Synthetic Control with Covariates.ipynb

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     "end_time": "2022-12-22T12:22:06.681764Z",
     "start_time": "2022-12-22T12:22:04.829523Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b353f4dd",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:06.741740Z",
     "start_time": "2022-12-22T12:22:06.683384Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/matheusfacure/python-causality-handbook/master/causal-inference-for-the-brave-and-true/data/smoking.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dfd3256b",
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    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:06.754899Z",
     "start_time": "2022-12-22T12:22:06.742902Z"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>cigsale</th>\n",
       "      <th>lnincome</th>\n",
       "      <th>beer</th>\n",
       "      <th>age15to24</th>\n",
       "      <th>retprice</th>\n",
       "      <th>california</th>\n",
       "      <th>after_treatment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1970</td>\n",
       "      <td>89.800003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178862</td>\n",
       "      <td>39.599998</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1971</td>\n",
       "      <td>95.400002</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.179928</td>\n",
       "      <td>42.700001</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1972</td>\n",
       "      <td>101.099998</td>\n",
       "      <td>9.498476</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.180994</td>\n",
       "      <td>42.299999</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1973</td>\n",
       "      <td>102.900002</td>\n",
       "      <td>9.550107</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.182060</td>\n",
       "      <td>42.099998</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1974</td>\n",
       "      <td>108.199997</td>\n",
       "      <td>9.537163</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.183126</td>\n",
       "      <td>43.099998</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state  year     cigsale  lnincome  beer  age15to24   retprice  california  \\\n",
       "0      1  1970   89.800003       NaN   NaN   0.178862  39.599998       False   \n",
       "1      1  1971   95.400002       NaN   NaN   0.179928  42.700001       False   \n",
       "2      1  1972  101.099998  9.498476   NaN   0.180994  42.299999       False   \n",
       "3      1  1973  102.900002  9.550107   NaN   0.182060  42.099998       False   \n",
       "4      1  1974  108.199997  9.537163   NaN   0.183126  43.099998       False   \n",
       "\n",
       "   after_treatment  \n",
       "0            False  \n",
       "1            False  \n",
       "2            False  \n",
       "3            False  \n",
       "4            False  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3b8d7d8",
   "metadata": {},
   "source": [
    "$$\n",
    "min \\ (Xw - y)^2\n",
    "$$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4fb1db62",
   "metadata": {},
   "source": [
    "## Helper Functions\n",
    "\n",
    "\n",
    "$$\n",
    "min \\ \\bigg( \\big[\\sum_i^{K}(X_i v_i)\\big]w - y\\bigg)^2\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca9c3297",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:06.774279Z",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>state</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
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       "      <th>11</th>\n",
       "      <th>...</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1970</th>\n",
       "      <td>39.599998</td>\n",
       "      <td>36.700001</td>\n",
       "      <td>29.400000</td>\n",
       "      <td>42.200001</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>34.299999</td>\n",
       "      <td>33.799999</td>\n",
       "      <td>41.400002</td>\n",
       "      <td>30.600000</td>\n",
       "      <td>37.700001</td>\n",
       "      <td>...</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>39.900002</td>\n",
       "      <td>40.400002</td>\n",
       "      <td>34.599998</td>\n",
       "      <td>37.700001</td>\n",
       "      <td>28.799999</td>\n",
       "      <td>33.700001</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>34.099998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1971</th>\n",
       "      <td>42.700001</td>\n",
       "      <td>38.799999</td>\n",
       "      <td>31.100000</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>41.299999</td>\n",
       "      <td>35.799999</td>\n",
       "      <td>33.599998</td>\n",
       "      <td>41.400002</td>\n",
       "      <td>32.200001</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>34.299999</td>\n",
       "      <td>38.500000</td>\n",
       "      <td>41.599998</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>36.599998</td>\n",
       "      <td>39.500000</td>\n",
       "      <td>30.200001</td>\n",
       "      <td>41.599998</td>\n",
       "      <td>40.200001</td>\n",
       "      <td>34.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1972</th>\n",
       "      <td>42.299999</td>\n",
       "      <td>44.099998</td>\n",
       "      <td>31.200001</td>\n",
       "      <td>51.299999</td>\n",
       "      <td>44.700001</td>\n",
       "      <td>40.900002</td>\n",
       "      <td>33.700001</td>\n",
       "      <td>41.900002</td>\n",
       "      <td>32.500000</td>\n",
       "      <td>41.900002</td>\n",
       "      <td>...</td>\n",
       "      <td>34.099998</td>\n",
       "      <td>39.099998</td>\n",
       "      <td>41.599998</td>\n",
       "      <td>46.900002</td>\n",
       "      <td>37.200001</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>29.900000</td>\n",
       "      <td>41.299999</td>\n",
       "      <td>40.299999</td>\n",
       "      <td>34.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1973</th>\n",
       "      <td>42.099998</td>\n",
       "      <td>45.099998</td>\n",
       "      <td>32.700001</td>\n",
       "      <td>50.599998</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>42.400002</td>\n",
       "      <td>36.299999</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>32.900002</td>\n",
       "      <td>41.900002</td>\n",
       "      <td>...</td>\n",
       "      <td>33.500000</td>\n",
       "      <td>39.599998</td>\n",
       "      <td>40.799999</td>\n",
       "      <td>46.400002</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>39.799999</td>\n",
       "      <td>30.100000</td>\n",
       "      <td>39.900002</td>\n",
       "      <td>42.599998</td>\n",
       "      <td>34.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1974</th>\n",
       "      <td>43.099998</td>\n",
       "      <td>45.500000</td>\n",
       "      <td>38.099998</td>\n",
       "      <td>52.500000</td>\n",
       "      <td>44.200001</td>\n",
       "      <td>42.400002</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>41.900002</td>\n",
       "      <td>34.500000</td>\n",
       "      <td>43.200001</td>\n",
       "      <td>...</td>\n",
       "      <td>35.200001</td>\n",
       "      <td>40.400002</td>\n",
       "      <td>42.500000</td>\n",
       "      <td>47.500000</td>\n",
       "      <td>37.799999</td>\n",
       "      <td>41.299999</td>\n",
       "      <td>31.299999</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>43.900002</td>\n",
       "      <td>35.799999</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 38 columns</p>\n",
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      ],
      "text/plain": [
       "state         1          2          4          5          6          7   \\\n",
       "year                                                                      \n",
       "1970   39.599998  36.700001  29.400000  42.200001  39.000000  34.299999   \n",
       "1971   42.700001  38.799999  31.100000  45.500000  41.299999  35.799999   \n",
       "1972   42.299999  44.099998  31.200001  51.299999  44.700001  40.900002   \n",
       "1973   42.099998  45.099998  32.700001  50.599998  44.000000  42.400002   \n",
       "1974   43.099998  45.500000  38.099998  52.500000  44.200001  42.400002   \n",
       "\n",
       "state         8          9          10         11  ...         30         31  \\\n",
       "year                                               ...                         \n",
       "1970   33.799999  41.400002  30.600000  37.700001  ...  32.500000  38.500000   \n",
       "1971   33.599998  41.400002  32.200001  38.500000  ...  34.299999  38.500000   \n",
       "1972   33.700001  41.900002  32.500000  41.900002  ...  34.099998  39.099998   \n",
       "1973   36.299999  41.000000  32.900002  41.900002  ...  33.500000  39.599998   \n",
       "1974   38.000000  41.900002  34.500000  43.200001  ...  35.200001  40.400002   \n",
       "\n",
       "state         32         33         34         35         36         37  \\\n",
       "year                                                                      \n",
       "1970   39.900002  40.400002  34.599998  37.700001  28.799999  33.700001   \n",
       "1971   41.599998  42.000000  36.599998  39.500000  30.200001  41.599998   \n",
       "1972   41.599998  46.900002  37.200001  40.000000  29.900000  41.299999   \n",
       "1973   40.799999  46.400002  36.500000  39.799999  30.100000  39.900002   \n",
       "1974   42.500000  47.500000  37.799999  41.299999  31.299999  42.000000   \n",
       "\n",
       "state         38         39  \n",
       "year                         \n",
       "1970   38.500000  34.099998  \n",
       "1971   40.200001  34.400002  \n",
       "1972   40.299999  34.400002  \n",
       "1973   42.599998  34.400002  \n",
       "1974   43.900002  35.799999  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_x_matrix(df, time_column, geo_column, x_columns):\n",
    "    return [df.pivot(time_column, geo_column, covariate)\n",
    "            for covariate in x_columns]\n",
    "\n",
    "\n",
    "x_co_list = build_x_matrix(df.query(f\"~after_treatment & ~california\"),\n",
    "                           time_column=\"year\",\n",
    "                           geo_column=\"state\",\n",
    "                           x_columns=[\"retprice\", \"cigsale\"],)\n",
    "\n",
    "x_co_list[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b9332bb",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:06.967418Z",
     "start_time": "2022-12-22T12:22:06.775771Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': 52.12957126425171,\n",
       " 'w': array([-4.99169159e-15, -4.23182897e-15,  1.48107704e-02,  1.09089624e-01,\n",
       "        -5.06196148e-16, -3.37682034e-15, -1.17698409e-15, -1.18142274e-15,\n",
       "        -2.83793251e-15, -2.25062029e-15, -2.51938955e-15, -4.60408280e-15,\n",
       "        -3.09984604e-15, -1.74984136e-15, -1.85047125e-15, -4.12797157e-15,\n",
       "        -2.81853017e-15,  2.31839951e-01, -1.37688357e-15,  2.04922584e-01,\n",
