diff --git a/eis_toolkit/vector_processing/proximity_tool.py b/eis_toolkit/vector_processing/proximity_tool.py new file mode 100644 index 00000000..a408359a --- /dev/null +++ b/eis_toolkit/vector_processing/proximity_tool.py @@ -0,0 +1,73 @@ +import numpy as np +import geopandas as gpd +from numbers import Number +from beartype import beartype +from beartype.typing import Union +from rasterio import profiles, transform +from eis_toolkit.vector_processing.distance_computation import distance_computation + +@beartype +def calculate_proximity(geodataframe: gpd.GeoDataFrame, raster_profile: Union[profiles.Profile, dict], maximum_distance: Number) -> np.ndarray: + + """ Interpolates the distance values calculated by the distance_computation function between 0 and 1. + 1 denots the value inside the polygon and 0 at the maximum distance. + If maximum_distance value is not provided, the program sets this value to the maximum value + in the provided distance matrix. + Uses linear interpolation to calculate the distance from the polygon. + + Args: + geodataframe: The GeoDataFrame with geometries to determine distance to. + raster_profile: The raster profile of the raster in which the distances + to the nearest geometry are determined. + max_distance: The maximum distance in the output array. + + Returns: + A 2D numpy array with the the distance values inverted. + """ + + out_matrix = distance_computation(geodataframe, raster_profile, maximum_distance) + + minimum = np.min(out_matrix) + difference = maximum_distance - minimum + out_matrix = maximum_distance - out_matrix + out_matrix = out_matrix/difference + + return out_matrix + +@beartype +def calculate_logarithmic_proximity(geodataframe: gpd.GeoDataFrame, raster_profile: Union[profiles.Profile, dict], maximum_distance: Number) -> np.ndarray: + + """ Logarithmically interpolates the distance values calculated by the distance_computation function between 0 and 1. + 1 denots the value inside the polygon and 0 at the maximum distance. + If maximum_distance value is not provided, the program sets this value to the maximum value + in the provided distance matrix. + Uses linear interpolation to calculate the distance from the polygon. + + Args: + geodataframe: The GeoDataFrame with geometries to determine distance to. + raster_profile: The raster profile of the raster in which the distances + to the nearest geometry are determined. + max_distance: The maximum distance in the output array. + + Returns: + A 2D numpy array with the the distance values inverted. + """ + + distance_array = distance_computation(geodataframe, raster_profile,maximum_distance) + + modified_distance_array = np.where(distance_array==0.0,np.nan,distance_array) + out_matrix = np.log(modified_distance_array) + + log_maximum = np.log(maximum_distance) + minimum = np.min(distance_array) + if(minimum != 0): + log_minimum = np.log(minimum) + else : + log_minimum = minimum + difference = log_maximum - log_minimum + out_matrix = log_maximum - out_matrix + out_matrix = out_matrix/difference + + out_matrix = np.nan_to_num(out_matrix,nan=log_maximum) + + return out_matrix \ No newline at end of file diff --git a/notebooks/testing_proximity_tool.ipynb b/notebooks/testing_proximity_tool.ipynb new file mode 100644 index 00000000..536f1f09 --- /dev/null +++ b/notebooks/testing_proximity_tool.ipynb @@ -0,0 +1,172 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "\n", + "sys.path.append(\"..\")" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import rasterio\n", + "from rasterio import plot\n", + "\n", + "import geopandas as gpd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "from textwrap import fill\n", + "from functools import partial\n", + "\n", + "from tests.raster_processing.clip_test import raster_path as SMALL_RASTER_PATH\n", + "from tests.raster_processing.clip_test import polygon_path as polygon_path\n", + "\n", + "from eis_toolkit.vector_processing.distance_computation import distance_computation\n", + "from eis_toolkit.vector_processing.proximity_tool import calculate_proximity\n", + "from