0\n",
       "1568    3.0   3.0     3.0\n",
       "1569    4.0   4.0     3.0\n",
       "1570    3.0   3.0     3.0\n",
       "1571    NaN   NaN     NaN\n",
       "\n",
       "[1572 rows x 3 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sup_evil_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e2a6186",
   "metadata": {},
   "source": [
    "* First, we calculate several metrics about the amount of people that responded positively regarding the existence of those three entities  \n",
    "* Below we can see that most respondents belive that devil exists, whereas less -but still plenty- belive that hell and demons also exist  \n",
    "* Moreover, the most frequent answer received for these questions is 3, which represents *Probably*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d2e9feaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of people asked: 1572\n",
      "Numer of people who believe in devil: 1103\n",
      "Numer of people who believe in hell: 1063\n",
      "Numer of people who believe in demons: 1036\n"
     ]
    }
   ],
   "source": [
    "print('Number of people asked:', len(sup_evil_data))\n",
    "print('Numer of people who believe in devil:', len(sup_evil_data[sup_evil_data.devil >=3]))\n",
    "print('Numer of people who believe in hell:', len(sup_evil_data[sup_evil_data.hell >=3]))\n",
    "print('Numer of people who believe in demons:', len(sup_evil_data[sup_evil_data.demons >=3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fd18249d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "devil     3.149601\n",
       "hell      3.088726\n",
       "demons    3.016086\n",
       "Name: mean, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sup_evil_data.describe().transpose()['mean']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7167398c",
   "metadata": {},
   "source": [
    "* To create the supernatural evil metric, we will sum the answers of those three questions and then divide them with the average number of responses received  \n",
    "* The metric created ranges from 1 to 4 with a mean of 3.09, which indicates high levels of belief in supernatural evil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c3f3b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.090909090909091"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sup_evil_metric = ((sup_evil_data.devil + sup_evil_data.hell + sup_evil_data.demons) / 3).round()\n",
    "sup_evil_metric.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68e5dd11",
   "metadata": {},
   "source": [
    "* In order to check the accuracy of the metric we created, we will perform PCA analysis on the variables devil, hell and demons \n",
    "* When we do that, we see that belief in supernatural evil (one component created) explains the majority of the total variance (aprox. 90% !)  \n",
    "* Consequently, the supernatural evil metric represents a big part of the three questions we used to create it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "848c4f3b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "explained variance ratio: [0.90171495]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca = PCA(n_components=1)\n",
    "X = pca.fit(sup_evil_data.dropna())\n",
    "print(f'explained variance ratio: {pca.explained_variance_ratio_}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ca889a1",
   "metadata": {},
   "source": [
    "### Q2: Variables Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14762ece",
   "metadata": {},
   "source": [
    "#### Dependent Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09383896",
   "metadata": {},
   "source": [
    "* First of all, we will form the dataframe containing the eight gun policies (dependent variables).  \n",
    "* Since the answer range of these variables is 1-2, we substract 1 from all the columns in order to give them an initial binary format (0-1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "85a67f51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>semi_auto_weapons_ban</th>\n",
       "      <th>gun_safety_programs</th>\n",
       "      <th>armed_security_in_schools</th>\n",
       "      <th>mental_health_screening</th>\n",
       "      <th>high_capacity_magazine_ban</th>\n",
       "      <th>teachers_with_guns</th>\n",
       "      <th>civilian_gun_possesion_ban</th>\n",
       "      <th>support_for_concealed_carry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <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",
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       "    <tr>\n",
       "      <th>1567</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1568</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1570</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1571</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1572 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      semi_auto_weapons_ban  gun_safety_programs  armed_security_in_schools  \\\n",
       "0                       1.0                  0.0                        0.0   \n",
       "1                       NaN                  NaN                        NaN   \n",
       "2                       0.0                  0.0                        0.0   \n",
       "3                       0.0                  0.0                        0.0   \n",
       "4                       0.0                  0.0                        0.0   \n",
       "...                     ...                  ...                        ...   \n",
       "1567                    NaN                  NaN                        0.0   \n",
       "1568                    0.0                  0.0                        1.0   \n",
       "1569                    0.0                  0.0                        1.0   \n",
       "1570                    0.0                  0.0                        1.0   \n",
       "1571                    NaN                  NaN                        NaN   \n",
       "\n",
       "      mental_health_screening  high_capacity_magazine_ban  teachers_with_guns  \\\n",
       "0                         0.0                         1.0                 0.0   \n",
       "1                         NaN                         NaN                 NaN   \n",
       "2                         0.0                         0.0                 1.0   \n",
       "3                         0.0                         0.0                 1.0   \n",
       "4                         0.0                         0.0                 0.0   \n",
       "...                       ...                         ...                 ...   \n",
       "1567                      0.0                         0.0                 NaN   \n",
       "1568                      0.0                         1.0                 1.0   \n",
       "1569                      0.0                         0.0                 1.0   \n",
       "1570                      0.0                         0.0                 1.0   \n",
       "1571                      NaN                         NaN                 NaN   \n",
       "\n",
       "      civilian_gun_possesion_ban  support_for_concealed_carry  \n",
       "0                            1.0                          0.0  \n",
       "1                            NaN                          NaN  \n",
       "2                            1.0                          1.0  \n",
       "3                            0.0                          1.0  \n",
       "4                            0.0                          1.0  \n",
       "...                          ...                          ...  \n",
       "1567                         NaN                          1.0  \n",
       "1568                         0.0                          1.0  \n",
       "1569                         0.0                          1.0  \n",
       "1570                         0.0                          1.0  \n",
       "1571                         NaN                          NaN  \n",
       "\n",
       "[1572 rows x 8 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dependent = initial_data.loc[:,'Q73A':'Q73H'].copy()\n",
    "dependent.columns=['semi_auto_weapons_ban', 'gun_safety_programs', 'armed_security_in_schools',\n",
    "                  'mental_health_screening', 'high_capacity_magazine_ban', 'teachers_with_guns',\n",
    "                  'civilian_gun_possesion_ban', 'support_for_concealed_carry']\n",
    "dependent = dependent - 1 # values 0/1\n",
    "dependent"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7516d50",
   "metadata": {},
   "source": [
    "* After the initial transformation of the variables above, their values have been shaped like so:  \n",
    "    * 0 shows that the initial value of the cell was 1, which means that this specific answer was \"Favor\"\n",
    "    * 1 shows that the initial value of the cell was 2, which means that this specific answer was \"Oppose\"\n",
    "* We will reverse this notation in order to get:\n",
    "    * 0 = Oppose\n",
    "    * 1 = Favor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "148b5032",
   "metadata": {},
   "outputs": [],
   "source": [
    "dependent.replace(0, -1, inplace=True)\n",
    "dependent.replace(1, 0, inplace=True)\n",
    "dependent.replace(-1, 1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6baad18d",
   "metadata": {},
   "source": [
    "* Below we can see some descriptive statistics for the dependent variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cf495d7c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>semi_auto_weapons_ban</th>\n",
       "      <th>gun_safety_programs</th>\n",
       "      <th>armed_security_in_schools</th>\n",
       "      <th>mental_health_screening</th>\n",
       "      <th>high_capacity_magazine_ban</th>\n",
       "      <th>teachers_with_guns</th>\n",
       "      <th>civilian_gun_possesion_ban</th>\n",
       "      <th>support_for_concealed_carry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1502.000000</td>\n",
       "      <td>1515.000000</td>\n",
       "      <td>1510.000000</td>\n",
       "      <td>1512.000000</td>\n",
       "      <td>1502.000000</td>\n",
       "      <td>1504.000000</td>\n",
       "      <td>1501.000000</td>\n",
       "      <td>1502.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.629161</td>\n",
       "      <td>0.927393</td>\n",
       "      <td>0.621854</td>\n",
       "      <td>0.908069</td>\n",
       "      <td>0.671105</td>\n",
       "      <td>0.294548</td>\n",
       "      <td>0.290473</td>\n",
       "      <td>0.557923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.483190</td>\n",
       "      <td>0.259576</td>\n",
       "      <td>0.485085</td>\n",
       "      <td>0.289024</td>\n",
       "      <td>0.469968</td>\n",
       "      <td>0.455991</td>\n",
       "      <td>0.454132</td>\n",
       "      <td>0.496799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       semi_auto_weapons_ban  gun_safety_programs  armed_security_in_schools  \\\n",
       "count            1502.000000          1515.000000                1510.000000   \n",
       "mean                0.629161             0.927393                   0.621854   \n",
       "std                 0.483190             0.259576                   0.485085   \n",
       "min                 0.000000             0.000000                   0.000000   \n",
       "25%                 0.000000             1.000000                   0.000000   \n",
       "50%                 1.000000             1.000000                   1.000000   \n",
       "75%                 1.000000             1.000000                   1.000000   \n",
       "max                 1.000000             1.000000                   1.000000   \n",
       "\n",
       "       mental_health_screening  high_capacity_magazine_ban  \\\n",
       "count              1512.000000                 1502.000000   \n",
       "mean                  0.908069                    0.671105   \n",
       "std                   0.289024                    0.469968   \n",
       "min                   0.000000                    0.000000   \n",
       "25%                   1.000000                    0.000000   \n",
       "50%                   1.000000                    1.000000   \n",
       "75%                   1.000000                    1.000000   \n",
       "max                   1.000000                    1.000000   \n",
       "\n",
       "       teachers_with_guns  civilian_gun_possesion_ban  \\\n",
       "count         1504.000000                 1501.000000   \n",
       "mean             0.294548                    0.290473   \n",
       "std              0.455991                    0.454132   \n",
       "min              0.000000                    0.000000   \n",
       "25%              0.000000                    0.000000   \n",
       "50%              0.000000                    0.000000   \n",
       "75%              1.000000                    1.000000   \n",
       "max              1.000000                    1.000000   \n",
       "\n",
       "       support_for_concealed_carry  \n",
       "count                  1502.000000  \n",
       "mean                      0.557923  \n",
       "std                       0.496799  \n",
       "min                       0.000000  \n",
       "25%                       0.000000  \n",
       "50%                       1.000000  \n",
       "75%                       1.000000  \n",
       "max                       1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dependent.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6095b3e6",
   "metadata": {},
   "source": [
    "* At the table below we can see the mean percentages of the dependent variables (unweighted).  \n",
    "* The table presents the means of the dependent variables, which are identical to the ones in the Appendix A of the original publication.\n",
    "\n",
    "| \t|Range  \t|Mean (%) \t| \n",
    "|---\t|---\t|---\t|\n",
    "|**Ban on semi-auto guns**   \t|  0-1 \t| 62,9  \t|      \t\n",
    "|**Ban on high capacity Ammo Clips**   \t|   0-1\t| 67,1  \t|   \t\n",
    "|**Banning Civilian handguns**   \t|   0-1\t| 29,04  \t|   \t\n",
    "|**Support for Concealed Carry Laws**   \t|  0-1 \t| 55,8  \t|    \t\n",
    "|**More Armed Security at Schools**   \t|  0-1 \t|62,2   \t|\t\n",
    "|**More Teachers/Faculty Having Guns**   \t| 0-1  \t| 29,5  \t|  \t\n",
    "|**More Gun Safety Programs**   \t| 0-1  \t| 92,7  \t|  \t\n",
    "|**Expanded Mental Health Screening**   \t| 0-1  \t|90,8   \t|  \t"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0879c2c2",
   "metadata": {},
   "source": [
    "#### Independent Variables"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96d25709",
   "metadata": {},
   "source": [
    "* Now we will gather all the independent variables in the dataframe `total_independents`  \n",
    "* We will also add a column containing the supernatural evil metric we calculated in Q1  \n",
    "* Then, we will rename the columns in order to clearly demonstrate their content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d7658887",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_independents = initial_data.loc[:, ('Q17','Q4','I_RELIGION','Q31','Q88A', 'Q88B','Q88C','Q88D','Q88E','Q88F', 'Q89', 'AGE', 'I_GENDER', 'I_EDUC', 'Q51A','Q95','Q93','Q93_NONE','Q80')]\n",
    "total_independents['sup_evil'] = sup_evil_metric\n",
    "total_independents.columns=['bible','attendance','religion','political_ideol', 'white', 'black', 'indian_alaska','asian','pacific_islander', 'other','hispanic','age','gender','education','marital','income','children','no_children','area_demographics','sup_evil']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a789fdf3",
   "metadata": {},
   "source": [
    "* We will create the Region variable from the answers we have regarding the state  \n",
    "* First, we save inside a list the [south states of America](https://en.wikipedia.org/wiki/Southern_United_States)  \n",
    "* Then, we check if each state of the dataframe exists inside the south states list or not, and we save the result at the `region` column  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "faf8c939",
   "metadata": {},
   "outputs": [],
   "source": [
    "south_states = ['AL', 'AR','DE','FL', 'GA', 'KY', 'LA', 'MS', 'MD', 'OK', 'NC','DC', 'SC', 'TN', 'TX','VA', 'WV']\n",
    "total_independents['region'] = initial_data['STATE'].isin(south_states)\n",
    "total_independents.region.replace(True, 'south', inplace=True)\n",
    "total_independents.region.replace(False, 'not_south', inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20fe88e9",
   "metadata": {},
   "source": [
    "#### Dummy Variables\n",
    "\n",
    "* We will now start creating dummy variables according to Appedix A.  \n",
    "* First, we have the religious variables which contain the below variables:\n",
    "    * Attendance: ranges from 0 to 8 and needs no dummies  \n",
    "    * Bible study: ranges from 1 to 4. This variable has been separated into three different variables like so:  \n",
    "        * Bible (answers 3 and 4 combined): The Bible contains Human Error, History and Fables\n",
    "        * Literalism (answer = 1): The Bible means exactly what it says\n",
    "        * Inerrancy (answer = 2): The Bible is true but shouldn't be taken literally  \n",
    "* To create the dummies of the bible study, we add two new columns containing True or False if the corresponding answer at the 'bible' column is 1 (literalism) or 2 (inerrancy).  \n",
    "* Then, we also check the values of the 'bible' column and add True where the cell value equals to 3(human error) or 4(history) and False at the opposite case.\n",
    "* Finally, we replace at all three columns the value True with 1 and the value False with 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d94ce2d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummies for bible\n",
    "# 3,4 = 0 | literalism = 1 | inerrancy = 1\n",
    "total_independents['literalism'] = total_independents.bible == 1\n",
    "total_independents['inerrancy'] = total_independents.bible == 2\n",
    "total_independents['bible'] = (total_independents.bible == 3) | (total_independents.bible == 4)\n",
    "total_independents.bible.replace(True, 1, inplace =True)\n",
    "total_independents.bible.replace(False, 0, inplace =True)\n",
    "total_independents.literalism.replace(True, 1, inplace =True)\n",
    "total_independents.literalism.replace(False, 0, inplace =True)\n",
    "total_independents.inerrancy.replace(True, 1, inplace =True)\n",
    "total_independents.inerrancy.replace(False, 0, inplace =True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92263af9",
   "metadata": {},
   "source": [
    "* We will now create dummies for the religion variable. We have six possible answers.  \n",
    "* We use the `get_dummies` function to automaticaly create them and then we rename the output columns to better understand the result.  \n",
    "* We merge the output dataframe with the `total_independents` one, which contains all the independent variables.  \n",
    "* Lastly, we delete the religion column since all the information we need exists at the dummies we just created."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "10e5ec5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummies for religion\n",
    "dummies_relig = pd.get_dummies(total_independents['religion']).\\\n",
    "    rename(columns = {1:'conservative_prot', 2:'mainline_prot', 3:'black_prot', 4:'catholic', 5:'jewish_other', 6:'no_affil'})\n",
    "total_independents = total_independents.merge(dummies_relig, left_index=True, right_index=True)\n",
    "del total_independents['religion']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a1a3d67",
   "metadata": {},
   "source": [
    "* The variable political ideology needs no dummies since its range is 1-7 in Appendix A of the original publication as well  \n",
    "* Regarding the variable 'age', we replace the invalid value 0 with NaN  \n",
    "* To create variables for the gender, we use again the `get_dummies` function to generate two new columns, one for male and one for female. We merge those two columns with the `total_independents` dataframe and then, we delete the initial column called 'gender', since the information we need is now at the two new dummy columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d0d732e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove invalid ages\n",
    "total_independents.age.replace(0, np.nan, inplace=True)\n",
    "\n",
    "# gender dummies\n",
    "dummies_gender = pd.get_dummies(total_independents['gender']).rename(columns={1:'male',2: 'female'})\n",
    "total_independents = total_independents.merge(dummies_gender, left_index=True, right_index=True)\n",
    "del total_independents['gender']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "429c96a1",
   "metadata": {},
   "source": [
    "* Now let's get to the variable describing the race.  \n",
    "* According to the original publication, the dummy formation here is as follows:    \n",
    "    * White\n",
    "    * Black\n",
    "    * Hispanic\n",
    "    * Other\n",
    "* First we create a new column where we will save the ones who answered 'Yes' to whether they are white or not\n",
    "* Then, we do the same for the ones that said they are black/African American\n",
    "* For the hispanic question, we will first replace the answer 'Don't know' (number 8) with `NaN` since it doesn't give us any information for the analysis. We will next save as Hispanic the answers: 2 (Mexican, Mexican-American), 3 (Puerto Rican), 4 (Cuban) and 5 (Other Hispanic, Latino, or Spanish origin group)\n",
    "* Lastly, for the 'other race' category, that's where we will save the answers of the ones who are Asian, American Indian/Alaska Native, Pasific Islander or other\n",
    "* We then delete the initial columns that we don't need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bd242fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummies for race\n",
    "\n",
    "# white\n",
    "total_independents['non_hisp_white'] = (total_independents.white == 1)\n",
    "total_independents.non_hisp_white.replace(True, 1, inplace =True) # our reference group\n",
    "total_independents.non_hisp_white.replace(False, 0, inplace =True)\n",
    "# black\n",
    "total_independents['non_hisp_black'] = (total_independents.black==1)\n",
    "total_independents.non_hisp_black.replace(True, 1, inplace =True)\n",
    "total_independents.non_hisp_black.replace(False, 0, inplace =True)\n",
    "# ignore 'don't know' answers for hispanic\n",
    "total_independents.hispanic.replace(8, np.NaN, inplace =True)\n",
    "# hispanic\n",
    "total_independents['hispanic'] = ((total_independents.hispanic == 2) | (total_independents.hispanic == 5) | \n",
    "                                    (total_independents.hispanic == 3) | (total_independents.hispanic == 4) )\n",
    "total_independents.hispanic.replace(True, 1, inplace =True)\n",
    "total_independents.hispanic.replace(False, 0, inplace =True)\n",
    "# other race\n",
    "total_independents['other_race'] = ((total_independents.other == 1) | (total_independents.asian == 1) | \n",
    "                                    (total_independents.indian_alaska == 1) | (total_independents.pacific_islander == 1))\n",
    "total_independents.other_race.replace(True, 1, inplace =True)\n",
    "total_independents.other_race.replace(False, 0, inplace =True)\n",
    "\n",
    "# delete race columns that we don't need\n",
    "del total_independents['white']\n",
    "del total_independents['black']\n",
    "del total_independents['asian']\n",
    "del total_independents['pacific_islander']\n",
    "del total_independents['indian_alaska']\n",
    "del total_independents['other']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59c6167d",
   "metadata": {},
   "source": [
    "* We will now create the dummies for the education variable.\n",
    "* First, we will do some changes to the answers. We will consider the answer 3 (some college) and 4 (some kind of training) as one and include them both in the same category: `some_college`. That way, we will have five dummies in total, just like the original publication.  \n",
    "* We will use again the method `get_dummies` to automatically create them.  \n",
    "* Lastly, we will delete the unecessary column 'education'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2515cd15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dummies for education\n",
    "# join choices 3 and 4 (some college or some sort of training, no degree)\n",
    "total_independents.education.replace(4, 3, inplace =True)\n",
    "\n",
    "educ_dummies = pd.get_dummies(total_independents['education']).\\\n",
    "    rename(columns={1:'less_than_highschool', 2:'highschool', 3:'some_college', 5:'college', 6:'postgraduate'})\n",
    "total_independents = total_independents.merge(educ_dummies, left_index=True, right_index=True)\n",
    "\n",
    "del total_independents['education']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3ba3b55",
   "metadata": {},
   "source": [
    "* Next are the dummies for marital status  \n",
    "* Here we have two groups:\n",
    "    * Married/Cohabitating\n",
    "    * Single/Not partnered\n",
    "* We consider married the people who have answered number 2 and single the ones who have given any other response  \n",
    "* We replace the answer 2 with the string 'married' and all the other responses with the string 'single'. Then, we use the `get_dummies` method to create two dummy columns\n",
    "* Lastly, we reverse the positions of 1 and 0 in the columns like so:\n",
    "    * 0 = False\n",
    "    * 1 = True\n",
    "* We merge the dummy columns into `total_independents` and we delete the initial `marital` column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a1d85168",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_independents.marital.replace(2, 'married', inplace =True)\n",
    "total_independents.marital.replace([1,3,4,5,6], 'single', inplace =True)\n",
    "\n",
    "mar_dummies = pd.get_dummies(total_independents['marital'])\n",
    "mar_dummies.replace(1,2,inplace=True)\n",
    "mar_dummies.replace(0,1,inplace=True)\n",
    "mar_dummies.replace(2,0,inplace=True)\n",
    "total_independents = total_independents.merge(mar_dummies, left_index=True, right_index=True)\n",
    "\n",
    "del total_independents['marital']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "736a6426",
   "metadata": {},
   "source": [
    "* To create the dummies for the kids at home we firstly create a new column which checks whether someone has answered that has one or more children at home (column `children`)  \n",
    "* We create another column where we note whether someone has answered that he has no kids at all (`no_children` = 1)\n",
    "* Finally we delete the two initial columns since we don't need them anymore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b75fbb96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# kids under 18 dummies\n",
    "total_independents['kids_18'] = (total_independents.children > 0)\n",
    "total_independents.kids_18.replace(True, 1, inplace =True)\n",
    "total_independents.kids_18.replace(False, 0, inplace =True)\n",
    "\n",
    "total_independents['no_kids_18'] = (total_independents.no_children == 1)\n",
    "total_independents.no_kids_18.replace(True, 1, inplace =True)\n",
    "total_independents.no_kids_18.replace(False, 0, inplace =True)\n",
    "\n",
    "del total_independents['children']\n",
    "del total_independents['no_children']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb225cc4",
   "metadata": {},
   "source": [
    "* For the area dummies, we had to join several responses\n",
    "* The two groups we want to create are: urban area and small town, so we consider that\n",
    "    * Urban area: answers 1 (large city) and 2 (suburb near city)\n",
    "    * Small town: answers 3 (small city/town), 4 (rural area)\n",
    "* So we replace number 2 with 1 and number 4 with 3 considering they are the same kind of answers\n",
    "* Also, answer number 8 (don't know) doesn't give us any extra information for the analysis so we replace it with `NaN`\n",
    "* We delete the initial column with the answers all together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "81d9a20b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# area demographics dummies\n",
    "# join answers 1, 2 and 3, 4\n",
    "\n",
    "total_independents.area_demographics.replace(2,1,inplace = True)\n",
    "total_independents.area_demographics.replace(4,3,inplace = True)\n",
    "total_independents.area_demographics.replace(8,np.NaN,inplace = True) # no extra info\n",
    "\n",
    "# urban area\n",
    "total_independents['urban_area'] = total_independents.area_demographics == 1\n",
    "total_independents.urban_area.replace(True, 1, inplace =True)\n",
    "total_independents.urban_area.replace(False, 0, inplace =True)\n",
    "\n",
    "# small town/rural\n",
    "total_independents['small_town'] = total_independents.area_demographics == 3\n",
    "total_independents.small_town.replace(True, 1, inplace =True)\n",
    "total_independents.small_town.replace(False, 0, inplace =True)\n",
    "\n",
    "del total_independents['area_demographics']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f058d58c",
   "metadata": {},
   "source": [
    "* Finally, for the region dummies, we use the `get_dummies` method and we merge the two new columns created (`south` and `not_south`) into the `total_independents` dataframe\n",
    "* We delete the initial column since we don't need it anymore"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e21faecc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# region dummies\n",
    "south_dummies = pd.get_dummies(total_independents['region'])\n",
    "total_independents = total_independents.merge(south_dummies, left_index=True, right_index=True)\n",
    "\n",
    "del total_independents['region']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f90b4874",
   "metadata": {},
   "source": [
    "**Descriptive Statistics**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "764dc840",
   "metadata": {},
   "source": [
    "**[By clicking here](https://drive.google.com/file/d/1sEXN8Ml5vAzIbZs9gND7O_-VapH0Mucc/view?usp=sharing) you can see the below table formated in a similar way to the Appendix A of the original publication.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "99e26684",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>bible</th>\n",
       "      <td>0.334606</td>\n",
       "      <td>0.472003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attendance</th>\n",
       "      <td>3.676735</td>\n",
       "      <td>2.932510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>political_ideol</th>\n",
       "      <td>3.800789</td>\n",
       "      <td>1.554416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hispanic</th>\n",
       "      <td>0.111323</td>\n",
       "      <td>0.314632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>52.645095</td>\n",
       "      <td>17.018217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>income</th>\n",
       "      <td>4.151329</td>\n",
       "      <td>1.707939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sup_evil</th>\n",
       "      <td>3.090909</td>\n",
       "      <td>1.079909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>literalism</th>\n",
       "      <td>0.225827</td>\n",
       "      <td>0.418259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inerrancy</th>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.471555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conservative_prot</th>\n",
       "      <td>0.267812</td>\n",
       "      <td>0.442960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mainline_prot</th>\n",
       "      <td>0.159033</td>\n",
       "      <td>0.365823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>black_prot</th>\n",
       "      <td>0.067430</td>\n",
       "      <td>0.250845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>catholic</th>\n",
       "      <td>0.234733</td>\n",
       "      <td>0.423967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>jewish_other</th>\n",
       "      <td>0.076972</td>\n",
       "      <td>0.266632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_affil</th>\n",
       "      <td>0.136132</td>\n",
       "      <td>0.343038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>0.411578</td>\n",
       "      <td>0.492276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>0.565522</td>\n",
       "      <td>0.495846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_hisp_white</th>\n",
       "      <td>0.746183</td>\n",
       "      <td>0.435332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non_hisp_black</th>\n",
       "      <td>0.113868</td>\n",
       "      <td>0.317752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>other_race</th>\n",
       "      <td>0.076972</td>\n",
       "      <td>0.266632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>less_than_highschool</th>\n",
       "      <td>0.056616</td>\n",
       "      <td>0.231181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>highschool</th>\n",
       "      <td>0.127863</td>\n",
       "      <td>0.334043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>some_college</th>\n",
       "      <td>0.307252</td>\n",
       "      <td>0.461502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>college</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.433150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>postgraduate</th>\n",
       "      <td>0.213104</td>\n",
       "      <td>0.409631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>married</th>\n",
       "      <td>0.492366</td>\n",
       "      <td>0.500101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>single</th>\n",
       "      <td>0.519084</td>\n",
       "      <td>0.499795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kids_18</th>\n",
       "      <td>0.254453</td>\n",
       "      <td>0.435692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>no_kids_18</th>\n",
       "      <td>0.447837</td>\n",
       "      <td>0.497430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>urban_area</th>\n",
       "      <td>0.510178</td>\n",
       "      <td>0.500055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>small_town</th>\n",
       "      <td>0.456743</td>\n",
       "      <td>0.498284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>not_south</th>\n",
       "      <td>0.612595</td>\n",
       "      <td>0.487312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>south</th>\n",
       "      <td>0.387405</td>\n",
       "      <td>0.487312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           mean        std\n",
       "bible                  0.334606   0.472003\n",
       "attendance             3.676735   2.932510\n",
       "political_ideol        3.800789   1.554416\n",
       "hispanic               0.111323   0.314632\n",
       "age                   52.645095  17.018217\n",
       "income                 4.151329   1.707939\n",
       "sup_evil               3.090909   1.079909\n",
       "literalism             0.225827   0.418259\n",
       "inerrancy              0.333333   0.471555\n",
       "conservative_prot      0.267812   0.442960\n",
       "mainline_prot          0.159033   0.365823\n",
       "black_prot             0.067430   0.250845\n",
       "catholic               0.234733   0.423967\n",
       "jewish_other           0.076972   0.266632\n",
       "no_affil               0.136132   0.343038\n",
       "male                   0.411578   0.492276\n",
       "female                 0.565522   0.495846\n",
       "non_hisp_white         0.746183   0.435332\n",
       "non_hisp_black         0.113868   0.317752\n",
       "other_race             0.076972   0.266632\n",
       "less_than_highschool   0.056616   0.231181\n",
       "highschool             0.127863   0.334043\n",
       "some_college           0.307252   0.461502\n",
       "college                0.250000   0.433150\n",
       "postgraduate           0.213104   0.409631\n",
       "married                0.492366   0.500101\n",
       "single                 0.519084   0.499795\n",
       "kids_18                0.254453   0.435692\n",
       "no_kids_18             0.447837   0.497430\n",
       "urban_area             0.510178   0.500055\n",
       "small_town             0.456743   0.498284\n",
       "not_south              0.612595   0.487312\n",
       "south                  0.387405   0.487312"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_independents.describe().transpose().loc[:,('mean','std')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "adcf9def",
   "metadata": {},
   "source": [
    "According to the above descriptive statistics, the average respondent of the research is white (74,6%), female (56,5%), single (51,9%), with no kids under 18 living at home (44,7%), with some college or technical education (30,7%) and with an average income of 35K$ per year. Also, lives in an urban area (51%) and a big part lives in the South (38,7%)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82fa9724",
   "metadata": {},
   "source": [
    "### Q3: Predict Support for Various Gun Policies"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d69769e",
   "metadata": {},
   "source": [
    "* In order to predict support for the different gun policies, we will have to run several regression models  \n",
    "* The value range of those policies (dependent variables) is 0-1, so in that case, we have to perform logistic regression  \n",
    "* We will do that in two models in order to see how much the gun policy predictors change depending on the independent variables taken into account each time\n",
    "    * Model 1: Gun Policies with religious predictors\n",