       "         4.54290464e-02, -5.75380220e-16, -1.58639895e-15, -3.36815045e-15,\n",
       "        -1.83380312e-15, -3.53906228e-15, -2.79647641e-15, -3.08583630e-15,\n",
       "        -4.47773819e-15, -3.33919761e-15, -4.27691089e-15, -2.65688344e-15,\n",
       "         3.93908024e-01, -4.40947417e-15, -3.86138132e-15, -2.18503264e-15,\n",
       "        -2.11418927e-15, -2.50831292e-15])}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.utils.validation import check_X_y\n",
    "import cvxpy as cp\n",
    "\n",
    "def find_w_given_vs(vs, x_co_list, y_tr_pre):\n",
    "    X_times_v = sum([x*v for x, v in zip(x_co_list, vs)])\n",
    "    X, y = check_X_y(X_times_v, y_tr_pre)\n",
    "    \n",
    "    w = cp.Variable(X.shape[1])\n",
    "    \n",
    "    objective = cp.Minimize(cp.sum_squares(X@w - y))\n",
    "    \n",
    "    constraints = [cp.sum(w) == 1, w >= 0]\n",
    "    \n",
    "    problem = cp.Problem(objective, constraints)\n",
    "            \n",
    "    loss = problem.solve(verbose=False, max_iter = 20000)\n",
    "    \n",
    "    return {\"loss\": loss, \"w\": w.value} \n",
    "\n",
    "\n",
    "y_tr_pre = df.query(\"~after_treatment\").query(\"california\")[\"cigsale\"]\n",
    "\n",
    "find_w_given_vs([0, 1], x_co_list, y_tr_pre)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23de3a9e",
   "metadata": {},
   "source": [
    "## Checking the Shape of the Loss on Vs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3083a08a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:16.930018Z",
     "start_time": "2022-12-22T12:22:06.969078Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "x_co_list = build_x_matrix(df.query(f\"~after_treatment & ~california\"),\n",
    "                                   time_column=\"year\",\n",
    "                                   geo_column=\"state\",\n",
    "                                   x_columns=[\"retprice\", \"cigsale\"])\n",
    "        \n",
    "        \n",
    "y_tr_pre = df.query(f\"~after_treatment\").query(f\"california\")[\"cigsale\"]\n",
    "\n",
    "\n",
    "def v_loss(vs):\n",
    "    return find_w_given_vs(vs, x_co_list, y_tr_pre).get(\"loss\")\n",
    "\n",
    "\n",
    "x1 = np.linspace(-.2,0, 40) # retprice\n",
    "x2 = np.linspace(0.8,1.2, 40) # cigsale\n",
    "\n",
    "\n",
    "cordinates =[[i, j, v_loss([i, j])] for i in x1 for j in x2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "300f7c78",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:17.129281Z",
     "start_time": "2022-12-22T12:22:16.931440Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='cigsale', ylabel='retprice'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yhicfSi0HmP3k66nlH09JXy704ZfZy4mmK90E4Zua9DHA7BxDhGp8bdZl/HV2ySjvnuP7tHdD+pKjfrVzU8v7952VeY5+S89MLe+xSrbRQt2qQ1LLtfpaqeW1a2ySeY4cRgoVmxbMuvv83N85PXY+IZokRCKRSCRSEe1wHW/seCORSCTSeWmH73hjxxuJRCKRzksrZivnJXa8kUgkEum8tMMRb5tmNctZaDOEULZFWP87RdKYxP6SZgiSzpP0UjjfHZL6hf0jEoL/EyTtlqizvtwk4bUQr8L+CxJ1XpE0PVFniKQH5MYKL4Zs5rJmCJL2DzFNlPRkWFOLpO6SngkxTZF0euIcAyQ9KOnV8P/+ibaSpgcNktYJZV0lXRrifUnSHmF/STMESetIeiqce6KkBTrQiTj+LunrHL/ySCQSaV3M8m+tRFuPeCsyQwid5sXAdmb2jqQlwv40M4QHgZPNbL6kc/C1qSfiy4WGh/1L42Ia9wRN50uAw4Gx+FKn7XCd5eMTsRxDUwGJfwNnmtmDknoDDUo3Q3gT2NzMvpC0Pb40aENgDrCVmX0tqQvwP0kjg4b1ScBDZna2pJPCzyea2XXAdSGutYC7zGx8iOsU4GMzWyXEMyDsL2eGMBM40MxeDcu4npc02symh/aHA/1K/M4iVWDOq0+mltsrxQqnTZn/0rup5QDTX+uWWv7+l71Tyz+o7Zp5ji8yPA5mZgwB5uRIhs+2J8gmy4qhLiPJtrtlJ8X2bUj/2p09P/1mNHyZfY66uvRRXpe+2UYLtQOnp5br809Syxumf5h5DpbPPqRi4oi3GZWaIfwYuN3M3gnHFUwEypohmNkDYR94Rzo47J+Z2N+dsOwlxNM3KEsZ3qHuWiKWBeITYW1snZk9GNr+OssMwcyeTKwpTsZlZlYYTXYJW+FbaBe8Iyf8PzWuwCF4p4qZNZjZp2F/STMEM3uloPUclKo+BgaF66zFXaB+U+K8kUgk0vZEychmVGqGsArQP0yNPi/pwLC/rBlCEYcAIws/SNpQLnYxCTgidMTLhrjKxihpeVxysiB6sQowXdLtksaF6e1aUswQiji0KK5auZTmx8CDQTwDYMmColf4/xLFDQH70PhA0C/sOyNMd98iacmwL80MoRDHCKArUFj4eTRwd0tVxSKRSKTVaIcCGm3d8VZqhlAHrA/sCGwL/E7SKuXMEJqcWDol7FugQGBmT5vZd4ENgJPlxvJ5YtwXuDVhZFCHOwOdENpaETg4wwyhENeWeMd7YiKuenPf28HAiDCVnolcVnKmmU1OxDUYN45YD3gKn2KGdDOEwsj/P8BPzawhTDvvBfw9RxxRqzkSibQN9fX5t1ai1d/xSjoKf/8KLsFYiRnCNODTIAv5jaTHgLWBV8zsChqN5M8iMWqVdBDwI/xda7OO3symSvoGN4svSFKmxbgvcFRRXOOCeQCS7sQ1nq8ws6fwThlJP8RHx4W4hgGXA9ub2Wcl4pou6VH8HfNk4CNJS5vZB6FjLPbrLZaN/Ax/Z3tH+PkWvJMvTCPvHuLoDexRMEOQ1Be4Dzg1vBIAf5+9MvCaPNesp6TXzGzlEnFHreZIJNI2VHEKWdKVeN/xsZmtGfatg9u2dscHK78ws2fS2mn1Ea+ZXWRm64QR3J3AgXI2ouVmCHcBm0qqC1PKGwJTwU0Hwv8LZgiF6dbt8NHkzuG9K2H/CmrMMF4eWBV4K8TzlaSN5D3MgeG8hXqrAv3x0WOBZ/Ep8EHh562AF4viKpgh/DMR5+3ATxLvgJE0SI2Z1z0I5geh+G48OYvw/2RcNfiI9MbCvvCQcQ+uvwxurFCIa2CoA03NELriHfW/zeyWRFv3mdlSZjbUzIbiI+tmnW4kEom0KdV9x3s1PvBJci5weujTfk8Zs5gkbZ3VfD/+/vU1fCT200KBpPuBw8zsfUnH4gk8S+FmCPeb2WFhZDoKNxZoAC5PTKveFt5RzqOpGcI/gG7Ag2GkNtbMjgA2AU6SNC+09YtE4tGR+A3vgb97XfD+FU9eujE5cjazekknAA+Fzvp53OwB3AzhR/hDzyUJM4Tf48lXF4e45pt73S4NXBPeEdcAN5vZvaHO2cDNkg4F3sE72gKbAdMKo+4EJwL/kXQh8Eninm8B/FmS4baEhRH83qGtxSUdHPYdnMiSjkQikfZLFd/dmtljCktDk7uBvuHzYuSYtY0mCZHWJv7BtYBZ912YWl7/2GOp5TMezzLfgjffWDy1/PXadEH9j7PW4ABf1KR/+c3MsDiYk8MCob4Kf1o1GcuFumSU98gxidjX0o8ZWJ9+jmXmZ7+LHNw13SRhqSEzMtvovWb6MrPatb+TWq7vrp95jh6bHZx1SMWmBTMvPT73H0bPwy/IPF/oeO9NTDWvDozGY60Bvmdmb6e10dbJVZFIJBKJLDrSjO+LtmQiaNgOz3GGI4HjzWw54HhCblEabT3VHIlEIpHIoqMh/0xIUSJoXg4Cfhk+34InyKYSR7yRSCQS6bwsegGN9/ElmOCJtK9mVehwHW/IgF5ofWe5tvOXatQx/n2i7Hi5JvFkSTeEdbxIWluuVzxJ0j1heU2qJnKizbslTU78fERoZ7yk/8lVrgpl9Ym27k7s31oueFGos3LY31+uNz1RrudceOewalFcM5TQqw7HnJC8N0rXqn5U0suJ8kJmdkl950gkEmk3VLHjlXQDvoJlVUnTQmLrz4C/SJoAnIXLC6fSEaeaK9J3DjxuZj9K7pC0LHAssIaZzZJ0M74O9mp86uAEMxsj6RDg18DvMjSRkbQ7UGwecL2ZFZYQ7Qz8lcb09FkhJb2YS4BdQhb3L4BTgYOB3wLjzWw3SasBF+Frk18G1gnnqMXFMAprd5G0HLANngldIE2rGmB/M3uuKK5y+s6RSCTSPqhiArGZ7VemKDuTLEFH7HgX6DsDYyX1UxCRSB5kZuMAwtKcvNQBPcKSop40poWvii+xAVfDGg38rqhuE01kuQjF/+FPPzcn4kqmE/YiX5ZvuXT1NWjUXX5J0lBJS5rZR4m6WwOvF2XZXYAvz1qw7je5ppmEVnUGa+DJBOD6znfmqBMJzB5/b+Yx9vKU1PK5LzXTWWnCx+8tlnmOaUrPXn0v41viE2X7nc4g/ZiZlp6pOzeHBcK8Kiwbycpq7q70FO5eyv5KrVeX1PLa2oxzNGSnkfebm36O2V9lx9njyzmp5TXT0zOj9dX0zHPMnTYptbzr4LUy28gkmiRUhUr1nQE2DtOpIyV9F8DM3sNHcO8AH+BiHg+E4ycDO4fPe1FaX3mBJnLgDOAv+PrkJkg6StLr+ELrYxNF3UMm3VhJuyb2HwbcL2kaPqI8O+yfQKPa1Ajc66N4ureJelUYZb9nZs1sbVRaq7rAVWGa+XdqfJrJ1HeORCKRNqUdSkZ2xI63Un3nF4DlzWxtXGf4TvD3pfhoegVgGaCXpANCnUOAoyQ9D/TBrQYbAyrSRA7veVc2szsoQVDvWgkXszg1UTQkiGb8GLhQ0kph//HADmY2GLgKn54G74D7yw0UjgHG0VRfuSv+wHBL+Lknbgu44L12UVyltKrBp5nXwqUuN6VxOjlV3zkSiUTanAbLv7USHaLjDSPE8aGDeZ8K9J3NbEbBZs/M7sfdggbiUoxvmtknZjYPl2/8XjjuJTP7oZmtj48eXy9qtlgTeWNgfUlvAf8DVpFrLBdzIwkrv6CXTFCbehRYVy47uXbCkeimRFwzzOyn4b3wgbhd35uJ9rcHXkhMPa+EP1hMCLENBl6QtFTRPZoKFLSqC7MBmNlXwPW4zzFm9r6Z7W5m6+IdOgV95ySKJgmRSKSNsIaG3Ftr0SE63mrqO0taqjBVGqZna3DzgHeAjST1DOVb01z3uQYfof4z0V4pTeRLzGyZoGG8CW7asEU4Pin3siMh9TxkKHcLnwcC38d1lL8AFpNUMFPYJhFXvzCqBZ+OfqzoHXKT985mNsnMlkjoK08D1jOzD1VGq1qug13IfO6CC4QXRvYl9Z2LMbNLzWy4mQ0//PA869EjkUikSrTDEW9HTK6qSN8Z98A9UtJ8YBawb0jUelrSrfhU9Hx82rYwPNtP7qoEPhK+KhFPOU3kchwt6Qe4hvQXNJocrA78S1ID/jBwtpkVDAx+hmtPN4Q6hyTq/FtSPd5JH5q4Fz3xTvrnOeMqqVUtqRcwOnS6tcB/adSd3oLS+s6RSCTSPmhFn928RK3mSGvzrfmDm/dp+rPY/IevzW7joSdSyz9+PL3+y58NyDzH1G7pWbLv1qS/tv/M5qaWA3yZccwsm5daPrchO3VgXoaec1bGMkBtlhZzTdfU8r4Z5QADlH7MkqSXLzs/+zqWn5d+v5bvm63VvMQq36SWd19vydTymrWHZZ6jZtgWqeXdVtmkYq3mb07bL/d3Tq/Tbqj4fHnoiCPeSCQSiUTy0YpTyHmJHW8kEolEOi/tcKo5dryRSCQS6bzEEW8kEolEIq1Hay4TykubLicKS4IW2vAgUb6B3GBgz8S+41Xa8OAmNYr9vxXWBiNpcUmPSPpa0j8S7fRRU8OBTyVdGMpSTQIk9ZX0XlF75QwPfp04x+RwPQNC2ZWSPlbCbCHjWrpKukpuxjBB0hYl7lmxecOQcP3jwvXsEPZvWXT9s9VUVQtJf5dUrEkdiUQibU9cTtSMig0P5CYA5+D6yYV9ZQ0PzGyfxHF/AQqCD7Nx/eU1wwYsEI1YJ1HneXxJEWSbBJwBjCkKuaThgZmdB5wXzrETbqz8eahzNfAP4N/JhlKu5WehfC35GuSRkjYw85cdKm3ecCpws5ldIndMuh8YamaP0Gi4MABfxlWQ0kTScKAfkUgk0h5pRSnIvLR1x1sNw4NjgNtwmcMk5QwPCG0J2Bv3T8TMvgEWjEBLIRe/WAIoLOIoaxIgaX1gSWAUMDx5OZQ2PEhSLHzxmKShKXE1uZYQ10Oh7seSpocYnlEZ84acce0JjCwYKoSHnvNwicvdShz/rab+tWdTyxteeSWzjZlT04XqP/i82QRQE97tmi2o/17GcqGPbXZq+WcN6eUAXzWkX8fs+vTlRnMtezlRQ8bSyJochildM0wO6jNWw9XmOEd30n8nM5U+NTqzJvt3Olvpk5lz5mZ/9c9vpjLfFJuV/jtlTkY5wLwcx1RKO3zH29bKVRUZHoSR7W4klKQg0/CgwKbAR2aWaVqcYD/gJmtc/FzSJECu5vQX3D6wmHKGB4Vr6onbBN7WgriKr2UCsItcdWoF3LKqILNZzrzhNOCAENf9+ANNMcXSmEcDd7dEOSwSiURaE2uw3Ftr0dYdb6WGBxcCJ5o19RRTuuFBgSajypwUdzzlTAJ+AdxvZu82b6Ks4UGBnYAnEtPMeSi+livxh5jn8Hv0JDBf6eYN++FT8YNxZbD/qFEOErlH71qEKX1Jy+BSmX9vQZyRSCTSusR3vG54QHgHCTxLBYYH+PTpjWEKeiCwg1wKsgvB8CCcs2B4cG34uQ6308ttXixpbaDOzJ4v7AumBgVbvt7AHmb2paSNgU3DO9zeQNeQfPQXmhsejCo6VXHnnhVXs2sJdn7HJ455EteE3pxG84Y6YAlJjwYd6UPxkTZm9lRIRhsIfBya2Ru4IxhIAKwLrAy8Fu5/T0mvmVmzqXpJh+NT2/zrX/8i6jVHIpFWox1mNbd6x2tmFwEXAUjaEdcuvhFPqmqR4YGZrVD4LOlq4F4zu1Nu07dRmLadhRsePJeo+gPgJTOb1oLQm42Q5RnWn4ekpQUmAWa2f+KYg4HhZnZS6CQXk7SKmb1CwvAgHLsY3jkWj87TaHYt4bplZt9I2gaYH3SfX8STuwjvjO8tmDfg0/JbA1dLWh3oDnxSdP0nF34ws/twHezCOb8u1emGYy+lUfe6/b1wiUQinZd2+I63rZOrKjU8KImZpRkeQJlRZRgJ9sVHqLsCPywYFeAjvh2KqmxBC0wCzGy+yhsegL8nfiAkeiXjuiGca2B4B/sHM7si5VqWwI0NGvDp75+Qza+AyyQdj3eOBxfeZYdOejmaZ2hHIpFIu8bq29+IN5okRFqbTvEHN/f9KZnH1P833QRhzuinU8sB3n2yV2r55Dl9U8undM2+3W/YrNTyj+rTxfK/qM9IfwW+np+e+TynIT2reX5D9pKQMqseFtBF2dnA3WrTDQp613ZPLe9X1zPzHINqeqSWL6H0cyxr2eOlFeam/95Xqstedr/sSs2stZvQa730v73a9dfOPEfNOluklnf77tYVmxbM+NkPc3/n9L3sgWiSEIlEIpFIRcSp5kgkEolEWo/WXCaUl9jxRiKRSKTzEjveSCQSiURaD5vf/jreDm2SIGmXUG+8pOckbZIoO14lTBIS5SeUaG+YpKdCvUlqNFY4U9K7KjICkHSwpE/UaCBwWNi/vKTnw74pko5I1ClpkpAoL2X40E/SrZJekjQ1rBNG0l6h/Qa5ZnLh+KGSZiXi+meibJTcOGGKpH/KZR/LmiQk6pUyfHg8cY73Jd1Z6vcXiUQibUYU0GhGpSYJD+GShSZpGK49vJpSTBIAJC2Hr6F9p9BQWGN7LfATM5sgaXGgIBZxD25SUEpe8iYzO7po3wfA98xsjlxYY7Kku4PgRkmThBBDM8OHwN+AUWa2p6SuuPY0wGRcPONfJeJ63czWKbF/bzObIU8BvRVXn7qRMiYJiXrNDB/MbNPCZ0m3AXeVOF+nJEuHGaDh1ddTy796JTuB8r2Z6VnN73ZPb+NDsrVwP21Iz2r+fH56VvP0edkZsjPnp8cxtz5di7khRzJ8bYY+cdfa7K87ZbQxryY9u7o+h+n6/IxrsSok/lclNVcZcdRmZInX5eheuqZneFeF9reaqM0lIxeYJJjZWKCfXJqwCWY2zszeKrH/64Ruci+aLlUpmCTU0dwk4QJ8XXDy+B8CE81sQmj7s4IUpZmNbaGwx1wzK3zTdKPpfU4zIygYPhTUopDUF9gMuCLR9vTweaqZvZw3rlBnRvhYB3Sl8R6UjUuNhg/FeteF8j64QcOdLYklEolEFjVRq7k5FZkkAEjaTdJLwH0EMYo0kwRJOwPvFTrYBKsAJml0mAr+Tc4Q9ghTs7eGkXQhruUkTQzXd04Y7UIZkwSVMXwAVsQVpK4K08CXS0ofBjkrhOPHSNo0WSBpNN65f4WPeqGMSYLSDR8K7AY8lOjUI5FIpH3Q0IKtlWjrjrdSkwTM7A4zWw3YFZ8OLWuSIJdSPAX4fYmm6oBNgP3D/3eTtHXG6e/BPWuHAf8FrknE9W7YvzJwkKQlQ1E5k4QLKWH4EOJaD7jEzNYFvgFOyojrA2BIOP7/gOvDyLkQ27bA0vhovGAlWM4kIc3wgUTdlhpORCKRyCInjnhxk4RCQg4+nVmJScICzOwxYKWQLPUDgklCEPUvmCSshHfGE+TykIOBFyQthY+2x5jZp8Fv9n68w0s752eJKeXLKGG6EEa6U3DThEE0N0n4XvhcMHx4C/e9vVguWzkNmJaoc2uOuOaY2Wfh8/PA6/iIPnnMbOBu/AEF3CTh5lD2FK7VPBDYGNfTfgufRThQ0gIrw/AufAQ+41ASSYfLk9+eu/TSS8sdFolEIlXH5uffWotW73jN7CIzWyck/tyJf5FL0ka00CRB0sohSQh5RnRX4DN8inkjST1D+dbAVDObZGZLmNlQMxuKd2rrmdmHeELTsFCnDjcreLHEaZPnT76P3plgeCBpsKQe4XN/4PvAy7g282KSCp3gApMEM1shEdetwC/M7M4Q27uSVg11ts4R16BEtvKKePLaG5J6F2IO17gD8FKoVjBJQAmTBDPb38yGhLhOwN/JJ0fce+FmC2U1Ac3sUjMbbmbDozNRJBJpVdrhVHNbZzVXapKwB95xz8NdiPYJyVZZJgnNMLMvJP0Vtyo0fHr1vhDLucCPceu7acDlZnYacGx4Zzwf+JyQnQysDvxFbp4g4HwzmxTaSjNJKMcxwHUho/mNwn2StBvuhzsIuE/S+DCNvBnwR7lFYj1whJl9Hqa775bUDagFHqbxnXJZk4QM9iW8p45EIpH2Ro5E81YnmiREWpsO8Qc39+0XUsvr/3t9ZhuzRz2XWv76U/0y25hA79TyyXXp82OvNWQv9flwfnpO3Gdzv0otnzE3fbkRwKz5lZkgKMcCmbqa9OUt3eu6ZLbRq0u6QcFiXdLzGgfUZec9DqxNN1JYRulLbIY0ZF/HynPTe5sVu6f/TgGWWTX976LHiCVTy2s2GJF5jroNdkwt77L06hWvjPp0281zf+cMHD0mmiREIpFIJFIJ7XHEGzveSCQSiXRaYscbiUQikUgrYvWtMnvcImLHG4lEIpFOS3sc8XZ0k4T9Q72Jkp6UtHai7HiVMEmQdJ7cbGCipDsk9UvUOTmc52VJ2yb2Pxr2FQwBlgj7