eis_toolkit.vector_processing.proximity_tool import calculate_logarithmic_proximity" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "def _plot_distance_matrix(ax, distance_array, title, transform):\n", + " plot.show(distance_array, transform=transform, ax=ax,cmap='gray')\n", + " ax.set_title(fill(title, width=25))\n", + " norm = plt.Normalize(vmax=np.nanmax(distance_array), vmin=np.nanmin(distance_array))\n", + " cmap = \"gray\"\n", + " plt.colorbar(matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap),ax=ax)\n", + "\n", + "\n", + "def _plot_example(path_polygon,path_raster,maximum_distance):\n", + " fig, axes = plt.subplots(1,3, figsize=(15,10))\n", + "\n", + "\n", + " gdf = gpd.read_file(path_polygon)\n", + " with rasterio.open(path_raster) as test_raster:\n", + " raster_profile = test_raster.profile\n", + " transform = test_raster.transform\n", + "\n", + " gdf = gpd.read_file(polygon_path)\n", + " _plot_image_with_transform = partial(_plot_distance_matrix,transform=transform)\n", + "\n", + " with rasterio.open(SMALL_RASTER_PATH) as test_raster:\n", + " raster_profile = test_raster.profile\n", + " transform = test_raster.transform\n", + "\n", + " distance_matrix=distance_computation(gdf,raster_profile) #Distance matrix from the distance computation function\n", + " _plot_image_with_transform(ax=axes[0], distance_array=distance_matrix,title=\"Distance Computation\") \n", + "\n", + " distance_matrix=distance_computation(gdf,raster_profile, maximum_distance) #Distance matrix from the distance computation function with maximum distance\n", + " _plot_image_with_transform(ax=axes[1], distance_array=distance_matrix,title=\"Distance Computation with maximum distance = 25\")\n", + "\n", + " output_mat2 = calculate_proximity(gdf,raster_profile, maximum_distance) #Output form the new proximity tool with maximum distance = 25\n", + " _plot_image_with_transform(ax=axes[2], distance_array=output_mat2,title=\"Inversion from Proximity tool with maximum distance = 25\") " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_plot_example(polygon_path,SMALL_RASTER_PATH,25)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "def _plot_log_example(path_polygon,path_raster,maximum_distance):\n", + " fig, axes = plt.subplots(1,3, figsize=(15,10))\n", + "\n", + " gdf = gpd.read_file(path_polygon)\n", + " with rasterio.open(path_raster) as test_raster:\n", + " raster_profile = test_raster.profile\n", + " transform = test_raster.transform\n", + "\n", + " gdf = gpd.read_file(polygon_path)\n", + " _plot_image_with_transform = partial(_plot_distance_matrix,transform=transform)\n", + "\n", + " with rasterio.open(SMALL_RASTER_PATH) as test_raster:\n", + " raster_profile = test_raster.profile\n", + " transform = test_raster.transform\n", + "\n", + " distance_matrix=distance_computation(gdf,raster_profile) #Distance matrix from the distance computation function\n", + " _plot_image_with_transform(ax=axes[0], distance_array=distance_matrix,title=\"Distance Computation\") \n", + "\n", + " distance_matrix=distance_computation(gdf,raster_profile,25) #Distance matrix from the distance computation function\n", + " _plot_image_with_transform(ax=axes[1], distance_array=distance_matrix,title=\"Distance Computation with maximum distance = 25\") \n", + " \n", + "\n", + " output_mat2 = calculate_logarithmic_proximity(gdf,raster_profile,25) #Output form the new proximity tool with maximum distance = 25\n", + " _plot_image_with_transform(ax=axes[2], distance_array=output_mat2,title=\"Logarithmic Proximity tool with maximum distance = 