    "    * Model 2: Gun Policies with religious predictors, political ideology and demographics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "05ec1baa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.formula.api import logit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da326315",
   "metadata": {},
   "source": [
    "### Model 1: Gun policies with religious predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971ed7be",
   "metadata": {},
   "source": [
    "* Firstly, we will gather all the variables we will need into one dataframe  \n",
    "* To do that, we will use the dataframe with the dummy variables we created in Q2  \n",
    "* We merge the dependent variables with the independent ones into one dataframe (model1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "92c7ca69",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = dependent.merge(total_independents, left_index=True, right_index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "645f8276",
   "metadata": {},
   "source": [
    "* We perform logistic regression for the eight gun policies using the corresponding variable columns from `model`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8f46c3db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.644764\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.255198\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.623914\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.299535\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.621622\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.582640\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.568701\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.658146\n",
      "         Iterations 5\n"
     ]
    }
   ],
   "source": [
    "regr1a = logit(\"semi_auto_weapons_ban ~ sup_evil + attendance + literalism + inerrancy + conservative_prot +\"\n",
    "         \" + mainline_prot + black_prot + catholic + jewish_other \",model).fit()\n",
    "regr2a = logit(\"gun_safety_programs ~ sup_evil + attendance + literalism + inerrancy + conservative_prot +\"\n",
    "         \" + mainline_prot + black_prot + catholic + jewish_other \",model).fit()\n",
    "regr3a = logit(\"armed_security_in_schools ~ sup_evil + attendance + literalism + inerrancy + conservative_prot +\"\n",
    "         \" + mainline_prot + black_prot + catholic + jewish_other \",model).fit()\n",
    "regr4a = logit(\"mental_health_screening ~ sup_evil + attendance + literalism + inerrancy + conservative_prot +\"\n",
    "         \" + mainline_prot + black_prot + catholic + jewish_other \",model).fit()\n",
    "regr5a = logit(\"high_capacity_magazine_ban ~ sup_evil + attendance + literalism + inerrancy + conservative_prot +\"\n",
    "         \" + mainline_prot + black_prot + catholic + jewish_other \",model).fit()\n",
    "regr6a = logit(\"teachers_with_guns ~ sup_evil + attendance + literalism + inerrancy + conservative_prot +\"\n",
    "         \" + mainline_prot + black_prot + catholic + jewish_other \",model).fit()\n",
    "regr7a = logit(\"civilian_gun_possesion_ban ~ sup_evil + attendance + literalism + inerrancy + conservative_prot +\"\n",
    "         \" + mainline_prot + black_prot + catholic + jewish_other \",model).fit()\n",
    "regr8a = logit(\"support_for_concealed_carry ~ sup_evil + attendance + literalism + inerrancy + conservative_prot +\"\n",
    "         \" + mainline_prot + black_prot + catholic + jewish_other \",model).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2de8d535",
   "metadata": {},
   "source": [
    "* Below we can see the table containing the odds ratios for the different gun policies and variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0521bbc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>semi_auto_weapons_ban</th>\n",
       "      <th>gun_safety_programs</th>\n",
       "      <th>armed_security_in_schools</th>\n",
       "      <th>mental_health_screening</th>\n",
       "      <th>high_capacity_magazine_ban</th>\n",
       "      <th>teachers_with_guns</th>\n",
       "      <th>civilian_gun_possesion_ban</th>\n",
       "      <th>support_for_concealed_carry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>3.179176</td>\n",
       "      <td>18.898381</td>\n",
       "      <td>0.378594</td>\n",
       "      <td>19.608004</td>\n",
       "      <td>3.735334</td>\n",
       "      <td>0.168522</td>\n",
       "      <td>0.928321</td>\n",
       "      <td>0.501725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sup_evil</td>\n",
       "      <td>0.731579</td>\n",
       "      <td>0.827492</td>\n",
       "      <td>1.540567</td>\n",
       "      <td>0.759499</td>\n",
       "      <td>0.754780</td>\n",
       "      <td>1.415570</td>\n",
       "      <td>0.669052</td>\n",
       "      <td>1.482267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>attendance</td>\n",
       "      <td>1.021204</td>\n",
       "      <td>0.955790</td>\n",
       "      <td>0.925394</td>\n",
       "      <td>0.992028</td>\n",
       "      <td>1.027865</td>\n",
       "      <td>0.957964</td>\n",
       "      <td>1.056332</td>\n",
       "      <td>0.983847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>literalism</td>\n",
       "      <td>0.944027</td>\n",
       "      <td>0.869563</td>\n",
       "      <td>2.061198</td>\n",
       "      <td>1.104254</td>\n",
       "      <td>0.907153</td>\n",
       "      <td>0.883956</td>\n",
       "      <td>1.274790</td>\n",
       "      <td>1.047767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>inerrancy</td>\n",
       "      <td>0.996512</td>\n",
       "      <td>1.088545</td>\n",
       "      <td>1.240414</td>\n",
       "      <td>1.227740</td>\n",
       "      <td>0.985676</td>\n",
       "      <td>0.904432</td>\n",
       "      <td>0.963205</td>\n",
       "      <td>0.783635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>conservative_prot</td>\n",
       "      <td>1.153290</td>\n",
       "      <td>1.538788</td>\n",
       "      <td>1.290376</td>\n",
       "      <td>0.777223</td>\n",
       "      <td>1.040191</td>\n",
       "      <td>1.483553</td>\n",
       "      <td>0.792280</td>\n",
       "      <td>1.090174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>mainline_prot</td>\n",
       "      <td>1.342797</td>\n",
       "      <td>1.508483</td>\n",
       "      <td>1.348325</td>\n",
       "      <td>1.420248</td>\n",
       "      <td>1.186542</td>\n",
       "      <td>0.980298</td>\n",
       "      <td>0.776629</td>\n",
       "      <td>1.109185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>black_prot</td>\n",
       "      <td>1.944769</td>\n",
       "      <td>1.751977</td>\n",
       "      <td>1.565029</td>\n",
       "      <td>1.591665</td>\n",
       "      <td>1.764892</td>\n",
       "      <td>0.538017</td>\n",
       "      <td>2.647879</td>\n",
       "      <td>0.410996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>catholic</td>\n",
       "      <td>1.536528</td>\n",
       "      <td>1.855287</td>\n",
       "      <td>1.208377</td>\n",
       "      <td>1.677888</td>\n",
       "      <td>1.378048</td>\n",
       "      <td>0.799509</td>\n",
       "      <td>1.425830</td>\n",
       "      <td>0.684985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>jewish_other</td>\n",
       "      <td>2.041308</td>\n",
       "      <td>1.792139</td>\n",
       "      <td>1.153514</td>\n",
       "      <td>1.503632</td>\n",
       "      <td>1.917890</td>\n",
       "      <td>0.814986</td>\n",
       "      <td>1.669410</td>\n",
       "      <td>0.573202</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            variable  semi_auto_weapons_ban  gun_safety_programs  \\\n",
       "0          Intercept               3.179176            18.898381   \n",
       "1           sup_evil               0.731579             0.827492   \n",
       "2         attendance               1.021204             0.955790   \n",
       "3         literalism               0.944027             0.869563   \n",
       "4          inerrancy               0.996512             1.088545   \n",
       "5  conservative_prot               1.153290             1.538788   \n",
       "6      mainline_prot               1.342797             1.508483   \n",
       "7         black_prot               1.944769             1.751977   \n",
       "8           catholic               1.536528             1.855287   \n",
       "9       jewish_other               2.041308             1.792139   \n",
       "\n",
       "   armed_security_in_schools  mental_health_screening  \\\n",
       "0                   0.378594                19.608004   \n",
       "1                   1.540567                 0.759499   \n",
       "2                   0.925394                 0.992028   \n",
       "3                   2.061198                 1.104254   \n",
       "4                   1.240414                 1.227740   \n",
       "5                   1.290376                 0.777223   \n",
       "6                   1.348325                 1.420248   \n",
       "7                   1.565029                 1.591665   \n",
       "8                   1.208377                 1.677888   \n",
       "9                   1.153514                 1.503632   \n",
       "\n",
       "   high_capacity_magazine_ban  teachers_with_guns  civilian_gun_possesion_ban  \\\n",
       "0                    3.735334            0.168522                    0.928321   \n",
       "1                    0.754780            1.415570                    0.669052   \n",
       "2                    1.027865            0.957964                    1.056332   \n",
       "3                    0.907153            0.883956                    1.274790   \n",
       "4                    0.985676            0.904432                    0.963205   \n",
       "5                    1.040191            1.483553                    0.792280   \n",
       "6                    1.186542            0.980298                    0.776629   \n",
       "7                    1.764892            0.538017                    2.647879   \n",
       "8                    1.378048            0.799509                    1.425830   \n",
       "9                    1.917890            0.814986                    1.669410   \n",
       "\n",
       "   support_for_concealed_carry  \n",
       "0                     0.501725  \n",
       "1                     1.482267  \n",
       "2                     0.983847  \n",
       "3                     1.047767  \n",
       "4                     0.783635  \n",
       "5                     1.090174  \n",
       "6                     1.109185  \n",
       "7                     0.410996  \n",
       "8                     0.684985  \n",
       "9                     0.573202  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1_odds = np.exp(regr1a.params).reset_index()\n",
    "model1_odds.columns = ['variable','semi_auto_weapons_ban']\n",
    "model1_odds['gun_safety_programs'] = np.exp(regr2a.params).reset_index().loc[:,0]\n",
    "model1_odds['armed_security_in_schools'] = np.exp(regr3a.params).reset_index().loc[:,0]\n",
    "model1_odds['mental_health_screening'] = np.exp(regr4a.params).reset_index().loc[:,0]\n",
    "model1_odds['high_capacity_magazine_ban'] = np.exp(regr5a.params).reset_index().loc[:,0]\n",
    "model1_odds['teachers_with_guns'] = np.exp(regr6a.params).reset_index().loc[:,0]\n",
    "model1_odds['civilian_gun_possesion_ban'] = np.exp(regr7a.params).reset_index().loc[:,0]\n",
    "model1_odds['support_for_concealed_carry'] = np.exp(regr8a.params).reset_index().loc[:,0]\n",
    "model1_odds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98a02db1",
   "metadata": {},
   "source": [
    "The above odds ratios show us some very interesting results. \n",
    "In this model, we see that belief in supernatural evil is positively associated with several gun policies. More specifically: \n",
    "* The odds of support for armed security in schools will be increased for someone who believes in supernatural evil rather than someone who doesn't (OR = 1,54)\n",
    "* The odds of someone to approve the arming of teachers are also higher for someone who believes in supernatural evil (OR = 1,41)\n",
    "* The same thing happens with the support for laws allowing the concealed carry of handguns (OR = 1,48)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ec65f7e",
   "metadata": {},
   "source": [
    "However, there is an inverse association in belief in supernatural evil with the approval of a semi auto weapons ban (OR ≃ 0,73). Also, people who believe in supernatural evil, tend to oppose with the existence of gun safety programs (OR ≃ 0,82). The same thing happens with the ban in civillian gun possesion (OR ≃ 0,66). This means that the ones who believe in supernatural evil are more likely to support civilian gun ownership as well as high capacity magazine ownership."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "811dc8b4",
   "metadata": {},
   "source": [
    "Moreover, we observe that the odds of approving a semi auto weapons ban are the same for people who belive that the bible contains inerrancies compared to the ones who belive that it contains humar error and history (OR = 0,99 ≃ 1). The same thing happens with the odds of approving a high-capacity Ammo clips ban (OR = 0,98 ≃ 1).  \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b3f7f8d",
   "metadata": {},
   "source": [
    "Religion also plays an important role since it is positively associated with the majority of those gun policies, compared to people with no religious affiliation. However, religion is negatively associated with the approval of teachers having guns, which means that the more religious someone is, the odds of him supporting gun ownership from teachers decrease."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d53f58f5",
   "metadata": {},
   "source": [
    "### Model 2: Gun Policies with religious predictors, political ideology and demographics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef58f265",
   "metadata": {},
   "source": [
    "* We will use the same dataframe as in Model 1 (`model`)\n",
    "* We save the column names of the independent variables that we will need in the `all_columns` list. We will not use the variables that are considered as reference groups so we remove them from the list.\n",
    "* Then, we perform logistic regression for the eight gun policies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "24ab3bd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_columns = list(total_independents.columns)\n",
    "ref_groups = ['no_affil', 'male', 'non_hisp_white', 'highschool', 'no_kids_18', 'small_town', 'not_south', 'single', 'bible']\n",
    "all_columns =[ele for ele in all_columns if ele not in ref_groups]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "14509a26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimization terminated successfully.\n",
      "         Current function value: 0.531460\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.235456\n",
      "         Iterations 7\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.589943\n",
      "         Iterations 5\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.272716\n",
      "         Iterations 8\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.529039\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.524175\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.517679\n",
      "         Iterations 6\n",
      "Optimization terminated successfully.\n",
      "         Current function value: 0.587264\n",
      "         Iterations 6\n"
     ]
    }
   ],
   "source": [
    "regr1b = logit(\"semi_auto_weapons_ban ~ \" +' + '.join(all_columns),model).fit()\n",
    "regr2b = logit(\"gun_safety_programs ~\" +' + '.join(all_columns),model).fit()\n",
    "regr3b = logit(\"armed_security_in_schools ~\" +' + '.join(all_columns),model).fit()\n",
    "regr4b = logit(\"mental_health_screening ~ \" +' + '.join(all_columns),model).fit()\n",
    "regr5b = logit(\"high_capacity_magazine_ban ~ \" +' + '.join(all_columns),model).fit()\n",
    "regr6b = logit(\"teachers_with_guns ~ \" +' + '.join(all_columns),model).fit()\n",
    "regr7b = logit(\"civilian_gun_possesion_ban ~ \" +' + '.join(all_columns),model).fit()\n",
    "regr8b = logit(\"support_for_concealed_carry ~ \" +' + '.join(all_columns),model).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2411720",
   "metadata": {},
   "source": [
    "* Below we can see the table containing the odds ratios for the different gun policies and variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "79c653fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>semi_auto_weapons_ban</th>\n",
       "      <th>gun_safety_programs</th>\n",
       "      <th>armed_security_in_schools</th>\n",
       "      <th>mental_health_screening</th>\n",
       "      <th>high_capacity_magazine_ban</th>\n",
       "      <th>teachers_with_guns</th>\n",
       "      <th>civilian_gun_possesion_ban</th>\n",
       "      <th>support_for_concealed_carry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>0.019238</td>\n",
       "      <td>0.969582</td>\n",
       "      <td>6.725033</td>\n",
       "      <td>0.779869</td>\n",
       "      <td>0.034009</td>\n",
       "      <td>6.225331</td>\n",
       "      <td>0.052418</td>\n",
       "      <td>13.005933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>attendance</td>\n",
       "      <td>1.039516</td>\n",
       "      <td>0.942228</td>\n",
       "      <td>0.899069</td>\n",
       "      <td>1.003111</td>\n",
       "      <td>1.036000</td>\n",
       "      <td>0.940892</td>\n",
       "      <td>1.066686</td>\n",
       "      <td>0.965753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>political_ideol</td>\n",
       "      <td>1.729463</td>\n",
       "      <td>1.471598</td>\n",
       "      <td>0.787194</td>\n",
       "      <td>1.537830</td>\n",
       "      <td>1.639252</td>\n",
       "      <td>0.668815</td>\n",
       "      <td>1.427820</td>\n",
       "      <td>0.666764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hispanic</td>\n",
       "      <td>1.566445</td>\n",
       "      <td>1.383903</td>\n",
       "      <td>1.079756</td>\n",
       "      <td>3.743916</td>\n",
       "      <td>1.318213</td>\n",
       "      <td>0.629372</td>\n",
       "      <td>2.044564</td>\n",
       "      <td>0.697881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>age</td>\n",
       "      <td>1.027903</td>\n",
       "      <td>1.014063</td>\n",
       "      <td>0.994018</td>\n",
       "      <td>1.014472</td>\n",
       "      <td>1.032823</td>\n",
       "      <td>0.986032</td>\n",
       "      <td>1.012266</td>\n",
       "      <td>0.991781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>income</td>\n",
       "      <td>1.087413</td>\n",
       "      <td>1.034639</td>\n",
       "      <td>0.845693</td>\n",
       "      <td>0.985306</td>\n",
       "      <td>0.988615</td>\n",
       "      <td>0.991473</td>\n",
       "      <td>0.965003</td>\n",
       "      <td>0.948347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sup_evil</td>\n",
       "      <td>0.782807</td>\n",
       "      <td>0.987627</td>\n",
       "      <td>1.397345</td>\n",
       "      <td>0.866084</td>\n",
       "      <td>0.841282</td>\n",
       "      <td>1.345509</td>\n",
       "      <td>0.737170</td>\n",
       "      <td>1.424358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>literalism</td>\n",
       "      <td>1.278039</td>\n",
       "      <td>1.018296</td>\n",
       "      <td>1.511995</td>\n",
       "      <td>1.023341</td>\n",
       "      <td>1.088931</td>\n",
       "      <td>0.660742</td>\n",
       "      <td>1.420664</td>\n",
       "      <td>0.698911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>inerrancy</td>\n",
       "      <td>1.146689</td>\n",
       "      <td>1.203292</td>\n",
       "      <td>1.097730</td>\n",
       "      <td>1.053254</td>\n",
       "      <td>1.099581</td>\n",
       "      <td>0.886684</td>\n",
       "      <td>0.986486</td>\n",
       "      <td>0.654439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>conservative_prot</td>\n",
       "      <td>1.827610</td>\n",
       "      <td>1.866710</td>\n",
       "      <td>1.181938</td>\n",
       "      <td>1.133062</td>\n",
       "      <td>1.222477</td>\n",
       "      <td>1.203354</td>\n",
       "      <td>0.977565</td>\n",
       "      <td>0.851695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mainline_prot</td>\n",
       "      <td>1.333142</td>\n",
       "      <td>1.828645</td>\n",
       "      <td>1.815185</td>\n",
       "      <td>1.665448</td>\n",
       "      <td>1.062378</td>\n",
       "      <td>0.961623</td>\n",
       "      <td>0.881845</td>\n",
       "      <td>1.029986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>black_prot</td>\n",
       "      <td>0.932156</td>\n",
       "      <td>1.155537</td>\n",
       "      <td>1.201772</td>\n",
       "      <td>0.622830</td>\n",
       "      <td>0.681313</td>\n",
       "      <td>1.395996</td>\n",
       "      <td>1.940999</td>\n",
       "      <td>0.415036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>catholic</td>\n",
       "      <td>1.792368</td>\n",
       "      <td>2.019571</td>\n",
       "      <td>1.427600</td>\n",
       "      <td>1.980527</td>\n",
       "      <td>1.347016</td>\n",
       "      <td>0.736109</td>\n",
       "      <td>1.310500</td>\n",
       "      <td>0.653350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>jewish_other</td>\n",
       "      <td>1.605845</td>\n",
       "      <td>1.527699</td>\n",
       "      <td>1.543176</td>\n",
       "      <td>1.367274</td>\n",
       "      <td>1.366501</td>\n",
       "      <td>0.943310</td>\n",
       "      <td>1.458650</td>\n",
       "      <td>0.680931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>female</td>\n",
       "      <td>3.423192</td>\n",
       "      <td>1.809046</td>\n",
       "      <td>0.948217</td>\n",
       "      <td>2.014142</td>\n",
       "      <td>2.870809</td>\n",
       "      <td>0.481573</td>\n",
       "      <td>1.393206</td>\n",
       "      <td>0.473651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>non_hisp_black</td>\n",
       "      <td>1.848720</td>\n",
       "      <td>1.641635</td>\n",
       "      <td>1.463108</td>\n",
       "      <td>2.028143</td>\n",
       "      <td>1.669433</td>\n",
       "      <td>0.451774</td>\n",
       "      <td>1.340525</td>\n",
       "      <td>0.976269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>other_race</td>\n",
       "      <td>1.020223</td>\n",
       "      <td>0.811242</td>\n",
       "      <td>1.053320</td>\n",
       "      <td>0.668256</td>\n",
       "      <td>1.153193</td>\n",
       "      <td>0.984990</td>\n",
       "      <td>1.983827</td>\n",
       "      <td>0.681038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>less_than_highschool</td>\n",
       "      <td>0.711196</td>\n",
       "      <td>0.337069</td>\n",
       "      <td>0.641198</td>\n",
       "      <td>0.769373</td>\n",
       "      <td>1.429859</td>\n",
       "      <td>0.862253</td>\n",
       "      <td>1.161528</td>\n",
       "      <td>0.712059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>some_college</td>\n",
       "      <td>0.935066</td>\n",
       "      <td>0.676001</td>\n",
       "      <td>0.679801</td>\n",
       "      <td>0.895511</td>\n",
       "      <td>1.117523</td>\n",
       "      <td>0.762031</td>\n",
       "      <td>0.814518</td>\n",
       "      <td>1.148360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>college</td>\n",
       "      <td>0.765138</td>\n",
       "      <td>0.799196</td>\n",
       "      <td>0.888955</td>\n",
       "      <td>1.068827</td>\n",
       "      <td>1.020150</td>\n",
       "      <td>0.843946</td>\n",
       "      <td>0.947125</td>\n",
       "      <td>1.243175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>postgraduate</td>\n",
       "      <td>1.449980</td>\n",
       "      <td>0.956791</td>\n",
       "      <td>0.505621</td>\n",
       "      <td>1.397320</td>\n",
       "      <td>1.824552</td>\n",
       "      <td>0.529034</td>\n",
       "      <td>1.270817</td>\n",
       "      <td>0.698072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>married</td>\n",
       "      <td>1.108415</td>\n",
       "      <td>0.844744</td>\n",
       "      <td>0.648473</td>\n",
       "      <td>0.824861</td>\n",
       "      <td>0.939293</td>\n",
       "      <td>0.909539</td>\n",
       "      <td>1.090891</td>\n",
       "      <td>0.907920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>kids_18</td>\n",
       "      <td>0.993690</td>\n",
       "      <td>0.707346</td>\n",
       "      <td>0.968158</td>\n",
       "      <td>0.680436</td>\n",
       "      <td>0.882179</td>\n",
       "      <td>1.316379</td>\n",
       "      <td>1.281077</td>\n",
       "      <td>0.842115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>urban_area</td>\n",
       "      <td>1.460002</td>\n",
       "      <td>1.663718</td>\n",
       "      <td>0.828020</td>\n",
       "      <td>1.471080</td>\n",
       "      <td>1.416460</td>\n",
       "      <td>0.597203</td>\n",
       "      <td>1.421842</td>\n",
       "      <td>0.731400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>south</td>\n",
       "      <td>0.715625</td>\n",
       "      <td>1.070285</td>\n",
       "      <td>1.545861</td>\n",
       "      <td>1.306435</td>\n",
       "      <td>0.907372</td>\n",
       "      <td>0.899229</td>\n",
       "      <td>0.700770</td>\n",
       "      <td>1.533365</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                variable  semi_auto_weapons_ban  gun_safety_programs  \\\n",
       "0              Intercept               0.019238             0.969582   \n",
       "1             attendance               1.039516             0.942228   \n",
       "2        political_ideol               1.729463             1.471598   \n",
       "3               hispanic               1.566445             1.383903   \n",
       "4                    age               1.027903             1.014063   \n",
       "5                 income               1.087413             1.034639   \n",
       "6               sup_evil               0.782807             0.987627   \n",
       "7             literalism               1.278039             1.018296   \n",
       "8              inerrancy               1.146689             1.203292   \n",
       "9      conservative_prot               1.827610             1.866710   \n",
       "10         mainline_prot               1.333142             1.828645   \n",
       "11            black_prot               0.932156             1.155537   \n",
       "12              catholic               1.792368             2.019571   \n",
       "13          jewish_other               1.605845             1.527699   \n",
       "14                female               3.423192             1.809046   \n",
       "15        non_hisp_black               1.848720             1.641635   \n",
       "16            other_race               1.020223             0.811242   \n",
       "17  less_than_highschool               0.711196             0.337069   \n",
       "18          some_college               0.935066             0.676001   \n",
       "19               college               0.765138             0.799196   \n",
       "20          postgraduate               1.449980             0.956791   \n",
       "21               married               1.108415             0.844744   \n",
       "22               kids_18               0.993690             0.707346   \n",
       "23            urban_area               1.460002             1.663718   \n",
       "24                 south               0.715625             1.070285   \n",
       "\n",
       "    armed_security_in_schools  mental_health_screening  \\\n",
       "0                    6.725033                 0.779869   \n",
       "1                    0.899069                 1.003111   \n",
       "2                    0.787194                 1.537830   \n",
       "3                    1.079756                 3.743916   \n",
       "4                    0.994018                 1.014472   \n",
       "5                    0.845693                 0.985306   \n",
       "6                    1.397345                 0.866084   \n",
       "7                    1.511995                 1.023341   \n",
       "8                    1.097730                 1.053254   \n",
       "9                    1.181938                 1.133062   \n",
       "10                   1.815185                 1.665448   \n",
       "11                   1.201772                 0.622830   \n",
       "12                   1.427600                 1.980527   \n",
       "13                   1.543176                 1.367274   \n",
       "14                   0.948217                 2.014142   \n",
       "15                   1.463108                 2.028143   \n",
       "16                   1.053320                 0.668256   \n",
       "17                   0.641198                 0.769373   \n",
       "18                   0.679801                 0.895511   \n",
       "19                   0.888955                 1.068827   \n",
       "20                   0.505621                 1.397320   \n",
       "21                   0.648473                 0.824861   \n",
       "22                   0.968158                 0.680436   \n",
       "23                   0.828020                 1.471080   \n",
       "24                   1.545861                 1.306435   \n",
       "\n",
       "    high_capacity_magazine_ban  teachers_with_guns  \\\n",
       "0                     0.034009            6.225331   \n",
       "1                     1.036000            0.940892   \n",
       "2                     1.639252            0.668815   \n",
       "3                     1.318213            0.629372   \n",
       "4                     1.032823            0.986032   \n",
       "5                     0.988615            0.991473   \n",
       "6                     0.841282            1.345509   \n",
       "7                     1.088931            0.660742   \n",
       "8                     1.099581            0.886684   \n",
       "9                     1.222477            1.203354   \n",
       "10                    1.062378            0.961623   \n",
       "11                    0.681313            1.395996   \n",
       "12                    1.347016            0.736109   \n",
       "13                    1.366501            0.943310   \n",
       "14                    2.870809            0.481573   \n",
       "15                    1.669433            0.451774   \n",
       "16                    1.153193            0.984990   \n",
       "17                    1.429859            0.862253   \n",
       "18                    1.117523            0.762031   \n",
       "19                    1.020150            0.843946   \n",
       "20                    1.824552            0.529034   \n",
       "21                    0.939293            0.909539   \n",
       "22                    0.882179            1.316379   \n",
       "23                    1.416460            0.597203   \n",
       "24                    0.907372            0.899229   \n",
       "\n",
       "    civilian_gun_possesion_ban  support_for_concealed_carry  \n",
       "0                     0.052418                    13.005933  \n",
       "1                     1.066686                     0.965753  \n",
       "2                     1.427820                     0.666764  \n",
       "3                     2.044564                     0.697881  \n",
       "4                     1.012266                     0.991781  \n",
       "5                     0.965003                     0.948347  \n",
       "6                     0.737170                     1.424358  \n",
       "7                     1.420664                     0.698911  \n",
       "8                     0.986486                     0.654439  \n",
       "9                     0.977565                     0.851695  \n",
       "10                    0.881845                     1.029986  \n",
       "11                    1.940999                     0.415036  \n",
       "12                    1.310500                     0.653350  \n",
       "13                    1.458650                     0.680931  \n",
       "14                    1.393206                     0.473651  \n",
       "15                    1.340525                     0.976269  \n",
       "16                    1.983827                     0.681038  \n",
       "17                    1.161528                     0.712059  \n",
       "18                    0.814518                     1.148360  \n",
       "19                    0.947125                     1.243175  \n",
       "20                    1.270817                     0.698072  \n",
       "21                    1.090891                     0.907920  \n",
       "22                    1.281077                     0.842115  \n",
       "23                    1.421842                     0.731400  \n",
       "24                    0.700770                     1.533365  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2_odds = np.exp(regr1b.params).reset_index()\n",
    "model2_odds.columns = ['variable','semi_auto_weapons_ban']\n",
    "model2_odds['gun_safety_programs'] = np.exp(regr2b.params).reset_index().loc[:,0]\n",
    "model2_odds['armed_security_in_schools'] = np.exp(regr3b.params).reset_index().loc[:,0]\n",
    "model2_odds['mental_health_screening'] = np.exp(regr4b.params).reset_index().loc[:,0]\n",
    "model2_odds['high_capacity_magazine_ban'] = np.exp(regr5b.params).reset_index().loc[:,0]\n",
    "model2_odds['teachers_with_guns'] = np.exp(regr6b.params).reset_index().loc[:,0]\n",
    "model2_odds['civilian_gun_possesion_ban'] = np.exp(regr7b.params).reset_index().loc[:,0]\n",
    "model2_odds['support_for_concealed_carry'] = np.exp(regr8b.params).reset_index().loc[:,0]\n",
    "model2_odds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "253fc5a6",
   "metadata": {},
   "source": [
    "After including several demographic factors and mediators in model 2, we result into more detailed and informative conclusions.  \n",
    "Belief in supernatural evil continues to have a positive association with the three gun policies mentioned in model 1. Those are: existence of armed security in schools (OR = 1.39), support for teachers having guns (OR = 1.34) and support for concealed carry laws (OR = 1.42).  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a67f6584",
   "metadata": {},
   "source": [
    "Let's see how and if political ideology controls, combined with belief in supernatural evil, influence the opinion of people regarding the eight gun policies:  \n",
    "* We found a strong inverse association between belief in supernatural evil and support for a ban on high capacity ammunition magazines (OR = 0.74, model 1). This effect is being slightly reduced by political ideology (OR = 0.83, model 2)\n",
    "* People who believe in supernatural evil are more likely to be in favor of a ban of civillian gun ownership (OR = 0.66, model 1). This behavior continues to be true after the adjustments of political ideology and other variables (OR = 0.73, model 2)\n",
    "* Belief in supernatural evil is positively associated with the approval of concealed carry of handguns (OR = 1.48, model 1), and this pattern doesn't change after the political ideology controls (OR = 1.42, model 2)\n",
    "* The odds of someone to support the arming of teachers in schools is positively associated with his belief in supernatural evil ( OR = 1.54, model 1). This association remains after taking into account the political ideology controls ( OR = 1.39, model 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2df0c7a7",
   "metadata": {},
   "source": [
    "Now regarding the demographics, females are way more likely -compared to males- to support a semi automatic weapons ban (OR = 3.42), the existence of gun safety programs (OR = 1.8), the ban of high capacity magazines (OR = 2.8) and the ban of civillian handgun possesion (OR = 1.39).  \n",
    "Consequently, they are more likely -compared to males- to oppose to the existence of armed security in schools (OR = 0.94), to the gun ownership by teachers (OR = 0.48) and to the existence of concealed carry laws (OR = 0.47)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b39adf0e",
   "metadata": {},
   "source": [
    "***\n",
    "According to the [research report](https://www.economist.com/united-states/2021/11/06/belief-in-supernatural-evil-is-a-strong-predictor-of-pro-gun-beliefs) in The Economist:  \n",
    "<span style=\"font-size:17px\"> <i>Each step up on a four-point scale measuring the strength of belief in supernatural evil correlated with 32% more support for arming teachers, and a 38% rise in backing for carrying concealed weapons.</i> </span>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1355b128",
   "metadata": {},
   "source": [
    "The above statement can be evaluated from the analysis we performed here since the ratios we came up with are very similar. In model 2, we found that the odds ratio of someone to be in favor of arming teachers is 1,34. This means that a one-unit increase in the strength of someone's belief in supernatural evil is associated with a 34% increase in support for arming teachers. The corresponding odds ratio for the support for concealed carry laws is 1,42, that is, a 42% increase. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f5ed1a",
   "metadata": {},
   "source": [
    "### Q4: Additional Estimations of the Strength of Predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77e2555",
   "metadata": {},
   "source": [
    "* We will perform linear regression with the variables of model 2 in order to get the standardized coefficients and to better understand our results.\n",