NwsqV/PV1NRgHTWaLUyUtE+irKRJQsa1bBfO/5qkkxL7T5MbFxTi2iHsX1xuePC1mpoa9JR0X7j+KUXrcUuaJGRcy3UhrsmSrpSUnfURiUQirYg1KPfWWrT1iLdSk4Q3gc3DUqDt8SVDGyrdJOFB4GQzmy/pHOBk4ES5McC+wHdxtav/SloloSS1v5kVp6m+gy8hOqFo/0zgQDN7VdIywPOSRgeN5XImCeWupRa4CF/zOw14Vm64UFjLe4GZnV90/tnA74A1w5bkfDN7JCxNekjS9mY2kvImCWnXch1wQGj3elwO8xI6AfWvpWck17/2ZmYbM15P/+f13vz07FaA97qlJ2R+WH75NADT69MNEAC+mp9+zMz56eeYPX9eajnAvCwThIzVFXm+EqX0o7LOAZC1yiNPG1lkZWjXZpTnebrtkhFn167pWeQAtb3T41Df9Ix7+g7IPEeXpVfPPKZSqjnilXQl8CPgYzNbM7H/GOBofGnpfWaWKjnc1pKRlZokPGlmX4Qfx+JKVAVKmiSY2QNmCzRKknV2AW4Mqk9v4muLU/PhzewtM5tI0dJrM3vFzF4Nn9/HdZEHFYopYUaQci0jgNfM7A0zm4s7CRXUpsrF9Y2Z/Q/vgJP7Z5rZI+HzXHydc+E85eIqey1mdn/43RnwDE3vfyQSibQ5Zsq95eBqYLvkDklb4t/Jw8zsu7jCXyq5O15JPdSonlQtKjZJSHAoMBLSTRKKOKRQJ0csV4Xp3N8p69E6gaQRuKJWwSOupElCuWvJEdfRYQr4SrlKVt64+gE74daKUMYkIeNaCvu7hGsZlff8kUgk0ho0zFfuLYsgTfx50e4jgbML8sFm9nGzikXk6ngl7QSMJ3yxhvd+d+epm9V0iX0tnssJTxyHAieGn0uaJBTVOQWfFrguRyz7m9lawKZh+0nOuJYG/gP81GzBhEc5k4SS15IR1yW4/vQ6+APGX3LGVYebGvw/M3sj7C5nkpB2LQUuBh4zs8fLnC9qNUcikTbBLP+W/K4KWx6N21VwLf6n5W5wG2RVyPuO9zR8yvNRvxAbL2lozrpNkHQU8LPw47NUaJIgaRhwObB9wRiAhElCOKZgknBt+PkgfJ5+64Qs4rRysYQRNGb2laTr8Xvx74y4+uLGAaeGaXRU2iRhVKJOqWtJi+ujRN3L8PfgebgUeNXMLkzsO5QwhWJmT8mT0QYCH5e6lsR5/4BPPf+83MnM7FIaJTs7hHJVJBLpHLQkaarouyovdUB/YCNgA+BmSSumSe7mnWqeb2ZftjCYklh1TRKG4M5DPzGzVxJFJU0SQp3t8NHkzuYuRAXuBvaV1E3SCnjC1zOS6hQyqcOU6o+AyRlxdQXuwN9f35IoKmuSkHItzwLfkbRCaHffEGuxScNuWXGFOn/C3+EeV1RU0iQh5VqQdBiwLbBfiVFwJBKJtDmtkNU8Dbg9pLs8g+f8DEyrkHfEO1nSj4FaSd/BM4afXNgoE1RqkvB7YHHcQg/8AWG4maWZJPwD96F9MNQZa2ZHmNmUkP38YqhzlJnVy03nR4dOtxb33b0sxLgB3in1B3aSdHp4ub43blSwuKSDw3kPDjMF5UwSyl3LfElH4+5JtcCVZjYl1DlX0jr4KPItEqNOuY1fX6Cr3F7wh8AM3I/4JdwOEeAfZnY5ZUwSJJW9Ftxg4W3gqdDW7Wb2x2a/5UgkEmkjWsGO4E7c1/zRMKjqCnyaViGXSYIaDeR/GHaNBv5kKVZwkUgZ2sVU89w3nkktr3/4xtTy2SPTTRQAXhubnuv2fE22oP6E2nRzgdfr04XsP5qXXg7wRYYJwldz05cbZRkgANRnmCBk/VHkGYvU1aaPI3rUdc1so0/XdIOCLJOEQXV9Ms+xZG16G8sp3ahhxfnpZhAA35mX/jtZcekvUssBBmyQsaxpw+KVik2pGbFtajlAt2GZx1S8uPaNtX6Y+ztnxUkPpJ5P0g3AFviI9iPgD3juy5V4rs1c4AQzezitnVwj3jAle0rYIpFIJBLpEDRUUTLSzPYrU3RAmf0lyZvV/KCaKjz1lzS6JSeKRCKRSKS1aTDl3lqLvO94BwalImCBafwSiyakSCQSiUSqQ05hjFYlb8fbIGmImb0DIGl52sm7ukgkEolEytGaGsx5ybuc6BTgf5L+I+k/wGO4xnFFhGVElZgkrCYX8J8j6YTE/lXVaBwwXtIMSceFsrVDnUmS7glrVJPnGiI3F0i2t184fqKkUYnlRRckzvGKpOmJOqMkTZd0b1H7jyfqvC/pzrC/v9y0YaKkZyStWVSvVm5gcG9i3zqSxoa2npMrSxUMF5LX3xCyn5F0pqR3JX1d1H7atZwjN0KYrKYmCQrtvSJpasg+j0QikXZDSwQ0Wou8yVWjQqe4EZ5ldryZpaZL56RSk4TP8aVNuxbF+zKeYYbcZOA9fNkPuEDFCWY2RtIhwK9xQ4ECF9Ao11hQefobbrjwqaRzcTHs08zs+MRxxwDrJto5D9eIbiIsYWabJurcBtwVfvwtMN7MdpO0Gm6MsHWi6i/xNb/JB4VzgdPNbKTcTehcYAszu46gyCVpLeCusPwH4B58SdWrRXGVvBZJOwLr4fezGzBG0kgzm4GbOywHrGZmDR3p9UP9m+PTy19/O7V8xpvZ/3Teq0/PkH2/S/a/9E8yFg7MqE8vn1k/J/MccxvSDQzqM5ZoWytMfimHSmtNxjG1NdnjjFqlZwx3VfrvvWtGfYDuGcf0sPQ4e+VYMd+rNv132r1vtrFFzYAMk4PF0/+51yw5NPMcrUGHG/GGDoDQ6Q7BFZPeA4aUG522kEpNEj42s2eBtL+irYHXzazwTboqPmIHdyrao3CgfL3rG8CURH2FrZf8X39fSqtr7YfLMBZiewgou05DUh987dedYdcaBN1kM3sJGCppyXDsYGBH/KEhSUljg4y4xuYQKUnWWQMYY2bzzewbYAKNIuFHAn8siGfk0SiNRCKR1qS+oSb31lpkPbb/H3A4pTWADe84KqGcAUBu9aoc7Eui48HVnXbGR5p7EeQY5UIZJ+JqUgummc1snqQjgUnAN/hI8ajkCcI77xWA1LVbRewGPBRGjuAd2u74lP4IYHlcHvIj4EJcQKR4keBxuLjH+fhD1PdKnGcfMtyMkpS4lgnAHyT9FR/Bb4mLjIDrRO8jaTfgE+DYgpNRJBKJtAdacwo5L6ldvJkdLhfKP9XMtizaKu10oUomCWUbd7nDnYGk1OEhwFGSnsc7ssJK89Nxb9vid59d8JHdurjhwkSav9/eF7jVGr1789BkJIq7FPWXNB53BhoHzJdU8H58vkQbR+LT/svh5gtXFMW+ITDTzDKlJBM0uZbg6nQ/rlR2A/AUruwFPvU828yG42peV5ZqUNEkIRKJtBEdcjlReHd3PrBxNU6oKpskZLA98ELSTCBM4/4wxLIKPoUL/m55z/AOtx+eyT0beDrUez3UuRk4qeg8+1I0Ck5D0uK40cJuibhmECQzw5T2m2HbF9g5vMPtDvSVdK2ZHQAchL/7BX+4KJ6KLh7t56HZtZjZmcCZIbbraXw/PA24LXy+A3dbakY0SYhEIm1Fe1xOlHdS+wFJeyhPhkMG1TRJyEHxqJJCAlBhJI/rDWNmm5rZUDMbik/tnmVm/8Dfaa8hdxaChLFBaGdVXKv5qRbEtRdwb1JyU1K/MEIH9+x9zMxmmNnJZjY4xLUv8HDodMEfUjYPn7cikTAVrm8vIF37MEGpawnZ1IuHz8OAYUDB2/hOGl83bA4kzR0ikUikzemwWc34u95eQL2kWfgUsZlZ3/RqmVRkkiBpKeA5PMGoQb5kaA0zmyHXl96G5nZ1+4VRN7gbUMlRWoFw/tOBxyTNw00BDk62B9xYbAEl6XFgNaC33Fz+UDMrqH3ti08tJ1kd+Lekevwd6qFpcQV+BvwtZF7Pxt/HF9gMmGaNfruFuM4Ffgz0DHFdbmanpVxLF+Dx8Mw1AzjAzApTzWcD18mNFb7GHxjanCwdZgDefCm1eN5rn6WWf/Jxv8xTvN8l/bn2I7Izjqc3pB8zqyFdk3d+RsYyZGctN1ThGynrmT3rib62JjtbuGtNRsZxRjlAt5ouFZX3yCgH6En6tfTOGKH1achOa+7bJz3bvdug1GIAapZcPLVcS6+QWt5lyVWzT9IKtGbSVF5ymSREIlVkkf/B5el4M00QRqebILz+VL/Mczyj3qnlE2qzO94sE4RP5qUbHMyY903mOb6en/4lPStDcH9ejs690u+ZPB1v99r0Tq9nl26ZbfTNMEEY0CX9d7pkXXo5wDJKX2Y2tCH9Or4zN7vjXaXP9NTypYbNTC0H6Pb976SW13zvB6nl3TfaJ7U8JxXPsj69zO65//g2fP/2VpmXzjviRdLuwCb4F+fjZnbnogoqEolEIpFq0B6Hlrk6XkkXAyvT+L70CEnbmFnuhKJIJBKJRFqb1sxWzkveEe/mwJqFd3+SrsHXtUYikUgk0m7pyFnNL+PKVQWWw9ezLhJClnMlGs4l6ytFwzmUHyPpZUlTQhISkrpKukqu1TxB0hZhf09J90l6KRx/dqKdI8Lx4yX9T9IaibKDJL0atoMS+0tqOIeyLcL+KZLGJPb/Uq6fPKXoOs4LcU2U6z/3C/tHJM4xQS58Uaizfoj5tXDvFPaX1HCWtGXRvZwtV/6KRCKRdkNDC7bWIu+Id3FgqqRC1soGwFhJdwOY2c5VjqtSDeeS9dM0nCVtiSs8DTOzOWrUHf4ZgJmtFfaNlLRBKDvfzB6RLwN6SNL2ZjYSuN7M/hna3Rn4K7CdpAHAH4Dh+KuH5yXdbWZflNNwDp3mxcB2ZvaOGpdDrRliG4GLgIySdF9QjnoQONnM5ks6Bxf8OBFX7Roe9i8NTJB0T8hSvgTPih6LZ5tvB4wsp+FsZo8k7uUAPDO9sMwoEolE2gX17XDEm7fj/f0ijaI5CzSc8Q6+n6Sli9f4mtk4KLlUIU/9Yg3nI4GzzWxOaLugO5zUUP44jPiGm9kzwCNh/1xJL+ACIAUxjAK9aHy/vy3woJl9HuJ+EO/gFqw1VqOGc2Fp1Y+B2wuWjIm4VgfGmtnMUG8MLshxblCbKjAW2DPUTaYydi/EFTrhvmb2VPj537jxxEiash/+4FDMnngnnZ0q2QrUv509GdPw5rup5V+9mZ5F+8H89MxUgA+7p6d1fG7p2cIAsxrSxeznZ4ilZS0VgsozjmuUPXGWZaSQZWCQZylQ97qu6eW12VnNvTKO6VWTfo5eOb5S+2QsJ1os41fWP1Wa3undLz1jvm7Z7OxrLTs4tbxm2dUy22gPNFSeGF118k4172BmY5Jbct8iiKuchnM16xerOq0CbCrpaUljEqPaCcAukuokrQCsT1O1rcKodCdCBx32HSXpddwxqGCXlyeuYg3nVXApyUclPS/pwLB/MrCZpMXla5Z3KI4rcAhN3ZY2lDQFf0d/RBjtLhtiKRuX0vWoF0YhKxKJRBY5hnJvrUXejnebEvu2r2YgRVSq4ZxaX6U1nOtw1aaNcKvAm8N7zivxjug5XNHqSRq1igu2gTcA/y8pVhEUulbCp3hPbcF1Fatt1eGd/Y74iPl3klYxs6nAOfi08ij8AaHJgkpJp4R91yXietrMvou/LjhZUveccZXUow6j5bWA0UQikUg7oz2+482yBTxS0iRg1ZCoU9jepMpZzWGEOF5uEvA+lWk4T8uo30zDOdS53Zxn8N/DwGCHd3yQudwF13FOOvBcCrxqZheWieVGGv2CU+NSo4bzfUVxjTKzb8w9kB8D1gYwsyvMbD0z2wz3Jk5KRh4E/AjYv1hVK9SdirstrRnOkZxXKnW/y41q9wbuMLOy81+KJgmRSKSN6Igj3uvxKdS7w/8L2/pmtn81A6myhvPdGfWbaTiT0B2Wmyd0BT6VZy/3Cvu3Aeab2Yvh5z/hPrjHJRuSlJR82ZHGDnE08ENJ/SX1x80akiPFZhrOeJLVpmGquyeeLDY1nKeQaDUEtxS8Ify8HT7S3jn53lXSCmGEXpg6XhV4K9ybryRtFEb5B4bzFuql6VGXupdNMLNLzWy4mQ0//PDD0w6NRCKRqjK/BVtrkZoJYGZfAl/i+sabAN8xs6skDZS0gpm9uYjiqkjDOaN+OQ3nK4ErJU3Gs4QPMjMLndtoSQ14FvRPQjuDgVOAl4AXQoLXP8zscuBoST8A5gFf4C5CmNnnks7AXZnATeQ/T8TQTMPZzKZKGoUv32rAtZULNn+3hVHyPOAoM/si7P8Hbtn3YIhrrJkdgSuPnSTXnG4AfhFG0eDJZVcDPfB3wsnEqnJ61EPxEfyieM8fiUQiFdOaI9m85NJqllRYArOqma0iaRngFjP7/qIOMNLpqFjBbe67E1LL6x/OzvOaM+rp1PI3n0j3/3imoU/mOSbUpWctv9XwdWo5wKfz07WWZ8xPTyT/ev6szHPMnp8e59z6yscCJVYeNKFLhhZztwwdZsjOWu5d1z2zjf51PVPLB9aml2fpMAMMtvRrWXlO+tvG7/RI1+cGWHZYusZ3901WymyjZpNSqT2JNjbeL7ONKlBxr3nPUvvl/s7Z6cMb2pVW8274+s0XYIFjT/Y3TyQSiUQibUh7XE6Ut+OdG6ZdC+s+0y08IpFIJBJpB7RHk4TM5UQh2eZeSf8C+kn6GfBf4LJFHVwkEolEIpXQHpcTZY54w0h3VzxLdgaeCft7M3twEccWiUQikUhF1GfkF7QFeQU0ngKmm9mvzeyEanW6YblPHjOEFYKi1KuSbgoCGIRlOXeEus/I9YszzRDCMScoYa6gdAOBR+XmCYXyBct4JD0iaVyIYYdEnXPkBgaTJe2T2F/WDCGUbyCpXtKe4efu4domyM0QTk8ce5qk9xLt7RD2d5F0jdz0YKqkkxN19gmxLjCCCPtLmiEkyvuGc/0jse9qSW8m6q1T7ncdiUQibUGHHPEGtgR+LultXHQBADMbVuH585ohnANcYGY3SvoncGg49rfAeDPbTdJqwEXA1mlmCGHfcviSoncS50gzEAAXoniuKK5TgZvN7BK5A9H9wFBJOwLrhRi6AWMkjTSzGeXMEBKxnkPTtb1zgK3M7GtJXYD/hbbGhvILzOz8orj2ArqZGzv0BF6UdAPwFXAevg77k9A5b21mD5UzQ0hwBqWXDf3azG4tsT8SiUTanIb2N+DN3fEuKnnITDOD8I55K9wsAOAa4DS8410D+DOAmb0kaaikJYsUqYrNEAAuwNf/Luj0yhkIZGBAYd3JYjSqPa0BjAmd9nxJE3AzhJsT11VshgBwDHAbLudYiMuAwrqTLmHLis2AXnKxjB74uuQZwErAK2b2STjuv8AeJDSmA03MECStDyyJS1MOzzj3Iqdh2ovp5e9OSy0HmPl2evlHc9KXhXycYYAA8A3pBgbzcxgYNGQc01ChwQFAbYbJQbe67KU8WdQpfblQ1nKiahgc9KnNXk60WE36Mf2Vfo4Blv2VOihjddYg0pd39RuY7UXSZYX05XBafmhmG7VD18k8piPQHrOac001m9nbpbYqnD+PacDi+DT3/BLHTMAVm5A0AlieptKHUCR1KLfpe8/Mmi0GVWkDgQJXhenU34WHAfAHgAMkTcNHu8ck4tperno1EJ8xKDYwaGKGIGnZsO+fJeKqlUtpfoy7GyUXoR4dpo6vlKthAdyKz0x8gI/qzw9CHa8Bq4UHlDpcyrLY8KGJGYKkGuAvuH51Kc4M579AyvhWikQikVbGWrC1Fnnf8S4q8ojzpx1zNu7cMx7v9MbR1MCgiRlCmHY9hTI2h2UMBMCnmdcCNg3bT8L+/YCrzWwwrpT1H0k15rZ89+OGCjfg78iLn3OLpRYvBE4sNiEIcdUHKc3BwAiFd9n4qH8lfEr7A7yDBNd7rgeWwTvRX0laMShbHQncBDwOvFUirmIzhF8A95tZKR+9k4HVwv0agCfgRSKRSLuhQfm31qLVO1613AzhU3wZU13xMeGd6U9Dp3QgMAhIylgWmyGshHdEEyS9Fdp6QdJSyRMWGQhgZu+F/3+F61ePCIceSpg+Nvey7Q4MDD+fGbSnt8EfHpIGBqXMEIYDN4a49gQulmeTJ+OaDjyKT1tjZh+FTrkBX95ViOvHuLHCPHP/3idC+5jZPWa2oZltDLxMU8MHaG6GsDE+qn4LOB/XwD47tPWBOXOAqxLnb4KiSUIkEmkj6luwtRat3vG21AwhvON8hGDmjuse3wXugxtGtQCHAY9ZUxP6JqNKM5tkZkuY2VAzG4pPW69nZh+qjIGA3JygkPncBXf8KWglv4O/Q0bS6njH+0mYGl487B8GDAOS5vTNzBDMbIVEXLfiOsp3Shok9/tFUg/gB7g+dMGSr8BuRXFtFe5rL9zqsFCnkJHdHx/NXl5oQCXMEMxsfzMbEuI6AX8nf1Ly/GHqfdfE+Ztg0SQhEom0Ee1xxJs3uWpRkcsMAZ/CvFHuBjQOuCIctjrwb0n1wIv4CLRQv5wZQjlKGgiEjmt06HRraSoe8ivgMknH49PfB4d1z12Ax8Or4BnAAUXvi5uZIaSwNHBNyHiuwbOo7w1l54YlPIZPGxeu9SJ8BDoZH21fZWYTQ9nfJK0dPv/RzF5JnKukGUIK10kaFM4xHjgiZ71IJBJpFVpzmVBecpkkRCJVpOI/uFm3/im1fN6Dj2e28d7D6Vm0z84ckFo+uUv2xNS7lm5Q8HF9dnbql/XpbczIMEGY11DWJnkB9Tmyq9PIyooGqKtJf8bvmlHeM0dWc++a9GP6ZWQsA/TPaGNJuqaWL1effS+Gzk1Pa15xsXSDg6XWzTa+6Pq9VVPLazbdIbUcoPt6O2ce0wpUPA791+ADcn/n/Hzata0y7m3r5KpIJBKJRBYZpvxbFmH1yMdy+9jisiaiTGnEjjcSiUQinZY04/viLQdXE5Jbk6i0KFNZYscbiUQikU5LNdfxmtljwOcligqiTLmmtds6uSoSiUQikUXGos5WTooyKachQ5uOeMNyl0pMEhaTdI8aDQR+GvaXNUmQtFc4tkHS8MQ59i+q0xAyhssaCyTq7hnm9pPtnRuOnxquUWF/SZMESatJekrSHEknJNpJu5a1Q51J4T70LYpriKSvi9rbLxw/UdKoxFKpsiYJ4bjpku4tan8rSS/IjSCuUeNa60gkEmkXtMQkQQnNgbClrn9UhihTOdr6i7JSk4SjgBfNbKewrOVlSddlmCRMxmUm/5U8gZldB1wX6qwF3GVm4+XrcUsaC4Rj+wDHAgtkHCV9D/g+vn4X4H/A5sCjKSYJn4d2di2KK+1aLgdOMLMxkg7BZR1/l6h+ATAycb464G/AGmGp1LnA0cBpGSYJ5wE9SSzNkktJXoObUrwi6Y/4GusrqJA5k9PNr+y9dC3mue/MTi0H+PSrQanlX2bIE8/JMaM0P+OYhhxtZD1Bd69NDzRPxrFlxKGMxNI858jKWu5Rk54t3CujHKBvxjFZOssAA0m/n8tkZC0vMy87Q3zpbulZyYsv/01qeZdVl04tB9Aqa6SWt5OM5VahJTn7ZnYp0BKVn6QoEzSKMo0wsw/LVWrrd7wLTBKC204/NRWFSJokFBxwrqGxczKgTzimN955Fb8jb2KSYGZTQ2eWRlJ4Y0VKGwsUOAM4F0h+2xsuptEVdyfqAiSNG5ImCXeGuD42s2eBtPUfxYYPqwKPhc8PJuOSq169AUxJnjZsvcI960tzpTBoLjzyEO5slGRxYE5iHXCT80cikUh7YFFqNaeJMqXVa+uOt1KThH/gIhrv48YGvwzyiUmKJRDzsE+iTlljAUnrAsslBC2ABfKRj+D6yR8Ao4MMZZImJgk5Kb6WybgWNbgaViGuXrjoyOnJymY2D9dqnoTfszUoGqGqyCQhhU+BLonp9T1pbgQRiUQibcp85d+ykNurPgWsKmmapEOz6pSirTveSk0StsUVk5bBp2P/kXzPqSKThFwBSRsCM81sMkA5Y4Ew1XoBrl5V3MbK+APBYPwhYStJmxUdVmySkBVXqWs5BDhK0vNAH1jgJ3Y6PjX/dVEbXcK1rIvfs4m40UGSYpOEkgR1q32BCyQ9g4+Ic2bkRyKRSOtQ5azm/cxsaTPrYmaDzeyKovKhZvZpVjsd2iQBl5i8PUxVv4YbJKyWqFtskpCHZiPkMsYCfXAThUflBgIbAXeHEeBuwFgz+zp0fiNDeeEelDJJyKLZtZjZS2b2QzNbP8T8eijaEJeTfAs4DvitpKMJ74rN7PXQcd4MfC/r+sthZk+Z2aZmNgKf8i42XACiSUIkEmk7GrDcW2vRoU0SaGpSsCT+zvONRPWWjipr8CnbG4v2NzMWMLMvzWxgYm5/LLCzmT0X4tpcbrDQBU+sSk41NzNJyEGza0nEVQOcSvDyDZ1hIa4LgbPM7B94YtYaIRENfMH31ER7zUwS0kicvxs+td3MSzjEE00SIpFIm9CSrObWoq2nmu/HO8rXcOOBXxQKJN0vaZnw44nA/0l6DX/nWxjenwF8T9Ik4CHcz/bTUL9gknB78oSSdpMb128M3CdpdKJ4M2CamSU7b3BjgRdxe72zi4wFSnErPvqcBEwAJpjZPYnyZqNKSUuFuP4PODW8P+ibdi3AfpJewZ2H3seNEcoSDCdOBx6TNBEfAZ+VbI8SJgmSHsenuLcOcW0bin4taSo+ZX2PmWW9F45EIpFWZVEmVy0s0SQh0tpk/sHN+m/JgfMC6h98ILX8swez89XGv79EavnL3dKfSd9RtvnAhxkmCTMa5qaWA8zKMDmYl/4qnvoqPMdnLSfqlmP5dres5URKX8bTJ6McoF/GMQNyrJ5cqsLlQsvVZhsYLLv89NTyPiN6p5bXbVhS7qAJtRvvlFredejw1PJ2RMXyF6ctv3/uTu60t69rFZOEtl7HG4lEIpHIImO+2t/gMna8kUgkEum0tL9uN3a8kUgkEunEtGbSVF5ixxuJRCKRTktrLhPKS0c3Sfi1GoX9J0uqlzQglPWTdKukl+RGBRuH/SWNBSRtI+n5sP95SVslzj9KjUYM/5RrJiOpW4jntRDf0LB/SzU1NpgdJBwL13ym3IhgqqRjw/7+ku4I9+EZSWuG/ctJeiQcO0XSLxNxnRGOHy/pgUQWOJJODnG9XMhCltSnKK5PJV2YqLO3pBfDea5P7D8n3