25\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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HtWzZMs2ePVurVq3SW2+9dTO7a3e953HgwIFas2aNRowYoejoaPv14oAbxUw1AAAAAB6TPSAQHx/vEJ87d679+kwvvviiLl68qL59++r06dOqW7eu1q1bp4iIiJvcW5i1adMmHT58OEe8TZs2SkxMVIsWLdSkSRMNHTpUgYGBeu+99/TDDz9o0aJF9kGyUaNGafXq1WrWrJlGjRqlkJAQzZgxQ6mpqZKuXl9NkurXr69ChQrp2Wef1ZgxYxQQEKCPPvpI33//fZ4fR9WqVZWUlKTVq1crNjZWERERqlChQp63mxdlypTRyJEj9dprr+nixYt69NFHFRUVpf/85z/666+/DKuZukOPHj307rvvqmfPnjp8+LCqVq2qrVu3avz48WrTpo2aN28uSZo1a5YWLFiguXPnqnLlyqpcubL69++v4cOHq0GDBrr33ns91kdnqlatqsWLF2vJkiW64447FBwc7FDw4vHHH9eIESO0ZcsWvfzyy/ZlpcCNYlANAPKIqdIAAE+yWp6RzOWa3LS12WxKSEhQQkJCHnoFbxg+fLjT+KFDh+wz0MaMGaNevXopMzNT1atX16pVq9SuXTt72+rVq2v9+vUaOnSoevTooUKFCql79+5q3Lixhg8frqioKElXlySuWbNGQ4YM0eOPP66wsDB17NhRS5YsyfN1saZOnap+/frpkUce0YULF9S4cWMlJSXlaZvu8Oqrr6pcuXJ655131K1bN/n7+6tcuXKmrlt2I4KDg/XVV19p1KhRmjx5sv7880/dfvvtGjp0qMaMGSNJ2rdvnwYOHKiePXvaB8gl6R//+Ie2b9+url276t///rcKFizo0b7+3dixY3X8+HH16dNH586dU+nSpR0GfUNCQtS+fXstWLBAzz777E3rly+wWq6xUl9csWX5Sk8BwGJSUlIUFRWlTZs2OVS18rbz58+radOmOnv2rNsuzgsAuPmsmmckcg1ujpYtW+rw4cP673//6+2uIJ+4dOmSypQpo4YNG+rjjz/2dncswaq5xlfyDDPVAAAAAABeNXjwYNWsWVMlS5bUqVOn9NFHH2n9+vWaPXu2t7uGfODPP//U/v37NXfuXP3xxx966aWXvN0l5BMMqgFAHjFVGgDgSVbLMxK5Bu6XkZGhV155RcnJybLZbKpUqZL++c9/6vHHH/d215APrFmzRk888YRiY2P13nvv5Xm5cH5ktVxjpb64wqAaAAAAAMCrpk6dqqlTp3q7G8inevXq5XDdN8BdCni7AwAAAAAAAICvYVANANwge7q0FW7wHfPmzZPNZrPfgoODFRMToyZNmigxMVEnTpzIcZ+EhATZbDZT+7lw4YISEhIsUcXMnVJSUjRu3DjVqVNHkZGRCgoKUpkyZfTkk09qz5493u6e24wfP14rVqy44fu7ev6zX4N/r4xmZYcPH5bNZtO8efPssW3btikhIUFnzpzJ0b5MmTIOFQZ9mbdzC7kGN8rVe9TdbiRHmlGmTBmH2U6///67EhIS9N133+Vo26tXL0td9D2vfC1f4MZ4O7f4Yp5hUA0AAC+bO3eutm/frvXr1+vdd99VjRo1NHHiRFWsWFEbNmxwaNu7d29t377d1PYvXLigsWPH5qtBtf/973+qWbOmJkyYoCZNmmjRokVat26dxo4dqz/++EO1a9fW2bNnvd1Nt3DHoJrR89+2bVtt375dsbGxN97Bmyg2Nlbbt29X27Zt7bFt27Zp7NixN+ULOwDz8tN7dPny5Ro9erT9599//11jx451OqiW3/havgBuFq6pBgCAl1WpUkV16tSx/9y5c2e98MILatiwoTp16qQDBw4oOjpaklSiRAmVKFHCW121hIyMDD344IP666+/tH37dlWpUsX+u8aNG6tnz5764osvFBAQ4MVe+obbbrtNt912m7e7kWtBQUGqV6+et7sB4BZVs2ZNb3fBa3wtX+DWk5iYqGXLlunnn39WSEiI6tevr4kTJ6pChQqG90lKSlKTJk1yxH/66SfdfffdudovM9UAII+8PTXal6dLw1ipUqX0xhtv6Ny5c3r//fftcWdLWzZt2qT4+HgVKVJEISEhKlWqlDp37qwLFy7o8OHD9g/BY8eOtS81zV6+cvDgQT3xxBMqV66cQkNDdfvtt6t9+/bat2+fwz6SkpJks9m0aNEijRo1SsWLF1dkZKSaN2+u/fv35+j/2rVr1axZM0VFRSk0NFQVK1ZUYmKiQ5tdu3apQ4cOKly4sIKDg1WzZk19/PHH1z02K1as0L59+zRixAiHAbW/a926tUJDQ+0/b926Vc2aNVNERIRCQ0NVv359rVmzxuE+2UtbNm3apD59+qhIkSKKjIxUjx49lJqaquTkZHXp0kUFCxZUbGyshg4dqsuXL9vvn700cdKkSRo3bpxKlSql4OBg1alTRxs3bnTYV69evVSmTJkc/b72+bXZbEpNTdX8+fPtz118fLwk6c8//1Tfvn1VqVIlhYeHq1ixYmratKm+/vprhz65ev6NlvPMmTNH1atXV3BwsAoXLqwHH3xQP/30U47HEB4eroMHD6pNmzYKDw9XyZIlNWTIEKWnpzt9XrINGzZMUVFRysjIsMcGDBggm82myZMn22MnT55UgQIF9M477zgc4+zlnwkJCRo2bJgkqWzZsvbHd+2svLVr16pWrVoKCQnR3XffrTlz5rjsn9V4O6eQa3CjrvcezczM1KRJk3T33XcrKChIxYoVU48ePfTrr7/m2FZuzku5sWbNGtlsNu3cudMeW7p0qWw2m8MsWEmqVq2aOnfubP/578s/k5KSdM8990iSnnjiCftjS0hIcNjGjZwjs/fVrl07ffbZZ6pZs6ZCQkJUsWJFffbZZ5Kunr8rVqyosLAw3Xvvvdq1a5fD/Xft2qVHHnlEZcqUUUhIiMqUKaNHH31UR44csbfJyspSmzZtVKRIER09etQev3DhgipXrqyKFSsqNTXVvr9r80V8fLyqVKmi7du3q379+vb9zJ07136sa9WqpdDQUFWtWlVr16516GNuc6F0NR/2799fc+fOVYUKFRQSEqI6depox44dysrK0uTJk1W2bFmFh4eradOmOnjw4HWPMRx5O6fkNc9s3rxZ/fr1044dO7R+/XpduXJFLVu2tL+GXdm/f7+OHz9uv5UrVy7X+2VQDQAAi2rTpo38/Py0ZcsWwzaHDx9W27ZtFRgYqDlz5mjt2rWaMGGCwsLCdOnSJcXGxto/xD711FPavn27tm/fbl++8vvvv6tIkSKaMGGC1q5dq3fffVf+/v6qW7eu08GykSNH6siRI5o1a5ZmzpypAwcOqH379g6DI7Nnz1abNm2UmZmpGTNmaPXq1Ro4cKDDl6SvvvpKDRo00JkzZzRjxgytXLlSNWrUUNeuXR2ul+XMunXrJEkPPPBAro7j5s2b1bRpU509e1azZ8/WokWLFBERofbt22vJkiU52vfu3VtRUVFavHixXn75ZS1cuFB9+vRR27ZtVb16dX366afq2bOn3njjDftgz99NmzZNa9eu1ZQpU7RgwQIVKFBArVu3Nr1sV5K2b9+ukJAQtWnTxv7cvffee5KkU6dOSZLGjBmjNWvWaO7cubrjjjsUHx9v/8J6veffmcTERD311FOqXLmyli1bpqlTp2rv3r2Ki4vTgQMHHNpevnxZHTp0ULNmzbRy5Uo9+eSTeuuttzRx4kSXj6t58+ZKSUnRt99+a49t2LBBISEhWr9+vT22ceNGZWVlqXnz5k6307t3bw0YMECStGzZMvvjq1Wrlr3N999/ryFDhuiFF17QypUrVa1aNT311FMu31cA3ON679HnnntOw4cPV4sWLbRq1Sq99tprWrt2rerXr6+//vrLvh0z56Xrady4sQICAhwur5B9/tm8ebP9jyUnTpzQDz/8YHj+qVWrln3w6OWXX7Y/tt69e9vb3Og5Mtv333+vESNGaPjw4Vq2bJmioqLUqVMnjRkzRrNmzdL48eP10Ucf6ezZs2rXrp0uXrxov+/hw4dVoUIFTZkyRV9++aUmTpyo48eP65577rEfW5vNpn/+858KDQ1Vly5d7I+9b9++OnTokD7++GOFhYW57GNycrKeeOIJ9e7dWytXrlTVqlX15JNP6tVXX9WIESP04osvaunSpQoPD9cDDzyg33//PVeP3ZnPPvtMs2bN0oQJE7Ro0SKdO3dObdu21ZAhQ/Svf/1L06ZN08yZM/Wf//xHnTt3ZvD/FrN27Vr16tVLlStXVvXq1TV37lwdPXpUu3fvvu59ixUrppiYGPvNz88v1/tl+ScAABYVFhamokWLuvwAunv3bqWlpWny5MmqXr26Pf7YY4/Z/1+7dm1JV5eOXrt07r777tN9991n/zkjI0Nt27ZV5cqV9f777+vNN990aF+pUiUtWLDA/rOfn5+6dOminTt3ql69ejp//rwGDx6sBg0aaNOmTfa/NDdr1sxhO3379lXlypW1adMm+ftf/TjSqlUr/fXXXxo5cqR69OihAgWc/+0v+6/pZcuWNTwuf/fSSy+pUKFCSkpKsl80