    "* We use the same dataframe (`model`) as in logistic regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59255021",
   "metadata": {},
   "source": [
    "#### ---- Standardized Values ----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "840284d8",
   "metadata": {},
   "source": [
    "* We will firstly standardize our values so that they have a mean of zero and standard deviation equal to one.\n",
    "* We will do that using `StandardScaler`\n",
    "* The table below contains descriptive statistics of the standardized values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f97cc846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>semi_auto_weapons_ban</th>\n",
       "      <th>gun_safety_programs</th>\n",
       "      <th>armed_security_in_schools</th>\n",
       "      <th>mental_health_screening</th>\n",
       "      <th>high_capacity_magazine_ban</th>\n",
       "      <th>teachers_with_guns</th>\n",
       "      <th>civilian_gun_possesion_ban</th>\n",
       "      <th>support_for_concealed_carry</th>\n",
       "      <th>bible</th>\n",
       "      <th>attendance</th>\n",
       "      <th>...</th>\n",
       "      <th>college</th>\n",
       "      <th>postgraduate</th>\n",
       "      <th>married</th>\n",
       "      <th>single</th>\n",
       "      <th>kids_18</th>\n",
       "      <th>no_kids_18</th>\n",
       "      <th>urban_area</th>\n",
       "      <th>small_town</th>\n",
       "      <th>not_south</th>\n",
       "      <th>south</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.502000e+03</td>\n",
       "      <td>1.515000e+03</td>\n",
       "      <td>1.510000e+03</td>\n",
       "      <td>1.512000e+03</td>\n",
       "      <td>1.502000e+03</td>\n",
       "      <td>1.504000e+03</td>\n",
       "      <td>1.501000e+03</td>\n",
       "      <td>1.502000e+03</td>\n",
       "      <td>1.572000e+03</td>\n",
       "      <td>1.556000e+03</td>\n",
       "      <td>...</td>\n",
       "      <td>1.572000e+03</td>\n",
       "      <td>1.572000e+03</td>\n",
       "      <td>1.572000e+03</td>\n",
       "      <td>1.572000e+03</td>\n",
       "      <td>1.572000e+03</td>\n",
       "      <td>1.572000e+03</td>\n",
       "      <td>1.572000e+03</td>\n",
       "      <td>1.572000e+03</td>\n",
       "      <td>1.572000e+03</td>\n",
       "      <td>1.572000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.666074e-16</td>\n",
       "      <td>-1.049399e-16</td>\n",
       "      <td>2.655712e-16</td>\n",
       "      <td>-2.413560e-16</td>\n",
       "      <td>-5.197795e-16</td>\n",
       "      <td>6.927083e-16</td>\n",
       "      <td>3.204188e-16</td>\n",
       "      <td>7.539464e-17</td>\n",
       "      <td>4.018555e-16</td>\n",
       "      <td>-3.031351e-16</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.202742e-16</td>\n",
       "      <td>-1.515610e-16</td>\n",
       "      <td>-1.737372e-16</td>\n",
       "      <td>-1.874384e-16</td>\n",
       "      <td>-5.368903e-16</td>\n",
       "      <td>1.222375e-15</td>\n",
       "      <td>4.610392e-16</td>\n",
       "      <td>1.591885e-16</td>\n",
       "      <td>-1.980321e-16</td>\n",
       "      <td>1.980321e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.000333e+00</td>\n",
       "      <td>1.000330e+00</td>\n",
       "      <td>1.000331e+00</td>\n",
       "      <td>1.000331e+00</td>\n",
       "      <td>1.000333e+00</td>\n",
       "      <td>1.000333e+00</td>\n",
       "      <td>1.000333e+00</td>\n",
       "      <td>1.000333e+00</td>\n",
       "      <td>1.000318e+00</td>\n",
       "      <td>1.000321e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000318e+00</td>\n",
       "      <td>1.000318e+00</td>\n",
       "      <td>1.000318e+00</td>\n",
       "      <td>1.000318e+00</td>\n",
       "      <td>1.000318e+00</td>\n",
       "      <td>1.000318e+00</td>\n",
       "      <td>1.000318e+00</td>\n",
       "      <td>1.000318e+00</td>\n",
       "      <td>1.000318e+00</td>\n",
       "      <td>1.000318e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.302532e+00</td>\n",
       "      <td>-3.573895e+00</td>\n",
       "      <td>-1.282374e+00</td>\n",
       "      <td>-3.142881e+00</td>\n",
       "      <td>-1.428456e+00</td>\n",
       "      <td>-6.461661e-01</td>\n",
       "      <td>-6.398357e-01</td>\n",
       "      <td>-1.123409e+00</td>\n",
       "      <td>-7.091319e-01</td>\n",
       "      <td>-1.254188e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.773503e-01</td>\n",
       "      <td>-5.204003e-01</td>\n",
       "      <td>-9.848476e-01</td>\n",
       "      <td>-1.038925e+00</td>\n",
       "      <td>-5.842062e-01</td>\n",
       "      <td>-9.005886e-01</td>\n",
       "      <td>-1.020568e+00</td>\n",
       "      <td>-9.169239e-01</td>\n",
       "      <td>-1.257490e+00</td>\n",
       "      <td>-7.952350e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>-1.302532e+00</td>\n",
       "      <td>2.798067e-01</td>\n",
       "      <td>-1.282374e+00</td>\n",
       "      <td>3.181795e-01</td>\n",
       "      <td>-1.428456e+00</td>\n",
       "      <td>-6.461661e-01</td>\n",
       "      <td>-6.398357e-01</td>\n",
       "      <td>-1.123409e+00</td>\n",
       "      <td>-7.091319e-01</td>\n",
       "      <td>-9.130731e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.773503e-01</td>\n",
       "      <td>-5.204003e-01</td>\n",
       "      <td>-9.848476e-01</td>\n",
       "      <td>-1.038925e+00</td>\n",
       "      <td>-5.842062e-01</td>\n",
       "      <td>-9.005886e-01</td>\n",
       "      <td>-1.020568e+00</td>\n",
       "      <td>-9.169239e-01</td>\n",
       "      <td>-1.257490e+00</td>\n",
       "      <td>-7.952350e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.677356e-01</td>\n",
       "      <td>2.798067e-01</td>\n",
       "      <td>7.798036e-01</td>\n",
       "      <td>3.181795e-01</td>\n",
       "      <td>7.000567e-01</td>\n",
       "      <td>-6.461661e-01</td>\n",
       "      <td>-6.398357e-01</td>\n",
       "      <td>8.901476e-01</td>\n",
       "      <td>-7.091319e-01</td>\n",
       "      <td>-2.308442e-01</td>\n",
       "      <td>...</td>\n",
       "      <td>-5.773503e-01</td>\n",
       "      <td>-5.204003e-01</td>\n",
       "      <td>-9.848476e-01</td>\n",
       "      <td>9.625334e-01</td>\n",
       "      <td>-5.842062e-01</td>\n",
       "      <td>-9.005886e-01</td>\n",
       "      <td>9.798468e-01</td>\n",
       "      <td>-9.169239e-01</td>\n",
       "      <td>7.952350e-01</td>\n",
       "      <td>-7.952350e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.677356e-01</td>\n",
       "      <td>2.798067e-01</td>\n",
       "      <td>7.798036e-01</td>\n",
       "      <td>3.181795e-01</td>\n",
       "      <td>7.000567e-01</td>\n",
       "      <td>1.547590e+00</td>\n",
       "      <td>1.562901e+00</td>\n",
       "      <td>8.901476e-01</td>\n",
       "      <td>1.410175e+00</td>\n",
       "      <td>1.133614e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-5.204003e-01</td>\n",
       "      <td>1.015386e+00</td>\n",
       "      <td>9.625334e-01</td>\n",
       "      <td>1.711724e+00</td>\n",
       "      <td>1.110385e+00</td>\n",
       "      <td>9.798468e-01</td>\n",
       "      <td>1.090603e+00</td>\n",
       "      <td>7.952350e-01</td>\n",
       "      <td>1.257490e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.677356e-01</td>\n",
       "      <td>2.798067e-01</td>\n",
       "      <td>7.798036e-01</td>\n",
       "      <td>3.181795e-01</td>\n",
       "      <td>7.000567e-01</td>\n",
       "      <td>1.547590e+00</td>\n",
       "      <td>1.562901e+00</td>\n",
       "      <td>8.901476e-01</td>\n",
       "      <td>1.410175e+00</td>\n",
       "      <td>1.474728e+00</td>\n",
       "      <td>...</td>\n",
       "      <td>1.732051e+00</td>\n",
       "      <td>1.921598e+00</td>\n",
       "      <td>1.015386e+00</td>\n",
       "      <td>9.625334e-01</td>\n",
       "      <td>1.711724e+00</td>\n",
       "      <td>1.110385e+00</td>\n",
       "      <td>9.798468e-01</td>\n",
       "      <td>1.090603e+00</td>\n",
       "      <td>7.952350e-01</td>\n",
       "      <td>1.257490e+00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       semi_auto_weapons_ban  gun_safety_programs  armed_security_in_schools  \\\n",
       "count           1.502000e+03         1.515000e+03               1.510000e+03   \n",
       "mean            1.666074e-16        -1.049399e-16               2.655712e-16   \n",
       "std             1.000333e+00         1.000330e+00               1.000331e+00   \n",
       "min            -1.302532e+00        -3.573895e+00              -1.282374e+00   \n",
       "25%            -1.302532e+00         2.798067e-01              -1.282374e+00   \n",
       "50%             7.677356e-01         2.798067e-01               7.798036e-01   \n",
       "75%             7.677356e-01         2.798067e-01               7.798036e-01   \n",
       "max             7.677356e-01         2.798067e-01               7.798036e-01   \n",
       "\n",
       "       mental_health_screening  high_capacity_magazine_ban  \\\n",
       "count             1.512000e+03                1.502000e+03   \n",
       "mean             -2.413560e-16               -5.197795e-16   \n",
       "std               1.000331e+00                1.000333e+00   \n",
       "min              -3.142881e+00               -1.428456e+00   \n",
       "25%               3.181795e-01               -1.428456e+00   \n",
       "50%               3.181795e-01                7.000567e-01   \n",
       "75%               3.181795e-01                7.000567e-01   \n",
       "max               3.181795e-01                7.000567e-01   \n",
       "\n",
       "       teachers_with_guns  civilian_gun_possesion_ban  \\\n",
       "count        1.504000e+03                1.501000e+03   \n",
       "mean         6.927083e-16                3.204188e-16   \n",
       "std          1.000333e+00                1.000333e+00   \n",
       "min         -6.461661e-01               -6.398357e-01   \n",
       "25%         -6.461661e-01               -6.398357e-01   \n",
       "50%         -6.461661e-01               -6.398357e-01   \n",
       "75%          1.547590e+00                1.562901e+00   \n",
       "max          1.547590e+00                1.562901e+00   \n",
       "\n",
       "       support_for_concealed_carry         bible    attendance  ...  \\\n",
       "count                 1.502000e+03  1.572000e+03  1.556000e+03  ...   \n",
       "mean                  7.539464e-17  4.018555e-16 -3.031351e-16  ...   \n",
       "std                   1.000333e+00  1.000318e+00  1.000321e+00  ...   \n",
       "min                  -1.123409e+00 -7.091319e-01 -1.254188e+00  ...   \n",
       "25%                  -1.123409e+00 -7.091319e-01 -9.130731e-01  ...   \n",
       "50%                   8.901476e-01 -7.091319e-01 -2.308442e-01  ...   \n",
       "75%                   8.901476e-01  1.410175e+00  1.133614e+00  ...   \n",
       "max                   8.901476e-01  1.410175e+00  1.474728e+00  ...   \n",
       "\n",
       "            college  postgraduate       married        single       kids_18  \\\n",
       "count  1.572000e+03  1.572000e+03  1.572000e+03  1.572000e+03  1.572000e+03   \n",
       "mean  -1.202742e-16 -1.515610e-16 -1.737372e-16 -1.874384e-16 -5.368903e-16   \n",
       "std    1.000318e+00  1.000318e+00  1.000318e+00  1.000318e+00  1.000318e+00   \n",
       "min   -5.773503e-01 -5.204003e-01 -9.848476e-01 -1.038925e+00 -5.842062e-01   \n",
       "25%   -5.773503e-01 -5.204003e-01 -9.848476e-01 -1.038925e+00 -5.842062e-01   \n",
       "50%   -5.773503e-01 -5.204003e-01 -9.848476e-01  9.625334e-01 -5.842062e-01   \n",
       "75%    0.000000e+00 -5.204003e-01  1.015386e+00  9.625334e-01  1.711724e+00   \n",
       "max    1.732051e+00  1.921598e+00  1.015386e+00  9.625334e-01  1.711724e+00   \n",
       "\n",
       "         no_kids_18    urban_area    small_town     not_south         south  \n",
       "count  1.572000e+03  1.572000e+03  1.572000e+03  1.572000e+03  1.572000e+03  \n",
       "mean   1.222375e-15  4.610392e-16  1.591885e-16 -1.980321e-16  1.980321e-16  \n",
       "std    1.000318e+00  1.000318e+00  1.000318e+00  1.000318e+00  1.000318e+00  \n",
       "min   -9.005886e-01 -1.020568e+00 -9.169239e-01 -1.257490e+00 -7.952350e-01  \n",
       "25%   -9.005886e-01 -1.020568e+00 -9.169239e-01 -1.257490e+00 -7.952350e-01  \n",
       "50%   -9.005886e-01  9.798468e-01 -9.169239e-01  7.952350e-01 -7.952350e-01  \n",
       "75%    1.110385e+00  9.798468e-01  1.090603e+00  7.952350e-01  1.257490e+00  \n",
       "max    1.110385e+00  9.798468e-01  1.090603e+00  7.952350e-01  1.257490e+00  \n",
       "\n",
       "[8 rows x 41 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import preprocessing\n",
    "\n",
    "standard_scaler = preprocessing.StandardScaler()\n",
    "model2_scaled = standard_scaler.fit_transform(model)\n",
    "model2_scaled = pd.DataFrame(model2_scaled)\n",
    "model2_scaled.columns = model.columns\n",
    "model2_scaled.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "182b3d1e",
   "metadata": {},
   "source": [
    "* We perform the linear regressions with the eight gun policies\n",
    "* At the table below we can see the standardized coefficients for each gun policy and predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "97ce0076",
   "metadata": {},
   "outputs": [],
   "source": [
    "ol1 = smf.ols(\"semi_auto_weapons_ban ~\" +' + '.join(all_columns), data=model2_scaled).fit()\n",
    "ol2 = smf.ols(\"gun_safety_programs ~\" +' + '.join(all_columns), data=model2_scaled).fit()\n",
    "ol3 = smf.ols(\"armed_security_in_schools ~\" +' + '.join(all_columns), data=model2_scaled).fit()\n",
    "ol4 = smf.ols(\"mental_health_screening ~\" +' + '.join(all_columns), data=model2_scaled).fit()\n",
    "ol5 = smf.ols(\"high_capacity_magazine_ban ~\" +' + '.join(all_columns), data=model2_scaled).fit()\n",
    "ol6 = smf.ols(\"teachers_with_guns ~\" +' + '.join(all_columns), data=model2_scaled).fit()\n",
    "ol7 = smf.ols(\"civilian_gun_possesion_ban ~\" +' + '.join(all_columns), data=model2_scaled).fit()\n",
    "ol8 = smf.ols(\"support_for_concealed_carry ~\" +' + '.join(all_columns), data=model2_scaled).fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "69bed003",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>semi_auto_weapons_ban</th>\n",
       "      <th>gun_safety_programs</th>\n",
       "      <th>armed_security_in_schools</th>\n",
       "      <th>mental_health_screening</th>\n",
       "      <th>high_capacity_magazine_ban</th>\n",
       "      <th>teachers_with_guns</th>\n",
       "      <th>civilian_gun_possesion_ban</th>\n",
       "      <th>support_for_concealed_carry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-0.004226</td>\n",
       "      <td>0.006502</td>\n",
       "      <td>0.001915</td>\n",
       "      <td>-0.002288</td>\n",
       "      <td>-0.007319</td>\n",
       "      <td>0.010464</td>\n",
       "      <td>-0.029946</td>\n",
       "      <td>0.010309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>attendance</td>\n",
       "      <td>0.042500</td>\n",
       "      <td>-0.043259</td>\n",
       "      <td>-0.124058</td>\n",
       "      <td>-0.000048</td>\n",
       "      <td>0.040174</td>\n",
       "      <td>-0.071628</td>\n",
       "      <td>0.063930</td>\n",
       "      <td>-0.037588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>political_ideol</td>\n",
       "      <td>0.314903</td>\n",
       "      <td>0.137067</td>\n",
       "      <td>-0.159465</td>\n",
       "      <td>0.161509</td>\n",
       "      <td>0.289149</td>\n",
       "      <td>-0.238977</td>\n",
       "      <td>0.216531</td>\n",
       "      <td>-0.263410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.052261</td>\n",
       "      <td>0.021673</td>\n",
       "      <td>0.010026</td>\n",
       "      <td>0.074931</td>\n",
       "      <td>0.033354</td>\n",
       "      <td>-0.051556</td>\n",
       "      <td>0.095999</td>\n",
       "      <td>-0.047126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>age</td>\n",
       "      <td>0.164573</td>\n",
       "      <td>0.055700</td>\n",
       "      <td>-0.041469</td>\n",
       "      <td>0.061388</td>\n",
       "      <td>0.199559</td>\n",
       "      <td>-0.086775</td>\n",
       "      <td>0.080553</td>\n",
       "      <td>-0.056020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>income</td>\n",
       "      <td>0.046941</td>\n",
       "      <td>0.010207</td>\n",
       "      <td>-0.117202</td>\n",
       "      <td>-0.011874</td>\n",
       "      <td>-0.011916</td>\n",
       "      <td>-0.001310</td>\n",
       "      <td>-0.022987</td>\n",
       "      <td>-0.034926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sup_evil</td>\n",
       "      <td>-0.088487</td>\n",
       "      <td>0.001960</td>\n",
       "      <td>0.166049</td>\n",
       "      <td>-0.034814</td>\n",
       "      <td>-0.062130</td>\n",
       "      <td>0.118431</td>\n",
       "      <td>-0.130257</td>\n",
       "      <td>0.158638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>literalism</td>\n",
       "      <td>0.039011</td>\n",
       "      <td>-0.002783</td>\n",
       "      <td>0.062607</td>\n",
       "      <td>-0.000764</td>\n",
       "      <td>0.011411</td>\n",
       "      <td>-0.068415</td>\n",
       "      <td>0.059262</td>\n",
       "      <td>-0.062846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>inerrancy</td>\n",
       "      <td>0.023725</td>\n",
       "      <td>0.023408</td>\n",
       "      <td>0.018420</td>\n",
       "      <td>0.012120</td>\n",
       "      <td>0.015164</td>\n",
       "      <td>-0.023826</td>\n",
       "      <td>0.002892</td>\n",
       "      <td>-0.081661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>conservative_prot</td>\n",
       "      <td>0.085970</td>\n",
       "      <td>0.061236</td>\n",
       "      <td>0.030007</td>\n",
       "      <td>-0.006732</td>\n",
       "      <td>0.021394</td>\n",
       "      <td>0.048972</td>\n",
       "      <td>0.009260</td>\n",
       "      <td>-0.032801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mainline_prot</td>\n",
       "      <td>0.033849</td>\n",
       "      <td>0.050771</td>\n",
       "      <td>0.092773</td>\n",
       "      <td>0.046370</td>\n",
       "      <td>0.006725</td>\n",
       "      <td>-0.005994</td>\n",
       "      <td>-0.015628</td>\n",
       "      <td>0.004525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>black_prot</td>\n",
       "      <td>-0.008975</td>\n",
       "      <td>0.012579</td>\n",
       "      <td>0.015983</td>\n",
       "      <td>-0.028838</td>\n",
       "      <td>-0.037820</td>\n",
       "      <td>0.030368</td>\n",
       "      <td>0.078792</td>\n",
       "      <td>-0.095384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>catholic</td>\n",
       "      <td>0.085008</td>\n",
       "      <td>0.064959</td>\n",
       "      <td>0.062993</td>\n",
       "      <td>0.063320</td>\n",
       "      <td>0.042974</td>\n",
       "      <td>-0.049415</td>\n",
       "      <td>0.046621</td>\n",
       "      <td>-0.078550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>jewish_other</td>\n",
       "      <td>0.034958</td>\n",
       "      <td>0.022328</td>\n",
       "      <td>0.048840</td>\n",
       "      <td>0.016489</td>\n",
       "      <td>0.021123</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.047244</td>\n",
       "      <td>-0.040084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>female</td>\n",
       "      <td>0.235938</td>\n",
       "      <td>0.070740</td>\n",
       "      <td>-0.010644</td>\n",
       "      <td>0.094916</td>\n",
       "      <td>0.205102</td>\n",
       "      <td>-0.142201</td>\n",
       "      <td>0.057415</td>\n",
       "      <td>-0.149387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>non_hisp_black</td>\n",
       "      <td>0.074898</td>\n",
       "      <td>0.036020</td>\n",
       "      <td>0.052445</td>\n",
       "      <td>0.061404</td>\n",
       "      <td>0.067322</td>\n",
       "      <td>-0.093579</td>\n",
       "      <td>0.029138</td>\n",
       "      <td>-0.001537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>other_race</td>\n",
       "      <td>0.006113</td>\n",
       "      <td>-0.009032</td>\n",
       "      <td>0.007128</td>\n",
       "      <td>-0.011565</td>\n",
       "      <td>0.021457</td>\n",
       "      <td>-0.010114</td>\n",
       "      <td>0.075993</td>\n",
       "      <td>-0.042826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>less_than_highschool</td>\n",
       "      <td>-0.031114</td>\n",
       "      <td>-0.074304</td>\n",
       "      <td>-0.039908</td>\n",
       "      <td>-0.014053</td>\n",
       "      <td>0.032877</td>\n",
       "      <td>-0.015577</td>\n",
       "      <td>0.012066</td>\n",
       "      <td>-0.032005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>some_college</td>\n",
       "      <td>-0.012841</td>\n",
       "      <td>-0.045208</td>\n",
       "      <td>-0.069980</td>\n",
       "      <td>-0.021893</td>\n",
       "      <td>0.018368</td>\n",
       "      <td>-0.048423</td>\n",
       "      <td>-0.031095</td>\n",
       "      <td>0.024987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>college</td>\n",
       "      <td>-0.046043</td>\n",
       "      <td>-0.026256</td>\n",
       "      <td>-0.016507</td>\n",
       "      <td>-0.002742</td>\n",
       "      <td>-0.000970</td>\n",
       "      <td>-0.024610</td>\n",
       "      <td>-0.004164</td>\n",
       "      <td>0.035431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>postgraduate</td>\n",
       "      <td>0.048554</td>\n",
       "      <td>-0.011321</td>\n",
       "      <td>-0.122606</td>\n",
       "      <td>0.020715</td>\n",
       "      <td>0.085012</td>\n",
       "      <td>-0.090547</td>\n",
       "      <td>0.045437</td>\n",
       "      <td>-0.061933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>married</td>\n",
       "      <td>0.006832</td>\n",
       "      <td>-0.025010</td>\n",
       "      <td>-0.090466</td>\n",
       "      <td>-0.028229</td>\n",
       "      <td>-0.026049</td>\n",
       "      <td>-0.012061</td>\n",
       "      <td>0.017466</td>\n",
       "      <td>-0.018662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>kids_18</td>\n",
       "      <td>-0.005582</td>\n",
       "      <td>-0.040762</td>\n",
       "      <td>-0.003682</td>\n",
       "      <td>-0.048447</td>\n",
       "      <td>-0.025478</td>\n",
       "      <td>0.048726</td>\n",
       "      <td>0.039083</td>\n",
       "      <td>-0.028520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>urban_area</td>\n",
       "      <td>0.069959</td>\n",
       "      <td>0.062874</td>\n",
       "      <td>-0.039569</td>\n",
       "      <td>0.049525</td>\n",
       "      <td>0.066407</td>\n",
       "      <td>-0.101759</td>\n",
       "      <td>0.065788</td>\n",
       "      <td>-0.064365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>south</td>\n",
       "      <td>-0.067100</td>\n",
       "      <td>0.004259</td>\n",
       "      <td>0.087196</td>\n",
       "      <td>0.028226</td>\n",
       "      <td>-0.023184</td>\n",
       "      <td>-0.016662</td>\n",
       "      <td>-0.061292</td>\n",
       "      <td>0.082688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                variable  semi_auto_weapons_ban  gun_safety_programs  \\\n",
       "0              Intercept              -0.004226             0.006502   \n",
       "1             attendance               0.042500            -0.043259   \n",
       "2        political_ideol               0.314903             0.137067   \n",
       "3               hispanic               0.052261             0.021673   \n",
       "4                    age               0.164573             0.055700   \n",
       "5                 income               0.046941             0.010207   \n",
       "6               sup_evil              -0.088487             0.001960   \n",
       "7             literalism               0.039011            -0.002783   \n",
       "8              inerrancy               0.023725             0.023408   \n",
       "9      conservative_prot               0.085970             0.061236   \n",
       "10         mainline_prot               0.033849             0.050771   \n",
       "11            black_prot              -0.008975             0.012579   \n",
       "12              catholic               0.085008             0.064959   \n",
       "13          jewish_other               0.034958             0.022328   \n",
       "14                female               0.235938             0.070740   \n",
       "15        non_hisp_black               0.074898             0.036020   \n",
       "16            other_race               0.006113            -0.009032   \n",
       "17  less_than_highschool              -0.031114            -0.074304   \n",
       "18          some_college              -0.012841            -0.045208   \n",
       "19               college              -0.046043            -0.026256   \n",
       "20          postgraduate               0.048554            -0.011321   \n",
       "21               married               0.006832            -0.025010   \n",
       "22               kids_18              -0.005582            -0.040762   \n",
       "23            urban_area               0.069959             0.062874   \n",
       "24                 south              -0.067100             0.004259   \n",
       "\n",
       "    armed_security_in_schools  mental_health_screening  \\\n",
       "0                    0.001915                -0.002288   \n",
       "1                   -0.124058                -0.000048   \n",
       "2                   -0.159465                 0.161509   \n",
       "3                    0.010026                 0.074931   \n",
       "4                   -0.041469                 0.061388   \n",
       "5                   -0.117202                -0.011874   \n",
       "6                    0.166049                -0.034814   \n",
       "7                    0.062607                -0.000764   \n",
       "8                    0.018420                 0.012120   \n",
       "9                    0.030007                -0.006732   \n",
       "10                   0.092773                 0.046370   \n",
       "11                   0.015983                -0.028838   \n",
       "12                   0.062993                 0.063320   \n",
       "13                   0.048840                 0.016489   \n",
       "14                  -0.010644                 0.094916   \n",
       "15                   0.052445                 0.061404   \n",
       "16                   0.007128                -0.011565   \n",
       "17                  -0.039908                -0.014053   \n",
       "18                  -0.069980                -0.021893   \n",
       "19                  -0.016507                -0.002742   \n",
       "20                  -0.122606                 0.020715   \n",
       "21                  -0.090466                -0.028229   \n",
       "22                  -0.003682                -0.048447   \n",
       "23                  -0.039569                 0.049525   \n",
       "24                   0.087196                 0.028226   \n",
       "\n",
       "    high_capacity_magazine_ban  teachers_with_guns  \\\n",
       "0                    -0.007319            0.010464   \n",
       "1                     0.040174           -0.071628   \n",
       "2                     0.289149           -0.238977   \n",
       "3                     0.033354           -0.051556   \n",
       "4                     0.199559           -0.086775   \n",
       "5                    -0.011916           -0.001310   \n",
       "6                    -0.062130            0.118431   \n",
       "7                     0.011411           -0.068415   \n",
       "8                     0.015164           -0.023826   \n",
       "9                     0.021394            0.048972   \n",
       "10                    0.006725           -0.005994   \n",
       "11                   -0.037820            0.030368   \n",
       "12                    0.042974           -0.049415   \n",
       "13                    0.021123            0.000315   \n",
       "14                    0.205102           -0.142201   \n",
       "15                    0.067322           -0.093579   \n",
       "16                    0.021457           -0.010114   \n",
       "17                    0.032877           -0.015577   \n",
       "18                    0.018368           -0.048423   \n",
       "19                   -0.000970           -0.024610   \n",
       "20                    0.085012           -0.090547   \n",
       "21                   -0.026049           -0.012061   \n",
       "22                   -0.025478            0.048726   \n",
       "23                    0.066407           -0.101759   \n",
       "24                   -0.023184           -0.016662   \n",
       "\n",
       "    civilian_gun_possesion_ban  support_for_concealed_carry  \n",
       "0                    -0.029946                     0.010309  \n",
       "1                     0.063930                    -0.037588  \n",
       "2                     0.216531                    -0.263410  \n",
       "3                     0.095999                    -0.047126  \n",
       "4                     0.080553                    -0.056020  \n",
       "5                    -0.022987                    -0.034926  \n",
       "6                    -0.130257                     0.158638  \n",
       "7                     0.059262                    -0.062846  \n",
       "8                     0.002892                    -0.081661  \n",
       "9                     0.009260                    -0.032801  \n",
       "10                   -0.015628                     0.004525  \n",
       "11                    0.078792                    -0.095384  \n",
       "12                    0.046621                    -0.078550  \n",
       "13                    0.047244                    -0.040084  \n",
       "14                    0.057415                    -0.149387  \n",
       "15                    0.029138                    -0.001537  \n",
       "16                    0.075993                    -0.042826  \n",
       "17                    0.012066                    -0.032005  \n",
       "18                   -0.031095                     0.024987  \n",
       "19                   -0.004164                     0.035431  \n",
       "20                    0.045437                    -0.061933  \n",
       "21                    0.017466                    -0.018662  \n",
       "22                    0.039083                    -0.028520  \n",
       "23                    0.065788                    -0.064365  \n",
       "24                   -0.061292                     0.082688  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2_linear_scaled = ol1.params.reset_index()\n",
    "model2_linear_scaled.columns = ['variable','semi_auto_weapons_ban']\n",
    "model2_linear_scaled['gun_safety_programs'] = ol2.params.reset_index().loc[:,0]\n",
    "model2_linear_scaled['armed_security_in_schools'] = ol3.params.reset_index().loc[:,0]\n",
    "model2_linear_scaled['mental_health_screening'] = ol4.params.reset_index().loc[:,0]\n",
    "model2_linear_scaled['high_capacity_magazine_ban'] = ol5.params.reset_index().loc[:,0]\n",
    "model2_linear_scaled['teachers_with_guns'] = ol6.params.reset_index().loc[:,0]\n",
    "model2_linear_scaled['civilian_gun_possesion_ban'] = ol7.params.reset_index().loc[:,0]\n",
    "model2_linear_scaled['support_for_concealed_carry'] = ol8.params.reset_index().loc[:,0]\n",
    "model2_linear_scaled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6d667e6",
   "metadata": {},
   "source": [
    "#### ---- Unstandardized Values ----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de3d6024",
   "metadata": {},
   "source": [
    "* We will use the unstandardized coefficients to interpret our results\n",
    "* We perform linear regression again to the unstandardized values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ec544788",
   "metadata": {},
   "outputs": [],
   "source": [
    "ol1 = smf.ols(\"semi_auto_weapons_ban ~\" +' + '.join(all_columns), data=model).fit()\n",
    "ol2 = smf.ols(\"gun_safety_programs ~\" +' + '.join(all_columns), data=model).fit()\n",
    "ol3 = smf.ols(\"armed_security_in_schools ~\" +' + '.join(all_columns), data=model).fit()\n",
    "ol4 = smf.ols(\"mental_health_screening ~\" +' + '.join(all_columns), data=model).fit()\n",
    "ol5 = smf.ols(\"high_capacity_magazine_ban ~\" +' + '.join(all_columns), data=model).fit()\n",
    "ol6 = smf.ols(\"teachers_with_guns ~\" +' + '.join(all_columns), data=model).fit()\n",
    "ol7 = smf.ols(\"civilian_gun_possesion_ban ~\" +' + '.join(all_columns), data=model).fit()\n",
    "ol8 = smf.ols(\"support_for_concealed_carry ~\" +' + '.join(all_columns), data=model).fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b6a9b14f",
   "metadata": {},
   "source": [
    "* Below we can see the coefficients of each gun policy and predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e7a17c73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>variable</th>\n",
       "      <th>semi_auto_weapons_ban</th>\n",
       "      <th>gun_safety_programs</th>\n",
       "      <th>armed_security_in_schools</th>\n",
       "      <th>mental_health_screening</th>\n",
       "      <th>high_capacity_magazine_ban</th>\n",
       "      <th>teachers_with_guns</th>\n",
       "      <th>civilian_gun_possesion_ban</th>\n",
       "      <th>support_for_concealed_carry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Intercept</td>\n",
       "      <td>-0.181095</td>\n",
       "      <td>0.759841</td>\n",
       "      <td>0.867490</td>\n",
       "      <td>0.712593</td>\n",
       "      <td>-0.069789</td>\n",
       "      <td>0.786035</td>\n",
       "      <td>-0.048873</td>\n",
       "      <td>1.021814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>attendance</td>\n",
       "      <td>0.007003</td>\n",
       "      <td>-0.003829</td>\n",
       "      <td>-0.020521</td>\n",
       "      <td>-0.000005</td>\n",
       "      <td>0.006438</td>\n",
       "      <td>-0.011138</td>\n",
       "      <td>0.009900</td>\n",
       "      <td>-0.006368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>political_ideol</td>\n",
       "      <td>0.097887</td>\n",
       "      <td>0.022889</td>\n",
       "      <td>-0.049764</td>\n",
       "      <td>0.030031</td>\n",
       "      <td>0.087422</td>\n",
       "      <td>-0.070104</td>\n",
       "      <td>0.063261</td>\n",
       "      <td>-0.084187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hispanic</td>\n",
       "      <td>0.080258</td>\n",
       "      <td>0.017880</td>\n",
       "      <td>0.015458</td>\n",
       "      <td>0.068832</td>\n",
       "      <td>0.049821</td>\n",
       "      <td>-0.074718</td>\n",
       "      <td>0.138560</td>\n",
       "      <td>-0.074410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>age</td>\n",
       "      <td>0.004673</td>\n",
       "      <td>0.000850</td>\n",
       "      <td>-0.001182</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>0.005511</td>\n",
       "      <td>-0.002325</td>\n",
       "      <td>0.002150</td>\n",
       "      <td>-0.001635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>income</td>\n",
       "      <td>0.013280</td>\n",
       "      <td>0.001551</td>\n",
       "      <td>-0.033288</td>\n",
       "      <td>-0.002009</td>\n",
       "      <td>-0.003279</td>\n",
       "      <td>-0.000350</td>\n",
       "      <td>-0.006112</td>\n",
       "      <td>-0.010159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>sup_evil</td>\n",
       "      <td>-0.039593</td>\n",
       "      <td>0.000471</td>\n",
       "      <td>0.074588</td>\n",
       "      <td>-0.009318</td>\n",
       "      <td>-0.027039</td>\n",
       "      <td>0.050008</td>\n",
       "      <td>-0.054777</td>\n",
       "      <td>0.072980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>literalism</td>\n",
       "      <td>0.045066</td>\n",
       "      <td>-0.001727</td>\n",
       "      <td>0.072608</td>\n",
       "      <td>-0.000528</td>\n",
       "      <td>0.012821</td>\n",
       "      <td>-0.074586</td>\n",
       "      <td>0.064344</td>\n",
       "      <td>-0.074646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>inerrancy</td>\n",
       "      <td>0.024310</td>\n",
       "      <td>0.012885</td>\n",
       "      <td>0.018948</td>\n",
       "      <td>0.007428</td>\n",
       "      <td>0.015113</td>\n",
       "      <td>-0.023039</td>\n",
       "      <td>0.002785</td>\n",
       "      <td>-0.086032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>conservative_prot</td>\n",
       "      <td>0.093777</td>\n",
       "      <td>0.035884</td>\n",
       "      <td>0.032861</td>\n",
       "      <td>-0.004392</td>\n",
       "      <td>0.022698</td>\n",
       "      <td>0.050412</td>\n",
       "      <td>0.009493</td>\n",
       "      <td>-0.036787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>mainline_prot</td>\n",
       "      <td>0.044709</td>\n",
       "      <td>0.036025</td>\n",
       "      <td>0.123016</td>\n",
       "      <td>0.036635</td>\n",
       "      <td>0.008640</td>\n",
       "      <td>-0.007471</td>\n",
       "      <td>-0.019400</td>\n",
       "      <td>0.006144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>black_prot</td>\n",
       "      <td>-0.017289</td>\n",
