jGaboardi/centered_norm_matplotlib_384.ipynb

## centered_norm_matplotlib_384.ipynb

      
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## centered_norm_matplotlib_391.ipynb
{"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.9"},"gist_info":{"create_date":"2024-07-17T02:01:19Z","gist_id":"a618ffefcdc59f9f4cfc59fc459e9246","gist_url":"https://gist.github.com/jGaboardi/a618ffefcdc59f9f4cfc59fc459e9246"}},"nbformat_minor":5,"nbformat":4,"cells":[{"id":"ba34be51-2191-48cc-88cb-395b960aa59d","cell_type":"markdown","source":"# matplotlib 3.9.1\n## `matplotlib.colors.CenteredNorm`","metadata":{}},{"id":"a697159b-a0f0-49c1-aa25-ef9a9a48ab25","cell_type":"code","source":"%load_ext watermark\n%watermark","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:11.998369Z","iopub.execute_input":"2024-07-17T22:57:11.998655Z","iopub.status.idle":"2024-07-17T22:57:12.024240Z","shell.execute_reply.started":"2024-07-17T22:57:11.998632Z","shell.execute_reply":"2024-07-17T22:57:12.023921Z"},"trusted":true},"outputs":[{"name":"stdout","text":"Last updated: 2024-07-17T18:57:12.016279-04:00\n\nPython implementation: CPython\nPython version       : 3.11.9\nIPython version      : 8.26.0\n\nCompiler    : Clang 16.0.6 \nOS          : Darwin\nRelease     : 23.5.0\nMachine     : arm64\nProcessor   : arm\nCPU cores   : 8\nArchitecture: 64bit\n\n","output_type":"stream"}],"execution_count":1},{"id":"65abdf80-3e61-4330-85f9-b20e8f3a5f9f","cell_type":"code","source":"import geopandas\nimport matplotlib\nimport numpy\nimport pandas\nimport shapely\n\n%watermark -iv","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:12.024844Z","iopub.execute_input":"2024-07-17T22:57:12.024961Z","iopub.status.idle":"2024-07-17T22:57:12.284014Z","shell.execute_reply.started":"2024-07-17T22:57:12.024945Z","shell.execute_reply":"2024-07-17T22:57:12.283744Z"},"trusted":true},"outputs":[{"name":"stdout","text":"matplotlib: 3.9.1\nnumpy     : 1.26.4\ngeopandas : 1.0.1\npandas    : 2.2.2\nshapely   : 2.0.5\n\n","output_type":"stream"}],"execution_count":2},{"id":"581f69c4-6f2c-43fb-9481-5d4f0a6e3ec5","cell_type":"code","source":"values = (\n    numpy.random.default_rng(850)\n    .uniform(low=.01, high=2.9, size=10)\n)\nvalues","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:12.284426Z","iopub.execute_input":"2024-07-17T22:57:12.284520Z","iopub.status.idle":"2024-07-17T22:57:12.288020Z","shell.execute_reply.started":"2024-07-17T22:57:12.284512Z","shell.execute_reply":"2024-07-17T22:57:12.287811Z"},"trusted":true},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"array([0.33735074, 1.08379739, 0.98264763, 1.5703785 , 1.30617177,\n       2.11096301, 2.87590001, 2.43072363, 0.66399704, 0.46799954])"},"metadata":{}}],"execution_count":3},{"id":"718e94a2-0625-4ea2-8b92-8d5db8a04751","cell_type":"code","source":"centered_norm = matplotlib.colors.CenteredNorm(vcenter=1)\ncentered_norm(values)","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:12.288458Z","iopub.execute_input":"2024-07-17T22:57:12.288539Z","iopub.status.idle":"2024-07-17T22:57:12.290953Z","shell.execute_reply.started":"2024-07-17T22:57:12.288531Z","shell.execute_reply":"2024-07-17T22:57:12.290747Z"},"trusted":true},"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"masked_array(data=[0.32337831, 0.52233525, 0.49537492, 0.65202796,\n                   0.58160663, 0.79611466, 1.        , 0.88134325,\n                   0.4104422 , 0.35820128],\n             mask=False,\n       fill_value=1e+20)"},"metadata":{}}],"execution_count":4},{"id":"08fe0064-2469-4c93-b6e0-3e801aac353f","cell_type":"markdown","source":"------------------------","metadata":{}},{"id":"3a4b47e6-8400-4c75-8e16-56459efe0c0c","cell_type":"code","source":"points = shapely.points(numpy.array([[i, i] for i in range(10)]))\ngdf = geopandas.GeoDataFrame({\"values\": values}, geometry=points)\ngdf","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:12.291337Z","iopub.execute_input":"2024-07-17T22:57:12.291407Z","iopub.status.idle":"2024-07-17T22:57:12.296791Z","shell.execute_reply.started":"2024-07-17T22:57:12.291400Z","shell.execute_reply":"2024-07-17T22:57:12.296575Z"},"trusted":true},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"     values     geometry\n0  0.337351  POINT (0 0)\n1  1.083797  POINT (1 1)\n2  0.982648  POINT (2 2)\n3  1.570379  POINT (3 3)\n4  1.306172  POINT (4 4)\n5  2.110963  POINT (5 5)\n6  2.875900  POINT (6 6)\n7  2.430724  POINT (7 7)\n8  0.663997  POINT (8 8)\n9  0.468000  POINT (9 9)","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>values</th>\n      <th>geometry</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.337351</td>\n      <td>POINT (0 0)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.083797</td>\n      <td>POINT (1 1)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.982648</td>\n      <td>POINT (2 2)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.570379</td>\n      <td>POINT (3 3)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.306172</td>\n      <td>POINT (4 4)</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2.110963</td>\n      <td>POINT (5 5)</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2.875900</td>\n      <td>POINT (6 6)</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2.430724</td>\n      <td>POINT (7 7)</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>0.663997</td>\n      <td>POINT (8 8)</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>0.468000</td>\n      <td>POINT (9 9)</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":5},{"id":"20f00ac7-4c50-4cb8-9252-4cbef6a039ce","cell_type":"code","source":"cmap = matplotlib.cm.coolwarm\nax = gdf.plot(\n    column=\"values\",\n    norm=matplotlib.colors.CenteredNorm(vcenter=1),\n    cmap=cmap,\n    legend=True,\n)\nax.set_axis_off()","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:12.298109Z","iopub.execute_input":"2024-07-17T22:57:12.298195Z","iopub.status.idle":"2024-07-17T22:57:12.473882Z","shell.execute_reply.started":"2024-07-17T22:57:12.298188Z","shell.execute_reply":"2024-07-17T22:57:12.473660Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 2 Axes>","image/png":"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"},"metadata":{}}],"execution_count":6},{"id":"00b00f70-a1d3-4270-9d87-27e342057b94","cell_type":"markdown","source":"---------------------","metadata":{}},{"id":"3f286434-4b63-4752-a38f-93086a433a9c","cell_type":"code","source":"import momepy as mm\nfrom core import stats, utils, viz","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:12.474226Z","iopub.execute_input":"2024-07-17T22:57:12.474296Z","iopub.status.idle":"2024-07-17T22:57:13.159111Z","shell.execute_reply.started":"2024-07-17T22:57:12.474288Z","shell.execute_reply":"2024-07-17T22:57:13.158759Z"},"trusted":true},"outputs":[],"execution_count":7},{"id":"6fc4e5e7-dfd7-4776-8495-5e368878bce6","cell_type":"code","source":"# which FUA?\nfua = 869\n\n# which h3 resolution?\nres = 7","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:13.159580Z","iopub.execute_input":"2024-07-17T22:57:13.159810Z","iopub.status.idle":"2024-07-17T22:57:13.161497Z","shell.execute_reply.started":"2024-07-17T22:57:13.159799Z","shell.execute_reply":"2024-07-17T22:57:13.161282Z"},"trusted":true},"outputs":[],"execution_count":8},{"id":"f0586a63-8bc0-4d3d-93cb-4ffe7569caf9","cell_type":"code","source":"meta = utils.read_sample_data()\ngeom = meta.loc[meta.eFUA_ID == fua, \"geometry\"]\ncity = meta.loc[meta.eFUA_ID == fua, \"eFUA_name\"].values[0]","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:13.161804Z","iopub.execute_input":"2024-07-17T22:57:13.161862Z","iopub.status.idle":"2024-07-17T22:57:13.201544Z","shell.execute_reply.started":"2024-07-17T22:57:13.161855Z","shell.execute_reply":"2024-07-17T22:57:13.201249Z"},"trusted":true},"outputs":[],"execution_count":9},{"id":"92541aab-fbf9-415f-bfc7-29f9eba629ee","cell_type":"code","source":"# read in orig OSM data\norig = utils.read_parquet_roads(fua)\n\n# read in base case (manual simp)\nbase = utils.read_manual(fua, orig.crs)\n\n# which file to compare with?\ncomp = utils.read_parenx(fua, \"voronoi\", orig.crs)","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:13.201965Z","iopub.execute_input":"2024-07-17T22:57:13.202031Z","iopub.status.idle":"2024-07-17T22:57:13.476946Z","shell.execute_reply.started":"2024-07-17T22:57:13.202023Z","shell.execute_reply":"2024-07-17T22:57:13.476645Z"},"trusted":true},"outputs":[],"execution_count":10},{"id":"4240437f-932a-445c-b253-54d9c27a251e","cell_type":"code","source":"# get graphs & node/edge gdfs for both data sets\nbase_graph = mm.gdf_to_nx(base, length=\"length\", integer_labels=True)\ncomp_graph = mm.gdf_to_nx(comp, length=\"length\", integer_labels=True)\n_, edges_manual = mm.nx_to_gdf(base_graph)\n_, edges_parenx = mm.nx_to_gdf(comp_graph)\n\ngrid = utils.make_grid(fua, res, orig.crs)","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:13.477361Z","iopub.execute_input":"2024-07-17T22:57:13.477435Z","iopub.status.idle":"2024-07-17T22:57:15.168923Z","shell.execute_reply.started":"2024-07-17T22:57:13.477427Z","shell.execute_reply":"2024-07-17T22:57:15.168592Z"},"trusted":true},"outputs":[],"execution_count":11},{"id":"d2c4615a-b9db-4052-a903-39d20d2283e7","cell_type":"code","source":"_m = \"manual\"\n_p = \"parenx\"\nvar = \"edge_count\"\nfor dname, df in [[_m, edges_manual], [_p, edges_parenx]]:\n    grid[[f\"{var}_{dname}\", \"IGNORE\"]] = grid.apply(\n        lambda x: stats.get_edge_stats(df, x.geometry),  # noqa: B023\n        axis=1,\n        result_type=\"expand\",\n    )\n\ngrid[f\"{var}_ratio\"] = grid[f\"{var}_{_p}\"] / grid[f\"{var}_{_m}\"]\n\nviz.h3_hex.plot_analysis(grid, f\"{var}_ratio\", info=f\"({_p}/{_m})\")","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:15.169368Z","iopub.execute_input":"2024-07-17T22:57:15.169501Z","iopub.status.idle":"2024-07-17T22:57:15.590911Z","shell.execute_reply.started":"2024-07-17T22:57:15.169494Z","shell.execute_reply":"2024-07-17T22:57:15.590694Z"},"trusted":true},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x1000 with 2 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAvgAAAMWCAYAAACEG/KlAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAADAKklEQVR4nOz9eZxsVXkv/n/W2mNV9dxnZD4gMnkZFOOIaAgoKN7EMfozTpgrinIVSYzDjWMkEpOLimJMUFSiMfEQJ4zDjeDwixoHxAEkgsBhOEPP3TXseX3/qK4+3ef0UN1d1Wvt3Z/3y7LpOtVdT1V3Vz372c96llBKKRARERERUSFI3QEQEREREVHnMMEnIiIiIioQJvhERERERAXCBJ+IiIiIqECY4BMRERERFQgTfCIiIiKiAmGCT0RERERUIEzwiYiIiIgKxNYdABEREREVXxAEiKJIdxiHcV0Xvu/rDqOjmOATERERUVcFQYAjSj2YQKo7lMPs2LED9957b6GSfCb4RERERNRVURRhAilusHahbFCHeB0ZXr7vXkRRxASfiIiIiGi1ypAoC0t3GAcp3QF0BxN8IiIiItoQwhEQQugOY45QAgZ2Da2bOedIiIiIiIho3ZjgExEREREVCFt0iIiIiGhDCFtAmtaiU0Cs4BMRERERFQgTfCIiIiKiAmGLDhERERFtCOFICGFOfVmoYs7JNOcZJiIiIiKidWOCT0RERERUIGzRISIiIqINIS0BKc2ZXCMzc2LpJFbwiYiIiIgKhAk+EREREVGBsEWHiIiIiDaEcASEQS06gi06RERERERkOib4REREREQFwhYdIiIiItoQ0uYUnY3ACj4RERERUYEwwSciIiIiKhC26BARERHRhuAUnY3BCj4RERERUYEwwSciIiIiKhC26BARERHRhpCWgLTMaYuRqTmxdBIr+EREREREBcIEn4iIiIioQNiiQ0REREQbQlgCwqAWHQFzYukkVvCJiIiIiAqECT4RERERUYGwRYeIiIiINoRxU3TYokNERERERKZjgk9EREREVCBs0SEiIiKiDSGkgJDmtMUIZU4sncQKPhERERFRgTDBJyIiIiIqELboEBEREdGGEJaEsMypLwso3SF0hTnPMBERERERrRsTfCIiIiKiAmGLDhERERFtCG50tTFYwSciIiIiKhAm+LTp3XrrrRBC4NZbb9UditHuuOMOvPOd78R9992n5f4ffvhhvPOd78TPf/7zw/7tne98J4TofBXm3e9+N0499VRkWdbx750XV1xxBc444wzdYWyol7/85TjuuOPmPo/jGCeccAKuueYabTEREa0GE3wiassdd9yBd73rXVoT/He9612LJvivetWr8IMf/KDj93f11Vfj3e9+N6TcvC+VN910E5773OfqDkMrx3Hwl3/5l3j3u9+NsbEx3eEQ5ZoQYm6zKyMuXSgOmWDzvmsRkVaNRgNKdWY82VFHHYXHP/7xHfleLR/84AcxMDCA5zznOR39vu2I4xhJkmz4/R7qxz/+Me6///5Nn+ADwIte9CIIIfD3f//3ukMhIloRE3wqhN/+9rd48YtfjG3btsHzPJxyyin4yEc+ctjtfvOb3+AZz3gGyuUytmzZgksvvRQzMzOH3U4phfe973049thj4fs+zj77bHzrW9/CU5/6VDz1qU9dcNvp6WlceeWV2LVrF1zXxZFHHok3vOENqNVqq34cP/rRj3DxxRdjeHgYvu/jhBNOwBve8IYFt/n+97+P8847D729vSiXy3jiE5+Im2++ecFtlmpZueGGGyCEWFCFP+644/CsZz0LX//61/HoRz8apVIJJ598Mj7xiU8s+LrnP//5AICnPe1pzQqMELjhhhvaelyt+/3mN7+JV77yldi6dSvK5TLCMMTdd9+NV7ziFTjxxBNRLpdx5JFH4uKLL8Yvf/nLua+/9dZb8djHPhYA8IpXvGLu/t/5zncu+XizLMPVV1+Nk08+GZ7nYdu2bXjpS1+KBx98cMV4oyjC9ddfjxe/+MULqvf33XcfhBC4+uqr8Vd/9Vc45phj5n4//uM//mPB92jncbUemxACn/nMZ/CmN70JRx55JDzPw9133w0A+H//7//hvPPOQ19fH8rlMp70pCcddl+tx//rX/8aL3rRi9Df34/t27fjla98JaampuZu98///M8QQuDaa69d8PXveMc7YFkWvvWtby24fvfu3TjppJNw2mmnLbifX/ziF3j+85+P/v5+DA0N4YorrkCSJLjrrrvwjGc8A729vTjuuONw9dVXL/h+QRDgTW96E84888y5r33CE56AL33pS4f9DIQQeN3rXofPfOYzOOWUU1Aul3HGGWfgq1/96oLbHdpOc+hzMt9HPvIRPOUpT8G2bdtQqVTwP/7H/8DVV1+NOI4P+/pDua6LF77whfj4xz/esQNTIqJuYYJPuXfHHXfgsY99LH71q1/hb//2b/HVr34Vz3zmM3H55ZfjXe9619zt9u/fj3PPPRe/+tWv8NGPfhSf+cxnUK1W8brXve6w7/m2t70Nb3vb2/CMZzwDX/rSl3DppZfiVa96Ff77v/97we3q9TrOPfdcfOpTn8Lll1+Of//3f8eb3/xm3HDDDXj2s5+9qkTgG9/4Bs455xzs2bMHf/d3f4d///d/x9vf/nbs379/7jbf+c538Pu///uYmprC9ddfj8997nPo7e3FxRdfjM9//vNrePaabr/9drzpTW/CG9/4RnzpS1/C6aefjksuuQTf/e53AQDPfOYz8b73vQ9AM0n6wQ9+gB/84Ad45jOfuar7eeUrXwnHcfCZz3wGX/jCF+A4Dh5++GEMDw/jr//6r/H1r38dH/nIR2DbNh73uMfhrrvuAgA8+tGPxic/+UkAwNvf/va5+3/Vq1615H295jWvwZvf/Gacf/75+PKXv4z3vOc9+PrXv44nPvGJGB0dXTbOH/3oRxgbG8PTnva0Rf/92muvxde//nVcc801uPHGGyGlxIUXXrigTaidxzXfW97yFuzZswcf+9jH8JWvfAXbtm3DjTfeiAsuuAB9fX341Kc+hX/5l3/B0NAQnv70px+W5APAc5/7XDzykY/E7t278Rd/8Rf47Gc/ize+8Y1z//7Hf/zHuPTSS/GmN70JP/nJTwAA3/72t/He974Xb33rW3H++ecv+H67d+9etHr/ghe8AGeccQZ2796NP/3TP8X//b//F2984xvxh3/4h3jmM5+Jf/u3f8Pv//7v481vfjNuuummua8LwxDj4+O48sor8cUvfhGf+9zn8OQnPxnPec5z8OlPf/qw+7n55ptx7bXX4t3vfjd2796NoaEh/NEf/RF+97vfLfpzWck999yDF7/4xfjMZz6Dr371q7jkkkvwN3/zN3j1q1/d1tc/9alPxf33349f/epXa7p/IgKEdXCSjgkXYel+RrpEEeXc05/+dHXUUUepqampBde/7nWvU77vq/HxcaWUUm9+85uVEEL9/Oc/X3C7888/XwFQt9xyi1JKqfHxceV5nnrhC1+44HY/+MEPFAB17rnnzl131VVXKSml+vGPf7zgtl/4whcUAPW1r32t7cdxwgknqBNOOEE1Go0lb/P4xz9ebdu2Tc3MzMxdlySJetSjHqWOOuoolWWZUkqpd7zjHWqxP+9PfvKTCoC6995756479thjle/76v7775+7rtFoqKGhIfXqV7967rp//dd/XfA8rUbrfl/60peueNskSVQURerEE09Ub3zjG+eu//GPf6wAqE9+8pOHfc2hj/fOO+9UANRrX/vaBbf70Y9+pACot771rcvG8P73v18BUPv27Vtw/b333qsAqCOOOGLBz2l6eloNDQ2pP/iDP1j147rlllsUAPWUpzxlwe1rtZoaGhpSF1988YLr0zRVZ5xxhvq93/u9wx7/1VdfveC2r33ta5Xv+3O/F0opFQSBOuuss9SuXbvUHXfcobZv367OPfdclSTJgq/9+c9/rgCon/70p4fdz9/+7d8uuO2ZZ56pAKibbrpp7ro4jtXWrVvVc57znGWfkziO1SWXXKLOOuusBf8GQG3fvl1NT0/PXbdv3z4lpVRXXXXV3HUve9nL1LHHHnvY917qb6AlTVMVx7H69Kc/rSzLmnudWO57/va3v1UA1HXXXbfk9yWixU1NTSkA6tuPe4z6ryc9zpjLtx/3GAXgsBxiKe973/vU2WefrXp6etTWrVvV//yf/1P95je/WfZrWq/zh17uvPPOTjy1i2IFn3ItCAL8x3/8B/7oj/4I5XIZSZLMXS666CIEQYAf/vCHAIBbbrkFp5122mETQV784hcv+PyHP/whwjDEC17wggXXP/7xjz+sFeCrX/0qHvWoR+HMM89ccN9Pf/rTVzWZ57//+79xzz334JJLLoHv+4veplar4Uc/+hGe97znoaenZ+56y7LwJ3/yJ3jwwQcXrQy348wzz8Qxxxwz97nv+3jkIx+J+++/f03fbymLVYOTJMH73vc+nHrqqXBdF7Ztw3Vd/Pa3v8Wdd965pvu55ZZbADTbN+b7vd/7PZxyyimLVr/ne/jhhyGEwJYtWxb99+c85zkLfk6tsyjf/e53kabpmh7Xoc/Nf/7nf2J8fBwve9nLFvxuZVmGZzzjGfjxj398WBvYs5/97AWfn3766QiCAAcOHJi7zvM8/Mu//AvGxsbw6Ec/GkopfO5zn4NlLSxj7d69G8cddxwe/ehHHxbrs571rAWfn3LKKRBC4MILL5y7zrZtPOIRjzjsd+hf//Vf8aQnPQk9PT2wbRuO4+D6669f9Dl52tOeht7e3rnPt2/fjm3btq359/K2227Ds5/9bAwPD8OyLDiOg5e+9KVI0/Sws3OL2bZtGwDgoYceWtP9E1H+fec738Fll12GH/7wh/jWt76FJElwwQUXtNWWe9ddd2Hv3r1zlxNPPLFrcXKjK8q1sbExJEmCD3/4w/jwhz+86G1a7RhjY2PYtWvXYf++Y8eOw74n0EwmDnXodfv378fdd98Nx3GWve+VjIyMAGguFl3KxMQElFLYuXPnYf92xBFHLIh9tYaHhw+7zvM8NBqNNX2/pSwW+xVXXIGPfOQjePOb34xzzz0Xg4ODkFLiVa961Zrvv/U8LPVcrZQgNhoNOI5zWNLbcujvTOu6KIpQrVbR39+/6sd1aKyt1qznPe95S8Y5Pj6OSqUy9/mhP0fP8+Yez3yPeMQjcM455+Dmm2/Ga17zmkWfpy984QtLLq4dGhpa8LnruiiXy4cdnLqui+np6bnPb7rpJrzgBS/A85//fPzZn/0ZduzYAdu2cd111y1Y87HU42k9prX8XuzZswfnnHMOTjrpJHzwgx/EcccdB9/38V//9V+47LLL2vqercfX6b8Los1EWALCoI2uhFpdLF//+tcXfP7JT34S27Ztw09/+lM85SlPWfZrt23bhoGBgdWGuCZM8CnXBgcH5yrYl1122aK3aSX1w8PD2Ldv32H/fuh1raRifu/7/NvOr+Jv2bIFpVJp0eSk9e/t2Lp1KwAsuwC0lSDu3bv3sH97+OGHF9xfKxEJw3AuyQPaP+DolsUW/t5444146UtfOtfj3zI6OrrmF8LWz3Dv3r2HHTQ9/PDDK/5ctmzZgiiKUKvVFiTQLUv9HrmuO3d2ZbWP69DnphXjhz/84SUnBC12ENqOf/zHf8TNN9+M3/u938O1116LF77whXjc4x439+933nkn7rzzTlx//fVr+v5LufHGG7Fr1y58/vOfX/B4wzBc8/f0fX/Rrz/0d/2LX/wiarUabrrpJhx77LFz1y82dnUp4+PjANr/uyai4msNMji08LGYs846C0EQ4NRTT8Xb3/72Jdd5dQJbdCjXyuUynva0p+G2227D6aefjrPPPvuwSyvZe9rTnoZf//rXuP322xd8j89+9rMLPn/c4x4Hz/MOW7T6wx/+8LDK77Oe9Szcc889GB4eXvS+F5vusZhHPvKROOGEE/CJT3xiyWSnUqngcY97HG666aYFFcQsy3DjjTfiqKOOwiMf+UgAmLvfX/ziFwu+x1e+8pW24lnMUtXg9RJCLDgIAZqLKw9tg1jN/f/+7/8+gGZCOd+Pf/xj3HnnnTjvvPOW/fqTTz4ZQHNR5mJuuukmBEEw9/nMzAy+8pWv4Jxzzpmr+rf7uJbypCc9CQMDA7jjjjsW/d06++yz4bpuW99rvl/+8pe4/PLL8dKXvhTf+973cPrpp+OFL3whJiYm5m6ze/duHHHEER0fPSqEgOu6C5L7ffv2LTpFp13HHXccDhw4sOCAPIoifOMb3zjsvgEs+JkopfAP//APbd9Xa3HvqaeeuuZ4ichM09PTCy7tFB6UUrjiiivw5Cc/GY961KOWvN3OnTvx8Y9/HLt378ZNN92Ek046Ceedd97cIItuYAWfcu+DH/wgnvzkJ+Occ87Ba17zGhx33HGYmZnB3Xffja985Sv49re/DQB4wxvegE984hN45jOfife+973Yvn07/umf/gm/+c1vFny/1ti/q666CoODg/ijP/ojPPjgg3jXu96FnTt3Lhib+IY3vAG7d+/GU57yFLzxjW/E6aefjizLsGfPHnzzm9/Em970pgWV0eV85CMfwcUXX4zHP/7xeOMb34hjjjkGe/bswTe+8Q380z/9EwDgqquuwvnnn4+nPe1puPLKK+G6Lj760Y/iV7/6FT73uc/NJTEXXXQRhoaGcMkll+Dd7343bNvGDTfcgAceeGDNz3PrxevjH/84ent74fs+du3atWgbxWo861nPwg033ICTTz4Zp59+On7605/ib/7mbw6rvJ9wwgkolUr4p3/6J5xyyino6enBEUccMdeeNN9JJ52E//W//hc+/OEPz024ue+++/B//s//wdFHH71gssxiWqNQf/jDH+L0008/7N8ty8L555+PK664AlmW4f3vfz+mp6cXTG1q93EtpaenBx/+8Ifxspe9DOPj43je856Hbdu2YWRkBLfffjtGRkZw3XXXtfW9Wmq1Gl7wghdg165d+OhHPwrXdfEv//IvePSjH41XvOIV+OIXvwig2Z7znOc8p+MbwDzrWc/CTTfdhNe+9rV43vOehwceeADvec97sHPnTvz2t79d0/d84QtfiL/8y7/EH//xH+PP/uzPEAQBPvShD82thWg5//zz4bouXvSiF+HP//zPEQQBrrvuugUHNiv54Q9/CMuyVjwNT0RLE1JCGLR5YCuWo48+esH173jHO+ZGMS/lda97HX7xi1/g+9///rK3O+mkk3DSSSfNff6EJzwBDzzwAD7wgQ907fXEnGeYaI1OPfVU/OxnP8OjHvUovP3tb8cFF1yASy65BF/4whcWVGp37NiB73znOzj11FPxmte8Bi95yUvg+/5hM8EB4K/+6q/w3ve+FzfffDOe/exn40Mf+hCuu+66w/rnKpUKvve97+HlL385Pv7xj+OZz3wmXvCCF+BDH/oQjjrqqLYr+ADw9Kc/Hd/97nexc+dOXH755XjGM56Bd7/73QvaMM4991x8+9vfRqVSwctf/nL88R//MaampvDlL38ZL3zhC+du19fXh69//evo7e3FS17yElx66aV41KMehbe97W2re3Ln2bVrF6655hrcfvvteOpTn4rHPvax6zoj0PLBD34QL3nJS3DVVVfh4osvxpe//GXcdNNNOOGEExbcrlwu4xOf+ATGxsZwwQUX4LGPfSw+/vGPL/l9r7vuOvz1X/81vva1r+FZz3oW3va2t+GCCy7Af/7nf654UHL00UfjnHPOWbKy/LrXvQ7nn38+Lr/8crz4xS9GkiS4+eab8aQnPWnVj2s5L3nJS3DLLbegWq3i1a9+Nf7gD/4A//t//2/87Gc/W/EsxGIuvfRS7NmzB//6r/8613p0/PHH4x//8R/xpS99Cddccw3uuece3H777V3Z3OoVr3gF/vqv/xr//u//josuugjvf//78Rd/8ReHLXRfjV27duFLX/oSJicn8bznPQ9/9md/huc///l46UtfuuB2J598Mnbv3o2JiQk85znPwetf/3qceeaZ+NCHPtT2fX3xi1/ERRddtGE9tES0cR544AFMTU3NXd7ylrcse/vXv/71+PKXv4xbbrml7cLNfI9//OPXXNhoh1CKO3YQtePee+/FySefjHe84x1461vfqjsc6rLdu3fjhS98Ie6//34ceeSRAJobXe3atQt/8zd/gyuvvFJzhN1x9dVX4wMf+AD27t275CLjzeiee+7BiSeeiG984xuH7RdARCubnp5Gf38/bn3y76HHNqeBpJokeOr3/wtTU1Po6+tb8fZKKbz+9a/Hv/3bv+HWW29d8ySc5z3veRgfH5/rMug0c55hIoPcfvvt+NznPocnPvGJ6Ovrw1133YWrr74afX19uOSSS3SHRxvgOc95Dh772MfiqquuWvQsT1H9+Z//Of78z/9cdxjGee9734vzzjuPyT3ROgkpIKRBU3RWGctll12Gz372s/jSl76E3t7euaEL/f39KJVKAJobFz700ENzG/hdc801OO6443DaaachiiLceOON2L17N3bv3t3ZBzMPE3yiRVQqFfzkJz/B9ddfj8nJSfT39+OpT30q/uqv/mrVk0uyLEOWZcvexjaomrEaSqnDep0PZVlWx3u5N4IQAv/wD/+AL3/5y8iybMHaC9pckiTBCSecsOIpeyIqvtbap9ZarZZPfvKTc3uv7N27F3v27Jn7tyiKcOWVV+Khhx5CqVTCaaedhptvvhkXXXRR1+Jkiw5Rl7385S/Hpz71qWVvk9c/wxtuuAGveMUrlr3NLbfcctgLIRERbS6tFp3vPOVxxrXonPvdH7XdopMXTPCJuuy+++5bcf782WefvUHRdNbY2BjuvffeZW9z0kknLdiNlIiINp9Wgv+9pz3euAT/nFt+WLgE35xnmKigjjvuuFVN08mT4eHhdY/JJCIios5iUykRERERUYGwgk9EREREGyLvU3TyghV8IiIiIqICYYJPRERERFQgbNEhIiIiog0hhIQwaF8RIcyJpZOK+aiIiIiIiDYpJvhERERERAXCFh0iIiIi2hCcorMxWMEnIiIiIioQJvhERERERAXCFh0iIiIi2hDSEpCWOW0xMjMnlk5iBZ+IiIiIqECY4BMRERERFQhbdIiIiIhoQ3CKzsZgBZ+IiIiIqECY4BMRERERFQhbdIiIiIhoQwgpIaQ59WWTYumkYj4qIiIiIqJNigk+EREREVGBsEWHiIiIiDYEp+hsDFbwiYiIiIgKhAk+EREREVGBsEWHiIiIiDYEW3Q2Biv4REREREQFwgSfiIiIiKhA2KJDRERERBuCLTobgxV8IiIiIqICYYJPRERERFQgbNEhIiIiog3RbNExp77MFh0iIiIiIjIeE3wiIiIiogJhiw5RjtQnx1CbGIGQEkJakLMfF36+8r8JUcxTkkREZDYhBaRlznuQSM2JpZOY4BPlRJrEmD7wEFSWrf+bCQF5WPLf+lwu+Dfb8+D39K//PomIiGhDMMEnyonp/R1K7gFAKWRpAqRt3FYIbDvhVFi205n7JiIioq5igk+UA2F1GsHMpJ47Vwq1sQPo236knvsnIqLC4EZXG4OLbIkMp7IMU/sf1BpDfXIMaRJrjYGIiIjawwSfyHDVsf1I40hrDEplqI2PaI2BiIiI2sMWHSKDxWGA6vgB3WEAAOoTo+gZ2gZp82WDiIjWpjXMwRQmxdJJxXxURAWglML0vgcApXSHAqBZxa9OmHGwQUREREtjgk9kqMbUOKJGTXcYC9QnRpvTd4iIiMhYPNdOZKAsSTA98rDuMA6jsmYvfu/WnbpDISKiHOIUnY3BCj6RgaZHHoZK2xlSv/FqEyOs4hMRERmMCT6RYcJ6FY2pcd1hLEllGWoTo7rDICIioiWwRYfIIEplzYW1hquNj6AyuBXSsnSHQkREOcIWnY3BCj6RQapjB5BEoe4wVqSyFLUJzsUnIiIyERN8IkMkUYjq2H7dYbStNj6CLDNznQAREdFmxhYdIkNM7X/QmJn37VBZ2tz8ani77lCIiCgnuNHVxijmoyLKmcb0BKLajO4wVo1VfCIiIvMwwSfSLEtTTO9/SHcYa5KlCeoTY7rDICIionnYokOk2czIw7meK18bP4DK4JbCnuYkIqLO4RSdjcF3ZCKNokYN9cl8V8CzNEFtknPxiYiITMEEn0gTpRSm9j2oO4yOqI0fgMoy3WEQERER2KJDpE1tYgRJ2NAdRkdkSYL65BgqQ1t1h0JERAbjFJ2NUcxHRWS4NI5QHdmnO4yOqrKKT0REZAQm+EQaTO1/EEoVKxnOkhj1qXHdYRAREW16bNEh2mDBzBTC6rTuMLqiOrYf5YEhCMHaARERLUKI5sUUJsXSQXwXJtpgeZ+as5wsidFgFZ+IiEgrJvhEG6w8OKw7hK6qjh2AUkp3GERERJsWW3SINpjf0w/HLyEOijFB51BpHKExNY7yQLEPZIiIaPWEMGyjq4K26DDBJ9KgZ3gHJh66V3cYXRNMTyCuTkJIa3Yk2sGPUloQVus66/DbFPTFloiIaKMwwSfSwOvpg+2VCjMHfz4hJdJGFUmarPnrFyb98/7bsiCX+HfL9WB7pQ4/GiIiovxhgk+kgRACPVu2Y/Kh+3SH0nGu6yGpz6z561WWrWmevrBsbDnx9MJuWkJEVATc6GpjFPNREeWA39MP2/N1h9FRtushXkdyvx4qTdCYOKDlvomIiEzCBJ9IEyEEeoa36w6jo0SWQmcHfX1sP3fTJSKiTY8tOkQa+b0DsN39SKJAdyjr5pbKSGt6N/DKkhiNiRGUC3bgRERUFEIaNkXHoFg6iRV8Io1avfh5J20bab2qOwwAQH1sH6v4RES0qTHBJ9LM7x2A5Xq6w1gX27IBZUZSnSUxGpOjusMgIiLShi06RJq1evGn9u7RHcqaOH4JqaaFtUupj+5DaXALhGANg4jIJJyiszGK+aiIcqbUNwjLcXWHsWpCSCgD1w9kSYRgckx3GERERFowwScyQF4n6jieD5XEusNYVG10L5QhbUNEREQbiS06RIYo9Q+hOrYfaRzpDqUtltPc0MrU+QNZ3Kzilwa36g6FiIhmCWnW5JqidnIW9GER5U/eqvgSGQSU7jCWVRvdB6XMjpGIiKjTmOATGaTUPwTLdnSHsSK3VEYWNnSHsaIsDhFMsRefiIg2F7boEBlECIHK8HZM739QdyhLkpaNtFHTHUbb6qN74fcPQwhzTgkTEW1W3OhqY7CCT2SY8sAQpMFVfNt2gCzVHUbb0ihEODWuOwwiIqINwwSfyDBCSPQMb9MdxqIcz0faMGvmfTuaE3XYi09ERJsDW3SIDFTuH0Z17AAyk0ZQCgHkZMLPodIoQDg9Ab9/SHcoRESbm5TNiylMiqWDivmoiHJOSImeIbOq+K5fQpbkM8EHgNrow6ziExHRpsAEn8hQ5YFhSMuMk2yW4yKp5681Z740DBDOTOgOg4iIqOvMyB6I6DBCSlSGt2HmwMO6Q4ElgLQA1e/6yF54vYOcqENEpIkQwqjXYJNi6SRW8IkMVhnYor2K7/hlpEFdawydkoQNRDOTusMgIiLqKib4RAYTUqIytFXj/VvICpLct9RG9+oOgYiIqKvYokNkuPLgFtTGDyBLN372vOO6SHPee3+oJKgjnJmE1zugOxQiok1HSAlh0OQak2LpJCb4RIZLM4nbaydDqBSOlcGTzY+2zODIFLbIYLUuSCFFBokMQqUQKgOQAlkGqAxqFRtU2Z6PpD6DInYn1kYeZoJPRESFxQSfyHC/fEBhKrAAWB35fo7M4FoZXCuFZ2VwZAbHSuGIDPa8A4b+bLKQyT3AKj4RERUbE3wig41XFe7Z39nvGWcScSZRi5f/83/UkIVh1Dp75wapje5lgk9EtMGEFBDSnPKRSbF0UjEbj4gKQCmFn/5O32jKOyaGIWxX2/13W9KoIaxO6Q6DiIio41jBJzLUb/cBkxoH2GRKYAzbMYQH9AXRZTPj49hbL8GSgCUFLClgS8CyxLzrDn4s6rxkIiIqFib4RAaqhwq/fkD/xlJ3TAzjnIF9UGmsO5TOEwJ7GoOozrT/2BZL+lsHA/bsAcLcv1k4eMAw77aOJWBbPFAgok1KSMCkyTXCoFg6iAk+kYF+fp9CkumOAkiVxLjcjsH0Qd2hdFxU2o5qzVnV16QZkGatA6+1HYCVPYHHnFDi2QAiIuqaYh62EOXYwxMKD03ojuKgX49vgbBWlwibTjg+7q0PaLnveqgwOrPxexoQEdHmwQo+kUGSVOG2e/W35syXKolJuR39Barij4gdUEpfBX3PSIwtvRar+ES0+Rg2RQcmxdJBrOATGeTXDyrUI91RHO7XE1sgrGLUA7LSEEbDktYYakGGMVbxiYioS5jgExlisqbw2326o1hcnElMWdt1h7FuwrJxX7BFdxgAmlV8IiKibmCCT2QApRR+dq+CMqs7Z4FfjW/NfRW/6u5AlHVmR+D1qgYZxmYS3WEQEW0oIaRxlyIq5qMiypnfHQDGqrqjWF6cSUzLbbrDWDPh9eCBeq/uMBZgFZ+IiLqBCT6RZkGk8Ms9Bpfu5/nVxFYIaUYFfFWEwIOpeS1GM40M46ziExFRh+X7fDtRAdx+v0Kck/WWUWZh2t6O3uhh3aGsSuxvxUzd1R3GovaMxBjq5UsxEW0SUpg1ucakWDqIFXwijfZPKuwZ0x3F6vw6Z1V84Xi4rzGoO4wlTTcyTFRzcoRHRES5wASfSJM0U/jZfflozZkvTC3M2PnpxR+TO5Aqs1/q7h8xcDYqEREd5qqrrsJjH/tY9Pb2Ytu2bfjDP/xD3HXXXSt+3Xe+8x085jGPge/7OP744/Gxj32sq3Ga/a5HVGC/eUihGuiOYm1+PbEtH1X80gAOBGXdUaxoup5hssYqPhEVn5DSuMtqfOc738Fll12GH/7wh/jWt76FJElwwQUXoFarLfk19957Ly666CKcc845uO222/DWt74Vl19+OXbv3r3ep3NJbPwk0mCmofCbfLWxLxCkFmr2NpSjvbpDWZKQFu4N83Om4f4DEQZ26d2Ai4iIlvf1r399weef/OQnsW3bNvz0pz/FU57ylEW/5mMf+xiOOeYYXHPNNQCAU045BT/5yU/wgQ98AM997nO7Eicr+EQa/PRehSx/3TkL/HJiK7DKysdGqnvbEaQ5OMswa6qeYYpVfCIiLaanpxdcwjBs6+umpqYAAENDQ0ve5gc/+AEuuOCCBdc9/elPx09+8hPEcXfGJZv77kxUUPeNKIxM645i/YLURt0xs0IuvDLur/fpDmPV2ItPREUnpDDuAgBHH300+vv75y5XXXXVio9FKYUrrrgCT37yk/GoRz1qydvt27cP27cvHNW8fft2JEmC0dHR9T2hS2CLDtEGihKFX9yf89L9PL+e2IbHVg4AKtMdygJ7050A8jf6bLKWYaqeor+cnzMPRERF8MADD6Cv72BhyPO8Fb/mda97HX7xi1/g+9///oq3FWLhe5Ka3br+0Os7hRV8og30i/sVwgLta1RPbDScrbrDWCApbcVkbObM+3Zwd1sioo3X19e34LJSgv/6178eX/7yl3HLLbfgqKOOWva2O3bswL59+xZcd+DAAdi2jeHh4XXHvhgm+EQbZHRa4d4R3VF03q8ntwHCjJcSYbu4t7F0H2QeTFRTTNfZi09EBSVE8z3DmMvqKuhKKbzuda/DTTfdhG9/+9vYtWvXil/zhCc8Ad/61rcWXPfNb34TZ599NhzHWdX9t8uMd2WigssyhZ/eW5zWnPlqiYPA3aI7DADApG3+zPt2sIpPRGSmyy67DDfeeCM++9nPore3F/v27cO+ffvQaDTmbvOWt7wFL33pS+c+v/TSS3H//ffjiiuuwJ133olPfOITuP7663HllVd2Lc78vxMS5cBUA5hurHy7vPr15PZVV0E6zu/H3kZFbwwdMl5NMdNgFZ+IyDTXXXcdpqam8NSnPhU7d+6cu3z+85+fu83evXuxZ8+euc937dqFr33ta7j11ltx5pln4j3veQ8+9KEPdW1EJsBFtkQbYrAisKVXYXRGdyTdUY0dhO5WeOEBPQEIifsjMyf6rNWekRinHcPFtkRULPMn15hgtbG0Fscu54YbbjjsunPPPRc/+9nPVnVf68EKPtEGOe0oc17QukFnFT8obUcjLVa9YmwmRZVVfCIiWoNivSMSGWxbf7Gr+LXEQeBvhStjSBwcUimgIDBb8VAZoNTsJYPKsrmPKltbMivcEu6r9XfkMZhmz0iMU1nFJyKiVWKCT7SBTjlS4Hu/KeZi2+MG68jSAMGyebrAwdTfOvipnPevUkAKAQkBIQApZq8XgGjNDcbsQQIU9qdb533PYhmdSVELMlR8nmwlooKQ0qxd0E2KpYOY4BNtoB0DAkM9CuNV3ZF0Vo+nMCj2rvv7KAAqU8jQ7kGQgHSKXeHeMxLhlKN93WEQEVGOFPOwhchgpx5ZvGrzrr5RSKHnzISXjMIq8CvZyHSKemjWTsFERGS2Ar8tEplp56DAYDGmOQIAjuyP4aspbfcvVIrhcoG2B17E3nHOxSeiYhBCGHcpIib4RBqcWpCJOq6tsN15WHcYha7iCwHsHOrOTodERFRMBX1LJDLbEYMCA2XdUazfroEZWNBfXRYqxVBBq/hHDTsoe3ypJiKi9nGRLZEmpx4l8J//nd+JOkOVDL3qgDEDbPx0DFJsR5bfp/QwviNwzFZW74moQIRhU3SEQbF0UDEfFVEOHDEI9Oe0ii8EcEx5n659rRYlsqRwvfiPOMKFZdCOj0RElA9M8Ik0EULkdqLOsYMBHFXXHcZh/GwcRcmHt/ZZGOrhSVYiIlo9vnsQaXTkENBXAqYbuiNpX9lV2CL1L6xdjMhiDJVTjNbyPRvfksDxO1zdYRARdZyQAsKgSoxJsXQSK/hEGgkhcErOqvjH949DwNy57KVsLPdV/F3bXHgOX56JiGht+A5CpNnRw0BvTjYq3dGXoKQmdIexrFYVP696SxI7h3hylYiI1o4JPpFmeani25bCEa6ZrTmHKqXjRi0AXo0Td7qF3XiFiAhCmncpoGI+KqKcOWYL0GN4Ff/4wRosRLrDaItQUS6r+EcO2egp5Xv9ABER6ccEn8gAplfxB8oZ+tQ+3WGsSjnLVxXftQWO28aFtUREtH5M8IkMccwWoOLpjuJwAsCxlQO5SpYBQGQRhsrmLgY+1CN2urCsnD3JRESrJYV5lwJigk9kCGloFf/owRCuquoOY03K2ZgpG+0ua6jXwpY+LqwlIqLOYIJPZJBjtwBlg6r4vqOw1dCZ9+0QWYTBitlVfCmBR3DmPRERdRBLRkQGkVLglCOAn96rdIcCADh+YBJS5W+x6nwVNYEJDMOMZ/Rwx2514LustRDR5iCEhDBoco1JsXRSMR8VUY4dtxUoG1DQ3daboqLGdIexbiINMGhoL37Fkzhq2NEdBhERFQwTfCLDSClw0hF6O8ctCRzp57c151BlmLk514lHcOY9ERF1Hlt0iAy0axvwm4eBhqax87sGa7BVqOfOu0DOVvEn6ubUNHYM2ugrc+Y9EW0ypk2uMSmWDjLn3Y6I5lgaq/h9vsIA9mq5727qwaTuEOY4FrBruwF9WEREVEis4BMZqsep45HDdUgpIMTBS3MyvYBqXZRApgRSJZBlAmkGxEogTQXieZcoad5+Jcf1jkCYuiJ1HUTawGBZYaKuv1pz/A4PDmfeExFRlzDBJzJQECU4MFGHApBmCmhjBoycvdgS8ABgke4PIZrz9qUUEBDNK2Y/KiUgBeDF0518KEbpwQQmMKQ1hoGKxPYBvvQSEVH38F2GyEAPHqhCdaGKrhSQKjV70LC4suNBJcXpv59PpA0MlBUmNVXxhQBO3GnQRgdERBtMSAkhzekQNymWTirmoyLKsfHpANVGrO3+Y6tH231vBJ29+MdscVDy+LJLRETdxQo+kUGSNMPDo1WtMVSDDIOOC5VoGuHTZSKt47h+B0oBGQQUJLL56xiUQJoJJApIM4kkBeKstfZh7UquwNFbOPOeiIi6jwk+kUH2jtaQpPpXuCZWL6wk/5tcLcYrlREECw+i5tYvzL9SoLmOYXYtQ3ORs4SQAgKy2W8jJAABJQSUknMLnw89WEiVwDHbK5AFHcdGRNQ20Vr/ZQiTYukgJvhEhqg2YoxNB7rDAADMFLSKLy0LYbi2x6SUglIp0MamuK0DhvmsTADoW9N9ExERrQabQYkMoJTCgwdmdIexQGL16g6h4yzHherG6uU2TE5MaLtvIiLaXFjBJzLAgYkGgijVHcYC1SDDgO1ApfoW/HaS6/kI1li974QkSVCdmUFvH6v4RLSJSQGYNLmmoK2TBj3DRJtTGKfYP17THcZhFIDELkgVXwjEaRu9NV02OTnJKj4REXUdE3wizR46UMUyY+m1qgYKsPI/+cX1S0hT/WdI4jhGtap3ShIRERUfW3SINJqcCTFdN3chqwKQOX2QaX4n6tiOiyAwZ+OuyYkJ9PT0QBR0cgMR0bI4RWdDsIJPpEmaZXhI88z7dswEGSDzWcVXAJQw62UujmPUaua1ZBERUXGY9c5HtInsHasjTvT3ha9EKSBz89mL75XKiGPzFglPcKIOERF1EVt0iDSoBzFGJxu6w2jbTKAwIG2oLNEdStukXPvM+26Lowi1Wg09PT26QyEi2lBCSgiDpuiYFEsnFfNRERlMKYUHDpjfmjOfUkDq5mu8o+V6RlfJORefiIi6hQk+0QYbnQrQCPNTCW+ZCRQg83HSz/F8hKE5C2sXE0UR6vW67jCIiKiA8vFuTVQQcZJi71g+F1gqBSi3DyIY1x3K8oRAaurc0UNMjI+jUqnoDoOIaOMI2byYwqRYOqiYj4rIUA+OVJHlJPlcTB6q+K5fQpLk4wxJNNuLT0RE1ElM8Ik2yHQtxFTVzEWf7coUkBnci2/ZjlEz79sxOTGhOwQiIioYs0txRAWRZQoP5mxh7VKqgUKfsAClf2fYQwnLBtJ8HUSFYYh6rYYyW3WIaDMQApAGbS7Fja6IaK32jdcQ5WDmfTsyBSjPvCq+65cQRflK7lsmWMUnIqIOYoJP1GWNMMHIRH5m3rdjJhRGLUwSQiKK89F3v5gwDDlRh4iIOoYtOkRdpJTCgwdmkN9ltYvLMgVV6ocIzKg8O76fu977Q01OTKBcLusOg4ioq4SQEIYViIqomI+KyBDj0wFqQX4ry8sxpYpvu17uk3sACIIAjUaxzvQQEZEe+t+diQpsupbPnvB2ZJmC8vq1xqDQXBNQFBPjhu8xQEREucAWHaIu2jZUxlSBk/xqKNALCUDPAmKvVC5E9b6lVcUvlUq6QyEi6g5p2BQdk2LpIFbwibqo4jvoLTu6w+iaNFOAr6eKb1k2wrB4B0+ci09EROvFCj5Rl+0YqmCmPqk7jK5REHAdG0IICCEACEAIiAW3af2HglJAphSgMmRKQWUZlMqaX7cK0nagcjoWczmNRgNBEMD3fd2hEBFRTjHBJ+qySslBT8lBtRHrDqXjbEtABhNI1tmiI9Dca0QIASHl7MGCnD1gwNzHOVKi3ihOa86hJsbHsfOII3SHQUTUeUIaMaBhjkmxdBATfKINsGO4jLsfnNIdRsf1ORFU2Jn+e6UUlFJA1sb3EwJSOsjauW0OsYpPRETrUczDFiLD9JRc9JSK1Ytf8SRUOKPnzpWC5xbr+TwUd7clIqK1YoJPtEG2DxVnEyMhADed1hpDFNQhZXFfwhr1OtI01R0GEVFnNfsxzboUUHHfHYkM01t2UfGLUXXu9wGkmhe4KgW3wFX83r4+WJalOwwiIsohJvhEG2hHAar4niOBhhkbMsVBo5BVfMuyMDQ0pDsMIiLKKS6yJdpAvRUXZd9GPUh0h7JmFVGHMuWMpsrguQ4aBdrsCgCGh4dZvSeiYpKyeTGFSbF0UDEfFZHBdgxVdIewZr2+gIrrusNYICpYFb9UKqGnt1d3GERElGPFeVckyom+iouyl7+TZ5YUsKNJ3WEcTmWF6cUXQmDL1q26wyAiopzLX5ZBVADbh8u492G9U2hWq89NgNDMqS5J0IAQVnOOfo4NDAzAcYpxsEJEtChudLUhivmoiAzXX/FQylEVv+xJIDR3oy6lMvieqzuMdXEcBwODg7rDICKiAmCCT6RJXibqCAF4mflnG+IggMjxPOMtW7fmOn4iIjJHfkqIRAXTV3FR8mw0QrMn6vT5AALNM+/boFQKz/cR5HCiTk9PD0qlku4wiIi6T4rmxRQmxdJBrOATaSKEMH53W9eWEMGE7jDaloT5q+JLKTG8ZYvuMIiIqECY4BNp1F9x4bvmzjvvsQII5GfhqspSeDnrxR/izHsiIuowtugQadSq4t+/b0Z3KIfp8SVUWNUdxqo1q/gyFxN1fN9HL2feE9FmIoRZk2tydta3XQY9w0Sb00CPZ1wV35ICTjypO4w1yVMVnwtriYioG5jgE2lmYi9+n5sCmdmLf5eT5qAXf2BgAK6bjwMRIiLKFyb4RAYY6PHgOWZU8UuuhAomdYexLpnhVXzbtjnznog2JyHMuxQQE3wiA5hUxS+paiFe79IwNLaKv2XLFkjJl18iIuoOvsMQGWKw14OruYrfXxJQSaA1hk7JssTIKn6lUkG5UtEdBhERFRin6BAZolXFf2C/nok6jiUgg3Et990taWTWpleCM++JaLOTsnkxhUmxdFAxHxVRTg31Nifq6Ogs6bUjIEcz79uRpQl8z9MdxpyhoSHYNusqRETUXXynITKIEAInHzsEAMiUQpYppFnz4/z/TjOFTGXL/Nvh1y2nx5NQkXmz+Dshjc2o4nueh76+Pt1hEBHRJsAEn8hQUghIS8DuUFv+goRfKWRZNnddY2oEcWfuxjhZmsAvVRAEehN9zrwnIoJ5k2tMiqWDmOATbRJSCki5+AuZJwcxsre+wRFtnExzFb+vvx+eQa1CRERUbOzBJyKUKz1w3OImoGmirxffsiwMDQ1puW8iItqcWMEnIgDAwNAwRvY9rDuMrhFCwfU8COCwVhml1NxHBUBlWfO/Z1uZ1mOYM++JiA4SsnkxhUmxdBATfCICAJQqPXBcF3EU6Q6l46SUCMMYmVr7lCAhBKSUEEJASHnwQGH2YKF1yDB3D0rBcRz09PSsJ3QiIqJVY4JPRACayWr/4DBG9+/VHUrHWa6POFzfgYtSCmmaruprkiRBlmWs4BMR0YZigk9Ec8o9vXAmxgpVxXdcrzlBR8OkhDRNMTMzg/7+/g2/byIiIwnDNroqaItOMR8VEa1Jq4pfJImC1jFokxMTcz3+REREG4EJPhEtUO7phe24usPoCLdURpKsrq2m09I0xcz0tNYYiIhoc2GCT0QLNKv4+R/raNk2gsCMVqPJyUlW8YmIgIMbXZl0KSAm+ER0mEpvH2zH0R3GugjLgSkpdZIkmJmZ0R0GERFtEkzwiegwee/Fd/0SwijWHcYC7MUnIqKNwik6RLSoSm8fpsbHkCRmJcorEVIiivX23S+mVcXv6+vTHQoRkT7c6GpDFPNREdG6CSHQl8NefMf1ka5z99luYRWfiIg2AhN8IlpST18/LDs/J/psx0UjCHWHsaQkSVBlLz4REXUZE3wiWlLeevEzSOMnInCiDhFtaron5nCKDhFRfqr4nl9GnCS6w1hRHMeoVqu6wyAiogJjgk9EyxJCoH/A7F58y7LQCM2Yed8O9uITEVE3mV+WIyLtevr7MTUxjjQ1s0IuHA8qRwl+HMeo1Wro6enRHQoR0caSsnkxhUmxdFAxHxURdZQQ0tiJOq7nI8xRct8yMT7OKj4REXUFE3wiaktPXz8sy9IdxgJCCERpPpPkVhWfiIio05jgE1FbpJToM6wX3/FLSFPzNrVqF3vxiWizUUIYdykiJvhE1Lae/gFIQ6r4tuOg0TB35n07oihCvV7XHQYREa3Cd7/7XVx88cU44ogjIITAF7/4xWVvf+utt0IIcdjlN7/5TddiZIJPRG1rVvEHdYcBAFDCKsT84onxcd0hEBHRKtRqNZxxxhm49tprV/V1d911F/bu3Tt3OfHEE7sUIafoENEq9fYPYnpiAlmmrzXG9UtohLG2+++kKIpQq9VQqVR0h0JE1H1CAMKg+vIaCkUXXnghLrzwwlV/3bZt2zAwMLDqr1sLg55hIsoD3VV8KS2EkZnjOtdqcmJCdwhERNRlZ511Fnbu3InzzjsPt9xyS1fviwk+Ea1a78AgpKbZwZbrISvYwtQwDFHnRB0iIm2mp6cXXMKwc2u8du7ciY9//OPYvXs3brrpJpx00kk477zz8N3vfrdj93EotugQ0aopBQRWH5RQkEJAyObISonW2U4FAUBAAbMXoRSUypqfZ1nzv7MUmcrQ7glSx/UQ5HDmfTsmJiZQZpsOERWdkIa16DRjOfrooxdc/Y53vAPvfOc7O3IXJ510Ek466aS5z5/whCfggQcewAc+8AE85SlP6ch9HIoJPhGt2oGxacTxanvwBYDFJ/AIgdkDBdH82Pp89t/E7AFDmkUA8jsWczlhGKJer6NcLusOhYho03nggQfQ19c397nneV29v8c//vG48cYbu/b9meAT0arUGxEmpjs72lEpIFUKyJZvvan4HoBiVvCB2So+E3wiog3X19e3IMHvtttuuw07d+7s2vdngk9EbVNKYe/IpLb7rwcJfMuC0jjBp5vCIECjXkeJST4RFZRpm0utJZZqtYq777577vN7770XP//5zzE0NIRjjjkGb3nLW/DQQw/h05/+NADgmmuuwXHHHYfTTjsNURThxhtvxO7du7F79+6OPY5DMcEnoraNTda0TrBRAKRTRhrOaIuh26amptBo1CGEhJTNS7N1qfXfs9cLMfffRES0cX7yk5/gaU972tznV1xxBQDgZS97GW644Qbs3bsXe/bsmfv3KIpw5ZVX4qGHHkKpVMJpp52Gm2++GRdddFHXYhSK+6QTURuiOME9e0ag+yVDCMCXUSGr+EIISAFkWbaqr5tL/IWElGLeQUDrgEA0b3PoQcPsbSzbhmXIDsVEVEzT09Po7+/H3puvR1/FnLOU07U6dj7zEkxNTW1oi063sYJPRG3ZNzKlPbkHmv36Ra3ie56HMGis+uuyLAOybM3Lj13XxRFHHQ1h0GlzIiooQ6foFE0xHxURddR0tYFqvXMzgderHiYQslgVZ8dxEKwhue+EKIpQr3MOPxFRUTDBJ6JlpVmGfaNTusNYoFXFLxSl2t4PoBsmx4u/m24jiBFGCeIkRZplRpyRIiLqBrboENGyRsZmkCSr6wnfCPUwhS/k7OZZ+eb7/ppaczopipq76RZ1s63JmQYe3H/4gaoUYnaNQvNizS5gXu46q7V+YXbfBmvutqyZEa1IiNaOiGYwKZYOYoJPREtqhDHGp8xs3VBKwfIqSHLei29ZFsIg0B0GAGByYryQCX6aZtg7uvjvSaYUslR1bP+0BUm/JXHczkFYFhN/ItpYfNUhokUppbD3wKTuMJZVC1OInC+Qsm0LzQGg+jV30zXzgG499o3NIE035kxPlikkaYYwTtEIYoxOFu/5JCLzsYJPRIuamKojCGPdYSxLKQXpV5AG+azie56HKDSjet8yOT6Ocrk4Vfzmzsv62p/GpurYMlBhFZ+oRcrmxRQmxdJBxXxURLQucZLiwPi07jDa0gjyWcUXQiBNzDuACsMQjXpddxgdoZTCwyN6f4+zTGFsqhjPJxHlR/7eFYmo6/aPTiHLzGgbWUmmFCw3fxN1PM9Dmpq5WdfExLjuEDpibLKOQOPOywfjqCFd5eZlRETrUcgWnQf/6Uu45/1/D7unAqu3Aru3Arun+dHqKS/43O6dvU3Pwdu1vsbyPd0PhWjDVWsBpqtmtY2spB5l8HI0Uac1897U2Q1hEKDRqKNUyt+BU0sUpzgwXtUdBgAgzRTGJ+vYOtSjOxQi7ZQQUAZNrjEplk4qXIIfjU3gjje9D/HY5Lq/l7DthQcBlfLhBwSzl/Lxx+CIF1y0/gdApFGWKewdMWvmfTuyTMEqVZDkpBdfzF5MNjk+gdKR+U3w945MIzNozv3oZA3DA2WO0iSiDVG4BP/ON1/dkeQeAFSSIJ6YQjzRRsIjJfrOOBk9Jx3fkfsm0mF0YgZxYmbbyErqYQpXSMDwKr7ne4gMGYu5nCBooNFooFQq6Q5l1aarAWYM2nkZaFbxx6bq2DrIKj4RdV+hSglj3/sxHvzUTXruPMtw9/uu03PfRB0QRjFGJ8xoaViLLFOwDe/Fl1IiDs1KPJczmcNe/DTL8PComQvERyfruVnbQtQ1Qpp3KaDCPKosivCry96hNYaHP38zanffrzUGorVozrzPX2vOoRpRBpObXxzHgTKobWQlQaOBQPMOu6t1YKxq5M7LQHPDrXFO1CGiDVCYBP+ev70e1Tvv0RqDSlPcfdXHtMZAtBZTMw3Ug0h3GOuWZgq2b+YMd9d1jZt5347J8QndIbStEcbGj6Qcnayxik9EXVeIBL92zx5j2mMe+uyXUf/dA7rDIGpbkqbYb2hLw1o0QvOq+EIIqCyfaxsajTqCHKwZUErh4QPm/x4naYaJabMPQoi6SQlp3KWICvGofn35u5AFZvS1qiTB3X/NKj7lx/7R6ULN6E4zBdszq4rveR6SRP889rXKQy/++HQDDcN3Xm4ZmagZNeGHiIon9wn+w//yNYx88/u6w1jgwRu/hPr9D+kOg3IkyxTiOEGaphvao11rhJiayVePdTsakYIpVXzbthHmoAK+nEa9jtDg9qI4SbF/LB8jUoHZKr7hrURElG+5HpMZT83gjje9T3cYh1FxjHve//f4Hx99t+5QKCdGxiYxOX1wgo1Ac+KKlAJi9qMUsx8P+3z2OjHv3w65jVhkI4+iLKxdTJplKPkVJKH+qUBSCqTIf7V2cnwC23fu1B3GovaNzuSur310sobB/jJkQTfZIVqSEM2LKUyKpYNyneDf9fa/Q7hvRHcYi3rwUzfhEW95DUpHm/mGSOZoBOGC5B4AFJpJapoBwPp7t4UQswcAB5N/S9qI4vy2jaykESs4EIDG5NrzvFwurF1MvV5DGIbwPLN2+J6ph5jK2c7LABAnGSanGxjqN3u0KxHlU25bdCb/6xe4/+P/rDuMJWVRjHuu/rjuMMhwSinsH+n+lBKlFNIsQ5ykCKMYjSBCtV6H5+b6GH9ZaZrB9vQlT1JKxHH+JxPNZ1ovfnPnZfMX1i5lZKKaq7GpRJQfuUzwVZril6/9S8DwhYEPfPILCB7arzsMMtjEVBVhpG9hoBRm/w2tVxADunrxXdeBMvw1arXqtRoigzbqGpmoIorzOZ0IaFbxJwq4BoZoOQr6p+YsuOQzFV5RLh/VvR/+DKZvv1N3GCvKwgj3/M0/6A6DDBUnCcbG9fbAR1Fc7Cp+pgC3B3B6IdxeSK8XltcL2+uF7ffC9npgexXYbhm2W4Lt+LBsF9KyIYTEWg8OHNfJxWjJtZicMGMufhAlGJ2o6Q5j3UbGa6ziE1HH5e6dvfHAXvz3uz6oO4y27bn+X3DCX7wa/o6tukMhwxwYnTRiVJ4U+mPolpJnox6s5gyJAGDNXg5qLlgWswuWAQExt05MoHUYoJr/oRSQRYbM8Om8Wq2KKIrguq7WOB4+MFWApcvNCUCTMw0M9rEXn4g6J3cV/F+/8b1Iq/kZL5YFIX73gX/UHQYZplproFoz49R8FEVwHWvlG+aM61irTO6XlmUKSZohilOEUYogStAIE9SDBLUgQTVIUA1SVBspqkEGJc1aiNppunvxJ6brHfvZmmBkglV82kTmqiMGXQooVwn+/q/8B/Z/6f/pDmPV9vzD5xEeGNMdBhkiyzLsHzWjzaHFytUrQZs0vmbXI/N20+2kWrVZxdchSTPsG83PzPt2RHGay0lARGSu3LytJ7U6fv2G9+oOY03SegO/+1tW8alpdGIaSWLWwsCiVfHLvoso0vccZ5mC5fdou/+NMD4xhfGpGqZm6pipBajVQzSCCGEUI05SpFnWlar0vtGZ5tqKghkZ50QdIuqc3PTg//e7PozGnod1h7Fm9//9P+OEP/tTuFuGdIdCGoVhhIlJM6uPthQowlBHS0o0Qv2PpBFmcDXP4e8egel6imRm5UXiQghYh23INn9Ttub6BksesnHb3LoHOff1jTDBZEGnzoSzVfyB3pLuUIi6SwhAGFRfLmiLTi4S/Onbf4P7Pvxp3WGsS1qr43f/95M4+a/epDsU0kQphX0bMPN+rYIwhOu4uR47CDR77xuh/vGUaaZg+z1IQjMP6NbD8nvRaLS3SZpSCkmqZvdrW9/vluc66/p6041M1NDf4y+68zQR0WoYdAi1OJVl+OVl74BK8r/j5n0fvRHRmLkJHnXX1EwNgQGV5aUIAdhWvhML33XQCM1ZfFnEXnzLdlFtM7nvtDCKC9VKdqgwSjBdM2efASLKL+MT/D3/8HlM/ujnusPoiLRax70fvEF3GKRBkqQYGdM7874dQRDmNoESAkgzs84+pJmC5VV0h9ExSgGx0DshyJbFOmA61Mh4FWmash+fCksJYdyliIxu0Qn3j+I3b/873WF01H0fuRHHv/GVcAb7dYdCG2hkbBJZDnY1FQKwJXLZi1/yXNQD8yJvxChML77j92Am0HsQ1QgjuI6T+1aypUgJ3H1fc72ZAObWI4i59QuHrFVYcN3yn7P1h2jzMDrBv+PKq5BMTusOo6OS6Sru/dCn8Mh3XK47FNogtXqA6Rzt3RCEERzbRWzYpJ/lOLZlZHIPAGmawS5VkARV3aGsi5AWapH+gxSB4iwIP5SUAml8sEVHAUizDGkGrHf9AtBc8HzYImchYdkSO7YO8QCAqECMbdEZ+X//fzz8z1/VHUZX3HvtZxBPFW/hHR0uU8q4mfcrEQJw7Hy90UvD2zYaEZD7XnyngsyQ8ZSNMIJr57OVbDllV3b1TJ9SCmmWIU5ShFGMRhCh1ggwPVPPVRGCck5I8y4FZOyj2vPxf9YdQtckk9O479p8TwWi9oxPTCOO87dAPAhCODlJoEq+gzAy+zlO0izXvfiWW0I9MOc5Fsj/gvBDea6FINC32dXYxDT7/okKxNgE/9hXv0h3CF1174c+jWQm36fsaXlRFGM8py1meaniSymMT+5bgri5SDV/BMLUvIO9Rhjl5iC0HVKlWsdxx3GCGVbxiQrD2AR/y3lPxOATztIdRtfE45O47yM36g6Dumj/6EROE7qmPFTxPdcxpm1kJUmawcnh7raW14M4MW+BuADgFKSKX/FtJIn+8a6s4tNGUBDGXYrI6EW2j3jba/HjZ/2p7jC6ZvxHP8eBvQ9BSgkhZPOjlLOLn+Tc1AQhD/k30fxvMtfUTA31Rr7nWbeq+KZ2GHmujYahC2uX0ogBW+Vn40RpO6hpnpqznGYV3zbyAKRdtiURhfpac+aL4gQztQb6esq6QyGidTI6wd/29Kdg4LGnY/LHv9AdSsdJ38OWN74Ejdra23TmJ/2LHiAccp2QErZtw/O5FXo3pWmGkbFJ3WF0RBCEsG0HiYEJVF4q9/MlaQbfryANa7pDaUsqSlAw9AgPs1V8W+Y6wfdsIDJgOlHL2MQ0eislTtQhyjmjE3wAeMTbL8NP/uerdYfRcUf+7z+BdcTWdX2PLMuALFvd8DQhcOSxx8O2jf/R59bI2CTSNL8Jx3xCAK4tjUvwy76ZM+/bESYCVg6q+LZXwYxBC2uX0gjyW8UveTaiqKE7jAWiKEa11kAvq/jUJUpIKIMm15gUSycZ/6i2X/RU9D/6NN1hdFTlpF3offFFeu5cKUxPjOu5702gEYSYmslHdbZdQRDCtsx5qbAtmdvkHgDiJIPjmz1RRwiJek6eYoHmQWjeCCGQpWY+yezFJ8q/XLwqnvj2y3SH0FFHvvMySFdfBb06PYk0Mb8ytx7VeojpaoBqPUQ9iBFGCeIkRZpmXXvjUkph30i+Zt63QwjAdcx5qbBzmMwdKkiE0QuwhduDNEctUPUgMuogtB0Vz0KWmrm+IYxiVOtmrAsgorXJRZ/G9ovPQ98Zp2D69jt1h7Ju21/0LLhnPlJrDEopTE2OY2jLNq1xdEsjiHHfw8sn2kIIWHPbvTd3d7TmbwE/u9vjgusWu93sdUIIjE/OIIr0T8LohjAIYVsOEs2tRyXPQSPM/3McJxk8Q3vxLcdHNQetOfMJNA9Cdf9+tst1LARBw+g2rbGJKfRWuF6LusC0zaVMiqWDcpHgA82JOj97wet1h7EuzvAAhi9/se4wAADVqUn0Dw7BsnLzK9AWpRQeGplq63ZJqjqx+zsAQAqBslfMFwkAgGgmJToTKCkEIlNH+qxBmEjjevGVAiLloGN/GBsoCCLYlp2LJN8WKWKDfu6LCcNmL34Pk3yiXMpNRrLjD89H76P0Vr7X6+i3vhpywIw52EopTE8Ur51kfKqOINz4JDBTCmGijG67WK8wDGBpbIPwPSdXbSMriZPUuF58u9SLKM5fct/iGdRKtpSKbyOO83EWamwinxv1EVGOEnwhBE5822t1h7FmA096NEoXPlF3GAvMTE0gTYtTEY2TFPvH9e0OHCcZfN/Xdv8bwXf1bHzlOlauF9YuJUylMQeF0rJRC8yvfi+nYXgvviUF4ig/ve1BGKFWN2vKD+WfEsK4SxHlqj9jx3OfgZ7TTkT117/VHcqqCMfBjv/zauPmCiulMD05gcHh9Y3rNMXekWnts9FbVXzDftQdJYRasPbg4AUQrR0B5z1+pQAoQEFBKYVMASpTyDIFpbLC7iLYjihO0eOXkYZ13aEgs8pQBVh872luJVuO70pEoSFHdG0aHZ9Gpcw2HaK8yVWCL4TAI/7iUvz8T96kO5RVOfK1L4J97E7dYSxqZnISfQNDsCw9ldlOmamFmK7p3zk2TjJUfN+YnSk7ybatuZ1js0whQwcSFQEILDxgaH3E7AGDlALVvMxsXIMotSA1HxRaXjl3C2uX0ghCWJZt3F4UvmcjCvNXDW9W8QNUysU+O0lUNOaey1zCES+4CJWTj9cdRttKu45C/yuerTuMJSmVYWYy3734Wabw8Ig5vaJxCmPaLjpJSKsTKf2iskwhSTPESYowThBEMYIwRiOMUGuEcOx8H4AuJ4pT2L7OTYUEgjh3bwXL8g3rxRcCQJaPvvvFsBefOqm10ZVJlyLK3aMSUuIRf3Gp7jDadtS7LoPwXN1hLGt6asLYecztODBeRZyYE38Up/A8T3cYHeX5LsJIX4XXKcDs++VEqb4DGOn1GtvSslaNIIYlzWn9qvh2rvceaQQh6o3inZUkKrJcvmse+cfPQuXE43SHsaKtf3Q+vMeavwuvyjJMT+Wzih+EMcYmzZslHmdmb2S0GlJIhJHeA6hGGBU6yY+TFMLrg5y9WF4vbK8XttcDy6vAdsuwnBKk40HaDoS0AKz/d8xyPNQK0pqzkNK2IPxQji0RNvLXmnOosXFW8YnyJFc9+C3CsnDCm1+NX7zqLbpDWZLd34utV75Udxhtm5mcQN/AIKQ0402xHUo1W3NMzKOjOEXF9xCF+tcFrJfjOWgE+tsLHNtCnBSr0txS8tw2pgQJANbs5SAJAdHanE0020EOLnpu9fYrQDXXO2B2sTNUhjinM+/b0aziS+2jVR2pjJ953456EKLeCFEuFevsJGkghFmTKEyKpYNyWxI78v/3bJSPP1p3GEs66s8vgRzq1x1G27Isw/TkpO4wVmViuoG6AYnnUuI0/1V81zUjuQeaVXyTRyCulWOvbwRophTSufULKYIoRSNMUA8S1IIE1UaCaiNFNUgxE2SYCRSqIVCNJGQBn8+DFHxXbw2r7NuI4+IsEB+bWHkTQSIyQ25f3aVt44Q3v1p3GIvqe8xpqPzhU3WHsWozk+PIsnxUSJM0w/6xGd1hLCvvvfgKgGmt2a6TnzNM7ZIae8UTg9audEMQRNp68S0pkEb5P4M3X70RohEU6zERFVVuE3wAOOpP/hCl447UHcYCwrJwxDtfCyHz99RmWYaZnPTi7xud1n7qvR1Jlt9TfyXfM2rxMlC8Kn7Jc7QuXo6TDCXf0Xb/3aagUPYsVDyBkivgOwKOJWDJ5n4O3VRyJTJl2BFyB7AXn9bNgKk5CyboFHSKTi578Fuk4+ARf/5q/PK1f6k7lDlH/OnzYT/C3NahlUxPTKC3fxDS4AOUaj3E5Ew+JjqEcYqK5yHKWSXPtiQaoRmtOYdyXQtJI/+Jk5QCYax/gWtS0HUNQLO1ViW1uSlhAvPe9ETzIqWcXbMg5zZta/bkirkbtjZjU82lDFAKyFqXTM19bN3Od20EQaOQrb21RoBGEKLk5/fsJNFmYG4W16ajXvZHKB1zhO4wAAD+UTvQ/6fP0R3GumRZipmpSd1hLClTZs28b0ceq/jSdoxdPxAEESwrf8/poTzX0b7zMtCc4FPyilnF7/UF0hVGAGdZhjRNkSQx4jhCFIWIwgBR2Ji91BGHNcRhDUlUQxrVkMU1IKlBpjXYqg4XdfiygbLVQMUO4NtxIZP7Fs7FJzJf7hN86bo4/spX6Q4DAHDUO14LWYDd/qYN7sUfnaghis1qG1lJGKdw3PxUuzzPRWBo9R5oVlA9J9cnH+G59tyuwCYo2hx8APAcgXCDd45VSiHLMkRhYNQc/k6r1QMEoTm/v5QvavasmEmXIsp9gg8AR7/y+fCP3K41hi0Xngv/yWdqjaFTsjRFdXpSdxiHCaMEIxNV3WGsSary8QIihECUg5aNINK3eLITTKjcz1e8Kr6CI/UVApQyZw5/t7AXn8hshUjwLc/F8Vf+qb77r5Sx9S9eoe3+u2F6YhzKsCr+wyPTxraNrCSMUriu2TsaA83qfZqDaq5SgOfls4pf9l3jFi8Dxari95Qs7eMpo6jYVXxR4MdGVASFSPAB4JhXvQDezq1a7vuoN70c1rYhLffdLWmaYmbanJnHkzMN1Br5PiWcKrMreo7jGL2vwKGCMM5dAmVbcl0z77spTlL4BajiW1IgjfUvwi9yFV9KgW3DA7rDoJzSPTVn0Uk6BZTPEtgiLN/Dub/4GqKxSSQzNaTVGpJqDclM85LOHPJ5tb7g86Q6e5uZGpJqHWizet3zqEei5/nnd/nR6TE9MY7e/n4Izb/8aZph36jZM+/bEUQJyq6rvbK4mOb+pvlKlpVS8H0H9UZ+DkpsWxpdKTetdWgtKp5CGJrxHEdRAEs4SPN66nEJW4cGYNvFPHghKorCJPgA4Az0wRno68j3SuuNg8n/vIODdPYAoHmQUIXzhNMhCvpCl6YJqtNT6O0f1BrHvrEZo5Oi1chg5u9KyfdyVb1vCcIYUopcJKYlzzF29GhLFCfwPcfoRdbLKXkSYVjXHcac5kGohVpD/zjUTvE9F/19Fd1hENEKCpXgd5JVLsEql+Bt37Ls7WrVGYzue3iDotp4UxPj6OkbaM6G1qDeiDAxvbGTMLrJxCq+JSUaYT4TEKUUSjmo4kshEBkw874dpk7QWomAglQRTIs+CgNI4SArSBV/+9ZBbe8HVBACMGqOrEGhdFIxG482ULnSAycHiyfXKk2aVXwdlFJ4KGcz79thWhXfchyoHCcfQRhDmvRmsQjfc3Kx8zIARHE+e/F7ShaSxLyDqOZBqFl/82s12N8L3yvu+x1RkbCCv05CCPQPDmN0/17doXRNdWYacZJACAkpmxchBaRo/ffs9ULM/XcnjE7WEUbmvWGvV7OK7yCO9VedPdc1vm1kJaZX8V3HMnZh7VJMm6C1EtcWiCNzz/Q1q/g2cnKMtyjbtrBlqDMtsETUfUzwO6Dc0wtnfMyototOUQDSTGFqcnJVX9fc/l1CSnHwIEC0DgYWXrfYAYNSwIHxfM68b4cSNgDdCalAXJC1DWHY3Dk0xycijBLGKXzPRpCT1i3PzhBF5v7wlVIoeTZqQT6ez8Vs3zLYseINbW4KEsqgBhKTYukkJvgdIIRA39AwxgpYxfdKZQRBuOqva/bxZlhhl/glCSkhUIa5b9nr0wj1V/F9383lwtrFZEqh5LvGVfHLvpu76n1LXqrNFd9CFNV0h7GiOA4hhZWb53W+nnIJPZWS7jCIaBWKediiQaWnF7aTv77V5UhpIdS0HbnKMvT5OXwnXIVmFV8Px7YLk9y3tKr4prAsiYamv59OiKIEnmt2DUhKIEvMbc2ZL8sylHK4OZsQAtu2DugOg4hWiQl+h7R68YvEcj2tiy/TqIGc7WO0Ko0wgee5cGwJy5KzC0W7/3wroJkZFUw224tvCtex2DLUZT2eyNXUn2YVX3cUq7NlqB+Onb8DEzKXEsK4SxHxr7aDKr19mJoYQ2LA4sn1cjwfYbj61pxOyrIMvT4wlY8C3ao5tkQS1tFK6gUAC83pYUJICCEgpZj97+YtDo6na35UwNxBmFLNJFcphUw1z4I0R/MtfPHK68z7doSRGb34vuegUYDnOJyt4pu42N13zJp5345mFd/LTS++5zoY7O/RHQYRrQET/A5qVfHHDuzTHcr6CGHMxlJZVIcU5Vz2ra7Es1Ik6eEPTClAqebzv9Y1DMD8AwYxe7DQPGjQnfx2U5bp78UXAkiSdfzgqA0KtowR5/BpjuMQQuTj7A5n3hPlV/HO02tW6e2DbZvTJrAWrl9Gup7MsoNaVfyiqfg2kijYkPtSSiHLMiRJ0lzUm+W/srycVhVfl5LnGnOA3Amhgb34vSXLiDGza5FlGco56MUf6Kug5Hu6w6ACUkIadymiYj4qjYQQ6Bsc0h3GmlmOgyDYmMSzXVlUN2rx5HpJKaBifVM/kjiCb1jC1knNKr6eg2wnhzPv22POH6BtmT3zvh3NKr7uKJZmWRJbhgZ0h0FE61Dcd3mNevr6MTUxhtTAXRWXowAIaQMwK0HJsgx9BerFr7gCcai3wiuQr9/N1ROQttPcfE2I2XUNS6SpSkFBzbZGNdcwqEzN7jy7uj4K03fUXaswio3pxS85Zs+8b0ezim9uL/624QFYFut/RHnGBL8LWr344yP7dYeyKp5fRqB5Ye1SsqgBIXwole8EynMtREFVe/UuiSL4bhmBAQlbp9mWhWqQrPN3RaB1OCDnFjvjsAOG5i2b04+kBOp1M/9+OkH37ywAlHMy874dSRxBCGlcL3655KGvt6I7DCowBQFl0FlBk2LpJCb4XZK3Kr4QFiKDe1qzLEWfJzBlVvfQqtkqRGrIa4mAGessOk1aNlQHF7lmSiFbZDH0YfcrAEvKXI1tXI0gTOA6NqJYz2uaEADSnL8AzJNmKUqei7pBVXwhBLZvHdQdBhF1AM/BdYkQAn0D+enFtz3P+MQkSxoQwrBy1yr0+BbSxJyDqCQKjVs8uV6+56Ie6jlwyRTguvleYL8SnTPce31hzOL/TkmTyKjXtKGBXrgF27CRaLMq1ru7YXr7+zE9MY40NadCsxjb9bTPvG9Hlqbo80Uue/FtSyIJzWstsApUxZdCINDcm90IU1hCIlNmHyyvVRjFqJRaCaCYXbMwO9oVs3swZIDKFDKVdezUt+cIhGEO//BXkKYpyl7JiF5817ExPNinOwzaBEybXGNSLJ3EBL+LhJDoGxzCxOgB3aEsSUHkasa8ivPZi+/bGRIDFwbGUQjPLRuxeHK9XNdFNdB7wNLaTTcIzD9gXotyqf3HJmcXNQspIGc3bju4fmF2jcPsbVpbvS11wODIJJcz79uRJBGEAT3JnHlPVCxM8Lusp68f0xNjxp5a9kpl48ZiLidN89eLX/ZsJNGM7jCWVIQqvuvYqAYJTBjn2AhTSCHmdhguCtuSCMPVTdhSmK3mr/N3rMiJZ5qmKPklrb34fT1llEsF3HCEaBMr5nkJg0gpje3Ft2w7F605h1JJA2KV4wt1kUIASV13GMuKoxCek+9j/VRJmJDcA80qvue5usPoONeGtoOW5s+3uNIkwmpHsnaKlBJbtwxouW/anJQQxl2KqNivmobo6R+AtCzdYRxGWE4uq4xpmqLXz8cfZNkTyDLzK+SWzG/PeMn3EMZmxd+I0kJVncu+jTDSt0A8jFK4bvEOmlrSNEWl5MKxJSwpZxczb8xr89bhftgGvj8R0frku2yXE80q/iAmx0Z1hzLH8UurPt1ulCQA4MGUqu1iPMdCbMDM+3bEYQDXKWsbgbhWliVR09x3v5jmbrpuIXrxpQASA6Y/ZQWu4gshEEcRstlWTgHAmr2+uQfD/DUMrQ0YxIL1C4CCUvPWMUAhm920rdkmpXDo62XJd9HPmfdEhcQEf4P09g9iemLCjGqukIg7OCdchzRN0Of7mDa4F98WkTEz79thi8ywPYxXZtsOQkN/l4MonV08mb+zZPOVfNuIA5UgSlFyXcRx3n5LV+a5HhqLPMdKKaSpArC+M1QLDxgOHiz0VvxCnWmifOBGVxujuCURw7Sq+CZwfX+uUpRraQBdfasrqfg20pwlInEUwHXyc6redx0jq/ctaabg+fluK3Edy4jkvkUhP7+f7bJtG40NGnTQPGBIkSQJ4jjG5OSU8fufENHaMMHfQL0Dg5BS71NuO8VoGwCANEnQZ2Avvm1JZFFVdxhrYkszD5gOJYRAkJj3sz9Uq4qfTwpSmJX8BVECp2AbMQmhb4F4mqaYnsnnaxURLY8J/gaSUqJX40QdhQJu6GBgFb9kq1wuXgaAKMxHFd9zXSSpWcnnYvJcxa/4DmID12QoUZzOUt/zEWlcvAwAkxOTuX29onxqbXRl0qWIivmoDNY3MKCtiu+Vyohj/YvlOilNEqMm6pQ8C3Fk9ljM5QiYX8V3bMuInT/bFUQpTF4MvhhLCoSRmS1mQViMKr6UEoEBgw6SNMX0tLn7dBDR2hSnFJITUlrYftQxSOOkuZV7liHLDn5c7DqlFn6+pvu1rHxPzVmGTEMALnQnUUIAIg0MO5+welEYwLFLxi7EVsKCWueiw42UZgqVnE3U8V2JIDT4IErYAPJdrHAMapecmJxEX18vF9wSFQgTfA1c1wNcb81fP5fsL3uAoBYcIMRphqTR6OCjMEeSxOj1fcwEelPrHs9CHOb/ORYAXAswsDsDvu+h2jDzwGM5Qdyq4pt/+FfybASGb4DXCBOUXCe3ZyRd15zkHgCSJMX0zAz6+/p0h0KbAKfobAwm+DkkpQSkXNU8iZnpKQQFTfABQKYBdFbxXcdClJOZ9+0Iw4ZxVXxLSjQMnpqznDTNRxVfCCBLDTyyW0yOq/jN0ZdmmZiYQl8vq/hERcEe/E2ip7cPtl3c47kkidHr6XtjckVcmOQeaB4mmbbW1nYcGJgXtc203XYXU/ZtJDkZodsIYzh2/nrxfd9Hkph3EJUkCWY4UYeoMIqb8dECQgj0DwxibHREdyhdY4sEFb/Z+qRU85Kp1m6OB/+701X+im8jCYu3SC0yqIrvGT7zvh1JmqHie8ZW8V1HGhvb4gQg81XFt20zNg1bysTEJHp7e1jFp65SMGtyjSporZsJ/ibS29eHyckJpAZWjzohURJhtPSbpwQgxcHt3+X8rd8FmvPKF2z/3jxIaH3MZreAn3/AIIRAFtW6/dC0MKUXXwCI853bzzG3iq9gCSBvT3MjjOE7tpEV8cVIYUEpc5/lOEkwU62ir7dXdyhEtE5M8DcRIQQGBgYwNjqqO5SOcz0fM2F7yVNr+/f1vs1KALaUUHnuG1mBCb34Xk4X1i7G1Cp+2XcQGr6wdnECwrKBHCT4nuchCMyfZDYxMYXeHlbxifKumOclaEm9ff2wLMOaq9dJCIl6rOfNKEkzuL6v5b43ggDgaiwD2Dmbed+OMM6MmqVjSYE4Nj/xXEojiI1fXySEQBTl4/c4jmNUq8U8K0lmaE3RMelSREzwN5lWL36R2F4JaaYvZYoMS9g6LQwacB1LS0VPCBtKFevFN0kzlPy1j8ntNN+VyDT+/ayfgJRmL7b1XG/Ne5joMMHdbYlyz+yyB3VFb18fpiYnkOZkWsZyHMdFNUigc5OrJM1Q9kqICjADfzG2ZSGMM7RywOYaBglLNp9zOX8dw2I/BtVsi1KYXcOQKSilmns2LJND+J6Las4X1i4lSpoHhboPXXzXMmI31fWqG9yL7zgOGkEA/T/t9kVxjGqtht6eHt2hENEaMcHfhKSU6B8YwPjYmO5Q1i1WNmDArqamJGzdICwP2VwPvoBSQJpmWP/xYXM5sxAClpQQUkACcwufk6yIz2ZTnGSzc/H1JddCKKMXfK6OgDS0F795EJu/3+WJiUn0VCrsxaeOU0KYNUWnoL/j5jzDtKF6+/ohc96L7/plhIn+5B5oVvE9v6Q7jI5zPQ/1sJtJYPOAIUkzxHGKME4RRAkaYQyrmK+5c6JEaW3tKvsOEgNGoHZKPYhhWWbVrHzPR6x7DNUaRVGMWq2uOwwiI333u9/FxRdfjCOOOAJCCHzxi19c8Wu+853v4DGPeQx838fxxx+Pj33sY12NkQn+JiWlRH//gO4w1syyLNQCM5L7Ft0JW6cJIRBqWrwMAI0wgmfablsdFCcZSr6r5b4dW+Z0as5yBCyDNr6yLAtBzp/jcfbiEy2qVqvhjDPOwLXXXtvW7e+9915cdNFFOOecc3DbbbfhrW99Ky6//HLs3r27azGaVe6gDdXX34+pyYlcLf6aY3vIUrPiTpK0UL34jlfSOp5SALAKXoIQQqDiydn1C62DqeZZDYjZ9o7ZNQyZAhQUsgzIsmxdm7Y5FtDVEzOa1IMYnm0Zsb7IsmwkSb7XN0RRhFq9jp5KRXcoVCCmTa5ZSywXXnghLrzwwrZv/7GPfQzHHHMMrrnmGgDAKaecgp/85Cf4wAc+gOc+97mrvv92MMHfxFq9+BPj47pDWZXVzLzfaHGqCtGL7zgOqg29i5cBIAgjuI6LqCg7Xc1jWwJJUMdazvvM37RNiNlN2+Tsxm04uOi5qfkfCs0DBimAhmFz+DunWcVPU70H2Z7rFWLxMgBMjE8ywSdapx/84Ae44IILFlz39Kc/Hddffz3iOIbjdP7sIxP8Ta6vfwBTk5O5qeILIdBI5u81a5Y4SVH2fERhoDuUdUlhxuJlAcCRQDFSpYV8B4jD9f0eKzU7kQhoextaKSWEwLITjPKsEcRwNVbxhRCIDVzsu1ZhFKFWq6NSKesOhairpqenF3zueR48rzMjjfft24ft27cvuG779u1IkgSjo6PYuXNnR+5nvoKfAKeVSCnRNzCgO4y2OV4JieE7x8apqYcf7fF8H0GkP7lvaYQR3IL14pc8G7Gmg8Asy+B7enr/N4KCgGXre3ye5yE1rH1wvcYnJnWHQAXSnKJj1gUAjj76aPT3989drrrqqo4+7kMnUrXWt3RrUhUr+IT+/gFM56CKbzsOZoIUuttGVpLnKr6UErXArMOTolXxhQCQ6v3diOMY5p4HW79GmKAyW3lr7sFw8GyHUgpZlnVl8ahjO2g08jXzvh1hGKJWr6NSZhWfiuuBBx5AX1/f3Oedqt4DwI4dO7Bv374F1x04cAC2bWN4eLhj9zMfE3xqVvH7+zE5MaE7lGUlcGBC20g7WlX8vL3NW46PzMDNpRphBMd2EBsyFnU9Kr6FWHMPfJpm8H0PDY1z+Lup5DkIg9qKt2utX5BSArPrGZrrF8S8JQyz/zV7QLD8AYNA/v7q2zMxPskEnwqtr69vQYLfSU94whPwla98ZcF13/zmN3H22Wd3pf8eYIJPs/r6BzA9NWVsFd/1S5gxbCzmcvJYxXddc3eOFQBcWyCnI8XnuI5E1KgvvuPvBovjWHcIXWFbEnGw8vx2AQBKIZtN0DshU/l5jVqtIAxRrzdQLhdvvw/aWEoJKGXAi+CstcRSrVZx9913z31+77334uc//zmGhoZwzDHH4C1veQseeughfPrTnwYAXHrppbj22mtxxRVX4E//9E/xgx/8ANdffz0+97nPdexxHIo9+ASgObO5r79fdxiLktJCbZ2LEXVIMpGbFgghgCg1++WgEURwbLNjXIkjMiOSe6BZxdc1h7+bXBvQ0XwkANh2sdaKHGrc8LO8RBvlJz/5Cc466yycddZZAIArrrgCZ511Fv7yL/8SALB3717s2bNn7va7du3C1772Ndx6660488wz8Z73vAcf+tCHujYiEwCE4i4WNCtNUzxw/33GbWxiexXUcjq0u+xZuajiu14J1RycIfF9FzVDzzKspOLbSMKV20Y2km1ZiAu0INT3HKQan2MFwLKLtUPwoY48YgdKJVbxafWmp6fR39+Pn912O3p7e3WHM2dmZgaPPusMTE1Nda1FRwe26NCcZhV/AFOT5lRpHNdDNafJPdCs4pvOtu3ZAyjzY21W8fPXi29JgTQybwO0JE1R8jw0CjCzXQgBFes9mBYAHMsqbIIvhIBtM22g9ZJQRjWQmBRL5xTzUdGa9Q8MdG1k02oJIRBm+T7lHcUJHNfXHcaylHCM6odcTrMXP38vW2VXQBnan52kOV/YMKvk2cgy/Yl1EAawrHy/bi1laHCgawsCiaiz8vdOSV1lUi++45VyV6ldTGpw8ux5PhoGzbxvRyMIYVv5eenyXQtRaF71viVJUpRyPhffdWxEbUzN2QgCgOMUr8rtOg4GBsx4byCileXnXZI2TH+//iq+bdvGTnRZrShO4HRwnm6nSClRz2FnhgDgOfl46RIApDL/Sc57Fd9CYlSDWRg0ClfF37p1i/b3BSoGBWHcpYjy8S5JG8qybfT26a3UZMIr1B9dlpn3p2Y7PtLMrAXV7QpyUsWv+BbSxPzkOUnS3O5uW/YdJLFZB1ECKNTuy329PSiVzG41JKKFzH+HJC109uK7fgmNuBjV+5YwTuC45lTxHcdFNTA/8VyO6VV8x5aIDW7NOVSa5u9vzrLMfY7DICxEFd+SEsPDQ7rDIKJVKl6jIHWEbdvwK31IknheHV3NjpdWgFJQmN3JMVNQKuvIZjFSStT1bvLZNZlBx9OxspCXXYGX0qziO0gMHfPoWRniND9nSJIkge95CHI0UcezBeLQzJ8/oOA6Nho5PHCab3h4qBAHKmQO09piTIqlk5jg06JmqnVMVVcaOTd/W/bWG4CCFBJSAnJu63dAioM7vi93wADpIC1I7/2hwihByfUQR3qPYDy/hJmGqUnR6niONDLBL/s2YsNm3rcjT1V833OMf47DoDlRJzXwd7QdJd9HX58588qJqH1M8OkwaZZh/+haZ+GL5tbvKbD63SQFSr4FID9JxmrpruJblpWLDa3aFQTRbAJlTqVcSgEVm9k2spK8VPGFEFBJHk71KbiOg0aah1gPt3XrsO4QiGiNzOkZIGOMjk1pqzg1gghSFvN0GdCs4uvsxReWC8M2Kl4nBd+wxYwVV3SkXU2XLAdV/JJnI8vJ5J8wCCBl/t5qBwcH4Lr5XHhNZtM9MYdTdGhTCoIIk9NVjREoeG6xN1JRwoLjurAdF7Ztw7IsCCFXfb5jtTzPQ93YfuW1C4IIliEHhZ5rIQzyWb1viZMEvmfu36BjW8bMvG9P/l7THMfGIGfeE+UaW3RojlIK+0bGdYeBcLaKn+V0hONKBIBgweZSrbUMcna9goSQzfULUgAQAhICs//DgtYnhdnFzlmzMq8yKKWQZQqZyubqEkIINCKB1bdN5YGC71qoGTAVyFYxEjOONdbF5L89W2bQ/5NenWYV38rNmZ2tW7bk8qwDER3EBJ/mTE5VEUax7jCgoFByXdQDs/uA18JznWX7m5UCUpWtc8DN4QcMlm0hDFOgoKcim1V8S+tc/56SjThXleWlxXEMz3MRhvpfD+Yr+Q6SXD7HzSp+IzC/F7+np4JyuaQ7DCow09piTIqlk3iITgCap+VHx6d0hzEnCIvXiy/Exvc3K9VcNB1GMUoGt12sX7OKr4ttScRBXdv9d4Vh1WYpJVJDZ963Iw+9+FJKbOHMe6JCMPvVhjbMgdFJZAatvlQqf32rKyl5LhKNCxiVSlHMFp2mUGMvvm8rFO25jeIEnkEHhb4roZRZBx2rY/5r2vDQIGybJ/aJioB/yYRqrYFqzbzKWBhGkEIYdeCxVo5taT89H8cJSp6HRpi3Dub2SCmQKMzuw3BwDYMQzTUIAji8Q2luDUPzoDJrfcxU2793Zc9GHOWxbWRlypAqvufahThDEoWhseuLPM/jzHvaEEoJKGXOGXqTYukkJvibXLaumffdpZRCyS9GL74p7UbNKn4xOY6DMGiepehUL37zIEEcPGCQsysc5h0wyCwq7M4NcZzAc12ta3MEAJGZtRZgrZTK4Hkl7Qf7i9m2dRhCmPE6RUTrxwR/kxubmEaSmJueBGHU3NQmx1X8kuciCM14Q29W8V00QnN/5mvhuQ6qHd8BWcwuel7+gKG3ZAPI/0Ho0vT+7ZV8pzCLl4FmFd+017SB/j54nr79OYio89iDv4mFYYTxyRndYSxLKWX0TO6VSCkQRYYlf7nuYz6cABBrPF6pBiksy6zNtjopimJtveO2bRWiNWc+pTL4njkbSNm2haGhQd1h0Caie1OrzbLRFSv4m5RSCvsMbc05VJTjKr7nOggMOx0fFawX3/c9zDR0Ll4WsGwXaWreOpZOsaWA7bfeLgQUFKAEmkuLZ9cwZAoZVPNjppq3WecbpysV4oItXgbMquJv2TJs/HQfIlo9Jvib1NRMDUFOetuznPbit+ZeG1kbKEgV37EtVIMEuuf7zwQZyrZEmhbjeZ2veRYqWNUmTa10sbluQTbXLMy7tP5t9laLHjDYUuRsx9r2KZXB9/X34lfKZfRUKlpjIKLuYIK/CSVJipExc2betyOM8lfFz7LUzOQeQBTH8F0PQZTzKr6wjBid2Kzie4Ws4nuOhXCNZ3uUUkjXOBpWOMV+e9JdxRdCYMuWYS33TZubaW0xJsXSSTwvtwmNjE3mZsv0lizLVy9+yXeNXrwMAELk63fgUL7nohGZ8xhmggxWwVodPNdBqGmBeBwncN3iLvzU3Ys/NDQIp+AHUUSbWbHejWhFtXqA6Wo+F61FUYQ8THGzbSsX7U9RFGvd/XU9pBQIIrPO5iglYDnFSUgFAJXpPcOTmvUj7rgoDGG7JdiOB8t2YVn2bD98d1/oXNfFQH9fV++DiPTi4fsmkill7Mz7dmSZgu97aBiePNtSIkU+Wl+EyGcG5bouqhoX1i5lJshQsmTuzpAtpuQ7CIJAawxxHMPzPMSGjJntNOlVMNPIcLDWtvCAW8rZfRgE5m3cJgChICAgoAAxu4pBKYjZjdugMiilmu1rWTa7advBv3XOvCed2KKzMZjgbyLjE9OI43wknkuJoxhCAKa24vueq62lYS2aVXwXQWResrwU17FRbehfWLsYpQRs10MU5LsX37al9uS+xYAlFl0hLRvVYPkHl81OJlrDd5/9ePgBgyUFHMecMZ1E1B1s0dkkoijG+OS07jDWLc3MmiE9nxQCcWz22YXFyJxV8VPV/RaG9ag2VO7HDtqG7LwMNBeEOwXsxU+t8oYXKrJMIU4yjE3ls02TiNqX73chatv+0Qljq96rFcexkemd5znIltnx1FRhFMPLSS9+yfcQxmaXdDMFODnuxfc9x7jN2fL3V7U8y6to3U16bLKGtABtZJRPCgJKGXQxMqNYPyb4m8D0TA31Rn7aRlaSphl836wqvuvY2mdar4eVgyq+bUnUgny0Es2ECtKgKni7pBBIDDwLFUVxgSbqCDRivW+9aaYwNskqPlGRMcEvuDTNcGBsUncYHRfHse4QFlL5rgGEUQzPMbuKLy0beTlBkmWA4/q6w1g137ONXSBsZlSrJ71eJAZsiMYqPlGxMcEvuJHxyULurpmmGUqGVPHLvos4yffiZQCwpLnZs+85qIf5+j2eCRRkjiaVuK5tzMLaxURRnPvFodJ2UTXkLFSaKYyzF580yCCMuxQRE/wCi+IYU9PF3OodABIDqviWJXMx874dYRTDNbCKL4RAoP9HvWr5quIriMyMxHM5KkcHTIdSCohh1u/D6GTd2DM2RLQ+TPALzLFtuAXeqTBJM5Q0T9RxbKs5d7ogbPPye3ieiySnOx5Vw3xU8cu+l4uzUFEUw3HzWcW3/R6EsVkHUWmaYXwq3yNdiWhxTPALTAiBocFi71aYpPqSEt9zEIbFqN63hGFkVC/+wZn3+ZRmgOOZVbU9lGVJhKG5rTmHUjl82xLSQs3Ql4rRyVoup39RfrU2ujLpUkT5e6WkVenrKcMpchU/SVEueSj5Hkq+C99z4XsOPNeB69iwbQuWlB3ftVEIgSQHFc+1sMzJ75HB7Jn37agGyuhdQ11bQuVohm4URbAdR3cYq6KcHmOT6CTNMD7NXnyioilu5kcAmono8EAf9o2M6w6lKzzXQa3Rfmmsud27gJQSYnbbdwEBIQAIMZtKKjSTSjW7d0Dzo1IKmVJQmZpdkJjfsZjLCcMIruMg0jxvvuS7mGnkvz84zQC35BlZJW+ehTIvrpUIYQHIx8IMyy1hJjC7GDA6UcNQXzmXo12JaHFM8DeBvt4yxiamECdm9X+ulwCQrCr/E1AKSJVCus4FhUWZyL0UxxKINOZPliVRC/Kf3LdUQ8AVwqhKuQSQJvlIkg8VRhE8xzFvXO5hBILUhulDPpM0w8R0HcMDFd2h0CbQ2mDKFCbF0kls0dkEitqL7/metoOWRpQY3XaxXkEYwXX0vTzYtpObmfftSDPANawX3/cdpGmOD/qlQb1kS7D8XsSrq0JoMzJRQ2bQASgRrQ8T/E2iv7cCx8QRKWtk2xYaGsdTZpmCp3mCT7c5loBtS0g528K0QTzXyc2OtatRC2DMQaFrW0bPvG9HGEawbXN78aXtoNrIz+9xs4rPiTpERcEWnU1CCIGhgT7sH53QHUpHSKl/PGUwW8U3qe2io4RAljZbIGaXKEAKASGb6xjE3KV5i9ZHAGg9JQoKUM3PM5UhU82DI6UUFnvahACitLn+oWiSTKFiRC++ghDFeH6FZQGGthklogSF/CT4ADA6UcVgXykXo10pvxRg1OSaYrwaHo4J/ibS31fB2OQ0kpz34vuei3qof9FalilUfBeNAi62dV179nEtfBHOmosY1p22LHXAYFkWpur5aGlYi1oo4Gg+KCz7bu6r9y1hGMG1HSSGJfmW14N6Ds9CxUmGyekGhvrLukMhonVii84m0qzi9+oOY12kFAgM2iymWcXXHUXnNTe37P4Dy5RCmmaIkxRRnKARhCi5xX1ZSjIF19O3RFtKiTAs1gGpMGmuKwAhJOqGzrxvx8hErbhnJYk2keK+k9Ki+vt6YBv2hrgarusaNU86zRQ8jQlbN5Q0Ll4GAFuacwDXDfUQ2g4KPSdfM+/bEYYRLJN68d0epAa9Rq1WnKSYnGEvPnVPa4qOSZciYovOJiNnq/gHxiZ1h7JqrmujHkQwbeOjME5RlK5x29K7eBkAwihGyfXQiIrwjB4uyQRmsq0AAEsCUihI0fwooJofhYJABigFITIIpQCks4sZUjTHLq7u+fE8u3DV+xbLso0Y+Wk5PmZy2JpzqJGJGgZ6S8YsCiei1WOCvwn19/VgbHIaaZqvXueNahtZrTTNUPa9Qmx8ZVkWYgN+L2yZwcSfdSdkVg9qCwqk4pCP7RFoHiBYEpASkGL5AwZk0x16BOYJwhCObSPVuLu0UkCoXCBnC2sXE8UpJmcCDPaVdIdCRGvEBH8TkrI5UWckR1X8ku+hFuiv0C0lMmhdwFr5notGaMZzHEYxfMdDEBerim/ZLg5UO/O9FJobvR0+Zn3xA4ZjBnzkZffXtbAsR2uCb/u9aBSget8yMlHFQK/PKj51XLP0YM7vlUmxdBJ78Depgb4KLCsfP37bslA3OLkHmjOkfT+/vfhCCOMOUhyrWMm9UkA10vc7snem2C0XQRjCsvXUrKRloxrqP/PVSVGcYqpajGlLRJtRPjI86jgpZW4m6kjLykV/u2kJ8mp4nos0MytBCaMIfoEm6lhuRet0lTiVEE6PvgA2gO248Hz/4MXz4LouHNeF7TiwLKt5kNPhA53UKi+6r0PejYxXC7com2izYIvOJjbQ14PxiRnjErv5PM9Fw4CZ9+1I0gxlz0UQ5mtGnuMsPvPeBI6VoQg1RCktjNX0H6zsnSlhh1/MpK05AjRo/7EJASnlvA3b5l3m3WZB5q5mN2kDmh+zDML2MV2g1pz5wjjFdDVAfy978alzTJtcY1IsncQEfxOTUmJwoBej41O6Q1lUs23E3IOPxUQGLFBdGzNf4MIwguf6CKO8Pq9NEcow4Tg6SiWEU4GKOrQQwCCuu/opQVkHfihFfxM9MFFDXw978YnyRn9JibQa7O+BJc38NTCxbWQlSZLB913dYbSt5HvGtxZ5Vr5+Bw5lOT6m6rqjOGhftXi9+K7rahsBmkR1eE5+9xZZSRglmK7lf0IY0WZjZmZHG6ZVxTeN67Rm3udPnOSj/cGyJBo5aCcKwgiek8+EVEBgsmHQJkwAwsSCdCq6w+iYZheN3oNU1zL7IHm9Rsankc2MI2vMQEUBVFbsx0vdpdDcycOUSz7esVev6GcXqQ0D/T0Yn5zpyOnqTskgYGrbyEriJM1FL75t20gMGYu5Es9WyEmoCyi7gsjARQT7qiVs82qF6MX3PA9hqPdJjsM6XKfX+LNhazU8fR+iO3+58EohAdsBLAfCtmc/OoDlAnbrv+3Zj07zukM+LrhOFvcsCJEOTPAJlpQY7O/B2IQZG+H4vmf8WMyVxKnZiZPnOghylDEHYQTX8RDlaC6+tB2MVM08SA0SC7KngjTnvfi2bcbuvEIAnpUiys+fVNt8FaK0987D/0FlQBwCcThXAV3XX+eCAwYHwq/AefT5EIKNBkRrwb8cAgAM9vdCSv3JiGXJXCWeS4mTFL5nZi++EM2JP3nj56gcoRRQj33dYSxrf7XU6WmRG86yBEw5wd6s4hevCn3ExB1AtgGTzFoHDEEVqjqBbPRBZHvv6f790oZrTdEx6VJETPAJQDOxHuzX34tv2w4yM96v183UJNr3PGNjW04QhnDtfLwQ224Zpq9LbOS8F9/3PUSROW1wrSp+kWyNRyEnHtZ2/8nvbi9EGxmRDkzwac5gfy+kxpKe5zloFKB63xIlmXFVfMe2Zmfe55PvmP9mL4SFsVo+Krn7a/lM8KUUiGNzkvuWOKzDsYvxtmqpFP37f7nyDbtI1aaQ7fud1hiI8qoYr0TUEZYlMdCvZ6dLIURups+sRmJYL76QEiqni5eB2V58w6v4iSwjLydIGrGE7eYvyXdd16ihAC1CAL5t1t/8Wh1V+x0Q6p/vmtzzc1bxC0ZBGHcpohx1tdJGGBrog+c13zyzTEGp5sfW51mmkB163ezn6+F5bu4X1i4mMmiiju+5hThD4jtAZOjmxpbjYWxGdxSrs79WxrBT0x1G21zHQRAExq4fiMMaHLsHcWLeAUi7erMqnAO/1R0GAEDVJpHtvw/Wjl26QyHKFSb4tIBlSfT1lNf0tQcT/vkHBNmCzxc7YFBQqNZD5HUs5kpMqOJLKRGamhWvUrMX3zUyyZ8OzGrJakc9trCtUkEa5SPJV1DGJvfAwSp+bODvZ1uUwrbRXzVXihsiuec2yO3HFW6DNqJuYoJPHSOlxFo2xc2Uwj3378vlws92REmKkucg1Fg9d10bjQKdIfEdgciwli7h9CAwcOZ9Ow7Uyxi2zU/wS76HQPPM+3bkuYq/M3oYYmZMdxgLqOoEsgP3w9p+nO5QqANMm1xjUiydxB580k4KgWEDd9PtJJ3HLp7roJHTXYGXEoQhHMucF2UpbYwZOvO+HfXIgu2u7czdRrEsC4EBM+/bIUQ+FoQfylMRKvt+rTuMRSX33KY7BKJcYYJPRhjoq8CyivvrGMUpSiUfnuvBdR04jgPbtmBJC0KIrp4NTzOFIrY/lTzdERwUZCWTOhrW5EDd7MW2tm3BlJn37YiDGuycvaYdMXUXkJh5pk/NjCM9cL/uMIhygy06ZAQpBYYHenBgzIzddDvNdWxU60u9cTaTAAFACgkhm2c1hBAQojlhqJWez7WgKnVw90iVQamDH1vrG4RotjTUC1a9bwmDEI7lad812HJKmMnZwtrF1CIL28plJJH+ySmH8jwXUZSP6n2LEEDJUZjJyWj8oXQc1tge3WEsK7nn57C2Has7DFon0ybXmBRLJzHBJ2MM9lUwNllFWrBe/GbyvfILiFJAqjJg3Q//4AFDUTYNW4wCUHKBuKEvBiEExuvFeRkdaVQwaJmV4AshkBpaVV5JHNZgWz3Gry+SKsXQPr0z79uhpkeRjjwAa+vRukMhMl6+zh9SoUkpMaxpDn83lUsuIg0jNZQC4kTlvnVkOWGktxc/kz1IclKhbUc1tGC7Jd1hLOB5LlIDZ963Q4jmQajpjmzcDwRV3WG0hb34RO1hgk9GGeyvwFrLKB5DWVIgCPTNy4uTFCXfoGb1DlOqmUDpOIaxbBfj5g+eWbWxhjm9+I5jI8jraKJZSWh2L35F1eHtu0t3GG1TUyNIRx/UHQatQ6bMuxRRcc4tUyFIKTE00IOR8WL04ju2o31zqThp9uYXdYR0BoEMzfUKUghIKSBn1y60PgKzy4zn1jA0/08pBajmbPVMKagMyDKFVK3QlamAmaiYB07ToY3hgRKSSGPv06zmGhTdUayXQskFZvQ/nYdTCjtGfw2ofJ0hSe65DdaWo3SHQWQ0JvhknKH+CsYnq7k9Ld/iufqTe6BZxS/7Zuym22mOY2Oq0ZwS1FzDoGanBq2HAFY4YMiUjbGpDjwAQ40FFfRLvRmp73sIczDzvh1JWINllZEasOndfNvj/RDTB3SHsWpq8gDSsYdhDR+hOxQiY5l73pA2rWYV35w2gbURRi2sK2ovfqwcdGsEqFLNEaNxkiGMMwRRinqYohYkqAcBfKcrd2uE6cDW2osvpczd1JzlKZRds05FOCpG775f6Q5jzdiLn1+tKTomXYqICT4ZabC/B1Lm94+u5LlIDNrFMk5S+F4OVvutgud5aER6jlqEAHr8Aq2uXcR4oO8g23XsZvtUgSRhDZZBr2lHzvwWiPN7EKUm9iEd36s7DCJjsUWHjGRJiaH+HoxO5G/AuGNZRs6eTzNVmF58S0rMhHofSBwF8J0KAv1dWF1RjSxUegeaDUsCzTrX7EeoZt2ruY4hm50Fm0Ehg8qal7X+nnmui7BQ1fsWhbInMNPQf+AykE7BHrlXdxjrlt5zG6yhnbrDIDISE3wy1lB/D8anqshytMRdKUBIiQ4Ms++4KC5OL760PaSB3t+LVhU/iC2tcXTL1p4UtWCp32OBg61Riz/+g2sYZj+K1nULDxgABTHvgCHNitF3v5hmFb/cgXUiaydUhi0HzJ95345sfC+yiX2Qgzt0h0KroJRoa2+YjWJSLJ3EFh0ylmVJDOZsLn7JdxFG+sZiriRJ89+L77kOpjUn9y1xFMArYC9+r5+hsc4DwYNrGBTCOEMjylAPM1SDDDONDNMNham6wlQdmGwITDQkJhoWlGXWHP7OUih7et92jwweAOrFWSHOXnyixTHBJ6MN9/dA5qSnRAphdHIPNKv4ee7FFwJoJOZUzIUA+grWiy+gULL19R3VIjv3B6HLScKqtl78sgrg77tTy313Szb2MLLJ/E0CIuo2JvhktGYVPx8TdVzXyUU7kc72gPVyPR+hYcdQcRTAK1Cz49beDGGs76AlSjJYXq+2++8+hYonUfZtlD0bvmvBcyy4toRtNVuZurV1247xO4CsWAekAKv4eaOUeZciKtDbEhXV0EAPJqZqyAz+K/QcG42crLaM4hQlz0UY5asX37GtuZn3RhFAXynDyEz+6yWenSE2YLJKPbbhFWRB+KGEkKiHybLFAAlACAUpBIQUzY+zl+b3OPyJmd27rblhmwKUam7elmXNDd22xqOQk8WcOpONPohsagSyf6vuUIiMwQSfjGdbFgb7KxibrOoOZXEKMGjkfVvyFi8ApMI1dnRiHDfg2hUY3qG1osFyiob+/B5hnKFc6kEWG/o3vw7SKSMLVv5FaW3chg6ccRMA7MyAH2wXJffcBvfRF+gOg8gY+S850aYwNNCzaNXKBKWShzjJ12nvKE7guflZHep5Hmqhmcl9S18ph0dN8wxWMiN2Xm6pJ07hTp3bjo96G8l9NxxwtwMaNy/rtmzkAWTTo7rDoDZkEMZdiogJPuWCbVkY7DOvF9+2JRoGzrxvR5aT0WBSClQ1z7xvRxI34Ob0nKgUChbM+j0O4wyWl68pWstRCohSfQvEM2GhsfVEbfe/EZJ7btcdApExmOBTbgwbWMW3pJXbKmMYJXBzUMW3HR8GbQq8rLxW8bf2pogNfJLrcXGq+K7fo/1M335vJ+D4WmPoJtEzoDsEImMwwafcsG0LA31l3WHM8T0XgWkjXVbJ9A0+XNfBVMO8xHMpSdyAY84Uz7ZUPIXQ0M3PwjiD5Zp35m61LMtGTVNrznyZkGhsK2YVX5T7YB9/hu4wqA2tja5MuhQRE3zKleGBXiMmawghEEX56rtfjOlV/DC1YdzUnBX0l/NzQAIoVNyoS0MZO6OR5nffhpZM+MaciWhW8T3dYXScfcoTIayc9sgRdQETfMoVx7Yw0Ku/oue7DtIsT4nc0kytXni+jyA2JCtahTRHVfytvRlCww9UgyiDdPT/za+V45YQGDReKRMWgoL14ssdx8PacqTuMIiMwgSfcmd4UG8V33Vs1HMy874dYZTAdcyq4ttWa+Z9/ijko4rvWAppYmZrzqHyWsUXQqIRmXcAvd8/ojhVfNuFc/LjdUdBq6B7U6vNstEVE3zKHce20K+pil/cFwOzkhAlXWPPLLQjjRuwDa/iD1eS3Oxq3Kzim7P+pl2WWzbyTF8qLARbH6E7jI6wTzwbwivu+E+itWLDGuXSlsFeOLaFLFPIVNb8mClkWTa3e2OWZXP/3qmkvOx7qOd0LOZygiiB7zqIYv1nJjzPxWROq/ctCsBAOcOoobvb9pfMmnnfjiD14KKuO4y2WY6HWsOc1pxD7fePwLH2b4GcnMVZjOjfCuvok3WHQWQkJviUS45tYctgb9u3V62kf4mDgTTLoDKFVM07MDjk9oDKXVK0KgasXpZCoBaamRSvVpoEsGXZuBGfQii4MkJkWFwraUQZ/FIZWWx+kq8UkGQ2AHPXN6TCRrj1EfD23qE7lLURAs6pTzJudDKtTEFAGXTW2KRYOokJPm0KQghYloC1jq60NM3w2/v3F7RFBwjC5u62scYqvu36qOW8et+ilMJAxbwq/rbeDEGYs+x+VpDlo4rv+hVUG+Ym9y37S0fiGPu3QJK/woV1zGmQfcO6wyAyllnvPEQGsyyJof78TvNoh85qmOvYuZp5345mFV93FAeVXIUwCnWHsWaNMIO0ze63ltJCLcjH73EyW8XPHb8C+xGP1h0FkdEMeushMt/wQA+kLObpPKBZxfc9B65twbYELCk27ORlpPI3834lSin0l805I9Hnxbk/AxUqw3ditUpQOXqSD5SOBHI2P945+fEQtlmTv6h9mTLvUkT5+qsm0qxVxR+dqOoOpSsc20Ic1Ofa8QWA1jAYKQSElBBCLLg0bygwPzlXqrnQVKGZ5KoMyKCgZtczHPp66vk+Js3vvFiTLG3AkmWkmou6wz2ZUfPY16oeZhgslZDFDd2hHMZ2S0bsWLsasXAQbXsE3L2/0R1KW+TWY2BtP053GETGY4JPtErDAz0Yn6why1GVrl2uTBEvUUTPlALS9fUVzz9gEEJAzh4wJOa3K69ZsxdfYWxG39kJWyogy29rzqHCzIcD0xJ8gTAWgNH7Ai9uf+loHG3dDaSGH5xYNpxTnqA7CqJcYIsO0SpZlsRgAXvxy76NON64JFAphTRNkSQJHBQn+VxMljRgaXy13dKTIknzl3gupR5mkLZZrTq2V0Gi+zTNGsXCRrT1BN1hrMg+4dEQpR7dYdB6KQFl0AU53nNlOUzwidZgeLBSqPFslhRII30V0TiO0OsVJwE9lFIKA5p68fv8DI0wv7POlxIqcxbbWrZr9Mz7duwvHw1Ic3dnEz1DsI49TXcYRLnBFh2iNbAtC0P9ZYxN1nSH0hG+KxFrHp3oIALgaY2hm3qcBrye+JD1C2Ju/YJSYm4+dKYEskwgU0CqBNIMSDOBJAOSTMz28698gCmg4Nsxirh9Qz3MMOj7yJJAaxxKASkcmDzzvh2xcBBvOwHOvv/WHcqinNOeBCFZkyRqFxN8ojUaHujB+FQ9VxMzFuO5NqKgpn2fqziO0ON5qBawW8dzBIJgdQ+stV7BOuyT2X8XAlLOW/DcOliYd8AAKTFTz3fiuZwIJdjQm+C7XhnVoBjP8f7y0ThK3gNkZj0e66iTIQe26Q6DOkQpGDXNy6RYOomHw0RrZNsWBvvKusNYN0tF2pP7FlcUMLuHgic7f3akuYYhQ5KkiOMEURwjiiJEYYgoDBBHDcRBHVaBx7rWAr29+FJaqEfFyQ4i4SLeerzuMBZyfdiPPFt3FES5wwSfaB22DPYYkxyvRaVkIzFoF8s4itBTsF783pKFMNL1HCtUvBz/grYhgr5efGGXkBVsiPaB8jFG9eI7Jz0Owilu6x5Rt7BFh2gdmlX8Csan8teLb9sScWDe8HlXFKcX3xJAonnn2DgMYEkPacES0ZZGlGFLqYTFxlMqpWZb6Jofsyxr7sWg1LoPzG3Xz93M+3aE0kW8dRec/XfrDgVy+AhYR+Rwp11aVgaBzKBNDU2KpZOY4BOt0/BgDyama7nr4/MshdjA0YlxFKHiuqhF+X/R7fGBIND7HKvZKv50w7yfdSf0+UAQrLYPv7lOYf5eDM3lC621DAtv2Xrm1GzzcKYUosRC3hfWLuVA5VgcKX4HKI0L76UF+5Qn6rt/opxjiw7ROjm2hYGc9eKXfQdxpHdx4nI8aU7b0FqVPYEgMGM8ZRwFhezF912JNJhe09e2KvpJkiCOY0RRjDCMEIQhguDgpTHvv8MwQhjFiOOkkM9nSyhcJFt3aY3B3nU6ZKVfawxEy/noRz+KXbt2wfd9POYxj8H3vve9JW976623HrYLvBACv/lN93aQZoJP1AFbBnpzc5JPCoEsNm0X0IXiKETF1R3F2gkoyMyc9g2litmL74uGtjUwEub8fLvhQM+xgNCTIohyP6zjz9By39R9rSk6Jl1W6/Of/zze8IY34G1vextuu+02nHPOObjwwguxZ8+eZb/urrvuwt69e+cuJ5544hqfxZUxwSfqAMfJTxW/7FvIDBuDtxhfmlH9Xou+kkScmPUcx1EAWaCqc19JIkv0rW9I4hC+W9wu10B4iI84GaJ/K0TPIFDuA7wyYLvo9lGVfeoTIQxa6Et0qL/7u7/DJZdcgle96lU45ZRTcM011+Doo4/Gddddt+zXbdu2DTt27Ji7WFb3fs+L++pEtMGavfjmLVqdz3MshA39M+/bEUUhyo6Les66dVwbCEPzxn0qpdBTkF582xIQ8dQiy2o3liWKW8W3pED6/7V3pzGWnXe973/PmvfeVbVr19CD7Y4HOJ6TC7Gx3eaEcyE6JvEBMUnXCF0Db4x8JYSIZV0IIBHyJldXICEkBgUIEkle8MJCAsUKsQ4yJwfbma6Jwzk+GR132+52u7tr7NrjWs99UYO7uquqq6p37edZq74fabm6du3d61/lrqr/+q//838GHXXjVNpmio0xobTRbqDV9QuyqyscNpVIi9WjyGWKQsoHskUu5YO1o79RRg2O/4DC6RtG94kCaxYXN7f7pWmqNL36336v19PXvvY1/fZv//amxx955BG98MILO57jh3/4h9XpdHT33Xfr937v9/TjP/7j1x/4NkjwgSFJ4kiTE3XNe5zkhxrIliC5X1cLe1rpl6tXJ4usPMzvJa1V8U2isg/UGU9y5R7Mn+/3ukqThrq96iX6LbuoIN/5Lpq1uWS3ml+0BRNIYbC6Wdt2FwxBqOTozXuOFeVi7dpGfJ5Yj+XEiRObHv/93/99fexjH7vq+efPn1ee5zp69Oimx48ePaqzZ89ueY7jx4/rk5/8pO677z51u119+tOf1gc/+EE9//zz+rEf+7HhfCJXIMEHhmimNeZtgt/IIvW75Rrn2et1VUtitUsyUWcsM+p2/W0tstaqkQVaajucjnKdGmmgvLe/hbUHITR+tWINQz02ys5/f+TntUWu3tuvK5l9Dy06GLnTp09rYmJi4/2tqveXM1fcCrfWXvXYujvuuEN33HHHxvsnT57U6dOn9Yd/+IcHluDTgw8MURJHao6723hnO1EYqN/188LjWuphOaqjoZEKjzYN207e66qsrfjGSJH16yJ10OsoTaqTjBojNZdfd3Z+2++qf/4NZ+fH4TUxMbHp2C7Bn5mZURiGV1Xrz507d1VVfycPPfSQvv3tb19XzDshwQeGbLY17jqEq6SxtMsb6d7pdTuqJf7HPpYZ5bn/lfHCFmpk5fzR38wkm/t3ERUZ//+/79Zk1FPYWXAaQ/fs92WL6nxNsVlh/Tv2IkkS3XfffXruuec2Pf7cc8/p4Yd3v3fDyy+/rOPHj+/t5HtAiw4wZEmyWsVfWPJjFGUtLV9rzpXq4UBtxa7D2FYtNup0PG2830Le7yowcal68dM4UN6d93KBeL/XURrX1e2Xu10njgLVL7jfwdb2O+pfeEPJ7HtchwJs6amnntLjjz+u+++/XydPntQnP/lJnTp1Sk8++aQk6aMf/ajefPNN/e3f/q0k6Y//+I91yy236J577lGv19NnPvMZPfPMM3rmmWcOLEYSfOAAzLTGvUjwjTGyeXkSz+30uh1lcayOf8VbSVaRKeRv5/3ViqJQIwu11C5PQloLO/K5qBsFVmX/TpvqvS1j/fg30T37muKZm2QczeIHdvLYY4/pwoUL+vjHP64zZ87o3nvv1bPPPqubb15dJH7mzJlNM/F7vZ6efvppvfnmm6rVarrnnnv0uc99To8++uiBxWis3c+IfwDX8sbZOS0uu03yG7VI/U65q/frkjTThbZ/VfyJmlG3RNX7dUEQqJ3H+9rkZdTGa4FMb951GNcW1dUraRV/PJGa73zDdRibZO+5W8nsiWs/EaWwuLioZrOpz/zXi6qPTVz7BSOysryo//ODU1pYWNi0yLbsuDQGDsjs1JjT8ydxqF67Gsm9tF7Fdx3FZnFo1PN4as5OiqLQWOb/4tAwMDJ9f6bm7CQOS3C1tIUgMBqfc9+ac6Xu2ddkrce3bQCPkeADByRNYk2MZc7OH5ncy37l69GI/OrRqcdWZb4Jmvc73v8bGU/z1U2SSqDXaSuJ/b9oulLLLCsYuG8pvJLttdW/cMZ1GEAp0YMPHKDZqXGFQaCisMqtVVEUKgq7dhQq7Oqfh62eRRqUfGHtVnrdjtIoVteDyZmNzKjj645Wu7Rexfe1F7+eBio8mnl/LcasVvF7fl2H7iiLA2Xnv+c6jG11z35X8fQN284XR/lYmbX9jv3gUyzDRIIPHKA0iXX8yOQ1n7eR+K8l/PnlFwL28ouCzRcGRVEo33je6vvGGOU9/6pxwzIWD9QduP3RFRgrDTy4yhiCfNCRMf714hsjxXaldMNde5224riufkl68SdXTnmd3thuW/2LbymZvtF1KECpkOADHgiCQMGQGuYGg4FeP1XdjWJ63bbSaEzdgbu0ZDwL1PFzpM+eFbmfVfyJmpEt4fqG9Sp+vwT/PCbjgaKFOddhXFPvzPcUT1HFB/aCHnygYqIo0sS42wW+B20sdpeMpiWbeb8bxaDrVRU3iYxstzytOVfqddqKI7978aMwUP3iwe2iOUxFd0WDOXrxq6KQ+42tNh2uvyAHhAQfqKBWa9J1CAeq122rnpjVCSsjPbNVEvhV6R6GPM/VqPmTkK4upi5bc867jJHSyO/4p/ILCorytJl1z3yv1AvagVGjRQeooCiKNDExrsXFJdehHIgoinTbiZaCtb6m9TUI6+sRNtYlbCxuvvpj+aY1D5sf2y6PmMgCdUu+sHY7dtCVD78SxrJAeX/FdRjXrdtpK47q6g/8uyBsJFLyTrna+IrOJQ3mziqeOu46FKAU3P80B3AgWpPNyib4szPTG8m9tDrHO5DRsLoi3l3EvDn5vzBX3raRa8nzXGO1VMsOe/GDwCjMl0tcu3+XMVIcSX3PiuSBMZpY8Hdqzk66Z76nqHWMXvySs1ZeLer3KZZhokUHqKg4jivZi99o1NVo1A/0HIExisJASRyqlkZq1GJNNBLNtKr39byczd3enZhIC9kStY1cS6+zojjy69dsK2gr7C27DmNfis6yBvNvuw4DKAW/fvIAGKqq9eIHgdHszLSz8483MmVJdW985oPc2e62WRIo71brjtNqL74/1eY0DpRd9G/H2r2gFx/Yner+pgKgOI41Pj6mpaVyVuyuNNVqKYrc/tiamRrXG2f9Hy24X816oBtnG8qttl+7cPnbK9qZ8mL7NQw7yUxbhT+58NB0OyuKwpoGuftZHZPtN2VK3gBVtJc0WHhH8eQR16Fgn2jRGQ0SfKDiWq3JSiT4aZqo2ZxwHYbGG5nSJFK3V51WknVJHGp2elzBdfY428sXL1td8yLBFgN1L1VzfYMxUhIbuV5r24wLxQvn3QYxJN0z3yXBB66BBB+ouCSONT42pqXlcif5s7MzXiyuM8ZotqJV/GOzk9ed3EurX6MwNAp32e0zGAz0+spcZVsveo6r+FFo1CjJzPvdKFYW1Z8/R5IP7IAefOAQKHsvfrM5oSxNXYexYb2KXyXNsZrG6m6+xqubs407OfcoGCNlsbuL01axoKAo367AO+meKeckIEiFNd4dVUSCDxwCSRJrbKzhOox9CcNQ01Mt12FsYoyp1ESdIDA6OuO2/anVanpxh+agDLptTWaFmlmhicxqPJXGEqmeSFkspZEUBWu/lId4J6MeG6Xzrw/t7/NFsbKgwcI7rsMAvFWtEhSAbU21JrW8fMl1GHt25cx7X0yM1fTOxWX1fBt0vg9HpycUDWsTgX2Kokjj42OV3buhnhr125vXGRhJ4dpxJaNAJjAyJpBMsHrxY4LV2wFaPezaBZG1RoXM6toHGVkrFWtHc/m1A/7M3Ome+Z6i5qzrMAAvkeADh0SSJBprNLR8qTxJfr1e8/bOgzFGM1NjeuvtedehXJdaFmty4mD3FditVmuykgl+Gofqt/e2BsbaQjaXpN2vzl2/YLhcUPKpOTvJL81rsHhe0cSM61CwB0zRGQ3/ymIADkyZevGNcTvzfjeaYzUlsdvK9/U6PjvpTWtMXMVefGsVFX1np++NVXshavfMd12HAHiJBB84RNI0OfBdYIdlqjWpOI5dh7Gj1V788iak05MNZalfX+NWq+k6hKFqZLEGfXc7BHd6fRWpn3fBhiFfntdguXoTrYDrRYIPHDJTJajiJ3GsyclyJHrN8Zpix/3r+xFHoWan/Ls4Wd+crQrCwCjvum+J648fdR3CgTFxqrDm379jbG+9Rceno4pI8IFDJk1TNep+V/F9mXm/G+u9+GVzbLbp5eJlqRwXobtRiyRbuN/BtspV/OzEnTIhywmBK/n50x3AgWpNTboOYVsT42Oq1TLXYezJ5Hi9VFX88Uam8Ya/X+N4bXO2MqslofqdFddhbBhUsIofTcwobh1zHQbgJRJ84BDK0lT1es11GFcJg0DT01Ouw9izMs3FD4zRsVn/25/KtCD8SkaSGXRch7FJuzeQTfy+c7cnQajsPXe7jgL7cPkYVx8OWnQAVMpUy6/NoyRpemZKYVieSvjlJifqzmfJ78bs9Hgp7jaUeXO2RhoqH/i2P4JVf6I6Vfz0+G0KUv+KFIAvSPCBQyrLUtVr/vyCrGVZqUcklqGKnyWRpprlSZrL2IufhMGeZ96PSrufy8b+fM/vV5CNKTl6i+swAK+R4AOH2JRHvfizs37PvN+NyYm6otDfH6vHj/gz8343kiQpVxXfWsVm9xtTjZy16jfL37Oe3Xz36g6/KCVrjXdHFfEdAhxiWZZ5saC11ZpUkiSuw7hugTGa9rSK32rWVcvK9zUuUy9+PYs06PnVe3+lslfx4+kbFY35114I+IYEHzjkXPfix3GkVklm3u9Ga6LhXRU/CgMdmZpwHca+pEk5NmcLjJHttl2HcW0lruKbKFF20+2uwwBKwa/fQgBGrlbLVMvcVfFnZ2a8nce+H0HgXxX/6ExToWcXHXvh+iJ0N+qxVBQet+dcptPPZSP3d+72Kr3pdpmofHehsJnrTa3Y6ArAoTE93VK9XlOWZUqTRHEcKQzDA+/XHhtreDmu83q1JhreJNSNWqrmeLm/xmnqdxU/jf2aeX8t1loNmsddh7En4VhLyfSNrsMASoPt3wAoyzLdcHzr2/bWWllrVRTF2mFV2EJ2/c9FoeKyj9u1j68/1xbF2vurz7Fr5ZIgCDQzXf6FtVsJAqPpyTGdu7DoNA5jpONHqtH+NNWa1KVLHibR1iosuipH7f5d7UGuKEpkBj3XoVybMcpuZuY9sBck+AB2ZIyRMWZobTTrFwzW2tLOvN+NqWZdF+aWlReFsxhmWuNK4mr8mE/XNmdbWfGrz30si9Rv+72wdivWWg0mb1B8/vuuQ7mm5OitCjO/2t6wf+sbTPnCp1iGyY97yAAOjfWLhSon99LqHYrplrsRj0kceT+Xf69868WPwkCDziXXYexbu1/Ihn73tAdpXenx21yHAZQOCT4AHJCpZkOhowXEx480SzXzfjeyLPVqzUYW5BstZ2W0XsX3Wfaeu2SCahcDgINQjXu3AOChIAh0x23HLlu7YFUUVvnG+gV72foEe8WahtU/51s871qa4zU1aukIPsPRm2pNetGmU08i9TtLrsO4bu2BVRTGMnnfdShXiVrHFE3MuA4DQ+bb5BqfYhkmEnwAOGBBEGiYhfytLgTWLyDywmq8Uc3kXnp3c7a2w773wBjZvocLfvfB2mK1F//C665D2SyMlJ2403UUQGmR4ANAyQSBUXCI2xamWi292T7j7Pz1xKjfLtvcnO21c3lXxc9u+A8K4upeqAIHjR58AECpuNycLY0D9dvlXVi7FVsUXvXiB/Wm4tkTrsPAAXG9qdVh2eiKCj4AoHQmUqO035NMICuzOvRfRlZGVpKVUWElq3fH8hXWbjy2L9YqKvoaDO2z8Ecnl6Igkilcf3ZGtZvvrtwCcWDUSPABAKUy6HW0cv6tHUtvRtJOTUwmCGWCQDLBxluZ9QuFQHbjgkFrFw1GJgjVay8P95PxRFEUyls3KnLci58ceY/C+oTTGIAqIMEHAJTK0tlT131f3Ra5bLH7PnojKUj8GdF5ENq5NOawim/iTOkNP+jk3BgdNroaDXrwAQCl0V64qN4lN+Mpi15bSeqm938UiqJQ7rAXPztxp0xI3REYBr6TAAClUOQDLb992mkMwcDdeM5R6ChUPHWrVOQKbC4VA5liIJP3Vyv7g97q+0M+b9ScVdw6OuS/FTi8SPABAKWwfO5NFbnbRaBFt62kMalet+s0jgMT13Wxu9azsL6QIZQUb36aMWZ1XKukYG3pQqDVVQury54LGbt+5DK2UFAMpCKXKfoy+UCm6EuDgYxyZe+5a6SfJtzxbXKNT7EMEwk+AMB7vfay2vPnXYchSTKDaib3UZJqsVOsLTTembVWeW61/SqGQFd1AW9zwRAERmMhM++BYaIHHwDgNWutls6cch3GBtttK0mrl5B2le4quR+2orCaW3CzrgKoKir4AACvrVw8p0G37TqMTYJBz3UIQxXVxrTSdderMLewpNbkuMKAumPVFcXq4QufYhkmvpMAAN7K+z1deuct12FcpeiuKE6qUcUPwkhLXbcbSxWF1TxVfGBoSPABAN5aOntK1vpZYgvzalTxbVzf/+6+Q3RxfllFVcupwIiR4AMAvNRZmld3ecF1GNuqQhU/Tmu61PMhvV+dwz+3UM2dgvGu9Sk6Ph1VRIIPAPCOLfLVHWs9FxZ91yHsmzFGl4r42k8cobn5Jar4wBCQ4AMAvLP8zhkVA/+T56JzqbRV/DAb0yD3q3yZF4XmqeID140pOgAAr/Q7K1q5+LbrMHYtLPry/1JkszBOtNixTsZiXsvFhSVNNscUMFGnknxri/EplmHiuwcA4A3fZt7vRhmr+P0g8zK5l6Q8LzS/eMl1GECpkeADALzRnj+vfqd8yV1oB65D2LW41lCn73fZ8uL8oorC7xgBn9GiAwDwQj7oa/ncm67D2Jeivay43lS/5/fozCAItdQLJS8GY24vzwstLC6rNTnuOhQMWSHJp2u3qi7ppoIPAPDC8ttvyBa56zD2LSxD7ElDRUmaji/OL5UmVsA3JPgAAOe6y4vqLF50HcZ1KTrLiuLEdRjbitJMy57MvN+NQZ5rYZGJOsB+0KIDAHDKFkUpZt7vRqRCXnbjG6N2kcj31pwrXZxf0uTEmIynC4Kxd9ZaWY/uzPgUyzBRwQcAOHXpwlnl/a7rMIaiaC95WcWPsjH1PZt5vxuDQa4FJuoAe0aCDwBwZtDt6NKFs67DGKrIs2V7YRRrqVu+5H7dhfnFylZZgYNCgg8AcGbx7KnK7TSTt5cUxbHrMDbkUU1W5W1xGQxyLSxRxa+K9Y2ufDqqiAQfAOBEe+GC+itLrsMYOiMpMn5kDXFW10qJFtZu5+IcVXxgL0jwAQBOXDp/xnUIByZfcV/FN0Gg5UHoNIZh6Q9yLS6tuA4DKA2m6AAAnMgmpiqb5BtJoZHTiTomHVNe4t77K12YX9TEeJ2JOiVnC6nwaJmK9SiWYaKCDwBwoj51RCao7q+hfGVRYeSmih8lqZY71cpc+v2BFpep4gO7QQUfAOBEEEaqtY5opWJTdNYZSXEUy8TZxvvS+qq+1bfWFhtvbWFVrL1/vbpKJU/WAQzTxblFTYxRxQeuhQQfAOBMY+qo2nPnZH26Zz8kQZxooSfZLcdmmrUjuPphWQVBoCCQAhkFwerjwfqrzPpT1xL4TRcMViaKtdKpXnIvSb3+QEvLK5oYb7gOBfvk2+Qan2IZJhJ8AIAzQRSp1prVyoW3XYcydL1kQra/jxcao8JaFbkkWSnf1Yu0fo8gDSK57f4/WBfmFjVOFR/YUXWbHwEApdCYOiqZav06CuvjWtlPcj8E3V5PSVzd+l2vP9DypbbrMACvVfcnAACgFIIoVr01q5WL1ajimyDUYpE6jaHCa5clSRfnlxSYtVYmY2SCYK2tKZAxhuq+xwq7evjCp1iGiQQfAOBcffqoVubOVaIh1tYmlTuq3q/rdnuK40T9fjVbdQIVevvs9ouzjTGryf7aBcDGn6+8INjpY2sXC+tvgTIhwQcAOBdGseqTs6tJfomFaU1zPa23wztjJEWBkePrjAORJpF63ZUdv8TWWuV5LuW7WsBwTcYYxXGsG2+6iWQfpVDxm3gAgLKoTx97d0RMSa0EDW8+h063qziqxk62lzPFYOTXT9Za9Xo9LS8vj/jMwP6Q4AMAvBDGsWqTM67D2LegManucArGQ2EkRWG1fs3Xs1j5oOfs/PNzc7IVaCNzaX1Mpk9HFVXrOx8AUGqNklbxgyjWQt+/rtcqVfGjMFS/43Yn236/TxUfpUCCDwDwRhgnqjWnXYexZ/20KR8LgVWq4ifR2oZejlHFRxn4V24AABxqjZnjai9