ul27dqpRo4aGDh2qLl26OPxFvF27dvrHP/4hSWrRooW2b9+uRYsW6c0339QLL7wg6eqg0JdffqmPPvpIgwcPdthfRkaG1q9fr+DgYPvjKlOmjF555RWHAaPcqFevngoUKKDbbrstx3NXoUIF+wBb9n5btWqlw4cP6+2331Z8fLyCgoJcPv/XOnPmjF577TW1adNGCxcutMfj4+NVrlw5JSQk6KOPPrLHL126pLFjx+rhhx+WdPV53rVrlxYuXKhXXnnFcD+NGjVSYGCgNmzYoLi4OP3222/6+eefNXz4cL399ttKT09XUFCQNmzYoOLFi6tixYpOt1OiRAmVKlVK0tVlWc5mPPz111/617/+ZW933333aePGjVq4cKHDax+A+7l6j/7888+aOXOm+vbt6/AHipo1a6pu3bp66623NG7cONPnpesJDw9X3bp1tWHDBo0YMULS1UG1/v3764033tD27dt13333acOGDS4H9SMjI+2zpe+8806n59cbPUdmO3nypHbs2KHbb79dklS8eHHVqFFDH3zwgQ4ePGifkW2z2fTAAw9ow4YNat++vSTpoYce0kMPPWTfVkZGhtq1a6fo6GgtXLhQAwcOlCQVKVJEixcvVnx8vF588UVVq1ZN8+fP16xZs1S1atVc9fHLL7+055o6deqoWLFimjBhgg4ePKjixYs79H3p0qX2gVaz0tPTtW7dOvtAX/bj/uqrr7Rnzx57Lv/zzz81aNAg/fDDD7l6DLC2lJQUh5+DgoIUFBR03ftlX1u3cOHC121bs2ZNpaWlqVKlSnr55ZedLgk1wkw1AMgjb0+LZklO/na957NGjRoKDAzU008/rfnz5+uXX34xtf0rV65o/PjxqlSpkgIDA+Xv76/AwEAdOHDA6bKaDh06OPxcrVo1SbIvJ9m2bZtSUlLUt29fwwpsBw8e1M8//6xu3brZ+5B9a9OmjY4fP+50ltyNSE1N1TfffKOHHnrIoQqbn5+funfvrl9//TXHvq6tFpk9oHPtsqCKFSs6LKPJ1qlTJ/uAmiT7rLgtW7Y4zOhzhxkzZqhWrVoKDg6Wv7+/AgICtHHjxhtaEiVdnRl38eJFh+p2klSyZEk1bdo0xzJWm81m/wKXrVq1ak6Py9+FhoYqLi7OPlNk/fr1KliwoIYNG6ZLly5p69atkq5+0TX6QptbNWrUsH+pl6Tg4GCVL1/+un20Em/nFHINPOGrr76SpBznm3vvvVcVK1a0n2/Mnpdyo1mzZvrXv/6lixcv6siRIzp48KAeeeQR1ahRw/7Hjw0bNqhUqVKmloFd60bPkdlq1KhhH1CT/j8fxcfHO1ziIDv+9+2eP39ew4cP11133SV/f3/5+/srPDxcqampOXJEgwYNNG7cOE2ZMkXPPfecHn/8cT311FO56mNsbKx9QE26OoBRrFgx1ahRwz6gZtRHs5o0aeIwcy57m61bt3b4zOGOfd2KvJ1TjPJMyZIlFRUVZb9dezkRo8cyePBgNWzY0PBSIdLV1+/MmTO1dOlSLVu2TBUqVFCzZs1MzWZnUA0AAItKTU3VyZMnHT6UXuvOO+/Uhg0bVKxYMfXr10933nmn7rzzTk2dOjVX+xg8eLBGjx6tBx54QKtXr9Y333yjnTt3qnr16g7LSLIVKVLE4efsvxRmt/3zzz8lyWUxhT/++EOSNHToUAUEBDjc+vbtK0kOy36ulT1AcujQoes+vtOnTysrK8tptbLs43ry5EmH+LV/0QwMDDSMp6Wl5dhuTEyM09ilS5d0/vz56/Y5t958800999xzqlu3rpYuXaodO3Zo586duv/++50+d7mRfSyMjte1xyo0NNRhAFG6+ppwdlyu1bx5c+3YsUOpqanasGGDmjZtqiJFiqh27drasGGDDh06pEOHDuV5UO3a12x2H2/0GAFwj9yeb8yel3KjefPmSk9P19atW7V+/XoVLVpUNWvWVPPmze2D/Rs3bszz+Scv50jJXD6S5LDdxx57TNOmTVPv3r315Zdf6ttvv9XOnTt12223OT3/devWTYGBgUpPT7dfB+9G+pjdn9z00ay8HA/4rmPHjuns2bP2W/YMU1f69++vvXv3atGiRS7bVahQQX369FGtWrUUFxen9957T23btrWvWMgNln8CAGBRa9asUUZGhv3C9EYaNWqkRo0aKSMjQ7t27dI777yjQYMGKTo6Wo888ojL+y5YsEA9evTQ+PHjHeJ//fWXChYsaLrP2RfFd3aR6WxFixaVJI0YMUKdOnVy2sZVpaZWrVpp5syZWrFihV566SWX/SlUqJAKFCig48eP5/hd9rLa7P64S3JystNYYGCgfbZccHCw0wtVuxpMvNaCBQsUHx+v6dOnO8TPnTtnssf/L3sAyuh4ufNYNWvWTKNHj9aWLVu0ceNGjRkzxh5ft26dfXnvtUuHAeQPfz/fXPuHmL+fbzxxXqpbt67Cw8O1YcMGHT58WM2aNZPNZlOzZs30xhtvaOfOnTp69GieB9W85ezZs/rss880ZswYhzyZnp5uvx7n32VkZKhbt24qVKiQgoKC9NRTT+lf//qXfXDKU9yRC5H/RUZGKjIyMtftBwwYoFWrVmnLli0u/8hrpF69eg6XOrkeZqoBQB55e1o0S3Lyp6NHj2ro0KGKiorSM888k6v7+Pn5qW7dunr33XclSXv27JGUczbZ39lsthzXpVizZo1+++23G+p3/fr1FRUVpRkzZhi+FitUqKBy5crp+++/V506dZzeIiIiDPfRsWNHVa1aVYmJifrhhx+ctvnyyy914cIFhYWFqW7dulq2bJnD48/MzNSCBQtUokQJlS9f/oYeq5Fly5Y5/HX83LlzWr16tRo1amS/8G2ZMmV04sQJ+6w96eq1d7788ssc2zOaVeXsudu7d2+Oggiunv9rxcXFKSQkJMeHyV9//VWbNm1y6wDXvffeq8jISE2ZMkXJyclq0aKFpKszSP7973/r448/VqVKlVzO1JTMPT5f5e2cQq5BXhi9R5s2bSpJOc43O3fu1E8//WQ/33jivBQQEKD77rtP69ev16ZNm+znn0aNGsnf318vv/yyfZDtRh6bt9lsNmVlZeXIEbNmzXJ6GYIxY8bo66+/1kcffaQlS5bo+++/NzVb7UaZyYXwPG/nlLzmmaysLPXv31/Lli3Tpk2bcn3t3Wv9+9//djoz1ggz1QAA8LIffvjBfk2xEydO6Ouvv9bcuXPl5+en5cuX22d/OTNjxgxt2rRJbdu2ValSpZSWlqY5c+ZIkv0v7BERESpdurRWrlypZs2aqXDhwipatKjKlCmjdu3aad68ebr77rtVrVo17d69W5MnT76hv+xJVy8A/cYbb6h3795q3ry5+vTpo+joaB08eFDff/+9pk2bJkl6//331bp1a7Vq1Uq9evXS7bffrlOnTumnn37Snj179MknnxjuI/u4tGzZUnFxcXruuefs11k5cuSIPv30U61evVqnT5+WdLVqXIsWLdSkSRMNHTpUgYGBeu+99/TDDz9o0aJFhtd+u1F+fn5q0aKFBg8erMzMTE2cOFEpKSkaO3asvU3Xrl31yiuv6JFHHtGwYcOUlpamt99+2+mXnapVqyopKUmrV69WbGysIiIiVKFCBbVr106vvfaaxowZo8aNG2v//v169dVXVbZsWV25csV+f1fP/7UKFiyo0aNH24tFPProozp58qTGjh2r4OBg+2wydx2nxo0ba/Xq1SpbtqzuvPNOSVev7RMUFKSNGzfaL6TtSvZFqKdOnaqePXsqICBAFSpUcDkwC+DmMXqPVqhQQU8//bTeeecde5Xkw4cPa/To0SpZsqS9MIynzkvNmjXTkCFDJP1/vgwJCVH9+vW1bt06VatWTcWKFXO5jTvvvFMhISH66KOPVLFiRYWHh6t48eLX/WOAp0VGRuq+++7T5MmT7ef7zZs3a/bs2Tlmoa9fv16JiYkaPXq0fRAxMTFRQ4cOVXx8vB588EGP9dNMLgSup1+/flq4cKFWrlypiIgI+8qBqKgohYSESLq6SuK3337Thx9+KEmaMmWKypQpo8qVK+vSpUtasGCBli5dqqVLl+Z6v8xUAwDAy5544