       "      <td>0.013017</td>\n",
       "      <td>0.030907</td>\n",
       "      <td>-0.033227</td>\n",
       "      <td>-0.070857</td>\n",
       "      <td>0.055203</td>\n",
       "      <td>0.142644</td>\n",
       "      <td>-0.188906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>catholic</td>\n",
       "      <td>0.096882</td>\n",
       "      <td>0.039771</td>\n",
       "      <td>0.072073</td>\n",
       "      <td>0.043165</td>\n",
       "      <td>0.047637</td>\n",
       "      <td>-0.053146</td>\n",
       "      <td>0.049937</td>\n",
       "      <td>-0.092042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>jewish_other</td>\n",
       "      <td>0.063350</td>\n",
       "      <td>0.021737</td>\n",
       "      <td>0.088854</td>\n",
       "      <td>0.017873</td>\n",
       "      <td>0.037231</td>\n",
       "      <td>0.000538</td>\n",
       "      <td>0.080465</td>\n",
       "      <td>-0.074686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>female</td>\n",
       "      <td>0.229912</td>\n",
       "      <td>0.037032</td>\n",
       "      <td>-0.010413</td>\n",
       "      <td>0.055325</td>\n",
       "      <td>0.194395</td>\n",
       "      <td>-0.130769</td>\n",
       "      <td>0.052584</td>\n",
       "      <td>-0.149672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>non_hisp_black</td>\n",
       "      <td>0.113893</td>\n",
       "      <td>0.029425</td>\n",
       "      <td>0.080062</td>\n",
       "      <td>0.055852</td>\n",
       "      <td>0.099570</td>\n",
       "      <td>-0.134289</td>\n",
       "      <td>0.041644</td>\n",
       "      <td>-0.002403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>other_race</td>\n",
       "      <td>0.011077</td>\n",
       "      <td>-0.008792</td>\n",
       "      <td>0.012967</td>\n",
       "      <td>-0.012536</td>\n",
       "      <td>0.037820</td>\n",
       "      <td>-0.017297</td>\n",
       "      <td>0.129431</td>\n",
       "      <td>-0.079795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>less_than_highschool</td>\n",
       "      <td>-0.065031</td>\n",
       "      <td>-0.083429</td>\n",
       "      <td>-0.083738</td>\n",
       "      <td>-0.017569</td>\n",
       "      <td>0.066836</td>\n",
       "      <td>-0.030725</td>\n",
       "      <td>0.023701</td>\n",
       "      <td>-0.068777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>some_college</td>\n",
       "      <td>-0.013444</td>\n",
       "      <td>-0.025428</td>\n",
       "      <td>-0.073555</td>\n",
       "      <td>-0.013711</td>\n",
       "      <td>0.018704</td>\n",
       "      <td>-0.047844</td>\n",
       "      <td>-0.030598</td>\n",
       "      <td>0.026898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>college</td>\n",
       "      <td>-0.051361</td>\n",
       "      <td>-0.015734</td>\n",
       "      <td>-0.018486</td>\n",
       "      <td>-0.001830</td>\n",
       "      <td>-0.001052</td>\n",
       "      <td>-0.025907</td>\n",
       "      <td>-0.004365</td>\n",
       "      <td>0.040636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>postgraduate</td>\n",
       "      <td>0.057272</td>\n",
       "      <td>-0.007174</td>\n",
       "      <td>-0.145188</td>\n",
       "      <td>0.014616</td>\n",
       "      <td>0.097532</td>\n",
       "      <td>-0.100793</td>\n",
       "      <td>0.050372</td>\n",
       "      <td>-0.075111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>married</td>\n",
       "      <td>0.006601</td>\n",
       "      <td>-0.012981</td>\n",
       "      <td>-0.087749</td>\n",
       "      <td>-0.016314</td>\n",
       "      <td>-0.024479</td>\n",
       "      <td>-0.010997</td>\n",
       "      <td>0.015860</td>\n",
       "      <td>-0.018538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>kids_18</td>\n",
       "      <td>-0.006191</td>\n",
       "      <td>-0.024285</td>\n",
       "      <td>-0.004100</td>\n",
       "      <td>-0.032137</td>\n",
       "      <td>-0.027482</td>\n",
       "      <td>0.050996</td>\n",
       "      <td>0.040736</td>\n",
       "      <td>-0.032519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>urban_area</td>\n",
       "      <td>0.067599</td>\n",
       "      <td>0.032637</td>\n",
       "      <td>-0.038384</td>\n",
       "      <td>0.028624</td>\n",
       "      <td>0.062410</td>\n",
       "      <td>-0.092791</td>\n",
       "      <td>0.059745</td>\n",
       "      <td>-0.063944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>south</td>\n",
       "      <td>-0.066532</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>0.086796</td>\n",
       "      <td>0.016741</td>\n",
       "      <td>-0.022359</td>\n",
       "      <td>-0.015591</td>\n",
       "      <td>-0.057118</td>\n",
       "      <td>0.084297</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                variable  semi_auto_weapons_ban  gun_safety_programs  \\\n",
       "0              Intercept              -0.181095             0.759841   \n",
       "1             attendance               0.007003            -0.003829   \n",
       "2        political_ideol               0.097887             0.022889   \n",
       "3               hispanic               0.080258             0.017880   \n",
       "4                    age               0.004673             0.000850   \n",
       "5                 income               0.013280             0.001551   \n",
       "6               sup_evil              -0.039593             0.000471   \n",
       "7             literalism               0.045066            -0.001727   \n",
       "8              inerrancy               0.024310             0.012885   \n",
       "9      conservative_prot               0.093777             0.035884   \n",
       "10         mainline_prot               0.044709             0.036025   \n",
       "11            black_prot              -0.017289             0.013017   \n",
       "12              catholic               0.096882             0.039771   \n",
       "13          jewish_other               0.063350             0.021737   \n",
       "14                female               0.229912             0.037032   \n",
       "15        non_hisp_black               0.113893             0.029425   \n",
       "16            other_race               0.011077            -0.008792   \n",
       "17  less_than_highschool              -0.065031            -0.083429   \n",
       "18          some_college              -0.013444            -0.025428   \n",
       "19               college              -0.051361            -0.015734   \n",
       "20          postgraduate               0.057272            -0.007174   \n",
       "21               married               0.006601            -0.012981   \n",
       "22               kids_18              -0.006191            -0.024285   \n",
       "23            urban_area               0.067599             0.032637   \n",
       "24                 south              -0.066532             0.002268   \n",
       "\n",
       "    armed_security_in_schools  mental_health_screening  \\\n",
       "0                    0.867490                 0.712593   \n",
       "1                   -0.020521                -0.000005   \n",
       "2                   -0.049764                 0.030031   \n",
       "3                    0.015458                 0.068832   \n",
       "4                   -0.001182                 0.001043   \n",
       "5                   -0.033288                -0.002009   \n",
       "6                    0.074588                -0.009318   \n",
       "7                    0.072608                -0.000528   \n",
       "8                    0.018948                 0.007428   \n",
       "9                    0.032861                -0.004392   \n",
       "10                   0.123016                 0.036635   \n",
       "11                   0.030907                -0.033227   \n",
       "12                   0.072073                 0.043165   \n",
       "13                   0.088854                 0.017873   \n",
       "14                  -0.010413                 0.055325   \n",
       "15                   0.080062                 0.055852   \n",
       "16                   0.012967                -0.012536   \n",
       "17                  -0.083738                -0.017569   \n",
       "18                  -0.073555                -0.013711   \n",
       "19                  -0.018486                -0.001830   \n",
       "20                  -0.145188                 0.014616   \n",
       "21                  -0.087749                -0.016314   \n",
       "22                  -0.004100                -0.032137   \n",
       "23                  -0.038384                 0.028624   \n",
       "24                   0.086796                 0.016741   \n",
       "\n",
       "    high_capacity_magazine_ban  teachers_with_guns  \\\n",
       "0                    -0.069789            0.786035   \n",
       "1                     0.006438           -0.011138   \n",
       "2                     0.087422           -0.070104   \n",
       "3                     0.049821           -0.074718   \n",
       "4                     0.005511           -0.002325   \n",
       "5                    -0.003279           -0.000350   \n",
       "6                    -0.027039            0.050008   \n",
       "7                     0.012821           -0.074586   \n",
       "8                     0.015113           -0.023039   \n",
       "9                     0.022698            0.050412   \n",
       "10                    0.008640           -0.007471   \n",
       "11                   -0.070857            0.055203   \n",
       "12                    0.047637           -0.053146   \n",
       "13                    0.037231            0.000538   \n",
       "14                    0.194395           -0.130769   \n",
       "15                    0.099570           -0.134289   \n",
       "16                    0.037820           -0.017297   \n",
       "17                    0.066836           -0.030725   \n",
       "18                    0.018704           -0.047844   \n",
       "19                   -0.001052           -0.025907   \n",
       "20                    0.097532           -0.100793   \n",
       "21                   -0.024479           -0.010997   \n",
       "22                   -0.027482            0.050996   \n",
       "23                    0.062410           -0.092791   \n",
       "24                   -0.022359           -0.015591   \n",
       "\n",
       "    civilian_gun_possesion_ban  support_for_concealed_carry  \n",
       "0                    -0.048873                     1.021814  \n",
       "1                     0.009900                    -0.006368  \n",
       "2                     0.063261                    -0.084187  \n",
       "3                     0.138560                    -0.074410  \n",
       "4                     0.002150                    -0.001635  \n",
       "5                    -0.006112                    -0.010159  \n",
       "6                    -0.054777                     0.072980  \n",
       "7                     0.064344                    -0.074646  \n",
       "8                     0.002785                    -0.086032  \n",
       "9                     0.009493                    -0.036787  \n",
       "10                   -0.019400                     0.006144  \n",
       "11                    0.142644                    -0.188906  \n",
       "12                    0.049937                    -0.092042  \n",
       "13                    0.080465                    -0.074686  \n",
       "14                    0.052584                    -0.149672  \n",
       "15                    0.041644                    -0.002403  \n",
       "16                    0.129431                    -0.079795  \n",
       "17                    0.023701                    -0.068777  \n",
       "18                   -0.030598                     0.026898  \n",
       "19                   -0.004365                     0.040636  \n",
       "20                    0.050372                    -0.075111  \n",
       "21                    0.015860                    -0.018538  \n",
       "22                    0.040736                    -0.032519  \n",
       "23                    0.059745                    -0.063944  \n",
       "24                   -0.057118                     0.084297  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model2_linear = ol1.params.reset_index()\n",
    "model2_linear.columns = ['variable','semi_auto_weapons_ban']\n",
    "model2_linear['gun_safety_programs'] = ol2.params.reset_index().loc[:,0]\n",
    "model2_linear['armed_security_in_schools'] = ol3.params.reset_index().loc[:,0]\n",
    "model2_linear['mental_health_screening'] = ol4.params.reset_index().loc[:,0]\n",
    "model2_linear['high_capacity_magazine_ban'] = ol5.params.reset_index().loc[:,0]\n",
    "model2_linear['teachers_with_guns'] = ol6.params.reset_index().loc[:,0]\n",
    "model2_linear['civilian_gun_possesion_ban'] = ol7.params.reset_index().loc[:,0]\n",
    "model2_linear['support_for_concealed_carry'] = ol8.params.reset_index().loc[:,0]\n",
    "model2_linear"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f99f0554",
   "metadata": {},
   "source": [
    "***\n",
    "At this point, we are able to evaluate another statement from the research paper of The Economist:  \n",
    "<span style=\"font-size:17px\"> <i>The effect of belief in supernatural evil on support for the right to carry concealed guns was roughly the same as having conservative politics or not possessing a college degree, but smaller than the effect of gender.</i> </span>\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c49232da",
   "metadata": {},
   "source": [
    "By observing the coefficients we found, we generally see that the effect of the belief in supernatural evil varies among the different gun policies. However, it is similar in range with that of the level of education and political opinions. For example, we see that regarding the support for concealed carry, the effect of belief in supernatural evil (0.07) is very close with the one of political ideology (0.08) and having a college degree (0.04). However, it is much smaller than the effect of gender (0.15).  \n",
    "Gender's coefficients vary with relatively big differences in its values. For example, the support for the semi automatic weapons ban is positively related with the gender with a coefficient of 0.23. Gender is also positively related with the existence of gun safety programs, but the coefficient here is almost 0.035, much lower compared to the previous gun policy."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5459302b",
   "metadata": {},
   "source": [
    "We also notice that the level of education is negatively related with several gun policies. Those are:  \n",
    "* Existence of gun safety programs\n",
    "* Armed security in schools\n",
    "* More teachers with guns\n",
    "* Support for concealed carry laws  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3907db48",
   "metadata": {},
   "source": [
    "### Q5: Compare with Decision Trees / Random Forest Estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd9337d8",
   "metadata": {},
   "source": [
    "* After having worked with both logistic and linear regression on our data, we will now work with decision trees and check our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "2a5a9849",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn import tree\n",
    "from io import StringIO\n",
    "from IPython.display import Image\n",
    "from sklearn.model_selection import cross_val_score, KFold\n",
    "from scipy.stats import sem\n",
    "import pydotplus\n",
    "\n",
    "from sklearn.ensemble import BaggingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa9f3bed",
   "metadata": {},
   "source": [
    "* First of all, we gather all the gun policy answers as well as the predictors in the same dataframe (`tree_data`)\n",
    "* In `xs` we save all the variables that will act as the features of the tree (predictors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "03d68680",
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_data = dependent.merge(total_independents, left_index = True, right_index = True)\n",
    "\n",
    "xs = tree_data.dropna().iloc[:,8:].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90c6441a",
   "metadata": {},
   "source": [
    "**Semi auto weapons ban**  \n",
    "* In `semi_auto_y` we put all the answers for the question regarding the support for a semi auto weapons ban  \n",
    "* Then we try different `max_depth` values and run k-fold validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b872ad5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth =  1 [0.08282467 0.03987328 0.02351024 0.04665327 0.08911829] 0.05639595075073707 0.012685852349559714\n",
      "max_depth =  2 [0.1923667  0.18098301 0.06502353 0.07548888 0.18193803] 0.13916003079640016 0.028249162966663688\n",
      "max_depth =  3 [0.22811468 0.17812596 0.0804343  0.08295459 0.15483928] 0.14489376387434902 0.02839057678021838\n",
      "max_depth =  4 [0.21520814 0.15049563 0.0776419  0.09917226 0.159353  ] 0.14037418443224978 0.024182325913322603\n",
      "max_depth =  5 [0.2436141  0.12654458 0.11185932 0.05836359 0.12887246] 0.1338508106878078 0.03025565302870142\n",
      "max_depth =  6 [ 0.16682787  0.03822039  0.00023284 -0.07124422  0.00422956] 0.027653288725860703 0.039091238319475696\n",
      "max_depth =  7 [ 0.02275542 -0.12776264  0.00746288 -0.16981602 -0.13394868] -0.0802618101754619 0.03966530969266155\n",
      "max_depth =  8 [-0.1222714  -0.22837263 -0.12069234 -0.19014982 -0.34895681] -0.202088599197828 0.042071245286664394\n",
      "max_depth =  9 [-0.24993897 -0.2850282  -0.26236736 -0.28128597 -0.53704395] -0.32313288875009244 0.05385669496109876\n",
      "max_depth =  10 [-0.28841973 -0.24546297 -0.33428443 -0.29164774 -0.63696994] -0.3593569611396102 0.0708117456515638\n",
      "max_depth =  11 [-0.46386893 -0.35870981 -0.49976656 -0.46589311 -0.72149956] -0.5019475961879633 0.05978076459631561\n"
     ]
    }
   ],
   "source": [
    "semi_auto_y = tree_data.dropna().loc[:,'semi_auto_weapons_ban'].transpose()\n",
    "all_depths = []\n",
    "all_mean_scores = []\n",
    "\n",
    "for max_depth in range(1, 12):\n",
    "    all_depths.append(max_depth)\n",
    "    simple_tree = DecisionTreeRegressor(max_depth=max_depth)\n",
    "    cv = KFold(n_splits=5, shuffle=True, random_state=13)\n",
    "    scores = cross_val_score(simple_tree, xs, semi_auto_y, cv=cv)\n",
    "    mean_score = np.mean(scores)\n",
    "    all_mean_scores.append(np.mean(scores))\n",
    "    print(\"max_depth = \", max_depth, scores, mean_score, sem(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d158e50",
   "metadata": {},
   "source": [
    "* We get the highest score for depth = 3 so we create the tree for that depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ae3d2803",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(DecisionTreeRegressor(max_depth=3).fit(xs, semi_auto_y), out_file=dot_data,\n",
    "                     special_characters=True, filled=True, rounded=True, feature_names=xs.columns)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52da2a52",
   "metadata": {},
   "source": [
    "* We see that political ideology is a feature of high significance for the semi auto weapons ban support since we can see it in two levels of the tree\n",
    "* Then we have gender and with less significance we have education, region, age and income\n",
    "* However, we will also use `BaggingClassifier` to improve our score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "efedf397",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imrpoved Score:  0.6952695396972505 0.0042008087760738305\n"
     ]
    }
   ],
   "source": [
    "semi_auto_tree = BaggingClassifier(DecisionTreeClassifier(criterion='entropy'),\n",
    "                                       n_estimators=50, \n",
    "                                       n_jobs=-1)\n",
    "bagged_cv_score = cross_val_score(semi_auto_tree, \n",
    "                                  xs, \n",
    "                                  semi_auto_y, \n",
    "                                  cv=3, scoring='accuracy')\n",
    "\n",
    "print('Imrpoved Score: ',np.mean(bagged_cv_score), sem(bagged_cv_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9235031e",
   "metadata": {},
   "source": [
    "**Gun safety programs**  \n",
    "* We will again try different depths in the case of gun safety programs policy\n",
    "* After many tries, we have max score at depth one so we create the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "baf75b33",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth =  1 [ 0.01286629  0.02077125  0.01527091 -0.00076726  0.02230429] 0.014089096196478667 0.004097218248514591\n",
      "max_depth =  2 [-0.02220497  0.02187973 -0.0182204   0.04071865  0.02000801] 0.00843620263085978 0.012260195548672614\n",
      "max_depth =  3 [-0.07564693  0.00331021 -0.10320086  0.05406606  0.00334932] -0.023624441738136027 0.028746842273301928\n",
      "max_depth =  4 [-0.10816308 -0.04662944 -0.19710505 -0.07690714 -0.13571033] -0.11290300924021227 0.025807471659920596\n",
      "max_depth =  5 [-0.20490175 -0.1470719  -0.23262155 -0.10447523 -0.20509819] -0.17883372285171767 0.02323843092108073\n",
      "max_depth =  6 [-0.30140361 -0.27381425 -0.68985509 -0.19970029 -0.30167772] -0.35329019157337804 0.08617954519796647\n",
      "max_depth =  7 [-0.48234869 -0.2161834  -0.65139698 -0.43509913 -0.31103137] -0.41921191455658774 0.074522536480616\n"
     ]
    }
   ],
   "source": [
    "gun_safety_y = tree_data.dropna().loc[:,'gun_safety_programs'].transpose()\n",
    "\n",
    "all_depths = []\n",
    "all_mean_scores = []\n",
    "for max_depth in range(1, 8):\n",
    "    all_depths.append(max_depth)\n",
    "    simple_tree = DecisionTreeRegressor(max_depth=max_depth)\n",
    "    cv = KFold(n_splits=5, shuffle=True, random_state=13)\n",
    "    scores = cross_val_score(simple_tree, xs, gun_safety_y, cv=cv)\n",
    "    mean_score = np.mean(scores)\n",
    "    all_mean_scores.append(np.mean(scores))\n",
    "    print(\"max_depth = \", max_depth, scores, mean_score, sem(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5adb3fb4",
   "metadata": {},
   "source": [
    "* We see again that political ideology plays a very significant role in support for gun safety programs since this is the tree we get for the highest score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7e38557d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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kctCHs1w5Y3mS8HZc5d8svyPIJtIS3RTyJQb0eUlQXopuoc7SQOCrg5NbUIx1MXFsOqJRLYmmujyS+WXT8dXC5qvVI0T/h8tGdTV9gHonFx+5WmW8yfjI1ZytQGVbGTNOnoV/6EYs81PeapVlc2w8XJwc8OEsV0H5KLqHdsMNAEn9HAIJ+JC6az0biJcrHRk7A+RjKsrq8qSkZ8ntAwcUx2RMSc+S0+/ho9EdHBEtVT65tv98LwT5eQu+b12h6eQJkc+VbZleunodznMWYdfaYLkAH8og0h1E/oNiWNCWpQHAVweHpMvcF8WmK8r4BYC0rg5BXV0ewv/2JUlpbPvP90JKepZSRUchWj1875sLQ9D02Z14EC5ODmwEdaB+Muir0A3wn+8lOEL6xh174eLkoPOXBEU9qLM0APjq4BAn0PelHsgtKEZKehZi9iUrLFddXR7C9/6fSmlsk66hMkcmRKuH730bIqT1vMxvnlR3PGJ7HFLSszDfc6ag8k5nX0RKehZVgzRgaDfcAOCrgwPId3GVoa4uD0F2goE4zui4RIXOTIhWj5D7lkWfaxTJ/+BU8k6pbvb+lN8QunkHMvdFCQ5Z92PCrwCA/4yw06qtFO1BnaUBQHRwcguKcfT4GfiHbmQXPi/zm8f+IGP2JSN08w54u0/FdGcndGhnhe6dO6LXmHf1fAfqwfe+DYU7dyuxOTYeeQWXkHt4j9zLhAxlKFqKpGgB/527lYiOS4T/fC8a/9KAoc7SgFClg7MgYBUASLXq+M5Oq4OsBg2Z0FAmDqaOVo+q++aioXe25BYUY3lEFIbYDFC6CF0drly/CUC1NAZFv9AxSwOArw4OgTgtsjBaV8TsS5bSq9mdeBAA4Dj6NYV5hGj1CL1vfXH9ZjlGuc7FEJsBCPLzVugoVU00KZp4ulB0GQAwsF8v3dwARSvQlqUBwFcHh+jCDJ04m7OcS1eva30h88C3pkt995/vpXRMUYhWD9/71jdH/jgFAJz3RNCkpZt9sRAA0I52wQ0a6iwNAL46OG4u41H94CHbHfef7wWPqZNR++gxRrnOxbHT57XqLIP8vNGurQX8QzcKmnzhq9UjRP9Hn5DnrSvIM2oIHSOK+lANHhU0RQ2exhJJiKIaqsHDG6rBQ6FQKHygzpJCoVB4QJ0lhUKh8IBO8FDkoGOVFIo8tGVJoVAoPKAtyyaMMc96V1XX4OfUdPya/ge7RfI90URMfHM0751D+1N+YzV1vN2n4mP3aZxbLPnq71BNncYNdZYUoyQwfAu7PhF4ETOTT7xNAHLBkKPjEhEdl4hda4OlQqtpor8jiyGtHaUIh3bDKUZHbkExG3hCMt6mt/tUpKRnsdtBFbE/5TekpGch1H8hG3OztiiL3SFFtngC0vo7svE5U9KzsDvpIJtW0XbHU8k7AYBq6hg51FlSjI4/c/MBAB5TJ0vF2/zYfRqAF9sHFUE0dbzcRFJddhLEl2xvlEwrVH+HQDV1Gg/UWRoRrQY6YFFQGOc5EpSCRCHio9XDVT7XuJui43x1dhSVp65+Dmn5dZHp/nbv3BEAkH/pitL8pEstO7ZJvks6W3X0dyShmjqNB+osjYhQ/4WIjkuUiuYDvIiHGOq/EFaWFkhJz8Io17lSrR6ip6PKYfIlOCIaznMWseOGKelZcJ6zCMER0VopXxlkH7mswyJjh6qCI5OxQ9nwduS75FioIhTp70hCJDYWer2nsjyK4UOdpRHx9hsjAECuBUe+v+P0HwDCtXqEIkRnhwt96+eQff6Hfz/JHhMa7o5Lf0cWqqnTuKDO0ogYavMyXJwc2HE0wl7xYXi7T2XHxIRq9QhFiM6OITLxzdFwcXLAnM+C2G6/IhkMLhTp70hCNXUaH3TpkApatjRHXd1zfZvBstDrPTjPWcTGrrx09TpS0rPk5FOFaPUIRYjODhf6Xo9oZWmBLSu/gjjtGBYErGLXaLq5jFf5zBTp78hiDJo6jx4/AQBYWNA4mnygzlIFHdp3wL37Vfo2g2XY4EEAwMauJJMR5DjQ+LR6ZCHd/arqGqmWHRlzVCZ7QejcsT0+nOUqNfFCJo5C/RfKpVelvyOb1hg0dUi97ty5s54tMQ6os1TB4MGDcfFSib7NYLGytMCmkC+xIGAVROPGYs5nQdgU8qXUj1KbWj2yk0mAejo7kmjaarQd0BcAcPtupdT1S8tuAYCUbhAXZEG6rP2Xr90AAPToKu08hOrvGIumzl/FVwEAtraGbaehQMcsVTD2zTdx4twFfZshxdiRwwCAbSlO+M8oznRCtXrILDHRxKmqrsHm2Hi5dEJ0dnTBoP59ANRPskhqBCUcPApAtZMiEzw/S+ifX7p6ndVDHz1sCHucr/6OJMaiqZP1Zw6GvDoYVlZW+jbFKKAtSxVMnToVy5YtQ2FJKQb1661vcwDU63mT1p23+1S5lpS6Wj3viSYiJT1LShOHq0sqRGdHF5CJLq7re7tPlRtLlN0DTyZ4FgSskpOM2LU2WOp5qqO/YyyaOuK0Y5jmpnhsmSINbVmqYMiQIRgzehS279XebLI2IK2796e9I3fOzWW8lOCX/3wv5B7ew267O3b6PGeZbi7jsWttMNvC3BTypcKJmiA/b+xaGwxv96nssU0hX2pdJlYRW1Z+hU0hX7K2ujg5YFPIl1ixxFdlXjLBI/uMTiXvlNoXDqinv2MMmjqnzl/AX5eu4KOPPtK3KUYD1eDhwdGjR/GuiwuyU39SOR5GoRgDk+cuhs3Q1xAZuVXfphgLC6mz5Mn0adPQ7Ek1foxYrm9TKBSNSDh4FPMDw1B06RKdCecPFSzjy5offkBK+h/4MSFV36ZQKGpz9cbfWBy8BsHLl1NHKRDqLHnSt29frF0bgQUBq9gdLBSKMVFVXQM3X38MGWqP+fPn69sco4M6SwH4+PjgY29veH62DOcvKA8DRqEYEtUPHuK9BV/jyXMT/JKQgObN6UIYoVBnKZD169fjTce3MeH9BUg+kqlvcygUlVy/WQ6n2b64VHoTKb/+StdVqgl1lgIxNTXF3n37MO8TH7h/GoAV67eh9tFjfZtFoXBy6PeTGOs2D+Zt2uL0mTN4+WXF+9kpyqHOUg1MTU3xww8/YMuWLVi/Yx+Gv+tJW5kUg+Jy6Q1Mm/cFpn78OZzGj0fm77/D2tpa32YZNXTpkIb8/fff+Pzzz7Fnzx4MG2yDD2a9C9G4sej6b9RuCqWhePT4CTJOnsWPCalIPpKJQQMHYcPGjXjrrbf0bVpjgK6z1BZnz57Fhg0bkPDLL/inuhq9rLujXy9rWFm2gampqb7NE8yjx0/Q0txM32Y0KI8eP0ZLc3N9myGY6pqHuHXnLgouX8Hz5wz+4+CAeZ98glmzZtGJHO1BnaW2efLkCY4fP44zZ86gpKQElZWVeP7ccOJh8uHWrVs4fvw4JkyYAEtLS32b0yCUlZXh9OnTmDhxItq0aaNvcwRhaWmJ7t27w87ODm+//TY6deqkb5MaI9RZUqS5f/8+hgwZAgcHB+zZs0ff5jQYjx8/xogRI9CuXTscPXrUKHsDFJ1Cd/BQpPHz88OzZ8+wefNmfZvSoJibmyM2NhanTp3C2rVr9W0OxQChzpLCkpSUhJ07d2Lr1q3o0KGDvs1pcOzs7BAUFISAgADk5eXp2xyKgUG74RQAQEVFBYYMGYLJkyfjf//7n77N0Rt1dXUYO3YsHj58iNOnT8PMrGlNclEUQrvhlHoWLFiA5s2bIyIiQt+m6BVTU1Ps2rULxcXFCA5WrAlOaXpQZ6kGJiYmvD7Gwr59+7B//35s376dboUD8PLLLyMsLAyrVq3CyZMnVWeQQBf1wtjqU2OFdsPVgG/FNYZHe+vWLbz66qtwc3PDli1b9G2OwcAwDJydnXH58mVkZ2fzXk7Ep24IrRekTGOoT40Y2g1XB4ZhpD6qjhsyPj4+sLKyQlhYmL5NMShMTEywfft23Lt3D1988YXqDDLI1gVjqxcUeaizbMLs3LkTYrEYMTExsLAwbHEtfWBtbY1NmzYhMjIShw4d0rc5FD1DnWUDQMacrl27BldXVwQGBkodV5RelvT0dPj6+sLExASurq5IT0+XS8OXGzduwM/PD4sWLYKjo6Pa5TR2Zs+eDTc3N3z00UeorJTXUNeUnJwcrFmzhv2fu7q68toMIFkXTExMEBgYiJycHJVpNa03TRqGojEAGGWPkpwPCAhgADBxcXFK83EdJ3llPwEBAYLtff78OTNp0iRm0KBBzMOHDwXnb2rcvXuX6d69OzN79myVaVXVBUmSk5M5/6eSdYSrTGX50tLSpK6hzXrTxFlAnaUW4OssJX8AyvLJHk9LS2Mr+P379xmGYZj79++zP4Ts7GxB9m7dupUxNTVlTpw4IShfUyY1NZUxMTFh9u7dqzSdIifG9b8mxyT/D6WlpXJpFX0vLS1lj504cYIBwPj4+LDHtF1vmjjUWWoDvs6yvLycVz7Z46RykwpPuH//PgOACQ8P521rSUkJY2Fhwfj7+/POQ6nnk08+YTp06MDcvHlTYRohzpJQXl7OZGdnM8nJyVItQdkyCSKRiP2/p6WlydULgjbrDYVZQJcOaQFVSzsUned7XFvLURiGwdtvv4179+7hzJkzMDfCcGT6pKamBvb29hg0aBAOHDigcLwZ4L/MJzAwECEhIZznZP//5HtOTg7s7e3ZdCKRCH5+fnBycuK0RRn058+bhbRlqQXAs2Wp7nEoaamourYkERERTIsWLZizZ8/ySk+R548//mBMTU2ZyMhIzvNC/h9RUVFs1zktLY3Jzs5mysvLVbYsCdnZ2Ux4eDh7XiQSSXWttVVvKAzD0G64dtCms+T6sfj4+HB2p4RQWFjItGrVivn222/VLoNSj7+/P2NhYcEUFxfLnRPihLjSki4yH2dJKC0tZccntV1vKCwL6NIhPSISiQCA3VJXVVWFDRs2yKVzc3MDAISHh+P27dvs8fT0dJiYmGDNmjVKr1NXVwcvLy/Y2tri66+/1pb5TZbg4GD069cPc+fORV1dncblFRUVAaj//4eHh6tMT5YBkXrTq1cvTiEyTesNRQZ9u+vGANRsWcbFxcl1iyS7VZIoWgIiEonkJo5kCQ0NZczNzZm8vDz1bpAiR05ODmNmZsaEhoZKHVdVFyTh+v9LfgoLCznLJDPfXJ+oqCipa2hSbyhS0G64NlDXWTJM/Q+GzG6Siq7MuZKuFUmvqsLn5eUx5ubmcj9qiuaEhoYyZmZmTE5ODntMiLNkmBfjlkD9Ep/CwkImOztbZX3Izs6WcoQBAQFMcnIy5zXUqTcUOehseGPm6dOnGDNmDMzMzHDs2DEqlaBl6urq4OjoiOrqapw5c4bGvmzc0EAajZnvvvsO+fn52LlzJ3WUOsDU1BQ7d+5ESUkJgoKC9G0ORcdQZ9lIOXfuHFauXInvv/8eAwYM0Lc5jZb+/fsjPDwcYWFhyMrK0rc5FB1Cu+GNkMePH+P1119Hp06d2JlPiu5gGAYuLi4oKipCdnY2jeDUOKHd8MbIt99+i6tXryImJoY6ygaAxL6srKzEkiVL9G0ORUdQZ9nIOHnyJMLCwrBmzRr07dtX3+Y0Gbp3744tW7YgKioKBw8e1Lc5FB1Au+GNiNraWtjb26Nv375ITU2lrUo94O7ujszMTFy4cKFJygk3Ymg3vDHx9ddf4/bt29i2bRt1lHpi06ZNaNasGXx9ffVtCkXLUGfZSMjMzMT69esRERGBnj176tucJkuHDh2wfft27N+/n1fEc4rxQLvhjYCamhoMHToUQ4cORWJior7NoQCYP38+9u7di7y8PPTo0UPf5lA0ZyF1lo0AX19fxMfHIy8vD926ddO3ORQADx48gL29Pfr370/HjxsHdMzS2Dl8+DC2bt2KTZs2UUdpQLRp0wa7du3Cb7/9hsjISH2bQ9ECtGVpxFRVVeHVV1/FG2+8gb179+rbHAoH33zzDdatW4fs7GzOMGoUo4F2w42BzMxMnDp1CgsXLqXSZYMAACAASURBVETr1q3Z4x988AEOHjyICxcuoGPHjnq0kKKIJ0+eYOTIkWjTpg1+//13do8+wzCIjY1Fp06d8M477+jZSgoPqKyEMTBz5kwGANOnTx/m+PHjDMMwTFJSEgOASUxM1LN1FFXk5uYy5ubmzMqVKxmGYZibN28ykydPptIOxgWNZ2kMdOnShQHANG/enGnWrBmzaNEipmvXrszcuXP1bRqFJ6tXr2bMzMyYsLAwpm3btkyLFi1YZ3njxg19m0dRDY1naegUFBTglVdekTpmamoKc3NzpKam4s0339STZRQhVFRUwN7eHmVlZTAxMWFVFZs1a4bt27fDy8tLvwZSVEFnww2dtLQ0NG/eXOpYXV0dnjx5AicnJwQFBeHp06d6so7Ch8OHD2Pw4MGsDo5k+6RZs2Y4dOiQvkyjCIA6SwPn0KFDnNrOz549Q11dHVauXIk+ffqgoKBAD9ZRlPH06VPMmTMHkydPRkVFBedL7dmzZzh48CCeP3+uBwspQqDO0oCpq6vD0aNHlSoI1tXV4ebNm8jIyGg4wyi8yMnJQWxsLBiGUeoM79+/j5ycnAa0jKIO1FkaMH/++SdqamoUnm/evDlatGiBrVu3wsfHpwEto/Dh9ddfR3JyMtq1a4cWLVooTNeiRQscPny4AS2jqAN1lgZMWlqawh9Z8+bN0bt3b5w7dw7z5s1rYMsofBGJRLhw4QJGjRqFZs24f27Pnj1DampqA1tGEQp1lgbMwYMH8ezZM85zHh4eyM7OxquvvtrAVlGEYm1tjYyMDHz77bdo1qyZnHgcwzA4fvw4Hj58qCcLKXygztJAqa2txcmTJ6Umd1q0aIFWrVphx44d2LlzJ9V6MSJMTU0RGBiIjIwMdOnSRa7H8PTpU2RmZurJOgofqLM0UI4dOyY1e9q8eXP069cP586dw9y5c/VoGUUTxo4diwsXLmDy5MlSkYjMzMxw5MgRPVpGUQV1lgYKGa8kP6gPPvgA2dnZsLGx0bNlFE3p0KEDkpOTsXHjRrRo0QLNmzfHkydPkJKSom/TKEqgO3gMFEtLS9TU1KB169aIiYnBe++9p2+TKDogNzcXM2bMQHFxMQCgrKyMBgs2TLQTdaiiogJHjx5FTk4O/v77b1RXV2vDuCYLwzCIj48HADg7O+t9bLJZs2Zo3749+vXrhxEjRuCNN96AmZmZXm3SBEOrr3V1dThz5gyuX7+O0aNH46WXXtKrPcaOjuqr+s7y2bNn2Lt3L6KiopCVlYVmzZrB1tYW3bp1Q9u2bTU1rMlz7949tGvXTuFyk4bk+fPnqKysxOXLl1FaWoq2bdti6tSpWLRoEYYPH65v83jB1tfIzcg6fhLNTICB3dqicxtTWCheAtmgVNU+g4W5KUyb0ajqmsAwQNUT4GrlY9y4W4O2Fm0wddp0LFq8WJP6qp6zzMjIwKefforCwkJMnz4dc+fOxdtvv42WLVuqawjFSLh16xaSk5MRFRWFc+fOYfbs2VizZg26d++ub9MUkpGRgYULfFFUeAnOth3gZtcJDn3bwry5/l9EFN1yu+YpDhfcw0/n7yKv7B/Mfm8W1vywVp36KsxZPnjwAN7e3oiLi8M777yDdevW0ejPTZiEhAQsWbIEt2/fxpo1awxucfyDBw/g/fFHiNuzF+MGdcTyyb3QpwN9oTdVUv+6hxW/leHuwzqsWRshtL7yd5ZlZWWYMmUKysrKEBMTA2dnZ/UspjQqamtrERoaipCQECxevBhhYWFyi671QVlZGVzfdcGNK0VYI+oDpwHt9G0SxQB49PQ5Nv5RhnWZZVjstxhhYeF86ys/Z1lcXAxHR0d07NgRBw4cQK9evTS3mtKoSEhIgKenJyZPnoy9e/fq1WEWFxfjzbEOaGfyCDvdX4a1lbnebKEYJql/3cOihBI4v+OCvfvj+dRX1c6yqqoKI0eOhLW1NZKSkmBpaak9iymNirNnz8LZ2Rlubm7YtGmTXmyoqqrCiOGvoQvuI+a9l2Fhrv9WLsUwyb35AO/vvoT33p+LTZu3qEquPPjvs2fPMH36dJibmyMhIYE6Sp5UVVUhOjoarq6uMDExgaurK/bs2YOqqiqt56+qqsKePXtUpjUxMVH50ZThw4dj79692LZtm17kX589e4ZpU1zRvPYuts+ijpIv1Y/q8NPZcnjtLoB10Al47S5AUl4Fqh8pDg2ojfxHCithHXRCaZqSu4+wOv06rINOwDroBH46W46KB9oJdj20RxtEzuiLbdHRvOqr0pbl+vXrERISglOnTqFv375aMbAp4Ovry/nwRSIRkpOTtZb/9u3b+PjjjyEWiznTbtu2DV26dAEAlc6Qr2182LlzJ3x8fJCfn9+g9Wb9+nVYHvAVDnz0Cnq1p11vvvgfKEHsmXK54xMGtccOD9U7xtTJn3/rASZsyQUAlAWPUZlGttwN0wfAsqV2Xob7s+/AP6UU+QWFyuqr4pblnTt3EBQUhM2bN1NHKYCcnBxERkYiICAApaWlYBgGpaWl8PHxgVgsRlFRkdbyJyUlQSwWIy4uDgzDsJ+4uDiIxWIkJSWxaSXPS36ys7MBAOHh4Vp7BnPnzoVIJMLnn3+utTJVcefOHSwLCMD377xEHaUA8m89QOyZcix27InT/30NZcFjcPq/r8FzRFccKaxEyd1HWs9/7kY1pxOUpPpRHSZsycWEQe3Zcgu+Gollk3rjSGEl0i9VanTfkrjZd8YEm474r98ipekUOsvAwEAMGzYMM2fO1JpRTYHTp08DADw9PdmJsF69erHBec+dO6e1/GTpw+zZs6XKIN9VLY24ffs27O3tERUVhYEDB6q+OQGEh4fj0KFDOHr0qFbLVUTgN1/j1W4t4WJL9dOFcL6sPrj0TLvO7ESYtZU55rzeFQCQd1Nx8Gl18m89fhOi6AvYPHOA0nIvVdSHq5s2pBNbrmVLU3i8Vl9uQl4FvxvkSeD4HirrK6ezLC0txbZt2/Ddd99pzRjJcTGxWMyOr0l2Iffs2cOm27Nnj1wZ6enp8PX1ZdMEBgYqDMcvmdbV1RXp6emC7FR3bO/atWsAgK5du0odJ4tgL168qLX8IpFIaVmqzm/YsAEikQje3t5K06lDr1698Mknn+Cbb77RetmylJaWYtv2GPi/1U1rZZIxMuDF2JrX7gIcKXzRoknKq2DTJXH8eLOuVMH/QAmbZnX6deTfesB5Pcm0XrsLkHWF3/g2KVvZRxllVU8AAJ3bSG9j6mJZvz2w8E6tVvMvP1SKHR42mDKkk9Jyz1yr34L6ei/peRLLlqYoCx7Da3hACNZW5nh/eGd87b9UYRrOMcvAwEAkJSUhN1d5U1kIxMkkJyfD1dVV6lx2djbi4+MREhIidTwuLo5tJYnFYrl8hLS0NDg5OUnZL1sWAAQEBGDFihW87FSGsgUEJD9XGmXn1MlPnonkcwLqXzru7u5ITk5W6DDT09Mxbtw4uWenTYiMb25uLoYMGaKTawD1/++fYzbgt09stVYmcTI7PGzgtVtaDO6I71AcyL+HdZk3pI5vnjmAdQJHCivl8hH2ednCoa8V+311+nW5sgBgsWNPLHVSvk9clTMEFI8JSubnSqPsnDbyKztPXkxlwWOQlFeBhLwKHCmsxLJJvTHDrjM6tdH+HtXiilo4bshWVF+5xyyTkpIwdepUrRsD1Hcz79+/D4ZhkJaWBgCwt7cHALnj7u7ubD7iKMk4HsMwOHGi/mHv37+fTZeeno6QkBAEBASw5d2/fx8BAQEICQlRKQylaGxP8mMoiEQipKWlYffu3VIt3927dyMtLU1pyzIiIgIikUhnjhIAbGxsYGNjg8TERJ1dAwASf47HpAG6WalxvqwGBV+NRFnwGOzzqnfGZLxN9vj8+EtsPuIoyXhbWfAYiL3ro9qLL95l02VdqcK6zBtY7NiTLa/gq5FY7NgT6zJvKGyJEkjZyj7GCGnBr06/jvnxl9jvyw+VYknSZd4z9UJ4uVMrDOjWVmF9lXOWlZWVyMvLw9ixY7VuDAB8+umnsLKqf6tK/lCXLFnCeZxAfvj79+9Heno6qqqqMHr0aDAMgy1bXqyRImMOkuVZWVlhyZIlAIDffvtNB3elP86fPy83Gy4Wi3H58mWFeU6ePAmxWKyT7rcsDg4OOHbsmM7Kr6ysxIW/CjCqt26Ct3w4qhs76yrZGvR9owfnccKEQe0BAAcu3kXWlSpUP6rDaz0tURY8BqHv9mPTZV35R648y5am8H2jPkzbsRJ+3fHGTM7S11nHv3nmAK1P8Ejyeo+W+D0zg/Ncc9kD+fn5AIDBgwfrxBiylEUW4tgUsWLFCojFYtbpiUQi+Pn5yTlW0v1u1457e9uSJUuUztJq2g1vSPbs2YMlS5Yo7IZbWlrKTf4A9Ut7AODNN9/UuY22trY4ePCgzson9XVQl1Y6KV9Rd0/VspWlTi/hSGEllh8qBVDvPL3HdJdzrKT7bfP9ac5ylh8qxSdvKI5vqWk33NCRfIkAgNOA+pdQQl6FynFPdRjYuTUycy5wnpNrWd69W99F6NChg9YN0QQ7Ozt2qUt4eDjEYjHGjRsHV1dXg9JcDggIAAC5ReHkOzmvjfxkmELRbPju3bvlyr99+za7NEnVC0obdOrUia1TuoCU3a6V3Htfr9h2a4Oy4DE44juUXe4ya0c+vHYXqOxaNySLHXsCgFy3lnwn53WVX1W5si8l8l1yok2bdGjdHPcq73Oek6thRKfaUMOt2dnZwc7ODm5ubiguLsa4ceMgFovZ1p6Pjw8iIyNx//59tZyBpq1G0iIvLy+Xuv7Vq1cBQOW+ek3zS8K1WL2kpAQAMHLkSN7laELz5s3x6JHytXqaQOqroYZbs+3WBrbd2uDdwR1x9d4jzNqRz05cAIDniK6IPVOOgq9GqrXIWtNW46DO9S3yOw+eSl3/+v36/5m1lfKguZrmV1VuWdVjqb39xAl7jujKmU9TTJuZ4NHjJ5znDLOGcUCWAZ08eRJAvdPgCg/n5uYGoH6d3+3bt9nj6enpMDExwZo1a3Rq5yuvvAIAiI2NZZcBXbt2jY18rspJCclPFpKTMVwCWXbFtdA8Ly8PADBo0CCBd0YRAlkGdO5G/RIYaytzzvBwosH160K3HL8ptY0v60oVrINOYOvxmzq1c8C/Tik+5w7Kqh4DqHdQB/LvAQCGWSuP0q9pfkWQJUM/nb0t1WolY5Xj/u2ONySG1XdRwty5cxEZGYkxY+TfpFFRUezfTk5O7My37PIhkUgET09PndppZ2cHkUjEeX0fHx/Y2dlJHZNdDiQkv6enJzIzMzFu3Dg5OxTdK1nUrmhMl6IdZtl3RuyZcoii5ce/Vru+mOBx6GvFznzLLh+aMKg9Zth11qmdtt3aYMKg9pzX9xzRFbbd2kgdk13uIzQ/X6ytzLF55gDMj7/EWS6ZQGtIjMZZjh49Wm49ZkBAAEaOHCm3RGbFihUYPHgwMjMz2T3WUVFRmDJlisIJJm2ybds2diuiWCyGSCSCSCTCrFmztJq/S5cuiI2NRWpqKnbv3s2m9fDwgLOzM+cwBHkeDfEcmjKv9bSUW4+52LEnhllbyP3Qlzq9hEGdW+FE6T/sHuvVrv0wyaaDTtYTyhI+pT8OFdzDkcJKHCmsxIRB7TFhUHu4DuY3gaJpfkVMGdIJL7U3x77sO4g9U44Jg9pj2pBOOpnY4YPcovTdu3fj//7v/wxmxpdi3Oi6PpHyjXnGl2I4JORWYOHPl7jqq/IQbRQKhUKphzpLCoVC4QF1lhQKhcID6iwpFAqFB9RZUigUCg+avLPUlv6MPtCl/o5kub6+vga1pbQpwydGpaFCtHok43sqi8TOV3+n+lEdkvIq1NYQ4ovRrLOkSMOlvyO5LlNSf0cVsutUZYMyR0ZGIjIyUi5gB4UihE9/uSS1p5ssZD/iO1Ru8TqX/s7S5BIcKayU0t+pePAUS5IuS5Urud4zfEp/ra1VbfItS2NFV/o7e/bsgVgsRnh4OBsPlJTr7u7ObsGkUISQ9G/w3tWu/dhwayQO6K4/pcXOhOjvkMXwm2cOkIrhSUK5HSq4p7V7oM7SSNGV/g6JVPTxxx9L7QBydnYGABw6dEhz4ylNDqKZI7mrh4Srk1WGFKK/szS5PjCM7K4e8p2c1wZac5Z89XFycnKwZs0aNh0ZZ5NEU70eyfyy6fhqd/PV8BGiC8Rlo7paP7rS3yHPWHarJPmuSnDNWOCrj5N/6wG2Hr/JpiPjYZJoqtcjmV82Hd9xN74aPkJ0gbhsVFfrZ4eHDcqCx0hFJiLPR1a8TIj+jqo94trcQ64VZ0liS0pqXYeEhMDe3l7KyYjFYtjb27MBfMkxd3d3ToEySd0d8ndOTg4CAwOlJCeU5ZdNxyeQRmBgoNT9kPsLDAxU6751AXFysvdNviuLgk6kN/z8/OTOESerKJ4mHzF6Q4fElpRs0azLvIEJW3KlnMyRwkpM2JLLBvAlx+bHX+IUKJPU3SF/5996wEojEJTll0336S+X5NLJsjr9utT9kPtbnX5drfvWNeTl47W7QEq3iHDian30eGsrc6mJm60ykZkA4P+G17c2ZZ8n+U7OawOtOEu++jgk3YkTJ9h0paX1FVHSqRHU1eshREdHS2lvBwQEQCwWK3VkQjR8+N43F5pq/ehKf8fDwwMAkJqayh6rqqrSqq64vuGrj0PSib1fZdOd/u9rAKT1dgjq6vUQfjpbLqW9vdixJ44UVip1ZEI0fPjeNxfa1Pp5tXsbLJvUGxMGted8cQjR35kwqD32edkiQaJFbh10Agl5FdjnZWt4LUu++jjECfTr1w85OTkQi8WIjo5WWK66ej2E8PBwKe1t0tpS5siEaPjwvW9doQv9HWdnZ4hEIri7u7MOuLGFc+Orj0OcQK/2LZF/6wGOFFbip7O3FRWrtl4PYdmkPlLa2/83vH41gzJHJkTDh+996xqHvlb45I0e2OFhg9Wu/TA//pLCFwIf/Z0Lfz+Qi5x+pLASV+9pN+i0VpYO8dXHARTL1HKhrl4PQXLiAngRZTwyMlKhMxOi4SPkvmXRVOtHV/o7VlZWbIi4efPmsSHfZs+ezfv/Zujw1ccBFMvUcqGuXg+hX0fp4MDEccaeKVfozIRo+Ai5b1l0pfXjOrgTliaXIPrE33J28NHfScqrwPJDpXLd+aS8CsyPvwQLM1OthXTTSsuSrz5OdHQ0QkJC4OPjg7S0NGRnZ6O8vFxJyYaNPnWBdKm/06VLF3h7e4NhGCQnJ2P27NnskqHG0B3nq4/z09lyrMu8Ac8RXbHPyxZHfIciZ+nrerRcMwxRF4hLU0eI/g4ZzlA0G57AMTasLlpdlK5KH4csZ5Fs1fGdnVaHa9euSWnWFBUVAVAuGqaOho+q++ZC1/FC1dXfIasOZO+/uLgYAGBtba1lS/WHKn0csuxEslWnC71qgqzeDNndokz0Sx0NH1X3zWmbhvFCyeoAWTvJhI2kpo429Xe0KWymlZYlX30cAnFaup44iI6OltKxiY2NBQC8/fbbCvMI0fARet/aRFf6O2SCZ9++feyxoqIidpz3jTfe0NBy/cNXH4dAnFb1ozps0aEmzk9nb0vp2MTn3AEAOPRVrIkuRMNH6H1rk2n/tvSSL75o6VU/qsPP/94juQ9AmP7Oskm9AYAdgyWQSSNyXhtopWXJVx+H7AJR9GMtKiqSG2fUlN69pR9WQECA0jFFIRo+fO9bF+hKf4dM8MybN09uYXtcXJwgdUlDha8+DtGAGbv+PGc5JXcfyY0zasrIH6TXsS527Kl0TFGIhg/f+9YFU4Z0QkJeBZYml8gtFJe9RyH6OzPsOuPE1X8wa0e+3DW1rWGklZYl0ceR7N4GBAQgOTlZatZ19uzZUk4kICAAhYWF7La7zMxMbZjDsmLFCraFRZbarFixgle+uLg4+Pj4sMeioqLk9lvzvW9dQPR34uLi2Fl5kUiEuLg4xMbGck6O8dHfIRM8sv+n7OzsRrMvnOjjSHZvFzv2xA4PG6l1eVOGdJJyIosde+LYomE44jsUAHDiqnaHkJY6vcS2hMiSmKVOL/HKt3nmAKnu6WrXfnL7ovnet67Y4WGDzTMHsM6OjAVz3eOUIZ0g9n6VvacJg9pj88wBchNdndq0wIbpA6TKJWk3TB+gVQ2jRqnBI6uYSNEfVINHNbKKiRT9QTV4KBQKRUOos6RQKBQeUGdJoVAoPGiUwX/pWCXFmKBjlcYBbVlSKBQKD4zGWRq7Vk50dLRKrRxl8NXEEXItklYyFifZMEDRjMaglaOJpo1kaDX/AyUKt1Nqop+Tf+tBgz5jo1k6ZMzLgXx9fTnjQIpEIiQnJ6vMLxv4mMCliSPkWorKzc7Ohp2dnUq7+NBUlw4Z83Ig/wMlctHLgfr1i5KBdxUhG/iYIBvsgks/R/JayvRzKh48hd3qPwFo9xnTpUN6JCcnhw1eIRlb08fHB2KxWGVLTogmjpBrkXKjoqLYMkls0MYQ4JeiHvm3HiD2TDkWO/aUiq3pOaIrjhRWKlVjBF5o7Syb1JuNr0nCq82Pv8Ru5wQ0088JP3pd4TldQZ2ljjl9uj50lqenp1RsTbI7SJVMgxBNHCHXIuXOmjWLPUa2gVJn2XQ5X1YDAJhp11kqtuac1+t30uTdrFGan0T58XitK2d4tYzi++wxdfVzth6/iVv/POF3Q1pEZ86SjK1xQQJQkHE0Pro8XOVzjWEqOs5XU0dReepq5ZCWX9eu0tvJunfvDgC4ePGi0vxCNHGEXCs5ORkMw0iVS64VFxen1KbGCBlb44IEoCDjaHx0ebjK5xpfU3Scr6aOovLU1copq6p3Qp1lur9dLM0AAIV3apXmJ11qReHV8v5+MXapjn5O1pUqLD9UymsbqLbRmbMMDw9HZGSkVOQe4EVMxfDwcFhZWQnW5VEHvpo6uoAE45B1dmR/tqqAukI0cdS9FnlRubq6Nllt8GWTeiP2TLmcxkvFg6eIPVOOZZN6w7KlqWBdHnXgq6mjC0jgCllnR8YOVQVCJg5OdoKGfJccCxWqn1Nytz6c3OaZA+R0xhsCna2zHD9+PID6Fp3kj4+06IgTkNTlGT16NID6FlLv3r3h7u6u8Q9XUlOHSEWQ0HAhISGYOXOm0skMfU8oeXh4QCwWIzU1lX0W2g5tN2zYMISHhyMzM1NhUOHGzth+9S+YrJIqqW5h1r+yDBMGdQAgrcvzWs/6UGJlVY8x8odzmB9/SeOo3JKaOiRSOAkNty7zBt617aDUUeh7QmnakE6s9AN5FopC25FgIdEn/pbSJCLHJSMRVT+qw/JDV7HYsafWIp8LRWctSzs7O4hEIrmI3bt374aPjw8bik2oLo9QhGjqGCINoYnj5OSEzz//HMnJyYiKioK7u7vO1SkNDdtubTBhUHu5yNoJeRXwHNGVDcUmVJdHKEI0dQwRpwHtWSEy0u1XJHkB8NfP2XL8Jo4UVuLDUd10Yjcf5FqWzZppz3/6+flh3LhxbJzKoqIiiMVidtaVIESXRyhCNHW40FQrR1MaWhNn1qxZmDdvHiIiInhpCekbbdZX7zHdMWtHPhunsuTuIxwprGQVGQlCdHmEIkRThwtdaeXwxbKlKcKn9MehgntYmlyCCYPaY9qQTpgypJPcM+Orn5OUV4F1mTcg9n5VqyHXhCJX00jrq7q6WuPChw8fDuBFnEoyGUGOA41Pl0cWEutS0ZijMokLAl9NHG1ci/z/udZfqkNtbS0sLS21UhYXxN6ax5rLPQztbgHgRZxKMvNLjgONT5dHFhLrUtGYozKJC0KnNi3wf8O7oix4DHZ42GDKkE7skiHJyOV89XNIOlH0Bc6JKm1uAHj07Dks27TmPCfXsuzRo/6tdfPmTaXyA3ywsrJCVFQU5s2bhylTpsDd3R1RUVFSExDa1OWRnUwC1NPUkUTTVuPgwYMBAOXl5VLXv3r1KgCojDwuRBNHyLUUlUueoWTgY00oKyvTqW4Pqa/l1U9gYd5Ko7IsW5pitWs/LE0uwSSbDpgffwmrXftJTXZoU5dHdjIJUE9TRxJNW41E/+bOg6dS179+v75bbG1lpjS/Iq0d0q3uZqk8vyTa1M/hy61/nqBHj+6c5+Ralq+88grMzc21pk7o6OgI4MVylkmTJnGmE6rLQyaIiP5NVVUVNmzYIJdOiKaOLnjllVcAALGxsVJ6QPHx8QCUi4cBwjRxhFyLq9yqqipWp4g8N03Jzc3F0KFDtVIWF6+88grMzVog/9ZDrZQ3pk/9i4PsDnnrZe7hG6G6PGSWmOjfVD+qQ8ypW3LphGjq6IIB/zrL+Jw7UnpAB/LrF4gPs7ZQmBfg1topufuI1T4n+joAf/0cyQXrkh+C7HdN+Ot2LezsX+M8J7fdEah3aL169dLaRAvZgufj4yOn1010rhVRWFiIgQMHym135MoXHh7OTtxI3paiMVGRSCQnFaELFG0r5HoesvdZVVUFT09P3tsdhVxLUdqAgABe8huqqKurQ5cuXbBq1Sp8/PHHGpeniIkTxqFz5UWEaUlHhmz38xzRVU7GgIynKeLYomHo17Gl3HZHrnzLJvVmlyBJ/tgVjYmq2gKoLRRtV+R6HrL3Wf2oDp/+cknn2x25rq0pdc8Z2K3Jxuq1G7jqK/d2x1mzZiExMRGPHz/mOi0Y0kqZO3eu3Dl1dXlmz54tpT8TFRWlcKKGr6aOriCaNpJaOVFRUQgNDVWZV6gmjpBrJScnSz1DMm6sDUcJAIcPH8aDBw8wdepUrZSniPdme+BQ0T948uy5VsojrbtZ9vJiV+rq8kwZ0klKJ2a1az+FEzV8NXV0RfiU/ljt2k9K02a1az98M161UiKZ4JF9Rkd8h8qNTTakfg4fMi9X4eGTOoX1lbNl+fDhQ/Tp0werVq3CBx98oHMjKY0TFxcXdO3aFTExMTq9zsOHD9H7JWt8/WZHvDdMupyTXAAAA6BJREFU9y8/SuNkTlwxeo1yRsyOnVynuVuWrVu3xvLlyxEYGIiaGuV7QSkULg4fPoyMjAyttVKV0bp1a6xY+T3CMsvx4Inms+KUpkfm5fs4cfUfrFj5ncI0ChepeXt7o3Pnzg1S2SmNi9raWixevBj+/v46nQmXxNvbG12seyHid91OgFAaH4+ePkfQ4Zvw/+prpfWVsxtOOHbsGN566y3s378f06dP14mhlMYFwzDw9PTEiRMncPHiRbRs2bLBrl1fXx2xddZAvPNKhwa7LsV4YRhgUWIJzleaIb+gSFl9VR7PcuzYsVi5ciU8PT1x6tQp7VtKaXQEBwcjISEBiYmJDeoogRf1dVFCCc7foMNHFNX8kHEDBwvuI0mcorK+qtwr5u/vj+nTp0MkEuGPP/7QmpGUxgXDMFixYgVWrlyJuLg4DBkyRC92+Pt/hRkzZ2LunmKcvqb5LjRK44RhgIjMG1h/7Cbi9u7jVV95bazdvn07HB0dMX78ePz4448aG0ppXDx+/Bhz5sxBSEgItm3bxkaS0hfbY3bg7YnOmL3rL/ycc0evtlAMjyfPnmNx4mWsO/Y3tm3fzru+mn777bffqkxkago3Nzc8ffoU//3vf1FaWoqRI0fqdM8vxTg4duwYZsyYgT///BMHDhzAlClT9G3Sv/V1Fp4+q8M30Um4UfUEw6zboI258O2DlMbFqdJ/MC/+MvLu1OHAr6lC6uuvvEO2mJiYYPny5di/fz/S0tJgY2OD8PBwurSoiVJcXAwPDw84Ojqia9euOH36NLu11RB4UV/jceK2Kd7clIfIrJt0aVET5eq9R1jw82XM+F8+rG1H4vSfZwXXV6Wz4Yqora3Fd999hx9++AHNmzfHjBkzMHHiRAwbNgzdu3dH27ZthRZJMWCeP3+OyspKXL58GadOnUJSUhKOHj2K/v37Y9WqVZg2bZq+TVQKW1/XhMMUz/GOTTs49rfCq93boIulGSxpi7NR8ZwBqmqfobTyEc7dqMHhoipkXa5Ev359sDpsjbr1dSEYDaisrGQ2btzIjB8/njEzM2MA0E8j/3Tu3Jnx9PRkDhw4wNTV1WlSfRocUl/HOb3FmLVorvdnST+6/3Tq0J7xfP99bdTXBWq1LLl48uQJ/vrrL/z9999aiYVJMRyaNWuG9u3bo1+/fujTp4++zdEKtL42XnRUXxdqzVlSKBRKI0b5onQKhUKh1EOdJYVCofCAOksKhULhwf8DNW1cocYb6RkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(DecisionTreeRegressor(max_depth=1).fit(xs, gun_safety_y), out_file=dot_data,\n",