N/JkvZJ7L8i3JeJ4V6nZ4VEIpFIK9Mes5rbesRbkUmCmZ2HC/gjaSfgeDMreCX+DRhlZnuGjrpn2F/OWOBTYCczez90eqNplKbc28xmSBK+VKiw1vdQ4AszW1nSviHOfczsERqNDQbgy6UKqbgH46Ihq5lZg8JaWOC3wHgz203SasBF+Brl+cCvzOwFub7z85IeNLMXgfPM7HfhPMfiDhlHSFoDX7L0XVyh6r+SVjGzrwpxhTrPE5YoSfoOrmL1fTP7Qo1rdHcE1gv1ugFjJI0MUpfHFyQvJf0VN1w4u8TvbwHzPihWzizBR++lFtd/+GVq+YwveqaWA8zOMB9IzxXO9xTdldrU8l45MnVra7LirNyIIYsuGc/nXZV+nQDdMzKf+2SU983xVdXf0uNYoj47YXWp+ekTk8tkZC0vtUx2Rn3vtdLNHGrXWDm1XGuU+opsSgfKWl7kZJmVtAVtvY63UpOEJAsEJsIodjPCel8zm2tm08NxJY0FzGxcWOcKbizQXS4MQUJPuQ43Pij8JncJ8RDi2zrEm2RPYKSZzQw/Hwn8saApbWYfh/1r4GuRMbOXgKGSljSzD8zshbD/K1zwYtmiuAB6FcV1o5nNMbM38Y5/RDKo0NEugctgAvwMuChIZBbHNcbM5pvZN/i65O2S5w/X3IP2mccQiUS+xbTHEW9bd7yVmiQACwQmtgNuC7tWBD4BrpI0TtLlcuMAKGMsUMQewDgzm5M4x2jgY1yTuPAQsCD+EN+XId4kxWIZKwH7yCUUR4YOELxD2z2cawSwPC6PmbzOobjOctKC8ExJ7wL70+gJmee+7gfclBDLWAVYRdITksZK2i4R1/aSesq9e7ckcc8kXQV8iEt1/p1IJBJpR0TlquZUapJQYCfgicQ0cx0+PXqJma0LfAOcFMrKGQv4yaTv4lPGP0/uN7NtgaXx6dbC+9/U2MLofS182rpAN2C2mQ3H1bquDPvPBvrLNayPAcaRMB0I709vA45LjnTN7BQzWw73Ej46T1yB4geCOnzKfwu8U75cUj8zewBXGHsyHP9UMi4z+yk+nT0Vd3VqhhJazZdfe3OpQyKRSGSRYC34r7Xo6CYJBYo7kWm49GNhZHgr3hGnGQsgaTBuMn+gmb1OEUFb+W58KrdwnoIVXx2wGO4JXGBv4A5zO75kbIWR+R3AsND2DDP7adCwPhAYhJs+FFyFbgOuM7Ni2cgC19Poh7sgrkCTeyZpbaDOzJ4viusuM5sXpqdfxjtizOzMoK+9Dd6pNzFDMHcyuokyfrxJrebDDti7TPiRSCRSfeKIl6qbJCBpMdyE4K5EnQ+Bd+Wi/+BJSi+G40saC0jqh7sFnWxmTyTa71147xw61x1wbWTwTvig8HlP4OEineNSJg130jhi3hx4pXD+kAQGcBjwWCKh6wpgqpn9NdlQYpoafPo8Gde+8qzrFfAO9JkccW0Z2h2ITz2/IalW7qaEpGH4g8ID4Xe2ctgvfNbhJSKRSKQd0R7dido6q/l+vCN7DZiJ2/wBbpIAHBYSnk4EbpT0J3wKNumBuBvwQEj8SXIMcF3ozN5ItL2fpKPC59tpNBY4GlgZ+J2k34V9P8RHeHeHRKta3CC+ICZ8BfAfuXnD5/jIuxD/UHzUOaYorrNDXMcDX+OdLLh/778l1eMPCQWD5e8DPwEmhVkCgN+a2f3A2eHhogF4GzgCwMymSLo5tDMfOMqa+uvujd/3JKOBH8rNIOqBX5vZZ5K6A4+HnLEZwAFmVvAjviYksgl/F3wkkUgk0o7IyvxvC6JJQqRVmTNxdOYfXP3D5WbTnZmj0wfWr00ozm8rcQzpS47ezVjp84nmpx8AfJOxKGl2jsmtuRkmCNVYKlGXoUNflzEx1j3HcqKeGUur+mSco39D9uTcoIw1YEvMz/6dLdktfbnQoKW/Si3vu2b2vahb5zup5TUbbJ5a3n2jkqkUnZWKTQt+NnSv3P9ILnvrlmiSEIlEIpFIJbRm0lReYscbiUQikU5L1GqORCKRSKQVaY8j3g6t1RzKtgjLk6ZIGpPYX1KrOZQdI9cwniLp3MT+YXId5ylyzebuYX9XSZfK9ZVfkrRHok45feMhcv3kqaF8aOKam2k1d9Br2VrSCyHm/xWynCORSKS90B6XE7X1iLcirWb5EqCLge3M7B016h5DGa1mSVvi63CHmdkcNS4vqgOuBX5iZhPCEprC+ttTgI/NbJWQzVswYiipbxz4N3CmmT0oF78o/F4PpoRWcwe9lkuAXcxsqqRf4MuzDm7+64tEIpG2ob4dJhC3dce7QKsZGBtGdksn1/JKC7Safxx2XQOchn/p/xi43czegUZ9YTVqNR8c9s+lUaHqSODsghxkQpP4h8BEM5sQ9n+WiPMQXBKRoLH8adhfUt9YblJQZ2YPhv1fJ9o6EvhxCa3mjngtBvQNnxejubBJMxo+ezfrEOyzL1LLZ3+enjlanyMDtrYm/R9jd0tPbuyXkaUL0CXDiGFujimweRnmAVlrD2tyJIXWZhzTLaO8p2Xf794Z93OxjOHG4hnmBQCDmorQNWNg35mp5QD9l04/pueq6QYHdcOyJ31q1tk4tfxblrW8yIm2gM2pVKt5FVxm8VG5ld+BYX+aVvMqwKZh6nqMpA0S+03S6DB9+htYMBIFOCPsv0XSkok6pfSNVwGmS7o9nP88acGai3JazR3xWg4D7pc0DV9rnOpMFIlEIq1NlIxsTqVazXXA+sCOwLa4+MUqpGs11wH9gY1wS8Cbw6i6DtgENxvYBNhN0tZh/2BcC3o9XKv4/ERbzfSNw/5NgROADfDO8+BQp5xWc0e8luOBHcxsMC5E0kRZKxKJRNqa9viOt6NrNU/D331+Y2af4nZ/a5Oi1RzKbjfnGfx+Dwz7x5jZp+YWfveHOp/hqlp3hPq3FLVVSt94Gu5u9EYYqd9ZVKeZVnNHuxZJg4C1E3HdBHyPEihhknDFvY+VOiQSiUQWCe1RMrKjazXfhU+11smtATfENY3LajWT0EoOI8queOc+Ghgmt7+rw3WUXwznvwcfCZZqq5m+MfAsPm08KBy3Vanzk9Bq7oDX8gWwWDgvwDa4Q1EzkiYJh/5os1KHRCKRyCKhHsu9tRZtnVxVkVZzyKYdBUzER3uXm9nk0EQ5reYrgSslTcaTlA4KHdIXkv6KdzQG3G9m94U6J+KazBfi71sLbZXUNw7xnwA8FKZ+n8enlaGMVnNHu5ag1/wz4DZJDXhHfAiRSCTSjmiPsshRqznSqsy6/azMP7j5/308tfzzsfNSyz/4sG9qOcBHDd1Ty7+sTZ8M+jrHXNGsjITiuTlUYSvVYs7SYQboknGK7hnlvXK8HFusPv2gfpauo9y/25zMc/QfkJ6R3GfZ9KxngK4r9Uktr119hdTymjVHZJ6j+6Y/yTwmsoCKtZN3GfKj3P+I7nrn3qjVHIlEIpFIJUTJyEgkEolEWpH2KBkZO95IJBKJdFrao4BG7HgjkUgk0mlpj5KRHdokQW4q8GVhXbCk3yfqlDUWCOUnSLKwdAZJi0t6RNLXkv5RdOz6cqOB10K8Cvv/T24aMFHSQ5KWT9SpT8R1d9E1lzRJCOUbhLp7JvZdKenjkL2cPHaApAfDfXlQUv+wfxu5+tWk8P+tEnUelZsqFGJbIlFW0iQhlPWV9F7y3ki6QtKEcP23ynWcI5FIpN0QlauakzRJOBzXXy5FwSThO/iylUMTZY8X1gWb2R8T+wvGAqvhQhQL1phKWg5fd/pO4vjZwO9whaZiLgnxFWItyCmOA4ab2TBc2OLcRJ1Zibh2Tuw/mEaThNWBGxNx1YZrHV10/qsT50xyEvBQuC8P0aho9Smwk5mtha97/k9Rvf0TsRU0mZMmCd8FjiuqcwYwpmjf8Wa2drj+d4CjS8QYiUQibUZ7FNBo66nmSk0SSqJ0YwGAC4Df0CjEgZl9AzSztpO0NNDXzJ4KP/8b2BUYaWaPJA4dCxyQ45rLmSSAr9e9DZdmXICZPaZgxVfELjSKYVwDPAqcaGbjEsdMAbpL6lYwUyhDSZME8BE/sCQwChieiGtGKBfQg+Zyn8354vPMQxq+SV/2Udct/TS9u2cvG6mfmf7M2W1++j+N3sp+Zp1Tk74yYV6GiQJAQ8ZqiqwoanN8l3TLmIrr2VCfWt5b6eUAfbqm/0769p2dfo5B6eUA3Qen/87qhiyRWg5Qs3L6ciF9d4PU8u4j9so8R6R1aY9LZtt6xFupSQLAxmG6c6Sk74Z9ZY0FJO0MvFdw7skZ47SMGMFH4SMTP3eXyySOlbRrYn9JkwRJywK7Af/MGRfAkoWHlPD/Ut8se+CSj8lO96owzfy7wrQ5ZUwS5NaBf8G1oJsh6SrgQ9zx6O8tiD0SiUQWOe1xxNvWHW+lJgkvAMub2dr4l/6dYX9JYwG5FOMpwO/JT2aMkg7AR4LnJXYPCUYIPwYulLRS2F/OJOFCfLSaPXzIG7g/iJwD/Dyxe/8wBb1p2Aqr+cuZJPwCV74q6ednZj8FlsGn8qOfWSQSaVdU8x1vqXwbuWPbSyHX5Q41usCVpUObJJjZjII/rJndD3QJyVLljAVWAlYAJkh6K7T1gqSlUkKeFo4rGaOkH+Cd+c7JUWWQusTM3sCngNdNtFfKJGE4Lov5Fq5LfXHRSLkUH4Wp8MKUeHJ6eHBo/0Azez0R13vh/18B1wMFqZ1yJgkbA0eHuM7HtbWb2P+Fh4Wb8NF1M5Q0SRgzMeOSIpFIpHrUm+XecnA1zfNtHgTWDLkur+C5Mql0aJMESUsVpkoljcCv57NyxgJmNsnMljCzoWY2FO9s1gvHl4v3A+ArSRuFcx2YOP+6wL/wTjfZ6fWX1C18Hgh8nwyTBDNbIRHXrcAvzOzOjNt5d7gfxfelH3AfcLKZPZGIq06NWdxdgB8BhSe3OylhkmBm+5vZkBDXCfg7+ZPC72zlcLyAnYCXytzDRpOEzYeVOiQSiUQWCdWcajazx4DPi/Y9kHgVOpamA7WStHVyVUUmCXhnfKSk+cAsYF9rfJNezligLGFU1xfoGkabPzSzF/GEqKvxBKKRNL7LPQ/oDdwS+v93Qgbz6sC/5OYBNcDZoR0oY5KQEdcN+BTwQLnp/B/M7IrQ1s2SDsWziguZHUcDK+Oevr8L+36IT7mPDp1uLfBfGs0bypoklAsLuCYksgmYEO5TJBKJtBtaWUDjEHz2L5VokhBpVWb+7YjMP7j6qW+lls95e1Zq+axPs58nv/myW2r57Dnpbcyel32OeQ3pE0r1Vrkee0biNHXKVqrtXpduUNCje7opRY9e6eUAPfqnZzV3Wyr9XtUtnW5eAFCzTHrWslZcMbuN1TdOLe+2+paZbUSqSsX/SDZaZovcndzTH4z5Ob50tMClZnZpk4B8hcm9ZrZm0f5T8FeGu1tGx9rWI95IJBKJRBYZLRnxhk720swDi5B0EP7qbuusThdixxuJRCKRTkyDLVp/orD08kRgczNL96YMxI43EolEIp2War7jLZVvg2cxdwMeDLk+Y83siLR2YscbiUQikU5LNfOYzGy/EruvKLEvlVZZThSWniy0GUKivJSBQEkzBEl7ycX+GyQNTxw/VNIsNZoE/DNRNkqugjVF0j/l2slIGiI3UBgX4t+hKK5SBgJSCTMESatJekrSHEknFLXzltzYYLyk5xL7zwjnHS/pAUnLhP1dJF0T6kyV1Gz9mKS71dxcoaQZQrj+6ZLuLTr+aklvJu7ZOomyLcK+KZKKtZwjkUikTWmPylWtNeJNmiFsiOssb1jiuIIZwo2hQzw0HJtmIFAwQ9gzdNQ9w/7JwO74OttiXg/riIvZ28xmhHWpt+LLc24ETgVuNrNLJK2BL4MamqhXykDgYBrNEBrU6AL0OXAsrvdcii3N7NOifeeZ2e8AQgf+e+CIEF83M1tLrsr1oqQbzOytcOzu+JKlBaipGcIXibjAl0f1pKnSVYFfm9mtRW31Ay4GtjOzd4raikQikTanNV2H8tJaHW81zBCaGQgoxQzBzKaGY3IHWRD9x+9LVxqlIQ1f3wuwGE2Vq0oaCFDGDCH8/2NJOy5EXAC9iuLqJVf16oFfe8G4oDfwf3hq/M2J+mXNEMzsIUlb5I0L/13dbmbvFLdVlm7py3gAagb1TS3vWp+eLFHTPc0LIrTRO30JzbyZtanl8+eklwPUz0+fUMqT85HlxVBTm95Il+7ZCqRdemS0sVj6F1ftgC6Z56gdOCC1vGbJxVPLtfTSmefQ0NXSY1g1fakQQJeB2UuOIh2Lhna4ZLa1lKsqMkNQeQOBsmYIGawQjh8jadNkgaTRuPTiV/ioF/wB4IDwMv1+/CEgy0CgpBlCBgY8IPfQTa4lI0xbvwvsT6PW9K24KMYHuIDG+WZWUFU5I8RWnGVX0gwhB2eG6e4LFFS5Qlv95R6/z0s6MGdbkUgk0irUW0PurbVorY63UjOECyltIFDSDCEjlg9wA4N18RHh9WHk7Cc02xZYGs9SK0g77gdcbWaDcaWt/4RON81AoJwZQhrfN7P18Kn5oyRtlojrFDNbDriORt/bEbjK1DK4BvWvJK0Y3sGubGZ3lDhHOTOENE7G3Yc2AAbgqfOFttYHdgS2xZWyVslxnZFIJNIqVNMkoVosso5XVTRDoLyBQDkzhLKY2ZyCFKKZPQ+8jo/cksfMxnWQdwm7DiVM15r78nYHBpJuIFDODCEttoKxwsehzogSh11PoxnBj/H32/NCnSfwe7UxsH6I63/4CPfRRFylzBDS4vrAnDnAVTQ1VhhlZt+E99KPAWsX11fCJOHKxycXF0cikcgio8Es99ZaLLKOt5pmCOUMBMqZIaTFJWmQGrOVV8Q7nTck9Vaj008dPrItiP6/E9pG0up4x/tJOQOBUOdOSpghpMTVS1KfwmdcW3ly+DnZMe5cFNdW4b72AjYCXjKzS8xsmRDXJsArZrZFIq5mZggZsRXui/CksELveRewqdx8oSeeMDe1uH7SJOGQTdcsLo5EIpFFRnsc8bZWclWlZghplDRDkLQb7tE7CLhP0vgwjbwZ8Ee5sUI9cISZfS5pSeDu8P6yFniYxnfKvwIukxsbGHBwDlmwkmYIcgvC5/BkrQZJxwFr4CPoO0IyWB1wvZmNKrQVHi4agLfxjGaAi/AR6GR8qv4qM8vy3StrhiDpcXxKuXd4n32omY0O1zEonGN84fxmNlXSKGBiiO1yM4tD2kgk0m5oj8lV0SQh0qrMuuPszD84++SjjPI00ySw6V9lxmFfpRstNMxKF/63udmJGDY//VKV5XAAmXNS6p6eXV3TIzvjWH16pJf3Szco0OLpGcsAGjgovXzQcqnlNUO+m3mOrsunvmWKdEwqNklYYfG1c3dyb342oXLnkhxE5apIJBKJdFpa2RYwF7HjjUQikUinpT3O6saONxKJRCKdljjijUQikUikFWmPI97WEtAoSVgGs9DmCZJ2UaN5wHOSNknUKWc4sE5QbCrUGRH2d5V0VagzQUE6UVIfNZoDjJf0qaQLi+LbU5IpmDFIWj4oORXMA45IHFvScEDSYpLuUaNJQzLz+5eSJof9xyX235Ro562wZjoZ1xBJXythxqDyRhAHS/ok0d5hiTrnhPNPlrRPYv/R4XdnYWlSJBKJtCva4zreth7xVmqe8BBwt5mZpGG4yEVSsLWU4cC5wOlmNlLuMnQuruL0M4BgOLAEMFLSBmb2FbBOobKk54HbEz/3wU0PnqaRD4DvmdkcuWbyZEl3FwQyKGE4ABwFvGhmO4WlOy9Lug5fZ/szXLRiLjBK0n1m9qqZJTvBvwBfFrV5ATCyaF85IwiAm8zs6OTBck3p9cI96AaMkTQy6Ec/AdwLPEpOtEKOdbx9+qW3MaD4V1rEVzPSywGbme5XXTsnQ++5PlsDOZMsIWaALhn/RHtkZCT37JlaDkDf/unl/dKfqWoGFKu/ljhm0JDU8i5LrppaHoksLA2tKAWZlzYd8ZIwTzCzsbhyVRM19NBBbEWjbvI1BGcfM/s6sZ42aR6QRjnDgzXwjrygHDWdpqYHBSGLJYDHE7vPwDvv2QtOYDY3qDyBd1Z57rMBfcL19sZdjOYDq+PGyjODjvUYXLc6GZeAvYEbEvt2xdc1T2lykvJGEOVYAxhjZvPN7BtgArBdaGtcwQkpEolE2iPt0RawrTveiswTwIUyJL0E3AcckqhXznDgOOA8ueHA+bgOMXiHsotchWkFXIO4eHHhfvio0MK51wWWM7N7i45D0nKSJobrOycx2oXShgP/wDvZ94FJwC+Ds9FkYDNJi8vVoXYoEdemwEdm9mo4dy9cjOT04rhCeSkjCIA9Qly3SiqcYwKwvaSeYTp5yxLnj0QikXaJmeXeWou27ngrNU/AzO4ws9XwUfAZiWPKGQ4cCRwfDAeOp1Ed60q8U38ON2V4Eh9xJtmXMKqUmyRcgKtaNQ/Q7F0zGwasDBwkV8aC8oYD2+KqUMvg07r/kNTX3N7wHOBB3HpwQom49iMx2sU73AvM7GtKYKWNIO4BhoaY/4vPLGBmD+DKY0+GczxV4vyRSCTSLmmP73hbveNVdc0TFmBmjwErFZJ8UgwHDqLxHe0thf1hKvX4oC+9C9APeDUR99pAnbmxAkAfYE3gUbkZwUa45GST6ekQxxR8VJpmOPBT3NvWzOw14E3C+2ozu8LM1jOzzfAp6GRcdcDuwE2J024InBviOg74raQm726tyAjCzD5LTI9fho/4C8eeGe7LNviD0Ku0ACVMEq64bVR2hUgkEqkSccRLdc0TJK0c3m8iz4juCnymFMMBvNPePHzeitCJhKnUXuHzNsB8M0saLjQZVZrZl2Y20BrNG8YCO5vZc5IGS+oR2uoPfB93AUozHEgaMSwJrEowLwjJXkgagneyydHtD3BjhGmJ2DZNxHUhcJaZ/UMpRhBF79Z3JpgdSKqVtHj4PAx3WXqAFmAJk4RD98hr/xuJRCKV0x7f8bZ1VnOl5gl74B33PGAWsE/IcF6S8oYDPwP+Fjqe2UDh/e8SwGhJDcB7wE+KYt07xJqH1YG/SDJ8hHi+mU0KZSUNB/Bp8qslTQplJ1pjRvZtofObBxxlZl8kzrVg+jsHvShvBHGspJ3xaeTPgYPD/i7A4+FezgAOKLxvl3Qs8BtgKWCipPvNbMEypEgkEmlr6hvaX1ZzNEmItDaZf3Bzp01KLbcvP04vnzk9O4q5s1OLbV7GcqI8SxQylgupNtvAgG7d09vo2S+jfLHMU3RdsZTtcyTSLqjYtKBXz6G5O7lvZr4VTRIikUgkEqmE9mgLGDveSCQSiXRa2uOsbux4I5FIJNJpsWiSEIlEIpFI69HQDpOrYscbiUQikU5L+xvv0rLFxXGLW7U34PDYRvuJob200R5iaC9ttIcY2st1dJatrSUjI5HDsw/51rTRHmJoL220hxjaSxvtIYZqtFGNGDoFseONRCKRSKQViR1vJBKJRCKtSOx4I23NpbGNdhVDe2mjPcTQXtpoDzFUo41qxNApiJKRkUgkEom0InHEG4lEIpFIKxI73kgkEolEWpHY8UYikUgk0orEjjfSqgQf5MLn3pKGSxrQljFFIpFIaxI73kirIelg4CNJr0jaHpgInANMkLRfzjaOljQwfF5Z0mOSpkt6WtJaFca3TWvGIKmvpJVK7B/WmnGUaTtXBuqijKGFcdwu6QBJvSs9ZySyyGlr6ay4fXs2YBIwEFgBmAGsFPYvCUzM2caUxOf7gN3C5y2AJyqM753WigHYG3gfGA9MATZIlL3QGnEAA8psiwPTWvFeVCOO94Bbgc+Bm4HdgK4V/j2sAOwOrNaCOsMSn7sApwJ3A2cBPXPUrwV+DpwBfL+o7NScMdwOHAD0ruDaewK/AX4NdAcODtdxbt52gRWBK4E/Ab2By4DJwC3A0Ep+Nx19iyPeSGtSb2afmtmbwNdm9jqAmX3UgjaSxh5LmNkdoY1HgT5ZlSXdXWa7B/+iX+QxBH4LrG9m6wA/Bf4jafdCmK0UxyfAc8Dzie25sC3RSjFUK46PzWxPYHngHuBnwHuSrpL0wzwNSLoz8XkX4GFgJ+CuMFuTh6sTn88GVgb+AvQA/pmj/r+AzYHPgP8n6a+Jst1LV2nGhsCuwDuSbpa0m6SuOesWuBp/IF4Bf6AaDpyP/21e0oI2ngW+BsYCLwHbA6PwDvlbS1zHG2k1JN2Nj+76AGsA4/Cn8x8A3zOzbXO0cSawLPBHYF9gZmhja2APM/tRRv0v8NHA18VFwE1mtuSijiG0McnM1kr8vDRwL3ANcLCZrbeo45D0KrC1mb1TouxdM1tuUcdQxTheKL5nIXdgb2BvM9sqRxvjzGzd8PlJYH8zezNMpT9kZmu3sI3x+EzGPEkCJphZ6msESRMLx4R8iIvxWaL9gLGFtvPEIKkP3gHvB2yA/33dYGYP5GhjvJmtE+L+AFjazCzvdSTjCJ/fMbMhpcq+lbT1kDtu354N6AucDJyETz3tgX8ZXIT/w87bzsHA08CnwFfAi/hU3mI56o4EtixT9lhrxBDqP0mYak/s6wM8BMxppXtxFLB2mbJjWvFeVBxHS353KW28kPj8TFHZuJxtvIFPc+8BTC0qm5Cj/ksl9v0eeAJ4taXXkdg3ADgCeDhnG+MTn69s6XWE454HVsE7/U+B4WH/yuR8tdRZtzjijUTaAElrAzPN7NWi/V3wEdp1bRPZtxdJ9cA3+OxHN2CImX0Ypmmfs3yjvKuKdp1kZh9JWgq4zsy2zqh/LXCtmY0q2n8YcImZdckRw2NmtlnWcRltXA4cZ2ZfF+1fCbjGzDbJ0cbW+Ii9AZ/6Px5YG38A/5mZ3VVJjB2Z2PFG2gWSDjezvBmsiwHb4VOchicpjTaz6Qt57vXM7IWFqVuirW3M7MGFrDvQzD5tYZ2+wCAL78sT+4eZ2cQc9ZcCCB3MIGBT4GUzm9KCGCpuo0SbZ5nZbxe2frXaCO30A1Y3s6cqbaszIEm2kB1HmLb/wszqqxxWhyJ2vJF2gaSfm9m/chx3IPAH4AE8kxVgMLANcLqZ/TujfvG7UwF34Uk0qrQDLn6XlXLc9vho4D3gGOBaPHu0G3CQmT2Uo429gQuBj/EM2oPN7NlQ1uydZ4n6P8en/YUv6zoYfwf/feBcM7siRwzVaOP/Fe8CfgL8G8DMjm2NNoraG+DV7IuW1CvRzgrAusCLZvZSjuOH4Ilis8P71IOB9fDp+8vMbH4rtbEz8ICZzc46NqON0WY2Z2Hb6KzEjjfSqkhaDdiFpqPVu81sas76LwMbFo9uJfUHnjazVTLqN+AZlskvg43CPrN8STh3lysCtjKzXjnaGI8nvfTD33PvaGZjJa2OT0nmSa4aD2xvZh9IGoF3Mr81s9vzJK9ImoRnwPYA3gZWDqPW/sAj5hnXWTFUo41pwKP4w1Qho/t84AQAM7umldoYgi+X2RqYHtrpi2c3n2Rmb+Vo404z2zV83gV/MHoU+B7wZzO7OqP+ZGCEmc2UdA6wEnAnsFW4jkNyxFCNNmbh0+4jgRvwDrRFo9RqtNFpaeuXzHH79mzAifi61ZPwzOIDwufx+BdbnjZeoUTSDrAYOZJPgD2BMcAOiX1vtvA6vgB2xJd9JLctgI9ytpFM5Hm3qGx8zjYmFf28NJ7Qciw51gIXxTChqGzcQlzHwrbRB++grgeWDfveaOHvpBptPAXsA9Qm9tXi2dpjc7YxLvH5SWCF8Hlg8f0pU//FxOfngZpy93cRtzEO6I+/m30I+AhfDrV5C+5nxW101i25Bi8SWdQcCnzXzOYld4a1ilPwdY9ZnAm8IOkB4N2wbwg+1XxGVmUzu1XSKOAMST8FfoWPvFvCWDwxakxxQRiR52F6mKbtC3wh6Xhc+OEHNF/qVI6vJK1kjeuhP5C0BT66+W6O+g2SuoTfx46Ja+hOflW7itsws6+A4yStD1wr6b4WnL9qbQADzeymonbrgRslZf5tFaokPteZr1nHzD4Nsy1ZvCtpKzN7GHgLWA54W1LeNebVasPMp9kvAy4L7/H3Bs6WNNhyLPGqUhudkjjVHGk1JL0EbGtmbxftXx5/n7Rqznb6A9vi09UCpuHTWC16HydpHeACYE0zG9SSupUiaTlc1agBOB2fdj4Un649wXJMvVeaGR2mVj8o8SC0LJ5M9N8cMQwB3rei94YtaaOonoBfABub2QEtqVtpG5JuxJWvrqHxoW454CC8U947RxsVZUaHv4t/4yPtL4FNaBw5nmD53v1Xo41xVuZVhaTli/8NL6o2Oiux4420GpK2A/4BvErT0erKwNFWtISilWISLoH3VQVt9AW+g09tVpSMU0EMVUkGqlIsFWWJV+t+tjRLPHSOh9KYgyD87/Qe4AqrIEmopZnR4V3/Krgy2DTgWTPLM2KuShuStjBXH1toqtFGp6Wt57rj9u3a8Om/jXCBgT3D59oW1F8OuBF4HJdd7JIouzNH/d2AAeHzIHx0Mwm4CRicM4Zr8REQ+Mj7XeC/+Gh1rxZcy7b4F/3Qov2H5Kw/JNyLT/CHmdfwDOcbi9tcFPcyHLde0bY+/iW/LrBea91PXIrwTeB/4dxTwv2Yhqtitfnf/sJuee9jSv2+4ffSv8J2BlZQd0Cl5+9MW5sHELe4tWQDHsQVeNYB/o4nsCweysblqJ9MPLkJX9Q/GF9y8WDOGCYlPj9Z6OTImUATjj0LeAxPCHqdhEIT+U0SKkoGqvRehuMaQr1HEtus8P+8KknVuJ/jgdWBjXGd443C/tXz3s9wfOFhaPmi/Xkfhip9MCx+kFmP9vMg8zoteJChwgfDzry1eQBxi1tLNooyfvHM6Cn4kok8mbwvJz4/n9Z2ShtTgL7h8/9omjU6JWcbk/DkG/AlRfcDF4Sfx+Vso2wWd1pZte5lqFONLPFq3M9qZIlX42Go0gfDTvMgQxWyxDvrFt2JIh2NLiFjFgAzuxb4JTAaX06TxaOS/iipR/i8K4CkLfFElDycDjwi6RBcQ/cWSQdKuhp3XslDnYWEJPM1yTsBfSXdAuR1knle0sWSNpS0TNg2lHQxnkyTRaX3EjO7Fc9m3kbSLSHZqqWJI9W4n9Ml/VzSrwlZ4pKWlXQQ+bPEd8LXYR+HT81uL+mCUJbXMWqQmf3TzMab2TG4SMpjcqnFPPdlb2AecJ6ZbWlmWwIfhs+Za8wDNeE9OXhH/g54ZjXkXsnSYGZTzd9JzzSzsaGNqeTPFh9oZjdZYu2umdWb2Y3kdwLrlMSON9LRuBwXbFiAeebsXrjXZxZH419GL4c6t0v6Cl9r+JM8AZjZzfiT/Kp48kpXfGRwg5n9Kt9l8LqkzRNt1pvZoSGu1XO2cSA+cj4d7ywfAE7D70Oea6n0XhbqfG1mx+NLva7BDTByU6X7eRA+LbsiULABHI13ZD/L2UY1HoYqepjpZA8ylT4YdlpiVnPkW4tc87nOzD5rg3P3ADCzWSXKljWz95rXav+ELPE+ZjajrWNpKZLuxUeaY4r2/wlXBMscqIT12C+UaGNdXEJzmxbEsw4LudxN0sr4A0cyq/lOMxuds341lruVyhKfBtxNhVniHZ3Y8UY6FGHZzNG41OQVeALLxsBU4Cxr+Vre3viX0xu2kCYLoZ2HWzAVmNXWapZD1zejjd+b2R9zHLclnmG+HDAfT4K53Mxey3mevwK3mdkTFcTaZNmPpAOAEfio+zLL8SUVOvy98NHhrbg84i7438W/LMcymvb4MNSRH2Qi5Ykdb6RDIel+fHq1Lz4lOwlXfNoG93TdJaP+xWb2i/B5E1xi8HV8LfHPzez+HDEUu/4I77xfBrAc9nEZ7ecyWqi0DUlnA0vicn674lmsr+DiE2eZ2S05zvMJPgoahGeJ32BmLZpGVMLQQdKpuLvR9cCPgGlhGjurjYuBJfAp4Rm4eMU9wA64jOcvc8YyBJhhZtMlDQWG4x65uafeJW2L38+kHvldlmOdeokHy5NxnefcD5aSdgPGmNnncreo82k0SfiVmU3Ley1F7bbo4TLlYegl4J95HoY6K7HjjXQoJI03s3XCP+ppZrZscVlG/eSX/CP4F9ELklYEbjaz4TliuBv/cv8TnnEqfPnIJgCWT9Wn2E1nQRHuTtS3THmyjXKjIAE9zCw1kUbSJDNbK3yuw7+svy9XBnvczNbMEcM4M1tX0nfwbNV98czVG/BO+JW8bYTPLwCbmtk3cgWuFwox5rmWUOdDYGkzmxuua1zONk4Cfo4baBQMFp7A15pfYWZ/zdHGhfhD2L/xaVXw5WoH4pnmqQ8AlT5YhjZeNLM1wuebcInTW3A50v3zTHdX4+GyWg9DnZGo1RzpaNSEjqEP0FvSUDN7S65DmzcBpkBfCwpLZvaGpNo8lcxs5zCquBQ438zuljQvT4eboKATXeo9134525gObGBmHxUXSHq3+eHNaJA0wMw+B5bBO0zM7IvwYJMHC3VexbWyz5A0DL+G+/GZhCx6hHegNfjSk29Cm/PkEox5KCRFzZP0rJnNDT/Pb0EbPwHWAHriGscrmtknknoBTwOZHS++rKqZQ1boAF/BE63SWMbMdkg8WG4R9j8ud6PKQ/LveGUz2yd8vlrScTnbeIvSD5c75awP/gBV6mHoer7lyVWx4410NP6MT1UBHAJcLsnwL8zTc9RfLTzNCxgqqX/oaGpwT9tcmNkdcqOGMyQdRss7/WeByWb2ZHGBpNNytvFvYHnc9aWY63PUPwsYJzd2WA04Mpx/EDAhZwzNOmgzmwhMxKdJ8/ABjZ3a55KWNjd8WJzQoebgQ0m9Q4b1dguCc2H+uTnbqDezWZLm4p3NZwBh9J2zCWZLGmFmzxTt3wDI421bjQfLRyX9Ef+38qikXc3sTrVgyVyVHi6r8TDUKYlTzZEORxiZKvwDrsPFCt4zsw9y1F2+aNf74YthILCZmd2+EPGsjQvy/7MFdQYAs81sZkvPV01CHCsCry1Mclmhs6t6YCz4PXer5B6F0WovM/s4x7FX451bL2Am3nGMwt9N9rF8JgnrAZfgHWdhqnk5fPT4CzN7PqP+friAB/i79iPxWYU1gNPN7NIcMXQBTsEfTMGnur/Bp3lPMrN3stpItNULn8lYGVfOGtyCuiNxpayvi/YvhXtwj8jbVmcjdryRDokareiS+1okil/h+StOwllEcf3CzC7OeWxXYF4haziMiNbDZTVHtvC8w0lkRttCZGVX83eqhchWDw9xyWSgEcCPcQGKiwpT4DnbWorEEhoz+7AFdRf6wbJEW1VZMrcwD5cpbeV+GOqsxI430qEIncN/8ESNccDhZvZWKFuQOJVSvy8+BToYGGlm1yfKFmQ8Z7RRjSSc7QpZruHL8a/4dORk4PhS721LtPF/xbvCtZ0FkBWHpAnAFmGq/de4gcT9wOa4nOZJOWLYHPgL/r55ffw+9MfVl35iZpnvmiv9nYbjKs5WX9Qo5zKx8NoDM2sID0drAm+Fd/F5ztNpH6g6DdYOdCvjFre8G/5u9Lvh8574utOCjuy4HPVvA87Gl3vcHX7uFsryatBOAXrgsndf4TKB4FOUk3O2kdQWvhxPYlkeN224M2cbX+FLeH4P/CFsXxQ+56g/OfH5OTwTGjz3Y2LOGMYlrn8F4I7weRvcY3mR/05L3M9HCIYC+DT6cznb6Iu/F/0P8OOisour8Lf7To5jdsXf2X+AL715GngYn7beKed5JhCcgIBf43rNp+I60n/O2cbm4W/iv+Fv6l78oepRYLmcbWwZ4v4EV1UbWur39W3c2jyAuMWtJRtFIu/Ad/ElDrvl+cdMc2OAU8IXyuIt6Hgnhv/X4m4rSVH/hel4i2Man7ONIfiU6DlAz7DvjRbcyydxVSTwd5mFL+vuLbiOiYnPtUXXldfgoKLfaYn7WWx+MS5nG9V4KPt/Zba/468msuqPA5bCH2JmAKuG/cuT/wGi0zxQddYtZjVHOhrzJC1l4Z2ZmU2RtDX+RL5SjvrdJNVYWLxvZmdKmoa70uTVGH4hLInohYtPXCOpkITzYs42lghTxcL1gGXhG4mcGurmSTJ7StoFeFCNgv55OQK4Lkw5fww8J2kMMIwwXZ2D5yRdgd+HXfAREZJ60nRZSxqV/k6hOtnqK5nZHuHznZJOAR6WtHPO+lCFZWKF+yAXQSmsm327MAWdgxmS1jTPN/gUf5CahXe8eduoNbNPwud38I4fM3tQvlY5D13NbEqod6ukqbg2+km0XH+6UxE73khH4yRcbWlBsoqZTQvvGo/OUf8evIP8b6L+NZI+wkcleTiM0kk4LwMX5WzjMjzzFdxYYCDwSUjKGZ+zDQDM7C5JD+LLqXKrEpnZxJCF+0M8EWlCqP9/lj/D+ee4JvD38Ht6ZaF53As2D+V+p1sAR+Vso9hYopBJOwCfis9DNR7KKl4mlojhkMS+WvIvJ+pMD1SdkphcFYl0IiQtbm1g+tAZkHQuPo3636L92wF/N7Pv5GijomVikjbA/XRnF+0fCmxi7naUp51aGh+oCiYJo/M+UIUlST/DlzFNAK40s3q5nvUSlk+d7QfAJ2Y2oWh/P+AoMzszTyydkdjxRjoNkkaa2fY5jluNRseUgpbu3ZbDcSXUXwpPYGrAR1PHALvjwh6/tJzLPqoQx9m4uMGnIfv0ZqAeHxkdaEUOOSXqvwDcjks7vp7nnCXaGA6cB7yHZ1Rfic8AvIJnJ2cqFIWs7pPxd6sFF56PgbuAs1sw+i7Xfq6/i0WFpCWsjZfOxAey9kX04410KCStV2ZbH1/vmFX/ROBG/F3gM/jUoIAbwrunPFyNv8t9F8+gnYUL+j8O5FrnWKU4drTGJRnnAfuEUdk2+BKfLPoD/XDv1mfknqvL5Dx3gYuBc4H78GStf5nZYvj0ca71xPgDwxf40qbFzWxxPCP2C1xjOJNK/y5ytP/TnMcNKNoWB56R1D+MhrPqJ1W3+km6QtJESddLWjJnDGfLBWGQNFzSG8DTkt5WwgM6o40XJJ0q1zBfKCT1lvRHSVMkfSnpE0ljJR28sG12FuKIN9KhkEvNjaGEVCGeMdkjo/4reKZl8brCrngWbp7pxHHWKOrfxAVIOYwaqhjHS3hW8nxJY81so0TZAgOElPpJw4hN8eSf3XEnnBssn0pS2r1YUJbRxstmtmpLy4qOq+jvIkf7uRyjJDXgbk1JBuNTvWZmqR1Z0e/kcvy992X472VzM9s1RwxJ84tHgN+Y2bOSVgGut3xGIG/iWd17hxhuAG4ys/ez6ibauAu4A3/3vzeejHgjvrTpPTP7bd62OhsxuSrS0ZiKCyK8WlygnMYAuCFA8Zfj0qEsD8mZon+nlC3qOC4C7g9TzqPk2aa3A1vT8gStx3Eh/mPwEfM+uE5vFrMl/RBYDDA16gJvjk975+FtSb8BrrEgHBJGdwfjswp5qPTvAjV35FlQhCd/5eE3uAvQr81sUmj3TTNbIWf9JMMTD3EXSDooZ70ukurMbD6+lOhZADN7RVK3nG18YWYnACckHspekGcm53oow9ftXh0+/1Wu13xGmD14EffS/lYSO95IR+M0yndux+SofxzwkKRXafxSH4IrHOXJiga4S42C/KcWdkpaGX+3mYeK4zCzv0uahOv5FpJoVgXuxAU5smgWq5nV42t6M71jA0fgU80NeBbzkXLN4/fw5Jw87INPTY+RtETY9xG+ljZTHzlwGpX9XYB3rtviU9xJhE+jZ2Jm50u6Ee8o38VzAVoyrVjxMjOq+EAGFT2UfSNpEzP7n6SdgM9Dew1SfteJzkicao5865CvhxxBQksXeDZ0Ot+6OCKOfPnMVWb2vxJl15vZj1vY3k64QMtQM1sqZ50/FO262NyacCngXDM7MGc7W9D0gWwa/kB2ZfHrjTL1bzSzffOcK6WNYbgq2yq4FOqhZvay3P1qPzMr50nd6Ykdb6RDI9flHYGvnXygwrYWymmnmjFUwsLEoQozq1NimGRmDy5MG0Xt/dTMrspx3IbAVDObIV/ychJBnxg4y8xy2eFVmxDLStbG5hmR9kXMao50KCQ9k/j8M+AfuBDFH5Q/G7gcuVSnqhGDpGEhw/NdSZfKPVibtb8o41AVMqtTYjitCr8PyOexDL6MqbB29m/4O+dzwr7MjrscypGJXHT8hnIjDhIPAH+WdI582dRCo/yZ1U1ikHS6pHuqEUN7iqMjE0e8kQ5FURbts8AOYSquFzA2RyZvsaPPgiLgFDPLs+SjohhCvf/h72HH4kpYPwV2NrPXW5ANXOm9qHaG98Lei7SkplXMLDMhSNJUM1s9fG7iaKT8meanmtmfwuc18KnZLiGOfczs6RxtTAHWDpnml+Id/634+9W1zWz3rDZS2s6bWb3IYmhPcXRkYnJVpKNRE0aHNfiD4ycAZvaNpPk56p+Fr3ktdWzeGaBKYwDobcEWEDhf0vN4IsxPyJ+MU2kcVcnwrsK9qDipCZicmJaeIGm4mT0nX0KT+U4zsDuNSWnn4WIoIyWNwM3pv5ejjZqQTQyelVx4APifpPFZlTMeQvJmVlcUQ3uKo7MSO95IR2Mx4Hn8C8AUtGDlxud5MiVfwG33ni8ukHRYK8UQTqfFCu8ezewRSXvgayfzTm9WGsdxVJ7hXY17cS/+IDK+uEDSoznbOAz4m6RTcWOAp+RZxe+GspayjAXvWjN7Jkwb56HSB4D28hDSXuLolMSp5kinQC7evqSZvZlx3KrAZ1bChFvSkpbDgL7SGMKxP8Yt/MYW7R8C/M7M8i7FqTSORZJZ3ZIYqomkPrgHbx0wrSW/T0nTcUMEARsBy1vQXJY02czWzNHGYvg75k3xB4D18M7/XeBYK9ItLlG/4szqSmNoT3F0VmLHG+mQyAUWFmTiVtJhVhMtZGZ0e0HSADP7vB20sbOZ3d2abai5nOLzZvZ1+Fvb08zyOk9V9ABQLdpDDO0pjvZE7HgjHQpJ6wKX4FOc74Xdg4HpwC/M7IWFaPMVM1ulSvHlTTxZEZfOex83X78A2BhXYPq1mb2Vo41huJDBssBI4EQz+yKUPWNmIzLqfx9fZ1mwoPsTbtfWBdjbzJ7KEUM12ihOshEuAvELADO7fSHaANeKzt3GoqKSBxG5KMva+FKpvF7PFbchqZ9Vbk5RcRudldjxRjoUISnj58UZppI2wgX6186o/xWNyUuFd5A98YxLM7O+OWKoRmb0Y7j+7WLAAfiSl5txK7f9zWyrHG1UlBktXwp0KO41ew+wq7nK0Hq4Dd73c8RQjTbm40pZH9P4O9kTz4A1MzukXN0qt1EXrmVXmq5rvgu4wvIJT1SUGS3XVt7L3HHqJ8Dv8OnvDYFLzSzTM7pKbczHPXhvAG5bmA60Gm10WswsbnHrMBvwakrZaznq/x3XV14yse/NFsYwGzgDlwMs3qbnbGNc4vM75coy2hhf9POWwKv4+8kXWhjD1KKyzPpVbGMD3HD9SBoHAy39nVSjjRvw2ZSN8FmUweHzJbhBQJ42Xkh8vg/YPnweATyZo/7kxOdngcXD557AxJwxVKONSbjj1nXAZ/jDx7649nPe+1lxG511i1nNkY7GSEn34Z1nIRN3OeBAcugLm9kxcqu4GyTdiQs+tHTapxqZ0Q0hu3MxoGci43NloDZnG5VmRieXT51cVNY1ZwwVt2HunLMNrqn8sFzYo0W/k2q0AaxnzZ2QpgFj5WueW8rCZEbPk7Ssmb0HfA18E/bPIf/fRVXaMLN7gXtD3DvhneZFkkZbPvnMarTRKYkdb6RDYWbHStqeRpnDQibuRWZ2f842npf0A3zJzBigewvD+Cn+BF+KTMu1wG/wqdkGfGrzZElrA33Jby5wDrA6PtUMgJlNlLQ1Pr2Yxe8k9TSzmWZ2Z2GnpJVo7rq0KNvAzBrw5UC34u+7W0yijVvwdbct5QtJe+HTog2wIOt7L5ovqynHipLuxv8uBxfuTSjrkqP+8cADkm4DpuAPEaPwzOC8ClzVaGPBUjAzm4W/Brk5ZCrv2optdEriO97ItxpJSwPr5u20F3EsA3E7tmiS0AZIGoo/zGxFY0fbD3gEOMnyLc+qODM6dEw/pqnBwV1m9lLOS6m4DUknmNn5ec+3qNrorMSON9KhkFSLJxINBkaa2ZOJsgWJLSn1hwAfm9lsScI9Xwti+pdZo9JOWhtHAzeaJ6+sjOsEDwNeBg6z4MOao52+wCAze71o/zAzK6cclDyukAy0G65A1aJkoKJ7OcrMnkiUZd7LlHZblCVe5n6uhdsW5rqf1cgSL2pvcfz7sdl670ikUqJJQqSj8S9gc3yq9++S/pooy6P9ej+Nf/dnAzsCT+PJOXk8RgGOTHwh/w24wMz6AScC/8zTgKS9gZeA2yRNkbRBovjqnHH8B1gH96LdAb+W0/GlI9fmqJ+8l/9vIe4lkr6SNCNsX4Ws8ZUK+3NeR6n72Z8W3E/8nj2Lv9Mci9/b7fH3/lfmbANJfSWtZGZNRFbC0q089Wsl/VzSGWGpVbLs1HL1Mtps0ftlSUeH2RMkrSTpMUlfSHpaUqZ2dqi3oqQrJf1JUm9Jl0maLOmWMDOQp426cC9GSZooaYKkkZKOkJRn2r3z0tbZXXGLW0s2ElmZ+BTapbjJdzdyZAMDLyY+P4/ryRZ+npAzhpcTn58tF19GG+OBpcPnEXhHsXv4OfM6iuMoUfbKor6XoV41ssSrcT/HJT4vbJb43viIeTz+bnSDRFneDO3LgetxOc7ngb+2pA3gK2BG+H9hqy/szxnDlMTn+4DdwuctgCdytvEYniF+Eu6l+ys8ifFQ4OGcbVScJd5ZtzjijXQ0FmTKmtl8Mzsc/6J8GF9LmsW7kgprZN/Cv0wKU4t5uVXS1WF68w5Jx0kaIrdLeydnG7Vm9kG4jmfwpUCnSDqW/Nm4X0jaKyQAEa6jRtI+5EsGqvReYmbH4KPUGyQdG2Jp6furatzPBkmrhJmDnpKGwwLxiLyZvL8F1jd3Mvop8B81CnPk1Z0eYWY/NrML8XWzvSXdLqlbzjauxtf+rmxmfcysD/4g0cdyrDEPJJNmlzCzOwDM7FHcsjEPfczsEjM7G+hrZn8xs3fN7Aqgf1blwHpmdqSZjTWzaWEba2ZHApnuW52atu754xa3lmz4FOp2JfYfhi9fyKq/HJ4s8xieVfwF3tGMA7ZuQRwH41PUn+KjkRdx56PFctZ/EjdIT+7rg69FnZOzjaHATcAn+PvQV3ABiZuAFRb1vSyqUwMcCzyOS3i29Pda6f3cGn/HPhXYBF9S9Vq4H7vkbGNS0c9L46PWY8k/4n2pxL7fA0+Qsga96Pj1w9/kseG+vtHCe3km3oGviD9MHIebX/wUuDdnG8/jiVkbhN/J8LB/ZfLPQozFM8KTs0o1wD7A0y39G+lMW0yuinwrkbQ6TTM+n7WwhKSVzr828I2ZvVa0vyC1eF0L22sXyUAdOUtc0pPATyyR7CbXGb4T2MTy+QJfC1xrjZaPhf2HAZeYWa53m2Hm4Gi841rJzJbJUy9R/2B8qngl/NXBu/h1nGNh3XdG/a1xyc0GfHnb8XjuQF/gZ2Z2V442htI0S1z4uvXcWeKdldjxRjockjYDPjKzlyVtgr83mmpm97VhTGeZ2W/b6vzFSNrGzB7McVxvYDt8JmA+rnz1QN6HEEk7h+NnVxJvUZsr4FORL1rLltAsBWBuSzgIX7f6kuXXJ14bmGlmrxbtX6iHoWrQkR9kiuq2iwfD9kLseCMdCkkX4slIdcBofIpxJJ6dO97MTsiof4iZXRk+L4snBq2HT1EebGaZGaSS/l/xLuAnoS3M7NgWXFKp9ieZWa7s05Q2Ms0aQmb1r4EJ+DvmJ/GpwLWAAyzfkqZZuDLSSDyZZnRLv5gl3Wlmu4bPu+DiF48C3wfOMrOrc7TxczwRSPgo62A8Qer7wLnm7yZbjKSBLe0s5Gtot6Op3vNoy6lVXOmDpaqwZK5Muy1+uCzzMPSymU1ZmBg6C7HjjXQoJE0B1gR64O5Ey5rZzDAqGWcZnqmSXjCz9cLnm/F3qpfhSlhHm9nWOWKYhncMD9CYMHM+cAKAmV2To41yy3UE/NPMBuVoo5zlnYCtzKxXRv2JwEbh/g0ErjOzbcPSmX+a2fdyxDAOn0rcE5cDXBO4A7jBzMZk1S+0YcHQIUz37m9mb4aYHrIM44tQbxKezNQDeBtPTvpQUn/gEfOEqaw2tsenV9/DpSevxVXNugEHmdlDOdo4ENfsfoCm7lnbAKebWaqaV8aD5Tgz+3WOGCbjSV4zJZ2DTzffif+esHyGERU/XC6qh6HOQJSMjHQ0zMxMUmEqtPDk2EDL16WvYmZ7h893SPp9znqr4yYJ2+HiDO9J+kOeDjfBTbh4fKkn37wSlpvizkbF/r/Cv7yzEDArfP4GWAIWyE7mzaA1cyvCy4DLwghnb+BsSYPNbLk8bSQ+1xXe/ZkLauR97z7PXJpxpqTXzezD0MYXkvKOLv6Mr4fuB/wX2NHMxoZ8gOvwUWMWp+CZ0dOTO8MDwNNky2huQ+kHy7PxBMDMjhdPZirIVP4AXxbVAFwrKa/5/O40f7jcF0+6ysvRwHcp8zAExI43Eukg3CfpcbxzuhzXfh2Ljwgey1F/cHiaFzBIUhdrVHjKlfhiZl8Bx8nNFq6Vmza0tNOfCJxvZpOLC+Q60nkYi7+TbDaylPRyjvr3A6MkjcHFJm4JdQeQf/lMk+NCh/f/cEGO5XO2sbZcbENAN0lLhS/oruRfCtSQ+F3uuCA4qTv5fzcNZjY11JtpZmPDNU1NLtnKQJR+mGog3z2txoPlu5K2MrOHaVwy97ZatmSuGg+X1XgY6pTEjjfSoTCzEyVt7B9trFyMfze8E741RxPJEcNz+HrVL8JIrdzUbblYnpevCf4F8L+W1MWXeJRTdtot5/m3TynbLEf9EyXtAKwB/DGRjDWdfKM78GzXcu2/nacBMyvXufYEfp4zjt0JnZSZTUvsXxwXf8jD9DA92hf/mzgeF/b/Ac1nFcpxJvCCpAdodM8ago9kz8hRv9IHS/DlYP+WdBrwJTA+vBLoD5Tzkm5ClR4uq/Ew1CmJ73gjkSqwMEk4iyiO9czshYWs2xf4Dr5uNK8bT7J+f2B++NLucEhaDtd7bsClN/fDlZreBk4ojIZztNMf2Jam7lmj897TMg+W7wC35s02D+1UZclcSND6BbCxmR3QgnpDgA+sSDM8JDWubmb/bWksnQZrB4uJ4xa3vBs+bXYjLtTwW6BLouzOnG1si3+hDi3af0jO+tsDb+Kj3HXxhJHX8C+3XCIcwMCinw/Ap2gPJzwQ52hjvaJt/RDDurhqUFb9awtxhHvyLv5u821gr5wxLIO/t/wSlzZ8J2ynJX83rfA7PSTxeTCeNDcdz9Repa3/bttyw0fw6wP9F7J+f1zJqs2vpbNsbR5A3OLWkg14EDgCNwf4e/hiXTyUjctR/8/4lN2FwOvAMYmyvOpE4/F3YBvjBgMbhf2rt6CNFxKfT8UzWA/C37NekLONhnD9jyS2WeH/mXq6JJSaQjtDw+eB5NetfhjYInzeHXcF6gX8Cbi0NX6nJe7nzfgUdQ0+WnyoBX9fFT2U5b3fKceshmcx34dnI18dHiCewUeJec7TaR6oOuvW5gHELW4t2fC1usmfD8BHnCvl6fSASXjmLHj26v2Fjm4hv+TfTYsvpY1xyfaAXuFzlzxf0OHYPYExwA6JfW+24F5OwXV4wUfvSWm/KTnbmFD08/OJz83kExfF77TE76S4vby/17Oo/KFs9zLbHsAnOeo/BuyET3O/jWcSK+zL9QBBJ3qg6qxbTK6KdDS6SOpuQSnJzK6V9CE+YkxdtxqosyAgYGbTJe0EXCrpFhKmARlUIwmnh6R18VFZrZl9E2KaJymXAIWZ3SppFHCG3FDgV7TMoOB04BFJF+FawrdIugtf7zkqtWYjn0g6AP+i3gPPoi28F8ybQFPp7xSqkK2Od27rmtn8kJh0vaQVzex48md5V7pMrI+Z3QMg6QwzuzHsv0fS6TljqJHU18xm4LMi78CC5Vl5v/MXNzdVwMxul3RK+Bs9VVJeNbFBZlawdTwm/J08Jlc7+1YnF8WON9LRuBwXSliwhMbM/itpL+DcHPVfl7S5hSU45ipLh0r6E95x5OEgGpNwfoiPTkbjI5Sf5WzjA6Dgf/u5pKXN7IOw5CO3spCZfQ0cL2kd4Bryu89gZjdLeiHEXEjC2RgXvxids5lDcPGQk/Ap+KPD/gHAyTnbqPR3CtXJVq/GQ1mly8SSGd5/LSrLG0NneqDqlMSs5si3Ckk9AMxsVomyZc3svea1Wg9JtUA3axRAaEldAb2tg2YVtzWS7gXOs6J10eGh7LdmltnhSNoUeNvMmtkZShpuZs9l1P85riD2ddH+lXFlteOyr2TB8ckHqmn4e9VcD1QhI/l8fKnZeHwtb+HBcAszuy1HG8fjU/TF93NdXLlqmzyxdEZixxvpNEj6vZn9McdxBWWnpJbuM5bzH0Oov1eoeys+ktgF13v+l+U3GKg0jt2AMWb2uVwH93waNXl/ZU3Xs5aq/1fgNjN7Is/5yrQxAB/lvgdciSfRbIzfi7Ms/xKabYFdaXov7rIil5+FjDHv30W7fiiLdB5ixxvpNCifMcAPcT3eV2mqpbsy8AszeyDHeS7G5RW74iIY3XBv3x1wcftf5mijGnG8aGZrhM834UpWt+DvmvfPGlFI+gSfHh+Ev5u8wczGZZ23qI378YS1vnhW9yT8ffc2wNpmtkuONi7ER2b/xkdm4PfiQNzDNvN+ZrSf+XeRo43VrAVOSUV1HzazrXIe22Q9eJjuHQFMxg0OMr+wK30gC21U/FAW2tkSn6pOul9dbkV2mN82Yscb6VDIpQVLFgE9zCw1b0HSVGB7M3uraP8KwP1mtnqOGCaZ2VpyY4YPgaXNbG5IXBlnOZyFqhTHy2a2avj8vJmtnygbbxnGAArmBJK+g2fP7ou/Y7wB74TzODWNN7N1wuh9mpkt25IYwnGvmNkqJfYLeMXMvpOjjYr+LnK0n6vzlhtPFJ9/FeBlADMbllE/aeJxKq7HfT3wI/z+llUKS7RR0QNZqFeNh7KzgSXxNdW74mvfX8HFOM4ys1ta0l5nIiZXRToa03HR94+KCyS92/zwZhTedxXzHvmzXwsJOPMkPWtmc8PP8/NmJFcpjkcl/RFfm/yopF3N7M4wysg0O6dRYvFVXM7wDLkz0X74MquVc7RRI1dq6gP0ljTUzN4K7wLzJgPNljTCzJ4p2r8BkNfndzqV/V2UcuRZUIQvPcvDW/gsyJ/wNdXC17HulLN+Mnt6d2BTM/tG0vX4srM8JBO0VjazfcLnqyUdl7ONaWY2PPFQdm3IP8j9UIabTKwFIOlGfBT+a0m34vckdryRSAfh38DyQLMvWHxkkMWVwLPhi6Dwhbwc/uWS1y3lQ0m9zexrM9uusDNk0M7N2UY14jgad8MpGCIcL+kbfNr7JznqN1siY+7BO5H8Gcl/BgpTsIcAl8sF8NfAs2vzcDBwiaQ+ND6MLId3YAfnbKPSvwuAwpKsOSXK9svTgJntHKZ6L8Wzm++WNM9y6lZThWVmVP5ABtV5KGuQNMDMPscFOWpDm1+E2YxvLXGqOfKtQ65huwtNtXTvNrMXK2y3Fy6E8XHO49cAdq5GHHLz9Toz+6wFdXoXZ88uDGEkpDDir8MFE94zsw9a2M5SJO6FBTeb1kLSw8CpZvZkibI3zWyFFrTVC++wVsblOwfnrPdI0a4fJ7KJR5vZ8BxtdMEfyAq+u4Nx28d7gJNKZVyXaGOcBY/khUXSPvhysJdxRa4jzey+8N75b2b240ra78jEjjfSoZHUG3+H9oYVeaB+m2JY2DgqzayuRgwZ7eVKapJbCM4rxB1Gd+sBL5rZyJznGgDMtoVYypXS5tq4ucA/Mw9Ob2ehlpktzANZqFeth7IBwIrAa235b6O98a22Zop0PEJGceHzJnim5l+ASXKLu0razvsFXXEMkvpK+rOk/0jar6js4nL1qhmHPLP6VVx/dwfcuu104NVQtshjyEFmdnfgWcJ7WEm/xu35egD/F5J8MjGzzyvtdCV1LZpGHQD0klTWwjFnbPW4vWCLYjCzL4Fhkn7VkhjSOl1Jq+WNA/jCzJ4zFyTZsqVxdFbiiDfSoSjK+nwEXx7xgqQVgZuzpuIklfOZFXCvmS29qGMI9W7DO72x+JTgPHxacU6y/UUZh6qTWV2Ne5GW1HSQmfXN0cZkM1szfH4OT0qaFaa+X8jKJg71trOwbjiMFP+KJ3hNBo4vlbhVoo0JuMDEF+EBYDf8nejmwHNmlvfdeam282ZWL7IYqhjH82Z2UiVxdGRiclWkI9PXgvesmb0RpuOyeBaXJiyV3NGvlWIAWMnMChKVd0o6BXhYrmO7MCxMHNXIrK40BqhCUhMwQ9Ka5lKNn+K6yLPwa8w7s3cWjZKKf8FlPXfCs4v/hS+JyaLWGkVD9qHxAeBsPCs5tdPLeAjpl+P8FcfQinHEjjcS6SCsJl8rKWCopP7hibqGfJ3FVODnIVuzCcq57KQKMQB0k1RjQeXKzM6UNA13p+ndSnFUI7O6GvfiWWBymaSm03K2cQRwXRhlfQw8J2nM/2/vzIPlqKow/vsSVlmCgEQgCqLsBQkSFaWUtQQKCktUQKACCiKKrCIEgoqWCoqCsZBiU1ZJqkghUMqOQLkQVl8CMYASICgFCEFZ5A8Ixz/OHV4zvPfmTrpn3ky/86vqyqTv3G/O9Huv7+3u754DbI0PqO0y1QbXIJ8t6eDMfmUnAL0yCemVOGpJDLxBv9F8+/PV9O+awHcy+p/G8H/0Ry1jDI3nYbkxgDtMd8brpAJgZpdKehYvodbxOMzsdHny/L3xNI8NZ/WBbTiry/48wMsbDrleN9dJbGbz02OET+Pmrnn4dzm+DVPPOpKOx4/D6pJUMJnlDhRlJwC9MgnplThqSTzjDYKg55C0Tu6yrKo0JH23ade5ZvZv+TKnn5jZtEyd8QxOABq382/KmQCoImd1mRh6KY66EgNv0Fck08vJ+PO296TdzwHXAmcsyx+0pMtyT6oZWjeYWUvXZnJ87g88bV4C7wDgE/it8AtssJbsSBqr48diEnCDmV1ZaDvXzL7eov97ge/i5Q2/g1/x74MnxDgmZx3uMBqfS98jV2PN5l3A/cA2+DlqyTJogD9HzNboVSSt1e5yoE5oBNURA2/QV0i6Ca8ReqmlBAvp5H8wsKu1LgzQXJtVwE5JEzNraW6qyBn9G/wK4F14usNVgauBXfC/y5bPFMs6oyXdCPwer416AF7AfRaeXGRXyytwUIXGm3he4CKT8KsjM7ONuqGRdDZjMLlKY13zdWa2MLN/KWd0Mh791Lxo/VS84MSb+PPyadZUYq+DGg/gv4+zzOyxVu/vlEZtMbPYYuubDXhkWdoK73kAuALYEV/WsCPuXt0B2CEzhqX4QH37ENtrmRrz07/L4WkOx6f/q9GWoTHQ9P8ZeOHztfAlNK36/7XwevFI2h3WOAF3E29V2Pd4m78XVWichNeenQ4clLbpjX2ZGg8UXl+E52zeADgOr4fbqv+Dhde34/mnwW/V3pcZQxUaj+NVjRYD96T412vzeJbWqOs26gHEFls7G55Q4URgYmHfxHTSvDWj/7h0ArgFmJL2LWozhoeAjYdpe6oNjRWAdwMvA2um/SsBCzM1FgLjmvYdDCzAi7G36j+v8PoHTW25g39pjfTeSXjS/LPwggtt/Uyq0MAr5yw/xP4V8PKEORrFgXegqW0go//DeKYpgLlNbQ9mxlCFRvF7fBIvYflMGsgP75ZGXbcxbekO+pL98Cu6OyUtkbQEuAN30e7bqrOZvWlmZ+PLJWZIOof23f2nUd4Z/Sv8BDmAX6leJelC3E06O1Oj4Yx+CzO7FF8GklOs4Vp5ikfM7NTGTkkfwgehHKrQwMz+aWZfwE/Kt+C34NuiAo038WT+zayb2nJYR9Lxkr5JckYX2nLOt78Erpe0M3CjpJ9L+pSk7+G/KzlUofEWZvZHc7/A+sCPcQd81zXqRDzjDcY0kvYEtjezU0bhs9cDMLOnJa2B10tdbO8sjzemkLQynmDkoW5qSNodOAd/bt5Y1/x+vNDBNyw9u22hUdoZLWlH4Gu83Qn8W+BiyzDdjaBxDfDrHA1Js81s/5zP6qRGXYmBN+g7kgFmffw22quF/btnnhyXM7M30utV8copiyzT+SrpY/jt4JfSCX46KSE/XuA7q/Ra2ThSv7JmoMaxvNsK+Xlzj+Uwmm25xKs4nlW4xJPOOAaLRjTWNd9rnis5CCohBt6gr5B0NHAkfkKdgi9ZuTa15Th5D8HTAb4AHIPflnscvzI40cxmZcSwAJhsXgbvAuB/wBzckTzZzPbJ0KgijpPwLEKzGUz9OAkfgGab2YjFAcoey/S+KlziVRzP0i7xgtZEChMZy8jR3NS/0slMn09kSk0M60oMvEFfIelBvMzaK5I2xE/Ql5vZTGXUEE39d8LNN/OAbczssXSyvcXykukvtFRAoHmAkjRgg6kGOx3Ho8CWzSfBdNJcYGYbZ8SwzMcyaTyAn9Avwk+swpcT7Q9geUtXqjie881sa3lRhH/h7tml6RnrvMzjOQU4D5iAT2SET2T+A3zdUh7qFhplJ4a1mciUnRjWmUgZGfQb4xtXEWb2RHqWNUfSBjBk4YNmlprZ88Dzkl6xtL7QzJ59uw9mRB6S9CUzuxiYJ2mqmd0naRN8LW0OVcTRMAM1r1/NNQOVPZYAU/Er9hnAt8xsQNJrOQNugSqO57g04VgFHywmAEuAFcnPGX0Jnsf77uJOSdsBFwOTMzS+AmxbnMxI2tDMZpJ3TCfxzonMVPzuSC7jGo8w8JzTjcH+T5IGMjW2GmYicwU+UczhUIaeGJ6FO+9j4A2CPuEZSVPMbAC8bqikvfCE/1tl9F8s6XT8SvNhST/DZ/K74ut5czgMmCnpVDz5+13yAgtPpbYcqojjWOA2SUOagTL6lz2WmBd5OFvSVenfZ2n/vFLF8Wy4xMcz6BJfBGxHvkt8leZBF8DM5kpaJVOj7GSmThOZshPD2hK3moO+QtIk4A1LWaua2rY3sz+36L86fivQcAfrbvjSoifxdai5gx6SVgM2IrlG23kWWFUcZcxAZY/lMJrL7BIvczxT/1IucXkpvA8Cl/H2ak3T8GQcLSczkv6AF2YYKOxbDp/MHGhmWaUS08/mbDy5yt6WUf+20HcCMBNfO/s8/ny3MZE52sxaXrFKOg5fGjcev9r+DNCYyMwxs+9laJR2ideVGHiDvkMVuIHrEEMhlvfgtyjfwAeIV1p0qbR/nTQk7cGgGagxkbnOzK7P7F/pZKafJzJJI1ziQ2E9kMUjtthyN+AQ3An8KLAHPgu/DZ9Rf7Gkdm5Gno7F0GYcW+BlBf+BJ8y4G3dGXwJM6HT/ETQWVaDR9Tgq/j2diF9pbkMhy1q3+veSxjC6q3b7Z9JL26gHEFts7WzAg8DawAeAl/AkCY0TRHaKwmG0vzraMbQZx1xg0/T6o3jhCHCDz5xO96+bRgv93MnQlBTLwjQRuBV/9jwXd66X6f/hCmLomkYL/cVlNfp5i1vNQV9RXF4i6WkzW6/QNt/ylo2UTTpROoaK4phnZpML/39ruYqkv5nZFp3sXzeNFvpfNbPzM943wPDO6POLMXaif49pHD9cEzDDzIYq5TgmCFdz0G+UcgM3rS1sPKuaBMySp7jLWeJQ2pFcURyPSfo2fpt7H1IeXknLk/e3XbZ/3TRGIif3NZR3RlfhrO4VjR8BZ+LP25sZ03UC4oo36CvKuoFVMulEFTFUGMcawCn48815wBlm9nJytW5uZnM72b9uGi30F1uGs7isM7oiZ3WvaPwFOMrM7h+i7Skze18rjboSA28wppD0MLCbmT3ZtH8D4GYz23QsxREMImn+cE3AJma2YqZOWWd0qf69oiFpU+AF80QxzW0TrU2XdZ2IgTeoDZION7MLWryno2sLc2LopTg61b8fNeTJP3YDXmxuAv5SfJYfBGWIZ7xBnWiZGcjMbpRn8OnU2sKsVIu9EkcH+/ejxu/wZS4D7xCQ7igdREyGKtXoZ2LgDfqOEdzALV2n8Faaw7LP/ErF0CtxVPE96qJhZoeO0HZAbhwjhTjK/eum0beMaWdZ0H8kN/Bs/A/3HuDe9HqWpOljJYYq4qjie9RJowvkOqM71b9uGn1LPOMN+ooq3MB1iKGKOCpyVtdGo9PkOqM71b9uGv1M3GoO+o1eqHjSCzFUEUcV36NOGqVp4Yye2On+ddOoKzHwBv3GsZQrhVeXGKqIo2z/umlUwURGcEZ3oX/dNGpJDLxBX9EFN3BfxFBFHFV8jzppVERZZ3QVzuo6adSSeMYbBEEQBF0kXM1BEARB0EVi4A2CIAiCLhIDbxAElSPpCEnTKtZ8QtLaVWoGwWgQ5qogCCrHzM4b7RiCoFeJK94gCEojaZqk+ZLmSbpc0mmSTkhtH0ltd0k6U9JDaf+Wku6RNJDaN077r5F0v6QFkg4f5vMOKvQ9X9L47n3bIChHDLxBEJRC0pbADGBnM5sMHNP0louBI8zs40Bxac8RwEwzmwJMxZf/AHzZzLZN+46WtFbT520O7Adsn/ouBQ6s9EsFQQeJW81BEJRlZ2BOo+6qmS2RPAe+vED9ambWSJhwJbBXen0XMEPSJOBqM/t72n+0pM+m1+8DNgZeKHzeLsC2wL3pc1YGnuvA9wqCjhADbxAEZRFeDWi4tiExsysl3Q3sCdwk6TA8veOuwMfN7H8p0cJKQ2heamYnl448CEaBuNUcBEFZbgP2bdwSlrRmo8HMXgRelrRd2rV/o03SRsAiM/sFcB2wNTABeDENupsBjX7Nn/d5Ses0Pk/SBh34XkHQEeKKNwiCUpjZAkk/BO6UtBT4K/BE4S2HAhdKehW4A/hv2r8fcJCk14FngO8DrwJHpAT7jzBEvWIz+5ukU4GbJY0DXgeO5J0FEoKgJ4mUkUEQdBRJq5rZK+n1dGBdM2s2YAXBmCGueIMg6DR7SjoZP988CRwyuuEEwegSV7xBEARB0EXCXBUEQRAEXSQG3iAIgiDoIjHwBkEQBEEXiYE3CIIgCLpIDLxBEARB0EVi4A2CIAiCLvJ/ajQS9tF7KIsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_loss = pd.DataFrame(cordinates, columns=[\"retprice\", \"cigsale\", \"loss\"]).query(\"loss<20\")\n",
    "\n",
    "sns.heatmap(df_loss.pivot(\"retprice\", \"cigsale\", \"loss\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45d87814",
   "metadata": {},
   "source": [
    "## Everything Together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d1a2fbe6",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:17.144764Z",
     "start_time": "2022-12-22T12:22:17.130611Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn.base import BaseEstimator, RegressorMixin\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.utils.validation import check_X_y, check_array, check_is_fitted\n",
    "import cvxpy as cp\n",
    "from scipy.optimize import minimize\n",
    "\n",
    "\n",
    "class SyntheticControl(object):\n",
    "\n",
    "    def __init__(self,\n",
    "                 time_column,\n",
    "                 geo_column,\n",
    "                 y_column,\n",
    "                 x_columns,\n",
    "                 post_column,\n",
    "                 tr_column):\n",
    "        \n",
    "        self.time_column = time_column\n",
    "        self.geo_column = geo_column\n",
    "        self.y_column = y_column\n",
    "        self.x_columns = x_columns\n",
    "        self.post_column = post_column\n",
    "        self.tr_column = tr_column\n",
    "        \n",
    "    \n",
    "    def fit(self, df):\n",
    "        \n",
    "        x_co_list = build_x_matrix(df.query(f\"~{self.post_column} & ~{self.tr_column}\"),\n",
    "                                   time_column=self.time_column,\n",
    "                                   geo_column=self.geo_column,\n",
    "                                   x_columns=self.x_columns)\n",
    "        \n",
    "        y_tr_pre = df.query(f\"~{self.post_column}\").query(f\"{self.tr_column}\")[self.y_column]\n",
    "        \n",
    "\n",
    "        def v_loss(vs):\n",
    "            return find_w_given_vs(vs, x_co_list, y_tr_pre).get(\"loss\")\n",
    "        \n",
    "       \n",
    "        v_solution = minimize(v_loss, [0, 0], method='L-BFGS-B')\n",
    "        \n",
    "        self.w = find_w_given_vs(v_solution.x, x_co_list, y_tr_pre).get(\"w\")\n",