gnFxcWpWbNmeu655/Tvf/9bw4cP188//3zd6kM1atTQlStXNGbMGLVu3Vrdu3fXn3/+qVWrVqlly5b2drNnz1ZoaKg6dOige+65RwkJCZKufsl5/PHHlZiYqPbt22vVqlVatmyZfYDjRjz11FP6/PPPlZGRod69e6tdu3aaMmWKw8XimzRpom+//VYFCxbUoEGD1Lx5cz333HO5vjD9nXfeqT179mj48OHauHGjunTpoubNm+vll19WZGSktm7dqqioKElS48aNtWnTJoWFhalXr1565JFHdPbsWa1atUpdu3a94cdppH///mrRooUGDhyoxx57TFeuXNGaNWvUoEEDe5uyZctq5cqVOnPmjB566CENGzZMDz/8sHr06JFje1OnTlW5cuX0yCOP6J577rHPXBw1apSGDBmi2bNnq23btpo1a5ZmzJihhg0b5tiG0fPvzIgRIzRr1ix9//33euCBB9S/f39VrlxZ27Zty9MFu53Jfq7//pwHBQXZH0NuXgvx8fEaMWKEVq9erYYNG+qee+7R7t273dpPADfO1Xt0+vTpmjBhgj7//HO1a9dOo0aNUsuWLbVt2zaH6yF64ryUfX4pV66cSpcunSOem/NPaGio5syZo5MnT6ply5a65557NHPmzBvqj7stXLhQTZo00YsvvqhOnTpp165dWr9+vT03SleX1D7++OOKj493qEY6ePBgtW/fXk8++aQOHz7ssT6ayYXA9UyfPl1nz55VfHy8YmNj7bclS5bY2xw/ftxeRV66OjNy6NChqlatmho1aqStW7dqzZo1hpcnccaWxdxtALghKSkpioqK0pdffulQicjbUlNT1apVK509e9bU9QcA5M3hw4dVtmxZTZ48WUOHDvV2d5APWDXPSOQaAMgvrJprfCXPMFMNAAAAAAAAMIlBNQAAAAAAAMAkChUAQB5ZrQqalfoC3ErKlCnD+w8eYbU8I5FrACC/sVqusVJfXGGmGgAAAAAAAGASg2oAAAAAAACASQyq3UTz5s2TzWaz34KDgxUTE6MmTZooMTFRJ06cyHGfhIQE2Ww2U/u5cOGCEhISlJSU5KaeW0NKSorGjRunOnXqKDIyUkFBQSpTpoyefPJJ7dmzx9vdc5vx48drxYoVN3x/V89/9mvQk6Wxb0XZU6WtdANuBpvNpoSEBG93w+MOHz4sm82mefPm2WPkZ/fKzvHx8fGKiYlReHi4qlatqokTJyotLc2hbfbz4ey2ePFiLz0Cz/J2TiHXwNc4O0eXKVNGvXr1MrWdbdu2KSEhQWfOnHFf5/KJ3bt3q1+/fqpataoiIiIUHR2t5s2ba9OmTTnaZj8f196Cg4O90HMY8XZO8dU8wzXVvGDu3Lm6++67dfnyZZ04cUJbt27VxIkT9Y9//ENLlixR8+bN7W179+6t+++/39T2L1y4oLFjx0qS4uPj3dl1r/nf//6nli1b6sSJE3r22Wc1duxYhYeH6/Dhw/r4449Vu3ZtnTlzRlFRUd7uap6NHz9eDz30kB544IEbur+r579t27bavn27YmNj89hLAJC2b9+uEiVKeLsbXkF+dq+jR49qypQp6t69uwYPHqzw8HB9/fXXSkhI0Pr167V+/focX5AHDBigxx57zCFWrly5m9ltAD5k+fLlioyMNHWfbdu2aezYserVq5cKFizomY75qEWLFunbb7/Vk08+qerVqys1NVUzZsxQs2bNNH/+fPXo0SPHfdauXevwfa1AAeb4wPcxqOYFVapUUZ06dew/d+7cWS+88IIaNmyoTp066cCBA4qOjpYklShR4pb9wpItIyNDDz74oP766y9t375dVapUsf+ucePG6tmzp7744gsFBAR4sZe+4bbbbtNtt93m7W4AyCfq1avn7S54DfnZvcqWLavDhw8rLCzMHmvatKnCwsI0bNgw/etf/1LDhg0d7lOqVKlb+jUIwJyaNWt6uwv5yosvvqh//OMfDrE2bdqoVq1aevXVV50OqtWuXVtFixa9WV0EbgqGhi2iVKlSeuONN3Tu3Dm9//779rizqcubNm1SfHy8ihQpopCQEJUqVUqdO3fWhQsXdPjwYfugydixY+1Ta7OnOh88eFBPPPGEypUrp9DQUN1+++1q37699u3b57CPpKQk2Ww2LVq0SKNGjVLx4sUVGRmp5s2ba//+/Tn6v3btWjVr1kxRUVEKDQ1VxYoVlZiY6NBm165d6tChgwoXLqzg4GDVrFlTH3/88XWPzYoVK7Rv3z6NGDHCYUDt71q3bq3Q0FD7z1u3blWzZs0UERGh0NBQ1a9fX2vWrHG4T/ZSyE2bNqlPnz4qUqSIIiMj1aNHD6Wmpio5OVldunRRwYIFFRsbq6FDh+ry5cv2+2cvP5k0aZLGjRunUqVKKTg4WHXq1NHGjRsd9tWrVy+VKVMmR7+vfX5tNptSU1M1f/58+3OXPZvhzz//VN++fVWpUiWFh4erWLFiatq0qb7++muHPrl6/o2Wf86ZM0fVq1dXcHCwChcurAcffFA//fRTjscQHh6ugwcPqk2bNgoPD1fJkiU1ZMgQpaenO31ebhXenhbtq1OlYU72+WLv3r16+OGHFRUVpcKFC2vw4MG6cuWK9u/fr/vvv18REREqU6aMJk2a5HD/tLQ0DRkyRDVq1LDfNy4uTitXrnRot3jxYtlsNk2bNs0hPmbMGPn5+Wn9+vX22LXLP/N6Xs3OPdcuj3S2/DL7nPTzzz+rVatWCgsLU2xsrCZMmCBJ2rFjhxo2bKiwsDCVL19e8+fPz9Vx/v3339WlSxdFREQoKipKXbt2VXJysuHz8Xe3Wn52p7CwMIcBtWz33nuvJOnYsWM3tT9W4+2cQq6Bla1Zs0Y1atRQUFCQypYtm2OgJ9u1yz8zMzP1+uuvq0KFCgoJCVHBggVVrVo1TZ06VdLV8/ywYcMkXR34zz5vZ+eoJUuWqGXLloqNjVVISIgqVqyol156SampqQ77NfMZOj09Xa+++qoqVqyo4OBgFSlSRE2aNNG2bdvsbbKysvTee++pRo0aCgkJUaFChfTQQw/pl19+yeuhNKVYsWI5Yn5+fqpdu/Ytf872Vd7OKb6aZxhUs5A2bdrIz89PW7ZsMWxz+PBhtW3bVoGBgZozZ47Wrl2rCRMmKCwsTJcuXVJsbKzWrl0rSXrqqae0fft2bd++XaNHj5Z09ctCkSJFNGHCBK1du1bvvvuu/P39VbduXacfxkeOHKkjR45o1qxZmjlzpg4cOKD27dsrIyPD3mb27Nlq06aNMjMzNWPGDK1evVoDBw7Ur7/+am/z1VdfqUGDBjpz5oxmzJihlStXqkaNGuratavDlyRn1q1bJ0m5Xg65efNmNW3aVGfPntXs2bO1aNEiRUREqH379lqyZEmO9r1791ZUVJQWL16sl19+WQsXLlSfPn3Utm1bVa9eXZ9++ql69uypN954Q++8806O+0+bNk1r167VlClTtGDBAhUoUECtW7fW9u3bc9Xfv9u+fbtCQkLUpk0b+3P33nvvSZJOnTol6eoX2zVr1mju3Lm64447FB8fb0/u13v+nUlMTNRTTz2lypUra9myZZo6dar27t2ruLg4HThwwKHt5cuX1aFDBzVr1kwrV67Uk08+qbfeeksTJ040/VgB3JguXbqoevXqWrp0qfr06aO33npLL7zwgh544AG1bdtWy5cvV9OmTTV8+HAtW7bMfr/09HSdOnVKQ4cO1YoVK7Ro0SL7DOkPP/zQ3u6RRx7Rs88+qyFDhmjXrl2Srg4Wvf766xo5cqRatGhx3T7m9byaW5cvX1anTp3Utm1brVy5Uq1bt9aIESM0cuRI9ezZU08++aSWL1+uChUqqFevXtq9e7fL7V28eFHNmzfXunXrlJiYqE8++UQxMTHq2rXrdftyK+Zn6eps8itXrlz3lpmZed1tOZN9bZ7KlSvn+N2ECRMUGBio0NBQNWzYUKtWrbqhfQDwXRs3blTHjh0VERGhxYsXa/Lkyfr44481d+7c69530qRJSkhI0KOPPqo1a9ZoyZIleuqpp+zXT+vdu7cGDBggSVq2bJn9vF2rVi1J0oEDB9SmTRvNnj1ba9eu1aBBg/Txxx+rffv2OfaVm8/QV65cUevWrfXaa6+pXbt2Wr58uebNm6f69evr6NGj9nbPPPOMBg0apObNm2vFihV677339OOPP6p+/fr6448/XD7mrKysXJ2zr1y5ct3j58yVK1f09ddfOz1nS1LVqlXl5+en6Oho9ejRw+FxAb6K5Z8WEhYWpqJFi+r33383bLN7926lpaVp8uTJql69uj3+92uK1K5dW9LVpSnXLou47777dN9999l/zsjIUNu2bVW5cmW9//77evPNNx3aV6pUSQsWLLD/7Ofnpy5dumjnzp2qV6+ezp8/r8GDB6tBgwbatGmT/a/2zZo1c9hO3759VblyZW3atEn+/ldfdq1atdJff/2lkSNHqkePHoZr6rNPtmXLljU8Ln/30ksvqVChQkpKSlJ4eLgkqV27dqpRo4aGDh2qLl26OMwuaNeunf0vWi1atND27du1aNEivfnmm3rhhRckSc2bN9eXX36pjz76SIMHD3bYX0ZGhtavX2+/0GarVq1UpkwZvfLKKw4zOnKjXr16KlCggG677bYcz12FChXsA2zZ+23VqpUOHz6st99+W/Hx8QoKCnL5/F/rzJkzeu2119SmTRstXLjQHo+Pj1e5cuWUkJCgjz76yB6/dOmSxo4dq4cffljS1ed5165dWrhwoV555RVTjxXAjXn66aft56HsAaBp06Zp2bJlevDBByVdfQ9/9tln+uijj9SpUydJUlRUlMOXjIyMDDVr1kynT5/WlClTHJZpTJkyRd988426dOmiNWvW6LHHHlOjRo1yXZQgr+fV3Lp06ZJef/11+2PMftyJiYnas2ePfalPnTp1VKxYMS1cuNB+jnRm/vz5+umnn7Ry5Up16NBBktSyZUtdvHhRH3zwgcu+3Ir5WZLuvPNOHTlyxOWxka7+QchsUYu9e/dq0qRJevDBB1WtWjV7PCgoSH369FGLFi0UGxuro0eP6p133lHHjh31wQcfqHfv3qb2A8B3jRo1StHR0U4/i1/Pv/71L1WtWtXh3NSqVSv7/0uUKKFSpUpJurp09Nptvvzyy/b/Z2VlqUGDBqpYsaIaN26svXv3Opy3cvMZetGiRfrqq69ynMf+Pki3Y8cOffDBB3rjjTcccmejRo1Uvnx5vfnmmy7/2L1582Y1adLkusdGkg4dOpSr4/h3CQkJOnjwYI6ia3feeafGjRunmjVrKjg4WN9++60mTZqkdevWaffu3br99ttN7QewEmaqWcz1pjjWqFFDgYGBevrppzV//nzT03yvXLmi8ePHq1KlSgoMDJS/v78CAwN14MCBHMv9JNm/VGTLTg7ZH6C3bdumlJQU9e3b17AK2sGDB/Xzzz+rW7du9j5k39q0aaPjx487/Sv8jUhNTdU333yjhx56yD6gJl39stG9e3f9+uuvOfbVrl07h58rVqwo6epF/a+NO/vi0KlTJ4fKNdmz4rZs2eIwY8AdZsyYoVq1aik4OFj+/v4KCAjQxo0bnT53ubF9+3ZdvHgxRyWkkiVLqmnTpjmWsdpsthx/fatWrVquvlDlZ96eFu2rU6VxY5yds2w2m1q3bm2P+fv766677srx3vzkk0/UoEEDhYeH288hs2fPznEOCQoK0scff6yTJ0+qVq1aysrK0qJFi+Tn53fDfZRyf17NLZvNpjZt2th/zn7csbGxDtfOKVy4sIoVK3bdfX311VeKiIjIkfuuvRi+M7dqfl69erV27tx53dvTTz9t6ngcPnxY7dq1U8mSJTVr1iyH38XGxmrmzJl6+OGH1bBhQz322GPasmWLatasqZdeeumGZ1hYmbdzCrkGVpSamqqdO3cafha/nnvvvVfff/+9+vbtqy+//FIpKSmm9v/LL7/oscceU0xMjPz8/BQQEKDGjRtLUo7zdm4+Q3/xxRcKDg7Wk08+abjPzz77TDabTY8//rjDOTsmJkbVq1e/bnXp2rVr5+qcvXPnThUvXtzU8Zg1a5bGjRunIUOGqGPHjg6/6969u0aOHKnWrVurSZMmGj58uL744gv9+eefOS5XAe/xdk7x1TzDTDULSU1N1cmTJ1W1alXDNnfeeac2bNigSZMmqV+/fkpNTdUdd9yhgQMH6vnnn7/uPgYPHqx3331Xw4cPV+PGjVWoUCEVKFBAvXv31sWLF3O0L1KkiMPPQUFBkmRv++eff0qSy4s1Z09DHjp0qIYOHeq0zV9//WV4/+y/EB06dEh33323YTtJOn36tLKyspxWt8xODCdPnnSIFy5c2OHnwMBAw3haWlqO7cbExDiNXbp0SefPn3dbRdI333xTQ4YM0bPPPqvXXntNRYsWlZ+fn0aPHn3Dg2rZx8LoeF070y40NDRH6eugoCCnxwWAZzg7Nzl7bwYGBjp8QVi2bJm6dOmihx9+WMOGDVNMTIz8/f01ffp0zZkzJ8d+7rrrLjVq1Ehr1qzRc889Z6pqcF7Pq7ll9Liv3U9u93Xy5El7oaC/c3aev9atmJ+lqzPmcvOh10yFtyNHjqhJkyby9/fXxo0bnT6f1woICFDXrl310ksv6cCBA/aBXAD51+nTp5WZmWn4Wfx6RowYobCwMC1YsEAzZsyQn5+f7rvvPk2cONGhqJwz58+fV6NGjRQcHKzXX39d5cuXV2hoqI4dO6ZOnTrlOG/n5jP0n3/+qeLFi7s8X/7xxx/Kyspymqsk6Y477nDZ7/DwcNWoUcNlm2zZs5dzY+7cuXrmmWf09NNPa/Lkybm6z7333qvy5ctrx44dud4PYEUMqlnImjVrlJGRYb8wvZFGjRqpUaNGysjI0K5du/TOO+9o0KBBio6O1iOPPOLyvgsWLFCPHj00fvx4h/hff/11Q2Wisy+6/Pfrs1wru8LLiBEj7Et0rlWhQgXD+7dq1UozZ87UihUr9NJLL7nsT/aXkOPHj+f4XfayWndXnHF2Aevk5GQFBgbaZ8sFBwc7vZj/9b6s/N2CBQsUHx+v6dOnO8TPnTtnssf/L/tLmdHxojoPkH8sWLBAZcuW1ZIlSxxmLhkVGpk1a5bWrFmje++9V9OmTVPXrl1Vt25dj/Yx+wvHtX0yc67MiyJFiujbb7/NEXd2nnfmVsvPkvuXfx45ckTx8fHKyspSUlKSqQqr2YN7ZgbwAPiuQoUKyWazGX4Wvx5/f38NHjxYgwcP1pkzZ7RhwwaNHDlSrVq10rFjxxyKoF1r06ZN+v3335WUlGSfnSbJfj22G3Hbbbdp69atyszMNDyPFS1aVDabTV9//bX9jyl/5yz2d55Y/jl37lz17t1bPXv21IwZMwxnRzuTlZXFORs+j1ewRRw9elRDhw5VVFSUnnnmmVzdx8/PT3Xr1tW7774rSdqzZ4+knH+t/jubzZbjZLtmzRr99ttvN9Tv+vXrKyoqSjNmzDD8S3WFChVUrlw5ff/996pTp47TW0REhOE+OnbsqKpVqyoxMVE//PCD0zZffvmlLly4oLCwMNWtW1fLli1zePyZmZlasGCBSpQoofLly9/QYzWybNkyh78ynTt3TqtXr1ajRo3sS6XKlCmjEydOOFw89NKlS/ryyy9zbC8oKCjXz93evXtzFERw9fxfKy4uTiEhIQ7X5ZGufgnbtGlTjmvvwDlvT4v21anSuLlsNpsCAwMdPuwmJyfnqP4pSfv27dPAgQPVo0cPff3116pWrZq6du2q06dPe7SP2R/e9+7d6xC/WRegb9Kkic6dO5djf3+/5mRu3Cr5WXLv8s+jR48qPj5eGRkZ2rRpk0qXLp3rx3v58mUtWbJERYsW1V133ZXr+/kKb+cUcg2sKCwsTPfee6/hZ3EzChYsqIceekj9+vXTqVOndPjwYUnG5+3sXHrtefv99983+zDsWrdurbS0NJdFYtq1a6esrCz99ttvTs/ZrlY8Se5f/jlv3jz17t1bjz/+uGbNmmVqQG3Hjh06cODAda8BjZvH2znFV/MMM9W84IcffrCvfz9x4oS+/vprzZ07V35+flq+fLn9r8vOzJgxQ5s2bVLbtm1VqlQppaWl2ZftNG/eXNLV6wiULl1aK1euVLNmzVS4cGEVLVpUZcqUUbt27TRv3jzdfffdqlatmnbv3q3Jkyeb+kvw34