    "                     special_characters=True, filled=True, rounded=True, feature_names=xs.columns)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dabb7145",
   "metadata": {},
   "source": [
    "* We then use `BaggingClassifier` to improve our score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2fa121f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improved Score:  0.9230170682730924 0.001336360593060244\n"
     ]
    }
   ],
   "source": [
    "gun_safety_tree = BaggingClassifier(DecisionTreeClassifier(criterion='entropy'),\n",
    "                                       n_estimators=50, \n",
    "                                       n_jobs=-1)\n",
    "bagged_cv_score = cross_val_score(gun_safety_tree, \n",
    "                                  xs, \n",
    "                                  gun_safety_y, \n",
    "                                  cv=3, scoring='accuracy')\n",
    "\n",
    "print('Improved Score: ',np.mean(bagged_cv_score), sem(bagged_cv_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8dc3b61",
   "metadata": {},
   "source": [
    "**Armed security in schools**\n",
    "* After trying different depths, we get the best score at depth = 3 so we create the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "587000c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth =  1 [0.06440321 0.0030547  0.00677318 0.04319472 0.04366996] 0.032219152063347134 0.011801398891425655\n",
      "max_depth =  2 [0.10136329 0.03194312 0.01415563 0.08929607 0.06797263] 0.06094614755409959 0.01660897866386345\n",
      "max_depth =  3 [0.10266606 0.05032687 0.00695711 0.10976848 0.07748695] 0.06944109443518284 0.01879432511188641\n",
      "max_depth =  4 [ 0.11439212  0.00742723 -0.02209055  0.11660096  0.04621486] 0.05250892217528669 0.02790553269579594\n",
      "max_depth =  5 [ 0.09400483 -0.04070329 -0.10061989  0.07162988 -0.01050849] 0.002760607728400255 0.035931317150883664\n",
      "max_depth =  6 [ 0.06004216 -0.10455752 -0.2026325  -0.01940926 -0.07182957] -0.06767733795002406 0.043722054196241575\n",
      "max_depth =  7 [ 0.05639738 -0.22831615 -0.2903236  -0.17364278 -0.27375216] -0.18192746341769278 0.06292883839148765\n"
     ]
    }
   ],
   "source": [
    "armed_security_y = tree_data.dropna().loc[:,'armed_security_in_schools'].transpose()\n",
    "\n",
    "all_depths = []\n",
    "all_mean_scores = []\n",
    "for max_depth in range(1, 8):\n",
    "    all_depths.append(max_depth)\n",
    "    simple_tree = DecisionTreeRegressor(max_depth=max_depth)\n",
    "    cv = KFold(n_splits=5, shuffle=True, random_state=13)\n",
    "    scores = cross_val_score(simple_tree, xs, armed_security_y, cv=cv)\n",
    "    mean_score = np.mean(scores)\n",
    "    all_mean_scores.append(np.mean(scores))\n",
    "    print(\"max_depth = \", max_depth, scores, mean_score, sem(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4d9eb590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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9L43KTec6juY7stffSNSWRQd+P639R07Iz8/P6lAAAAAsQwUlAADAX9zc3PR42zYKWb/jhs9tVDE+6/Vwjs96t3fT9KG+5vXIvp6KnfmKYqa9JOnv/Svt9Zs/oo+ZoJs+1DfXw25G+3tp/og+Nsuhpw/11UfDepqnYpemj4b11PShvmasPu6umj7UV+MHdipQ/97t3bT+v4PN+H3cXTV/RB+bZfFlUcj6HXq8bRu5uZXdQ3wAAABKm0NGRsaNPaoSAACgDNu8ebO6P91V2z8elu/eh0BxJCWn6rFXZmrNN+v01FNPWR0OAACAZUhQAgAAZNPbr6cyziTqsxHPWB0KbmIvTFkuhztdtGLVaqtDAQAAsBR7UAIAAGQzZdp0PfhAMy3avFPPPpVzyTVQXIs271R47D79sneV1aEAAABYjj0oAQAAsmnYsKGmTf9IwbO/1rfxh60OBzeZb+MPK3j215o2/SM1bNjQ6nAAAAAsxxJvAACAXAwbOlRLFn2hJWP89XDjelaHg5vAzoPH1O/dUPV79nnNnDXL6nAAAADKBBKUAAAAubh+/br69+urdeu+0ZxXeqn7Yw9YHRLKsTXb9yro45Xq2vVpLV6yVJUrV7Y6JAAAgDKBJd4AAAC5qFy5shYvWar/FzRUAR8u1qRFm3X5ylWrw0I5c/nKVU1atFkBHy7W/wsaSnISAAAgGyooAQAACmDu3Lka8Wqw/lGzut4Z2IlqShTImu17NW7hRp1OS9eUqdM0ZMgQq0MCAAAoc0hQAgAAFNDx48c14tVX9dXixWp5/70a2PERPd22me6qdYfVoaEMOXXugtZ+v1efb9qpuF9/14D+/TVl6lTdc889VocGAABQJpGgBAAAKKQdO3Zoxscfa+XKFUo7f0H1775TDevWlmP1W1TJwaHE5rl85Zqq3VKlxMbD3/64ek23Vi25d/tnRoZS06/o8ImzOnryjBxr1pBfr156+eVX1KpVqxKbBwAA4GZEghIAAOQiY+cAACAASURBVKCIrly5oq1btyo2NlaHDh3S2bNn9eeff5bI2OfOnVN0dLQeeeQRNWjQoETGRKbr168rPDxczs7OatmyZYmMWalSJdWuXVuNGzdW69at1a5dO91yyy0lMjYAAMDNjgQlAABAGbNnzx499dRTatmypcLCwnTbbbdZHdJNZ8mSJfL399eIESP03//+1+pwAAAAKjTWDAEAAJQh+/fvV6dOnfTAAw9o9erVJCdLSb9+/XT16lX9z//8j2655RZNmDDB6pAAAAAqLBKUAAAAZcTBgwfVsWNHNWzYUGvWrFH16tWtDumm9txzz+nq1at68cUXVaVKFY0fP97qkAAAACokEpQAAABlwJEjR9SxY0fVrVtX69atU40aNawOqUIICAjQ9evXNXjwYFWtWlWjR4+2OiQAAIAKhwQlAAAosxwKeCJ2ed9SOykpSR07dlStWrUUEREhR0dHq0OqUF588UVdvXpVQ4cOVZUqVfT666+XyLgF+f4W9rtrjFnev/MAAABZkaAEAACw0IkTJ9SpUyfdeuut2rBhg5ycnKwOqUIKCgrSlStXNHz4cFWtWlXBwcFWhwQAAFBhkKAEAABlVvYqsZuteiw5OVmdOnVSRkaGNm7cqDp16lgdUoX2yiuv6Pr16xoxYoQqV66sV155pUTGvVm+rwAAAKWFBCUAAIAFUlJS1LlzZ12+fFlRUVGqW7eu1SFBUnBwsK5evarhw4frlltuUVBQkNUhAQAA3PQqWR0AAABASXFwcJCDg4OOHDkiX19fjR071uZ+bu2zi4yM1EsvvSQHBwf5+voqMjKyRONMTU2Vt7e3zp49q02bNsnZ2blEx0fxvP7663rnnXc0dOhQffrppzds3ri4OE2ZMsX8Xvr6+uqrr77Kt1/W76uDg4PGjh2ruLi4fNuWxncbAACgKBwyWHMCAADKifyWeBufjxkzRu+++64WLVqkAQMG5NrP3v2xY8fq3XffzTH2mDFjNGHChGI/w/nz5+Xt7a2jR49q8+bNuv/++4s9JkrHW2+9pQkTJujTTz9VQEBAofsXZkuCr7/+Wr6+vnY/M77H9sbMq9+mTZvk5eVlXpf2dxsAAKCoqKAEAAA3nebNmysjI8NM6hRUZGSk3n33XY0ZM0bnzp1TRkaGzp07ZyY8c6tKK6j09HR1795dhw4d0saNG0lOlnFvvfWWRo0apcDAQH355ZdFHseobMz+Jysjybht2zZlZGQoIyNDv/32myTp2WefzXVso99vv/1m9tu2bZskaenSpWa70v5uAwAAFAcJSgAAcNPJWjVWGJs3b5YkjRw5Uo6OjpIkR0dHjRw5UpK0cePGIsd06dIl9ezZU3v37tXGjRvl6upa5LFw47z33nsKDg7W//zP/2jJkiWlNo+RXGzUqJHi4uL09ddfa968efn269Gjh6TMZGRkZKRSU1P12GOPKSMjQ7NnzzbbleZ3GwAAoLhY4g0AAMqNgi7xLujp39nv29uPMrui/Op05coV+fn5afv27YqMjNTDDz9c6DFgreDgYH3yySf66quv9MwzzxSoT2FPnc9tCXbWMbKPGRcXZ/N96tGjh4YPH54jSV9a320AAICSQAUlAABAKbpy5Yr69++vrVu3KiIiguRkOTV16lQFBQXJ399fq1evLvHx582bp3fffVdBQUHatGmTdu7cqZMnT+bbr2XLlsrIyNDOnTs1efJkff311+rYsaN8fX1Ztg0AAMoNKigBAEC5UZIVlKdOndLdd99tc/+ll17SnDlzdO7cOXMZbHFcv35dzz77rNatW6eIiAi1a9eu2GPCOhkZGQoKClJISIhWrlypp59+Os/2hamgtNc2NTVVtWrVsrmf35hHjhzRr7/+qo4dO9q0K+nvNgAAQEmighIAANz0jH36tm/fLikz8TNjxowc7fr27StJmjx5sk6dOmXej4yMlIODg6ZMmVLgOa9fv66AgACtXbtWa9asITl5E3BwcNCcOXP0/PPPq3fv3oqIiCjxOfbv3y8p8zs6efLkfNu/9NJLcnBwML/bDRo0sHv4Ukl+twEAAEoaFZQAAKDcKGoF5VdffZXjJOTJkyebB4RkbZ/bPoA9evTQ/Pnzddddd+UbZ0ZGhgIDAxUaGqqwsDB17tw53z4oP65fv64XX3xRS5cu1erVq9WpUye77QpTQWnvO5pVQkKCmjZtmmPM7du36/HHH7fbZ+7cuRo8eLB5XRLfbQAAgNJABSUAALjpDRgwQIsWLTIrKefOnasRI0bYbTthwgQtWrRIQUFB5r25c+cWKjk5bNgwffHFF1q+fDnJyZtQ5cqV9emnn6pnz57q2bOnoqOjiz3mgAEDNHfuXPN6zJgxSkhI0M6dOyUp1zkee+wx7dy5U2PGjLHpGxYWZpOclIr/3QYAACgtVFACAACUoODgYM2cOVOLFy9Wr169rA4HpcjYY/Sbb75ReHi42rdvb3VIAAAA5RIVlAAAACVk1KhRmjFjhr744guSkxVA5cqV9eWXX8rb21vdunXTtm3brA4JAACgXCJBCQAAUALeeustTZ48WZ999pn69etndTi4QapWrapFixbJ09NTXbt2VWxsrNUhAQAAlDss8QYAACim999/X//5z380b948vfjii1aHAwtcuXJFfn5+2rZtmzZt2qRHH33U6pAAAADKDRKUAAAAxTBt2jSNGDFCM2bM0LBhw6wOBxa6fPmyevbsqR07dmjTpk1q2bKl1SEBAACUCyQoAQAAimjmzJl6+eWXNWXKFAUHB1sdDsqA9PR09ejRQ/Hx8YqMjFSLFi2sDgkAAKDMI0EJAABQBJ9++qkGDx6s9957T2+++abV4aAMSU9PV9euXbVv3z5FRUXpgQcesDokAACAMo0EJQAAQCEtXLhQL7zwgsaMGaO33nrL6nBQBl24cEE+Pj46dOiQoqKi1LRpU6tDAgAAKLNIUAIAABTCkiVL5O/vr5EjR2rSpElWh4MyLDU1VT4+PkpKStLmzZt1//33Wx0SAABAmUSCEgAAoIBWrlyp/v37a9iwYZo2bZrV4aAcSE1NVadOnXTy5ElFR0erYcOGVocEAABQ5pCgBAAAKIBvvvlGvXr10uDBgzVjxgw5ODhYHRLKibNnz6pTp05KSUlRdHS0GjRoYHVIAAAAZQoJSgAAgHxs2LBBvr6+eu655zRv3jySkyi0M2fOqGPHjrpw4YKioqLk7OxsdUgAAABlBglKAACAPERHR+vpp59Wnz599Nlnn6ly5cpWh4RyKjk5WV5eXvrjjz8UFRWlevXqWR0SAABAmUCCEgAAIBdbt26Vt7e3nn76aYWGhpKcRLGdOnVKHTp0UEZGhqKionT33XdbHRIAAIDlSFACAADYERsbq86dO8vLy0uLFy9W1apVrQ4JN4njx4+rQ4cOqlq1qjZv3qw6depYHRIAAIClKlkdAAAAgFX69u2rAQMG6MqVKzb3d+7cKW9vbz3xxBP66quvSE6iRN1zzz3atGmT/vjjD3Xq1EmnT5+2+Tw1NVXdunXT6tWrLYoQAADgxqKCEgAAVEinT582K9e6du2qlStX6tZbb9Xu3bvl5eWlhx9+WGFhYapWrZrFkeJmdfToUXl6eqpWrVratGmTateurXPnzqljx4766aef5OjoqJSUFFWqRE0BAAC4ufHbDgAAqJA++eQTValSRVLmKd3du3dXXFycOnXqpAcffFCrVq0iOYlSVb9+fUVGRurMmTPq0qWLEhMT5enpqfj4eElSWlqali9fbnGUAAAApY8KSgAAUOGkp6fr3nvv1blz58x7VapUUd26dXXvvfdq48aNuuOOOyyMEBXJwYMH5enpqUqVKunEiRO6evWqJKly5cp68MEHtWvXLosjBAAAKF1UUAIAgArn008/1fnz523uXbt2TSdPnlRGRob+/PNPiyJDRVSjRg3dfvvtNslJSbp+/bri4+O1YcMGC6MDAAAofVRQAgCACuXatWu67777dPz4cdn7Nahq1ap66KGHtHHjRtWqVcuCCFGRnDhxQh4eHkpMTLRJThqqVKmidu3aKTo62oLoAAAAbgwqKAEAQIWyZMkSnThxwm5yUpKuXr2qXbt26b777tPRo0dvcHSoSI4fP64HH3ww1+SklJlQj4mJ0Q8//HCDowMAALhxSFACAIAKIyMjQ++//36+7a5du6a0tDQdO3bsBkSFimrdunU6e/asrl+/nme7qlWrauLEiTcoKgAAgBuPBCUAAKgw1q9fr927d+e6x2TVqlXl4OCgAQMGaN++fWrbtu0NjhAVyQsvvKCIiAi1adNGksxT5bO7evWqwsLCtG/fvhsZHgAAwA1DghIAAFQY7733nt0kkJGY7NOnj/bu3avQ0FC5urpaECEqmi5dumjbtm2KjIw0E+L2vqNVqlTRpEmTbnR4AAAANwSH5AAAgArhxx9/lLu7u829qlWr6tq1axowYIDGjx9PUhKWi46O1rhx4xQTE6MqVaro2rVr5mdVqlTR4cOH5ezsbGGEAAAAJY8KSgAAUCFMmjRJVatWlUTFJMouT09PRUdHa8uWLfLw8JD0d0Wlg4ODpk6damV4AAAApYIKSgAAcNPbv3+/mYA09pikYhLlwfbt2zV+/HitX7/evHfmzBk5OTlZGBUAAEDJIkEJAKjQ9u3bp2+//Va7d+9WSkqK/vjjD6tDQilISEjQrl275OzsrBYtWqhGjRqWxHHrrbfKyclJLVq00JNPPqlmzZpZEgcK7/Tp09q8ebPi4uJ0/PhxnT9//obOn5KSori4OJ0+fVru7u5ycXG5ofOjdFWqVEm1a9dWo0aN5O7urnbt2umWW26xOiwAAG4YEpQAgArn1KlTmjNnjkIWLNDhxETVqllDDzRpJCfHGrr1Vv5BeDO6du26/rhyRbdXv83SOP7444pSUs9r74FDOpd2Xg1dXBQwaJCCgoJ01113WRobcrp27ZoWL16suXNm67tt21TJwUGuzv/QXTVu1R23VrYkpot/XFW1qlVUuZKDJfOjdPyZIaVeuqbE0+d19NQ51bzjDvn16qVX/v1vtWrVyurwAAAodSQoAQAVxpUrVzRjxgy9O2GCbqlaRf/zTDf1ebqjHnqgidWhoQKK+2W/lq+LVMiyNbp67brGjB2rl19+maqpMiIqKkovDxuqhP371f1RF/V//H61b1ZPt1a1JjGJiuNU6iWFx/2mhd8e0K7EUxrQv7+mTJ2qe+65x+rQAAAoNSQoAQAVQnx8vPr366fExES9MmiAXg8aqOq3VbM6LEDply7rgzkL9fGCr+Ti4qLFS5bIzc3N6rAqrIsXL2pw4Ita9NVidWp5nyb2b6uGd9W0OixUUGt/StRby3/U6QtXNGXqNA0ZMsTqkAAAKBUkKAEAN71169apf79+av3QA5r93ijddy9VKCh7fvv9uF76zyT9uGuvFi9Zoq5du1odUoXz+++/q2eP7jqa+Ks+HviEOrrVtzokQJevXNNH6+I0de1O/fvf/9aHH05W5cpU8gIAbi4kKAEAN7X58+crKChIL/Tz1dRxr6oK/6hDGXbt+nW9+s5UfbYkTHPmzFFgYKDVIVUYv/76qzyfbK9aVf/Uly93lLPTHVaHBNhY+1Oihn4Wo65du2nx0qUkKQEANxUSlACAm1ZkZKS6dvXRW8OHKDjwOavDAQps2vwv9db0uVq3LlxeXl5Wh3PTS01NVZvWrXRX1T/0+bCOuqNaVatDAuyK++20Bny8Uf38n9fMWbOtDgcAgBJDghIAcFPav3+/2ri7K6BPd00a9a8bPv9tTdtJki7t32r3WpKOHjup+vXuzrVPacdUWn1L+zlu9DxZ7dr3q9r6DizwnEaM9uQ3xqhJnyhk2Rr9EBurpk2bFipOFNy1a9fUpXMnnTy0V1+/5qOat5XdQ4qSUi6UycrOOoHzJUnJ80uv4resPXvapStaHXtIEXFHFBF3RN4tG+iZto3V0a1+gb5DxjuzJ7/3uGXfcfX/KEIffTxDQUFBhY4dAICyqJLVAQAAUNIyMjIU+OILetK9pd57bajV4dj10WeL1LRDL6vDQCEknzmrtr4DC9z+6LGTxZrvvdeG6kn3lgp88QXx35NLz6xZMxX/8w59PvSpMp2cnLU+Xo+8/pXVYViiLD77hOWxenXhFkXEHZEkRcQd0ZC5mzV0flS+fZNSLhRr7vbN7tGUfz6h4OH/1uHDh4s1FgAAZUUVqwMAAKCkLVq0SL/s2aNdEYtUuXLZ+G9x2avlRk2akW+bsqQsx1YUu/b9qs1bY/XvF54tcJ8JH+de8ZSXSaNeLtQ8hsqVK+l/3x+th7yf1aJFi+Tv71+k+ZG75ORkjR87VlOfa6sG/6hhdTh5Gr/ke6tDsMyNfPY9R1MUvfd3De3ilmebkKi9erX7I/qnh6ucne5QUsoFffRNnEKi9urgyVQ1vtsx37ne7tc2z3nyMqBdE23cfUwjgv+tFavCijQGAABlSdn4VxsAACUkPT1db456Q+P+HSinWvn/AxEVyw879+iV8R+qre9Au0ni3Hz02SIdO5lcqLkOHkmSJLV8sOjLs51qOWrcvwP15qg3lJ6eXuRxYN/Y//xHLerXVo9WDa0OBRb78dApvfbFd+rw9op8E6I/HT4lSer3+P3msnNnpzsU4PmAJGnXb6fz7H/4VJokya3BncWK+a0+rRQREaHNmzcXaxwAAMoCEpQAgJvKnDlzlPHndb3Y369A7W9r2s7cJ3Dp2o3m9dK1G5V63v4yvKVrN6pP0Ou6rWk79Ql6XUvXbizUPFn3Jcx+P/uehannL9jM98r4D3Ug8UiO8Xft+1UffbbIHKOgcRWUvdgk23eR33xR23folfEfmvFFbd+Ra9uivOPcpJ6/oLWRW9Qn6HV59hssSVo25wMd2ba2QP2jtu/QqEkzNG74kCLHUBwv9vdTxp/XNWfOHEvmv1n99ttvmv/pp/pPz4cL1L5O4Hxz38CVPxw0r1f+cFBpl67Y7bPyh4N6fsZ61Qmcr+dnrNfKHw7abfftvmN67YvvzDHfX7VDe46m2MxtLw5787y/aocOnkzN0c64Tkq5oOdnrNf7q/7+39+eoymatT7ebJNXrFnnyq2NvRhzu5/f3Pk9e9Z39/yM9fp23zG7MdmTdumKIuKO6PkZ69V1YmYV4hcvd9HeaXkfqpaUcjEznpq32dy/u1bm9b5j5wocQ3E4O92hgR6u+s+oN27IfAAAlCaWeAMAbipz/3eOBvXpXuil3Wsjt2hg8DjzemDwOHXzaq9lcz6waff29LmaNCvEpt/ayC365cAhjS+FBNaLr72jtZFbzOt5i1Zq3qKV+j5soR5qdr8ZQ5+g13M8j9Gvb7dOJR6XJL0y/kPNW7TSvB4YPC7XKsPc3tuooQE53ltJveOjx05q+8/x5s+yf48umjZuhM3BRPk5kHhEXQe+rIXT3jHfd0HF/bJfUmYV5GdLwjRszCRJ0sx3R+mZrl5yrFGwAz8qV66kgGe6ae7/ztGrr75aqBiQu/nz58vV+R9q3eiuQvUz9ho0DJm7Wd4tG+iLl7vYtHt/1Q5NXfOzTb+IuCPad+yc3vRrZXP/+RnrbfpOXfOzpq75WStGPq0nm9XLM57s8xh9c/N5TIIi4o7ombaNc53fiFWSerVpbN5/7YvvFBK11+bZj58remVvYea2J7d3/Gr3R2zecXZJKRcU++tJ82f3TNvGmvRcuwIfwmPMmX3P0n/UuM38PK/544+ckSQ53V5Nn8fs06sLM/+/eurA9urp3qhQe6EO9HBVuzHLFB8fLze3oi0XBwCgLCBBCQC4aezbt08J+w+ox9Rx+TfOZsGSMO2PWqn69e7W0WMn9dmS1Zo0K0RR23eow2OZ/9CM2r5Dk2aFaNTQAL3Qr2eOtp6PtTLb5uXS/q0FOoE6axJv+Iv+cqxxh5au3aiBweM0f9FKffz2a5JkJiejl8xTm4ebS8pMzjXt0EsDg8eVSoIyavsOzVu00u67sNfWeG/Gc6Sev6Dpn4Zq0qwQ9fLxMpN/JfWOJZmHEC2c9k6R3kHq+Qt6c9InGjU0oFjvMPvBOsPGTNI3kVv06YfjCpyk9O3iqQkfz9e+ffvUrFmzIseCv61asVxdH7q30P0+j9mnnz8YYO47+HlMgqau+Vnf7jtmJhO/3XdMU9f8nGOPQqNt+2b3mG2NBJ0xppS53LjrxDCF/XhYTzarp+T5gXZPys5tHmMvRHua1atlM4Yx/7rRvmayNinlgh55/SsNmbvZTBJ+u++Y3X0XP49JKPQ7LMzcBXn2Yd5uqnnbLUq7dEUzI+I1dc3P8m3VUM3rO9md1zhwZ+6Qp/JNgpamDm+vsLk2Dt2ZFdihwEnKJnVrqcm9/9CqVatIUAIAyjWWeAMAbhoxMTFyrFFDLZoW/h+c74/6l1lZV7/e3XqhX09J0op1kWYb4+9G4iyvtiUhInqbJGnoP/uaiay+3Trp0v6tZnJSykxyXtq/VQ3r19Oufb9qbeQWu4nCkhT91/Ls7O/C388n17ZGclKSHGvcoeEvZh76snlrrNm2JN/x/qiVWjjtHQ0MHmcuEy/MydrTPw3V2sgtGvrPvgXuk5Wxx2X0knnmz+jS/q1aOC2zKnZ9zPYCj9WiaWM51qihmJiYIsUCW2fPntXuX/bqsaZ1C9337X5tbfYd/KeHqyQp7Me/T1M2/m4k8vJq692ygXnv233HlHbpilo3ukvJ8wP14fNP5BnLln3H7c4T1LlFrn2efMC2IjN5fqCS5wfKpU4N7Tmaooi4I3aTjrnN1e/xwlUWF2Vue4x4jOSklFnROMw7M0kXvff3XPv+/MEAzR3ylIbM3WwuKS/uydqFYexxuW60r/kOkucHau6QpxQRd0Sb4o8Warw2je7Ut9HRpREqAAA3DBWUAICbxt69e9WsSdEOu2ji0sDm2kiOzctSqWgsZ86+RNhe25JgzFfnztr5ts2+LLq0GXNlfxfZ32PWtnVbdcnxmZSZyDNOuS7Jd1y/3t2qX+9udfF4TFtid2rBkjANDB6nwc/2krfn42rTsnmu73bp2o2aNCtE0UvmFej925NbdWzfbp00MHicFn+9vlCVma73u2jvXvtVcSicX375RZLUrF7hf7bZT2c2knUhUXvNhKJRvZh9ybC9tm/6tVZE3BEzaeXdsoH+X+cW+S7tlv5eapx9nrxOkDaWIWeVfal0Sc1VEAWZO694Gr+80O7n45d8n+sJ2c5Od8i5zR3q6FZf2/af0Ocx+zRk7mYFdHhAndzqq1WjOnbfU0nJWgmaVa82jTVk7mYt//5goSo7Xe+ppahvd5dUeAAAWIIEJQDgpnH69Gn9o1ZNq8O44T5bEqZJs0I0+Nle6t3VS061HHVPnTvV4PFuVodWJjjWuEPdvNqrm1d7/bBzj75Y+Y25LD63JKKxH6lxqE52BVmin5+se4sWRJ3ajjpz5kyR58PfjPdY+/ZbLY5Eal7fScnzA7XnaIqi9/6u8Uu+V0TcEXm3bKA3/Vrnuky5pHwes09T1/ysgA4PyLd1QzndXk1317pNDwR/WarzWj23lFlx6d2ygbxbNtCPh05p8dYD5rLz3JKIkvRq90c0dc3PSrt0xWYptnFY0qvdHylWXMYenAXldEc1nUm5MQfzAABQWkhQAgBuGn/++afuuL16kfoePXbSpmrPOCl71NAA897gZ3tp3qKVubYd/GyvIs2dG2O+5DNn86ziMw5fyVpZmNsJ5CVl1NAATZoVogOJR2yqJu0toTae48SO9fnuuVja77jNw83V5uHmCny2l83S8tLQJ+h1rY3ckuO5jZ9NYZ/ljtur6/r16yUaY0V14ULmz+DWqpUL3Tcp5YJNFeHBk6mSbJNSAR0eUEjU3lzbBnR4IMe4zes7qXl9J/m2bqjDp9LUe/I3iog7UqBEWfZ5CrNc2TigJetycnunkhtzHTyZalM1WdC5Tp+/VOS57THe8cEZAwt1qExuWje6S60b3aUAzwfyXB4uZe7jKUnJaZds5j56OvNdODvdnmf/52esV0TckRyxG89u7/uRlyqVK+nyH38Uqg8AAGUNe1ACACDpsyWrzeTa0WMnFboqXJLkmeVAlt5dvfJs6+35eKHnzSuR+GSbzITHrM+Xmu2Wrt2o25q20yvjP8zR3kjiGQfQlCbjvbw56RObd2Fv70vjvU3/NFTJZ86a96O279BtTdvpo88W5Whbku/Ynoea3W8uK7cn656RWf9k/zwv/XtkLmnPvtekcW08K8qXz2MSzKRcUsoFLdn2qySpfbN7zDa+rRvm2baTW32z7WtffKc6gfP146FTkjKXHze8K/dK8KwJPGPO7PMU5eAaI3lqHDSTnTHX+CXf5zuXsa+m8Uxpl65o3qZfijy3IeuzG+94ZkS8TfLz233HVCdwvmatz32cvDSv75Tr0nBD03sy/4PRkm2/2ryLsB2Ze4s+2jDvk+GNE9Sz7zVpXBvPBgBARUIFJQAAfzFOfTaMGhpgc2J0h8damZWD2fd7HDU0QN282hd4rm5e7bU2covqtuqiwc/2sruvYt9unbT46/V25wvMUn1nHATzUJcBdufKXuVYErK+i6xLlWe+OyrPttmfo5tXe/n39ClQ28K+Y2MZdl6Ks0Q7t/mMMbt4PKZuXu01MHicuWTckP27hfLFOAXa8Gr3R2z2jHyyWT2z4jD7/oqvdn/ETOBJUv92TRQStVddJ4blmGfqwL+/794tGygi7ogav7xQAR0e0IfPP5HnPAVlHBbz2H+W2v3cqJjMOlfWJchZYzQ807axIuKO2DzT2/3aFnnuwj67d8sGeR7eY5wKnpe8Kleb13eSd8sGducO6PBAjmX52U8h7+hWX94tG2jI3M0aMnezTdvs3yUAACoKEpQAAEgaP3yIatWsoVGTZqibV3v9K6C/3QTS+OFD9GCTRlr89Xqtjdyibl7t1b9Hl0IdNf1dgQAAIABJREFUdiJJ44YPUb2762jeopU6djI513affjhOy9dFmsu4Rw0NkL+fj03CsW+3Tjp/MT1Hm0uXr6it70B9+8POEk9QSjnfxcJp76hvt05mHPbafvvDz+ZBODPfHaUeHZ/MsXy9pN6x1Rxr3KFPPxyn9THbzWcx9gklOVl+venXSo7Vb9H4Jd/neaDNm36t1KxeLS3//qC5p+QzbRvnOPykdaO7FDW+t8J2HDaTXa92f0SPNqxjk8h806+17ql9u0Ki9ur42Yu5zvNq90fU7/H7c036ZderTWNduHzVXG5t9L985bo6vL1CWxOOm0u6s881d8hT6tWmsdk365iSzHZTB7bXPz2amQcBFXbu/J596/4T5sFEUwe2V9dH7ivVQ24kaXrAk1r382+KiDti/ny9WzZQT/dG+fatedstmhXYQZvij5rvyNiHk+QkAKCicsjIyMiwOggAAErCc889p+vnTytkylsF7lMSh50AN0LAiLdUucY/9OWXN+YAkZtZaGionnvuuTyr5LLLXgVX1tUJnG9WG+Lmtvz7gwqat1n8sw4AUJ6xByUAAABQDtUJnG+zf6WUuU+jsf9iu6Z1rQoNAACgUFjiDQBABVSQ/RkNVJcCZdMXL3fR8zPW292/0rtlA3XMchgPAABAWUaCEgAAACiHvFs20IqRT2vLvuPm/pUBHR5Qu6Z11dGtvmredovFEQIAABQMCUoAQIVWUasDK+pzA0VVVveefLJZPT3ZrJ7e9OPgJQAAUH6xByUAAAAAAAAAy1BBCQAASlzq+Qtavi5S30Ru0drILerm1V79e3RRF4/H5FjjjgL1Xx+zXYu/Xm/2f9qrvXp0fFJ17qydZ99d+35VW9+BdqtE89p7k6pS4MZJu3RFq2MPKSLuiCLijsi7ZQM907ZxgZemp126ok3xR7X8+4Nmf++WDdT1kfv0jxq35dovIu6Inp+xPteKWOO0dnvKahUtAAA3AxKUAACgxI2dPFvzFq00r9dmSVQum/NBnn1Tz1/Qi6+9o7WRW3L0/yZyi2a/92auScrkM2fV1neg3c+OHjtZhCcBUBomLI9VSNRe8zprovKLl7vk2Tft0hUNnR+liLgjOfpHxB3R9IAn7SYp9xxN0fMz1uc6blLKhSI8CQAAKAkkKAEAQInate9XzVu0UqOGBuiFfj1Vv97dOnrspD7834Wat2ilDiQeUROX/8/encfVmL5/AP8gESqyTqPSgpIkjDAhMZLIZMmevoaYLENjyYyRMCOMGEtmYvr2zdLIEpHUkKhpKktCUyhtZA+JyNLvj/M7Zzqd06blqXzer5fXq/M893M/13NminN13detWez1oeeiERQWie1rXDDGygKqys3w7HkuNv++D+6ePth39CS+mT5R7rWrtxRf/STm7jKv2OuJqOolZGbDJzwRziNMMHVAZ7RXa4bb2bn45UQ8fMITkXL/GXTbqhZ7/emrmQiJz4CHvRlGfaYDFSVF5OTlY3vIVXgcj4P/38lwGmokdc2FWw/k7nYuj5udqcz1REREVLXYg5KIiIgq1YUr/wAAJn05DBrqbQEAGuptMWOiLQDgcsKNEq/ff0xU4TTdzkayHFxVuRkWfDUJAODivlXudb94+yHr/sNi503JuA0AMO7SqayPQkRV4FLqAwCAXV89tFcTfY+3V2sGh4EGAIAr6Y9KvP5QTAoAYOoAfclycBUlRcyxFCUVXf1jpMZ7hl6F1U+B8HIcVOK8qQ9yAABGmi3L8zhERERUCVhBSUREVE3E/Q/zbkQhKCwSY2cvgbWFGf5jZwNrCzMAwIGgU7BfuAIA4LtpFcZZD5GaIzz6Ig4Hh0mWT7s4OcB2mAW66evJ3K/wWGsLM8x1GA/zPqXv9FtSn0axkvo1ZmbdAwC0aakmdfyT1qIP/f/cvFXi3MUtAS+pd2V49EW4uG9FTKCv1NJwotpE3P/w4a4Zkl6JlsaamDpAH5bGoqrjgNgUOHqdAQB4OQ6CbW9dqTkikrIQeCFVsnzaeYQJbHpqw1BD+vux6FhLY03M+qIr+uurlznOkpTUr/F29gvRPCrSy7DbNhe9Tsp6WuLcxS0BL653pat/DPbMGwpLY03Je0dEREQ1CysoiYiIqpk4OVn46ytJyXDb7CVJTgKA/cIVOBB0Suo6K/t5Ur0d3T19YGpjj/Doi1L3cNvsJTVWfK3bZq+qfDRJTIBsQlHcN1J8vrxupon6zfluWiVz3Mp+Hnw3rZKbqBWL/0dUuanWXBXe/oFQ6tQPSp36wds/EM+es/cc1Rzi5GThrxMys7H2yEWpBJuj1xkExKZIXTf65xNSvR09jsfB3O0wIpKypO6x9shFqbHia9cekf5ZUhU8jscBkE0oivtGis+XV8r9ZwAgUyn5cNcMSYK3JFczHgMA1Jo2xu5zSWg9Yxdaz9iF3eeSkJOX/0ExERERUdmwgpKIiKiaXbjyD+5dDIWqcjOER1+Elf08mNrYw8XJQea4/cIVkipKcVLzRniAZOl07OUEDLSbicPBYZLqyPDoi3D39IGLkwMWfDVJpodjcRWXYjV1N+t9R07C2sIMQwf0kRx79jwXy9y3wcXJQabatDhFN9GZs9wdJ8Ii8fuGFWXaYZyoql1KfYiUrfZQUVJERFIWRv98AuZuh+E8wkTmuKPXGUkVpTipGbd+gmTptLj3YuCFVEl1ZERSFjyOx8F5hAnmWBrJ9HAsruJSrKbuZu3/dzIsjTUx2EijQvOYux2Weu3sG4mQ+Ax4zjAv0w7jREREVH6soCQiIqpmTlPHSRJhhZdci5OJRY+LiZeBHz4ZhvDoi3j2PBe9uxsi70YUtrgtlow7+//VlIXnK9zD8UzU+Sp4qqrlttkL7p4+WLHAUSqJuPn3fQgKi4TT1HGlziHuXXnWfyfybkRJ/vhuEu0YHnouusriJyqPmYO7SBJhhZdci5OJRY+LiasEAy+kIiIpCzl5+eil0wYPd83AhimfS8ZFJt2Vma9wD8eziXeq4Kmq1tojF+FxPA7Lvuz1wUlEce/K4O9s8HDXDMkfL8dBCInPwOmrmZUZMhERERXCCkoiIqJqJl7qXFRp1XsrFjgiKCxSkmgrrq+keAl1u57y+7S5uG8tcRfrivagrGzi5GRMoK9U5eeBoFNw9/TBWf+dxb6nhRUX8zjrIbBfuAL7j4WWuQqTqCqJlzoXVVribdmXvRASnyFJtBXXV1K8hFp3nq/ceVz9Y0rcxbqiPSgrmzg5Ge46usTKz9IUF7Ntb104ep3BoZgUmZ6fREREVDlYQUlERFRLdNPXQ96NKMQE+sLdZZ6kr6S4h2VN4eLkAAAyfR3Fr8XnS/Pw8RO4bfbC1aRkXAn9Q2ZZurhf50C7mZJ+koWTq0Vfl4ab61BtZ6ihhoe7ZiDcdTTc7EwlfSXFPSxrCucRJgAg09dR/Fp8vjSPnudh7ZGLSMh8jOgfx1UoOVkWIfEZVTo/ERHRx4wVlERERLVMN309dNPXw+hhFkjJuA0re1GyUlwhOHOiLXb6BUj6WZZXRasju3TUAQA8eJwtdf/0O6LdvTXU25U6x5WkZKza7AUjfT3s+HFZmSokSzN29hIEhUXKvC/ixOnMibYVvgdRTWCooQZDDTXY9NJG6oMcjP75BELiMyQVgg7mBvAJT5T0syyvilZH6qs3F82Tkyd1/8xHou/F9mpNS51DtGnQBRhqtMRmh/7FVp2Wx5StoQiJz5B5X8SJUwdzgwrfg4iIiORjBSUREVEtMd91A5Q69UPs5QQAgIZ6W+hqtpcZN9rKAoCoP+PDx08kx8OjL0KpUz/84u1XpXF21u0AQLSpTWbWfQBAZtZ9BJwMAwD06talxOszs+7D1MYeRvp6cF3gWGxysnAfycJ/ip4XGz9StOS9aK9J8Wvx+0ZUWy3e8xdaz9iFC7ceAADaqzWDdhsVmXE2vbQBANtDruLR8zzJ8YikLLSesQueoVerNM5On4i+p/3/TsbtbFFS8nZ2LgIvpgIAemi3KfH629m5MHc7DEONllj2Zc9KSU4CwBhT0fLtor0mxa/F7xsRERFVPlZQEhER1RJTbIdjp18ABtrNlDm3fY2L5GvzPj3h4uQAd08fST9KMWsLM0waNaxK4+ymrwdrCzO595850VZmqbZ4GbY4mfhnpKh/nrzrxT6kynPogD6wtjCD/cIVkuXhYi5ODnI3JiKqTcb36wif8ERY/RQoc87D3kzydX99dTiPMIHH8ThJP0oxS2NN2PXVK3p5pTLUUIOlsabc+zuYG8gs1Rb3vBRXbp65dhsA5F4v9iFVnoONNGBprAlHrzNw9Dojdc55hIncjYmIiIiocjBBSUREVEv07m6ImEBfBJwMkyTuXJwc0KtbF8kO32KuCxzRpaMOImLjsNMvAIAoiTlycP9KWS5dmh0/LsOx0xE4ERaJoLBIWFuYYbiFGcaUoUpxznL3KolJVbkZft+wAqHnorH/WCiCwiIxc6ItRltZMDlJdUIvnTYIdx2NwIupksSd8wgT9NBuLdnhW2zZlz2hr94cUTfuwSc8EYAoiWllolVpFYkl2ezQH8Fx6QiJz0BIfAYsjTVhaayJUZ/plHqts2/V9ItVUVKE5wxznL6aiUMxKQiJz4CDuQFsemkzOUlERFTF6hUUFBQIHQQREVFlmDx5Mt49fwSfjSuFDoWo0jl8uxINlFth7969QodS6+3btw+TJ0+u1p2miarKoZgUzN55BvxYR0REtRl7UBIREREREREREZFgmKAkIiIiIiIiIiIiwTBBSURERERERERERIJhgpKIiIiIiIiIiIgEwwQlERERERERERERCUZB6ACIiIhIWEqd+gEA8m5ECRxJ+T17notDwWGYs9wdAODi5IBJXw5Dxw6aUuPEz1iSws9f1nmJPgatZ+wCgFq563lOXj5OX83EoZgUhMRnwNJYE2NMdTHYSAMqSoqSceJnLElJz5+QmQ1zt8O18j0iIiKqCVhBSURERLXWV4tXSZKIAODu6YNuQyfgSlJyueaxtjCrknmJSDiPnufBaVc4HL3OICQ+AwAQEp8BR68zcNoVjkfP88o8l6Vx8b+cePQ8D+ZuhyscLxER0ceMFZRERERUKx0IOoWgsEhsX+OC6XY2AIDw6Iuwsp+HXX4B2OK2WDK2uOrQK0nJMLWxx1qXuR80LxHVXMFx6QiJz4CX4yDY9taVHA+ITYGj1xkEx6Vj6gB9AMVXR4orI93sTIu9z7qjlyo3cCIioo8QKyiJiIioVtp/LBQAMMbKQnLMvE9PAMBOv4BSr3/4+AlMbeyxfY2L1NLtis5LRDWDs28kAEglJwu/Fp8vjrgy0sPeDLptVeWO8Qy9irtPXlRCtERERB83VlASERFVkvDoizgcHCZJYrk4OcB2mAW66etJjbuSlIwzUefh4r4VgGh58fiRQzHOeohkTOG+kEFhkRg7ewmsLczwHzsbyXLkA0GnYL9wBQDAd9OqYq8vOm7ogD5QVW5WruextjDDXIfxkkTdhzx3UeXtC1nUwV/XyxwLChMlHHw3rSp1bs/dB2BtYSapkqyseYmKE5GUhcALqfAJTwQAOI8wgU1PbRhqqEmNS8jMxtnEO3D1jwEASd/Ewom2wn0hQ+IzMGVrKCyNNTF1gL5kObK4UhCATBVh4euLjivan7Esz2NprIlZX3RFf331D37uoiraF9LSWFOytLu48yXZefofyXsqT0RSFlz9YxDuOrrE+xAREVHpmKAkIiKqBOIkYmHunj5w9/RBsO9WSWJP3rigsEhJAqxwkrHoePG4mEBfBJwMg7unj2ScOAEp73rxOfE4awszuUm4wtw2e0nNL763i5MDXBc4lvu5q9ov3n6ShG/RZK084dEXJTFW5rxExREnEQvzOB4Hj+NxOLxouCSxJ29cSHyGJAFWtBqw8HjxuHDX0Qi8mAqP43GSceIEpLzrxefE4yyNNbFn3tASn2ftkYtS84vv7TzCBMu+7Cl1vCzPXRWmDtBHSHwGAmJTZJZ4i88XJyIpSxKjPCn3n2H0zyfg5Tio1EQrERERlY4JSiIiokogTtLdCA+AhnpbAEDs5QQMtJuJw8FhkkSdeNxZ/53o3d0QAJCZdR+dzG1hv3CFTALswpV/cO9iKFSVm0n6IJra2MPFyUHmuLzr/+sfKIkpM+s+vP2Pwt3TB+HRF4tNHoqTdy5ODljw1SSoKjfDs+e52Pz7Prh7+khVR5b1ueWpzF3Djbt0grvLPETExhWbrC1sm89+WFuYlZpALe+8RMURJ+ni1k9AezVRBfOFWw9g9VMgAi+kShJ14nHB39mgl04bAMDt7FyYLPkDjl5nZBKMl1IfImWrPVSUFBGRlIXRP5+AudthOI8wkTku7/rd55IkMd3OzsXuc9fhcTwOEUlZxSYPxck75xEmmGNpBBUlReTk5WN7yFV4HI+Tqo4s63PLU9EdsS2NNXF40XD89uc1qSSs+HhJ9/7tz2uwNNaUOyYnLx+u/jFwHmEi834SERHRh2EPSiIiokogXnZ9+GQYwqMv4tnzXPTuboi8G1Eym7Xk3YiCtoY6riQlIygsEt7+R4ud12nqOMly7MLJNHHisOjxota6zJUkDjXU22K63ShRnMFhxV5zNvqizD1UlZthwVeTAABnos6X+7mrmnmfnvhm+kQc/HU9tq9xgf3CFQj//+coKvZyAoLCIvGfIku7KzovUUnEy4kDL6QiIikLOXn56KXTBg93zcCGKZ9Lxj3cNQMPd81Ah9bKSMjMRkh8Bnafu17svDMHd5Esxy6cTBMnDoseL8rNzlSSOGyv1gxTB3SWxFmcyKS7MvdQUVLEHEsjAMDZxDvlfu6qcjXjsczy65D4DKQ9yCn2mgu3HiAkPqPYCsvtIVcREp+BmYO7VGqsREREHzNWUBIREVWCFQscRUugC/WVLK5nY9Hl0yVp3bKF3ONl6SEJQGrzFwCSZOXOEnajFsfWrqf8JZ4u7lvxzfSJAMr33EVVtAdlccZYWWDOcnds89kvN449AScAAGafda/UeYlKsuzLXgiJz5DqK1lcz8aiy6dL0kpZSe7xsvSQBCCz+Ys4WekTnlhsAlEcm+48X7nnXf1j4DRUlKwsz3MXVdEelAGxKXD1jyl2F+9mjRvKrYDcH3UTANC3Uzu5c3ocj0PwdzbFvvdERERUfkxQEhERVYJu+nrIuxEltQFOUFgkrC3MsGKBo2RJtLd/INw9fTBzoi1GW1lArbkqPmndEpp9rQV+gg9T1ueuTuLkrbivZ2EPHz/BTr8AuDg5lDnJW5Z5iUpjqKGGh7tmSG2AExKfAUtjTSz7spdkSfTuc0nwOB4HB3MD2PTShlrTxmjbXAkGC/cK/AQfpqzPXRWK67tp21sXjl5ncCgmRebco+d58AlPhPMIE7lJXvGcVj8Fyr1n4c2HiIiIqOyYoCQiIqpE3fT10E1fD6OHWSAl4zas7OchKCxSUgk4Z7k7AEhVLz57nltl8WRm3ZdUTQLAzTTRUkcXJ4dir5k50RY7/QIkPS7LorTnlqeiPSjHzl6CoLBImTgfPn4ieY6iUjOzAAC9uhW/NPND5iUqK0MNNRhqqMGmlzZSH+Rg9M8nEBKfIUloOfuKEuCFqxdz8vKrLJ7b2bmSqklAtPkLINppuzgO5gbwCU+U9Lgsi9KeW56qTvLJ23k77eFzAEAP7dZVem8iIiKSxh6URERElWC+6wYodeqH2MsJAERLqXU12xc7XpwoFG8+U1W8/Y8iM+s+AFGyct+RkwCAgSUsUR5tZQEA2Pz7PklSDhBtnqPUqR9+8faTHCvvc1em8SNFS9APFeqn+ex5LvYdFT2j+DkKu3ZDtHtvJx1NmXMVmZeoNIv3/IXWM3bhwq0HAERLqbXbqBQ7XpwoFG8+U1V2n7uO29miX5Lczs6F/9/JAAAz/U+KvcamlzYAUS/GR8/zJMcjkrLQesYueIb+G295n7syudmZSuIqnOQV7+ItPl9Y4u1sAIBeO1WZc8C/PUKL/il6noiIiMqHFZRERESVYIrtcOz0C8BAu5ky57avcZF87btpFewXrkC3oRPkznMzLUOmb2RFdTKXrvhzcXIosYeieZ+ecHFygLunj0yvTGsLM0waNUzyuqzPXRXGWQ/B/mOhmLPcXVKZKlbcM15OEG020lxZuVLnJSrN+H4d4ROeKHdpsIe9meRrL8dBcPQ6gz7fH5A7T8r9ZzJ9IyvKZMkfUq+dR5iU2COyv746nEeYwON4nEyvTEtjTdj1/be1Q1mfuyrY9dVD1PW7GP3zCZlzReMUu5LxGACg2qRslaFERERUOZigJCIiqgS9uxsiJtAXASfDJEk9FycH9OrWRbLTNSBKfj1/8VKS+HJxcsCkL4ch71U+TG3sERF7uVITlK4LHNFcRRku7lvLtYGN6wJHdOmog4jYOOz0CwAgSjiOHNxfauOesj53VTn463ocCDqF/cdCERQWKentWdwzip+luM2HPnReotL00mmDcNfRCLyYKknqOY8wQQ/t1pKdrgFRf8TcV28kS72dR5jArq8eXuW/g7nbYURdv1upCcplX/aEahNFuPrHlGsDm2Vf9oS+enNE3bgHn/BEAKKEo5WJltTmMWV97qrQSlkJnjPMcfpqJg7FpEh6X44x1cVgIw25y9PFz8INcIiIiKpXvYKCggKhgyAiIqoMkydPxrvnj+CzcaXQoQhOvEN2Rfs8Us3h8O1KNFBuhb17a+dmKTXJvn37MHny5I96KS43c6k7DsWkYPbOM+DHOiIiqs3Yg5KIiIiIiIiIiIgEwwQlERERERERERERCYYJSiIiIiIiIiIiIhIMN8khIiKqg9h7kohKwt6TREREVJOwgpKIiIiIiIiIiIgEwwpKIiKiWqI278z97HkuDgWH4URYJILCImFtYYbxI4di6IA+UFVuVu75riQlw9TGXu578ex5LkLPRWP/sVDJvYZbmGHk4P5o3bKFzPgDQackY2dOtMWMibbopq/3Qc9JJKTavDN3Tl4+jp6/hZD4DITEZ8DSWBNjTHUx2EgDKkqKZZoj5f4z+P+dDI/jcQAAD3szWJlooZWyksy9Tl/NxKGYlFLvVZ6xRERE9OGYoCQiIqIq98PPO7DTL0DyOqhQovLgr+vLNdfDx09gamMv99yz57n4avEqBIVFytzrRFgkdvy4TCpJOXb2EqmxO/0CsNMvAL6bVmGc9ZByxUVEH271ofPwCU+UvC6cqNwzb2ip1ydkZsPc7bDUMWffSITEZ8BzhrkkmfjoeR4W+EQgJD5D7r02O/SXJDTLM5aIiIgqhglKIiIiqlJXkpKx0y8ALk4OmG43ChrqbZGZdR8bfvPFTr8A3EzLQMcOmmWeb/WWXcWeCz0XjaCwSGxf44IxVhZQVW6GZ89zsfn3fXD39MG+oyfxzfSJAESVk0FhkXB3mQeHcSMllZwHgk7BfuEK9DExgoZ624o9PBGVKiEzGz7hiXAeYYKpAzqjvVoz3M7OxS8n4uETnoiU+8+g21a12Otz8vJh7nYYlsaacJ/cD+3VmiEnLx97Iq7D1T8Gp69mwra3LgAgOC4dIfEZ8HIcJDkGAAGxKXD0OoPguHRMHaBf7rFERERUMexBSURERFXqwpV/AACTvhwmSfhpqLfFjIm2AIDLCTfKPNcv3n7Iuv+w2PP7j4UCAKbb2UgSjqrKzbDgq0kAABf3rTJjCycnAWDogD4AgD8jY8ocFxF9uEupDwAAdn310F5N9L3YXq0ZHAYaAACupD8q8fobd58CAMaY6kquV1FSxJT+nQEAh2JSJGOdfUUV04UTjoVfi8+XdywRERFVDCsoiYiIqohSp36YOdEWW9wWy5yb77oBO/0CcO9iKFSVm+FKUjLORJ2XJNDEPRpLWmZcXE/K4o6HR1/E4eAw7PQLgLWFGeY6jId5n55leo7SlNQXMzPrHgCgTUs1qeOftG4JAPjn5q1S5wdE8bu4b0VMoK/UsuzCilsuLq/PpXiOoufEry8nXC9TXEQfqvWMXXAwN8CGKZ/LnFu85y9R9eBWe6goKSIhMxtnE+/A1V+UOBf3QiyaPCs6PyDbk7K44xFJWQi8kAqf8ERYGmti1hdd0V9fvUzPUZqS+mLezn4hmkdFerl02+ai10lZT0ucOzb5PgDgMz3pimcVJUWZ+1oaa0ot2S7K0ljzg8YSERFRxbCCkoiIqIq4u8zDTr8APHz8ROr4w8dPsNMvAO4u86Cq3AxBYZEwtbGXqu4LCouE/cIVOBB0qlJicdvsBSv7eZI+kEFhkbCynwe3zV6VMn9J3D19AMgmAsW9IMXnS3IzLQNW9vPgu2nVB21gczNNlGTw3bRKcszawgyAqG9lYeLXhXtmElUFNztT+IQn4tHzPKnjj57nwSc8EW52plBRUkRIfAbM3Q5LkpOAqBeio9cZBMSmFJ32g6w9chGjfz4h6QMZEp+B0T+fwNojFytl/pKIN7UpuumMuL+j+Hxxoq7fBSCqugyITcGUraFoPWMXPEOvyry34iXZRd838evCS7bLM5aIiIgqhhWUREREVWRQv88AiCr/CldChkeLPvAPtxBVTY2dvQQAcNZ/J3p3NwQAZGbdRydzW9gvXFHhzVrCoy/C3dMHLk4OWPDVJJm+jLbDLEpM+gm9a/iz57lY5r4NLk4OH/xe7DtyEtYWZpLl2wAwfuRQBIVFIvRctGRe8ftCVB0GGnwKAIhIzJKqhIxIzALwb4XelK2idgTB39mgl04bAMDt7FyYLPkDjl5nSqyiLIuIpCx4HI+D8wgTzLE0goqSInLy8rE95Co8jsfBpqc2DDXUir1e6F3DxVWOa49clEpmuvrHIOr6XalNciyNNXF40XD89uc1OHqdkYwVHy9cMVqesURERFQxrKAkIiKqIt309WBtYSbpdSi2/1goZk60lWwMk3cjCnk3oqCtoY4rSckICouEt/93fU0iAAAgAElEQVTRSovj7P8nRMXJSUC6L+OZqPOVdq+qsPn3fQgKi4TT1HEfdL3bZi+4e/pgxQJHmV6T1hZmsF+4Akqd+kGpUz+061n6bsFElcVQQw2WxppSPRIBUc9EB3MDycYwD3fNwMNdM9ChtTISMrMREp+B3ecqrwVBZJKoAlGcnARE1YxzLI0AAGcT71Tavapa4qbJkvfLy3EQQuIzcPpqptSYqxmPZZZuh8RnIO1Bjsx85RlLREREH44VlEREVKe8e/de6BCkzHUYDyv7eZKdqm+mZSAoLBLBvlulxomTaFVBPG9xyTcX962Sna3lqWgPyoo4EHQK7p4+OOu/U7IkvDzE72tMoK9MlaiqcjPs+HEZjp2OwJzl7lJ9P6vqv0VFvHv3Hg2EDqKOqF+/5vyOftYXXTH65xOSnapT7j9DSHwGDi8aLjWuaHVgZRLPqzvPV+55V/8YOA01Kvb6ivagrCyFE6wAMNhIA4Ao4SuuMg2ITYGrf0yxO3M3a9zwg8YSERFRxdScf50RERFVUKNGjZCT+0LoMKSYGIp2kY2IvQzg3x2rxccBwNs/EO6ePpg50RbBvqJNYDL+Dqr+YKuIi5MDgOJ7PYrPy2O/cAUAYKDdTEmVY+GEadHXYg8fP4HbZi9cTUrGldA/il3C3rplC0y3s0HejSgc/HU9xlkPQWaWaMMNd5d5ZX7G6vDseS4aN24sdBh1gqqqqDIx99UbgSMBjLVaAfi3j6J4x2rxcQDYfS4JHsfj4GBugMOLhiPcdTQSN02u/mCriPMIEwBATl6+1HHxa/H50q4v2sNS/LpwBaR4qXZxO3MXrmYtz1ghvcp/C+VmTYUOg4iIqEJYQUlERHWGuro6zv8tf3dnoagqN8P2NS6Ys9wdIwf3h/3CFdi+xkVqqfGc5e4AILXbd9FkXlkV3ZAHAGZOtJXaMby8Klod2aWjDgDgweNsqfun3xHt7q2h3q5C8xd1JSkZqzZ7wUhfDzt+XFZs5eXY2UsQFBYp876kZNwGAKi3bV2pcVXU3YeP0XvAJ0KHUSeoq4t6B957+hJ67VQFjUVFSREe9mZw9o2ElYkWHL3OwMPeTCrZ5uwr+rlWeLfvosm8siq6aQwAOJgbSO0YXl4VrY7UV28umicnT+r+mY9EPwfbq5WcfBNffzs7F+3V/v1eFr9HDuYGZY6lpF27KzK2Kt19+hLqn/BnAxER1W6soCQiojrD2NgY11NS8Tpf+Kqowvr37g4A0OxrDQD4wsxU7jjxTtNl3ahFvAt17OUEyXWeuw/IjBttZQFA1MuxcAIzPPoilDr1wy/efmV9lA/SWbcDANFGNeLqxMys+wg4GQYA6NWtS7HXivtzFv1T9LxYZtZ9mNrYw0hfD64LHEtcFj5+pGjJ+6HgMMmxm2kZOPz/r/uYFL+ktbq9zn+D6ympMDY2FjqUOsHAwACNFBWRkPlY6FAAAP06i5JLBgv3AgAGdW0vd1zK/WcAINnApjTiTXYu3HoguW7n6X9kxtn00gYAbA+R3vU6IilLsht2Ver0iej71P/vZNzOFiUlb2fnIvBiKgCgh3abEq//TK8tAGD3uetSiVtx78kh/7/UGxDtnA6Inq3wWPHO3OLz5R0rpH/uPIFx95KrTImIiGo6VlASEVGdMXjwYLx/X4CI2DgMMestdDgSHTtoSqoYZ060hYZ6W6nzvptWwX7hCnQbOkHu9eL+lUWJd6EeaDdTckzesmTzPj3h4uQAd08fmd6K1hZmmDRq2Ac8VdmJNwuSd/+ZE21lll+Ll2x/SOXmn5ExACD3XmLiecWb5MxZ7i6pYhXz3bRK5r+TkCJi4/D+fQEGDx4sdCh1gqKiIgYO6I/wxDsY9ZmO0OFAt62qpIrRwdxAqgoQALwcB8HR6wz6fC/7CwgAkv6VRY0x1UVIfAasfgqUHJOXVOuvrw7nESbwOB4n0+fS0lgTdn3lt0ioLOLNguTd38HcQGYHcXHPS3HlZnu1ZpL3SN714kQtANj11UPU9bsY/fMJmTiKPmt5xgrl3fsCRF6/h/WzuMEXERHVbqygJCKiOkNNTQ1fDBmCgydOCx2KDHEV4xTb4TLnxlkPwfY1LpLXLk4OuBL6B2ICRRtWiPtXyrvOd9MqSSXl9jUuxW5247rAEb6bVmHmRFvJse1rXEpcAl2Zdvy4DNvXuEhitbYww/Y1Lli96OtKvU/RRGNJxJvkFH3vYwJ9Mc56SKXGVVEHgk7hiyFDoKamVvpgKhO7CRMRfDkT+W/fCR0KgH+rGMf36yhzzra3LjzszSSvnUeYIPrHcQh3HQ3g3/6V8q7zchwkSdB52JsVu9nNsi97wstxkNRyaA97M2x26I9Wykof9lDlsNmhPzzszSSxWhprwsPeDD+M+axM19v21kXwdzaS+C2NNeHlOEhqWTwAtFJWgucMc6n3RTzWc4a51LOWZ6xQwhNu4+XrN/jyyy+FDoWIiKhC6hUUFBQIHQQREVFlCQoKwrixY3Ej/DBaqTUXOhyiCnuU/RSdzEfjwMGDsLa2FjqcOuPly5fooNkeP9gYYeLnnYQOh+iDTNp2Gu27D4D3f32EDoWIiKhCWEFJRER1irW1Nfr164sVHr8JHQpRpVjh8Rv69evL5GQla9KkCVat+QlrA+Px4nXN6ltLVBZnEu7gr6S7WL3mR6FDISIiqjAmKImIqM7Z/MsW7D1yAheuyG4GQVSbXLjyD/YeOYHNv2wROpQ6aebMmWij3h4bj8tvo0BUU73Kf4vv/c/DZdkyfPrpp0KHQ0REVGFMUBIRUZ3TtWtXODk5YcK873Hn3gOhwyH6IHfuPcCEed/DyckJXbt2FTqcOqlBgwbYun0HtodcwfFLaUKHQ1QmBQXAwt1ReKvQBIuXLBE6HCIiokrBHpRERFQnvXr1CuYDB+J9/kuc/N8WNFFqLHRIRGX2Mu8Vhk2bj/qKTRB+9iwaN+b/v1XJ3X0tVq1ciYBvh6GnThuhwyEq0frAS9j+5z+IjomFkZH8TY+IiIhqGyYoiYioznr48CF6f/YZNNq2xP7tP6GFqorQIRGV6smzHIyf8x0y7z9G7PnzaN26tdAhfRSmTpmMk8ePwmf2IJh2bCd0OEQyCgoAj6A4/HzsMg4dPgwbGxuhQyIiIqo0XOJNRER1VuvWrXEiOBh3Hj7BgHGOSE7LFDokohIlp2ViwDhH3Hn4BCeCg5mcrEa/e/8X5kMsMcbjJA5EJwsdDpGU/LfvMMf7HDyCrmDX778zOUlERHUOE5RERFSnGRgYICY2Fu0+1YDZmBnYsecg3r57J3RYRFLevnuHHXsOwmzMDLT7VAMxsbEwMDAQOqyPiqKiIvwPHMQSl2WY630W830icP/ZS6HDIkL0zXsYtjYIpxPvI/TPPzFt2jShQyIiIqp0TFASEVGd16pVK5w6fRqznZzg4r4NvW2mIfRcNNjlhIRWUFCAkHN/o7fNNLi4b8NsJyecOn0arVq1Ejq0j1K9evWwatUqHDhwEH+l5aLvD4exPeQqXrx+I3Ro9BFKfZCDWTvDYbM+COqdjRF74SIGDhwod+zTp08RHR1dzRESERFVHvagJCKiOuHWrVvIysqCmZlZieOSk5Px7bfOCAw8Bj1tTdgONcfAPj1goKcNteaqaNxIsZoipo/Rq9f5yH76DInJqTgbfQkBoeFITs2Ajc1IbNzoAT09PaFDpP+Xl5eHn376CR4bf0aDesCIHloY1EUdRpqt0Fa1CZSVGgodItUh7wsK8PRFPtIe5uDSrQcIvnIbkYm3oautjXUbfoatrW2J1+/atQszZ86Eubk5XFxcYGlpWU2RExERVQ4mKImIqFaLj4/HunXr4O/vjyFDhuDkyZNlui4xMRE+Pj44EXQc1xL+qeIoiWR1NeyC4dYj4ODgwOXcNdjTp0+xd+9eHDl8COciIpH/htWUVLVat1TDMKvhGD9hAqysrFC/ftkWvYWHh8Pd3R0hISEwMTGBi4sLxowZgwYNGlRxxERERBXHBCUREdVK586dg7u7O06ePIlu3bph6dKlGDduHBQUFMo919OnT5GQkIDHjx/j9evXVRBt3bRlyxYAwPz58wWOpPZo1KgRWrZsCUNDQzRv3lzocKic8vPzkZiYiLt37+L58+dCh1PjRUZGYsuWLfD39xc6lBqvfv36aNGiBXR0dNChQ4cKzRUXFwd3d3ccPHgQOjo6WLJkCezt7dGoUaPKCZaIiKgKMEFJRES1RkFBAY4fP45169bhr7/+woABA7B06VJYWVmhXr16Qof30Zk8eTIAYO/evQJHQkQ10b59+zB58mT2+xVIcnIy1q9fD19fX6ipqWHhwoWYPXs2lJWVhQ6NiIhIBjfJISKiGu/t27fYs2cPunXrhlGjRkFNTQ2RkZE4e/Yshg8fzuQkERFREXp6evDy8sKtW7cwZcoUrF69Gpqamli+fDkePHggdHhERERSmKAkIqIa6+XLl9i+fTs6duyI//znPzA2NsaVK1cQGBiIzz//XOjwiIiIajx1dXWsX78e6enpWLRoEXbu3AltbW3MnTsXaWlpQodHREQEgAlKIiKqgZ4+fYoff/wR2traWLx4MaytrXHz5k3s2bMHXbt2FTo8IiKiWqdFixb4/vvvkZqainXr1iEoKAgdO3aEvb09rl27JnR4RET0kWOCkoiIaoy7d+9iyZIl0NTUxM8//4yZM2ciLS0N27Ztq/CmAURERAQ0adIEc+fOxc2bN+Ht7Y24uDh069YNNjY2iIqKEjo8IiL6SDFBSUREgktOTsasWbOgra2NPXv24IcffkB6ejrWrFmDNm3aCB0eERFRnaOgoICpU6fiypUrOHr0KB49eoTPP/8cAwcORHBwMDc3IiKiasUEJRERCSYuLg4TJkyAvr4+wsLCsGXLFqSmpmLx4sVQUVEROjwiIqI6r169ehg5ciSioqJw9uxZNG3aFNbW1jAxMYGfnx/evXsndIhERPQRYIKSiIiqXXh4OKysrNCjRw9cv34de/fuRVJSEhwdHdGoUSOhwyMiIvooDRgwACdOnMClS5fQpUsXTJ06FZ06dcKvv/6KV69eCR0eERHVYUxQEhFRtXj//j2OHj2Kvn37YtCgQcjLy0NwcDDi4uIwfvx4NGjQQOgQiYiICED37t2xb98+XL9+HUOHDsXChQuhra0Nd3d35OTkCB0eERHVQUxQEhFRlXrz5g18fX1hZGQEW1tbtG3bFn///TfCw8MxbNgwocMjIiKiYujq6mLHjh1ITU3FtGnTsHbtWmhqauK7777D/fv3hQ6PiIjqECYoiYioSrx8+RJbtmxBx44dMWPGDPTq1QvXrl3DkSNH0KdPH6HDIyIiojJq164d3N3dkZGRgaVLl8Lb2xsdOnTAnDlzcOvWLaHDIyKiOoAJSiIiqlTZ2dlYvXo1OnTogGXLlmHUqFG4efMm/ve//6FLly5Ch0dEREQfSFVVFcuWLUNaWho2btyI4OBgdO7cGZMnT8aVK1eEDo+IiGoxJiiJiKhS3LlzB4sWLUKHDh2wefNmzJo1C2lpafjll1+gpaUldHhERERUSRo3bgwnJyfcuHED//vf/3Dt2jV0794d1tbWiIyMFDo8IiKqhZigJCKiCrlx4wZmzpwJHR0d+Pn5wdXVFWlpaVi9ejVat24tdHhERERURRQUFDBp0iRcvnwZx44dQ05ODvr37w8zMzMcP34cBQUFQodIRES1BBOURET0QS5duoRx48bBwMAAZ8+exfbt25Gamopvv/0WysrKQodHRERE1aRevXqwtrZGREQEIiIi0KJFC9jY2KB79+7Yu3cv3r59K3SIRERUwzFBSURE5RIWFoahQ4eiZ8+euHXrFvbv34+kpCTMmDEDioqKQodHREREAjIzM8OxY8dw+fJlGBkZwcHBAZ06dYKnpyfy8vKEDo+IiGooJiiJiKhU79+/R0BAAExNTTF48GC8e/cOoaGhuHjxIsaOHYv69fnXCREREf2rW7du2LNnD27cuAErKytJn+q1a9fi6dOnQodHREQ1DD9REhFRsfLz8/Hf//4XXbt2xdixY/Hpp58iOjoap0+fxhdffCF0eERERFTDaWtrY/v27UhLS8NXX32FdevWQUtLC0uXLsW9e/eEDo+IiGoIJiiJiEjGixcvsHnzZujp6WH27NkwNTXFtWvXcPjwYZiamgodHhEREdUybdq0wU8//YSMjAx8//338PX1RYcOHTB79mykpKQIHR4REQmMCUoiIpLIzs6Gm5sbtLS0sHz5cowePRrJycn473//CwMDA6HDIyIiolpORUUFS5YsQWpqKn755RecOnUKnTt3xoQJE3D58mWhwyMiIoEwQUlERLh9+zacnZ2hqamJLVu2YO7cuUhPT8fmzZuhoaEhdHhERERUxzRu3BizZs3C9evXsWfPHly/fh09evTA8OHDcfbsWaHDIyKiasYEJRHRRywpKQnTp0+Hrq4uDhw4gNWrVyMjIwMrV65Ey5YthQ6PiIiI6rgGDRpgwoQJuHTpEoKCgvDy5UuYm5ujX79+CAwMREFBgdAhEhFRNWCCkojoI3T+/HmMGTMGhoaG+Pvvv7Fjxw6kpKRg4cKFaNq0qdDhERER0UemXr16sLKyQnh4OKKiotCqVSt8+eWXMDIywu7du/HmzRuhQyQioirEBCUR0Ufkzz//xJAhQ9C7d29kZmbC398fCQkJmD59OhQVFYUOj4iIiAh9+/ZFYGAgrl69ih49emD69Ono2LEjtm3bhpcvXwodHhERVQEmKImI6rj379/j4MGD+OyzzzB06FDUq1cPp06dQmxsLMaMGYP69flXAREREdU8hoaG8PX1RXJyMkaOHImlS5eiQ4cOWLNmDZ48eSJ0eEREVIn4qZSIqI7Kz8+Ht7c3DAwMMH78eGhpaeH8+fP4888/MXjwYKHDIyIiIioTLS0tbN26FampqZg1axY8PDygpaWFRYsWISsrS+jwiIioEjBBSURUx+Tm5sLDwwM6Ojr4+uuvYWZmhsTERBw8eBC9evUSOjwiIiKiD9KmTRusXr0a6enpWLFiBfz8/KCjowNHR0fcvHlT6PCIiKgCmKAkIqojHj16hBUrVkBLSwuurq4YP348bt26hd9//x2dOnUSOjwiIiKiSqGsrIxFixbh1q1b2Lp1K8LDw6Gvr4/x48fj0qVLQodHREQfgAlKIqJaLiMjA9988w06dOiAHTt24JtvvkF6ejo2btyITz/9VOjwiIiIiKpEo0aNMHPmTCQlJcHPzw83b95Ez549YWlpiTNnzggdHhERlQMTlEREtdQ///wDBwcH6Onp4ciRI/jxxx+RlpaGFStWQE1NTejwiIiIiKpF/fr1YWdnh0uXLiEkJAT5+fmwsLBAnz59cOTIEbx//17oEImIqBRMUBIR1TIxMTGwtbWFkZERzp8/Dy8vLyQnJ+Obb75B06ZNhQ6PiIiISDBDhw7FmTNnEB0djU8++QRjxoxB165d4ePjgzdv3ggdHhERFYMJSiKiWiI0NBSDBg1Cnz59cPfuXRw6dAhXr16Fg4MDGjZsKHR4RERERDWGqakpAgICcPXqVZiamsLR0RG6urr45Zdf8OLFC6HDIyKiIuoVFBQUCB0EERHJ9/79exw8eBDu7u6Ii4vD0KFDsWzZMpibmwsdGn1kMjIyMHToULRr107qGABoampKjt27dw9//fUXWrZsWe0xEpGwpkyZgvj4eMn3/9OnT5GRkYFu3bpJxty+fRsbN27EqFGjhAqTPlIZGRnw8PDArl27oKSkhHnz5mHu3Llsi0NEVEMwQUlEVAO9fv0avr6+WL9+PW7duoWxY8di6dKl6NGjh9Ch0Ufq2rVrMDIyKvNYQ0PDKo6IiGqaevXqlWncypUr4erqWsXREMn36NEjbN26Fdu2bUN+fj4cHR2xcOFCtG/fXujQiIg+alziTURUgzx//hwbNmyAjo4O5s2bh0GDBiEpKQn79+9ncpIE1bVrV+jr65c6Tl9fn8lJoo+Um5tbmVqOTJgwoRqiIZKvVatWcHNzQ3p6Otzc3LB//37o6uriq6++wvXr14UOj4joo8UEJRFRDfDgwQP88MMP0NLSwurVqzFp0iTcunULXl5e6Nixo9DhEQEApk2bVmLyoWHDhpg2bVo1RkRENcmkSZNK3ISkXr16MDQ0ROfOnasxKiL5mjVrBmdnZ9y6dQs7duxAZGQkunTpgrFjx+LChQtCh0dE9NFhgpKISEDp6emYN28etLW14eXlBWdnZ6Snp2PDhg1QV1cXOjwiKXZ2dnj79m2x59++fQs7O7tqjIiIahI9PT2YmJgUu9S7QYMG/CUG1TiKioqYPn06EhMT4e/vj/T0dHz22WcYMmQITp8+LXR4REQfDSYoiYgEkJCQAHt7e+jp6eHYsWNYt24dUlNTsXz5crRo0ULo8Ijk0tHRQc+ePeUmH+rVq4eePXtCR0dHgMiIqKaYNm0aGjRoIPfcu3fvuLybaqz69etjzJgxOH/+PP78808AwJAhQ9C7d28cOnQI79+/FzhCIqK6jQlKIqJq9Pfff8PGxgZGRka4dOkSvL29cfPmTcydOxdNmjQROjyiUhWXfGBlFBEBwLhx4/Du3TuZ4/Xr10e/fv2goaEhQFRE5TNkyBCcOnUKsbGx0NTUhJ2dHQwNDeHt7Y38/HyhwyMiqpOYoCQiqmIFBQUIDg7GwIED0a9fPzx69AhHjhzB1atXMXXq1DJtKEBUU9jZ2aGgoEDmeEFBAZd3ExHU1dXRv39/1K8v/TGjXr16mDJlikBREX2Yzz77DAcPHkRCQgL69u2Lr7/+Grq6uti0aRNyc3OFDo+IqE5hgpKIqIq8e/cOf/zxB3r06AFra2s0bdoU4eHhiIqKgo2NTbE9uohqsjZt2mDAgAFSVZQNGjTAgAED0KZNGwEjI6Kawt7eXu7fcWPHjhUgGqKK09fXh7e3N1JSUjBu3DjJxoYrV67Eo0ePhA6PiKhOYIKSiKiSvXr1Cr/99hs6d+6MKVOmQF9fH5cuXcKJEycwcOBAocMjqjB7e3upKsqCggLY29sLGBER1SRjxoyRSlA2aNAAX3zxBVq1aiVgVEQV1759e3h4eCAjIwPz58/Htm3b0KFDByxYsACZmZlCh0dEVKvVK5C3TouIiMotJycHv/76KzZt2oQnT57AwcEBixcvhq6urtChEVWqnJwctGrVCm/evAEANGzYEI8ePYKKiorAkRFRTTFixAiEhITg7du3qF+/Pnbv3o1JkyYJHRZRpXrx4gV27dqFjRs34t69e5g8eTKWLFkCAwMDoUMjIqp1WEFJRFRBDx48wHfffQdNTU38+OOPsLe3R1paGn799VcmJ6lOUlFRwciRI6GgoAAFBQWMHDmSyUkikjJ16lTJZjkNGzbEqFGjBI6IqPI1bdoU33zzDZKTk+Hl5YWYmBh07doVo0ePRkxMjNDhERHVKkxQEhEV8dtvv2HChAmljktNTcWcOXOgpaWF33//HUuXLkV6ejrWrVuHdu3aVUOkRMKZNGkS3r59i7dv37IqiohkjBw5Eo0bNwYA2NjYoGnTpgJHRFR1FBUV4eDggGvXruHgwYPIyspCnz59MHjwYISGhgodHhFRrcAEJRFRIVu3bsXs2bOxf/9+hIWFyR1z5coVTJkyBZ06dUJwcDA2btyItLQ0LFu2DM2bN6/miImEYW1tLfdrIiIAaNKkCXr16gUA3L2bPhr169eHra0toqOjcfr0aTRo0ACWlpbo2bMnDhw4gPfv35d4fWxsLIYNG4Y7d+5UU8RERDUHe1BSrZWXl4dTp07h5MmTOB8TjVupqXjy9Fmpf/FT7abcrCk+adcOxiYmGDLkC4wcORKffPJJpczt4eGBRYsWoaCgAAoKCujfv79UkjIyMhLr1q1DUFAQjIyMsGTJEowfPx4KCgqVcn+q+S5cuIDg4GBEREQgISEB2dnZePXqldBhkYAaN24MNTU1GBoaon///rCyspIkZYjEsrKycPz4cZz6809cjruIe/ce4PmLF0KHRZWofv36aKGqAm1tbfTu0xfDhg3DF198IakipY/XpUuXsHbtWhw+fBi6urpYsmQJpk6dikaNGsmMdXR0xM6dO6GlpYWIiAhoaGhUSgzSn5ticCv1Fj831UHKzZrhk3Ztq+RzElF1YIKSap2nT59i3bp18PrtV+TkPEfvLtro2UkD2p+0hGozJTSoz8Lguuz5y1e4l52DKylZOHclGbkv8zDCegRWurmhe/fuHzzv+vXrsXTpUpnj58+fx/379+Hu7o7IyEh8/vnncHFxgbW1tdQOpVR3FRQUwM/PD+7u7rh69Sr0dHUxyHwADAz00VKtJRo3lv2AQR+PV69e43H2YyQmJuFM+Dkkp6TAyMgILi4umDhxIn9OfOQuX76Mla4rcDwoCMpKjfC5Xit0a98cbVQao1lj/nKrLnn/vgDP8t4g7VEuLmU8w4VbD6CirAzH2V9j6dKlXGFBuHnzJtavXw9fX1+0atUKzs7OcHR0hLKyMgDg7t270NLSwps3b9CwYUO0bdsWkZGR0NLS+uB7ynxu6qqHXvodoP1pa6g2a4IG9fl3VF3y/MUr3Mt+his3M3E2Lgm5L19hhLV1hT8nEVUXJiip1nj//j18fHzgsnQJ3r/Nx7zRAzFlaG+0Um0mdGgkkDdv3yE4OgFbDp/F5ZsZcHScBTc3N7Rq1apc8/z000/4/vvvZY43bNgQWlpaSElJgZWVFZYtWwYzM7PKCp9qgYsXL2Lu3LmIjY2F/ZRJmDfHCSbdjYUOi2qwuMvx2LrdE7579qF3797Ytm0bevbsKXRYVM0ePXoEV1dXeP32G4w11eBk0RGWRupo2IC/RP1YPHr+Gn7RqdhxJhn1FRvDff0GODg4oD5/kf7Ry8rKgoeHB7y8vKCgoIA5c+Zg/vz52LhxIzzZNIwAACAASURBVDw8PPDmzRsAon+Htm7dGhEREdDR0SnXPaQ/N73BfLsvMHX452jVXLkqHolqoDdv3+FEVDx++SMUl2+kffDnJKLqxAQl1QrPnj2D3bixCAs7g69G9MN3U4dBtamS0GFRDVFQUIB9f57Hat+TgIIijgYeQ+/evct0rZubG1auXFns+Xr16uHo0aMYOXJkJUVLtcWGDRvg4uICs8/7YdPP65mYpHKJuxyPhYuWIPKvKLi7u2Px4sVCh0TVJDY2FqNGjkBBfh6WWRtggqk2WEj78XqW9wbrg67BJzIFFoMs4H/wIFRVVYUOi2qA7OxseHp6YsuWLcjNzUVBQYFM25iGDRuiZcuWOHfuHDp27FimeUWfm8YhLCwMX40yx/LpNlBt1qQqHoFqgYKCAuw9GYVVvx8FGjQs1+ckourGBCXVeKmpqRhpbY2c7AfYt8IBRjqfCh0S1VC5ea/x9cY/cOridfzP1xfjxo0rcfz333+Pn376qcQxDRs2xJQpU+Dt7V2ZoVIN9ubNGzg5OcHHxwebNq6H0yxHLtOlD1JQUADP37yw8NslcHBwgKenJxo2bCh0WFSFDhw4gGn2UzFIvy22TumFZo24jJtErt1+imm/R0Ol9Sc4HhQMbW1toUOiGuLly5eYOHEiTpw4gbdv38qcV1BQQIsWLRAREYHOnTuXOFdqaipGjrBGTvZD+K1xQje9yulhSbVf7stXmOXug1OxCWX6nEQkBCYoqUa7ffs2+pr2RluVRti34j9o24LLEqhk7wsKsNL7OLYeCsfu3bsxefJkueOWLl2KDRs2oCw/AhUUFJCeng51dfXKDpdqmIKCAowbNw6nTp3C/n27MXTIYKFDojog9NRpjJ80FUOGDMGBAweY8K6j9u7di6lTp8LJojOWjzJCff53piIe5LzCtF1/48HrBvg75jzat28vdEhUA+Tl5aF9+/bIzs4udoyCggJUVVUREREBAwMDuWNu376Nvn1M0VZVCX+s+Rpt1VipS9Levy+Aq9chbNkfWuLnJCKhMEFJNdbLly/R17Q3lOvn49DqGWisyKoTKjuP/aex3u8Uws+ehampqeR4QUEBFi1ahE2bNpUpOSk2a9Ys/Prrr1URKtUgy5Ytw44dOxB+KgTG3YyEDofqkPgrV2E+xBJff/011q5dK3Q4VMliYmJgPnAAnId2xjdD5ScPiADg1Zt3mPDrX8hVVEN0zHk0acKltx87T09PzJ8/H+/evStxnIKCAlRUVHD27Fl07dpV6tzLly/Rt48plBu8xeF186HUiJ+bqHgb957AOt8TMp+TiITGBCXVWOPGjsGlmL8Q+vOcat8I5/aDJ2jfpkW13rOyFI29uZUzAOBpsEe1x5Lz4hUCIi4jODoBJ2MSMMzUEOMG9cAXvQyg0rRxqdeLY5enLM+zcNshHI9JwpWr19CuXTsUFBRg/vz52LZtm9zxDRo0gIKCAgoKCpCfny91bsKECfDz8yv1nlR7HThwABMnTsTRwwcwfJhlsePqN2oKAHj/+kWJ85V1XGVfWxFC3bc67//sWQ78Dx7C8aATOBZ0AiOth2PiBDtYWVpCVVWlzDHKU1rcJ06GYNTocfDz8+PSqjrk3r176NbVEMO7qGG9XQ+hw6lSd568xKct/k2otZ3nDwC4v9Wu2mPJyXuDwLhMhFzNQui1LAztqo4xvTRh0eUTqCiVnpwRxy5PVT/P49zXsN58Fj0/H4QDhw5X6b2oZnv79m25Wn8oKCigWbNmOHPmjNSuzOPGjsGl2Cic2rqEG+GUgYr5TABATvhOua+rQs6LPBw+cwHzf/YFACyZao0JQ/tCT6Ntma4Xxyh37g+Ie4HHHhyLuib5nERUE3AbOaqRgoKCcDI4GH+s+E+1Jye3HQpH12mrq/WelaWmxb7yv8fxzS/+OBmTAAA4GZOAr9x3w3HD3lKvvf3gSYXvv272l+j4iRpcli4BAHh7e8skJ5WUlKCjo4MBAwZg0qRJWLBgATZs2ICDBw/ir7/+Qnp6Ol6/fs3kZB2Xk5OD+fPnY8O6n0pMTlLtt2z5D5jlNBfHgk4AAI4FncCkqQ6w/89XpV6bkZlZoXsPH2aJDet+wvz585GTk1OhuajmcFmyGLotG+HHMd1LH1yL7Qi7jh4rjgsdhsSawCv41u8CQq9lAQBCr2Vhlk805vjGlHrtnScvqzq8ErVs1gi7Z/ZFcPAJBAUFCRoLCev169dSr+vVqwdFRUUoKirK3fH97du3eP78OQYOHIhLly4B+Pdzk/+Pc5icrMFm/vi7JDkJAOt3B6HH1OW4mlL6vy1u3y9++f+HWj9vAjp+2kryOYmoJmDnbqpx3rx5g28XfoNvxw9GJ4021X7/5bsCq/2elaW6Yr92KwvhcTcwd4x5iWO8g6KweOIXmDasD9q3aYHbD57Aw/80vIOikHznIfQ+bV3qvdbMsCnxPiVRVGiAzfPGwGzORsz+P/buPK7Kam34+K/e01PGCYeOmihqmGUMcRwID2rOjM6KioqaOOScllMRLx8ey1lTTHPAVDQHxAFFFBVBkQOBEglmKh6TwcSOwjZCX8+p94/dfctmD7AZBOz6fj59HvZ9r7XutbbPUfa1r3WtyVPo06cP8+bNw93dnVdeeQVra2ssLIxnQ4k/j4ULF9KsqRUzpk6ptDGrKwvxzyrtu4ucjDnNB+/PMNnmy42b8V8wj/F+79Lc2pqbWVksXrqcLzdu5srVq7xehlNSly1ZZPI5psyYOoWvd+1h4cKFLF26tFxjiJojKSmJXbt3EzOvN8/9n6f7e//AA2lP5DkZOfmc+eE2k3sYPwwkIyefbfGZzHKzxbeTDU3rv0jOvV9ZHf092+Izycy7T6tGpQdqAgc6mnxOVWrd+CXe7/0Gs9+fgaurqxyg9SdlYWFBUVERubm55ObmkpOTw61bt8jOzuann34iMzOTW7du8dNPP6nBzP/+979oNBrat2/P3bt3+WDW+3w4ypPXm0sWXFlVZqbkxcwsYlO+Z/owV6Nt9sUkE5WQxpoPRzO2TxcA4i5cpu/sFWw5FMeq2aPK9KxPJ3ubfI45/ue5v7Dmg1G4+AXx3uQpdOzYsVLGFaIiJEApapzg4GAeFRUyZeA71T0VUULy5R/ZdTKZLZEJACYDh+ev3ARgWM8O6pbzZo3qM87ThS2RCaRdyzYZoLx+62cA3nqtYqe2v27diAn9OjNr5nQSEr9h8eLFFRpPPH2uXbvGmjVrOH3imMFsBVGzJSZ9w/YdO/ly42YAk4HDb5JTABg10ofm1tqTTZtbWzNpwni+3LiZC6nfmgxQXsu8DkDbvzuWe77PPvssa1atoHtvdyZOnMhrr71W7rFE9fr999+ZOX0qfu+0pnVjyVqqqPM3/s2epBtsi88EMBk4TP1Rm03k/XYLdct50/ovMqZzK7bFZ3Ix657JAOW/7vwCgEOz6i3n817319n1zSmCg4OZPdt4WRvxdHvhhRewsbHBxsbGZLuCggJycnLIycnh+vXr3L9/ny1btvCfB4VM9e71hGYrFMmXrvP1sQRCIuIATAYOw05qM7sHde+gXuvarg0AIRGlBygzc/IAeKt18wrNuaTXm7/CxEE9mDVzBgmJSXKIn6h28klM1Cj/+c9/WLFsKZP7ufD8c6XHz+t5zKaex2zu5P/C2vBY6nnMZnhgCOFxqQbbh8elMjwwxGi74jUPlbHL40zaVWav3aeO8en2KNKv55ZrTqbmUvx6WeYeHpda6ntUkqbwAceSMhgeGELvWasB2B3ox9VdQSb7KVu0G5XYatK4gbbG2+UffyrT8yvD9IFdSTmfypkzZ57YM0XtsWrVKjo6v01H57fN7rt7bxjPPm9B/0He7N4bpnPv2ectDNYr3L03jP6DvHn2eQsCAoO4cvWq0balPQMgJjaOKdNnqmMEBAaR9t1FvXYFBRqdZ0+ZPpMrV6+Wa22G1mKqnbltS1NQoOFw5FH6D/LG5Z3uABzaH8ZP2TdM9sv6Y4t240a69Z6aNNFmnVy69H2552SOjs5v4/y2E59//vkTeZ6oGnFxcZy/kMp73Z9skDn+Sh5z95yn8fS9NJ6+l8VH0snIyTfY9uD5m/huiKfx9L34bojn4Pmbem2UcUxdL37fWPuD52+afI4hmqJHRKfn4rshHs8VpwAIndSZjM/6m+yXfVe7RbvhS7r1rBtb1gHg8q3aUULhf/7yLBO6vMrypUv4z3/+U93TETVc3bp1sbW1pXfv3kyaNIn333+flcuXMXlQ9zJ9bjIm7sJlZq3cgWW3CVh2m8DCkIN6246VeyWVvF789b6YZPX1vphkNIVF5Z6jobkO+2gtcRcuq/c0hUVYdpvArJU7DPZV+intTNV0NEZTWERUQhrDPlpLzynaA+/2fDaNzAOma+Pv+WwamthNWFrUUa9FJWiz0rcETDR7HpVpuncvUs5fkM9JokaQAKWoUaKjo7nz878Z1qND6Y2Lmf75HnV7s1LnsGQA7tPtUfgtDtWrh/jp9qjKmfwfjiVl0G/+ejXLEGDZrhN0nrqcM2m6wYAnNSfQ1qf0Wxyq8xxTQcrsvHuEx6XSfMhHbI1KxLt7O9K3fcLKaUNwd7ajYT3TtUGX7ToBoHcYjtJPuW/Md9dyAGjwkgXbjiWqQddtxxLRFD4wvdgSXnnZkh7t27B161dm9RNPv0ePHrFz507G+JZta01xKz5fwwjfscDjOoalBd0CAoMY4TtWrX+4cNES2tgbr1tX2jMORx6ll5unmj2ojNnWqSMxsXE6Y41+10/n2V9u3Ewb+78bDGaWZW0l16K0CwjU//LCnLam3MzKYvfeMOo3asLmkK/wGT6UG9cusy54NX29PGnU0HTZiIWLlgDoHYaj9FPuG5P6rfbDxMsNGrAp5Cs1KLwp5CsKCswLiIwd7cuOHTt49OiRWf1EzbFt61a6vWnFK3XrlN64kkSn5zI4OFbNMgRYdfwSPRZHE38lT6ft4iPpTNqaqFejcfGR9Eqf1/qYH5i0NVHnOaaClDn3fuXg+Zu0nnuA0HPXGdyhOReC+rB0WHtc7a3420vPm3zequOXAPQOw1H6KfeNuZit/RK1vsX/sCPhuhp03ZFwHU3Rk/3f5GCnFvz8738THR39RJ8raj/lc9Nw1/JvzY1KSKPv7BVqFiBoayN28gvSCf6VZ9xxQRvV1+OCNjLh05ByjwewMOSgzlyVuS8MOQiApUUdPp3sTUhEHHfu3dfpe+fefUIi4vh0srdOkLCssm/fZV9MMs28ZrD1yFm8ezlzac8SVs0ehYeLIw3rlz2LPnhPtBpg3RIwkSE9nErt891V7d+nDepasPXIWTXAuvXI2QoHfpv8rR4937aXz0miRpAApahRIiMjcbazKdMJz8U52Fhxc99n5EetJGLxZADCTl9Q759Ju8qyXSeY49Ob9G2fkB+1kvRtnzDHpzfLdp1QA4fFT4bOj1pZrpOvhwdq//FVnpMftZITq2YCcPDs4/pNZZ1TWZU294LCIvU92h2oPQyi+HtUkv2Y/8VvcSgh833ZHejH4K5tq+Vk885TlzNz9eNMjZmr9zJx2U6zg5SuTm04ekQK0Qtd586do6CgAE8P8w/GKcjP517eLX57WMih/drg3a7dxk+FjYmNY+GiJfgvmMeNa5f57WEhN65d5r2J48v9jP6DtCdBK+P99rCQhDOnAdhX7GTYw3+cWO2/YJ463tehWwHYsOlxcLOszzW2Fv8F81i4aIlOcNSctqVp+VobRviO5evQrRzaH8bwod7qVu0nqa1TRyZNmaa+njRlGqPf9TMrSOnp4UZBQQEJCQmlNxY1UmTkYXrZPtla2b4b4gG4ENSH28FDuR08lKMf9AQgIvVxxlP8lTxWHb/ELDdbte2FoD7McrNl1fFLesHM0hQ/1Vp5bnEFvz7i6tKB3A4eSuikzgCEpxgPULYLOMKkrYlsGNuR0EmdGdC+uc7p4E9Kj8XRfLArRX39wa4Upm5PeqJByrp1nsOpVSM5LEeYLTIyEmeH1uUKuCmGfaQ9PPLSniVoYjehid3EqXULADgYm2Kqq0lbj5xVx7y0Zwlzfb2ISkgrd9Az7sJlloZGMtfXi+zINWhiN5EduYa5vl4sDY1UMz67dXhT2z5V9znKaw+X8pVosR02j3FBG9kSMJE9n01jSA8nmjVuUK6x3mrdnE8ne+Ph4si4oI3si0kuc99OfkE6B+3MWL6dCZ+GVDhI6epsz1H5O0jUABKgFDVKyjdJ/P01K7P7TezXRQ1qvuOorR+mZCXC48CgclgLaOshjnHvqHO/Mrg726ljnkm7iqbwAU5tWpAftZKV04ZUy5xA9z1S5lj8PSopfdsnhMz3xW9xqLolvDJO1i4rJSP2xKqZasA1P2olIfN9OZaUwYkU87Zitm1tTd7PP/Pjjz9WxXRFLZWcnExza+tSM+8MmTZ1spqJ19fLE0DNEDQk9o9AnHI4C2jrH74/Y5rRPqU9Q7kWFn6AmNg4Cgo0dHR+m98eFrIueLXaLurYcb3xhg/11mtX1ucqwc+Saxnv967OfXPblubGtct8HbqVEb5j1W3iFT1Z2xxz5mk/tCWcOa0GhJVg7+HIo0QdP17msRo1bEhza2u++eabqpquqEL/+te/uPPzXf7evHwfUMvL1V77O1JEahbxV/LQFD2ifcuXuR08lKXD2qvtlGClcoAMaGs0+nay0blfWcZ3ba1mMypzVDI3DbkQ1IcNYzsyaWuiuiX8SZ6srRz4c/SDnmrA9XbwUDaM7Uh0ei4xl249sbkAvNX0JZKTEp/oM0Xtl5KcRNvXK/YlnRKwOxCbQtyFy2gKi3CytUETu6nMB7cY8ulkbzWA16xxA8b20Z4tUN6g59k/AowzhrupAVlLizrMGK79gjn2j88FDq2s8XBxVGs+KsJOJuHXryuvWeuWeCmrS3uWsCVgIuOCNjLso7Xsi0ku98naXdu1YfowV/Z8No01H45mXNDGUgO3H6/Xfll8at0CNZCsid3EloCJRCWkEZ1Uscz4dm+0IO+OfE4S1U8ClKJGuZaZiU2Tv5ndr7Ttxsp265IZgMrr4tuxK8p/tIf2/26OoN/89UxcttNgNuSTnBOU/h6V1KxRfQZ3bcvNfZ8x1qMjYacvYD/mf5m9dh/HkjK4k/9Lpc6vJCUg6dSmhc71wV3bAqazPw15tcnLgPZAFCEUmZmZtC7nISXmBjWV7cMlM/5MHcpS2jOCAgMAbeCsl5sno9/1M5iRqGwBL+ucS2unjFdyLcrr4lvOzWlbmubW1gwf6s29vFuM93uXXbv30vK1NkyZPpPDkUfJu3OnzGOVhxKQLFmvdPhQbSarqQxaQ15r1Yrr169X2vzEk5OZqd1i/WpD8/5traj5fewBbYBtcHAsU7cnGcyGVLaAl8xKVF4X3yJeGUrbkl1S0/ovMqB9c64uHYhvJxvCU27SLuAIc/ecJzo9l5/vP6zU+ZWkBCTbt3xZ5/qA9toDKExlf1aFln/7q/xdIMx27VomNk0rlsXt76et9/rx+jD6zl7BhE9DKrS1W1EyEKgEK4tvJTfH0lBtdl8zrxnq9mbLbhNo5qU9GE8J4AFMGdKLqIQ0rmXdBuBa1m2iEtIY0M28EmIl5z+khxPZkWsY26cLYSeTsB02j1krdxCVkKa3pbyslENz1u07abKdEpB0stU9SEnZHl4yIGuuV/84uFQ+J4nqJgFKUaMUaO7z0ovmbe+uaextrMiPWkn8Fx+ycHw/tSbl8MAQowfl1GSWFi/g7mzH7kA/dav68MAQWvsEmOw3x6c3gN5WbOW1cr+8TGV/GlL3r9pvW/PzDR8kIP6c7t+/r1ePsDZxfMuB3x4WkpqcyLIli9SalP0HeRusLfm0qFvXkr5enhzaH6Zuae8/yJtXmrU02c9/wTwAva3YymvlfnmZyqA1pF69umg0teMwD6GroKAAgJdeeK6UlpXLrmk9bgcPJWa+K4EDHdWalL4b4o0elFOTWdZ5Dld7K0IndVa3qvtuiMfuo0Mm+81yswXQ24qtvFbul5ep7M+qULfO/5AvfxcIMxVo7mNZwc9NDq2s0cRu4lxIAJ9O9lbrOg77aK3eQTm1Rds3tMkN8WlXAPj2j/qNyvWKsLSog4eLI3s+m6ZuhR/20VpaDSzfwapKNqhyYE55VbR/3b9qv7ySz0miukmAUtQo//3vf6tk3HFeLgB6W5Sv5dzRuV+Z7G2smDa4G+nbPiFi8WSOJWXQeerySptTVWcwGuLUpgUrpw1Rg6+mtGmhPRU3L1/3G8Wbf2yHKK2epXKyubEAp7l/Zv/nWe1fdw8fVm1Whqh9XnjBvMyf8lKCXyW3JFfGFmXHtxz44P0Z3Lh2mZPHtfUm2zo9Lpqv1LmsrAxDZbySc1dOBS9eV9OctuXR0flt1gWvVoO0ptjaamtT3c67rXP9xh9bmqxLqWepnEJuLMBp7lqe1P/vicqn/Fvyf559plqeb9e0HpN7vMGFoD6ET+9GdHouPRY/PmRlTOdWAHrbpjPz7uvcN6aqMxgNad/yZZYOa68GX01p00T7xdKd+7q/I2TdLQSgWQPT9SyVk82NBThLe3+qwn//+9sTf6ao3Srzc5NDK2umD3Pl0p4lHF75AVEJaXTyM32QnamswZLbn5Vsxrm+XuWan1+/rtpx/6g/aeg/haVFHdZ8OJoZy7dz5959xgVtZM2HoytUq9MQJ1sbVs0epQZ3TRn20Vr1BPHilPdQWZ+5/ZXXpfUvjXxOEjWFBCjFn8KALtpfdLcdS1QDgtl599hzSlsHxdXpTb0+5h7Copi9dh/1PGaTfFn7gbdZo/oGt62bMyelZqQypqbwARsjzhqdQ3nnXlZK8NWUN/7Y2rHnVIrO+g7Fa7/ha/96c5P9vbu3A9CrNam8Vt4/IWqLbt20vzxuDvlKDdbdzMpic0j5T02cMn0mzz5vQWKSto5hc2trXmtlo9funS7aQyvWfrFeDabt3hvGs89bMGX6TLOfO2TwIEB/LTt27gLAw92tXG0rQgnSmvJmmzYA7Ni5S2cu4fsPAPC2k+ntXz7DtQeDlKw1qbxW1ipEVZm75zyNp+/l/I1/A9qt0oa2mfdrqw22h567rgYpc+79Stg32t8jetk1UdsqNSOVMTVFj9gcZ/ygvqo+QEYJvprS+hVtgDLsmx911nc4NRuAti1M1wYd3EH7O0jJWpPKa+X9E+JpN2vlDiy7TSD5krbEQLPGDWhlYNu4UqtSaacpLGLD/lNGx9165IwapMy+fZfd0f8EoEvbNuWap7I9e83u4zqB0bgLl7HsNoHgPdE67Ts7vg6gZjb2crIr13PLQgnumuLdyxmA/acf1+DUFBap70tp28+V/iVrTSqvK7J9XYia5C/VPQEhnoR3HFurp2Mv23VC594cn95qABC0wcBjSRk0H/IR47xcdA62KQufXk5siUyg9yz9gydWz3x86qU5c/Lu3o5jSRk6YxrKYKzo3Iur51H6VgVTp5zb21jh7mxncH3jvFywt9E9DEl5njJm7w5v4u5sh9/iUPwWh+q0nePTWz0MSYjaoke3rurJ1Uo9yooaPWokX27cjMs73fXubVi3Vv15+FBvdu3ea/DZkyaYn8Foai3+C+apB+uY27Y0zz5vUWqb3x4WGr3n+JYDfb08Dc7lvYnjcXzLweDzlDE93Nzo6+XJCN+xjPAdq9PWf8E8enSrWAaDEKUZ5tySbfGZeK7QDwys8Hn8AbXz643UE7tXHb+k026Wm60alARtsC46PVdnTEMZjK72VkSn59J67gHGdG6lcyiPuRpPL71ea8mTwouza1oPV3srg+sb07kVdk3rGXyeMmYP2ya42lsxaWsik7bqHk4zy82Wzq8/2dPZhaguI9xdCImIo+cU/R0Iaz4crf7s3cuZqIQ0nXalZQ3aDtMtmzLX14uu7coXoOzaro16YrdSj1Lh4eLIcNd/6Fx7zboxfv26EhIRh1+/ruU+cVth2W1CqW2KZ3GWNKSHE2Enk5ixfLvOKdxg+H1RnqeM6epsr576PS5oY6n9haitJEAp/jQ+Hu1BmxavEHb6AseSMnB3tsO7ezv10BWF/2gPrP5Wly2RCeT+XGD2c5zatCD+iw85FJ+mBubm+PSm/RvNdYKO5syp+MEwx5IyWD1zKGPcO6onXVfW3Ctb8PvDOJqYTlRihro+j452DOzy91L7Wlq8wMY5IzmR8r267nFeLgzo4ijBSVFrBQUGYGv7Jrt27+Vw5FH8F8xj1Egf2tiX/r8JQzo6v01qciLh+w+oATf/BfNwcuqgF/jb/lUIe/eFM2nKNLXdqJE+Jg/pMWctfb088Rk+VD0wprxtq9qmDes4FHGEI5FH1bn08fJk6JDBpfatW9eS7V+FEHX8uLqW9yaOZ8jgQRKcFE9E+5YvEzPflcOp2WpgbpabLe1aNtAJOoL2QJ02TSwJT7lJdHourvZWDO7QXD0IRlH8YJjo9FxW+HRglIuNetJ18fGa1KvDtvhMbuXrbjOsDqtGOHHsYg7HL+aq63NzsCpT9qNlnef4YrQzMZduqese07kV/dpaS3BS/Kk42dpwLiSAQ7Hn1cDfXF8v2r/5qpo1CbqHsUQlpLHmw9GM7dNF53Ca4vz9BlD3ry/y8fowPFwcmTKkV4WDaP5+A2jzalPOffuDetjOmg9H49Xp7zSs/5Je+wHdOhASEccI98ov5VUeez6bxr6YZPU99OvXlQHdOpTpfbG0qMOmj/2ITkovV38haotnfv/999+rexJCKJ555hk2zR2lbu8VojLV85jNzp07GTFiRHVPRdQQI0eOhN/+w45t5d9mXRmefd6C9yaOZ12wfua1eHqNGvMuPPsXdu7cWd1TEWb6+uuvGTlypMksPyHKan/KTSZvS0Q+lglzPPPMM4T4j1e3/9YEJTP/RO1h2W2CfE4S1U5qUAohXE2E7AAAIABJREFUhBBPwLPPW+jUiwTt4SorPl8DPK4TKYQQQgghhBB/NrLFW4hSlKUWo8JUTUYhxJ/bof1h9B/kbbBeZF8vTzzcKuewGCGEEEKImqwsNR0Vko0pxJ+HZFAKIYQQT0BfL09OHtfWnVS8N3E8X4duZftXIdSta1mNsxNCCCGEEEKI6iMZlEKUQrIihRCVpUe3rvTo1pWgwIDqnooQQgghRKUqa7ajZEUKIQyRDEohhBBCCCGEEEIIIUS1kQClEE8pTeEDth1LZHhgCPU8ZjM8MITwuFQ0hQ/K3D88LlWn/7ZjidzJ/6XUvunXc8tcu9OctkKI2qOgQMOmkK/oP8ibZ5+3oP8gb3bvDaOgQFPm/rv3hun03xTyFXl37pjsdzjyKM8+b1GmZ5jTVghRvTRFj9iRcB3fDfE0nr4X3w3xHDx/E03RozL3P3j+pk7/HQnX+fn+wyqeuRCiptIUFrH1yFmGfbQWy24TGPbRWvbFJKMpLCpz/30xyTr9tx45y51790vtezEzy2gtTkPjmjMvIWor2eItxFMq8KsjbIlMUF8fS8rgWFIG7s527A70M9lXU/iAict2ciwpQ69/VGIGwe8Po2G9vxrseyf/FzpPXV6mOZrTVghRuyzw/4QvN25WXx+OPMrhyKP09fLk0P4wk30LCjSMftePw5FH9fofiTzKpg3raNSwoV6/tO8u0n+Qd5nmZ05bIUT1WxjxHdviM9XX0em5RKfn4mpvReikzib7aooeMXV7EtHpuXr9j1/MZdUIJ/720vNVNnchRM30fzeEExIRp76OSkgjKiENDxdH9nw2zWRfTWEREz4NISohTa9/VEIaa+eMoWH9lwz2vXPvPp38gozem7Zsm8FxPVwcTY4rRG0nGZRCPIXSr+eyJTKBOT69Sd/2CflRK0nf9gnjvFw4lpTBtRzTGUgnUr7nWFIGq2cO5ea+z8iPWsnNfZ8xx6c3x5Iy2HMqxWjfRTuOlXme5rQVQtQead9d5MuNm/FfMI8b1y7z28NCbly7zHsTx3M48ihXrl412T/q+HEORx5lw7q13Mu7xW8PC7mXdwv/BfM4HHmU0J279PokJn1DW6eOZZqfOW2FENUvIyefbfGZzHKz5UJQH24HD+VCUB/GdG5FdHoumXmms5ViLt0iOj2XFT4duLp0ILeDh3J16UBmudkSnZ5LWPKNJ7MQIUSNcTEzi5CIOOb6enFpzxI0sZu4tGcJfv26EpWQxrWs2yb7RyelE5WQxpoPR5MduQZN7CayI9cw19eLqIQ0dkf/02jfz746ZPRe5LlviUpIY0vARDSxm9T/tgRMJCohjchz35Z7zULUdBKgFOIpdP7KTQCG9exAs0b1AWjWqD7jPF0ASLuWbbJ/2OkLAIxx74ilxQsAWFq8wPTB3QHw3xxhsN/a8Fhyfy4o0xzNaSuEqF2+SdZ+iTFqpA/Nra0BaG5tzaQJ4wG4kGr6l+tdu/cCMMHvXfV087p1Lflg1vsAzJm3QKf9is/X4PJOd74O3Vrq3MxpK4SoGVJ/vAuA99staFr/RQCa1n+RMZ1bAXAx657J/uEp2t+LRrnYYFnnOQAs6zzHlJ5vABB4IM1oXyHE0+n89zcAGO76D5o1bgBAs8YNGNe/KwDfXr1psn/YySQAxvbpgqVFHQAsLeowY7gbAB+vN7xbJHhPNLk/5xsdd8by7QAM6eGkc115rdwX4mkkW7zFn4pS6zA/aiXHkjIYHhiCu7MdYz064u5sB0B4XCp+i0MBCJnvy+CubXXGOJN2lYNn09Tt03N8etO/syP2NlZ6zyve1t3ZjikD3+Edx9Zlnqcppk4Xz87T/qLeqJ5u+n/jBtoP+pd//Mnk2Ma2gCvBSkPOpF3Ff3ME8V98qLM1vKJthXiaKPUOf3tYyOHIo/Qf5E1fL0/G+71LXy9PAHbvDWOE71gAvg7dyvChutuQY2Lj2Be+X90+7b9gHoMHDcTxLQe95xVv29fLk5kzptGjW9cyz9OU3x4WGr2XlZUFQONGjXWuN2nyCgCXLn1vcmxjW8CVYGVJc+Yt4ND+MPp6earvnTHmtBWipms8XRvMvx08lOj0XHw3xONqb4VvJxtc7bW/lxw8f5NJWxMB2DC2IwPaN9cZI/5KHhGpWer26VlutvRt2wy7pvX0nle8rau9FZO6v07n1xuVeZ6m3A4eavRe9t1fAWj4ku7vIY0ttUGBy7dM17Y1tgVcCVYKIR5T6iJqYjcRlZDGsI/W4uHiyNg+XfBwcQRgX0wy44I2ArAlYKJeMC3uwmUOxqao26fn+nrRv1t7HFpZ6z2veFsPF0emDOlF13ZtyjxPU0ydFp59+98ANGqg+7vFKw20f/dd/lcO4FSym8rYFnAlWGlI3IXLfLw+jHMhATpbuIvzcHE0ek+5L8TTSjIoxZ+SEpws/nP69Vw+3R6lBicB/BaHEh6XqtOv3/z1OrUdl+06QeepyzmTprtl8dPtUTptlb6fbo+qyqWpcwL9gKJSN1K5by5la3jIfF+96/3mrydkvq/BQG152wrxtFKCk8V/TvvuIgGBQTpBsxG+Y9m9N0ynXy83T53ajgsXLaGtU0diYh/XUAIICAzSaav0DQg0XPOoMi1ctATQDygqdSOV++ZStoaXzH787WGhGuAtjTlthagtlOBk8Z8zcvJZfCRdDU4CTNqayMHzN3X6DQ6O1antuOr4JXosjib+Sp7OMxYfSddpq/RdfCS9Kpemzgn0A4pK3UjlvrmUreEbxkrJByFKUoKTxX++mJnFwpCDanASYFzQRvbFJOv06zt7hU5tx6WhkXTyCyLuwmWdZywMOajTVum7MORgVS5NnRPoBxSV+o7KfXMpW8O3BEzUu9539gq2BEw0GKhVjO3TBUDnPS3+WrkvxNNIMijFn9L5H25yc99nWFq8wJm0q/Sbv57OU5czx6e33nW/xaFqFqUS1Ezf9om6dTr58o/0nrWag2fT1OzIM2lXWbbrBHN8ejN9cHcsLV5AU/iA4PDTLNt1wmjGpcJUdmR12nMqBXdnO3p3eFO9pil8gP+mCOb49NbLNi3JnLZCPM2Sk1O4l3eLunUtiYmNo5ebJ22dOuK/YJ7e9RG+Y9UsSiWoeePaZXXrdGLSN7i805194fvV7MiY2DgWLlqC/4J5fDDrferWtaSgQMOKVZ+zcNESoxmXClPZkdVpx85d9PXyxMPNrbqnIkSNcuHGXa4uHYhlneeIv5LH4OBYeiyOZpabrd71SVsT1SxKJah5IaiPunX6/I1/47niFBGpWWp2ZPyVPFYdv8QsN1um9HwDyzrPoSl6xLpTP7Dq+CWjGZcKU9mR1Snsmx9xtbeih22T6p6KEDXO+e//RXbkGiwt6hB34TJ9Z6+gk18Qc3299K6PC9qoZlEqQc1Le5aoW6eTL12n55RFHIxNUbMj4y5cZmloJHN9vZgx3A1LizpoCotYs/s4S0MjjWZcKkxlR1an3dH/xMPFEVdne/WaprCIj9eHMdfXSy/btCQPF0cOr/yAdftO6gSCletlyS4VoraSDErxpzSxXxc1u7D4lmslmFjyukLZBn7wbBpn0q6iKXyAU5sW5EetZOW0IWq7s2nX9MYrXsMxNvVKFayqan26PYplu07gP9pDJzMzOPw0x5IymNiv9G/zzGkrxNNs2tTJanZh8S3XSjCx5HWFkvkXFn6AmNg4Cgo0dHR+m98eFrIueLXaLvaPbMri4xWv4Xgy5nQVrKpqBQQGsXDREoICA4xu9Rbiz2p819ZqdmHxLddKMLHkdYWyDTwiNYv4K3loih7RvuXL3A4eytJh7dV2SjZl8fGK13A884PpwyRqosVH0ll1/BLz+9jLVm8hDJg0qKeaXVg8KKYEE0teVyhbkA/EphB34TKawiKcbG3QxG5i1exRaruzqZf1xitewzE2xXQ5mJpoYchBloZG4u/XXyczc83u40QlpDFpUM8yjfPd1Zt627yjEtL4V67pg06FqO0kg1L8KSlbnUsyVWMRwH+0B8eSMtRDYozVlVS2UDcf8pHhcTZHMG1wN6PPqWgNysqmBCfjv/hQJ/MzPC6VZbtOcGLVTKPvaXnaCvG0U7Y6l1Ra4C0oMIDDkUfVQ2KM1ZVUtlDXb2Q4K2jOvAV88P4Mo8+paA3KyqYEJ1OTE01mfgrxZ6VsdS6ptMDb/D72RKfnqofEGKsrqWyhbj33gMFxAg+kMbnHG0afU9EalJVNCU7GzHc1mfkpxJ+ZstW5JFM1FgH8/foTlZCmHhJjrK6ksoW6mZfh30c+Xh/G9GGuRp9T0RqUlU0JTp4LCdDJ/NwXk8zS0EhOrVtg9D0tbl9MMh+vD9Or7anU/fzriy+UmoUpRG0lAUohzGBvY0V+1ErSr+cSm3oF/80RHEvKwN3ZDv/RHjWmpuIcn94s23UCTeEDnaCrpvCBer8s7uT/wsaIs1y8nkvK5gW81lQ3qKLU6+w9a7Wh7jqHEpnTVghhmONbDvz2sJC07y5yMuY0c+Yt4HDkUfp6eRIUGFBjgnf+C+axcNESCgo0OkHXggKNer8s8u7cYe0X60n77iKX07/l9dalHzImhCg7u6b1uB08lIycfM78cJvAA2lEp+fiam/F/D72NSZ4N8vNllXHL6EpeqQTdNUUPVLvl8XP9x+yOe4qGTn5JHziQatGpQcLhBDmcWhljSZ2Exczs4hN+Z6P14cRlZCGh4sj/n79TW7bfpLm+nqxNDQSTWGRTtBVU1ik3i+LO/fus2H/KS5mZnMhdCGvWeseEKhs0+45ZZHB/sUPJSre3tAp3uOCNhJ2MkkClOKpJQFKIcrB3sYKexsrBnRx5Pqtn+k3fz3HkjLU4No4Lxe2RCao9SzNVdEgXZsW2pNy8/Lv6zz/5u27AGr9TFPSr+eycHsUDjZWBL8/TLIehahBHN9ywPEtB7wHD+Ra5nV6uXlyOPKomtX43sTxfLlxs1rP0lwVzY60tdXWqb2dd1vn+Td+/BEAa+vSP5wohwY5vuXApg3rjGadCiEqzq5pPeya1qNfW2v+decXBgfHEp2eq2Y1juncim3xmWo9S3NVNDuyTRPt3yN37j/QeX7WXe3fVc0avFjqGMqhQXZN67FqhJPRrFMhROVwaGWNQytrBnbrQGZOHn1nryAqIU0NxPn160pIRJxaz9JcFc2ObPNqUwDy7mp0nv/jTz8D0Kzxy6WOoT006BAOrZqxds6YMmVIVpSpE76FqO2kBqUQZpi9dh/1PGaTfFn7IbtZo/rYNPmbXrsBXbS1V4LDT3Mn/xf1+pm0q9TzmM3a8Ngqnecbf3xzt+dUCtl59wDIzrvHoXjtP2jtX29usn923j06T12Og40VH4/2MBqczI9aafC/kvfNbSuEMGzK9Jk8+7wFiUnfANDc2prXWtnotRsyeBAAK1Z9Tt6dx/WKYmLjePZ5C1Z8vqZK5/lmG+02rh07d3EzKwuAm1lZhO/Xbg9926mDyf43s7Jo69QRx7ccCAoMkOCkEFVk7p7zNJ6+l/M3/g1A0/ov8mpD/X/z+7XVfqmw7tQP/Hz/oXo9/koejafvZX3MD1U6z9avaAOUYd/8SM69XwHIufcrh1OzAWjbooHJ/jn3fqXH4mjsmtZjfh97CU4KUYVmrdyBZbcJJF+6DkCzxg1o1VS/Bu6AbtrfBdbsPs6de/fV63EXLmPZbQLBe6KrdJ5v/JHQsTv6n2T/kcSRffsuh2LPA9D+zZYm+2ffvksnvyAcWjXD32+A0eCkJnaTwf9K3ld8Oll7IKJSv1OhnOKt3BfiaSQZlEKYwaeXE1siEwxuU14983F2wDuOrdVt1ko9SoW7sx3Depr+cF5R9jZWuDvbGXz+OC8Xva3oJbdXn7qg/aBhqL9CgolCPHmjR43ky42bcXmnu969DevWqj/36NZV3Wat1KNU9PXyxHekT5XO0/EtB/p6eRp8/nsTx+ttRVdqXiqZm8ejTwIY7K+oqSeNC1GbDHNuybb4TDxXnNK7t8Ln8e8qnV9vpG6zVupRKlztrfB2alml87RrWg9XeyuDzx/TuZXeVnSl5qWSuXn6+58ADPZX1NSTxoWobUa4uxASEWdwS/OaD0erP3dt10bdZq3Uo1R4uDgy3PUfVTpPh1bWeLg4Gny+X7+uelvRS27FPpmcAWCwv6I8WZ7DXf9BfNoV+s5eoXfvSbwvQlQnCVAKYQanNi2I/+JDDsWnqYG7OT69af9Gc/WEb8XHoz1o0+IVzl3MZEtkAqANYnp2tH8i26WD3x/G0cR0ohIz1DqZHh3tGNjl76X2nbm69GL2Qognr6Pz26QmJxK+/4AauPNfMA8npw7qCd+KoMAAbG3f5MzZeL7cuBnQBjH79+vzRDISN21Yx6GIIxyJPKrWyezj5cnQIYNL7TtpyrQqn58QAtq3fJmY+a4cTs1WA3ez3Gxp17KBesK3Yn4fe9o0sSTh2h22xWcC2iCmu0PTJ5KRuGqEE8cu5nD8Yq5aJ9PNwUrN7jTlg10pVT4/IYSWk60N50ICOBR7Xg3czfX1ov2br6onfCv8/QbQ5tWmnPv2B0Ii4gBtENOr09+fyHbptXPGEHnuW6IS0tQ6mR4ujgzqXnoyyYzl26tkTg3rv8Smj/2ITkon7GSSOi/vXs64OtuXazu8ELXFM7///vvv1T0JIRTPPPMMm+aOwrt7u+qeingK1fOYzc6dOxkxYkR1T0XUECNHjoTf/sOObV9V91TEn9CoMe/Cs39h586d1T0VYaavv/6akSNHStadqBT7U24yeVsi8rFMmOOZZ54hxH883r2cq3sq4ilg2W2CfE4S1U5qUAohhBBCCCGEEEIIIaqNBCiFEEIIIYQQQgghhBDVRgKUQgghhBBCCCGEEEKIaiMBSiGEEEIIIYQQQgghRLWRAKUQQgghhBBCCCGEEKLa/KW6JyDEn109j9kA5EetrOaZVMyxpAyGB4YYXIeyRlOK99MUPuBEyveEnb7AsaQM3J3t8Ohoh2dHexrW+2ulzlsIAc8+bwHAbw8Lq3km5iso0BB1/Di7du/lcORR+np50sfLk/79+tCoYUO9tnv3hXMk8qja1mf4UDzc3Khb19LoMw5HHqX/IO9a+f4IURkaT98LUOtOLVfmbUrxNWmKHhGRmsXxi7lEp+fiam/F4A7N6WHbBMs6z+n00xQ9IubSLcJTbqpt3RyscHdoyt9eer7S1yLEn5lltwkAaGI3VfNMKiYqIY1hH601uA5ljaYU76cpLCI6KZ2wk0lEJaTh4eKIh4sjXp3+TsP6L1XqvIV4UiRAKYSosPTruQwPDCl3f3dnO/VnTeEDJi7bybGkDPXasaQMjiVlEJWYQfD7wyRIKYQAtAHH0e/6cTjyqHrt8B/BxyORR9m0YZ1OkHKB/yd8uXGzXtu+Xp4c2h9m8Blp312k/yDvqluEEKLauNpb6bxeGPEd2+Iz1dfR6Y8DlaGTOqvXNUWPmLo9iej0XL22xy/msmqEkwQphRA6LmZmMeyjteXu7+HiqP6sKSxiwqchRCWkqdeiEtLU/9bOGSNBSlErSYBSCFEhyZd/pPes1SbbGMsOTb+eS+epy1k4oZ967UTK9xxLymD1zKEM7PJ3LC1eQFP4gODw0yzbdYI9p1KYNrhbZS5BCFFLRR0/zuHIo2xYt5ahQwZTt64lBQUaVqz6nIWLlhC6cxcfvD8D0AYav9y4Gf8F8xjv9y7Nra25mZXF4qXL+XLjZq5cvcrrrVvrjJ+Y9A0u73SvjqUJISqBsYzPjJx8eiyOJnCgo861bfGZzHKzxbeTDU3rv0jOvV9ZHf092+Izycy7T6tG2g/8MZduEZ2eywqfDvRra41lnefQFD1i3akfWHX8EmHJN5jc440nskYhRM2XfOk6PacsMtnGWHboxcwsOvkF8enkx1+WRielE5WQxpoPRzOoewcsLeqgKSxize7jLA2NZHf0P5k+zLVS1yDEkyA1KIUQ5bY2PJbes1YTMt/X7L538n+h89TlrJ45lNeaPs5wCjt9AYAx7h2xtHgBAEuLF5g+WBsk8N8cUQkzF0I8DXbt1m7fnOD3rrpFu25dSz6Y9T4Ac+YtUNt+k5wCwKiRPjS3tgagubU1kyaMB+BC6rc6Y6/4fA0u73Tn69CtVboGIcST9fP9h/RYHM0Knw5qwBEg9ce7AHi/3YKm9V8EoGn9FxnTuRUAF7PuqW3DU24CMMrFRt36bVnnOab01AYlAw88zmoSQvy5Be+JpueURWwJmGh23zv37tPJL4g1H47mNevG6vWwk0kAjO3TBUuLOgBYWtRhxnA3AD5eb3hXiBA1nWRQiqfGmbSrHDybxpbIBADm+PSmf2dH7G10t++kX88lNvWKGuhyd7bDu3s7Bndtq7YpXhdSqa3o7mzHWI+O6nbk8LhU/BaHAhAy39do/5Ltend4Uw28lXU97s52TBn4Du84tjbZztS6SzK3LqQh/psj2B3oh7uznbrGstoYcRZ3ZzvGuHfUub470M9g+7K8Z0JUt5jYOPaF71e3EfsvmMfgQQNxfMtBp13adxc5GXNaDaAptRCHD3387XjxupBKDcS+Xp6M93uXvl6eAOzeG8YI37EAfB261Wj/ku1Kq7loaD19vTyZOWMaPbp1Lfe6S1LmaIqpuo/GtmUbWltWVhYAjRs11rnepMkrAFy69L3O9TnzFnBofxh9vTzV906Iioq/kkdEapa6jXiWmy192zbDrmk9nXYZOfmc+eG2GuhSaiEOaN9cbVO8LmR0ei6+G+JxtbfCt5ONunX54PmbTNqaCMCGsR2N9i/ZzlDNxdLW42pvxaTur9P59UblXndJ5taQLIvNcVdxtbdilIuNzvXsu78C0PAl3d83GltqP/xfvqVRrxXf7l1cWd4zIapT3IXLHIxNISQiDoC5vl7079Yeh1bWOu0uZmYRm/K9GujycHHEu5czQ3o4qW2K14VUait6uDgytk8XdTvyvphkxgVtBGBLwESj/Uu2c3W2VwNvZV2Ph4sjU4b0omu7NuVed0nm1oU05OP1Yez5bBoeLo7qGstqw/5T6nta3J7PphlsX5b3TIiaTAKU4qmgBBGLW7brBMt2nSBi8WQ1sGeonVLfENAJMpZsr7SL/+JDDsWnsWzXCbWdEpwz1L944M5vcSjuznZGg3CKT7dH6YyvPHuOT28+Hu1h9rqrSnkP9jmTdlWdY1ldy7kDUK5sTSGeBCWIWNzCRUtYuGgJJ48fVQN7htoptRABnSBjyfZKu9TkRML3H2DhoiVqOyWIZqh/8QDbCN+xJmsuKgICg3TGV57tv2AeQYEBZq/7Sbpy9SqATvajspaSwUulRuXCRUt01iUH4ojKpgQRi1t1/BKrjl8ifHo3NbBnqJ1S3xDQCTKWbK+0i5nvyuHUbFYdv6S2UwKQhvor95R2JWsuGrL4SLrO+MqzZ7nZMr+PvdnrfhLir+Spzy1JWUvJIKNSS3LV8Us66zIkM+8+oA3yClHTKEHE4paGRrI0NJLDKz9QA3uG2im1DQGdIGPJ9kq7cyEBHIo9z9LQSLWdEpwz1L944G5c0EY8XByNBuEUC0MO6oyvPHuurxf+fgPMXndVKe/BPnEXLqtzLKtrWbcBypWtKURNIAFK8VRQgnTp2z6hWaP6wOPaiAfPpqmBOqXdiVUzcWrTAoDsvHvYj/lf/BaH6gUYz/9wk5v7PsPS4gXOpF2l3/z1dJ66nDk+vfWuG+q/NSpRnVN23j22HUtk2a4TnEm7ajR4qATv5vj0Zvrg7no1GItnR5Z13YZU56nh6w6cwd3ZzqwA6p5TKbg729G7w5tVODMhyk8J0t24dlndQqzUMNwXvl8N1CntEs6cpqPz2wDczMqi5WttGOE7Vi/AmJycwr28W9Sta0lMbBy93Dxp69QR/wXz9K4b6r855Ct1Tjezstgc8hULFy0hJjbOaPAwJjaOhYuW4L9gHh/Mel+vtmPx7MiyrtuQqgoC7ti5i75enni4uVXJ+EKUhxKkuxDUR91CfP7Gv/FccYqI1Cw1UKe0O/pBT9q3fBmAnHu/0i7gCJO2JuoFGC/cuMvVpQOxrPMc8VfyGBwcS4/F0cxys9W7bqh/6Lnr6pxy7v1K6LnrrDp+ifgreUaDh0qgb5abLVN6vqFXg7F4dmRZ121IZZ8avuH0FVztraosKBr2zY+42lvRw7ZJlYwvREUoQbpLe5bQrHED4HFtxIOxKWqgTml3at0CnGy1mcbZt+9iO2we44I26gUYz3//L7Ij12BpUYe4C5fpO3sFnfyCmOvrpXfdUP+tR86qc8q+fZetR86wNDSSuAuXjQYPleDdXF8vZgx306vBWDw7sqzrNqQ6Tw1ft+8kHi6OZgVQd0f/Ew8XR1ydTX+ZIkRNJTUoxVNB2XZ98GwaZ9Kuoil8gFObFuRHrWTltCFqu/yoleRHraTlKy+Tfj2XY0kZbDuWaGxYJvbrom4tLh5MUwKHJa+XtHBCPzVw2KxRfXU788GzxmsTnU27pveM4jUYY1OvmL3umiT58o8cS8pgrEfZswuUjFL/0R6y1VvUWMq267DwA8TExlFQoKGj89v89rCQdcGPD5L67WEhvz0sxMbmVdK+u8jhyKNsDvnK6LjTpk5Ws/6KB/uUwGHJ6yUtW/KZTs3F8X7vArAvfL/RPrGxcXrPKF7b8WTMabPX/aQomZ9BgQFl2sYuxJOibLuOSM0i/koemqJHtG/5MreDh7J0WHu13e3godwOHkqLl/9KRk4+0em5hJ67bnTc8V1bq1l/xQNvSuCw5PWSAgc66tRc9O1ko87TmPgreXrPKF6D8cwPt81ed1U7f+Pf2mzOTjalNy4HJaN0fh972eotaiRl2/WB2BTiLlxGU1iEk60NmthNrJo9Sm2nid2EJnYTLZs05GJmFlEJaWw9csbouJMG9VS3FhcPpimBw5LXS/p0srcaOGzWuAE2ZHCSAAAgAElEQVRj+7wDwMHYFKN9zqZe1ntG8RqMsSmPy7aUdd01SfKl60QlpOlt7TZFySj19+svW71FrSUZlOKp4D/ag2NJGTp1JY3VbCy5fdqUhvX+avB6WYNkxQ9/AdRg5ZbIBKMBRGVuzYd8ZPC+/+YI9RRrc9ZdUmXUoCyPXSeTAXCxb1Wm9sqfV/wXH5ZaV1OI6hQUGMDhyKM6dSWN1WwsuX3aFGULckllDb6VPJlaCVZ+uXGz0QCiMrf6jQxnAc2Zt0A9HducdZdU0RqUJSnva2pyYqn1L4V40ub3sSc6PVenrqSxmo0lt0+bomxBLqmsQbLiB8UAarByW3ym0QCiMrfWcw8YvB94IE09xdqcdZdUmTUo9yTdAKBjK8N/p1aE8ucVM9+11LqaQlQXf7/+RCWk6dSVNFazseT2aVMa1n/J4PWyBsmKH/4CqMHKkIg4owFEZW7NvGYYvP/x+jD1FGtz1q23hkqoQVkeXx/Tni3QyfH1MrVX/rzOhQSUWldTiJpMApTiqWBvY0V+1EqdA3COJWXg7myH/2gPNbClbLEe5+XCgC6ONHjJgsYNLGntE1DKE2qmsq67priT/wtbIhOY49O71CDvnfxf2BhxlovXc0nZvEAv2CtETeP4lgO/PSzUOQDncORR+np5EhQYoAbMNv2xxfq9ieMZMngQLzdoQJMmr/BKs5bVu4ByKuu6q1LenTus/WI9ad9d5HL6t3pBWdAe3LNw0RIKCjQ6wd2CAo16X4iqZNe0HreDh+ocgBOdnourvRXz+9irga0dCdot1mM6t6JfW2vqW/wPjS3rYPfRoWpeQfmUdd1V6ef7D9kWn8ksN1ujgdtZbrasOn4JTdEjnTaaokfqfUPjbo67SkZOPgmfeOgFe4WoSRxaWaOJ3aRzAE5UQhoeLo74+/VXA1tbj5xlaWgkfv26MqBbBxrUteCVBvVoNbD05IaaqKzrrinu3LtPSEQcc329Sg3y3rl3nw37T3ExM5sLoQv1gr1C1DYSoBRPFXsbK+xtrBjQxZHrt36m3/z1HEvKUDMBZ67WfhNfPHtRU/igyuaTnXdPzZqExwe9zPHpbbTPOC8XtkQmqDUuy6K0dRtSHTUob/z0bwDav9HcZLv067ks3B6Fg40Vwe8PM5rJKkRN5PiWA45vOeA9eCDXMq/Ty82Tw5FH1UzASVO0Rd+LZy8qQbKqcDMrS82ahMcHyJgKyL03cTxfbtys1rgsi9LWbUhl1KBM++4iAYFBOL7lwKYN64xmnNraauvX3s67rbOmGz/+CIC1dc36gCKeXnZN62HXtB792lrzrzu/MDg4luj0XDUT8INd2m2NxbMXlSBZVci596uaNQmPD3oxFJBTjOncim3xmWqNy7Iobd2GVFYNyh///QsA7Vo2MNqmTRPt3wt37j/QWVPWXe3fU80avKjTPiMnn8VH0rFrWo9VI5yMZrIKUdM4tLLGoZU1A7t1IDMnj76zVxCVkKZmAs5Yvh1Ad9t3YVGVzSf79l01axIeH/Qy19fLaB+/fl0JiYhTa1yWRWnrNqQ6alDeuKX9vNj+zVdNtruYmcXCkEM4tGrG2jljjGayClGbSA1K8VSYvXYf9Txmk3xZ+0GzWaP62DT5m9H2SqBQOXymqmw7lkh23j1AG6zcc0r7oaOL42tG+wzooq2TEhx+mjv5v6jXz6RdpZ7HbNaGx6rXzF13dbt04xYArzUzvq0rO+8enacux8HGio9He0hwUtQaU6bP5NnnLUhM+gbQbqV+rZXxWmdKoFA5fKaqbA75iptZ2lpyN7Oy2LFzFwDdTGzBHjJ4EAArVn1O3p076vWY2Diefd6CFZ+vUa+Zu+7KdDMri7ZOHXF8y4GgwACjwUmAN9tot3Lt2LlL5/0I36/dovq2U4eqn7D4U5u75zyNp+/l/A3tl3VN67/Iqw2N/xunBAqVw2eqSui56+Tc+xXQBivDvtH+TmFqC3a/ttqA/rpTP/Dz/Yfq9fgreTSevpf1MY/na+66q8L3uQWA/nb24lq/og1Qhn3zo877cTg1G4C2LR4HUHLu/UqPxdHYNa3H/D72EpwUtcKslTuw7DaB5EvamrbNGjegVVPj/ztXAoXK4TNVZeuRM2Tfvgtog5W7o/8JQJe2xrdgD+im/Td7ze7j3Ll3X70ed+Eylt0mELwnWr1m7rqrW8b1HABaW79itE327bt08gvCoVUz/P0GSHBSPDUkg1I8FXx6ObElMoHes/Trqa2e+fjb95D5vvgtDqXD+EUGx7mWc6fStxLbj/lfnddzfHqbrBH5jmNr5vj0ZtmuE3q1Mt2d7RjW8/GH6LKuu6ZIu6b9Jb+uiW86T13QfqgxtH5FdZ5ALoQxo0eN5MuNm3F5p7vevQ3r1qo/fx26lRG+Y2lj/3eD41y5etXgFuWKaPma7i/5/gvmmawR2aNbV3VLdMlamX29PPEd6aO+Luu6q8Lx6JMABuepULI0Hd9yoK+Xp8G2700cLzUrRZUb5tySbfGZeK44pXdvhc/jf9s3jO3IpK2JuPxvlMFxMvPuV/pW4nYBR3Rez3KzNRmg7Px6I3VLdMlama72Vng7tVRfl3XdVem7LO2XxXXr/I/RNnZN6+Fqb2VwTWM6t9LZin76+58ADLZVVPYJ5EJU1Ah3F0Ii4ug5Rf9z0JoPR6s/bwmYyLigjbTz9Tc4zrWs25W+ldh2mO6ujrm+XiZrRHZt14a5vl4sDY3Uq5Xp4eLIcNd/qK/Luu6aIu2K9kuiun990Wibk8kZAAbXr6jOE8iFKC8JUIqnglObFsR/8SGH4tPUoNYcn960f6O5etI1wOCubfml6KG61XuOT2+G9ezAg4eP6Dx1OecuZlZqgPLj0R7UtaiD/+YIsw6w+Xi0B21avMK5i5lsidQWSV49cyieHe11MgrLuu6aQlmLqaxI5c9GiNqmo/PbpCYnEr7/gBoA818wDyenDupJ1wDDh3pz//4v6lZv/wXzGDXSh6KiB7R16kjcmfhKDVAGBQZQt1495sxbYNYBNkGBAdjavsmZs/F8uXEzoA049u/XRydTsazrrgrKe1hWmzas41DEEY5EHlXrZPbx8mTokMFVNEMhHmvf8mVi5rtyODVbDWrNcrOlXcsG6knXAAPaN+eXh/9Rt3rPcrPF++0WPHj0X3osjuaf1+5UaoByfh976r74HIEH0sw6wGZ+H3vaNLEk4dodtsVnAtqAo7tDU52MwrKuuyop8yst03HVCCeOXczh+MVctU6mm4OVmjGqUP5shKhNnGxtOBcSwKHY82pQa66vF+3ffFU96RpgSA8nfvn1gbrVe66vF8Nd/0HR//t/dPILIj7tSqUGKP39BlD3ry/y8fowsw6w8fcbQJtXm3Lu2x8IiYgDtAFHr05/18koLOu6awplLaayIpU/GyGeNs/8/vvvv1f3JIRQPPPMM2yaOwrv7u2qeyoVopyQLZl+NUs9j9ns3LmTESNGVPdURA0xcuRI+O0/7Nj2VXVPpdIpJ2RXRp1HUTVGjXkXnv0LO3furO6pCDN9/fXXjBw5slZnySknZNfmNTwt9qfcZPK2RORjmTDHM888Q4j/eLx7OVf3VMpFOSFbMv1qBstuE+Rzkqh2UoNSCCGEEEIIIYQQQghRbSRAKYQQQgghhBBCCCGEqDYSoBRCCCGEEEIIIYQQQlQbOSRHiCogtSeFENVNak8KIUyR2pNCiOoktSeFECVJBqUQQgghhBBCCCGEEKLaSAalEGVUm0/m1hQ+4MDZb4lKzOBYUgbuznZ4d29H7w5vYmnxQqn9lbUbUvz9MNXOUHuAazl32HMqhWW7TgCweuZQPDva07DeX0sdS4g/m9p8MndBgYa9+8I5EnmUw5FH6evlic/woXi4uVG3rmWZ+0+aMg0A/wXzGDXSh9dbt67Qs8wZV4iarjafzK0pekREahbHL+YSnZ6Lq70Vgzs0p4dtEyzrPGeyr7JuU4q/J5qiR8RcukV4yk31WW4OVrg7NOVvLz1vcpyMnHx6LI6ule+xEFWtNp/MrSksYv/pFKIS0ohKSMPDxRHvXs64OttjaVGn1P7K2g2OXcr7cTEzi05+QaW2i0pIY9hHa2vl+ytEWUiAUog/gcCvjrAlMkF9fSzpcaByd6Cfyb7ZefcqbR7uznY6r9Ov59J56nKdazNX7yUqMYONc0aWKXgqhKgdFvh/wpcbN6uvDxcLHh7aH1Zq/9Hv+nE48qj6euGiJSxctITU5EQc33Io97PMGVcIUXUWRnzHtvhM9XV0+uNAZeikzhUa29XeSv1ZU/SIqduTiE7P1XvW8Yu5rBrhZDRI+fP9h/RYHF2huQghaqb/uyGckIg49XXxQOWez6aZ7Jt9+265n3vn3n06+QWV2u5iZhbDPlpb7ucIURtIgFKIp1z69Vy2RCYwx6c3Y9w70qxRfbLz7rFy7ym2RCZwLecOrzVtWOo4C8f3Y9rgbibbGMsuVQKRCyf0U69pCh/Qeepy3J3tWD5lEM0a1UdT+IDtxxLx3xzBiZTvGdy1rVlrFULUTGnfXeTLjZvxXzCP8X7v0tzamptZWSxeupwvN27mytWrJjMWd+8N43DkUTasW8sEv3cBiImNo5ebJxs2bWZd8OpyPcuccYUQVScjJ59t8ZnMcrPFt5MNTeu/SM69X1kd/T3b4jPJzLtPq0YvGe1vLJtRyXYMHOioXou5dIvo9FxW+HSgX1trLOs8h6boEetO/cCq45cIS77B5B5vGBxv6dH0ii1UCFEjXczMIiQijrm+Xozt8w7NGjcg+/ZdVuw8SkhEHNeybvOadeNSx/l0sjfTh7ma9ezPvjpUapvkS9fpOWWRWeMKURtJDUohnnLnr9wEYFjPDjRrVB+AZo3qM87TBYC0a9km+1+/9TMAb73WtFzPv5P/C52nLmf1zKE6gdAfsm4D4N29nTovS4sXGO3eEYCw0xfK9TwhRM3zTXIKAKNG+tDc2hqA5tbWTJowHoALqd+a7L9rt3b75tAhg9VrPbp1BdDJlDT3WeaMK4SoOqk/arOPvN9uQdP6LwLQtP6LjOncCoCLWebv5lCyHVf4dNAJboanaH8vGuVio24dt6zzHFN6aoOSgQfSDI63PuYHbuUXmT0PIUTNd/77GwAMd/0HzRo3AKBZ4waM66/9neDbqzdN9s/MyQPgrdbNzXpu8J5ocn/OL7VNzymL2BIw0ayxhaiNJINSPLXqecxmnJcLK6cN0bs3e+0+tkQmcHPfZ1havED69VxiU6/gvzkCQK3RaCqDz1hNSmPXz6Rd5eDZNLZEJuDubMeUge/wjmPpNc7KU9exOGWLdqN6upkHjRto67Bd/vGnUseviI0RZ3F3tmPMH4FHRVLGvwBwfrOlznVLixdqZZ1PIQx59nkL3ps43mAm3pTpM/ly42bu5d2ibl1L0r67yMmY08yZtwBArZs4fKi3yfFBvyalsesxsXHsC9/Plxs309fLk5kzpqkBudLWURpTdTGzsrIAaNxIN/ugSZNXALh06XuTYxvaAq5sy/46dGu5n2XOuEJUhcbT9zKmcyuWDmuvd2/unvNsi8/k6tKBWNZ5joycfM78cFsNoCk1Gge0N/6B2FhNSmPX46/kEZGaxbb4TFztrZjU/XU6v96oTOsojamajdl3fwWg4Uu6pV0aW2rrvl2+pSl1/JI2x13F1d6KUS42OteNbRc3Vecy/koegQfSiJnvqrM1XIjazrLbBPz6dWXV7FF692at3EFIRBzZkWuwtKjDxcwsYlO+5+P12n87lRqNQ3o4mRz//7N333E1738cwF+nI5VoueGSlU0uUUJG0tRAiFSkknGFuMjeZCabigiZoaGd0iAtFD97j8hoqZQ6vz+651xpz+859X4+Hj0et3O+43Xyveecz/v7GUDJORjLejw84SGuhMXB1SscukP7Ye5EDYwc0LNSr6Mi5c3b+PbjFwBAK5ni81S3kZECADx88Q5A2a+zOsITHmLloQuIcl0Dv+jSb4wAwMpDF3BuyzzoDu0Hyw1HazUDIfyGelCSBmuTtSGO+UYjNS2r2OOpaVk45huNTdaGkBAXhX/MfQz7eyevOAkUzdFo5eCOS+GJtZJl80k/GNof4s0D6R9zH4b2h7D5pF+tHL883MVnfp/PkbsIDff5stx7+g4AINNCHCf8b0FKdxGkdBfhhP8tZHzPLXffG3efYIdHEOaOH1Hiucikonmm5FpJ41J4Iqasc4WU7iLsvxRW4t+MEEG1Y9tWHD7qgk+pqcUe/5SaisNHXbBj21ZISkrA2/caFJUH84qTQFGhbKq5Bc6er3h+xspYs24DNLTH8HoGevteg4b2GKxZV/G8RzW1aes2ACixQE0rWdliz1fGrj17ISQijrFGk3DG3a1EAbe656rouITUhXXj++FE5DN8zvxR7PHPmT9wIvIZ1o3vBwkxYQQmvy8aqvxL777A5PeY5XYLV+LL79lTWQ4+yZiwL4w3D2Rg8ntM2BcGB5+6H9bsGPAAQMkiIXcuSO7zlRX5+BMcAx5g1qjuld7n2adMAMARi8ElHp+wLwxHLAajTzupKuUghN9tnjMJrl7hSP2WWezx1G+ZcPUKx+Y5kyAhLga/6LtQtdrAK04CRXM0Wm44iouhsbWSZZPrFRgs2sWbB9Iv+i4MFu3CJtcrtXL88mx39wWAEovhyEq3KPZ8We7928NSRlIcbj4RkFCbCQm1mXDziUDG95I9r5+++QiDRbtwbI0N+nZpX+6xM8KcoTu0X7nbENJQUA9K0mCpKRZ9Kb1x90mxnpA37j4BAOgMLlqwZco6VwBAkOMCKPfsCKCo16HC9I2wcnCv8TyI3CLdEhNN2E4YBQlxUWR8z8W+S9exwyMIY4f1g4J82zL355fehNVZzObg5RvQUelTak9R/5j7AIqKt78WSVe5eCEy6RktkkMaBA31UQCA0OthxQpeodfDAAAGeroAgLFGRc9F37iOwSqDAACv37xBp649MdXcosbFstCwcGzaug2rli/DYruFkJSUQHp6BnY57sGmrdswwWh8uQvC8NOq4Yr9+2HHtq24cSMCU80tAKBWiol1dVxCyjOiR1FP38jHH4v1hIx8XDQNCndxF/MjkQCAa4tHY2CnlgCAd9+yMWCND2a53Sq3F2VlcAt6dtq9MXd0jxLzMhooypVbnOO3Fa2PXH8MLYW2ler9yXXh9itoKbSFeu8/eY9l5ORj3eW7sNPuXeO/MSH8SE2pFwAgPPFhsZ6Q4YkPAYBXGOMuzhJycDmUexf1Sn778St6T14Gyw1Hy+1FWRnhCQ+x3d0XS831MH+KNiTExZDxPQd7zwZgu7svxqoNLLeQxy+rWv++2M38nSfhF30XziuteMXPjO85WHnoApaa69X470ZIQ0M9KEmDpSDfFjoqfUrMZXjhegIs9Yby5kNM89uNNL/d6NSmJZKfv4d/zH2c8L9Vazki7j4FAF5xEijqzWg7oahwEZb4uNbOVRe4PUuDHBfw/lZpfrvham8O/5j7CIorfWhm7MNX8I+5DwvdwaU+/6snHhsqfVxCBEm/v/rCQG8Mb65DLo+z5zHbxpq3WEvhj+8o/PEd8vKdcfdeErx9r8HF9Xit5QgLK+qNwC1OAkU9DBfbLQQABIder7Vz1TV1tZFYvHA+rnpewJGD+zHV3AKhYeEV78jQcQkpT592UtBSaMubF5HrUtxrTB/WhTd34sd9xvi4zxgdWzbH/XdpCEx+D/eo57WWI/Jx0fxp3OIkUHxexhuPPtbauepa/MsvCEx+D3NV+Yo3/peDTzIcAx7AXl+hWC/OgyGPEJj8HtYjK56ShxBB1LdLe+gO7YcLwTHFHr8QHAMrw5G8hWEywpyREeaMTn/KIunZG/hF34Wbz41ayxHxb0GUW5wEinozzp+iDQAI4/N2AbdnacjB5by/VUaYM46tsYFf9F0ExvzXE33v2QD4Rd/FLKPRTMUlhG9RD0rCV9hsdq0eb+74ETC0P8Rbqfrpu1T4x9yHl8OcYtv93ouvNnGP22HiilKfX+XiVe7q2DWdg7Kmyjr2hJGKsHJwx4XrCaX2MvUILhruMVShS7nH/7VwCwCa/97JLeu41VVQWAgAEBERqbVjkoYhN/dHxRvVwIL586ChPYa3evTjJ0/g7XsNwQHXim23Zt2GKg11rgrucaVb/Vnq80uWLcfihfPL3L+mc1DWFeOJEzBr7jw47d1fqbk0mT7u73Jzf0C0GX0VE0Tcz5KCQg7YQqwaHWvWqO6YsC+Mt1L1s0+ZCEx+j0u2asW24xbR6gL3uN2WXi71+XWX75a5sjVQ8zkoa9O5mJcAgMFdZMvf8F/cv2uovVaxXqJX4l/DMeABri0ezRtqXpfYbOo3QqqmttpNcydqwGDRLt5K1U/ffIRf9F14715cbLtNrlcqHOpcXdzjyumV/l1k5aEL5a6OXdM5KGuqrGNPVFeG5YajuBAcg4nqyrgYGovt7r4IObicN3ycH1A7ifAL+iQkfEVSogUys8uf17Aq+nctGgoQ9e98h9wVq7mPA8AJ/1vY4REES72h8HKYg8gD/+CJR93PyVZflphoAkCJ+SK5v3Ofry7uUO1fcef5XGKiWeYwbe55f3+e+3tpx62J9Kyi+V+kpGj+KPKfFi1aID296osvVMVAxaJCe/iNoiGa3FWkuY8DgLPrcWzaug2zbawRHHANibG3kPL2ZZ3mqk+rli8DgBJ/a+7v3OeritsblLuwTW2dq7Tj1oW0tHRISEhUvCHhO5KSkgCAzNz8Gh/rr/bSAICbT4vmquWuWM19HABORT+HY8ADTB/WBZds1RBqr4X7W8bW+Nz8wk67N4CiIdW/4v7Ofb4i3Lk77bR7l7voDXdbB59k3H+XhujVuiWGsM9yKxpNM2ZXCFrbnuf9cP3+e02k5+RBit4LSBVJSrRARi20mxR7FE1xFXm3aFQXd8Vq7uMA4OYTge3uvrAyHAnv3YsR5boGzy7zxzRUtWGpuR4AlJgvkvs79/nq4i6Cw13kZvTcrbx5Kn8trv7+e31JzypaqIzaSYRpdNue8JWuXbrg+YfPtXY8CXFROC0wxgKn8xgzWAFWDu5wWmBcrCi2wKnoy+Wvq31XtPhLWUpb3MVSb2ixFcOrqqa9I3t2LFq59lNaZrHzv/74FUDRIjXlmbLOFf4x90vk5/6NLPWGltjnZUrRSngDe5Q9XxM319tP34plKO+4NfHiQ1Gmbt1omBb5T5cuXeB3rW6LUJKSEjhycD9mzZ2HsYb6mGpugSMH9xdbxGXW3HkAUGy17+oWTn9fkAcAZttYF1sxvKpq2juyd++intEfP30sdv6Xr14BANq3L3+C+LFGk+Dte61Efu5rnW1jXa1zVeW4deHps2fQGTOmTs9B6kbXrl0BAC9Ss6DYUaZGx5IQE8YuEyUs9oiDTt92mOV2C7tMlIoV2BZ7xAFAsdW+fy/mVdbvC/IAwPRhXYqtGF5VNe0d2fPPov//UjNzi53/zdei9x45mWaVOs6rL0XfwwZ0Kv/f5P67NDj4JKNPOyk4TlWulx6S5Xn5OQvy8pUfkk4IAHTt2gXP332q8XEkxMWw959pmL/zJPRU+8Nyw1Hs/WdasQVj5u88CQDFVvsubfGXyvh9QR4AsDIcWWzF8Kqqae/Inp3bAQA+fc0odv5XKUXtUrnWLcvdf/KK/fCLvlsiP/dvZGVYd6MxasOLd0Xfe6idRJhGPSgJX1EapII7T9/X6jFV+xYNMe5msgYAMHpA6UOUnv77xsxdwKYiOipFi+zEPnzF2++oV0SJ7cYNL5pcet+l68UKmDfuPuGtWl2Xevw7d8y5kDi8/VTUK+Ptp2+4Gll0J29g9/InfZ80agAAlJgTkvs79/X96sHLDwCArnJlT06v0qsTAJRYDZx7XC3lXuXmqqrEJ2/Q6o8/0KEDTXJP/qOsrIzXb96UWtSrTSNHDAMAtJHrBADQ1tIodbvHT4oW8eIuYFMRA72i4tatmNu8/fYfOFRiu4kTjAAAuxz3FHutoWHhEBIRx649eyv5SqqnV8+eAIBTpz3w+s0bAEWLAF3yLBpOOkhZqdz9TaYUFT/OX7zEeyw9PQPupz0A/Pf6qnquqhy3tn1KTcXrN28waNCgOjsHqTudOnWC7B8yuPP6a60cb0jXouHIfVZcBQCM6tWm1O24K01zF7CpCHeRnfiXX3j7uYQ/KbGdoWJR4f5gyKNiBczIx5/Q2vY8DoVWfK6a6NamqEB54fYrvPtW1JPn3bdseCcWjXypbBH4f+/TAYA3d2dp3n3LhrpDIPq0k4K9vkKZxUnuvJ+///z+fG249y4TyioVz9lNyK+UlFWQ+PhNrRxrWL+ixUW7jC+aWkpDuU+p2z19UzQfLXcBm4pwF9mJffCct98Rz5AS241TK/ps3ns2oFgBMzzhISTUZmLfucDKvpRq6fFvx4mzgTfx9t9OHG8/fsXVsHgAwMB/2y1lmaShAgDF5pr89Xfu6/t1fspff7h+/72+JDx6hVay1E4izKMelISv6OnpwfnoUaR/z4FkNe6elaZrO1leL0ZLvaElegy62pvDysEdStZbS92fO3/l7yaNGgD/mPvQtPuvx9Mma8MS243o1w1LTDSxwyOoxDyXOip9MHl0+Q3zmuIuFlTa+S31hpZYQZw75yW356amUi/oqPSBlYM7rBzci227xESz1BW6uUPpy/s3lGslzfvbl5aLWwCuLYFxDzFGv2bDM0jDo6qqCklJSVzzC4DFNLOKd6im7t268XoxzraxRoffegyecXfDVHML9FToX+r+3Pkrf2cyxRjevtcwdMQo3mM7tpV8L1NXG4lVy5dh09ZtJea5NNAbA3NTk+q8rErjLhZU2vln21iXWEGcO+clt+fmFONJ8Dh7HrPmzuP1NuVatXxZsXkiq3Kuqhy3tl3zC4CUlBSGDq3d3uKk/ujpGT4tU8cAACAASURBVCA4PgQzhnet8bG6tGrB68U4fVgXtJMu3mPwiMVgzHK7haEb/Urdnzt/5e8mKHVAYPJ7jNn1X0Fg3fiSNxaHdW8FO+3ecAx4UGKeSy2Ftpik3Kkar6ryuIsFlXb+6cO6lBh+zR1a/XuB8N6/w+MlxZqWea7r/0sBgFLPxVWfq5KnZech9tknrNxN31FI1fDaTVnZkGxeuV7GZenavjWvF6OV4UjItS5+U+DYGhtYbjiKAearSt2fO3/l7yZpqMAv+i5Gz/3vu8nmOZNKbDdyQE8sNdfDdnffEvNc6g7thylaQ6rzsiqNu1hQaee3MhxZYgVx7jBsbjFRS0UBukP7wXLDUd4wbq6l5noYOaBnHaavucCYZIzRo/cgwjzqQUn4ipaWFmT/aInzofG1elxuLz8TDeUSz00YqQinBf99EV1iook4l+WIPPAPgP/mryxtP1d7c14hzWmBcZmL3aycpgtXe/Niw5adFhhj38LJkJVqXq3XVBX7Fk6G0wJjXlYdlT5wWmCMdTP0K9xXQlwUR5eYFnut3Pk6V07TLXWfY77RAFDha5swUhFBjgt4fxcdlT5wtTcvNty+NqR8yUBo3ENYWMyo1eMSwScsLAxTU1OccD9V5+fi9sabZmZa4rkpxpNw5OB+3u+rli/Dw+Q7SIwtmgONO39lafudcXfj9aQ8cnB/mYvdbFi3Bmfc3YoNWz5ycD+cjxxEK9nKLSZRE85HDuLIwf28rAZ6Y3Dk4H5s3bSxUvtf9bxQ7LVy5+vcsG5Njc5VlePWJreT7jA1NYWwcNWH0xL+MN3CAmH/e4+U9OoNc/wdtxfjZJVOJZ4bN7ADdpn8d0PTTrs3olfrItS+aNEI7vyVpe13xGIwryflLhOlMhe7sddXwBGLwZg+7L/F7XaZKNXbEGjHqcrYZaLEy6ql0Ba7TJSwyvCvSh/jRGTRd7by8nKHy/MLz7jX+KNlS2hplb0ACCGl4babzgXFVLxxJXB7+U3VKXnjbKK6Mvb+M433+1JzPSS4b0KUa9FnJXf+ytL2O7bGhteTcu8/08pc7GaV1TgcW2NTbDj03n+mYf+S6fWyoMz+JdOx959pvKy6Q/th7z/TsH7WhAr3lRAXg/NKq2KvlTtf5yqrcXWau6Y+fE5DyO1kaicRvsDicDgcpkMQ8qvdu3fjgOMO3Dy4CCLC1MmX1NxKF2/Ev8lE9K3bYLFqttoqaXiePn0KBQUFXA/yx2AVGm5L6t6tmNsYpamD5ORk3lyGRPBwOBwMUVHGAIlsrBvXt+IdCPlN3s9CjNwWgr+XrMKiRYuYjkME0O7du3Fwz07cOraG2k2kWlYcuoi4F18RfSuG2kmEcdSDkvAdW1tbCIuJ4+DlG0xHIQ3A4zef4OwVCUenffShS0rVtWtXzJ8/H/PtFqOwsJDpOKSBKywsxHy7xZg/fz4VJwUci8WC074DcL3xBE8+llz0gZCKHL7+GE2aS8PW1pbpKERA2draoomoOA5cCGY6ChFAj1+n4KhnKByd9lI7ifAFKlASviMsLIxdjk7YdS4Ej9/UfGU60njl/SzAwn2XYGIyBYMH0+TzpGyrVq3C23fvsffAQaajkAZu74GDePvuPVatKn0eLyJYVFRUYDJlCv45fwf5BXSDg1Tek4+Z2BP0CLv37KWpHki1FbWb9mDnqWt4/DqF6ThEgOTl/8T8XaeonUT4Cg3xJnxr0sQJSIiJQuDOv/GHZN3P00gaHrv9l+AT8xD3kpLRpk3pK6ISwnXhwgWYmJjgqucFjNHRZjoOaYCu+QdgrNEkeHh4YNKkkosEEMGUkpKCvxT6YExvGWw3HsB0HCIAvmT9gN6ecAxUHYULlzyZjkMagEkTJyDhdjSC9y3FH1J1P18jEXwLd5+Cd3QytZMIX6ECJeFb2dnZGKIyCC2E8nBpozVEm9LdZVJ5u8+FYLtHMMLCw6GiosJ0HCIgli9fjkOHDiEsOKDEytKE1MTde0lQ09DGnDlzsHVryZXWiWCLiYmB2sgRWKTVAwu0ejEdh/Cx3PwCTDkchaymMrgVE4tmzWq2+jIhwL/tpsEqaMH+Cc9t8yEmQu0mUrZdp69h28lr1E4ifIeGeBO+1axZM/j6+ePV5yzoLTuEj99ofidSsUIOB2tcvbHxxDW4uLrShy6pki1btkBDQwNqGtoIDA5hOg5pIAKDQ6CmoQ0NDQ1s2bKF6TikDqioqMDF9Ri2+iRjw5V7KKT7/6QUnzJyMX7fDbzJAq75BVBxktSaZs2awfeaH16lZmCM3S58/JrOdCTChwoLOVh9+CI2uFyhdhLhS1SgJHxNTk4ONyKjkMsSg4bdXiQ9f8d0JMLHsnJ+YPrmk3D2uYlz587B1NSU6UhEwLBYLN7wW/2xRjhw+AhooAGpLg6HgwOHj0B/rBEmTSoa2k2T0DdcpqamOHfuHI5FvYDVsRhk/fjJdCTCR5LfpkHXMQy5ojK4ERkNOTk5piORBkZOTg43IiKRi6ZQn+uAe0/fMB2J8JGs7FyYrzuCo1fCqZ1E+BYN8SYCIT09HcaTJiI09Dqs9IdihbkOJMXFmI5F+ASHw8GZoFhsPOkPNGmKq17eGDRoENOxiIDbsWMH7O3tMUx1KBx3bodi/35MRyICJPHOXdj9sxSRUdFwcHDAkiVLmI5E6snt27cx1kAfnLwcLNfrhSkqnUF16cYrPScf232T4Rb5DOqj1HH+4kVISkoyHYs0YEXtpkkIDQ2F1Vg1rLI0hGRz6q3bWHE4HJz2j8YG16sAW5jaSYSvUYGSCIzCwkK4ubnBftlSFP7Mg63RSJhqDoKsFC2g01jl/yzAtVvJ2Od5A3eevIaNzSysX78ef/zxB9PRSAMRHx+PefPm4fbt25hmNhW2f8+lQiUpV0LiHew/eAgnT53BoEGDsH//fgwcOJDpWKSeff78GWvXrsXRI0fQr4MM5qh3g07fthBm0+ClxuJz5g+cufUCh68/hVBTUThs3wELCwsICdE1QOpe8XZTPuYba8JMVxWy0rSATmOR/7MAvlF3sPdcEO48fkntJCIQqEBJBE5aWhq2bduGo0cOIz09Ayp95DGguxw6//kHpJqLAWBBSIgF6qzQMGVm5+LDl3QkPf+A8LtP8D0nFwb6Bli7bh369+/PdDzSAHE4HHh4eMDBwQFJSUno2qULRqmNQK9ePSEjLQMxMVGmIxIG5eTk4uu3r3jw4H8IC4/A02fP0LdvX9jb28PExISGdDdyd+7cwfp1a+Ht44Pmok0xrJss+spJorWEGJqLNmE6HvkNB6j298fCQg7SsvPx8nMWEt+kI/bZJ0hJSmDmrNlYtmwZpKSkajMqIZVSrN2UkQEVhW4Y2LMjOreVhVQLcbCF6DOqISgoKASbLYTM77n48CUNSU/fIizh4b/tJH1qJxGBQQVKIrByc3MRHBwMPz8/xN2OwbPnz/EtLR2FhYVMRyN1SKJFc/zZpg36KSpCQ0MT+vr6+PPPP5mORRqJ+Ph4XLt2DREREXjw4AG+fPmC3NxcpmMRBomKiqJly5bo3bs3hg8fjjFjxlCPSVLChw8f4OPjg+CgINxJjEdKykdkZH1nOhapRUJCQpCWlIC8vDyUVQZDV1cXGhoaEBWlm1iEedRuahyonUQEHRUoSYMRGBgIMzMztGzZEhcuXICCggLTkeoci8XC6dOnMXXqVKajEEJqydu3b6GmpoaWLVsiMDCwwc9V9u3bN7Rr1w579uyBjY0N03EIIQA+ffqE9u3bw8XFBebm5kzHqRccDgdWVlY4e/YsvLy8oKGhwXQkQkgtaOjtpfz8fPzzzz/Yu3cvFixYgB07dkBYWJjpWIRUC02CQgReQUEB1qxZA11dXWhpaSE2NrZRFCcJIQ3P58+fMWbMGIiIiMDHx6fBFycBQFpaGkZGRnBxcWE6CiHkXydOnECzZs0wceJEpqPUGxaLBWdnZxgZGWHs2LEIDw9nOhIhhFRIWFgYTk5OOHPmDFxdXaGuro73798zHYuQaqECJRFoKSkp0NTUxPbt2+Hk5IRTp06heXNaNIcQIniysrKgr6+PjIwMBAQEQFZWlulI9WbmzJmIjY3FvXv3mI5CSKPH4XDg6uqKqVOnQkxMjOk49YrNZuPEiRPQ09ODvr4+oqOjmY5ECCGVYmJigps3b+Ljx48