    "        self.v = v_solution.x\n",
    "        \n",
    "        \n",
    "    def predict(self, df):\n",
    "        \n",
    "        x_co_list = build_x_matrix(df.query(f\"~{self.tr_column}\"),\n",
    "                                   time_column=self.time_column,\n",
    "                                   geo_column=self.geo_column,\n",
    "                                   x_columns=self.x_columns)\n",
    "        \n",
    "        X_times_v = sum([x*v for x, v in zip(x_co_list, self.v)])\n",
    "        X = check_array(X_times_v)\n",
    "        \n",
    "        return X @ self.w\n",
    "        \n",
    "        return self.y_scaler.inverse_transform(y_scaled.reshape(-1,1)).flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "786b19b3",
   "metadata": {},
   "source": [
    "## Use Model with Covariates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1b17d296",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:18.206100Z",
     "start_time": "2022-12-22T12:22:17.146169Z"
    }
   },
   "outputs": [],
   "source": [
    "# model with 2 covariates\n",
    "model1 = SyntheticControl(\n",
    "    time_column=\"year\",\n",
    "    geo_column=\"state\",\n",
    "    y_column=\"cigsale\",\n",
    "    x_columns=[\"retprice\", \"cigsale\"],\n",
    "    post_column=\"after_treatment\",\n",
    "    tr_column=\"california\"\n",
    ")\n",
    "\n",
    "\n",
    "# model with 1 covariates (Outcome)\n",
    "model2 = SyntheticControl(\n",
    "    time_column=\"year\",\n",
    "    geo_column=\"state\",\n",
    "    y_column=\"cigsale\",\n",
    "    x_columns=[\"cigsale\"],\n",
    "    post_column=\"after_treatment\",\n",
    "    tr_column=\"california\"\n",
    ")\n",
    "\n",
    "\n",
    "model1.fit(df)\n",
    "model2.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "88da578b",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:18.212036Z",
     "start_time": "2022-12-22T12:22:18.209199Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0. , -0. ,  0. ,  0.4,  0. , -0. ,  0.1,  0.1, -0. , -0. ,  0.1,\n",
       "       -0. , -0. , -0. , -0. , -0. , -0. ,  0. ,  0.1,  0.1, -0. , -0. ,\n",
       "        0.1, -0. , -0. , -0. , -0. , -0. , -0. , -0. , -0. , -0. ,  0. ,\n",
       "       -0. , -0. ,  0. , -0. , -0. ])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.w.round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "86891c68",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:18.216669Z",
     "start_time": "2022-12-22T12:22:18.213746Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.12344947,  0.96484268])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model1.v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2c635844",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:18.226946Z",
     "start_time": "2022-12-22T12:22:18.218001Z"
    }
   },
   "outputs": [],
   "source": [
    "sc1 = model1.predict(df)\n",
    "sc2 = model2.predict(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2b198d07",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-22T12:22:18.301702Z",
     "start_time": "2022-12-22T12:22:18.228046Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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MM89YOgRxl26b3JVSXwM9gWitdcOcZR8BDwLpwClgmNY6LmfdBGA4kAWM1Vr/bp7QoXpFF2YMvPG2ZFLjYe/3cGQemfHn2ODly3wPH740RuHj6MyU1lPoUqOLJJ27YTBA+xepWjuU/xxazuiT69gdf5IVbq6szVhOivqZ6hUr8ExgD1IT27B2fyIjv99Do2plefEBf9rV9pDP28bMmzePKVOmoJQiKCiIWrVq4ebmxosvvsiuXbsYPnw4rq6utGvXjjVr1nDw4EEOHTrEsGHDSE9PJzs7m6VLl1KnTh0eeughIiIiSE1NZdy4cflOSzB//nymT59Oeno6LVu2ZNasWfnORyPu3G27ZZRS7YFEYF6e5N4F2KC1zlRK/RdAa/2KUioA+BFoAVQB1gF1tdZZtyqjSMa5Xz0LO740TR+QngA12kLrMVC3KxjsiEqMwsPZA6OdjPQoEtei4NR6kk78ztqobfziqNjl7ITS0NypErVc7ufXw8FciIOWNSvw0gP+NPOtcNvDlnY3fPVeMx4uHijaArwDodvkW25y6NAh+vbty19//YWHhwdXrlxh+vTpucm9YcOGzJ49mzZt2jB+/Hh+/fVXDh48yLPPPkurVq0YPHgw6enpZGVl4ezsnDs/S0pKCs2bN2fz5s1UrFgxt1smJiaGl19+mWXLlmE0Ghk9ejStWrUq0i6gkqDIu2W01luUUr7/WvZHnrfbgX45r3sDC7XWacAZpdRJTIl+2x3X4G5F7oa/Z8CRX0AZoEEfaDUaqja5YbPKbpXNFkKp5F4ZGg/BtfEQHsrK5KELe4k8+jMrz23gl8Tz7ExbiKP3QjpWqUJ4dGv6fRlNR/8qvNClLg2qlLV09OIWNmzYQL9+/XL7wvNOnBUXF0dCQgJt2rQBYNCgQblTDrRu3Zr33nuPyMhI+vbtS506dQCYPn06y5cvByAiIoITJ05QsWLF3GOuX7+ePXv20Lx5cwBSUlIKPa+KKJo+9yeB61OjVcWU7K+LzFl2E6XUSGAkkO/c0Hfk9CaY19s0wVebZ6HFSChb7d6OJe6dnT34tKCaTwtGAc8kXmbf3i9YfepXfs+M4IrnBbwrLiMmsSoD5oZwX61QXuhSj1qe5n2GpM27TQvbXG417e2tvukPGjSIli1bsmrVKh544AHmzp2LwWBg3bp1bNu2DRcXF0JCQvKd8nbo0KF88MEHRVqP0q5Qg5SVUq8BmcD1+TPz+xeR778GrfVsrXUzrXUzT0/PewvA9z54cBr832Ho/LYkdiuh3DwIbv86rw7bzvpev/BlpU6EZipiXM9BjfnsSx3OqAWP8cyPi4m4Ig/4zk9cXBxxcXEWKTs0NJTFixcTGxsLcMPTiMqXL0+ZMmXYvt3Uhlu4cGHuutOnT+Pn58fYsWPp1asX+/fvJz4+nvLly+Pi4sLRo0dz9/t3eUuWLCE6Ojq3vNtN4Stu755b7kqpoZgutIbq//05jwR88mxWDbhw7+HdhsEOmj5htsOLwrP3qE2brp/SRmsmRuxgy57PWRW9kz/LnSAq/R2GLfmQbp7P8p8Hh5ScqRyKwPWnCpUrV67Yy27QoAGvvfYaHTp0wM7OjsaNG+Pr65u7/quvvuKpp57C1dWVkJAQypY1dbMtWrSI+fPnYzQa8fb2ZtKkSbi6uvLFF18QFBSEv79/7hOe8goICODdd9+lS5cuZGdnYzQamTlzJjVq1CiuKpdIdzTOPafP/dc8F1S7Ap8AHbTWMXm2awD8wP8uqK4H6hTLBVVhO7IyuXZ8Nav2zObL9NNcMxh4KM6N3o1folH7vlCKR9Zcv2iWkZEBgNFofQMArk95CzB58mSioqKYNm2ahaMq+e72guptu2WUUj9iuiDqr5SKVEoNBz4DygBrlVJhSqkvALTWh4DFwGHgN2DM7RK7KIXs7HGv34uBQ35lWe8VNLGvwk/lk5hxZDz7/xtE/NY5kJFi6Sgtymg0WmViB1i1ahXBwcE0bNiQP//8k9dff93SIYl82PQdqqJk0Fqz+PAiPtz9X4xZmbwZe5mQdAccWjyJoeVT4F7F0iEWm+uts6tXrwKmPm4hwAwtdyHMTSnFow0GsLzPCqpVbMBLXh484+5Fwt9Tyf40EJaOMM1zU4pER0fnXmAU4l5IchdWo7p7dRY+OJ9RjUbxj3s6IdUCeNeuDamHVqHndISjqywdYrGpVasWtWpZ6aMahU2Q5C6sir3BntHBo5nXfR7e5cryk89ZWrj1JkzXJPunYXBmi6VDLBb29vbY29v01E/CwiS5C6vUyLMRS3st4eE6fdEV/2J4VU/2ZnuRtWAARO6xdHhmd+XKlRvGlwtxtyS5C6vlYnThzTZvMjVkKsrpMs9U9eZwlhsZ8/pC9BFLh2dWMTExxMTE3H5DM7l48SIDBgygVq1aBAQE0L17d44fP17g9teHRl64cIF+/frlLh84cCBBQUF8+umnhY5p9+7djB07ttDHKS1ktIywCdsubOM/65/FLr0c8yNPUsPJiOPItVDe19KhFanrIyKyskwjiC0xM6LWmjZt2jB06NDcqX7DwsJISEjgvvvuy3cfNzc3EhMTb1h28eJFWrZseVd3m2ZmZkp3VAFktIwokVpXac2MjtPJcrjK0Gq1uZSaTNLcByHhoqVDMws7OzuLTXm7ceNGjEbjDXO4BwcH07hxY0JDQ2nSpAmBgYGsWLHipn3Dw8Np2LAhAF26dCE6Oprg4GD+/PNPwsLCaNWqFUFBQfTp0yd3uGdISAivvvoqHTp0YNq0aYSEhPDKK6/QokUL6taty59//gnApk2b6NmzJwA7d+6kTZs2NG7cmDZt2nDs2DFzfyw2R/5ECpvRpmobpnWcxtgNYxnkU4+fIg6SOftByo76HVxK1nTCsbGxzDg0g7PJRTvHSr0K9XilxSu33ObgwYM0bXrzg2ycnJxYvnw57u7uXL58mVatWtGrV68CJxn75Zdf6NmzJ2FhYQAEBQUxY8YMOnTowKRJk3jrrbeYOnUqYJpLZ/PmzQCsXLmSzMxMdu7cyerVq3nrrbdYt27djfWoV48tW7Zgb2/PunXrePXVV1m6dOldfholm7TchU1pV7UdU++fSrLxKgN8GpCReIaYL3tDWuLtd7Yhly9fJjUl9fYbFiOtNa+++ipBQUF06tSJ8+fP586Bczvx8fHExcXRoUMHAIYOHcqWLf8b+fToo4/esH3fvn0BaNq0KeHh4fke75FHHqFhw4Y8//zzHDp06B5rVXJJy13YnPbV2vNpyKc8t+k5HvUJZOm5MCK/eJhqY34Be0dLh1ck6tSpw/t13sdgKP72V4MGDViyZMlNyxcsWEBMTAx79uzBaDTi6+t70/S998rV1fWG946OpvNoZ2dHZmbmTdtPnDiR+++/n+XLlxMeHk5ISEiRxFGSSMtd2KQOPh34NORTrhjj6F+9EeXidnDi8wHorAxLh1YkDAaDRRI7QMeOHUlLS2POnDm5y3bt2sXZs2fx8vLCaDSycePGu7pQWrZsWcqXL5/bf/7999/ntuLvRXx8PFWrmh4V8e23397zcUoySe7CZoX4hPBxh4+JsY+nv08Qla9sZN/nw9DZ2ZYOrdAuX77M5cuXLVK2Uorly5ezdu1aatWqRYMGDXjzzTfp3r07u3fvplmzZixYsIB69erd1XG/++47XnrpJYKCgggLC2PSpEn3HOPLL7/MhAkTaNu2be7IInEjGQopbN76s+t5cfOL+GS68GPEIY41/S9Nej5t6bDuyfXhbtdHf/j7+1s4ImEtZCikKHVCa4TyYYcPOWefxDDv6njtmUxKYrylwyoUf39/SeyiUCS5ixKhc43OTG4/mSNOmjkV4cDCNywdkhAWJcldlBhdfbvyVOBTLCvjRsTVpUSfPWrpkO6J1tri0w8I63Iv3eeS3EWJMjp4NEHlGvKBR1l2LXvW0uHcNScnJ2JjY4mNjZWJwwRgSuyxsbE4OTnd1X4yzl2UKPYGe6Z2nk7vxd340i2Can8toVHbfrff0UpUq1aNyMhItNZorTlypGRPkCbujJOTE9WqVburfWS0jCiRtpzZzLObx9A+WTF1xG7sHErGzU1C5CWjZUSp075mBx50vY9NrjBtyWhLh3PXZs2axaxZsywdhrBhktxFifVmnxkEphqZn76DPWf+tHQ4d2XlypWsXLnS0mEIGybJXZRY9vb2jGv2ERWysnh503NcS79m6ZDu2Jo1a1izZo2lwxA2TJK7KNFaNg1lSEpDrqg0Xv7t2XsaUiaELZLkLkq8bo/M4OkrKfx1dS/zD39v6XDuyLRp05g2bZqlwxA2TJK7KPEqVfKmjucI7k9K5uPdHxMWHWbpkG5r/fr1rF+/3tJhCBsmQyFFqZCals6RD5vzSuVMst29+anXEso7lbd0WEIUigyFFKWek6MDKW3f5dPoS8SmXObVra+SlS1TxYqSS5K7KDVa39+TOEMbXrh8la3nt9J7RW9+PPojyRnJlg7tJlOmTGHKlCmWDkPYMEnuotRQSuHR57/0vZbCc0neuDu48/6O9+n0Uyc+3v0xUYlRlg4x17Zt29i2bZulwxA2TPrcRamzdtbzdI7+mnMe7YmoHcRyFcO6SzsBCK0eymMBjxHsFWzZIIW4A7fqc5fkLkqdy1fj+Hv2OAKTtlHTcAmA084e/FSlJiuIIyE7jSCPIIYEDKFTjU4YDUYLRyxE/iS5C5GPUzGJ/LlzL1cOrsUvcTdtDYdwNVzjFzdXvi9fgXN24GV0Z1zT53mw7sMopYottsmTJwMwfvz4YitT2J5bJXeZ8leUWrU83ajVoz30aM/J6AR+2BfFwX07qHplJ+PiDkCZ03xXNo3Xtr/F+tOrmRjyIR7OHsUSW1hYWLGUI0ouabkL8S/HLyXw6/4oVu+LoMaVrTT1mM9XFRxxtXdmYrv36OzbxdIhCgFIt4wQ90RrzY4zV3jvp80MTPuEFd7RHHZ0pLtPJ15t+yZlHctaOkRRyhXqJial1NdKqWil1ME8yyoopdYqpU7k/C6fZ90EpdRJpdQxpdQDRVMFIYqfUopWfhX58ble7Ks/k3pnO/HM1Wv8cW4tfZf14M9I800j/M477/DOO++Y7fii5LuTce7fAl3/tWw8sF5rXQdYn/MepVQAMABokLPPLKWUXZFFK4QFuDna899HGtFu8BtsvfYaH57PokziZUavH82bf00iKSOpyMs8duwYx44dK/LjitLjjrpllFK+wK9a64Y5748BIVrrKKVUZWCT1tpfKTUBQGv9Qc52vwNvaq1veTeGdMsIWxGTkMYbS3bQ+vTHXPb4h2/LlqWyixfvtJ9Mc+/mlg5PlDLmmFumktY6CiDnt1fO8qpARJ7tInOW5RfUSKXUbqXU7piYmHsMQ4ji5VnGkZlP3IfxwRmcjX2SWVHxGK5F8eTvTzLxr4mcu3bO0iEKART99AP5DQTO96uB1nq21rqZ1rqZp6dnEYchhPkopRjQojrPj3uJeWWmMyHClcfjr7H6xM88uLwnr6wbw8mrJwtVxqRJk5g0aVIRRSxKo3tN7pdyumPI+R2dszwS8MmzXTXgwr2HJ4T1qlHRlVmje3Ogw/dkRvdgxll4PC6ejRGb6PNLH55b1JVDR5ZCdvZdHzsiIoKIiIjbbyhEAe61z/0jIFZrPVkpNR6ooLV+WSnVAPgBaAFUwXSxtY7W+pZzq0qfu7B1p2ISWbInkl17/8E/ZRPZFXaxqWwaiXYG2qZn87RHCxoHDgbf+8De0dLhihKiUOPclVI/AiGAB3AJeAP4GVgMVAfOAY9ora/kbP8a8CSQCTyntb7tU34luYuSQmvN3nNXWRF2gS3791PeeSlRFU6QZKdpnpLKU0kZtGo0DBU6CYpxOgNRMslNTEJYQEZWNn+dvMzysNNsilyOXbkNZNin0j0xickNn0F1eLnAfSdMmADABx98UFzhChskc8sIYQFGOwMh/l6E+HuRmtGc3w5FMnX3dFa7/UGXHZ8QWrYaBA/Kd9/Y2NhijlaUNNJyF6IYJaSm0n5BL8pwkTUXLuA6aDHU6mjpsISNkmeoCmElyjg5Man1G1y113xUvgp60eMQtd/SYYkSSJK7EMWsT0BbAt27sNQN9hldYcEjEHfjzU8vvvgiL774ooUiFCWBJHchLGBm19exV64ML1OdjPRkmN8Pkq/krk9JSSElJcWCEQpbJ8ldCAso71yeF5u9QLrzRQa79UJfPQMLB0NGKgAzZ85k5syZFo5S2DJJ7kJYyKCAh6njHsRht118Vfl5OPc3LH/6nu5oFeLfJLkLYSFKKT66/20MdulMSTrOoYYvweGf4Y/Xee6553juuecsHaKwYZLchbCgWuVq8USDoRjL7WHAiQokNBoO22dC1D5LhyZsnCR3ISxsVPAzVHKuDB7LGHqhJ9n1HmRq/X+YOrydpUMTNkySuxAW5mzvzMTWr4HDJQ4mr2G6+0vg0xKWDofvekH4VkuHKGyQJHchrEAHnw6EVg/F2WsD07fv5+EN3ozZHwTRR+DbHvB1Nzi1AazgjnJhGyS5C2ElxrcYj6O9HeV9VvHXuWSUTzN4bj90/S9cDYfv+8DcTnD8d0ny4rYkuQthJbxdvRkTPIZ0h0OU696C5MaDyLZzglbPwLgw6PEJJEbDD/1hdgc4slKGTYoCSXIXwooMqj+IOuXrUKH6ajYci+CTtcdNK+wdoflwGLsXes+EtARYNAS+aAuHV1g2aGGVJLkLYUWMBiOTWk3i6Jx96B1v8dnGk/yyL8+TKu2M0HgIjNkFfedAdhYsfhzWvGJ6LUQOSe5CWJlgr2ACfAJIdblIo5qpvPTTPvZHxt24kZ09BPWH0dug1RjY8QUsHARpiRaJWVgfSe5CWKGVc1ZSe3BtyvmsoqKbA0/N282la6k3b2iwg67vQ4+P4cRa+KYrxJ8v/oCF1ZHkLoQVKu9Unv80/g97Y3Yx4oEEElIzGfn9HlIzCuh6aT4CBi2GK+EwNxQuhBVnuMIKSXIXwgoNGzaM3yb/hn95f348NZPJ/fzZFxHH+KX7KfDpaXU6wfDfwWAP33SDo6uKN2hhVSS5C2GFfHx8qFG9BhNaTuBi0kXOZv3Ki13q8nPYBT7ffKrgHSs1gBHrwbOeaQrhbTNlTHwpJQ/IFsIKvf3227mve/j14JuD3/Bz794cu1SFj34/Rl2vMnQKqJT/zmUqwROrTNMH//4qxJ6Cbh+aLsKKUkNa7kJYuf9r+n8YDUY+2vURH/ULIrBqWcYt/IdjFxMK3snBBR75DtqOg91fmW58Sr1WfEELi5PkLoQVGjJkCEOGDAHAy8WLpxs9zabITey89BezH2uGq6M9w7/bxZWk9IIPYjBA57fhwWlwZjN8/QAkxhRTDYSlSXIXwgr5+/vj7++f+/6x+o/h6+7Lf3f+lwpuBmY/3ozohDRGzd9DeuZtpiBo+gQMXgJXzsDCgZAhz2YtDSS5C2GFJk6cyMSJE3PfG+2MjG8xnnMJ55h3eB7BPuX4qF8QO85c4c2Vh25/wFr3Q9/ZELkbfh4lc9KUApLchbARbau25X6f+5m9fzYXky7SO7gqo0Jq8cOOczdOUVCQgF7Q+S04tBw2vmv+gIVFSXIXwgoNGDCAAQMG3LT85eYvk5WdxSe7PwHghc51aVy9HK8vP8CFuDvobmkzFpoMhT8/hn/mF3XYwopIchfCCgUHBxMcHHzT8mplqvFk4JOsCV/Drou7sLczMPXRYDKzNS8s3kd29m3GtCtlmqrA735YOQ5ObzZPBYTFqQLvditGzZo107t377Z0GELYhJTMFB76+SFcHVxZ3HMx9gZ7Fu+K4OWl+3mte32eau93+4OkxsNXXSAhCoavA8+65g9cFDml1B6tdbP81knLXQgb42zvzEvNX+LE1RMsOrYIgEeaVaNLQCU++v0YR6LuYDy7U1nTXDR2DrCgHyRdNnPUorhJchfCCj388MM8/PDDBa4PrR5Kq8qtmPnPTGJTYlFKMfnhIMq6GHluYVjBE4zlVb4GDFwIiZdM0wVn5DPrpLBZktyFsEKtW7emdevWBa5XSjGhxQRSMlOY/s90ACq4OvBRvyCOXUrgo9+P3VlB1ZpBny8hYgesGC1DJEsQmWxCCCv04osv3nYbv3J+DAkYwreHvqVBxQb09+9PiL8Xj7euwVdbz3C/vxft6njcvrAGD8GVN2D9W1DBDzq+XvgKCIuTlrsQNuzZxs/SoVoH3tn+Dj8d/wmACd3qU8vTlRd+CiMu+RbTE+TV7nlo/Bhs+QjCfjBjxKK4SHIXwgr16tWLXr163XY7BzsHPgn5hPuq3sfb295m2YllODvYMW1AY2IT03lt+cGC53/PSyno+SnU7AC/jIWlI2DnHIjaL89mtVGF6pZRSj0PjAA0cAAYBrgAiwBfIBzor7W+WqgohShlQkND73hbBzsHPr3/U8ZtHMebf7+JQtGnTh+e71yXj34/Rug/XvRtUu32B7IzQv95sOZlOL0JDvyUU4CbqW/epyX4tIBqzU2jbYRVu+dx7kqpqsBWIEBrnaKUWgysBgKAK1rryUqp8UB5rfUrtzqWjHMXovDSstIYu2Es2y5s452279DTrxcDZm/jSFQCa8bdh08Flzs/mNYQd850ofX6z6VDoLMBBV4BpkRfsRa4VQI3r5zflcC5vOmbgDC7W41zL2xy3w40Aq4BPwPTgRlAiNY6SilVGdiktfYv8EBIcheiqKRmpvLshmfZEbWD99q9R3D5ULpN+5P6lcuwcGRr7AyFSLppCXB+D5zLSfaRuyEt/ubtDMacZO/1v8Tv1QD8OpieECWJv8iYJbnnHHgc8B6QAvyhtR6slIrTWpfLs81VrXX5fPYdCYwEqF69etOzZ8/ecxxClDTdunUDYM2aNXe9b0pmCs+uf5Zdl3bxXrv3SLsazAs/7ePlrv6MDqlddEFqbUr4idGmsfKJl/K8zrMs4SIkRZv2catk6tf362D6Xc6n6OIphW6V3O+5z10pVR7oDdQE4oCflFJD7nR/rfVsYDaYWu73GocQJdGDDz54z/s62zszI3QG/1n/H17b+hrvt3ufHoGV+eSP47Sp5UGwT7miCVIpcHI3/Xjc5o9G3DnTPDanN8HpjXBgsWl5hVr/S/Q124NLhf/tk5UJmSmmm6syc34yUiAzzbRdxVpFU48SqjDdMo8AXbXWw3PePw60AkKRbhkhLC45I5kx68ewN3ovk1q+xyfLTX3uvz7bjvKuDpYLTGuIPmJK9Gc2Q/hfkJ4AKHCp+L8krm8zSsf3Pmg+Aur1MF0MNqfsbLh8DC4ehGpNTfcDWAFz9bm3BL4GmmPqlvkW2A1UB2LzXFCtoLV++VbHkuQuhHkkZyQzat0o9sXsY3SDN/homSPtanvw1dDmGArT/16UsjLgwj+mZJ8QBfbOYO8IRmewd8r57WhabnQyLbt0yPRs2LhzUKYKNHsSmg419e8XhfSkf11f2GmabO26ai2g0aPQoO+N3zaKmTn73N8CHgUygX8wDYt0AxZjSvLngEe01ldudRxJ7kLcqFOnTgCsW7eu0MdKykhi1LpR7I/ZzxCfT/js9xReesCfMfcXYf+7JWRnwYk/YOdsOLXBdCG3wUPQYqRpuOadXrjNzoZr500JPGInnNsOFw/875uDZz3TMNDqrcCrvql7af8iiD4MBnuo0wWC+kPdrqY/RMXIbMm9qEhyF+JGc+bMAeCpp54qkuPFp8XTbVk3mldqjr70BL/uv8D84S1pU/sOpiewBZdPwq65ELYA0q5B5UamJB/wkOmi77ULcC0y5/f5nN/XX0dBdobpOPbOecb0twSf5qahnf+mNVw6aEryB5aYvnE4upuedhX0KNRoZ3pAuZlJchdC8Pm+z5kVNovvHviRl3+IJS45nVVj76OSu5OlQys6aYmmhLtrrqllnR87R3CvAu5Vc35XgbLVoGpT8A68+/777CwI/xP2L4bDKyA90XTsXjOg9p3fjHYvJLkLIbiWfo2uS7vSrFIznm3wHr0++4uGVd354alWGO1K2EwkWsPZvyB8K7h65knkVU195OYaa5+eDMfXwKbJpj76MTvBuZx5ykIe1iGEzQkJCSEkJKRIj+nu4M5jAY+xMWIjmfaRTH44kF3hV5lyp9MD2xKlwLcdhIyH5sPBvytUDgLXiua9icrBBRo+DH3nQFIMrHvTfGXdhiR3IazQE088wRNPPFHkxx1cfzBlHMrw+b7P6R1clSGtqvPlltP8fuhikZdVqlUJhlajYc83cHabRUKQ5C6EFTJXcs/bej8Se4SJPQMIqlaWF3/ax9nYpCIvr1S7/1UoW930IPLMtGIvXpK7EFYoIyODjIwMsxx7SP0hua13R3s7Zg5qgkEpRs3fe2eP5xN3xsEVenxsuvnpr2nFXrwkdyGsUOfOnencubNZjl3GoQyPBzye23r3qeDCp4824nDUNd5aecgsZZZadbuY+uC3fASXTxRr0ZLchbBCI0aMYMSIEWY7ft6+d4CO9Sox5v5a/LgzgqV7Is1WbqnUdbLp5qaVz5lG8RQTSe5CWKEhQ4YwZMgdz8N31/K23g/HmsaDP9+pLq39KvLazwc4eD6fqXzFvXHzgs7vwNmt8M/8YitWkrsQVig5OZnk5GSzlvHv1ru9nYHpAxtT0dWRx7/eycnoBLOWX6o0fgxqtIU/XjdNh1wMJLkLYYW6d+9O9+7dzVrG9db7pohNua13zzKOzB/REoNSDJ67g3Ox5v0DU2oYDNBzKmQkw28TiqfIYilFCHFXRo0axahRo8xezr9b7wA1PVxZMKIlaZnZDJq7naj4FLPHUSp41oX7XoCDS+BE4SeEux1J7kJYoUcffZRHH33U7OXk13oH8Pcuw7wnWxCXnMHguTu4nFj847RLpHbPg0ddWPW8aVphM5LkLoQVio+PJz6+eC5q5td6BwiqVo5vhjXnQlwKQ+buIC45vVjiKdHsHU3dM3HnYNMHZi1KkrsQVqh379707t27WMoqqPUO0Ny3AnMeb8bpmCSGfrOLxLTMYompRPNtC02GwrZZELXPbMVIchfCCo0dO5axY8cWW3mD6w/G3cGdz8M+v2ndfXU8+WxQYw6ej2f4t7tISZe7WAut81umRwr+MtY0ZbAZSHIXwgr17duXvn37Flt5ua33yE0cir35LtUuDbz5pH8jdoZfYdSCPaRnZhdbbCWSc3noNhmiwmDHl2YpQpK7EFbo8uXLXL58uVjLHFR/EO4O7nwR9kW+63sHV+WDPoFsOhbDuIX/kJklCb5QGvQ1PZov8ZJZDi/JXQgr1K9fP/r161esZeZtvb+/4302R2wmKePGER0DWlRnYs8A1hy8yMtL9pOdbfmH/dgspWDAD6YuGjOwN8tRhRCF8sILL1ik3MH1B3Mo9hDLTizjx6M/YqfsCPQIpGXllrSq3IpGno0Y3q4myWmZfLz2OOGxSTzbsQ4h/p4ocz4Eo6Qy2Jnt0PKYPSHETdKy0giLDmNH1A62R23nUOwhsnU2zvbONKnUhFberYi7UoPFf2dzIS6VBlXcebZjbboEeGMwSJIvLvIMVSFszMWLpicjeXt7WzgSk2vp19h1cRc7onawI2oHp+NPA9Cpeheau45m9uZzhMcmU8fLjTH316ZnUGXsS9pzWa2QJHchbMz156du2rTJonEU5FLSJZadXMassFk0927OJx2msvloArM2nuLYpQRqVHRhdEgt+jSuhoO9JHlzkeQuhI357bffAOjatauFI7m1X0//ysS/JuJX1o/PO32Oh5Mna49c4rMNJzlwPp4qZZ14ukMtHm3ug5PRfP3LpZUkdyGE2fx94W+e3/g85RzL8UXnL6hZtiZaa7acuMxnG06wK/wqFV0deLy1L4+1rkEFVwdLh1xiSHIXwsZEREQA4OPjY+FI7syh2EOMXjeabJ3NzNCZBHkG5a7bcTqWL7ecZsPRaJyMBvo382F4u5rUqOhqwYhLBknuQtgYa+9zz0/EtQieXvc0MckxfBzyMe2rtb9h/YlLCcz58zQ//3OBzOxsujb05qn7/GhcvbyFIrZ9ktyFsDHr1pnm++7UqZOFI7k7l1MuM3rdaI5fPc4brd+gT50+N20TfS2Vb/4OZ/72sySkZtLCtwIj2/vRsZ6XDKO8S5LchRDFJikjiec3Ps+2qG2MbTyWEYEj8r3BKTEtk0W7Ivh66xnOx6VQy9OVzgHeONgbMBoU9nYGjHYK+xteG7C3U1Sv4CItfiS5C2FzTp82jSP38/OzcCT3JiMrg9f/ep3VZ1YzsN5AXmn+CnYF3I2ZmZXNqgNRzP3zDEeirpF5h1MahNbz4tUe9anl6VaUodsUSe5C2Bhb7HP/t2ydzce7P2be4Xn08OvBe23fKzDB56W1JjNbk5mlycjOJjNLk5mVTUZ2zu8szfqc4ZYpGVkMaVWDcaF1KF8KR+HcKrnL3DJCWKG33jLPZFLFyaAMvNT8Jco6lmXGPzNwsnPijdZv3HYOGqUURjuF0Q6cyf+PQW0vNx5uWo1P1x5n3rZwlu2NZGxoHR5v7Ss3TeWQlrsQwuym7Z3G3ANzeTzgcV5s9mKRTjJ2/FIC7646wpbjMfhWdGFC9/p0CahUKiYyu1XLXf7ECWGFjh07xrFjxywdRpEZ23gsA+sNZN7heXyxL//54u9V3Uqmh3l/O6w5RjsDT3+/h4FztnPwfPE8g9ZaSbeMEFbo6aefBmy7zz0vpRTjW4wnOSOZWftm4WJ0YWiDoUVaRoi/F+1qe/Djrgg+XXucBz/bykPBVXmkWTVa1qyIXSkbZlmo5K6UKgfMBRoCGngSOAYsAnyBcKC/1vpqYcoRorR5//33LR1CkTMoA2+2eZPkzGSm7J6Ci9GFR+o+UqRl2NsZeKxVDXoHV2HmhpPM23aW5f+cx7OMI90betOzURWaVi9fKsbTF6rPXSn1HfCn1nquUsoBcAFeBa5orScrpcYD5bXWr9zqONLnLkTpkZGVwbiN49h6fivv3/c+Pf16mq2s5PRMNhyNZtX+KDYcjSYtMxtvdye6B1amZ6PKNPYpZ9N982YZCqmUcgf2AX46z0GUUseAEK11lFKqMrBJa+1/q2NJchfiRgcPHgSgYcOGFo7EPFIzUxm9fjR7L+3l45CPCa0eavYyE9MyWX/kEr/uj2LzsRjSs7KpWs6ZHkGV6RxQCWejHelZ2WRkmoZbZmRl5/yYXqdnZWOnFA809MbN0Tp6tM2V3IOB2cBhoBGwBxgHnNdal8uz3VWt9U23kimlRgIjAapXr9707Nmz9xSHECVRSRjnfjtJGUmM/GMkR64c4bPQz2hTpU2xlX0tNYN1h02J/s8TMWRk3XkebFy9HN892QJ3J6MZI7wz5kruzYDtQFut9Q6l1DTgGvDsnST3vKTlLsSNdu3aBUDz5s0tHIl5xafF8+TvT3Lu2jm+7PwlTSo1Kf4YkjPYGX4FAKOdwsHOgNHegL1BYbQzmKZDyJn+YO+5OP5vURgNqpZl3pMtKOts2QRvruTuDWzXWvvmvL8PGA/URrplhBB36HLKZYb9NozLKZeZ3Xk2gZ6Blg7plv44dJExP+wloLI784a3tGiCN8s4d631RSBCKXU9cYdi6qL5Bbg+xmkosOJeyxCitAoLCyMsLMzSYRQLD2cP5nSZg7uDO0PWDOHlLS9z4uoJS4dVoC4NvJk1uCmHo67x2Fc7iE/OsHRI+SrsaJlgTEMhHYDTwDBMfzAWA9WBc8AjWusrtzqOtNyFuFFp6HP/t9iUWL479B0Ljy0kJTOFjj4dGRk0kgYeDSwdWr7WH7nEqPl7qevtxvzhLSnnUvxz28jEYULYmOut9uDgYIvGYQlxqXEsOLqABUcWkJCeQNuqbRkZONIi/fG3s/FoNE9/v4c6ldxYMKL4E7wkdyGEzUlIT2DRsUXMOzSPq2lXaVapGSODRtKqciurGpu+6Vg0I7/fQ21PU4IvztkpJbkLYWNKy2iZO5GckczSE0v59uC3RKdEE+gRyPDA4YRUC7mjKYSLw+bjMTw1bze1chJ8cT0EXJK7EDamNPa5305aVhorTq7g64Nfcz7xPFVcq/CI/yP0rdOXCk4VLB0eW3ISfE0PVxaMaElFN0ezlynJXQgbU9LvUC2MjOwMNp7byMJjC9l1cRdGg5Guvl0ZUG8AgR6BFu2y2XriMsO/24VvRVd+eMr8CV6SuxCiRDp59SSLji3il1O/kJyZTEDFAAb4D6BbzW442TtZJKa/Tl5m2De7GNjCh7d6m/ePsyR3IWzM33//DUCbNsV3S74tS8pIYuWplSw8upBT8adwd3CnT+0+9K3bF193XwyqeB9dMWr+Hnafvcr2CaFmnWpYkrsQNkb63O+N1prdl3az8OhC1p9bT5bOwsnOiRruNfAt60vNsjXxdfc1vXaviYvRxSxxrNx3gWd//IdFI1vR0q+iWcoAeYaqEDbnyy+/tHQINkkpRXPv5jT3bk50cjSbIzdzJv4M4fHhHI49zNqza8nW2bnbe7l4UdO9Jn7l/GhfrT0tK7fEaCj8dAId63nhaG9g9YEosyb3W5GWuxCi1EjPSufctXOEXws3Jf1r4YTHh3Mi7gQpmSm4O7hzv8/9dPHtQuvKrTHa3Xuif/r73fxzLo7tE0LN9nAQabkLYWM2b94MQIcOHSwcScniYOdA7fK1qV2+9g3L07LS2HZhG3+E/8GGcxtYcWoFZYxlCPEJMSX6Kq1xtLu7kS/dAyvz+6FL7Dl3lea+xT9UU5K7EFbojTfeAKTPvbg42jkS4hNCiE8I6VnpbI/aztqza9lwbgMrT6/E1ehKh2odeMD3AUJ8Qu7oAm1o/Uo42BtYtT/KIsldumWEsEKnT58GwM/Pz8KRlG4ZWRnsvLiTtWfXsv7ceuLS4uhbpy9vtn7zjsbTj5y3m32RcWwbb56uGbNM+SuEMB8/Pz9J7FbAaGekbdW2vNnmTTb238iIwBEsO7GMz/d9fkf79wiqzKVraew9d9XMkd5MkrsQVmjdunWsW7fO0mGIPOwN9oxtPJaHaj/E5/s+56fjP912n471vExdMweiiiHCG0lyF8IKvfvuu7z77ruWDkP8i1KKSa0n0a5qO97d/i6bIjbdcvsyTkba1/FkzYGLZGcXbxe4JHchrND333/P999/b+kwRD6MBiMfd/iYgAoBvLT5JfbF7Lvl9j2CvLl4LZV/Ioq3a0aSuxBWyMfHBx8fH0uHIQrgYnThs9DP8HLx4j/r/8OZ+DMFbhtavxIOdgZWH7hYjBFKchfCKv3222/89ttvlg5D3EJF54p80ekLDMrAqHWjiEmOyXc7dycj7et6sOZAVLF2zUhyF8IKTZ48mcmTJ1s6DHEbPu4+zAqdxZXUK4xeP5rE9MR8t+seWJkL8amERcYVW2yS3IWwQgsXLmThwoWWDkPcgQYeDfgk5BNOXj3J85ueJyMr46ZtOgVUwminWL2/+EbNSHIXwgp5e3vj7e1t6TDEHWpXtR1vtnmT7VHbmfj3xBsmJwNT18x9dTxZc/AixXXjqCR3IazQypUrWblypaXDEHehd+3ejG08llWnVzF179Sb1ncPrMz5uBTCIuKKJR6ZW0YIK/Txxx8D8OCDD1o4EnE3RgSO4FLyJb45+A1VXKswoN6A3HWdr3fNHIiicfXyZo9FkrsQVmjJkiWWDkHcA6UUE1pM4FLSJSbvnIxfWT9aVG4BQFlnI+1qe7D6wEVe7V7f7M96lW4ZIayQh4cHHh4elg5D3AM7gx0f3PcBNdxr8MLmF4hMiMxdd71rZn9kvNnjkOQuhBVatmwZy5Yts3QY4h65ObgxveN0snQW4zaOIzkjGYAuAd7YG0xdM+YmyV0IKzR9+nSmT59u6TBEIdRwr8GU9lM4GXeS1/96Ha01ZV2MtK3twaoDUWYfNSPJXQgrtGLFClasWGHpMEQhtanahv9r+n+sPbuW2ftnA9AjsDKRV1M4cN68XTOS3IWwQmXLlqVs2bKWDkMUgccDHudBvwf5LOwzNpzbQJcGlbA3KLNPAyzJXQgrtGjRIhYtWmTpMEQRuD5NcMOKDZnw5wQup52jTW0PVpu5a0aSuxBW6PPPP+fzz+/saT/C+jnZOzH1/qm4GF0Yu3EsHQNcibiSwsHz18xWpiR3IazQ6tWrWb16taXDEEWokmslPg35lItJF9l89VPsDNmsPmi+rhlJ7kJYIRcXF1xcXCwdhihiwV7BTGw1kd3RO6hRZ6NZu2YkuQthhebPn8/8+fMtHYYwgz51+jCo3iBiDGs5n/knhy6Yp2tGkrsQVmju3LnMnTvX0mEIM3mx+Ys08WyOk/dy5u3dYpYyJLkLYYXWrl3L2rVrLR2GMBOjwci0jp/gbCjPwaSlZimj0BOHKaXsgN3Aea11T6VUBWAR4AuEA/211sX7ZFghbJzRaLR0CMLMyjmVY1mf7/BwMc8cQkXRch8HHMnzfjywXmtdB1if814IcRe+/fZbvv32W0uHIczMx90HZ3tnsxy7UMldKVUN6AHk7RzsDXyX8/o74KHClCFEaSTJXRRWYbtlpgIvA2XyLKuktY4C0FpHKaW88ttRKTUSGAlQvXr1QoYhRMmyadMmS4cgbNw9t9yVUj2BaK31nnvZX2s9W2vdTGvdzNPT817DEEIIkY/CtNzbAr2UUt0BJ8BdKTUfuKSUqpzTaq8MRBdFoEIIIe7cPbfctdYTtNbVtNa+wABgg9Z6CPALMDRns6GAzFsqhBDFzBzj3CcDnZVSJ4DOOe+FEEIUoyJ5QLbWehOwKed1LBBaFMcVQghxb+QOVSGEKIEkuQshRAkkyV0IIUogSe5CCFECKXM+w++Og1AqBjhbiEN4AJeLKBxLKin1AKmLNSop9QCpy3U1tNb53gVqFcm9sJRSu7XWzSwdR2GVlHqA1MUalZR6gNTlTki3jBBClECS3IUQogQqKcl9tqUDKCIlpR4gdbFGJaUeIHW5rRLR5y6EEOJGJaXlLoQQIg9J7kIIUQJZZXJXSn2tlIpWSh3Ms6yRUmqbUuqAUmqlUso9Z/lgpVRYnp9spVRwzrqmOdufVEpNV0opG67LJqXUsTzr8n3ClZXUw6iU+i5n+RGl1IQ8+9jaOblVXSx6Tu6hLg5KqW9ylu9TSoXk2cfWzsut6mLp/ys+SqmNOf9eDimlxuUsr6CUWquUOpHzu3yefSbkfPbHlFIP5Fl+7+dFa211P0B7oAlwMM+yXUCHnNdPAu/ks18gcDrP+51Aa0ABa4BuNlyXTUAzWzgnwCBgYc5rFyAc8LXFc3Kbulj0nNxDXcYA3+S89gL2AAYbPS+3qoul/69UBprkvC4DHAcCgA+B8TnLxwP/zXkdAOwDHIGawCnArrDnxSpb7lrrLcCVfy32B7bkvF4LPJzPrgOBHwGU6SlQ7lrrbdr0Kc3DAg/rLoq6WIO7rIcGXJVS9oAzkA5cs9Fzkm9diiPOO3GXdQkA1ufsFw3EAc1s9LzkWxfzR3l7WusorfXenNcJwBGgKtAb+C5ns+/432fcG1MDIk1rfQY4CbQo7HmxyuRegINAr5zXjwA++WzzKP9LiFWByDzrInOWWYO7rct13+R8zZxoia/N+SioHkuAJCAKOAdM0VpfwTbPSUF1uc7azgkUXJd9QG+llL1SqibQNGedLZ6XgupynVWcF6WUL9AY2AFU0lpHgekPAKZvHGD6rCPy7Hb98y/UebGl5P4kMEYptQfTV530vCuVUi2BZK319f66/E6otYz7vNu6AAzWWgcC9+X8PFZcwd5CQfVoAWQBVTB9zXxBKeWHbZ6TguoC1nlOoOC6fI0pQewGpgJ/A5nY5nkpqC5gJedFKeUGLAWe01rf6tteQZ9/oc5LkTyJqThorY8CXQCUUnWBHv/aZAA3tnQjgWp53lcDLpgzxjt1D3VBa30+53eCUuoHTElnnvmjLdgt6jEI+E1rnQFEK6X+wvSV+U9s75wUVJfT1nhOcuLJty5a60zg+evbKaX+Bk4AV7Gx83KLuljF/xWllBFTYl+gtV6Ws/iSUqqy1joqp8slOmd5JDd+67j++Rcqh9lMy/36FW+llAF4HfgizzoDpq9sC68vy/nak6CUapXztexxrORh3Xdbl5yvnh45r41AT0xfVy3qFvU4B3RUJq5AK+CojZ6TfOtirecECq6LUsolpw4opToDmVrrw7Z4XgqqizWcl5zP8CvgiNb6kzyrfgGG5rweyv8+41+AAUopx5wupjrAzkKfF0tdUb7N1eYfMfVxZmD66zUcGIfpqvNxTA/dVnm2DwG253OcZphO7Cngs7z72FJdAFdMowH2A4eAaeRcTbfGegBuwE85sR4GXrLVc1JQXazhnNxDXXyBY5gu8K3DNF2srZ6XfOtiDecFaIep+2Q/EJbz0x2oiOki8Imc3xXy7PNazmd/jDwjYgpzXmT6ASGEKIFspltGCCHEnZPkLoQQJZAkdyGEKIEkuQshRAkkyV0IIUogSe5CCFECSXIXQogS6P8BHxUP+J2dRsEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = df.query(\"california\")[\"year\"]\n",
    "\n",
    "plt.plot(x, sc1, label=\"retprice and cigsale\")\n",
    "plt.plot(x, sc2, label=\"cigsale\")\n",
    "plt.plot(x, df.query(\"california\")[\"cigsale\"].values, label=\"California\")\n",
    "plt.vlines(df.query(\"after_treatment\")[\"year\"].min()-1, 40, 120, color=\"black\", ls=\"dotted\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "758eb586",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:root] *",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}