WHh+uNN95Q79691bx5c/Xp00fR0dE6ePCgvv/+e02bNk3S1QTTunVrtWrVSr169dLtt9+uU6dO6aefftKePXv0ySefGO4j+7i0bNlScXFxeu6559SkSROFhYXpyJEj+vTTT7V69Wr7l73ExES1aNFCTZo00dChQxUYGKj33ntPP/zwgxYtWmTqZJ8bfn5+atGihQYPHqzMzExNnDhRKSkpGjt2rL1N165d9corr+iRRx7RsGHDlJaWprffftvpNdeqVq2qpKQkrV69WrGxsYqIiFCFChXUrl07vfbaaxozZowaN26s/fv369VXX1XZsmUdrh/j6vm/VsGCBTV69Gj7xagfffRRnTx5UmPHjlVwcLDGjBnj1mMFwHvatWunZcuWqW/fvnrooYd07Ngxvfbaa4qNjXWo9JuamqouXbqobNmyeu+99xQYGKiPP/5YtWrV0hNPPJHj4sPuFBMTo+bNmysxMVGFChVS6dKltXHjRocqpp7Uo0cPvfXWW+rRo4fGjRuncuXK6fPPP3f6B5Br3Yr5WdJ1v8Dl1okTJ9SkSRMdP35cs2fP1okTJ3TixAn770uUKGE/FoMHD9bly5fVoEEDxcTE6NixY3rnnXf03Xff2T9PAbg1vPbaa7r//vvVokULDRkyRBkZGZo4caLCwsJ06tQpl/dt3769qlSpojp16ui2227TkSNHNGXKFJUuXVrlypWT9P/nuKlTp6pnz54KCAhQhQoVVL9+fRUqVEjPPvusxowZo4CAAH300Uf6/vvvb/ixPProo5o7d66effZZ7d+/X02aNFFmZqa++eYbVaxYUY888ogaNGigp59+Wk888YR27dql++67T2FhYTp+/Li2bt2qqlWr6rnnnjPcR0RExHWXtubWJ598oqeeeko1atTQM888k2Omd82aNe2DjtWrV9fjjz+uihUr2gsVTJ48WTExMXrxxRfd0h/AWxhU84InnnhC0tXruxQsWFAVK1bU8OHD1bt3b5cDatLVCyGvW7dOY8aMUXJyssLDw1WlShWtWrVKLVu2tLebPXu2hg0bpg4dOig9PV09e/bUvHnzNHXqVAUEBCgxMVHnz59XrVq1tGzZMofqNWY99dRTKl68uCZOnKjevXsrKytLZcqUUc+ePe1tmjRpom+//Vbjxo3ToEGDdPr0aRUpUkSVKlVSly5drruPO++8U3v27NE777yj5cuXa/r06UpPT1dsbKzuu+8+bd261X7tssaNG2vTpk0aM2aMevXqpczMTFWvXl2rVq3KcfFsd+jfv7/S0tI0cOBAnThxQpUrV9aaNWvUoEEDe5uyZctq5cqVGjlypB566CHFxsZq8ODB+vPPPx0G36SrSbtfv3565JFHdOHCBTVu3FhJSUkaNWqULly4oNmzZ2vSpEmqVKmSZsyYoeXLl+e4KKnR8+/MiBEjVKxYMb399ttasmSJQkJCFB8fr/Hjx9s/UADwfU888YROnDihGTNmaM6cObrjjjv00ksv6ddff3U4Dz377LM6evSodu7cqbCwMElXr9Eya9YsPfzww5oyZYoGDRrksX7+85//1IABAzR8+HBlZGSoffv2WrRokdu+BLgSGhqqTZs26fnnn9dLL70km82mli1bavHixapfv77L+96q+dld/vOf/9iLOzz++OM5fv/35aNVqlTR+++/r4ULFyolJUURERG699579eWXXzocawD5X4sWLbRixQq9/PLL6tq1q2JiYtS3b19dvHgxx2fsazVp0kRLly7VrFmzlJKSopiYGLVo0UKjR49WQECApKtVpUeMGKH58+frgw8+UGZmpr766ivFx8drzZo1GjJkiB5//HGFhYWpY8eOWrJkiWrVqnVDj8Xf31+ff/65EhMTtWjRIk2ZMkURERGqXr267r//fnu7999/X/Xq1dP777+v9957T5mZmSpevLgaNGige++994b2fSPWrFmjzMxM7dmzx+F7T7a/Lx+tVKmSZs6cqePHj+vSpUsqXry4HnnkEb3yyiumrtkKWJEty1fm1AEWc/jwYZUtW1aTJ082vMAz8reUlBRFRUVp9erV9sEHK0hNTVX79u119uxZRUZGers7AIAbZNU8I5FrACC/sGqu8ZU8wzXVAAAAAAAAAJMYVAMAAAAAAABM4ppqwA0qU6aMz1QkgWdZrTqNlfoCAMg7q+UZiVwDAPmN1XKNlfriCjPVAAAAAAAAAJMYVAMAAAAAAABMYvknAOQRU6UBAJ5ktTwjkWsAIL+xWq6xUl9cYaYaAAAAAAAAYBKDagAAAAAAAIBJt9yg2vbt29W0aVOFhYWpYMGCio+P18WLF13e57ffftPjjz+uIkWKKDQ0VDVq1NDu3bvtv7fZbE5vkydPtreZOXOm4uPjFRkZKZvNpjNnztxQ/8+cOaN+/fopNjZWwcHBqlixoj7//PMb2hYA98ieKm2lG7yHPAPA3bydU8g11kKeAeAJ3s4pvppn8t2gWnx8vObNm+f0d9u3b9f999+vli1b6ttvv9XOnTvVv39/FShgfBhOnz6tBg0aKCAgQF988YX+85//6I033lDBggXtbY4fP+5wmzNnjmw2mzp37mxvc+HCBd1///0aOXLkDT+2S5cuqUWLFjp8+LA+/fRT7d+/Xx988IFuv/32G94mAMAc8gwAwJPIMwDgO26pQgUvvPCCBg4cqJdeeskeK1eunMv7TJw4USVLltTcuXPtsTJlyji0iYmJcfh55cqVatKkie644w57bNCgQZKkpKQkw3399ttvGjx4sNatW6cCBQqoYcOGmjp1qn1/c+bM0alTp7Rt2zYFBARIkkqXLu2y/wCAm4c8AwDwJPIMAFhLvpupZuTEiRP65ptvVKxYMdWvX1/R0dFq3Lixtm7d6vJ+q1atUp06dfTwww+rWLFiqlmzpj744APD9n/88YfWrFmjp556ylT/Lly4oCZNmig8PFxbtmzR1q1bFR4ervvvv1+XLl2y9yUuLk79+vVTdHS0qlSpovHjxysjI8PUvgC4l7enRfvqVOn8hjwDwFO8nVPINdZAngHgSd7OKb6aZ26ZQbVffvlFkpSQkKA+ffpo7dq1qlWrlpo1a6YDBw64vN/06dNVrlw5ffnll3r22Wc1cOBAffjhh07bz58/XxEREerUqZOp/i1evFgFChTQrFmzVLVqVVWsWFFz587V0aNH7X8N+uWXX/Tpp58qIyNDn3/+uV5++WW98cYbGjdunKl9AQDcjzwDAPAk8gwAWI/PL/8cP368xo8fb//54sWL2rFjh/r372+PffHFF/Lz85MkPfPMM3riiSckSTVr1tTGjRs1Z84cJSYmOt1+Zmam6tSpY99HzZo19eOPP2r69Onq0aNHjvZz5sxRt27dFBwcbOpx7N69WwcPHlRERIRDPC0tTf/73//sfSlWrJhmzpwpPz8/1a5dW7///rsmT56sV155xdT+AAC5Q54hzwCAJ5FnyDMAfJfPD6o9++yz6tKli/3nbt26qXPnzg5/Wbn99tuVnJwsSapUqZLD/StWrKijR48abj82NtbpfZYuXZqj7ddff639+/dryZIlph9HZmamateurY8++ijH72677TZ7XwICAuwJNbsvycnJunTpkgIDA03vF0DeWW16spX6kh+QZ8gzgLdZLc9I5Bp3Is+QZwArsFqusVJfXPH5QbXChQurcOHC9p9DQkJUrFgx3XXXXQ7typQpo+LFi2v//v0O8f/+979q3bq14fYbNGjg9D7OLqg5e/Zs1a5dW9WrVzf9OGrVqqUlS5aoWLFiioyMNOzLwoULlZmZaa/w89///lexsbEkIADwEPIMeQYAPIk8Q54B4LtumWuq2Ww2DRs2TG+//bY+/fRTHTx4UKNHj9bPP//scBHOZs2aadq0afafX3jhBe3YsUPjx4/XwYMHtXDhQs2cOVP9+vVz2H5KSoo++eQT9e7d2+n+k5OT9d133+ngwYOSpH379um7777TqVOnJF39i1TRokXVsWNHff311zp06JA2b96s559/Xr/++qsk6bnnntPJkyf1/PPP67///a/WrFmj8ePH5+gLAODmI88AADyJPAMA1uPzM9XMGDRokNLS0vTCCy/