YOHAgIiIimI5ESJVRgZIIrOvXr0NRUREvX75EZGQk5s2bx3QkQgiplry8PIwdOxbPnz9HcHAw5OTkmI5Ur0aMGIHu3bvD2dmZ6SiENHqRkZF49OgRZs6cyXQURrDZbJw6dQqampoYM2YMbt++zXQkQgipFAUFBcTFxUFFRQXq6urYs2cP05EIqRIqUBKBU1hYiI0bN0JTUxODBw9GQkIClJSUmI5FCCHVUlBQAGNjY8THxyMkJARdu3ZlOlK9Y7FYsLS0xOnTp5GTk8N0HEIaNWdnZygpKTXqFV+bNm2Ks2fPQlVVFdra2khMTGQ6EiGEVIqEhAQuX76MDRs24J9//sGUKVOQlZXFdCxCKoUKlESgpKamQldXFxs3bsSOHTvg6ekJKSkppmMRQki1cBdlCAoKgq+vL/r27ct0JMZYWFggKysLnp6eTEchpNFKS0vDxYsXYWVlxXQUxjVt2hSenp4YNGgQtLS0kJSUxHQkQgipFBaLheXLlyMgIAChoaFQUVHBo0ePmI5FSIWoQEkERkREBBQVFfHw4UPcuHEDdnZ2YLFYTMcihJBqW7x4MTw8PHDx4kWoqqoyHYdRrVu3hp6eHg3zJoRBZ86cgZCQUINd7baqREREcPnyZSgoKGD06NH43//+x3QkQgiptNGjRyMuLg7NmzfHoEGD6CYw4XtUoCR8j8PhwNHREerq6ujfvz8SEhIwePBgpmMRQkiNbNq0CXv37sWJEyegq6vLdBy+YG1tjRs3buDp06dMRyGkUXJ2doaxsTEkJCSYjsI3mjVrBl9fX/To0QOjR4/GkydPmI5ECCGV1qFDB9y4cQMmJiaYOHEili1bhoKCAqZjEVIqKlASvpaWlgYjIyMsXboUmzZtgre3N1q2bMl0LEIIqZGDBw9izZo12L9/P6ZMmcJ0HL6ho6ODdu3awdXVlekohDQ68fHxuHPnDiwtLZmOwneaNWsGHx8fdOzYEerq6nj+/DnTkQghpNJERERw+PBhHDt2DHv37oWmpiZSU1OZjkVICVSgJHwrLi4OAwYMQGxsLEJDQ7Fs2TIa0k0IEXinT5/GvHnzsGXLFsyePZvpOHyFzWZjxowZcHNzw8+fP5mOQ0ij4urqip49e2LYsGFMR+FLkpKS8Pf3R+vWraGuro7Xr18zHYkQQqrEwsIC0dHRePnyJQYMGIBbt24xHYmQYqhASfjS/v37MWzYMHTr1g2JiYkYPnw405EIIaTGvLy8MGPGDCxduhT29vZMx+FLlpaW+PTpE3x8fJiOQkijkZ2djTNnzsDa2prpKHxNUlISAQEBkJKSwujRo/H27VumIxFCSJUoKioiLi4Of/31F9TU1HDw4EGmIxHCQwVKwlcyMjJgbGyMhQsXYuXKlfDz84OsrCzTsQghpMauX78OExMTzJgxA1u3bmU6Dt/q1KkTNDQ04OLiwnQUQhqNCxcuICcnB9OmTWM6Ct9r2bIlgoKCICIigtGjRyMlJYXpSIQQUiUyMjLw9vaGvb09bG1tMX36dGRnZzMdixAqUBL+cefOHSgpKSEiIgJBQUFYvXo1hIToEiWECL64uDiMHz8e+vr6OHjwIE1XUQFra2v4+/tT7yRC6omLiwvGjh1LN4UrSVZWFsHBwWCxWNDQ0KC53AghAkdISAjr1q2Dt7c3vL29oaqqSvPrEsZR9YfwhaNHj2LIkCGQk5NDYmIiRo0axXQkQgipFQ8fPsSYMWMwePBguLu7g81mMx2J740dOxYyMjI4fvw401EIafAePnyIyMhIGt5dRW3atEFwcDB+/PgBDQ0NfPnyhelIhBBSZWPGjEF8fDwAYODAgfD19WU4EWnMqEBJGJWVlQUzMzPMmTMHS5YsQVBQENq0acN0LEIIqRVv3ryBpqYmunbtCk9PTzRt2pTpSAKhadOmmDZtGo4dO4bCwkKm4xDSoLm4uPCmViBVIycnh5CQEGRkZEBbWxvp6elMRyKEkCrr3Lkzbt68ibFjx8LAwABr166l71+EEVSgJIxJTk6GsrIyAgMD4efnhw0bNlDPIkJIg5Gamgp1dXVIS0vDz88PzZo1YzqSQLG2tsbLly8RHBzMdBRCGqy8vDycPHkSlpaWNK1ONXXo0AEhISH4+PEjFSkJIQJLVFQUbm5uOHjwILZt2wY9PT3qGU7qHX0TIYxwc3ODiooKZGVlkZiYCC0tLaYjEUJIrUlPT4empiYAIDg4GJKSkgwnEjw9e/aEqqoqLZZDSB26evUqvn79ihkzZjAdRaDJy8sjNDQUb968gb6+Pr5//850JEIIqZbZs2cjPDwc9+/fh7KyMhISEpiORBoRKlCSepWdnY0ZM2bA0tIStra2CA0NRbt27ZiORQghtSY7Oxv6+vr48uULgoKC0KpVK6YjCSwrKytcvXoVnz9/ZjoKIQ2Si4sLdHR0ICcnx3QUgdetWzcEBwfj0aNH0NfXpxVxCSECS0VFBfHx8ZCXl4eqqiqOHTvGdCTSSFCBktSbhw8fQkVFhbdSmIODA5o0acJ0LEIIqTV5eXmYNGkSHj16BH9/f3Tq1InpSALN2NgYoqKicHd3ZzoKIQ0OdwoFKysrpqM0GL169UJISAju37+P8ePHIzc3l+lIhBBSLbKysggICMDChQthbW0NGxsb/Pjxg+lYpIGjAiWpF6dPn4aysjKaN2+OhIQE6OnpMR2JEEJqVUFBASwsLBAVFQVvb2/06dOH6UgCT1xcHFOnTqVh3oTUgePHj0NWVhb6+vpMR2lQ+vbti4CAANy+fRsTJkxAXl4e05EIIaRa2Gw2tm7dikuXLuHcuXMYPnw4Xr9+zXQs0oBRgZLUqdzcXMyaNQvm5uaYOXMmbty4gQ4dOjAdixBCahWHw4GtrS0uX74MT09PqKioMB2pwbCyssKDBw8QFRXFdBRCGozCwkIcP34cFhYWEBYWZjpOg6OoqIjAwEBERUVhypQpVKQkhAi08ePHIzY2Fjk5ORg4cCCCgoKYjkQaKCpQkjrz5MkTDBkyBOfPn4enpyd2795NX4IJIQ3SihUrcPToUZw7dw7q6upMx2lQlJSU0L9/f7i6ujIdhZAGw9/fH2/fvoWlpSXTURosZWVlXLt2DUFBQTAzM0NBQQHTkQghpNq6d++OmzdvQkNDA7q6utiyZQs4HA7TsUgDQwVKUicuXLgAJSUlNGnSBAkJCRg3bhzTkQghpE5s374d27Ztg5ubGwwNDZmO0yDNnDkT58+fR0ZGBtNRCGkQXF1dMWLECHTv3p3pKA3a0KFD4evrC19fX0yfPp2KlIQQgda8eXN4eHhg165dWLduHcaNG4f09HSmY5EGhAqUpFb9+PEDtra2MDY2xrRp0xAZGYnOnTszHYsQQuqEs7Mz7O3t4eTkBDMzM6bjNFhTp05FYWEhzpw5w3QUQgTex48f4e3tjZkzZzIdpVEYMWIErl69Ck9PT8ycOZN6HBFCBN6CBQsQEhKC2NhYKCsrIykpielIpIGgAiWpNS9evMCwYcNw8uRJnD9/Hvv27YOIiAjTsQghpE5cuHABc+bMwZo1a2Bra8t0nAZNSkoKEydOpGHehNQCNzc3NG/eHEZGRkxHaTQ0NDTg6emJ06dPY/bs2VSkJIQIvOHDhyMhIQFt2rTB4MGD6SYyqRVUoCS14sqVKxgwYAB+/vyJuLg4TJo0ielIhBBSZ7hzis2dOxfr1q1jOk6jMHPmTMTFxeHOnTtMRyFEYHE4HBw7dgympqYQExNjOk6joqOjgwsXLsDNzQ0LFy5kOg4hhNRYmzZtEBISAhsbG5iammL+/PnIz89nOhYRYFSgJDWSn5+PRYsWwcjICMbGxrh58ya6devGdCxCCKkzMTExGDduHIyNjeHk5MR0nEZj2LBh6NGjB5ydnZmOQojAunHjBh4/fkzDuxliaGiIM2fO4MCBA1i8eDHTcQghpMaEhYXh6OiIs2fP4vjx41BTU8O7d++YjkUEFBUoSbW9fv0aI0aMgLOzM9zd3XHkyBGIiooyHYsQQupMUlIStLW1oaGhATc3N7BYLKYjNRosFgszZsyAh4cHcnJymI5DiEBydnaGsrIy/vrrL6ajNFoTJkzAqVOn4OTkhFWrVjEdhxBCasXkyZNx69YtfPnyBUpKSggPD2c6EhFAVKAk1eLr64sBAwYgKysLsbGxMDU1ZToSIYTUqadPn0JLSwsDBw7EuXPnwGazmY7U6FhYWCArKwuenp5MRyFE4Hz79g2enp6wsrJiOkqjN2XKFLi6umLr1q1Yv34903EIIaRW9OnTB7dv38aQIUOgoaGB3bt305y7pEqoQEmq5OfPn7C3t4eBgQEMDAwQExODnj17Mh2LEELq1Nu3b6GtrY2OHTvC09OTeoszpHXr1jAwMKBh3oRUw5kzZyAkJAQTExOmoxAA06dPx5EjR7B+/Xps27aN6TiEEFIrJCQkcOnSJWzevBlLly7F5MmTkZWVxXQsIiCaMB2ACI53797BxMQE8fHxOHbsGCwsLJiORAghdS41NRVjxoyBmJgYvL29ISkpyXSkRs3Kygr6+vp4/PgxunfvznQcQgSGi4sLjI2NISEhwXQU8i9ra2v8+PEDtra2aNq0Kezs7JiORAghNcZisbB06VIoKSlhypQpGDRoEDw9PaljE6kQ9aAklRIYGAhFRUWkpqYiJiaGipOEkEYhMzMTBgYGyMzMhL+/P2RlZZmO1Ojp6OhATk4Ox44dYzoKIQIjLi4Od+7coeHdfOjvv//Grl27sHjxYhw4cIDpOIQQUmvU1dWRkJAACQkJDBo0CJcuXWI6EuFzVKAk5SooKMCaNWugq6sLLS0txMbGQkFBgelYhBBS5/Ly8jBu3Di8ePECQUFBkJOTYzoSASAkJARLS0u4ubkhPz8fAJCSkoJNmzbB0NAQhYWFDCckhFkODg4wMzNDXFwc7zFXV1f07t0bqqqqDCYjZbGzs8PWrVtha2sLFxcXpuMQQkitkZOTQ3h4OMzMzDBp0iQsWbIEP3/+ZDoW4VMsDs1aSsqQkpKCqVOn4ubNm3BycoKNjQ3TkRq1tWvXYsOGDRVuJyMjgy9fvtRDIkIE3927dxEVFQUbGxs0afLfrCcFBQUwMjJCeHg4IiIi0LdvXwZTkt+9evUK8vLyWL58Oe7evQs/Pz8UFBQAKFoIREpKiuGEhDCnW7duePr0KQCgV69esLa2xrp167B+/XoaQszn1q9fjw0bNsDV1bXEaCUOhwMPDw/o6+vTMH1CykDtJf528uRJzJ49GyoqKjh37hxatWrFdCTCZ6gHZSOVkpKC5OTkMp+/fv06FBUV8fbtW9y8eZOKk3ygskNLW7RoUcdJCGk4li9fjr///hvjxo1Dbm4ugKJGoKWlJYKDg+Hn50fFST7z8uVLuLq6olmzZti8eTP8/f15xUkAyMjIYDAdIcyTlpbm/ffDhw+xdOlSZGdnIzIyEuHh4bSiKh9bu3Ytli9fDmtra5w9e5b3eGFhIaytrWFqaort27czmJAQ/kbtJf42bdo0REdH4/Xr1xgwYABu3rxZ6nbp6ek4d+5cse93pHGgAmUjxOFwMHz4cPTt2xfh4eHFnissLMTGjRuhqamJ4cOHIy4uDv3792coKfmVsbEx2Gx2uduw2WysWLGinhIRItg+f/6M4OBgAEBAQAC0tLSQmZkJOzs7nD17Fp6enhgyZAjDKQlXcHAwhg8fDnl5eTg4OPBWhPx9mNCPHz+YiEcI38jMzOT9N4fDQUFBAQoKCuDt7Q01NTXIy8tj8+bNNMSOT23atAkLFiyAmZkZLl68iIKCAkyfPh0nTpwAAOzbt49WxCWkDNRe4n/9+/fn1RjU1NRKzL3L4XAwYcIETJkyBY6OjgylJEyhId6N0IEDB2BrawsAaNmyJZKTk9G6dWukpqbCzMwM4eHh2LlzJ+bNm8dwUvI7TU1NXL9+vcy7SWw2G58+fYKMjEw9JyNE8GzatAnr16/nNdKFhYXRrl07vHnzBmfOnIGxsTHDCcmvWCxWpba7d+8e9XoljZq8vDxevHhR4XZ3797FX3/9VQ+JSHUsWLAAhw8fxqBBg3Dz5k3edz82m42dO3di4cKFDCckhD9Re0kwFBYW8r6LT506FUeOHEGzZs2wadMmrF27FoWFhRAREcH9+/fRpUsXpuOSekI9KBuZFy9eYMmSJeBwOOBwOEhPT8fkyZMRHh4ORUVFPHnyBJGRkVSc5FPm5uZlDs1is9nQ1NSkD1tCKiEvLw9OTk7FehDl5+fj3bt3aNOmDS0kwYdu3boFERGRCguVv/YeI6Qxys7OLvd5FouFLVu2UHGSz23fvh3t27dHdHR0sUJLQUEBtm/fzlskjBBSHLWXBIOQkBDWrFkDHx8f+Pn5YejQoTh+/DivOAn8N70F9alrPKhA2YhwOBzMmDGjRIM8IiIC2tra6N+/P2JjY6GkpMRgSlKecePGlTlsgcPhwNzcvJ4TESKYzp49i69fv5Z4PD8/H58+fYKKigqePHnCQDJSFhUVFZw/f77CAmVeXl49JSKEP+Xk5JT5XJMmTTBlyhTY29vXYyJSVXl5eRg/fjxevXrFa6j/KiUlBR4eHgwkI4T/UXtJsOjq6iI2NhZsNhvz588v9lx+fj7Cw8Nx/PhxhtKR+kYFykbk6NGjiIiIKHHHtbCwEHl5eZg/fz5atmzJUDpSGRISEjAwMCi22jBX06ZNMXbsWAZSESJ4duzYUeZz+fn5+PjxI5SUlBATE1OPqUhFDA0NsXv37nKLlN+/f6/HRITwZj+RkwAAIABJREFUn7J61gkLC2Po0KFwc3Or9JQJpP7l5ORAX18fQUFBZc4TymKxsHnzZupVREgpqL0keP7880/8/PkTP378KHFThsPhYOHChUhJSWEoHalPVKBsJF68eAE7O7tS78ICRV90TExM8OHDh3pORqrKzMysxJwqTZo0gaGhIcTFxRlKRYjgCAsLQ3Jycpnvh0DRwisZGRlYu3ZtPSYjlbFgwQLMmjWrzN4RFQ1vJaShK60HpbCwMNq2bQsvLy80bdqUgVSkspYtW1ZucRIo6lzw+PFj+Pr61mMyQgQHtZcEy9y5c/HgwYMyb7Dl5ubSFHSNBBUoG4HShnb/rrCwEJmZmZg0aVKZEwoT/jBmzJgSH6wFBQUwNTVlKBEhgmXXrl2l3lXnEhYWhqioKJYvX46zZ8/WYzJSWfv374eOjk6Jf0chISEa4k0atdzc3BKPsdlsiIuLIzg4GJKSkgykIlVhY2PDm26pvM8qNpuNzZs311csQgQKtZcEx5EjR3D8+PFyaxX5+fm4dOkSrl69Wo/JCBOoQNkIHD16FDdu3KhwMu38/HxERUXRlx0+JyIiggkTJkBYWJj3WIsWLaCjo8NgKkIEw9OnT+Hr61vqlyBhYWE0adIEs2bNwosXL7BlyxZISUkxkJJUhM1mw8PDA127di32XigkJISMjAwGkxHCrB8/fpR4jM1mw9/fH127dmUgEakqBQUFxMbGIiIigrdgW2mFyoKCAty6dQvR0dH1HZEQvkftJcExe/ZsAKhw6hEhISHY2NggPT29PmIRhlCBsoF7/fo17OzsypyjhsVi8d64u3fvjpUrV2LhwoX1GZFUg6mpKa/gLCwsjMmTJ9OQLUIqYe/evSUaesLCwrxpLp48eYJ9+/ahTZs2DCUkldWiRQsEBQVBRkaG92/KZrOpByVp1H5fxZ7FYuHYsWNQUVFhKBGprmHDhiEsLAwREREYPnw4gJKFyiZNmmDr1q1MxCOE71F7STC8fPkSW7duRe/evQGgWFH5V4WFhfj27RuWLVtWn/FIPWNxaHblBovD4UBDQ6PEwjgsFgtsNhsFBQXo168fpkyZgnHjxqFHjx4MpiVVUVBQgNatW+PLly8AgJCQEKirqzOcihD+lpaWhrZt2/LmZxMWFsbPnz9haGiILVu28L4YEcGSlJQEZWVl5OXlQVhYGOvXr6cVikmj9fz5c3Tp0gVA0fe9LVu20P8PDURUVBTWrFmD0NBQNGnShDcSgMViITk5mT7DCPkNtZcEz+PHj3Hu3DmcPn0ajx49grCwcIlRoCwWC+Hh4bwbN6RhoR6UDZizszNCQ0ORn58PNpsNNpsNISEhDBkyBDt37sTLly+RmJiIZcuWUXFSwLDZbJiZmQEAZGVloaamxmwgQgTAiRMnkJOTw1tcRVVVFTExMbhy5Qo17ARY3759cf78ebBYLOTl5SErK4vpSIQw5tdV7KdPn07FyQZEVVUVISEhiIqKwogRIwAU9aDkcDjYtm0bw+kI4T/UXhI83bt3x+rVq/Hw4UPcv38fK1as4N10+7Vn5fTp00udc5kIvlrrQZmXl4cHDx7gw4cP1DjgA2lpabCxsQFQ9Obcu3dvDBkyBMrKyrwJ0ps3b44///wTvXv3bjDd3V+8eIEXL17g69evZQ5rbygeP36MVatWQVlZGUuWLGE6Tp2j65XUlLGxMQBAXl4eZmZmUFBQqLNz0fVa/wIDA+Hi4oL+/ftjxYoVTMdhnIiICGRkZKCgoNBg5lJNS0tDcnIyvn79WupciwR48uQJVq5cidatW8PR0bHcRVb4CV2vVcftaZSUlASgaKEJaWnpWj8PKYmuV8HRUNtLLBYLMjIy6Ny5Mzp37sx0nFpRXj3p1atXuHnzJiIiIpCamgoA6NKlC01xwbA6ae9wauDbt2+cffv2cdRHqXGaCgtzANCPAP40FRbmqI9S4+zbt4/z7du3mlwS9a6goIDj7e3NMTMz48j+0ZLxvyX91MP12lSYM1p9lOBfr7KyjP8t6ac+rtemnNGjRwv29WpqyvlDRprxvyX9VP+nT68enKVLl3IePHjA9GVVZffv3+csXbqU06dXT8b/jvRTX9drzwZwvfZg/O9IP/V1vQr++6tCnz6M/x3pp/o/srJ/cMzMzDje3t6cgoICpi+rKuHWk0ZTPUmgf5oKC3NG11I9qVo9KHNycrBlyxbs3rUTbBYH+n/9CbUeslCQk0RrCVE0FxGMu7WNXdaPn/iYkYvkt+m4/vATfJNSUMBhYdHif7BixQqIiYkxHbFcly9fxrKlS/Ds+QuMHNgHY1T7Q7lPF3Ru2wpSLcQhJFT+SmBEsGRl5yLlSxruPn6F4NtJ8AqPw88CDhYtXiw41+uyZXj27BnU1dVhaGiIwYMHQ15eHtLS0hASohk3GpLMzEx8+PABiYmJCAgIwKVLl/Dz508sWrRIYK7XpUsW4/nzlxjWVRqa3SQxQK45OkqLQlKsCejtlf/9+FmItJyfePQpBzdfpsPnYQaef8qEob4+djk68v2Kzk+fPsXiRXbw8vZB17YtYTCwM1R7tkNPORlIi4tCRJjNdERSi37kF+Db91w8fPsVUQ/fwSv+BZ69/wJDA33s2i0g16udHbx8fNClVQvo9ZTAkE6S6NFKDFJiTSDShD7jG5IG8f66eDG8vLzQvYs8jAx0MVJ1MHr37A4ZaSmIiogwHZFUoLCwEN/S0vHi1WvExCfC2z8Y1yOi0aWLPLZt247x48czHbFcxetJgMHAThjVpx36dvgDraWaoblo6QvlEP6SlZuPj2nZSHr9GaHJ7+CT8BIFHNSonlTlAqWnpyfsFszHt6+fYafRDdNVO0GcCpINwvcfP3Ei6iUcg59AWuYPODrthZGREdOxSnj27Bnm/f03AgIDMVFjMFZZGUFerjXTsUg9+56TC5crodh+wgtS0jJw3OPEv9frvHkICAjAlClTsGHDBr7/4kpqX1ZWFg4fPoyNGzdCSkoKjo6O/Hu9zp2DgKBgjO0riyWj2qGTjCjTsUgt4HCAsGdp2BD0Fi+/5MBu0WKsX78eInzWEP3x4wfWrl0Lx9270bm1FDZMGQJ1hQ5gUVG8UeFwgNDk11hz9iZefEyD3aJFfH697kKnlmJYoykHtS5SdL02MgL3/uroiC6dO2HHhpXQGjUCLLpgG4RnL15inYMjzl32gpaWFg4cOMCbv5Gf/FdP+oLF+v1hMaoXxEWoINkQfP+RD7fr/8MunzuQlmlZrXpSpQuUHA4Ha9euxebNm2A8qCOWj+mJ1hLUaGmIPmbkYuu1hzh/+xVWrlyF9evX880HV3h4OIzGj4OcrBR2LDDF0H60uE9jl/IlDeuPXsQZv0isXLmS/65XIyN06NABe/fupdXmCD58+ICVK1fixIkT/Hm9jhuLtuIcbNBuD5WOEkxHInXgZyEH7rEfsT3sPf7qr4jLV73xxx9/MB0LAPD582eMH2uIe3fvYIWRMixGKaAJm3qeNWY/Cwrhdj0ZWzxj8Ve//rh81YvPrlcD3LuTiKVqbWGu3BpNqHt5o8b376/jxyPp3j1sWL4YNhamAjNHLamayFu3sXDFerx59wGenp4YOXIk05EAFK8nTVbtgZVGSmgt2YzpWKQOfEzPxmbPOJyLelTlelKlCpR5eXkwnWoCby8v7JrcDxOV2tc4NOF/F+PeYPG5uzAwNMTpMx6ML/Rw8uRJzLS2hpH6IOy3t4KIMH2okv+cDYjGvG2uMDAwxOkzZ/jjep05E8bGxnBxceG7u+iEWadOnYK1tTUMDAxw+vRp/rhera1g0EcGOw06oykNR2zwXnzJxTSPJ+A0k4Gvnz969erFaJ7//e9/0B+jC9aPLHjY6UK+dcNYeILUjucf02Di6AeOSHP4XPPji+tVT1cHrOyvOGnSDZ1bUqcN8h++fH/V1wObBXidOYau8g1jURVSth95eZi1cBkuXPXF0aNHMX36dEbz/FpPcrQYjklDujGah9SPCzefwM4tokr1pEoVKM3NTOHvfQWuFgOhIt+yVsISwRDz/Aus3OKhYzAO7qdOM5bDy8sLEyYYwd5iHJZON+SbHkeEv9y89xhTV+6Dzhh9uJ86xViOout1AtasWYNVq1bR9UpKFRkZCSMjI2hra8Pd3Z2xHF5eXphgZAS7kW2xYIQcDU1sRNJyfsL6/FO8zxdHbHwCZGVlGcmRmpqKQUoD0U4ccLPVhrQ4FXtISd++58JiXwDefQdux8Uzer0qDxyAtsLf4WLcFVJidMOclMRX76/KyujQ7k9ccDsMGWm6+dNYcDgcbNm9D5t27sWlS5dgaGjIWBZzM1P4+1yF29zRUOnWhrEcpP7FPEmBxcEQ6OiPrVQ9ib1u3bp15W3g4LAVhw8exNlZKlDuLFNbOYmAkJNuhsHyMlh3IhDsJk0wbFj9D1FNSkqCro4OFpnpYfmMcVTsIWVq37olhvXvgdWOzmCzm2DYsGH1niEpKQm6urqwt7fH2rVr6XolZerQoQNGjBgBe3t7sNls5q5XbS38rdoGi9SoONnYiAoLwaCPDHyTUnD2ii/Mp02v9yF/ubm50NbUgOjPTFz8Rx8txJjtTUz4l1jTJhg3qCu8Yh7hnKcXzMynMXK9ammoQzTnEzzMuqOFCC3WRErHN++v2loQFxWB30V3SLRoUa/nJ8xisVgYMVQFhYWFWLxsBQwNDdG6df2v2+DgsBWHDx3EeTsdKHeldSMaG7mWzTGkexusdb1aqXpSuQXKiIgITJtmjsPTBmJkj1a1nbVByMjNx4XYN9jp/xBz3ONw700aWCygrbQYRJpU7UtL4P0UqG4Jxj86Pcvc5krCW2zxeYA57nFIzfyBtlJiaFXKXKDcXFq7wrDT/yEKCjn4U0oMMuJV/+L/p5QYurUWx8Ld7lBXV0fHjh2rfIzqys3NxYhhqhg1sAe2LzClYk8VZGRlw8M/CluOXYbl+kO48+glWCwW5FrJQKRpxRMRZ2Rlw/tGPNYducDb/3vOD7RrJQNxsfKHKyc9fY2uhrZYYVn6CnIXg2/xjvvpazratZJBaxnJar3O37WVlUGPjn9i3ioHZq7XESOgqakJJycnul7rSHp6Otzd3bF+/XpMnToVCQkJYLFYaN++PURFK+55lZ6ejitXrmDFihW8/bOystC+fXuIi4vXwyv4j5ycHHr16oXZs2czcr0OVx2C4e2FsUGnIxUnqyAztwAX76Zid9hb/H3xCZI+fAcLQDtJkSqv1hv06BtG7LuDxaPKnj7n+ZdcON/6AGO3B9gd9hZtJJqiraQImjWteXFEmM3CqC4SOBL6CCmfPkNHV7fGx6yKJf8sxs0bobi4WA8yzannZGVl5OThfNRDbL8Si1mHAnH3VSqEwEK7li0qtcJ5Rk4ersU/x6aLt3j7Z+fmo51MCzT7bbGCutq2OoTZQhit0B4HvaKQ8ikVOjr1fL0uXoSbYcE4Y9oN0s2o52RNNKT30bIw/v66ZAlibkbj2oWTaCkjXa/nFmTpGZk4fd4TG3c4wXzWfCTeSy76ntmubaVWOE/PyMTVa4FYvXkHb//v379Drl1biDdrVmLb0+c9oaJhgI079qCgoABybf+s1X+vEUNV8PDxU+zcvQezZs2q10I5t5501EYdan3k6u28giQjJw/no59gh1c8Zh0Jxb1Xn8ECC+1aNq/853nCS2z2jOXt/z33J9rJiJf7uRtw5xWGrDiPpWMHlnhO1vIodlyNL/entP3K0lZaHN3aSGLBNpcK2ztlDvEuKCjAwP5/YVibn1it37vSJ29sll24ixNRL0o8rtWnDU7OHFzp49x/l47RO64DAFL2jCt1m2nOtxB4P6XE44enKWHcALlKbRuyZBT6tKteIWijzwNEpjRB/J17YLPr547xxo0bcOqYMyJd10FMhHpVVMXCnW5wvRJa4nFdVUWc32ZX7r4ZWdmw3ngEflGJpe5/wN4KstKlL6CR+i0D8gbzAACZkSdLPG+8zLHU4x5fNxcTNSr//0xFVh86h7Ck14hLSKzH63Ujzpw5g4SEBIiJidXLORujOXPm4PDhwyUeNzAwgJeXV7n7pqenw9zcHN7e3qXu7+Liglat6v+G3LJlyxAYGIi4uLj6u143bID7od3wt+4JUWGac7Iq7H2ewz32Y4nHNXtIw21q2TcZf/cg5Ts0D90DALxbP6TCbX4/1z6jbmghWjvXy513WRh//H+IT0iEgoJCrRyzIsnJyRg4QBE+K8ZjgDz1qqiKf06EwS00ucTj2oqdcXqhXrn7ZuTkYc6RIAQklvz+qq3YGU6W6vhDQqxOt62phOcfob/lMiPX6+UZvdC/XfN6OWdD1hDfR8vC2PvrwIEI874A5QH96uWcDcXfS1biqFvJoaj62hq4fMql3H3TMzJhMdcOPgHBpe5/ZM82tPrjvynzxptZl7ptfJgf/upTe3OX5uTmYpC6PqaaT8Pq1atr7bjlKSgowEDFfhjRXhhrJg6ql3MKoiUnI+EW9qDE49r9O+LUfO1y983IycNc5+sIuPOq1P33WIwo9XP3/psvUFt7CQCQesymxPOylkfLPW9lspVmw8XbuPEmH/GJd8ts75TZInF2dsan92+wSLN7lU/cWNx/l44TUS9gp9UD8Wu1kbJnHOLXamO6amcE3k/Bs9SsSh0n/uVXXnGyLFcS3iLwfgrWjlXAYwc9pOwZh5Q943B4mhJmn4zDu285JbbdObk/b7uLf6sCAE5Gv6z2612k2R2f3r+Bs7NztY9RFe/evYPDVgdsnz+VipNVlPT0NVyvhGLp9LF4cMkRmZEn8eCSI6zGqcMvKhFP35QsXv8q8NY9+EUlYt9SS7zzP4zMyJN4538YS6ePhV9UIjwCosrcd7OrZ5nPXQy+Bb+oRGyeZ8I7bmbkSRxfNxcz1h3Em49fqv2af2dvMRafUj7U7/Xq4AAnJycqTtahu3fv4vDhw1i1ahVevXoFDoeDV69eYfbs2fD29sbjx4/L3d/Pzw/e3t44evQo0tLSwOFwkJaWhlWrVsHb25uxuSBXr16N1NTUen5/3YL1Wm2pOFlFD1K+wz32IxaMlMPtRQPwbv0Q3F40AObKrRH06Buef8mt1HES3maW2mD+VWZuATQP3YNmD2neuR4uH4Q12h0R9OgbQp98q42XBADo3645JvZvhQW2c2vtmBVZYDsPk4f1ouJkFd1//RluoclYbKiMO7un4/OJebizezos1BUQkPgCz1LSyt0/5O4rBCS+gOOMUXh+2AafT8zD88M2WGyojIDEFzgf9bDOt62pAfKtMXlYLyywnVdrx6zIAtu5mNi/FRUna0FDfR8tCxPvrwsXLIDZZCMqTlbRvfv/w1G301ixyBbPEqORn/oSzxKjYWNhCp+AYDx5VvIGzK/8Q8LgExCMw7u34vOzJOSnvsTnZ0lYscgWPgHBOH3+v3bSucvevG3zU18iP/UlAj3PAACOuNXuXPpioqLYvXktHBwc8O7du1o9dlmK6klvsdhAsV7OJ4juv/kCt7AHWGQwAIk7piL1mA0Sd0yFhVpvBNx5hWcp6eXuH3LvDQLuvMLu6SPw7IAFUo/Z4NkBCywyGICAO69w/uaTEvvEPfvEK06WJfWYTak/YesnAADWG1evU9FiA0V8ev+23PZOqa2S7OxsrF65Asu0u0FchIYvlCXxddEH2kTl9mgnXVSQaCcthmlDOwEAkt6U/wURAA5dfwq9PTdweJpSudt5xr8FAJgO6QgJ0f+66qr3LvpSf/3hxxLbGiq24z02rFvRxMyl9fasLHGRJlim3Q2rV65AdnZ2tY9TWatXrcRwxZ4YPah+7jQ2JPEPngMATHRU0b510V269q1bwmqcOgDgzqOX5e5/PugmAMDCUA0SzYuGIkg0b4YFJkVDU1bu9yh1v71n/fA+tewverzj6o/kHRcAtAb/BQAIiUkqN1dViIuJYrXVOKxZ/X/2zjyuiqr/4x+fHrcUyAAlQRQ3lKuyKCpiihAg5QVRIlER0kC0eFQ0SoNIoUxU1ExFyR4QFVSI5VJ4WUVFUZBFBSVFdkKBjKVwefr5+2M6w527cYF7WXTer1ev7sycM3O+w3hm5jPfxad7rldfX5iZmcHKykrhx3qVuX79OgDA2dkZ2traAKhcjh4eHgCA3Nxcqf1Pn6Ye/Nzc3KCiQnmTq6ioYMuWLQBA/7+7GTp0KPz9/fHll192z/X6xTaYjFHG/HFssvyOkldNfXx00FeHpgoV6qWpMhCrZlD341s17X+cPHqlBtyQ2zjsIL2K5b166lqwn6pGH0tp0GtYbkQdK+ZWfeeMkMDnC0biypUs/Pzzz3Ldrzh+/vlnZGVdhc9S1quio+Q+oJ75HE11oaVK5ZTTUlXChwuo56WbZXVS+0dlUR9ynM04UP4n56fy4AH4+F3qJfLLyEyFt5UHPktn4urVq912vV69moXPF4xU+LFeBV7meVQS3T2/Xs26ioAvvBV+rJeN7Nx8AMBKxyXQ1qL+vWtrjcRa15UAgNybop7rgkRGxwEA1jg7QUWZmp9VlJXg9THlpebt97VIWwe7RfS6BW/PAQCxHpxdxXLB25hvOrtbPCj/+usvfOmzDVvtDDBEDuk9XlZyH1D3a0eTCdBSpT5+aakOhesCynv2Zrn0+Sn62n0AgPP8Scz77kLq3drvTBaj/WH+Tdh8HYtjay06PNb6plaY+UUjyGUexml0LiJ3yMD+2GpngC99JOtJYtXHiIgIvPjfUyyd0bk8ARobYwFQocpJhbVYFZIFK44GVs4ZAysOVbUpNrcKHidyAIgPUb58rw68/BpaUNtkpYtF+iPFhicLtrXiaMDdbBwtyMkyTmlICrcGQHstqisxc1GMUKFyKBXXNre7/+1xt3HCbTasOBr0+RAHCdcWFCcFl29Vtanr4kLLSf/2hND2WDJdC/4/30VkZCRWr17dpX1Jo76+HqdOnUbktxvkul+luasAUKHHiZl5cPxsH2xMDfGhrRlsTKkH6KiULHz41WEA4sOOM24UISb9Oh0+7e1ih8ULjDF1vLbI8QTb2pga4mNHa8yf3n7KBDJOaYgLnyYQT8ThQmHYGqqUGHGnVPqXM0kh4IKiojAZN4rwxfcRuBIaIDaEGwC9Xng/ZDn/1zKp4+oo71vNgU/wuW66Xk8hLi5OYcdoD5Lv8sWLF+DxeLC1tQWXy4Wbmxu4XC4AIDIyEk5OTgCoeX7ZsmWMfaSlpeHcuXN0+LSPjw8cHBygry/69V2wLZfLxcaNG2Fubi7zOKUhIfMIAKCiogIARJJ8v/XWWwCAwsJCqfuWFAJOxMqeZPny5fD29u6e6/X0afy4TPpLXUfR9KM+QFRvN0Fy8WO4nr4LS91hWDF9BCx1qVxKcbfqsT6K+pp72GEC7KaqMfaRWdoIXmEDHfa3Yb4WFum9CT0N0dyggm0tdYfBzeQtmOq0/3ck45SGpDBBAKhufAYAUB/CvB8PV6IeDIvrWkX6CLODX47Q5ZNgqTuMPh/iyK6gniNmaDMLGygNek3qGDuL6pD+WDxVFQcP7MN770kPE+4q3393APazJkBVSX4e52ou3wMA6sM+AT+vFCv2/wxrQx2smq8Ha0MdAEBM1j24HeEDAELWWcN+NvPfwaWiKsRl36fDpzfbGsPWeBw42sxrVbittaEOPKz08bZe+8/OZJzSqA+T7BlY1UCJN+oqzHvpiDeofyd3q3+Xum9JIeDKYgoUKaqtPFBVGowlsyfg++8OKPx6PXhgH+ymqEJ1iGJftNl5tO/Po5Lo1vn14EE4LuZCXVV+BW77q48BADyvK0MCPwX2Kz/CIut3sMZ5GRZZvwOA8ghc6e4JADh57CA+sOcy9pF+6Qqi4n+mxbdtXp5Yavuu2FBmwbaLrN/Bf9aupsU7WcYpjed1ZRK3VVTVAACGqzP/XWmMoNL/FN2VHqkjKQSciJXttSXh3iePHZR6nM6yfs0qOLisRWBgINTURO9r8iIiIgL/979nWDq7c8+aJMS47kd38PPLsfI7PqwNRsN53iRYG1D5C2OulcD9aCoA4NhaC9jPGsfYx6U7NYjPfkCHT3txjWA7QwecUaoQRrCttcForLWcircnt/9Bqr1QaGKDJKp+J/dz5rPQiH/u73drpHt4SwqzlnTf9TuThZP/sYa1wWj63MlKSGoh9TeYL3sKDnEsnT0e26NzJL7viPWgPBMZgYWc4ej/WtfCvog4Kfi7sLoRu365wxDjPE7kIDa3itHP4VAmw9tvX1IxLHan4/I95lfhXb/cYbQlfXf9cqdLY5eFfUnFAERFQ7WhAxnbpVG7fzEt2kqDtGl68pyxnixL8ow8kn4fGhtjsSokS6wQ3FEG/PtfWMgZjsiI013aT3vExsZiyOuDYG6sGO9JIk4K/r51vwL+IdG0OAkAH351GFEpWYx+izZ8y8jtGBgWhzmuPsi4wcwd4R8SzWhL+vqHSHeplgeBYZRQJiwEkryRZHtHIaHh//1qvcj6RRu+xX+/Wi9WqCUQEbiphfnFhCyLy5nZFQb2/zcWzTXEmUjxHp/yIjY2FkOHDoWlpaVCjyMLRJwU/F1QUABfX19anAQAJycnREZGMvpZWFgwcjsGBATAwMAAaWnMv4uvry+jLenbHV9kAwICAIgKiiRvJNneUUhoeESEYq8VaQwcOBCLFy/GmTNnFHqc2NhYDBn4b8wbqxhRlrxUC/4uqv0TgWmVjJfI9VH3ECfguZJc/BiOoUWMnGQHMqpgeeQmMkuZIS6BaZWMtqRvYFqlQmwS5EAG9bwinLNM7Z8XbbJdGtXbTWixQRpXy5oAUJ5Fcbfq4Xr6LjT9ruLolRrU//m8nd6dw5YzDCmp6fj9d+kiV1f4/fffkZyaisUzx7XfuBMQcVLwd2FFPXZGX6PFSQBwO8JHTNY9Rj/7XbGM3I5747Mx3zcSl4qYf9ed0dcYbUkgQZRRAAAgAElEQVTfndHXFGKTIHvjswGIvoCQPFNke0choeEh69rPK6Woth1l8cxxSE5NVfj1mpKaDltO9xUZYefRvj2PSqLb5teUFDgu5rbfuBMQcVLw983CO/DbuZcWJwFgpbsnzsTwGP2slixneAZ+E3QQ081skH7pCuMYfjv3MtqSvn479yrEJkG+CaKEQWFBkeSNJNs7CgkNlyQ87jscgv7qY2C/8iOx4q68sDR7G0NeH4zY2PYdtbrC2cgI2OhrY0AHC14JQ8RJwd+FlQ3YGZPDENjcj6Yi5loJo9+S3QmM3I5BvFyY+UXj0p0axjF2xuQw2pK+O2MkO4/JiyAeFfkl6X5OtncUEhou7ClZ96M7LfB2hEt3ahDEy8Vay6mdGo8gA/79Gmz0tXFGgp4k4kH57NkzXLx4EQedDLp88Lzyx/j12/egPKg/Lt+rg8OhTFjsTscmK12R9R4ncmjxjIiaN/ys6dDpG2W/4739F8HLr6G9Iy/fq8O+pGJsstLFOvPxUB7UH01PnuNI2n3sSyqW6HFJkOYd2dtYMl0LSYW1SCt6SJ8nYqs0pmqpwM9uCq7er6dF4a6KlPMnqsMz4iKeP3+O/v0V8yU5OTkJ8wwn47V/KSY3Wk7RA1SfD4by0NeRcaMIizZ8izmuPvB2sRNZ/+FXh2kvSiJqFkXvo0Onswvvw3ztDsSkX6e9IzNuFCEwLA7eLnbY4GQD5aGvo6nlLxyISERgWJxEj0uCNO/IniTifCZsTA3pkGyAEhe3fR8Bbxe7dovcOFqaIDEzD0lZN+m25LwoCnNjDtwDQhR8vSZjwYIF3VbcRBrXr1/HH3/8ARUVFaSlpcHCwgIGBgbw8fERWe/k5ER7URJRs7y8nA6dzsrKgomJCc6dO0d7R6alpSEgIAA+Pj7YsmULVFRU0NjYiD179iAgIECixyVBmndkTxIeHg4ulwubbq6wKYylpSVWrVql2Os1KQlzRg/Fa/9STNnuvOoW3N06E0qDXkNmaSMcQ4tgeeQmNszXElm/Puoe7f1DXsavexnRYXi5Vc3ghtwGr7CB9urJLG3EgYwqbJivhXVzRkJp0GtofvI3jlypwYGMKomeQoTu9JjpKsnF1JfzwLRKxgv7Dn45rpY1KaS4g8loZfyrH5Camor3339frvsmpKam4l/9+sF0kmb7jTtB7oNHeBDsDuXBA3CpqAr2u2Ix3zcSm22NRda7HeHTXpRE1MwPcqFDp3NKarFwRxTisu/T3pGXiqqwNz4bm22N8fG7hlAePABNrc9w6Jc87I3PluhxSZDmHdmTnM0shrWhDiz02395UVTbjmI6SRP/6tevG65X6t9Gd8HOo/KjJ+ZRSXTb/Pqvf2Ge6SyF7D87twD1JbegoqyE9EtXYLVkOaab2WCbl6fI+pXunrTQRkTNkrwrdOj0tZw8zLWxR1T8z7R3ZPqlK/gm6CC2eXnC62N3qCgrobGpGUGHjuGboIMSPS4J0rwje5KTZ3/CIut3sNDCTOx2g6kcBG7/AhevXKOFXkWIlK+99hrM5pogOTkZH330kdz3D1B6UsbFSzi0Zl6X95VbWoeSQ67UfftODZbsToCZXzS8uEYi692PptJelETUzNu9nA6dzil5BJuvYxGf/YD2jiTCmxfXCB8vnNZ2Pz9/E0G8XIkelwRp3pE9ydmr92BtMBoW00bJZX9Hk2/B2mC0TF6lsmDG0cTHx8XrSSLqz507d/D02fNOV3oWZM28sbR3oWDINREThdcTiLdgfH41Lt+rQ9OT55g+5k3U7l+MXe+3vfhm3qsX2Z/yoP5YZz4eAHDxV+k5ePoS5noj6DBwjY2x0NgYi4mft5/DZO4EdaxbMB4n3GZjzwcG8DiRI+KF2lH0Rirj6bPnuHNHcV6qBXl50J8gWcDrKh4OlrR3oWDINREThdcTiAdgTPp1ZNwoQlPLXzDmjEfz5RPYv8WVbncx947I/gRzOKbnSA9D7Y34h0QjMCwOvm5LGZ6ZByISkZiZBw+H9r0HrWZPo0LqvzoMpbmroDR3FTQXeihy2JgyThtPnz1T7PVaUABDw96RANrT05P2LhQMuSZiovB6AgkDP3fuHNLS0tDY2IjZs2fjxYsXOHLkCN0uPT1dZH+CORxTUkQrEfZ2fH19ERAQAH9//x4P9Z42bRqePn2q0Os1PzcHUzQkp2voKqtnadAve4KhguQlWHg9gXjCJBQ2ILO0Ec1P/oaRlhKqt5vg20Vj6XaZpU0i+1Ma9BrWzfnnYfOB9ITifZUC7xmo3m6C6u0mOOwwQWHFHQb8+18YP0IZBQUFct83oaCgABO11DDg34oRBdwsp9HeCIIh10RMFF5PIGHg8dfv41JRFZpan2HGOA3Uh32CPS5mdLvLd6pF9ieYazGjUPEeaPJmZ/Q17I3PxrYls9oNyVZU284w4N+vYYKmmsKv1wkayl32BOoI7DyqGLprHpVEd82vkyaOx8ABikmt8LGbK+1dKBhyTcRE4fUEEgYeHf8z0i9dQWNTM2bNMMTzujIc2t2Wl/HC5asi+xPM4ZiacVkBVikWv5178U3QQWzfullsqDdAnbNN690Qc/IHBAftxEp3TxHPUnkxVW8SbirwGqT0pGfgjOp6igE3i7acxoLiGBEThdcTiJdgfM4DXLpT88/9fDjqfnTH7lVz6XaX79aI7E8wh2NGUfcUFJInO2NyEMTLxVb7GXK57+aUPAI/vxzO87oW2i2IntabEt/PRTwoa2qoPxLJo9gVSKizMMIh0cJ89u5kJBXWYnscFTYjKa8kCaGWJNRtj7uNdQvGSzxOV3NQdifKg/ojyMkQ52/9hi1n8mHF0cCS6VpYbKQlUyg5QBXN2XImH8culMiUo1MSGm9QXq01NTWYNm1aO607R81vtdBQU1zxBvVh4r+CS8uxCAC+bkuRmJlHF4mRlFeShFBLEt+++D4C/1km2VOrqzko5Q0RJ6+EBjA8P6NSshAYFoe0o19KPKeCKA99HYc+X4OfL+XCM/BH2JgawtHSBA7vzO502Hl7jFSnHtYVer3W1NA5EHsaEuosTHvCm7+/P3g8Hi00SsorSUKo33hD/L/PLVu2YPPmzRKP09UclPKGiJP5+flSPT+7C01NyqNMkdfrb7UPMXyK4q5XNQk52trzUPE2H4Xk4sfYwS8HAIn50IgHzKSd18XuZwe/HGvnSP7C29XcaT2BoIgAAOYTqHkt5la9SP45eTBi6Gv47bff5L5fQk1NDTRU5Jd7UhgSGiVMew/q25bMAj+vlC7mIimvJAmhHushPvfUl5GZWG8j+aNVV3NQyhsiImb4L5Pq+anItl1BQ2Wwwq/XEUO6N0KCnUflT3fPo5Lojvl1pMaI9ht2EhLqLIwk4Y2wfetmJPBT6CIxkvJKkhBqtXHiQ0m9/b7GpvVuEo/T1RyU8oaIkzcuJEr1/BTEwW4RPLy24rujP8qUd7OjaL6lgeqamvYbdhKiJ2m8IdkLW1Y6ez/faj8D/PxyukiMpLySJIR63MehYvfjdyYL660lP493NQelvCHi5IXtS6V6fnaEM5lUGiwTXfm9O7w1jLo2xL3viHwKbGykvpgN7cHq3RxNFdTuX4zUTxfAz24KnVeS5LDsLWyy0gUgOS8k2S4v1IYOxEqTMajdvxgn3GZjsZEWXajHz679XI1EGCYFczoLuTb++KP9KuWdpbmlBQMH9L6KX1PHa6P58glcCQ3A15840XklSQ7L3oK3ix0Aybkeyfb2qHvcBP+QaNy6X4G8iECRsHSSr9N87Q7aI1JQXBVeBihx2NXWDM2XT+Dsrk1weGc2XdTn60+cIG+Gvk59bFHo9drcjEGDuv5RpyfR19fHixcvkJ+fjz179tB5JUkOy96Cj48PgLZ7FYEsk+3t8ejRI/j6+qKgoADFxcW9QpwEACUl6gFfodfrn39hYDd6AsmKnsYQqjDEumn40no0nQ+N5F7rLWyYT4lVzU/+Zqwny2S7PI8lLEqQZRK6KG+UBwBPnjxRyL4B4OnTp+1+rO4JONpqqA/7BBn+y7BjmSmdV5LksOwtbLY1BgA0tT5jrCfLZHt71De1Ymf0NdyurMe1XSulh6UrqK08UBncX+HXq5JinNHkDjuPSj5Wd8+jkuiO+VVFaajC9t9ZpnEm43ldGW5cSETg9i/ovJIkh2VvYZsXFV7d2MQsdkuWyfb2eFTfAL+de3Gz8A6KstJlFieBNrGXFMyRN4MGDUJzc/vFfDsLrSf14H2eM0oVdT+648L2pdj+wWw6ryTJYdlb8OIaAZB8Pyfb26O+qRU7Y3JQWNmArG8+kJs4Wd/UitALRfDiGsk1CoJcG+Led0RUyP/7v/+T24G7CkdTBRxNFdgaaKK0vgUOhzKRVFhLezW6mOogLLOUzmfZUbrqHamrQU0edc3MB+3KBkoEIvkz5cGqkCwkFdaK2FpaT1V+ekvA41VS2/qWpwCo8yYPetO10t1MHa+NqeO1Yb9gJh5UPcSiDd8iMTOP9mpcs9gcx2PT6HyWHaWr3pGTdSgvrEePmxjHL6+lXrBI/kxpkKJBU8dr49Dna2TykGwPx8/2ITEzT+S8PKiiErSPVFNcAvpX+XrtCPr6+tDX18f777+P+/fvw8LCAjwej/Zq9PDwQHBwMJ3PsqN01TuSw+EAAB4+fMg4fllZGQDQ+TOlQYoG6evr44cffpDoddqTvMrXq57GEOhpDMEijirKfn8Cx9AiJBc/pr1xnI1HIDz7IZ2HraN01atHV526t9f9+Zxx/Mo/qBdOTRX5PcCRY1U3PqXzyQFtL/HOxorxklFUflJB/tUNx+gsHG01cLTVYDtzPEofNsJ+Vyz4eaW0V6Or+RSEpt2m81l2lK56R07SpMLm6hr/Yhy/so4K2yX5tqRRWFGPb366himj1HBgtblELxVFtpUX3XEtdce/CXnCzqOix+rueVQS3XEt9Yac6JKYxpmMaZzJWGr7HkpKy2C1ZDkS+Cm0V6O76wocCz1F57PsKF31jtSbNBEA8KiunnH88krK65jkz5QGKRo0jTMZR/fvkuh1ar/yIyTwU0RsfVRPCWjuris6bUdP0pueYTmjVMEZpQrbGWNR+qgJS3YngJ9fTns1uprpIfRCEZ3PsqN01Tty0kjq3beusZV5P6+nBGStN2W4n/9TNIgzShX7XefJ9b5bVkeNw0in89G30hB3rfQ+FwoAn50rgMbGWNwooyqcaQ4bDB010T8O14CaII6k3afFN4AqnqOxMRZH0qUXkOkqE0ZQE0lUdiXtyVj9uBUJBZRbs6G2/MSWJdOpr3/xeW15EErqWsDLp45lrKMqtW3Tk+c4l03lRCLnjaXjbNwTCqW5q5BdSF1bo0aoYqyW6ION/YKZAKj8jHWPm+j1GTeKoDR3Fb6LVFxRGADQHUP9jSPOZ9LeiZUPGxCbToWlTdcbK7EvaTvH1QdTx2vD122pRHGy+fIJsf8Jbyc4WlIPtD+ltYUU3a+sRUw6tTxr6oSOmsoiJ9atW4d+/fohK4sKhdDW1sb48aIpMkhS9z179uDRo0f0+rS0NPTr1w979yq2wuLkydQX6PDwcFRUUF7LFRUViIqKAgDMnDlTav+KigoYGBhAX18f/v7+vVKcfFX5POEBNP2uIreKehjSVBmIMW+KeiZzOdT97ohQBdbM0ka6MqsimfDPy25UQR2qG6lnj+rGp0goop5ZDDXl57kyQ5t6zjh14xHD04jkTLOY0H1VhV8FtoRdgJrL98gpoSJNtFSVoDNC9EOMnTE1Nx76JQ/1Ta30+ktFVVBz+R6HE/MUOs6J/7zQnM0sRlUD9e+lqqEZ8dlUBVOjsdIFl6qGZsz3jcSUUWrYunSW1JcZRbVlUQzsPCoKO4/2Dj7+9Av0Vx+DaznU/KitNRLjdMaItHOwfQ8AEHToGC3UAVTxnP7qY7DvcIhCxzl5IjW/nzz7EyqqqH8HFVU1iI7/BQBgbCS9kHBFVQ2mm9lgGmcytm/dLFGcBIBlS6mItqi4BHpdY1MzTp39CUDbuWDpOJ+euAz11ceQU0K9q2ipDoXOcNH3WVtj6p340PmbzPv5nRqorz6Gw/ybCh3nxJFUyqyzV++hqoFyPKtqaEF8DlX13WisdGGwqqEFZn7R4IxSxVb7GXK/796poubk8RqKS70nTM/FcUvB0XgUwjJL8d7+iyLb9nzQNinMnaCOTVa62JdULJKH0YqjgfeN5VO1SBIcTRVYcTTEHt/FVEek0BDJedkZz01SJGfLmXxsOZPP2Ba8agbDW3OxkRZ+ulEltu0mK90u5Z981VlhMxfHY9NgvnaHyLaD3qvp3/On68HbxQ6BYXEiuRVtTA3hZG2q0HFOHa8NG1NDscdfs9hcJFSbhGETMTH12i0AENuf0BkvT1IkxzPwR3gG/sjY9t+v1svk2cmiGFxcXBAcHAwTE1GviGPH2vKrmJubw8fHBwEBAXQ+SgKXy4Wzs7NCx6mvrw8ulyv2+B4eHiKh2iTnJfHc5POpqn7i+hN6a6Xxlx1HA3WEZz8EN+S2yLZA27aPKqY6KtgwXwsHMqoYFVkBKt/aUn3F3uP0NIbAUneY2OM7G48QqXxLcrV1xuNIU2UgDjtMwPqoe2KPRQpisMiHZXMnITTtNhbuiBLZtu/DBfTvt/W0sNnWGHvjs+l8lARrQx04msovkbw4ONpqsDbUEXt8V/MpIiHVJOcl8dxMv0V93BHXn6DotiyKgZ1HRWHn0d7Bqg8ccCz0FOba2ItsCw7aSf9e8PYcbPPyxDdBB+l8lIRF1u9gheMShY5zGmcyFlm/I/b47q4rREK1Sc5L4rmZnJ4BAGL7E0jbD+y5iIyOg4fXVnh4bWW02eblqZD8k68KH5hOROiFIth8LVpzJMilrbr425NHwotrhCBeLp2PkmBtMBqOJop1nuGMUoW1wWixx3c10xMJ1SY5L4nnZvptak4T15/QFS/Pm+VU9KXK692X56RXCpTTx7yJ1E8XIKGghhb+NlnpwnD0MLrCN+GzdydDV0MJV0saEJZJKc17PjDAwqlvSSzSI09I4Zqk27VIKqyFFUcDVlM0YGuoKdfjCBfJAahzskh/pNiK6yfcZiM2two/3ahCUmEtXEx1wDUYyYqTXcSYMx5XQgMQm55NC3feLnaYoTeWrvBN8HVbisk6mricfxfHY9MAUCLme28bySVcuj1IMZpfMvOQmJkHG1NDvGtqiCXm0j3MAIiIh/JCuEgOQJ2/xQuMRURTlu5l9uzZyM/PR1RUFC3c+fj4YObMmXSFb4K/vz84HA4yMjIQHBwMgBIx7ezsusUj8YcffkBcXBx4PB54PB64XC64XC4cHR3b7evu3n2Jqlk6hpGWEpLXTUNC0e/0S+SG+Vow1Bwq8gLpbT4KuuqDcbW8CeHZVIqIQNuxsJ70psTiEvJkj9048O/+juTix0gufgxL3WGw1B0GW478c+3ZTVXDqGEDcTa/DuHZD2GpOwz2U9W6tajDq8KMcRrI8F+G+OwSWmDbbGsMo7HD6QrfhK1LZ2GS5pvILK5GaBolBu37cAFsjMZ2i+fggdXmSMx9gPP5ZeDnlcLaUAcLDcbAblb7L1Ob/psu83EU1ZZFMbDzqHjYebTnmTXDEDcuJCI6/hdauNvm5QljI326wjdh+9bN0Js0ERevZOFY6CkAlIjJtbGS6pEoL47u3wVeYhIS+KlI4KdgkfU7WGRtAQe7Re32FRYa2yPm5A84E8NDZHQcEvgpcHddAQfb91hxsovMGDccF7YvRXxOKS3ceXGNYKSjTlf4Jmy1n4FJI4fhSvFvCL1QBIASMW0MR3fL/Xy/6zwk5pWDX1AOfn45rA1Gw1p/NOxmSo94BACvMFGHPnlCzkd3RkT0eyHkKnL69GmsWLGi11SvZumdaGyMxalTp7B8+XKF7L9fv3447reODglmYekKSnNXKfx6VeT+WV4tFH099evXD98vnQD7aeyLGYt4Pom+h8FTrHDq1CmF7H/FihV4WpqDox5WCtk/y6vF2uAkDNSZodDrtfV2Er5fyqahYek63TG/vnj6J04EH1DI/ln6PhHRcVjlsUFhEUNET+rO6tUsfQ/11cfEvu/0yhyULCwsLCwsLCwsLCwsLCwsLCwsLK8GrEDJwsLCwsLCwsLCwsLCwsLCwsLC0mOwAiULCwsLCwsLCwsLCwsLCwsLCwtLj8EKlCwsLCwsLCwsLCwsLCwsLCwsLCw9BitQsrCwsLCwsLCwsLCwsLCwsLCwsPQY/+7pAfQ0GhtjAeClqFpeWN0Ii93pYm1pevIcaUUP8dONKiQV1sKKowGrKRpYOPUtqA0d2AOjZSEozV0FAGi+fKKHR9I1EjPz4PjZPol2NLX8hZ/SrsMz8EcAgLeLHZwWmmL8KA2JbX/JzENiZh5sTA3haGkCq9nToDz0dYXawSKdfv36AYDCKv91FzweD7a2tlLtiIyMxOnTp8Hj8eDh4QEPDw/o6+t3eb8s3Yem31UAQPV2kx4eSddILn4M19N3JdrR/ORvxBfWI7n4MZKLH8NSdxjsp6rBfMIwKA16rZtHyyKMmsv3AID6sE96eCRdp7CiHvN9I8Xa0tT6DHHX7uF8fhn4eaWwNtSBw+yJsNAfDeXBA0TaphaUIyrrV7rtQoMxsDEaCzXlwd1lDosY+vK8SeZC7/gHAIAN87XgoK+OsaqDJLaVZd5k59jeS3/1MQCA53VlPTqOjkLGLQ1Bm6S172u2v2yorz4GAH22annMtRJEX7sPfn45XM304LpgMjijVBltiI3SkJf9r7xA+bJQ3/IUFrvTxW5revIcn4TfQFJhLb0uqbCW+u92LYKcDFmRkqVL3LpfAcfP9klt85H/USRm5tHLgWFxCAyLw5XQAEwdr81o+2XwWRyPTaOXEwWEyrO7Nsl38CyvHAUFBbC1tZXaxtbWFjwej14ODg5GcHAwIiIisGzZsk7vl4WloxTV/gnX03eltvk6pRzh2Q/pZcGX6NDlkxQ9RJZXhPqmVsz3jZS4fcfZKwhNu00v8/NKafHx1Mb36PVNrc+w7mgy+HmlIm3P55fhwGpzVqRk6RSeP91DcvFjevlARhUOZFQhed006GkMYbTtyLzJzrEs3c0i63fo3xVVNT04EpaXmZXf8cHPL6eXQy8UIfRCEY6ttYD9rHEy78faYLTcxsQKlC8JuxMlv7ykFT1EUmEt9nxgAFtDTSgP6o+mJ89xJO0+9iUV41x2JdYtGN+No2V5mcguvA/ztTuktolKyUJiZh4Oeq+Gq60ZACDjRhEWbfgWx2PTsH+LK9321v0KHI9Ng7eLHVxtzTBqhCoqHzZgbzgPx2PTcL+yVqzXJQuLLGRlZcHERLpXSGRkJHg8Hvbs2YOPPvoIKioq9HonJyfMmTMH2tpMUV2W/bKwdJTcqmZwQ25LbVNU+yfCsx9iw3wtrJg+HJoqA1Hd+BQHL1UjPPshHjQ8Ees9xMLSUb6NuSZxW2FFPULTbmOzrTGczfSgpaqEqoZm7E+4gdC02yip/QPjNN4AAKQWlIOfV4p9Hy6A3awJUB48AE2tz3Dolzzsjc/G2cy7WG9j2F1msbwkxN2iPBwDbcdixfQRAIDM0kY4hhbhRM5DfLtoLN22I/MmO8eyKAJJXo83C+9gupkNArd/IbItcPsX2LTeTcEjY3lViLlWAn5+ObZ/MBsr502iIx1irpXA/WgqjMePgJbqUACSvSMLKxtg5heN7Y6z5TYuNgflS8CR9Pv47Y9Widt/ulEFAFhpMgbKg/oDAJQH9cc6c0qU3B4n/eWHhUUS30UmwnztDvz3q/VS251NpsKFlpjPpNfNn64HAAxPSQC4UUSF5TgtNMWoEZR7+agRqliz2BwAkF9cJpexs7x67N27FyYmJoiIiJDa7vTp0wDAECcBwMbGBgDA5/M7tV8Wlo5w9EoNuCG3cdhhgtR2edUtAAAHfXVoqlDREJoqA7FqBvWCfqumRbEDZXklOJyYh98e/ylxe+4DyrvM0VQXWqpKAAAtVSV8uGAKAOBmWR3dNirrVwCAsxmHfiFSHjwAH79LiZJfRmbK3wCWl56YW/UAAFuOGr3OVIe6hwt6PwIdmzfZOZalu3hU34DpZjYIDtqJCeN06PUlpWUAAIOpnB4aGcvLSPS1+wDAECcBwGLaKABA+u0qqf3rm1ph5heNIJd5GKehIrVtR5CbB+Xle3Xg5dcgLJMK19hkpYtF+iPB0WQOtrC6ERd/raNFMSuOBpZM18JiIy26jWBeyKTCWqwKyYIVRwMr54yBFYfynIrNrYLHiRwAQPCqGRL7C7cz1xtBi3Sy2mPF0YC72TjMnaDeabuFIWOUhix5MS/fo85l6qcLGCHcgpxwE69oy3Ie+hIZN4oQk36dFry8XeyweIGxSPjwrfsVSM8pxBffU2ICyW/o8E7beRLMC0lyK9qYGuJDWzPYmFIP0FEpWfjwq8MAgP9+tV5if+F2suZRFLTHxtQQHzta06JeZ+wWhoxRGu3lxfzi+wic3bWJOjf/2CgOcWHZJNxbWNysfNgAABg+TJmxXkOV8ry4U1rd7rj7AmlpaTh37hyCg4MBAD4+PnBwcBDJcVhQUICUlBRs2bIFAMDlcrF8+XJGmLFgXkiSA5HL5cLNzQ1cLhdAm/cfAJEwZcH+wu1sbGwYIp0s9nC5XGzcuBHm5uadtlsYMkZptJf3ccuWLYiPjweXy6VtFAcJ7Ra2myzn5uZ2ar99mczSRvAKG+iXvA3ztbBI702RkLmi2j9x6UEjdvCpcBGSp8tuatsLo2B+M5Jb0VJ3GFZMHwFL3WEAKE+Y9VH3AACHHSZI7C/cTtZ8YIL2WOoOg5vJW/SLbGfsFoaMURrt5XfbwS9H6PJJsNQdRtsodj+NzwAA6kOY9/ThStTDZnGd5A+YfY1LRVWIy75PhxFvtjWGrfE4cLTVGO0KK+qRUYD+l1YAACAASURBVFhJC10kF6L97DaxVzAvJD+vFCv2/wxrQx2smq8Ha0PqxTAm6x7cjlAfJELWWUvsL9xOXM7F9uyxNtSBh5U+3tbTktpOmt3CkDFKQ5a8mJeKqvBlZCYy/JcxwrIFqWqgRBp1FebzzYg3qH8rd6t/p9cJhnsLIss560uw82b3zpviQq1JuLfwh56OzJuvyhybfukKouJ/xrHQUwCAbV6eWGr7LqZxJjPa3Sy8g9SMy/D2+xoAFYq8bKkdPrDn0m0E80Im8FNgv/IjLLJ+B2ucl9Ghy2dieFjp7gkAOHnsoMT+wu0WWphBRVmpQ/Yssn4H/1m7GgventNpu4XpaA5JWTgUEvrPeXo5nyXb49KdGsRnP0DohSIAgBfXCLYzdETyIRZWNiCjqBp+Z7IAUOHFS2eNZ4QkC+aF5OeXY+V3fFgbjIbzvEl0ODLxFAQgEtIs2F+4ncW0UbLd4wXssTYYjbWWU/H25JGdtluYruaFJKHdwraQ5Zvl9VL3HZJaSJ3T+fJNcyEXD8qkwlo4HMqkRToA2JdUDIvd6bh8r47RzmJ3OsNjL6mwFh4nchCbK6rQEnFS8HdhdSN2/XKHFh0BSO0v3O6T8Bvt2rPrlzsMe4h9u3650ym7FUVJXQscDmUieNWMdgVRSf0BSrjt6yRm5tHhwoTAsDjMcfVBxo0iRrs5rj60OEnWffjVYUSlZIndL8mtSH7ful8B/5BohiAnrb9wu4/8j7Zrj39INMMeYp9/SHSn7FYUzZdP0IKtrHwXmQiluavg+Nk+EWEXoMYPQETEVf9HsCTb+zI8Hg8WFha0SAcAAQEBMDAwQFpaGqOdgYEBLU6SdU5OToiMFM0DRsRJwd8FBQXw9fVlCGfS+gu3c3Z2btceX19fhj3EPl9f307ZrShevHhBC7bSIG0aGxsZ68my4Pg7st++SnLxYziGFjE8UA5kVMHyyE1kljYy2lkeuUm/ZJN166PuIe6W6EMOeckW/F1U+ycC0yoZgpy0/sLtPH+SLOQRAtMqGfYQ+wLTKjtlt6Ko3m5CCw/SOJBBPf8ICwxq/7xMk+19HX5eKex3xTJyHO6Nz8Z830hcKqpitJvvG8nwwuPnlcLtCB8xWaLXBxEnBX8XVtRjZ/Q1WnQEILW/cLt1R5PbtWdn9DWGPcS+ndHMMGpZ7VYUJbV/wH5XLELWWUsVRPfGZwMQfdEhuSTJ9vaOBVAib1+HnTd7Zt4kHL1SA02/q3A9fVdErCVjAmSbN1+FOTaBnwKrJctpkQ4Avgk6iOlmNki/dIXRbrqZDS1OknUr3T1xJoYHYYg4Kfj7ZuEd+O3cS4uOAKT2F27nur79XPR+O/cy7CH2+e3c2ym7u4P0S1fwTdBB/GftapFt+bcKAQCqbw7D8fAI9Fcfg/7qY3A8PAKNTc3dOk5Fwc8vx5LdCbRIBwBBvFyY+UXj0p0aRjszv2hanCTr3I+mIuZaidj9rvyOz/hdWNmAnTE5tOgIQGp/4XbrQ8TX/RBkZ0wOwx5i386YHEY7We1WBESobWp9xlhPlgXHJMylOzUI4uVireVUuY9LLh6URES84WcNzWHUg8iNst/x3v6L4OXX0J6HpN3PG+dh+pg3AQDVj1sxfTsfHidyGF6QAJBX/hi/fvselAf1x+V7dXA4lAmL3enYZKUrsl5c/5NXyugxVT9uxcmrZdiXVIzL9+rEekMClEfivqRibLLSxTrz8SL5GgW9I2W1WxxdrRre9OQ5tsfexiYrXRG7ZSUquxJWHA2Y643o0lh6A0RELIreR4cFk9yIMenXac9D0i7t6Jcw5lAh7pUPG6C3dBM+/OqwiFiWU/QA1eeDoTz0dTpn4hxXH3i72ImsF9f/v/EX6DFVPmxAaPwFBIbFIeNGkVhvSIDyiAwMi4O3ix02ONlAeejraGr5CwciEhEYFsfwjpTVbnH0VNVw/Qmj8fUnTricd5cWb4XP28sOERHLy8vpXIYkh+G5c+doz0PS7urVq5g9mzpHFRUVGD16NJycnESKtVy/fh1//PEHVFRUkJaWBgsLCxgYGMDHx0dkvbj+ISEh9JgqKioQEhKCgIAApKWlifWGBCiPyICAAPj4+GDLli1QUVFBY2Mj9uzZg4CAAIZ3pKx2i6M7q2IvX74cPB4PiYmJ9DkiNr2KkJfh615GdHgbyY3IK2ygPWhIO57bFBhpUd4N1Y1PMTMoF+uj7om8IOZVt+Du1plQGvQanSfM8shNbJivJbJeXP9TNx7SY6pufIpTNx7hQEYVMksbxXr1AJRnz4GMKmyYr4V1c0ZCadBraH7yN45cqcGBjCqGl4+sdoujL1a/7e0QETE/yIUOIc4pqcXCHVGIy75Pex6Sdue/dMCMcVTUTVVDMwy8wuB2hM/wggSA3AeP8CDYHcqDB+BSURXsd8Vivm8kNtsai6wX1/9ERhE9pqqGZoRfKMLe+GxcKqoS6w0JUB6Je+OzsdnWGB+/ayiSg1HQO1JWu8XR1arhTa3P8GVkJjbbGovYrQjOZhbD2lAHFvryS7bfU7DzZs/Om1PeGoIvrUfjalkTLcgKnwuWNoiIWJJ3BdpalIfXtZw8zLWxR1T8z7TnIWl3OTEGs2ZQDgoVVTUYZzgHK909GV6QAJCdW4D6kltQUVZC+qUrsFqyHNPNbLDNy1Nkvbj+x8Mj6TFVVNXgeHgEvgk6iPRLV8R6QwJtQt82L094fewOFWUlNDY1I+jQMXwTdJDhHSmr3eKQd+Xs747+iEXW70g95nQzG8ayh9dWJPBTEXp4n0xepb0ZIiLm7V5O5z3MKXkEm69jEZ/9gPY8JO0Sv1iMGeOGA6A8+A0/PQ33o6kihV1yS+tQcsiVupffqcGS3Qkw84uGF9dIZL24/uEX79JjqmpoQfjFuwji5eLSnRqx3pBAm3jnxTXCxwuntd3jz99EEC+X4R0pq93i6GrV7KWzxoOfX47Um5W03WSc7XE0+RasDUZLHV9nkYsHJQm7js+vxuV7dWh68hzTx7yJ2v2Lsev9trC92v2LUbt/MUarDUFhdSOSCmtx8mqZxP2umTeWDkMWFPuIcCi8Xhi/xVNo4VBz2GCsNBkDAODlS1ajM+/VixxDMF/jxV/bPCNltVsRHEm7j6TCWqyZN7b9xmLY9csd7EsqxmfvTn4pQr2JF19M+nVk3ChCU8tfMOaMR/PlE4wCLM2XT6D58gmMGTkct+5XIDEzD6HxFyTu18PBkvbkExT7iHAovF6Ybz5xYuRRJAViYtKvS+xzMfeOyDGUh76ODU7UTSk9p7DDdvcm5k/Xw3+W2eDsrk046L0aH351uFu8PXsTxNvu3LlzSEtLQ2NjI2bPno0XL17gyJEjdLsXL17gxYsXGDt2LAoKCsDj8RASEiJxv56ennQYsqDYR4RD4fXC7NmzhxYOtbW14ebmRo9TEunp6SLHUFFRob0+U1JSOmx3T2NjY0OHa/fr1w/9+vXDG2+80dPD6jGIF19CYQMySxvR/ORvGGkpoXq7CaPoQPV2E1RvN4H2sEEoqv0TycWPcerGI4n7XT1Lg/ZIEXxpJS/AwuuF+dJ6DCMf2Irp1IMqr7BBYp/M0iaRYygNeg3r5lAPWJcetHn4yGo3S/dAwq7jr9/HpaIqNLU+w4xxGqgP+wR7XMzodvVhn6A+7BOMUVdBYUU9+HmlCJfiBeBmOY32+hMU+4hwKLxemB3LTBk5F53NqGeCuOz7EvtcvlMtcgzBHIwZhW1eabLarQgO/ZJHeYhaTlPocQDKo3RvfDa2LZn1UoR6s/Nmz86bpjoqWDtnJEKXT0Kg7Visj7rXrR6cfQ0Sdh0d/zPSL11BY1MzZs0wxPO6Mhza3eYt+byuDM/ryqAzRhs3C+8ggZ+C4+GS829/7OZKC2eCwhsRDoXXCxO4/QtaONTWGkmHPkfF/yyxz4XLV0WOoaKsBK+PKTEnNeNyh+1WNNdy8pDAT8Ea52VitxOP1cuJMfTf4HldGU4eO4gEfgrOp17otrEqCuLNF5/zAJfu1PxzrxuOuh/dsXvVXLpd3Y/uqPvRHWPUlVBY2QB+fjnCL0ouFuxm0ZbrWFBMI8Kh8HphtjvOpoVDLdWhcJ5HhTPHZz+Q2Ofy3RqRYygPHoCPF1L30oyitnRlstqtCCymjYK1wWi4H02F+upjUF99DOM+Dm23X07JI/Dzy+lzIW/k4kH52buTkVRYy8grKSlnIxHGZEFt6ECx62UV1MapD2UsE7EyLLNUooBIxjbxc/ET3/a423TF647YLUxXclDG5lZhX1Ixft44T+I5kgb5G6R+uqBToeG9EV+3pUjMzGPklZSUs9E/JFrmMGF1oTyIBFlySAIQqTZNxErhytWCkLFpLvQQu/2L7yPwn2WUWNkRu4WRRw7KrrLEfCY8A3/EobN8mcb8suDv7w8ej8fIKykpZ6Ovry8CAgJk2u/w4cPFrpclhyQATJw4kbFMxMrg4GCJAiIZmyQBb8uWLdi8eTOAjtktjDxyUMqKiooKfvjhB8TFxcHd3Z2R91PWv8XLhLf5KCQXP2bkR5OUeywwrVLmcDe1IeLv5bLkQgMgUjWVvHSHZz+U+CJMxjZpp/iPRDv45Vj7z0t3R+wWRh45KFmYbFsyC/y8UkZeSUk5G4nYJQskBFkYWUUyUpmaQMTK0LTbEgVEMraxHuLzR30ZmUlXse6I3cJ0JQdlTNY97I3PxvkvHSSeI3lB/l4Z/svazavZV2Dnzd4zb9py1OAd/wAhV3+TaRyvItu3bkYCP4WRV1JSzka/nXvxTdBBmfY7XE18Dj1Zvf0EC8UAoMXKY6GnJAqIZGxq48SHnnr7fU1Xwu6I3cLIMwfliTNRAIC3TWZ1aD8f2HOx0t0TkdFxIt6nfY2t9jPAzy9n5JWUlLNxZ0wOgni5IuvF0fV7PHPOIGJl6IUiiQIiGZsksc/vTBbWW1NiZUfsFqarOSiVBw/Aftd5SMwrh1fYRUYuT2nn90wmVejORPetdo/fGeQiUHI0VVC7fzGjAE5SYS2sOBr47N3JtAhGQqxdTHXANRiJYa8PwAiVQZjikyiPYXQ7stotb0hezff2XxS7XbBIkCD1LU9x/OIDFFY3IvOLd0QE3L7M1PHaaL58glEAJzEzDzamhvB1W0qHRJMQ6zWLzWG/YCbeVBkKDdU3MJbbtTConkJWu3srROglBXMAqshPYFgcmlr+YgjBTS1/0dv7Ovr6+njx4gWjAA6PxwOXy4W/vz8dEk1CrD08PPD+++9DVVUVb731FkaM6JtpGWS1uzcwfPhwuLm50V6kABVeD+CVC/XW0xiC6u0mjEIOycWPYak7DN7mo+jQvlM3HuJARhWcjUeAy1HFsMH/xnClAdAPzGnnCL0TWe3uaTbM18KBjCo0P/mbIVI0P/mb3v4ywNFWQ33YJ4wCOPy8Ulgb6mDbklm0sBV+oRB747Phaj4Fdsbj8ebQQRjxxhBM8jzewxZ0Dlntljckr+bCHVFitwsWCQKowj1747PR1PqM8eJHcllttjUW2Ud9UytCkm/idmU9ru1aKSL29mXYebP3zJtkXiQFc4COzZuvwhw7jTMZz+vKGAVwEvgpWGT9DrZv3UyHRJMQa3fXFXCwfQ+qbw6Dxojh0Jw8vYct6Byy2q1IHtU34FjoKWzz8ux0mHYCP6X9Rr0czihV1P3oziiAw88vh7XBaGy1n0GHRIdnUCHWrmZ6sDUeizeHDsQIldcxeWN4D1vQOWS1W1GoKQ+G8/xJjEI3pOjd9g9EU7DVN7Ui9EIRvLhGCot2kFsVb4AS7DiaKrA10ERpPVXAJamwlhbKtpzJBwCG92LTk+fyHAKD6settNck0FYUZpOVrsQ+LqY6CMsspXNcykJ7doujqzkoOwopLsTRVEGQk2GnPC/7AlPHa2PqeG3YL5iJB1UPsWjDt0jMzKM9AT0DfwQAhvciEb4UQeXDBtprEgDuV1KV1qWJbGsWm+N4bBqd41IW2rNbHN2Zg9Lxs31IzMwTsanuMRU2tGZxmwfdZB1NAMCjx02MtuW1VPoFwfPZ19HX14e+vj7ef/993L9/HxYWFuDxeLQnoLs79dVL0HtRuHCLPKmoqKC9JgHg11+pL2Q+Pj4S+3h4eCA4OJjOcSkL7dktju7MQWlrawsejydi0/37VMimpqZmt42lN6GnMQR6GkOwiKOKst+fwDG0CMnFj2mPFu94KtxF0AuHvMApgurGp7T3DwA8aHgCQPrLorPxCIRnP6RztclCe3aLHVs3ekfqqlPPOXV/PmfYVPkHdT40Vfp+uKwgHG01cLTVYDtzPEofNsJ+Vyz4eaW0ULbpv1TaCUHvReEE8PKkqqGZ9poE2gq9iBPkCK7mUxCadpvOcSkL7dktjq7moOwIkzSp3PJ1jX8xbKqso+7zxOuEUFhRj29+uoYpo9RwYLW5wr00ewp23uy+edP19F0kFz8WGWf9n89pOwgdmTdfpTl2GmcypnEmY6nteygpLYPVkuVI4KfQHnweXlsBgOG9qMgiLRVVNbTXJADcK6EK027z8pTUBe6uK3As9BSd41IW2rNbHPLKQVlaRn38NjaS/JHefuVHSOCniNhEzr276wq5jKU3wBmlCs4oVdjOGIvSR01YsjsB/Pxy2hPQK4xy0BL0XlTsPb6Fcf8qqaXew7y4RhL7uJrpIfRCEZ3jUhbas1scXc1BufI7Pvj55SLjLH1E3bffekP0g1JZHXXNGem0HzHcWeSSg/KzcwXQ2BiLG2W/A6BCqXXUJHvnEaGQFJ9RFCevlqH6cSsASqyMyqZy+phOkPy1mWtATYJH0u6jvuUpvf7yvTpobIzFkfS28XbUbnlBcnkK/ye8nVD9uBUWu9PB0VTBZ+9OfinFyY17QqE0dxWyC6m/z6gRqhirJdnLjAiFpPiMogiNv4DKh1Ren8qHDYg4T4VozTOS/EXOfsFMAMCBiERawAOo4jlKc1fhu8i28XbU7p7C0ZJ66PwprS08qKnlL0TwqfNBbAYA3THUv8GI85mMcxebToXETdfr+7nf1q1bh379+iEri3Ln19bWxvjx4yW2J0Khogu1hISE0F6CFRUVCA+nvkYuWLBAYp/3338fAOVV+OhRW96stLQ09OvXD3v3tlVM7KjdPcXy5csBAGfPnqXX/frrr3Quzjlz2g/9eZn4POEBNP2uIreKeijRVBmIMW8OktievPCSIgqK4tSNR6hupO7T1Y1PEVVA5Yg21RGfmgMAuBzqA8eRKzX0SytAFYHQ9LuKowLj7ajdPcWEf16eowrqGOcjoYh6NjHUfDmiJbaEXYCay/fIKaHu31qqStAZIfmjCBEKSfEZRRF+oQhVDdQ1UtXQjLOZVKqguZMlf8iwM6bmvUO/5KG+qZVef6moCmou3+NwYtt4O2q3vCC5PIX/E95OmDiSyj14NrOYcT7is6mqqEZj255NqhqaMd83ElNGqWHr0lkvpTjJzpvdP2/a/1MEJ76wrXp585O/Ef2PjcQOoGPz5qswx3786Rforz4G13KouUdbayTG6YyR2J4IhaT4jKI4Hh6Biirq+qqoqsHJsz8BAMzmShazHWzfAwAEHTqGR/VtuVXTL11Bf/Ux2He4LZd7R+1WBLfvUPkTdcePk9hm2VLKsUU41yRZJjb3ZT49cRnqq48hp4R6l9BSHQqd4ZLnJSIUylrUpbOEX7xLexRWNbTg7FWq6NbcSZJDsG2NqXfVQ+dvMu/xd2qgvvoYDvPbxttRu+XJ0lnUs0jc9bZ8miW1jXR+TePxoprCnSpq3huvwIgHuXhQOhqPQlhmqdiQ4z0fGNC/g1fNgMeJHJh+Ld4NuaSuRe5hx9O38xnLm6x0peaInDtBHZusdLEvqVgkV6YVRwPvG4+il2W1u6dJv/sQAMTaROhuj055s8JmLo7HpsF87Q6RbQe9V9O///vVenz41WEYOnmL3c/9ylqRvJFdRW/pJsayt4ud1HyL86fr0WHOwrkybUwN4WRtSi/LandP4/DObJxNvgrPwB9pL1aC8PmYOl4bNqaGYu1fs9i814ety4KLiwuCg4NhYiL6gHXsWNuDXkREBJycnKCrK97r+9dffxXJG9lVRo9mVk/18fGRmiPS3NwcPj4+CAgIEMnPyOVy4ezsTC/LandPQ4rkuLu7016shIiICIaX6auAo4E6wrMfghtyW2RboG3bB4PDDhOwPuoe3v5OvBj0oOGJSP6zrjIziJkjZ8N8Lak5xkx1VOhwPeGcb5a6w7BUv+35QFa7exo9jSGw1B0m1iZn4xG9JhS9qyybOwmhabfFhhzv+7DtI0rIOmu4HeFj1mcnxe6npPYPuYcSG3iFMZY32xpLzRH5tp4WHRItnCvT2lAHjqZtoVay2t3TcLTVYG2oI9YmV/MpjFD09FvUhzBxbQnd6f2pCNh5s/vnTbupaoi5VQ/v+Ae0ZypB2MaOzJuvwhy76gMHHAs9hbk29iLbgoN20r9PHjuIle6e0Jstfu65V1Iqkjeyq4wzZH4U3ublKTVH5IK352Cblye+CTookitzkfU7WOG4hF6W1W5FknuT+reioiJZlFpoYYZF1u9gpbsnVrozvUfbOx99hQ9MJyL0QhFsvhat0xHkMo/+fWytBdyPpmL2tjNi91NS2yiSN7KrGH56mrHsxTWSmiPy7ckj4cU1QhAvVySXo7XBaDiaTKCXZbVbEZAiOV5hF2nPVMKxtRYikQ8AcLOc+gCk8rriPMflIlBOH/MmUj9dgISCGloA22SlC8PRw+hK1wCw2EgLLU//R4d6b7LShYPxKDx59jcsdqfj6v16uQqUn707GcqD+2N73O0OFbD57N3J0NVQwtWSBoRlUl+I9nxggIVT32J4H8pqd09DzvfLjDFnPK6EBiA2PZsWtbxd7DBDbyxd6RqghLKWv57QIpm3ix2cFpqi9ekzzHH1weW8u3IVKH3dlkJF6XV88X1EhwrY+LotxWQdTVzOv4vjsWkAKMHxvbeNGIV7ZLW7N3B21yZEpWThbPJVJGbm0XlAxZ2PQ5+vwc+XcvFLZh6dU/NdU0MsMZ8pZs99j9mzZyM/Px9RUVG0qOfj44OZM2fSla4BYNmyZWhubqZFMh8fHzg7O6O1tRUGBgbIyMiQq0Dp7++PN954A1u2bOlQARt/f39wOBxkZGQgODgYACU42tnZMQr3yGp3TyNcJAegxung4NCr8mR2F0ZaSkheNw0JRb/TL2cb5mvBUHMoXbEVoF4OW579Tb8YbpivBQd9dTx5/jcsj9zE1bJGub5oe5uPgsqg17CDX96hQgze5qOgqz4YV8ubEJ5NfcALtB0L60lvMgpQyGp3b2CP3Tjw7/6O5OLHdL43S91hsOW8HAVHAGDGOA1k+C9DfHYJLWpttjWG0djhdKVrALCfPQEtT57Rod6bbY3haKqLJ8/+h/m+kbhyt1quAuXWpbOg8voAfBmZ2aECNluXzsIkzTeRWVyN0DTqBXXfhwtgYzSW4VEoq929gQOrzZGY+wDn88voPJkLDcbAbtYERjvyt3mZYefNnpk3Q5dPQtytesTcqkdy8WM6t6c4Gzsyb77sc+ysGYa4cSER0fG/0KLeNi9PGBvp05WuAaooS0tLCx3qvc3LEysdl6D1yRNMN7PBxStZchUot2/djDdUlOHt93WHCths37oZepMm4uKVLBwLPQWAEhy5NlaMwj2y2q1IyPgkFRQCqKJCoYf34XzqBURGxyGBn0LnAX0ZxEkAmDFuOC5sX4r4nFJa1PPiGsFIR52udA0A9rPGoeXJc1pQ8+IawdFkAp48/x/M/KJxpfg3uQqUW+1nQOX1AfA7k9WhAjZb7Wdg0shhuFL8G0IvFAGgBEcbw9FC93jZ7FYEwkVyyLFtZ+hIzH1JbFFk5EO/F0KJvU6fPo0VK1b0aY86SUViWOSHxsZYnDp1ig6FlDf9+vXDcb91dGhwX4NUyO7OPI8sklGau0rh16si969oSIXs7szzyCIZRV9P/fr1w/dLJ8B+Wt98sSKVXtkq2Irjk+h7GDzFCqdOnVLI/lesWIGnpTk46mGlkP13B8JFYlh6jrXBSRioM0Oh12vr7SR8v3RC+417Key82Xvojvn1xdM/cSL4gEL23x2QCtnyyvPIwiQiOg6rPDYo7Lmf6EldzZHYk5AK2X3Zht6O+upjYt935JKDkoWFhYWFhYWFhYWFhYWFhYWFhYWlM7ACJQsLCwsLCwsLCwsLCwsLCwsLC0uPwQqULCwsLCwsLCwsLCwsLCwsLCwsLD2GXIrk9DbY3JMsPQ2be5KlL8HmnmTpS7A51Fh6A2zuSZa+BDtvsvQl2NyTLD0Nm3uy52A9KFlYWFhYWFhYWFhYWFhYWFhYWFh6jF7pQdmXq3A3PXmO+LxqJN2uRVJhLaw4GlgyXQvmeiOgPKi/1L7EbmkInhNyrC1n8gEAm6x04WA8CuPUh4odV1rRQ/x0o6rD42Kh6MuVuZta/sJPadfxS2YeEjPzYGNqCEdLE1jNngbloa+325/YLg7h89HU8heSsm7ibPJV+ljvmhrivbeNoD5MWey4PAN/BAB4u9jBaaEpxo/S6ISVLIL05crcjY2NOHv2LHg8Hng8HrhcLpYvXw4bGxuoqKjI1D8xMRGnT5+m+3O5XNjZ2WH48OF0O3KOpEHOX0fassiPvlx5tvnJ34gvrEdy8WMkFz+Gpe4w2E9Vg/mEYVAa9JpM/dPuPUbMrfpO9WfpOH25MndT6zPEXbuH8/ll4OeVwtpQBw6zJ8JCfzSUBw+QaR8ltX/gbGYx9sZnAwD2fbgANkZjoaY8uFNtyfmURl88130Ndh5l59HeQF+uzN3Y1IyouAQk8FORwE/BIut3sGypHRZamEFFWUnm/h5eWwEA27w8sdJxCSaM0xHb/l5JKU6e/QnfBB0EAAQH7QTXxgrD1VTlZ9QrSF+uzN3U+gxx1x+AX1AOFGSyuAAAIABJREFUfn45rA1GY+ms8bCYNqoD9/hGnL16D0G8XABAkMs82BiOFrnHN7U+Q+rNSkRfu9/pY3WFXilQ9mW+5hUhLLOUXk4qbBMqT7jN7tK+rThM0eaT8BtIKqyll/clFWNfUjFSP10AjmbbS3x9y1N4ReQx2gqOK8jJEGpDB3ZpbCy9my+Dz+J4bBq9nCggVJ7dtUlq38qHDTIfp6nlL3zkfxSJmXkix/olMw+HPl/DECmF2waGxSEwLA5XQgMwdby2zMdlebn4/PPPERwcTC8LCpXx8fFS+zY2NsLZ2Rk8Hk+kP4/Hww8//MAQKaXB5XJlHnNH2rK8GnydUo7w7If0suALdujySVL71v/5HFviSpBc/Fhs/z1246A2hP24yNLGjrNXEJp2m17m55XSQuWpje+127+woh7zfSMZ6zb9Nx3n88twZK0l46WkI22lYW0o/uWchYXAzqMsvYFt/t/iWOgpejmBn0ILlTEnf2i3v+v6TUjgp9DL3wQdxDdBB3HjQiKmcSYz2t4svIPpZjaMdR5eW5HAT0Xo4X0yCaIsLx/+564j9EIRvczPbxMqT/7Hut3+hZUNMPOLZqzzCrsIfsFoHHZbQN+365tasTH0Ivj55WKPtd91ntiPlvKEFSjlSGF1I8IyS7HJShcrTcZAc9hgVD9uxXcpvyIssxQldS1ivRsJkjxGC6sbYbE7HX6Lp9DrYnMpT8g9HxhgpckYAMDle3VwOJSJE1fKsOt9fbrt+Vu/IamwFsGrZmCxkRZjHx4ncnD+1m/0PlhePm7dr8Dx2DR4u9jB1dYMo0aoovJhA/aG83A8Ng33K2tl8lj8+hMn/GeZjdQ2SVk3kZiZh4Peq7HEfCaUh76Oppa/cCAiEYFhcYjgZ9L7iErJotu62poBADJuFGHRhm9xPDYN+7e4dtV0lj5IQUEBgoOD4ePjAzc3N2hra6OiogI7d+5EcHAwfv31V0ycOFFi/8TERPB4PBw7dgyOjo5QUVFBY2Mj9uzZg4CAAISHh2Pz5s0AJHs8FhQUwMDAAHv27KHXdaQtC0tR7Z8Iz36IDfO1sGL6cGiqDER141McvFSN8OyHeNDwBGNVB0nsz7/7O5KLH+OwwwTYTVWj18fdqsf6qHvg3/0dK6aP6A5TWPoAhRX1CE27jc22xnA204OWqhKqGpqxP+EGQtNuo6T2D4zTeENi/6bWZ5jvGwlrQx3scp4HLVUlNLU+w8kLhfgyMhOpBeWwnz2hw20leUcSgXPHMlP5nwyWlwZ2HmXpDdwsvINjoaewzcsTa5ydoK01EhVVNdh14BCOhZ7CvZJSiZ6QAHAmhocEfgqCg3ZijbMTACD90hVYLVmOo6EncWj313TbxqZmTDezwSLrd3Dg2x3Q1hqJxqZm/HgyEt5+X+N86gV8YM9+EH/VKKxsQOiFInhxjeA8bxK0VIeiqqEFB37OR+iFIpTUNmKchuQIs6bWZzDzi4a1wWh8u8IUWqpDqfv2xbvwO5OF1JuVsJ81DgCQmEeJkcfWWtDrACDmWgncj6YiMa8czvOlfxzqKmwOSjmSV0F9oXMwHgXNYZSyrDlsMFbNGQMAuFX5R4f3Wd/yFBa707HnAwOGuPnTjSoAgK2hJr1u7gR1APj/9s48rqb8jePvMYMYyr4PshTFyNKMnUKNGVkb9LNlKWGMdcxmjzHTEKNByhjbjGVQZJClspQQ2YqylGSvTNm3/P44ztW5a3XvbeG8//G653y3c3z7nvN9zud5HomCE1C4gGc1Tmb9LZ6XeTs5EXsFAJfP2vBRZcE14KPK5Rne0x6AU3GJWutfSRa+XDepX0tnX5v2Cm5Ert07KlzHTUuVZJyLYJT88ff1KmV723+iONahuRWARO0p825x7NgxAAYNGkTNmoKKtmbNmnh4eABw8uRJrfX//vtvANzc3BTu4GZmZkyePBlA8a8m7ty5g42NDX5+floNoTktK/NuEX39AQDOTSpS3UzwUKhuVpzBLYTN8NkbD7TWn7JdWLezbqqz/hbPy8gAnLwiPKf7trGkRnlBXVOjfGmG2gkfts8k3tVaP/5GGgDOLS0U9U1LFGNgR2sANkfG56qsOlIyHtNh2gYWDrXTajSVkZHXUZmCwPGTwj55YN/e1KxRDYCaNaox0nUgACfPnNNYF2DDlm0AOPfopjhm1641gESVCXAh/hIA/fv0UPRlZlqaYQP7S9qSebc4eUV4hvdtVZ8a5QV7UI3ypXC1E9S3Z66maK0ff0OwQfX5tJ6ivmmJYgxsLxgatxy9pCg7cfVBAIlxMutv8bwxMYiCssr4QIa0MZeo9kS+/ec0q8MTiP/5C0xNihJzPZ2D8XeZtU34YxZjISobz5TbB1WFoabjhy/eJejUDVaHJ+BgXQX3jnUVxjtd16ELbXExr997DEDF0lJ36cpmwte9uFv3dbavzB8Hr+BgXUVF4ajOXVx04fYd3EJy3MG6isS9Wxll1/G3hdJtBzO8p71aJd74+av4IzCE67t9MS1VkrOXkgiNilEY0MQYjc6dNbvla4pJqen4gROxBIQe44/AELq2acqYvo4Kg5yu69CFtriYoot2JaX4j1XKCxuD8wnXdbafXTS5i6uLc6murOju/efM0QYbU2Hhvffew8PDg2XLlqmcGzVqFL6+vvz333+YmZlx+vRp9u3bpzC2iTEa+/fvr7V9UFUCajoeEhLCP//8g6+vL05OTowfPx57e/tsXYcutMVqTEpKAqByZamqoWrVqgDExMRobVuTC3h2YlcC+Pj44OTkhJubm0HLvitUn3GEQbaV+blbHZVz3+24wtrjt7nw/SeUNnmf2FsPOXQlndnBghuJGBtMeTOp3D6oxlLTdDw8IZ2gmFTWHr9NF8uyuLWqShtz3XNBbE8b2uK5XU9/BkBFJffBSqUFF5q4u4+1tt3FsqzELVHdeRkhxqGrfSPmD+mocm7y6jBWhZzjiq87piWKEZOUwoGYa0zfEA6giNEoqv00tQ+qSkBNxw/FJrPt+CVWhZzDsak5Hg5NaGel+f1WuT1taIvVmJwqGGoqmkmftZXLfAjAhetpWts+Fn8TANv60vdB0xLFVPrNSVl1+O89g2NTcwa9NmjKqCKvo6/PyetovlK0Ym3cXQdIFH4iY775Eb9Vf5Fy+SxmpqU5E3Oe/QcOM2WGUFaM0ahN7acpJqWm46GHIti8/V/8Vv1FN8fOfD1ymMLQp+s6dKEtLmZS8g0AKlWU/k1VqSyEC4q9oP2jjDoXcNHde52fj+R4xLEoAFrZNpccNzMtXShjd+pLxWF+uHa04tfBbVXOfbPmsKAeXOIqPOOvpXIg9jozNkYCKOImKhvalNsH1ZiUmo4fOn+D7cevsCosFkebWozs0ph2Datl6zp0oS0uZnKa+IyXulZXfv3Mv3BD8zoHcOySYAeyrSfdW5mWKKbSr6NNLYl7tzKONroFS/piEAXljB6NWB2eQMqDp5LjKQ+esjo8gRk9GmFqUpQ9Mbfo9GuowjgJglHNY00UgSeTDTEUftl5Hucl4QoV4Z6YWzgvCeeXnecN0r42Fu6JA1BJOiPGdxTPZ5fDF++ycE8c7h01/2EBLAu9RJXxgQz2j1Rx4wYY+FrBqXyPxd/i+beNuV+58EdgCHfvZUiO372XwR+BIcz9ygXTUiXZFR5Na9epEnXfrvBohs5cyuZ9kQYZi6f/FoXrsth+t3E/4+m/RUdN/fFaLXxtUzYSirEgxfOaOH1RWKTKmZVi1fYwSrcdTOm2g1m1PYyMB4+yNYZL14SFUZPhcfGGXZRuO5i+3y7kz5mjtRqG31bmz5+Pr68vd+7ckRy/c+cOvr6+zJ8/HzMzM4KCgrCxsZEoAYOCgnBxcWHDhg3KzeaKadOm0alTJ0UcyKCgIDp16sS0adMM0r425syZA6gaFMW4keL5nBIfL7xArl+/XmOZkJAQ5syZw/jx43W2l5Oy7xLTHWux9vhtUh4+lxxPefictcdvM92xFqVN3hdigC07o9hUgxAbbPTmi2w7q/1LcHbxCrlG31Wxivhle+Pu0XdVLF4h1wzSvjZ+OyA8X5WTMIjxzsTzmhDdDpXvhfhbdksUmN2/DatCzpGSITVUpGQ8ZlXIOWb3b4NpiWIERyfQYdoGhXEShBiNbsuCCYi8aJCxzNtylF6/BCriQAZHJ9Drl0DmbTlqkPa1ISaqUY79KMaJEs9rIjxO2IDXKF+agMiLDFj0LxWG/M7SXdEq9zYnZZU5FJvMgu3H8XBQFTTIvEFeRwXkdTR/8ZolGCHvpEhj0d9JScVv1V94zfoRM9PS7AjeR/OOXRXGSRAMcAPdx7IxIEi52VwxY94CHHr/T6E43BG8D4fe/2PGvAUGaV8bYqIa5diPYsIa8Xx2WLjUn6IVa9Nr4AjW+fmoGHAPRgjPi5o1qrExIIheA0dQtGJtFi71V/l/eBeY1a8lq8Ji1T/jw2KZ1a+l8Iw/dZWOM7YojJMgxE10X76fgKOXDTKWeQFR9P51hyIOZPCpq/T+dQfzAqIM0r42xKQ2mp7x4nlNRMQJHxZrlC9FwNHLDFwcTMVhfiwNPqNybwe9VlUq3zfxt3jemBhEQdneQlAnHo6/KzGOHY4X5KgOjYSvrIP9hUnz7/j2NK9dDhBUh81nBeOxJkqrijI7iAa9CQ6WjLKvh6lJUTKePGdZyCUW7omjW5NqkuQxyhS0rOF+YZdxsK6iU/3ZuIYZM3o04silFDzWCH8kWe+lg3UVNo9pg1/YZcX5rMezoy4tjNi1EL7OHzgRKzF4HTghLCyft2kKQN9vFwIQsnw6ttb1AEF1aNVnAkNnLtXbWHbgRCxeq7cxZUgPxrl0VYnL2NPOVmtCmIKSNby161TJ77FeK9kZHs2KaSN1ZgJfvzucrm2a4tDyY7Xnm9SvxdyvXDgcfYGhM5cCvHNGys6dOwOC4SurEjIkRDBqi0lYunfvDsCRI0do2VK4R0lJSdSqVQsXFxetKsrsIBrepk6dyuTJk1ViODo7O9OkiebNZUHNZL127VqcnJzo2lVzHNVFixbh5OSULaVoTsq+S7SrIzxjw6+kSxQ84VfSAehiKTz7Xf++AECQWyOa1RBe+q+nP+UT75OM3nxRq/onO4QnpPPbgWTGdajBqNbVKG3yPvefvGRZxA1+O5BMN6tyWFX5UGP9/M5228WyLJtcrfA/cpPRmy+qHM+OeuldoIP1R4Bg+MqqhDwUKxguxCQsAxb9C8Du6c60qCu8kyan3sdm4mrclgVrVVFmB9HwNqm7LWM+b4ppiWJkPH7Gkp3RLNh+nO62dbGuqXlO53cm6+Bo4aP+vC1HJcbM6RvCCY+7IUl8k5OyyvjuOY1jU/NsqUrfZeR11DDI66h+dOogqNZCD0VIDGmhhyIAQSUJ0GvgCAAO7wrg0xbC3iop+QZ1m7ZmoPtYvWMmhh6K4CdvH36YOJaJY9wxMy1NesZ9vJf48ZO3D326f66SaCYrBUl5aNPYGq9ZP3Iw4igD3ccCSO6PqKycMW+BxPA5ZcZcDkYcfeeS5HSwEkLZHTp/Q6KEPHRe+FDm2ERQ8w1cHAzArh970qKuIGhITn1A02/+xn35fq0qyuxw6PwNvINOMtGpGWM++/jNM373GbyDTtK9hTnWH2nOsJ7fWcNFReS8gCiJMXPGxkgi4m5KkuQ42tRi6zfdWL73LO7L9yvKisezoxjVF4MoKK2rm+FgXUURF1Fk64lkhrQxV8ROvLWoJ7cW9aRWhQ+JuZ7OnphbrDuSaIghABB+UfgiJhonQVAzjrIXjE4H47XH4ClInEhMY0/MrWypG9vWr8gou3qscWvJ/H42eKyJ4vBF6bWeTU5XcfPeE3OLxJSHhhx2gaJxvZpCluq9UheTTXuPMLynvSIxzP3Da7h/eA21q1Xi7KUkdoVHs2p7mMHGcfCkoN4VjZMgjcsYGqXdZTW/EZWlIcunK+7V/cNr+HPmaHaFR7Mn8ozW+p7+W/BavY1pbn00GjI7NLfi6/5d2fTLBHymDGPozKUKQ/K7QpMmTXByclLEUBT5+++/8fDwUMQ4fPXqFa9evaJOnTqcPn2aoKAg/P39DTaO0NBQAIVxEqQxHPft26exbkFl2rRpzJkzB09PT42u3pGRkQQFBWXLXTsnZd81rKp8SBfLsgQoKVYCzqYwyLayIqHB9VmtuD6rFTXLmhB76yF74+7x14k76prMFeEJgnJe3FSDoMIZ1Vp4sTr0eqNfkDl386GKe+LeuHskpj3JpxEVPKxrVsCxqblK3MPNkfG42jdSxDhMWf0VKau/onZFM2KSUgiOTmBtmOGeMYfPC6FSROMkCEqHMZ8Lm/UDMcZXmxmKCz7DFffLf5QjwdEJ7D+t3t0rJ2WjLt8iODqBwR10h7Z515HXUcMhr6O552PrhnRz7KwS93DDlm24uw5QJIZ5fjeR53cTMa9dkzMx59kRvI8/1mr2VskpYYeFfZxonARBzThxjGD02X/gsMH6MjZ27VozYbQbAetW4Os9j4HuYxUGX2Wunz+huLfr/HzYEbyP3fvD8nbA+Yz1R+VxtKkliZEIQsxE145WisQwd1e6c3elO7UrlibmWirBp66y9uAFg43j8AXBICoaJ+H1M/4zQXhzINZw4dKMzflFgxT3y29kJ4JPXWX/Gek7ytmkFBU37+BTV0m8I/VKNRYqCspSpQRj4tPnLyle9H2VCppw71gX5yXhikzVl+8+YE/MLTaPkWbo+2Xn+Ry7OmcXsV2L7/5Ve37WtnOMsqunsb6+MSgNyabjwkRpWVezNV4d3ZtWZ/LGU/iFXVYoIwNPJjNr2zmNWbxLFf8gR+rVp89fAm/mijEwKV6cl5mZerczpq8j3cb9rMhUfenaLXaFR7Pjt+8k5UQjmjEQ263+mYfa8z/+vl5rdmx9Y1Dqi6a2nTu3ZOjMpWzae0Sj2lG8rxGr5mhViWalt/0njPVayZJNwdmK0amLJ88EFymjzlcTE16+fKl3O+PHj6dTp06KTNXx8fEEBQWxf/9+STnR4GYMxHbLlFGfvGDy5MmKLNjq0DcGpaER79WpU6e0Kj9Xr14NQPv27XW2mZOyOeXJE2HTZNz1tRgvjfh/4NaqKn1XxSoyrF5JfcLeuHtscpX+PXuFXNPpopdbxHYbzDum9vzs4KuMbK35K7C+sdP0ZdvZFGYHX9WYfbZUsff1Vkdp48GzV5R6P/vvgDmlSJEiPHz6wiBteTg0odcvgYpM1Zdv/UdwdAIB30rf15QVf4ZEbLeOh/o4U9M3hDO6a1ON9fWNQWkoshpYATq9VqdsjoxXUZnmpCzAhsPCZrFVg+oq5/Tl4dMXlDTyfH3wLG+9A+R1VH/yex3VRF6sr/89MIwA5euRw3Do/T9FpuqLlxPYEbyPPVulH9OVFX+GRGy3Qt3Gas9PmTGXCaM1fzDWNwalsXDu0Q2Pid+zePlKlViaWY2xAJ916ggIxmFDZPF+8eIFJiYmerejidzak9Qxsktjev+6Q5Gp+vKtdIJPXWXrN90k5ZTVgYZEbLfumFVqz8/YGMloR/VegqB/DEpDkdXACtDpY8ELZcvRSwqVacDRy8zYGKkxi3cpk6J6K1JBuz1JRUFZvrxgELv36LlKYW18/JGwmT1ySfjaJ2asFo8DrDuSyMI9cQxpY87mMW3Y/40d5+ZoNswUNiY4WAKQ8UR678Tf4nldiLE7JzhYqsSz1IVYPqtaUp3bd9bfyspXXaS9nhsVKxrPNbxcubKkpWvPzpcdmlrWBuBwtPBiLGasFo8DrNoehtfqbQzvac+O374jYtUcrgTp3iwUFqYM6QGgEi9S/C2ezy1iYpus3L2Xgaf/Fs5eSiJ6vVe2jZPwJlamunZzgziPjDtfy5Gaqn9smObNhaDYBw4cAN5krBaPA/j7+zNnzhw8PDzYv38/p06d4vbt23r3XVCYOlUIJZCeLlVmiL/F87q4c+cO06ZN4/Tp08TFxWk1TopxPqdOnaozmU5OyuYGcR4Zc76WLVOGe48MYxxSx8dVhZeNI4nC/5mYaVU8DvDXidv8diCZQbaV2eRqxd5RH3N6SgvVxgop4zoIz9f7T6QfLsTf4nlNiO6ImrLPKiurDE3a40zF+6AxqFChAqlKcctzSxNzwZ0r4oKgYBAzVovHAdaGxbBg+3Fc7RsR8G1PDnj254LPcIP0XxCY1N0WgIzHzyTHxd/ieV31lV2zld26c1pWRIwJOqm7rUb3b31IefDU6PP13hP9P5rnBHkdLfzrqCbyYn1NSdOeNCO7NGsiGAUPRghh2sSM1eJxgD/Wrucnbx/cXQewZ+vfnAjbxfXzJwzSf0Hgh4mCK3Z6hjTZrfhbPJ9TRAOk6NadtS1lN251ZfUhNe0e5cuXM0hb6lDYkx7q/5xvUlv4exXjKIoZq8XjAGsPXMA76CSuHa3Y+k03wmb14fyiQXr3XVCY6NQM0PyMF8/rqq/xuZ1FLSm6dWvK4q2sZs0taQ8EQYa6/Y6KgtLKSvgyF3crgypm2besm5oUZX4/GyZvPMVnjavisSaK+f1sJAa2yRtPAUiyfSsb87KLckIegCFtzCUZw3OKvupIyyrC4nH3/lNJ/9dSBUNQ9bIl1NZT5uprt+umtTRnlxvsH8memFsq1yrelyFtzLM9bm0ZvtURf0uQ94pzxRhYWzci9or+X4RNS5XEZ8owxnqt5It2zRg6cyk+U4ZJXI3Heq0EkGT7zm7yF2WUE/IADO9pL8kYnlP0VUc2NBfUCnfuZUj6v3pLWOA/qqz9JanvtwvZFR6tMn7xHg3vKY3Bd/ZSEp7+W2hcryZLvhuuSMaT3XbFe6jcbm65kChsWo07X605d+6c7oI6MDMzw8/PD3d3d3r06IGLiwt+fn4SQ5i7u/CFLWu2b2VjXnZRTsgD4OHhIckYnlP0VUdaWwuxY2/fvi3pPzExEYCaNXUbu0+fPs20adNo0qQJK1asUCTY0cSVK1cA+OSTT3S2nZOyuSE2VnA7Nep8bdSIuDv6z1dNlDZ5H6/udZiy/QqODcoxevNFvLrXkSQ6mLJduI9Zs9Qqb0Kzi3IiCYBBtpUlmW5zir6qHsuKwvP+7sPnkv6v/Se8kFU3089Ioy0zrSG4ePcR7g01x/TSl4YNG7LSX3tm6exiWqIYC4faMeHPULo2q4PbsmAWDrWTvIhP+FMIXZE127fyi352UZcIxtW+kSRjeI7b1FMd2aC6sNG8m/5I0v+1u8LztEZ57YpssX5y6n1qlH+zMRbvkat9o1yVFUm8KzyjmtXRvhbnlvjrabgZe7765e69MLfI62jhX0c1kRfr658rVxqkLTPT0vh6z8Nj4vc4dXVgoPtYfL3nSQxoHhO/B5Bk+1Y25mUXdYlg3F0HSDKG5xR91ZFWDYQQS3fupkj6v3pN2KfWrKE9Jl+vgSPYEbxPZfzitbq7DlDpKyn5hqRd8X5mLasPsXEXjfqeKbZ94XoaVcrkfO+bFdMSxfAe0p6Jqw/StWkt3Jfvx3tIe8mzbuLqgwCSbN8GfcZ3tJJkDM8p+qojG1QTbEJ30x9Ln/EpwryoUU7HM/51/eTUB5L3AcVzu2P254K2DN85Ie515nF181BFQVm2bFkaWTXg6JWcq4Fa1RMs2Y2m7gLAroH67GiX7wpfAcUENrpwsBZiBZ5ITFPU++PgFZVyTjbCH/KykEsSA+bhi3epMj6QZaGGsfhqon5lYdHZfPwa1+8Jk/v6vcfsOC3ELWhaU7PBMSvnbwovlHUraZ5svZsLXw23R7+JeZDx5Dn/vHYNF+8FCFnWQbgPWQ3CYhZv8Xx2OXollUZWDYyiHhJp1749kecMk3WrbVMh21QdJ2ED0OlT9S4CYqZpMYGNLrq+TrJzPOaSop7v5r0q5XrZCYaM39bvkhgwD5yIpXTbwSzeoLsvfbCsLcyF9bvDuXZb+Lu+djuVwFDBJa25VR2NdQH6dhFeMJVjTYq/xesT223tOpXG9Woyza2PRuNk1na3hrxxHcp48Ij1weEq7epDxOk4GltbGXe+tmtHeHi47oLZoEOHDgBUriysn46OjmrLiVmpxQQ2uhCT7ERGRirq+fiouuN8+eWXgJBVPKsBMyQkhPfee48FC4ybMbHh65f2tWvXkpSUBAhJgDZv3gzoNgwmJSVhY2NDkyZN8PT01GmcBDh79iwAlpa6Ve45KZsbDh06ROPGjY06X9t36EjUDePG32pVWxh/Ey9Bwd+xnvqQAVdShXGIiRd00cVSeI6eTL6vqLfyqOpHNidr4cPLsogbko13eEI61WccYXk2+tKH+q831ptP3+V6uvA+cj39KTtihfeYptW1v0xOdxTcZcMT0iUGBzH7rHjeGFy4/YiMR0+NEsJApH379mQ8fExssmGykrZ+7TbcYOwfANg1Vv8h4/ItwbtHTGCjCzHJTtTlW4p6/ntV4y73sBXCBy3ZKc1kfSg2WZHh2phYvN58bAqPIzlV+NtITr3P9uPCe1SzOtqzFdvWF96z14bFSjZ1YjzJLh/XylVZkfPXhP/nelWz9x6cE2KTU8l4+Nj48/XRUy7czlsjpbyOFt51VBN5tb6mZ2Rw7rxhYvC1by2EcareUPDm6WLXQW25i5cF9bSYwEYXYpKdo1HRinpL/FeplHPu/gUA3kv8JAbM0EMRigzXxqShhbC+r9u0laRkYc4nJd9gy/adANg2s9Fav38fwVNt87YdimPpGff5a9NW4M31AbSyFe7xH2vXS4y8YuzJrp3t9LkUBUeOnaBdO+PNQcGe1JDI+JyJoDTR2rIqAA3HrwXArpF69fTlW8LHMDGBjS4cbYQ1IOryHUU9//2quSG62wp75SW7pVmvD52/ociGbUwsqglr/6YjF0lOFexoyakP2B4l/M01q6Pd68q2nvAOsPbgBelz+3Xsyc6vXb1ByJwOwrVlLStm8RbP60tk/C0aWTXdDWPeAAAUcklEQVRUu99Rm8W7Z29ntq5axpQcel/XrVhKoWIc0sZcRTHoO7gFHmuiaDNXvTxZjF+pTO/mNdgTc4svFh1UHFNnVGtbvyITHCxZuCdOJc6lg3UVvrT9SKWOIRGTBanrf0gbc5UM4mLMS2Xl5tlk4Y/LrIRmFWjPZjXYeiKZyRtPKZSpIhMcLCWZub+0/Ygjl1JwXqJqQMnNfdkdk0KfoaNzVCen9OzZk+nTpxN/9SYWtarq1Va9j6ooVIzDe9qrKAb/nDmaoTOX0tRlitr6YvxKZfp2acWu8GjsR85WHJv7lYtKuQ7NrZgypAdeq7epxLns2qYpLo5tVOoYEjFZkLr+h/e0V3G/FmNeispNh5Yf07VNU4bOXKrIsC0yZUgPSZzI/UcF4426vkTEdp07t2TT3iOM9VqpULFqalcfdhw+RW8X3XE89UGcrxcuXKBBgwZ6tWVhYaFQMXp4eKgoBtevX4+Li4tGA5kYv1KZ//3vfwQFBdGq1RtFgzrDpr29PVOnTmXOnDkqcS6dnJwYNMi4LhNisiB1/Xt4eKi4aosxL0XlZnCwkMlPXX0RZZWn6EqvKe5mbsvmhsDAQHr2NG6sY3G+Xkp5TL0K2VP255Q65U0U6ptBtpWpblZccn6pc31Gb75Iu8XqDTdi3DVlejWuwN64ezj5v1GAqttktjE3Y1yHGvx2IFklPlsXy7L0aWI8F3p4k+RCXf+DbCurZL4VY7WJiqM+TSpyJDGDvqtUE7kYe/y7zqdhWb+u3muZNho0aIBlvbrsPHEFqxr6uzrWrVJGoWJ0tW8kUfYB+I9yxG1ZMJ9+u05tfTF+pTLOLS0Ijk7gs9mbFcdm91d9ZrezqsGk7rYs2H5cJc6lY1Nz+rYx3r2EN8mC1PXvat9IJYO4GPNSVG7WKF9acY/U1RcNtTktK3L6quB2b1ayuMo5ffk36goN6tcz+ny1qFeH3RfSaFBZPzVQTpDX0cK7jmoiz9ZXCwu27dxDo4b691O/rrlCxejuOkBFMbjOz4eB7mOxaqneeCbGr1Smf58e7AjeR9uuvRTHvGb9qFLOrl1rfpg4lp+8fVTiXHZz7MyAvr1zc1nZRkwWpK5/d9cBKhnExZiXonKzXy8nNmzZhsfE7xVqU5EfJo6VxJ+sWaOa4n6q60s06upD3MXLXLh4yfjvmr37sHWtH9/10j/sRN0qZgoVo2tHKxWvAL+RnXBfvp+WP2xUW1+MX6lMn0/rEXzqKl3nvslDos4A165hNSY6NcM76KRKnEtHm1r0baUad9mQiMmC1PXv2tFKJYO4GPNSVG7WKF9KcY/U1RcNtQB9W9UnIu4mvX/dgTKGvNZdZ5LpM3ik2nNqs3iPGDGCuBv3iErMufuNqNzrq8bo1bNZDeb3e/OVYYKDJeE/dmb/N8KCJsavVFfPd3ALhZJyfj8bjcluvv28Ib6DW0hcnOf3s8HbpSkVShn+xUgZb5emzO9noxirg3UV5vez4Uen7BtbVocL1nBd413j1lJyX8TYnt9+Ll0oK5Qqzu+DmkvKOlhXwXdwC34f1DxH9yUqMY24G/cYPty4sZsaN25Mq08/5c/toQZpT1TjDejaVuWcc+eW+EwZpvg9ZUgPotd7EbFKMG6I8SvV1ftz5miFktJnyjCNyW6mufXhz5mjJW7LPlOGaXWBNiRLvhuOz5RhirF2bdMUnynDmO3RV2dd01IlWTFtpORaxXid09z6SMoqGxp1semXCdlqN7ccO3eJCwnJeTNfW7XCz0/3F+PsIKoYhwwZonKuf//+kn6mTp1KXFwcp04JHyrE+JXq6q1fv16hpPTz89OY7MbT05P169fj4fEmsZOfn1+23KUNwYoVK/Dz81OM1cnJCT8/P37++WeddUUX+Jzg6+sLkK1ry0nZnHLkyBFiY2PzZL62/KQF6wyY7VUdovqmr43qJrBH4wp4dX+j3h7XoQaHvm7K3lFCkHEx7pq6ekud6ysUQF7d62hM0jDF/iOWOtdnkO0b9ZhX9zrM71GXCh/mPAxMTpnfoy5e3esoxtrFsixe3evwY2fdqp0KHxbFp3d9ybV2sSzLUuf6+PSub7Txv8x8xYbT9xg5aoxR2s+K+6jRrDsUz8tMwyQfEVWM/duqbsh7tazPwqFvNs+Tutty9JeBHPDsD7yJX6munv8oR4XRbeFQO43Jbr7v8yn+oxwlLs4Lh9rx2zB7Kpga50NAVn4bZs/CoXaKsTo2NWfhUDum922to6ZAr5b12T3dWTF+x6bm+I9ylLjF56YswKoQwRBm6PvwMvMVfx2Ox32UcT+aA4wcNYb1p+4ZbL5mF3kdLXzrqCbydH0dOZKV6zYaJIEjvFH5De7nrHKuXy8nfL3nKX7/MHEssZGhnAgTPMTE+JXq6q3z81EY3Xy952lMdjPr+0ms8/ORuDj7es9j+aJfqFTBePE8RZYv+gVf73mKsXZz7Iyv9zx+mvadjpoCAetWSK5VjNc563vV9/B+vZw4vCtAca3dHDuzzs9H4kKvD/5r/qZVq5Y0bqzeo9BQjBgxgrhrKRy/bJg4+aKKsV8bVRFGr0/r4j3kjSJ0olMzIn/qR9gsYT8pxq9UV89vZCeFgc57SHuNyW6+79UCv5GdJO7Q3kPas8i1fZ484xe5tsd7SHvFWB1tauE9pD3Tvsyex2GvT+uy68eeivE72tTCb2QniVs8CM/ppW52kvsill3qZmeQaz1++TZx11I07nfee6UhYJiHuxsxh3ay2cM4cbZkCifOvsewbvc5vn7GldMDhIaG0u2Lz4laN09nnEQZGXV8Md6Lhs1a4bvcMIZDbYSGhtKtWzfOnz+frTiJMjLK2NvbY2FhoTCCGpPQ0FC+6OrIgTGNVVQ5Mu8ua47fZunJR1y4eJmSJY2rFHv06BENLOrzdWcLhqqJWygjo4s/Q86xeF88F+Iv5s18rV+X0c1KMthWu8u8jIw68np9bdigAVPGjmTk0IFG7Uum8JCUfIPGbTqxY8e/2NkZxl1cGx7u7sRE7CFgkvpwVTLvJr0WBGPd2gFfDcIetQpKAM+5P3H2RoYifqKMzI7TNzh7IwPPuT/lSX92dnY4Ojry41L1cm0ZGW0Ehh7nzMUkPOcY5oujLsT5Onny5DzpT+btYvPmzURHR+Pp6Zkn/Ynz1XOf/IyXEbj36AW/HrjJz78uMPrmGaBkyZL87PUr8wKiFNkcZWSyS9qDJ8wLiOJnr1/zbr7+uoBfD9zk3qMXRu9P5u0iP9bXeT//zIyfF5BqoIzeMoWfb2fOxdHRMU+MkwCec+dy7loaQVGquUNk3k2Coq5w7loannM17881GigrVqzIbM85fLflHEmpeRsUWqbgkZT6iO+2nGO25xy16eCNxQLvhew6HM1fuw7nWZ8yhZ+rN+8ywXsNs2Z75u18XbCAoKAgVq9enWd9yhR+EhISGD16NLNmzcrT+eq9aDF7L6Tyz6m7edanTMHkZeYrJgUlYtXoY1xcVGMpGwsXFxesGzXi65Vhee46K1N4eZn5iq9XhmHdqFGez1erRh8zKShRnq8y2SY/11crK2vcxk0xmKu3TOFlzYYt7Ajez4IF3nnWZ8WKFZnl6cmUvyJJSsldZneZt4eklPtM+SuSWZ7a9+caXbwBXrx4gUOXTty6FMO2r1piapK3sTpkCgYZT57T4/dIqtSzZs/e/XzwgdrcSkbD19eXcV9/TcCCybRv1lB3BZl3mowHj3D4ah6VPzIneM/e/Jmv48axe/fuPPtCKVN4SU9Pp127dlSsWJHg4OD8ma9jv+KvgZa0Njde5nCZgs3sPUlsOJPOsagTapNsGZP4+Hg+adGCAW3rM7t/9uIlyrzbTN8QwV+HL3IsKiqf5mtz+n9sxnQHOZyLjG7yfX39xJZhA/qpTUAj824QdvgIX/Qbwm+//SaJL58XCPakzty+cp6gbz/HtESxPO1fpmCQ8fgZTr/spHKdhuzZu0/rfkejghLggw8+ICBwOy+LmzF0ZRQPnsouDe8aD56+YOjKKF4WNyMgcHueb55ByNw7wm0EQ2YsJTouMc/7lyk8PHj0BJcffXj+XlG2BgTm33wdMYJ+/fpx4sSJPO9fpvBw//59evXqxdOnT9m6dWs+rq9ueGxJ4MyNh3nev0z+syz8Bn8eu83WwG15vnkGsLCwYGtgICv2neX3nSd1V5B5p/l950lW7DvL1sDAfJyv2/jz2G2WhcshMmS0UyDW160BLFmxmgW/L8/z/mXyn5Onz/I/t68YMWJEnhsnQbQnbeNFsdIMWbKPB0+e5/kYZPKXB0+eM2TJPl4UK01A4Dad+533Z86cOVNbARMTE7p+/gULfVeyNSqRLg0rY1pCVlK+C1y/95gvfSO59fh99oaEUq2a+gx/ecFnn33G8agTzFq8Esva1bCslX9jkSmYXLuditMEL26kPWDvvv35P1+PH2fq1Kk0bNiQhg1l5a+MlKSkJDp37sy1a9fYu3dvPs/XrkSdOMncfyKoX8GE+hWNn41QJv95kfmKH/5NZFn4TZb5+tKnT598G4u5uTlVq1Zlipcvt/97hH3jmhQp8l6+jUem4PHiZSZT1h7EZ1c0y5YVhPlaje8W/8XtB8/pWK8MRd6T56vMGwri+jpxyvfcvH0HR/sOFCmiVaMk85YQ+G8wvQaOoFPnLqxcuTLf/t8Fe9LnLFy6gq1H4ujS+CNMS8pKyneB5NQH9PHezc0Hr9i7PyRb+x2dBkqAcuXK0d/FhS3b/mXp7lNYVi6FecVShhizTAEl5PxtBv1xnArVzQkJO0Dt2rXzdTxFihShj7Mzaffu8e28xWRmZmJrXZeiH7yfr+OSKRjsiTxD3+8WUa5SNUJCwwrGfO3Th7S0NCZNmkRmZiYtW7akaFH5444M7Nq1CycnJ8qWLUtISEjBmK/OX5L2339M/WMnma9e0axGaT54X95wv61c++8pozZfJizhIVsDA+nXr19+D4lmzZpha/sJsxf5cfj8dVpZVsWspJxhXgaSUjIYvmwvITHX2RpQgObrJ58wZ+laIhLS+bRWacxM8l4FL1PwKLjrqy0zZs8l7PAR2rf+lDJmpvk9LBkj8fjJE35a4MO476YzZswY/Pz8eP/9/N0zi/akzYE7WBIUSYNqZahTWQ4t9Daz/+w1Bvjso3y1WjmyJ2mNQanMw4cPcRsxnPUbNtLZuhqevawxr/BhbscsUwBJSHnItIAY9sXcwKV/P/xX/MGHHxas/2M/Pz8mTZxIxTKlmDumP07tm+f3kGTyiSvJt5my+C+CI07h0r8//itWFMz5OmkSlSpVYv78+fTq1Su/hySTT1y6dIlx48axc+dOXFxc8Pf3L5jzdcJ4ypd8n2mdq9O1Ybn8HpKMAXn8PJPFB5Pxi7yNubk5G//ZQuPGjfN7WBLOnj1Lvy+dSUxMYJRjEyY4taBEMdnw8y7y+NkLFgZFsSz4NLVrm7Pxn80FdL72ISEhAfeWlfm6fQ1KFJXVae8ihWZ97deXxMRExnuM4LsJYyhZQvaaeJsI/DeYb2f+xJ2UVBYsWIC7u3t+D0mCxJ7UpDY/ubTEvJJsLH+bSLiTwQ/rI9l3OjFX9qQcGShFwsLC+GrMaOLj4/n842r0bVGDtvUrULyorGYrjDx9/pLDF1PYFJXMzjM3sLCw4PclS+nYsWN+D00jN2/eZNKkiWzYsBEbS3NcnTrwRbtmVC4nf4l523ny7DkHTsTy9+5wgg5EYWlpgc/vSwrBfJ3Ehg0baNasGW5ubvTo0YMqVark99BkjMyTJ08ICQlh9erVBAQEYGlpiY+PT8GfrxMnsGHjJhpXN2VA0/I4NChHpVKyAriwcu7mQ3bEpLL+dBov+ICp02cwduxYihUrmC5Wz549w8fHhzmzZ1O0SCYD2lrS45N6NK6Vd1nuZfKPs1fvsu3YJf46HMfzzCJMnT69kMzXWXzAC1yalKObdXkaVS1YH6BkjEOhXV/neFKsaFGGDuiLc/cvsGlsnd9Dk8klt+7cZfvOPfyxbgPRZ87Rv39/FixYQNWqVfN7aBoJCwtj7JjRxMXH80Uzc/q1rke7htVle1Ih5enzlxw6f52NEZf492QClhYW+OTSnpQrAyUIGZk2btyI/3JfDodHUKTIe1hULUOl0sUoVUz+clgYePAskzv3nxF/8z8yM1/Rtk1r3D1G0bdv33xJ1pAbTpw4gY/PYgK2BpBx/z41q1bCvHolzD404X05vspbxf1HT7iVlkFcQjKZr17P15EehXC++hAQEEBGRga1atWibt26lClTJt9dL2QMS0ZGBjdv3uT8+fNkZmbStm1b3N3dC998XbyYgK1byHjwkI/Kl6JW2eKYFgM5PGDB5+lL+O/JK+LvPCT90VPMa33E0OFujBw5kkqVKuX38LLFnTt3WL58OX/+sYKEq0mYlSqBZfXylPuwGMU/kJ/xbxNPX2SS9vAZcddTSX/wmDq1a+E6bHihnK8rV/iTmHQNs5LFsaj0IWVM3qO4/Ih/q3ir1tc/V5KQkEgZMzMaWtanfNkymBSXw2sUdF5mvuS/9AyuJCZx9VoyZmam9OzZi7Fjx9K8eeHwLpTYkyIiKPLee1hUr0BlUxNKmciLZmHgwZOX3M54Qvz1FGF/3lp/e1KuDZRZSUlJITQ0lNOnT3Pz5k3u37+vb5MyeUDp0qWpWrUqTZo0wc7OjgoVKuT3kHLNs2fPiIiI4Pjx41y5coV79+6RmZmZ38OSMSDyfJUpTMjzVSa/MTExoVy5clhbW9OuXTsaNGiQ30PSiwsXLnDo0CFiYmJIS0vjyZMn+T0kGQMiz1eZwoQ8X2XymyJFilC2bFnq1q1LixYtaN26dYFV7WYH2Z5UODHGfscgBkoZGRkZGRkZGRkZGRkZGRkZGRkZmdwg+8fIyMjIyMjIyMjIyMjIyMjIyMjI5BuygVJGRkZGRkZGRkZGRkZGRkZGRkYm35ANlDIyMjIyMjIyMjIyMjIyMjIyMjL5xv8BPg/pgzUKAIcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(DecisionTreeRegressor(max_depth=3).fit(xs, armed_security_y), out_file=dot_data,\n",
    "                     special_characters=True, filled=True, rounded=True, feature_names=xs.columns)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da0ce5d9",
   "metadata": {},
   "source": [
    "* We see that the feature with the most significance is the belief in supernatural evil being on top of the tree  \n",
    "* Then, we have political ideology again together with postgraduate level of education\n",
    "* Finally, we have region (south or not), highschool education and belief in supernatural evil again"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e4d51ac",
   "metadata": {},
   "source": [
    "* We then use `BaggingClassifier` to improve our score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4fc33fa6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improved Score:  0.668794408402842 0.00787672838907885\n"
     ]
    }
   ],
   "source": [
    "armed_security_tree = BaggingClassifier(DecisionTreeClassifier(criterion='entropy'),\n",
    "                                       n_estimators=50, \n",
    "                                       n_jobs=-1)\n",
    "bagged_cv_score = cross_val_score(armed_security_tree, \n",
    "                                  xs, \n",
    "                                  armed_security_y, \n",
    "                                  cv=3, scoring='accuracy')\n",
    "\n",
    "print('Improved Score: ',np.mean(bagged_cv_score), sem(bagged_cv_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c488fedd",
   "metadata": {},
   "source": [
    "**Mental Health screening**\n",
    "* After trying different depths, we get the best score at depth = 1 so we create the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ff1075d1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth =  1 [ 0.01435918  0.03158575 -0.02488171  0.01910757  0.03711023] 0.015456205458922479 0.010887475711152443\n",
      "max_depth =  2 [ 0.03179944 -0.00626423 -0.02467689  0.03414432 -0.03568283] -0.00013603884803146116 0.014315027632265978\n",
      "max_depth =  3 [-0.00598748 -0.02011298 -0.08588561 -0.02008662 -0.10938644] -0.04829182568145609 0.02064592360230743\n",
      "max_depth =  4 [-0.02371393 -0.19803195 -0.19555611 -0.14956853 -0.17055461] -0.147485023624016 0.0321882361138855\n",
      "max_depth =  5 [-0.10283201 -0.21200067 -0.49445042 -0.23431004 -0.30665301] -0.27004922953442373 0.0649246695886327\n",
      "max_depth =  6 [-0.38751411 -0.67862327 -0.65725856 -0.3169766  -0.38552687] -0.4851798830231494 0.07576050663353387\n",
      "max_depth =  7 [-0.39107344 -0.89500983 -0.83374804 -0.5857537  -0.64653965] -0.6704249335100794 0.09024682616541453\n"
     ]
    }
   ],
   "source": [
    "mental_health_y = tree_data.dropna().loc[:,'mental_health_screening'].transpose()\n",
    "\n",
    "all_depths = []\n",
    "all_mean_scores = []\n",
    "for max_depth in range(1, 8):\n",
    "    all_depths.append(max_depth)\n",
    "    simple_tree = DecisionTreeRegressor(max_depth=max_depth)\n",
    "    cv = KFold(n_splits=5, shuffle=True, random_state=13)\n",
    "    scores = cross_val_score(simple_tree, xs, mental_health_y, cv=cv)\n",
    "    mean_score = np.mean(scores)\n",
    "    all_mean_scores.append(np.mean(scores))\n",
    "    print(\"max_depth = \", max_depth, scores, mean_score, sem(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bdaf7bde",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(DecisionTreeRegressor(max_depth=1).fit(xs, mental_health_y), out_file=dot_data,\n",
    "                     special_characters=True, filled=True, rounded=True, feature_names=xs.columns)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e32e7cf7",
   "metadata": {},
   "source": [
    "* Political ideology is the deatire with the most significance for the mental health screening factor  \n",
    "* We will now use `BaggingClassifier` to improve our score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "4d519783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improved Score:  0.8997586499845536 0.0008447531881099846\n"
     ]
    }
   ],
   "source": [
    "mental_health_tree = BaggingClassifier(DecisionTreeClassifier(criterion='entropy'),\n",
    "                                       n_estimators=50, \n",
    "                                       n_jobs=-1)\n",
    "bagged_cv_score = cross_val_score(mental_health_tree, \n",
    "                                  xs, \n",
    "                                  mental_health_y, \n",
    "                                  cv=3, scoring='accuracy')\n",
    "\n",
    "print('Improved Score: ',np.mean(bagged_cv_score), sem(bagged_cv_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8444741",
   "metadata": {},
   "source": [
    "**High Capacity Magazine Ban**\n",
    "* After trying different depths, we get the best score at depth = 3 so we create the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e76f14a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth =  1 [0.08465911 0.06059322 0.04840479 0.01792189 0.10651717] 0.06361923543166932 0.01518517632396489\n",
      "max_depth =  2 [0.09430895 0.0962979  0.03379462 0.0580707  0.12446376] 0.08138718652419903 0.01589591793137267\n",
      "max_depth =  3 [0.15685457 0.11603058 0.04749482 0.05716154 0.11191651] 0.09789160447466402 0.020249846726410493\n",
      "max_depth =  4 [0.13171906 0.12795835 0.06525011 0.0513546  0.10484544] 0.09622551233342518 0.016300218995086595\n",
      "max_depth =  5 [0.08252711 0.1052613  0.06539008 0.03979491 0.01838036] 0.06227075006926517 0.015329330268140469\n",
      "max_depth =  6 [-0.01288948  0.0084686   0.0005305  -0.01837404 -0.05394117] -0.015241118707616352 0.010779457194272332\n",
      "max_depth =  7 [-0.06422907 -0.02120469 -0.11527595 -0.14174121 -0.10017236] -0.08852465434039045 0.020982797533133694\n",
      "max_depth =  8 [-0.15388789 -0.24406469 -0.27612283 -0.29772346 -0.24278157] -0.2429160886532417 0.024529464786181017\n",
      "max_depth =  9 [-0.30809973 -0.20797029 -0.37245482 -0.322883   -0.2745244 ] -0.297186449479428 0.027311015006738524\n",
      "max_depth =  10 [-0.3664627  -0.42795763 -0.43570017 -0.48986585 -0.37851517] -0.41970030282275045 0.02211090220450335\n"
     ]
    }
   ],
   "source": [
    "magazine_ban_y = tree_data.dropna().loc[:,'high_capacity_magazine_ban'].transpose()\n",
    "\n",
    "all_depths = []\n",
    "all_mean_scores = []\n",
    "for max_depth in range(1, 11):\n",
    "    all_depths.append(max_depth)\n",
    "    simple_tree = DecisionTreeRegressor(max_depth=max_depth)\n",
    "    cv = KFold(n_splits=5, shuffle=True, random_state=13)\n",
    "    scores = cross_val_score(simple_tree, xs, magazine_ban_y, cv=cv)\n",
    "    mean_score = np.mean(scores)\n",
    "    all_mean_scores.append(np.mean(scores))\n",
    "    print(\"max_depth = \", max_depth, scores, mean_score, sem(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "101a3721",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(DecisionTreeRegressor(max_depth=3).fit(xs, magazine_ban_y), out_file=dot_data,\n",
    "                     special_characters=True, filled=True, rounded=True, feature_names=xs.columns)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5044c7a",
   "metadata": {},
   "source": [
    "* Political ideology is the feature with the highest significance\n",
    "* Then, we gender and age playing also an importan role in support for high capacity magazine ban\n",
    "* We will now use `BaggingClassifier` to improve our score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5ba30a25",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improved score:  0.7201227988878592 0.005397584811718652\n"
     ]
    }
   ],
   "source": [
    "magazine_ban_tree = BaggingClassifier(DecisionTreeClassifier(criterion='entropy'),\n",
    "                                       n_estimators=50, \n",
    "                                       n_jobs=-1)\n",
    "bagged_cv_score = cross_val_score(magazine_ban_tree, \n",
    "                                  xs, \n",
    "                                  magazine_ban_y, \n",
    "                                  cv=3, scoring='accuracy')\n",
    "\n",
    "print('Improved score: ',np.mean(bagged_cv_score), sem(bagged_cv_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bdedfd7",
   "metadata": {},
   "source": [
    "**Teachers with guns**\n",
    "* After trying different depths, we get the best score at depth = 2 so we create the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "bf20543f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth =  1 [ 0.09187013  0.093256   -0.00339895  0.0590025   0.06665482] 0.06147689974430655 0.017571951642863508\n",
      "max_depth =  2 [ 0.09773414  0.10097467 -0.01261921  0.10242318  0.08789314] 0.07528118271118897 0.0221207121716371\n",
      "max_depth =  3 [0.08116175 0.07991383 0.00305438 0.11385118 0.05535334] 0.0666668961379722 0.018420760583717586\n",
      "max_depth =  4 [ 0.0680451   0.09784875 -0.01698703  0.10121583  0.05282075] 0.06058867774913177 0.021415891211603696\n",
      "max_depth =  5 [-0.01067821  0.07927567 -0.06969208  0.03384506  0.01037006] 0.008624101888074808 0.02463411566892441\n",
      "max_depth =  6 [-0.12332406  0.09705633 -0.14417766 -0.04389534 -0.08273879] -0.059415905197172415 0.04274178941887398\n",
      "max_depth =  7 [-0.29040559 -0.06054975 -0.28488196 -0.13132313 -0.26778923] -0.2069899329344082 0.0468466044198058\n"
     ]
    }
   ],
   "source": [
    "teachers_with_guns_y = tree_data.dropna().loc[:,'teachers_with_guns'].transpose()\n",
    "\n",
    "all_depths = []\n",
    "all_mean_scores = []\n",
    "for max_depth in range(1, 8):\n",
    "    all_depths.append(max_depth)\n",
    "    simple_tree = DecisionTreeRegressor(max_depth=max_depth)\n",
    "    cv = KFold(n_splits=5, shuffle=True, random_state=13)\n",
    "    scores = cross_val_score(simple_tree, xs, teachers_with_guns_y, cv=cv)\n",
    "    mean_score = np.mean(scores)\n",
    "    all_mean_scores.append(np.mean(scores))\n",
    "    print(\"max_depth = \", max_depth, scores, mean_score, sem(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "644f40d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(DecisionTreeRegressor(max_depth=2).fit(xs, teachers_with_guns_y), out_file=dot_data,\n",
    "                     special_characters=True, filled=True, rounded=True, feature_names=xs.columns)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e05f87a",
   "metadata": {},
   "source": [
    "* After creating the tree, we see that political ideology has the most significance. Then, we have gender and also whether someone lives in a small town or not\n",
    "* To improve our score, we will now use `BaggingClassifier`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "1a734e5e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improved score:  0.696866311399444 0.004381057810332734\n"
     ]
    }
   ],
   "source": [
    "teachers_with_guns_tree = BaggingClassifier(DecisionTreeClassifier(criterion='entropy'),\n",
    "                                       n_estimators=50, \n",
    "                                       n_jobs=-1)\n",
    "bagged_cv_score = cross_val_score(teachers_with_guns_tree, \n",
    "                                  xs, \n",
    "                                  teachers_with_guns_y, \n",
    "                                  cv=3, scoring='accuracy')\n",
    "\n",
    "print('Improved score: ',np.mean(bagged_cv_score), sem(bagged_cv_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53ecdb4c",
   "metadata": {},
   "source": [
    "**Civilian Gun Possesion Ban**\n",
    "* After trying different depths, we get the best score at depth = 3 so we create the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7acd8bcc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth =  1 [0.08009633 0.05727892 0.01735961 0.02422257 0.06493916] 0.04877931887276514 0.01205066494075131\n",
      "max_depth =  2 [ 0.08817133  0.0863434  -0.014476    0.03471437  0.07351214] 0.0536530486545528 0.01956373620823774\n",
      "max_depth =  3 [0.07438815 0.08903108 0.00287892 0.02986366 0.08965146] 0.05716265514105763 0.017406602401050852\n",
      "max_depth =  4 [-0.01680185  0.03672404 -0.06673473  0.02977658  0.03621617] 0.0038360415525868774 0.02025976462405771\n",
      "max_depth =  5 [-0.1094602  -0.00334433 -0.08002697  0.03615636 -0.04665088] -0.040665204848365374 0.026088626650258094\n",
      "max_depth =  6 [-0.12023418 -0.12222807 -0.22377837 -0.01064035 -0.08676438] -0.11272907182449221 0.03433276552109837\n",
      "max_depth =  7 [-0.16922848 -0.23514336 -0.19763739 -0.11052454 -0.18447189] -0.1794011305084219 0.02038742029817059\n",
      "max_depth =  8 [-0.2141663  -0.44773987 -0.39605484 -0.26154468 -0.37403994] -0.33870912613558807 0.043525720981294404\n",
      "max_depth =  9 [-0.29262269 -0.5177743  -0.58211423 -0.38911767 -0.49306893] -0.45493956324239493 0.051114938832166716\n"
     ]
    }
   ],
   "source": [
    "civilian_gun_posses_y = tree_data.dropna().loc[:,'civilian_gun_possesion_ban'].transpose()\n",
    "\n",
    "all_depths = []\n",
    "all_mean_scores = []\n",
    "for max_depth in range(1, 10):\n",
    "    all_depths.append(max_depth)\n",
    "    simple_tree = DecisionTreeRegressor(max_depth=max_depth)\n",
    "    cv = KFold(n_splits=5, shuffle=True, random_state=13)\n",
    "    scores = cross_val_score(simple_tree, xs, civilian_gun_posses_y, cv=cv)\n",
    "    mean_score = np.mean(scores)\n",
    "    all_mean_scores.append(np.mean(scores))\n",
    "    print(\"max_depth = \", max_depth, scores, mean_score, sem(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "f2fd0167",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(DecisionTreeRegressor(max_depth=3).fit(xs, civilian_gun_posses_y), out_file=dot_data,\n",
    "                     special_characters=True, filled=True, rounded=True, feature_names=xs.columns)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ac7e361",
   "metadata": {},
   "source": [
    "* After creating the tree, we see that political ideology is the most important feature\n",
    "* Other features with significant importance are race, education, attendance and belief in supernatural evil\n",
    "* Now in order to improve our score, we will use `BaggingClassifier`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "b97887fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improved Score:  0.7217253629904233 0.006983102681187177\n"
     ]
    }
   ],
   "source": [
    "civilian_gun_posses_tree = BaggingClassifier(DecisionTreeClassifier(criterion='entropy'),\n",
    "                                       n_estimators=50, \n",
    "                                       n_jobs=-1)\n",
    "bagged_cv_score = cross_val_score(semi_auto_tree, \n",
    "                                  xs, \n",
    "                                  civilian_gun_posses_y, \n",
    "                                  cv=3, scoring='accuracy')\n",
    "\n",
    "print('Improved Score: ',np.mean(bagged_cv_score), sem(bagged_cv_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f163dc67",
   "metadata": {},
   "source": [
    "**Concealed Carry Laws**\n",
    "* After trying different depths, we get the best score at depth = 2 so we create the tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "1f36e4cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max_depth =  1 [0.12897407 0.09371078 0.04061414 0.00864701 0.08130836] 0.07065087130276118 0.020968573753455996\n",
      "max_depth =  2 [0.15914364 0.13675839 0.05979081 0.04918873 0.11386025] 0.10374836222538737 0.021412109307267835\n",
      "max_depth =  3 [0.14495553 0.1386447  0.04400415 0.05588281 0.07053849] 0.09080513672733086 0.0212620869505116\n",
      "max_depth =  4 [0.11664459 0.05287011 0.05164846 0.02668784 0.05570567] 0.060711333977381085 0.014924482934221993\n",
      "max_depth =  5 [ 0.04109396  0.01755463  0.03125427 -0.05329264  0.03615986] 0.01455401387936366 0.01741012833774567\n",
      "max_depth =  6 [ 0.04866416 -0.01341606 -0.08461022 -0.02940118  0.00731578] -0.01428950450636659 0.021913558519435057\n",
      "max_depth =  7 [-0.06980598 -0.0849536  -0.19066261 -0.11236015 -0.17181079] -0.12591862589526648 0.023778633821042876\n"
     ]
    }
   ],
   "source": [
    "concealed_carry_y = tree_data.dropna().loc[:,'support_for_concealed_carry'].transpose()\n",
    "\n",
    "all_depths = []\n",
    "all_mean_scores = []\n",
    "for max_depth in range(1, 8):\n",
    "    all_depths.append(max_depth)\n",
    "    simple_tree = DecisionTreeRegressor(max_depth=max_depth)\n",
    "    cv = KFold(n_splits=5, shuffle=True, random_state=13)\n",
    "    scores = cross_val_score(simple_tree, xs, concealed_carry_y, cv=cv)\n",
    "    mean_score = np.mean(scores)\n",
    "    all_mean_scores.append(np.mean(scores))\n",
    "    print(\"max_depth = \", max_depth, scores, mean_score, sem(scores))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "4f830797",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dot_data = StringIO()  \n",
    "tree.export_graphviz(DecisionTreeRegressor(max_depth=2).fit(xs, concealed_carry_y), out_file=dot_data,\n",
    "                     special_characters=True, filled=True, rounded=True, feature_names=xs.columns)  \n",
    "graph = pydotplus.graph_from_dot_data(dot_data.getvalue())\n",
    "Image(graph.create_png()) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "85488d48",
   "metadata": {},
   "source": [
    "* After creating the tree, we see that political ideology and gender are the two most significant features for the support for concealed carry law policy \n",
    "* We will also use `BaggingClassifier` to improve our score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "f4bf3515",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Improved Score:  0.6367392647513129 0.0074666814348572684\n"
     ]
    }
   ],
   "source": [
    "concealed_carry_tree = BaggingClassifier(DecisionTreeClassifier(criterion='entropy'),\n",
    "                                       n_estimators=50, \n",
    "                                       n_jobs=-1)\n",
    "bagged_cv_score = cross_val_score(concealed_carry_tree, \n",
    "                                  xs, \n",
    "                                  concealed_carry_y, \n",
    "                                  cv=3, scoring='accuracy')\n",
    "\n",
    "print('Improved Score: ',np.mean(bagged_cv_score), sem(bagged_cv_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b6a6443",
   "metadata": {},
   "source": [
    "* Overall we see that political ideology is one of the most important factors that influence support for the gun policies we analyzed here\n",
    "* Belief in supernatural evil is also important with bigger significance in some of the policies\n",
    "* We also observe that its influence is smaller than that of political ideology"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86ad050c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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