o1KlTql69utavX68777zT3uZ///uf/vrrL/vP99xzj5YvX64RI0bo1VdfVdmyZTVlyhR169bNYduLFy9WVlaWHn30Uaf7njFjhsaOHWv/+b777pMkzZ07V7169VJoaKi2bNmi4cOHq1OnTjp37pxuv/12NWvWzP6XnpIlS2rdunV64YUXVK1aNd1+++16/vnnNXz4cLcdIwDmMVUa2cgzADzBanlGItd4C3kGgKdYLddYqS+u2LJ8pacAYDEpKSmKiorS0qVLFRYW5u3u2KWmpqpz5846e/as4fILAID1WTXPSOQaAMgvrJprfCXP3DLLPwEAAAAAAAB3uaWWfwKAJzBVGgDgSVbLMxK5BgDyG6vlGiv1xRWfHFTLzMzU77//roiICNlsNm93B4APysrK0rlz51S8eHF79Sng78g1APKCPIPrIc8AyAvyjDX45KDa77//rpIlS3q7GwDygWPHjqlEiRLe7gYsiFwDwB3IMzBCngHgDuQZ7/LJQbWIiAhJ0tNPP63AwECH312+fNnpfYziV65ccUs8MzPTVNxoKqPZKY7ORqSN/tLl5+dnKu7v7/zlYRQPCAhwS3tPb8cobnQcrn2N3ej2PX0czPTT158rs69NZ8fm3LlzuuOOO+znk7zylenJyL3s18axY8dyfXHUjIwMp/HU1FSn8YsXL5qKX7p0yWncKMcZ9ccoN5nJH2bPRUFBQU7joaGhTuNGF8o12j5gNSkpKSpZsiR5Bobc9dpwxei8bjZudvtmmXl9m/0exXsH+Z07zyW8X8zzyUG17JN3YGBgjg/pRtMejU74Ru3Nxs1+cbHSoJrZAQpPD5h4K+6OwSpvxt0xqObrcbPPoeS+D4PIf7JfG5GRkXkeVHPX+Tc9Pd1p3EqDakaDZ0Zxo8EzBtWQX5BnYMSdrw13DZ55a1DNGxg8QH7hy+/D/ICFtwAAAAAAAIBJPjlTDQCshEo5AABPslqekcg1AJDfWC3XWKkvrjBTDQAAAAAAADDJp2eqXb58Ocf6YaOLOBvFjQoPmC1sYPa6NUZxs5ytnza67punCxIYPSZ3HRtvxY1GyD1dnMJdcWf98fQxs9pz7uzYGF2bCsgNo5xy7tw5U/ELFy6YiqelpTmNm81ZRucLM9dUM8oFRtdOCwkJcRo3ei8aHWOja62Fh4c7jQNAfmL2+tHeutaaO3jrszMFDwCYYWqm2vTp01WtWjX7RZvj4uL0xRdf2H9//vx59e/fXyVKlFBISIgqVqyo6dOnO2wjPT1dAwYMUNGiRRUWFqYOHTro119/dc+jAQAvyJ4qbaWbryLPAEBO3s4p5BpyDYD8z9s5xVfzjKlBtRIlSmjChAnatWuXdu3apaZNm6pjx4768ccfJUkvvPCC1q5dqwULFuinn37SCy+8oAEDBmjlypX2bQwaNEjLly/X4sWLtXXrVp0/f17t2rUznNkCALh1kGcAAJ5GrgEAuIupQbX27durTZs2Kl++vMqXL69x48YpPDxcO3bskCRt375dPXv2VHx8vMqUKaOnn35a1atX165duyRJZ8+e1ezZs/XGG2+oefPmqlmzphYsWKB9+/Zpw4YN7n90AACfQp4BAHgauQYA4C43XKggIyNDixcvVmpqquLi4iRJDRs21KpVq/Tbb78pKytLX331lf773/+qVatWkqTdu3fr8uXLatmypX07xYsXV5UqVbRt27Y8PhQA8A5vT4v21anS10OeAYCrvJ1TyDXkGgD5n7dziq/mGdOFCvbt26e4uDilpaUpPDxcy5cvV6VKlSRJb7/9tvr06aMSJUrI399fBQoU0KxZs9SwYUNJUnJysgIDA1WoUCGHbUZHRys5Odlwn+np6Q4XMk5JSTHbbQCAj/BGnpHINQBwK+E7DQDAHUwPqlWoUEHfffedzpw5o6VLl6pnz57avHmzKlWqpLfffls7duzQqlWrVLp0aW3ZskV9+/ZVbGysmjdvbrjNrKwslxVlEhMTNXbs2BxxZ5XBzFb/NKqYZraSmtmqoEajrmZHY51VADKqCmRUzdOoj56u4Gj0WD1dUdLssfd03BuVOK1UhdNb/TE6J8D7vJFnJNe55trXy9mzZ51uw+gLklH1z/PnzzuNG1X/vHjxotO42QrXRudTM/nDqPpncHCw03hoaKjTuFH1T7P52egxGVUFNaqIDeDWYKXvNM6YrfLprqqgZrfjSZ7+rG22vRFfmU0DwDNMD6oFBgbqrrvukiTVqVNHO3fu1NSpUzVlyhSNHDlSy5cvV9u2bSVJ1apV03fffad//OMfat68uWJiYnTp0iWdPn3a4S87J06cUP369Q33OWLECA0ePNj+c0pKikqWLGm26wDgEVabnmylvtwIb+QZiVwDwLqslmckcg3faQDkN1bLNVbqiys3fE21bFlZWUpPT9fly5d1+fLlHH/d8PPzs4/2165dWwEBAVq/fr3998ePH9cPP/zgMgEFBQXZS15n3wAAt4abkWckcg0A3Mr4TgMAuBGmZqqNHDlSrVu3VsmSJXXu3DktXrxYSUlJWrt2rSIjI9W4cWMNGzZMISEhKl26tDZv3qwPP/xQb775piQpKipKTz31lIYMGaIiRYqocOHCGjp0qKpWrepyKjUA4NZAngEAeBq5BgDgLqYG1f744w91795dx48fV1RUlKpVq6a1a9eqRYsWkqTFixdrxIgR6tatm06dOqXSpUtr3LhxevbZZ+3beOutt+Tv768uXbro4sWLatasmebNm8e1TQD4LKZKuw95BgByslqekcg15BoA+Y3Vco2V+uKKqUG12bNnu/x9TEyM5s6d67JNcHCw3nnnHb3zzjtmdg0AuAWQZwAAnkauAQC4i+lCBVbijuqfZuPuqv5ptqqMEXdU/zSKG1V281ZVULMj1b5QhfNm7NdZ3EpVOG9G3NmxT0tLc9oWuNb58+dzVD0zqvLprqqgqampTuNGVUGNKmga5Saj94pRdTdneSIwMNBpW6Pqn2arfJrNt0Zxo+0bVQU16j8AeNK151+z1Tk9XS3UqL0Ro+2Y+TzvrmqeZrdvtr27qogC8E0+PagGAFbAVGkAgCdZLc9I5BoAyG+slmus1BdX8lz9EwAAAAAAALjVMKgGAAAAAAAAmMTyTwDII6ZKAwA8yWp5RiLXAEB+Y7VcY6W+uMJMNQAAAAAAAMAkn56pduXKlRyVaIyqfLmr+qe7qpSZrVpjVEHHGXdV+fRWFU6zFXSsVFXzRuLeqMRppSqc3uqP0XscuJaz6p9GVTvdVRX0/PnzTuNGVUGNqtkavc6N3itmqkcHBQU5bRsaGuo0bpQ/3RV3V7VQo6qgRnEAyKsCBQrkyDNmq3y6K+6uaqFmOfs8bPZ7kdnK1marc5r9zG50zNz1HQuANfj0oBoAWAFTpQEAnmS1PCORawAgv7FarrFSX1xh+ScAAAAAAABgEoNqAAAAAAAAgEks/wSAPGKqNADAk6yWZyRyDQDkN1bLNVbqiyvMVAMAAAAAAABM8umZaleuXMlRzcVd1T/T09Odxt1Vjcxs9UKjqjXO4kZVPgMDA53G82sVTl/vvyfjVnus3qiASvVP5NaFCxdyVPAyqs5pFPd0VdCLFy86jRvlMrO5xln+CA4OdtrW6L3lruqcZuOerjpqVBXUqLI2AFzLZrPlOP+a+ewvma/y6efn55btmO2nETPV6s3u02yVTyNmZ8246zuWr8zWAW5VPj2oBgBWwFRpAIAnWS3PSOQaAMhvrJZrrNQXV1j+CQAAAAAAAJjEoBoAAAAAAABgEss/ASCPmCoNAPAkq+UZiVwDAPmN1XKNlfriCjPVAAAOEhMTZbPZNGjQIHssKytLCQkJKl68uEJCQhQfH68ff/zRe50EAAAAAC/z6ZlqGRkZOSrReLoqmLuqghpVXzQajTVT5cZslU9PV8P09Sqcnu6/Nx6vlapw3oy4s8dF9U/ndu7cqZkzZ6patWoO8UmTJunNN9/UvHnzVL58eb3++utq0aKF9u/fr4iICC/19ua4ePFijippRtU2U1NTncY9XRXUaL9mc5ZRNbi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