cKM2987A2pkWPV7OuVvFj9Qce9Q/tUZbG6ncvuQ5D0moV/9KlSxobG3MdSinZwsp6NJvSp1iGqRqX9QCAyihTFd+YQMs2cx3GjspexQ+Mke13XIexyRxVfHiuvN/xAIDKakwfcx3C7tQn1S9cB3FtnW5XUUl78bM0UlH4dfeh3+vp0iU/7igAW6FFBwDgnTBJlTWn1Vm44DqUbYVJprmecT7zfjeMpDgKNChZm04SR+p1Lnn5JZ6fm1Oj0WAu/h6xk+1oUMEHAHipMXPcdQg7akdjpZr40+l0FYXlquKHGv3M+93q9XpaWXE71QfYDgk+AMBLUZIqa065DmNLQb2pzsB1FHuzWsUvT4Jfz2IN+u5m3u/G3MWLrkMAtkSCDwDwVmPavyq+CSMt5uXscO10O6Wo4odhoH637TqMa+rRi79nrje1YqMrAAAci9JM2YRfVfw8a6qwvjaO7MxISmL/f/WnkZFsCVYva7UXH/CN/9/lAIBDzade/DBraLlfzuR+XafTVejx2MzVmff+V+/XdbtdevHhnXLeYwQAHBpRmimdaKm76LhSaowumZrbGIYkiSO1c//6240xsoOu6zD2bO7iRdXrdddhlEJRWBUeja7xKZZh8vcSHgCANWMeVPGD+qR65Zoyua1up+NlFb+WRirykq1eFlV8+Me/724AAK4QpTWl45POzh/EqRZ61fqVmcZ+3cSPo1C9TnkXrNKLD59U66cVAKCyXPbi9+Jx2RLNvN+NTqerMPAnDYiCwtuZ97vR6XTUbpdn7YArrifmHJYpOn5dvgMAsI04q2v61rtVFAPZvJAtchXF6tvVo7jqbbHF43sV1ie0WL6ukV2wSpJI7Y77XvxaGmvQLW/1ft3cxYuq3Xij6zAAEnwAQHlE2fUvcl1P9IurLgjyqz+W5zrfMZKqWebrdroKglBF4W4kZRAEynvVqHyvV/FrtWosxkZ5keADAA4VEwQyQbDrHtX2ufNaXCp/dXlrVlkSacVhFT9LAvU75Zh5vxvzc3Mk+DvwrS3Gp1iGyZ/mOwAAPNRqNV2HcKA6na4CR734aRKp16nW9Jl2u61Op+M6DBxyVPABANhBEscaH2toabm6Vfx6GqlYK2Xatf9YK1lZFVay1soWVoW1skMseZqiX+qFtduZu3hRx2+4wXUYOMRI8AEAuIapVrOyCX4Uheq0l3dMtM3aEWj1AiAwgYwxq+1OxkgmWH29MavHxqsuu2DQ2oXC2gVDHAXql3gs5k7Wq/hZlrkOxTuFtRsXkz7wKZZhIsEHAOAakiTW+FhdS8vVaieRpMhY9ffwfCPJ2mK1d3kfU4nWhVG2p/OWzfzcnI4dd79BGw4nevABANiFqdak6xCGLksT9ftuFth2Oh2FYejk3KOwsrKibrfrOgwcUiT4AADsQpLEGmvUXYcxNEEQaNBzuxg0imOn5z9o8/PzrkPwji38O6qIBB8AgF2aqtBEnTQOZR1nN91OR0GFq/iNenUuCLHZn/3Zn+nWW29VlmW677779MUvfnHH5//Lv/yL7rvvPmVZpttuu01/8Rd/caDxkeADALBLaZpUooqfJLG6XT82l0oqWsWv1WoaGx93HQYOwN/93d/pN3/zN/W7v/u7evnll/WBD3xAH/7wh3Xq1Kktn//aa6/p0Ucf1Qc+8AG9/PLL+p3f+R39xm/8hp555pkDi9HYYc67AgCg4rrdnk69ccZ1GNcliaR8MHAdxoYgCJzupjtsxhjddOKE4opevOzH4uKims2mfvuT55XVJlyHs6HTXtT/82szWlhY0MTE7uJ68MEH9f73v19//ud/vvHYXXfdpZ/92Z/VJz7xiaue/1u/9Vv6h3/4B7366qsbjz355JP6+te/rhdffPH6P4ktUMEHAGAP0jRRo17enUprWeJVci9JSZK4DmGoJicnSe4rqtfr6Wtf+5oeeeSRTY8/8sgjeuGFF7Z8zYsvvnjV83/yJ39SX/3qV9XvH8wsKRJ8AAD2aGqqnL34URiq1/GjNedynU7H2W66wxbHsSZbLddhYI8WFxc3HdtNQDp//rzyPNfRo0c3PX706FGdPXt2y9ecPXt2y+cPBgOdP39+OJ/AFarx3QQAwAhlaVrKKn4UGa1tPeWdqlTxZ2ZnVzf/wpZsIRUeHevrzE+cOKFms7lxbNVqc7kr/x9ba3f8/77V87d6fFjY6AoAgH2YajV1acW/avh2sjRRv+vvRl3rVfwy9+KPjY2pVivfhR+k06dPb+rBT9N0y+fNzMwoDMOrqvXnzp27qkq/7tixY1s+P4oiTU9PX2fkW6OCDwDAPmRZqnotcx3GrhhjlPf933SpzFX8IAgOLFnDwZuYmNh0bJfgJ0mi++67T88999ymx5977jk9/PDDW77m5MmTVz3/C1/4gu6///4DW6tBgg8AwD5NTU26DmFXsjRWUeSuw7imbrdb2l78qelphRGNEddirfXu2KunnnpKf/VXf6VPfepTevXVV/WRj3xEp06d0pNPPilJ+uhHP6pf/uVf3nj+k08+qddff11PPfWUXn31VX3qU5/SX//1X+vpp58e2tf1SvxLBABgn2prVfyVttsdYXeSxJG6nRWVoSvcWqs0TdXp+Pv13EqaZRpn5v2h8dhjj+nChQv6+Mc/rjNnzujee+/Vs88+q5tvvlmSdObMmU0z8W+99VY9++yz+shHPqI//dM/1Q033KA/+ZM/0S/8wi8cWIzMwQcA4Dq02x298dbbrsPYVhoHGvR7rsPYtfVFh2VKT2666SYl27R0YNX6HPz/+8/fUerRHPxue1H/7/81u6c5+GVABR8AgOtQq2WqZanaHf963GtZol7H34W1W7HWKsuy0lTxm5OTJPd7UNjVwxc+xTJM5Wx0AwDAIz724odhoF63HEnylbrdbilGTUZRpBYz7+EhEnwAAK5Tfa2K75MkCt4d8l0y6734vpuemSntomBUGy06AAAMwVSrqTfPnHMdhiQpTWL1uuWZ0b+V9Sq+r7349UZDjUbDdRilYwsr61FfjE+xDBOXnQAADEG9XlPmQRXfGCOb912Hcd18ruIbYzQzM+M6DGBbJPgAAAzJVKvpOgTV0lh5PnAdxlD0ej0ve/FbU1OKmHkPj/GvEwCAIWnUa8rSRJ2um7GUcRSpU5KZ97tRFIXSLFPXo4k6SZKo2XR/IVdW1q4evvAplmGigg8AwBBNtSadnTsMbGWS+3V9z6r4M7OzXsUDbIUKPgAAQ9Ro1HTD8SMqimLtsCqKQtbaTe8Xa+/bK97fryxL1C/ZzPvdKIrCm7n4ExMTyrLMdRjANZHgAwAwZI16bd+vXU/27RYXA0Wx9vgVFwvWWnVWlof4GfhlvRff5USdMAw1NTXl7PxVsfpv1p++GJ9iGSYSfAAAPBIEwWr/bBju6XVn3uqr3S73aMzt+FDFn56eVrDH/yeAK/TgAwBQAVXfUbXXdzf6s1araWx83Nn5gb2igg8AQAVktZrzKvdBKvJcrZULUm9FRRDLhqHyIFJhQhXh6ts8CFWYUBriIlhjjGZmZ4f29x121lqvNi/zKZZhIsEHAKAiWlNTOvPWW67DOBC13rLSl78g2V0sRI4SKU5W30axFCWyUSyFsWwUy4axbBjJBpGKKF59G0QqglBFGG9cKORBqOb0rOI4PvhPEBgiEnwAACqiVtUqvrVqfvcru0vuJWnQWz0uY654u1vx/f9Zmjq5x1cBbtGDDwBAhUxWsBe/tfCGdMHNnYn+/3hRtiI7A/vAFv4dVUSCDwBAhdTrdaUVmtUe5T2l3/qyuwDayxp862vuzg/sAwk+AAAVU6WJOlNvfEPquW05GnzjBar4KBV68AEAqJh6va40TdXtdl2Hcl3GVi4qOP2/XIch217S4NsvK77zR1yHUnqFtSo8mlzjUyzDRAUfAIAKKnsV39hCY9/+kuswNgz+/V9l89x1GMCukOADAFBB9UZDSZq6DmPfpt/5jrR00XUYG+ylRQ2+82+uwwB2hQQfAICKKmsVP+2vKPrO/+c6jKsMvvHfZQuq+NdjfaMrn44qIsEHAKCi6vW6kiRxHcaetV77muRhIm0vLSj/ztddhwFcEwk+AAAVZYwpXRV/cvEt6dwp12Fsq/+Nf5UtKjo8HZXBFB0AACqs3mgoSRL1er1rP9mxMO8r+5Y/C2u3YpfnlH/vFUU/+EOuQymlorAqCn/aYnyKZZio4AMAUGHGmNLsbjt95n9KnRXXYVxT/5X/ThUfXiPBBwCg4hqNhuI4dh3GjuqdBQXf/3fXYeyKXbqo/LVyxIrDiQQfAICK874X3xaa+I7frTlX6r/yxcpOYDlI1vp3VBEJPgAAh0BjbMzbKv70he9L8++4DmNP7OIF5a/9D9dhAFsiwQcA4BDwtRc/GXQUf+errsPYF6r48BVTdAAAOCTGxsY0NzenQb/vOpQNrddflgb+xLMXduEd5a+/quiWu12HUhrWWlmPJtdU9QKNCj4AAIeEMUatyUnXYWyYWH5H5sz3XIdxXfqv/LfKJokoLxJ8AAAOkbHxcUWR+xv4QTFQ/VsvuQ7jutm5c8pP/S/XYQCbkOADAHCI+NKLP/32t6SVRddhDEX/lS+6DqE0rLUqPDqqeveFBB8AgENm3HEVv9ZdUvi9f3N2/mGzF89qcOqbrsMANpDgAwBwyBhjNOmqF99aNb/7lcoNIB+88t9chwBscN+EBwAARm58YkLGGBVFsdqqUBSrf77y/Ss+dr2m5k9LF88M4TPwS3HhjPI3vqXwpttdh+I1W3g2RcejWIaJBB8AgEPIGKPxiYk9v85au5Hs27ULgC3/vNXFQb+r5FtfPoDPxg/9r3+RBB9eIMEHAAC7ZoxRGIYKw3DPr7XWqvPyuOxC9wAic684/6byN7+j8MYfdB0KDjl68AEAwEgYYxS/7wOuwzhQ/a/Ti7+T9RYdn44qIsEHAAAjE95yj8zEtOswDkzxzhsqLp51HQYOORJ8AAAwMiYIFL/3P7oO48AEMzfKtI66DgOHHAk+AAAYqfC298qMu99sa+iMUXLyv8gY4zoSbxXWv6OKSPABAMBIVbWKH931gIKpY67DAEjwAQDA6IU/8D6ZsUnXYQyNaUwo/qEfdx0GIIkEHwAAOGCCUPF7f9R1GEMTP/AhmThxHYb3XE/MYYoOAADAAQp/4IdkGk3XYVy38MTtit5zp+swgA0k+AAAwAkThorufdh1GNcnihU/8GHXUQCbkOADAABnov/wwzL1cddh7Fv8v/0nBWPlvwsxKtZa744qIsEHAADOmDAqbRXftI4quvsh12EAVyHBBwAATkW33ydTG3Mdxp4lDz0qE5BKwT+R6wAAAMDhZsJI0T0Pq//VL7gOZdei29+v8MgJ12GUTlFIhUeTa4rCdQQHg8tOAADgXHTHfVLWcB3G7mQNxe//oOsogG2R4AMAAOdMFCu+56TrMHYluf8/y6Q112EA2yLBBwAAXojuuF9K667D2FFw/FZFP/A+12GUluuJOUzRAQAAGCETJ4rv8XgqTRAqefBR11EA10SCDwAAvBHd+YDkaftL9N4fVdCcdh0GcE1M0QEAAN4wcaL4rgfV/7fnXYeyiZmYUvze/+g6jNKzhZX1aIqOT7EMExV8AADgleiuB6Ukcx3GJslDj8qE1EVRDiT4AADAKyZJFd31gOswNoS33qvw+G2uwwB2jUtRAADgnfjuhzT4n1+S+l23gSSZkh95xG0MFUKLzmhQwQcAAN4xSeZFFT9+/wdlamOuwwD2hAQfAAB4Kb77ISlOnJ0/mL1J0e3vd3Z+YL9o0QEAAF4yaU3RHT+iwb//q4OTB0oe+i8yxoz+3BVWyKrwaHOpQv7EMkwk+AAAwFvxPSdl0ky235P6PdlBT+p3t35/0JMG/aGcN7rrAQVTR4fydwGjRoIPAAC8ZbK64nt/dNfPt9auJv79rjTorV0IdNcuBHobFwK235U2/vzu89XvSUGo+If+94P7pIADRoIPAAAqwxgjJalMkroOBVtgis5osMgWAAAAqBASfAAAAKBCaNEBAADASFhrV9dJeMKnWIaJCj4AAABQIST4AAAAQIXQogMAAICRsIVV4dHkGqboAAAAAPAeCT4AAABQIbToAAAAYCTY6Go0qOADAAAAFUKCDwAAAFQILToAAAAYCTa6Gg0q+AAAAECFkOADAAAAFUKLDgAAAEbCFoVsUbgOY4NPsQwTFXwAAACgQkjwAQAAgAqhRQcAAAAjURRWhUebS/kUyzBRwQcAAAAqhAQfAAAAqBBadAAAADASbHQ1GlTwAQAAgAohwQcAAAAqhBYdAAAAjIQtrKxHk2t8imWYqOADAAAAFUKCDwAAAFQILToAAAAYCVp0RoMKPgAAAFAhJPgAAABAhdCiAwAAgJEoVKiwheswNhTyJ5ZhooIPAAAAVAgJPgAAAFAhtOgAAABgJGzh1+Qaj7qFhooKPgAAAFAhJPgAAADAkM3Nzenxxx9Xs9lUs9nU448/rvn5+R1f86u/+qsyxmw6HnrooT2fmxYdAAAAjMRh2ujql37pl/TGG2/o85//vCTp137t1/T444/rH//xH3d83Yc+9CH9zd/8zcb7SZLs+dwk+AAAAMAQvfrqq/r85z+vl156SQ8++KAk6S//8i918uRJffOb39Qdd9yx7WvTNNWxY8eu6/y06AAAAOBQW1xc3HR0u93r+vtefPFFNZvNjeRekh566CE1m0298MILO772+eef15EjR3T77bfriSee0Llz5/Z8fhJ8AAAAjIS11rtDkk6cOLHRK99sNvWJT3ziuj7Ps2fP6siRI1c9fuTIEZ09e3bb1334wx/WZz/7Wf3zP/+z/uiP/khf+cpX9BM/8RN7vuCgRQcAAACH2unTpzUxMbHxfpqmWz7vYx/7mP7gD/5gx7/rK1/5iiTJGHPVx6y1Wz6+7rHHHtv487333qv7779fN998sz73uc/p53/+53c87+VI8AEAAHCoTUxMbErwt/Prv/7r+sVf/MUdn3PLLbfolVde0dtvv33Vx9555x0dPXp013EdP35cN998s7797W/v+jUSCT4AAABGpCgKFYU/u0vtNZaZmRnNzMxc83knT57UwsKCvvzlL+uBBx6QJH3pS1/SwsKCHn744V2f78KFCzp9+rSOHz++pzjpwQcAAACG6K677tKHPvQhPfHEE3rppZf00ksv6YknntBP/dRPbZqgc+edd+rv//7vJUnLy8t6+umn9eKLL+r73/++nn/+ef30T/+0ZmZm9HM/93N7Oj8JPgAAADBkn/3sZ/Xe975XjzzyiB555BG9733v06c//elNz/nmN7+phYUFSVIYhvrGN76hn/mZn9Htt9+uX/mVX9Htt9+uF198UePj43s6Ny06AAAAGInDtNHV1NSUPvOZz+x8fvvu+Wu1mv7pn/5pKOemgg8AAABUCAk+AAAAUCG06AAAAGAkrC1krT9TdHyKZZio4AMAAAAVQoIPAAAAVAgtOgAAABiJwzRFxyUq+AAAAECFkOADAAAAFUKLDgAAAEbDsxYd+RTLEFHBBwAAACqEBB8AAACoEFp0AAAAMBKFLVR4tLmUT7EMExV8AAAAoEJI8AEAAIAKoUUHAAAAI8FGV6NBBR8AAACoEBJ8AAAAoEJo0QEAAMBIWFvIFv5MrrFM0QEAAADgOxJ8AAAAoEJo0QEAAMBIMEVnNKjgAwAAABVCgg8AAABUCC06AAAAGAlrC68m1/gUyzBRwQcAAAAqhAQfAAAAqBBadAAAADASRSEVHk2u8WjPraGigg8AAABUCAk+AAAAUCG06AAAAGAkbFHIetQX41Msw0QFHwAAAKgQEnwAAACgQmjRAQAAwEjYwsp6NEXHp1iGiQo+AAAAUCEk+AAAAECF0KIDAACAkbC2kLX+TK7xKZZhooIPAAAAVAgJPgAAAFAhtOgAAABgJJiiMxpU8AEAAIAKIcEHAAAAKoQWHQAAAIyELQrZwp/JNT7FMkxU8AEAAIAKoYIPAACAkcgHl1yHsIlv8QwLCT4AAAAOVJIkOnbsmL76X/8P16Fc5dixY0qSxHUYQ2WstdWcDwQAAABvdDod9Xo912FcJUkSZVnmOoyhIsEHAAAAKoRFtgAAAECFkOADAAAAFUKCDwAAAFQICT4AAABQIST4AAAAQIWQ4AMAAAAVQoIPAAAAVMj/DyS2WKqiJc4AAAAAAElFTkSuQmCC"},"metadata":{}}],"execution_count":12},{"id":"5b66aef8-6b29-433e-9398-d070f5a3a8f0","cell_type":"markdown","source":"-----","metadata":{}},{"id":"e5dd82fb-2b9c-4e5a-a802-d4009f4e4de0","cell_type":"code","source":"grid[f\"{var}_ratio\"]","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:15.591307Z","iopub.execute_input":"2024-07-17T22:57:15.591378Z","iopub.status.idle":"2024-07-17T22:57:15.593632Z","shell.execute_reply.started":"2024-07-17T22:57:15.591370Z","shell.execute_reply":"2024-07-17T22:57:15.593467Z"},"trusted":true},"outputs":[{"execution_count":13,"output_type":"execute_result","data":{"text/plain":"0     1.025806\n1     1.000000\n2     0.899522\n3     0.902741\n4     0.905263\n5     1.030568\n6     0.428571\n7     0.666667\n8     1.168317\n9     1.052265\n10    0.833333\n11    1.000000\n12    0.945946\n13    0.866667\n14    0.842881\n15    0.896154\n16    0.946320\n17    0.861226\n18    0.819355\n19    0.931592\n20    0.635553\n21    0.945205\n22    0.894374\n23    0.976631\n24    0.875000\n25    0.909148\n26    1.009872\n27    0.935484\n28    0.975836\n29    0.990453\n30    0.986586\n31    0.976230\n32    0.876392\n33    1.125000\n34    0.973404\n35    1.004739\n36    0.971698\n37    0.903153\n38    0.979424\n39    0.806147\n40    1.160000\n41    0.907051\n42    0.945946\n43    0.937860\n44    0.932384\n45    0.903448\n46    1.051724\n47    2.727273\n48    1.018797\n49    1.000000\n50    1.800000\n51    1.333333\n52    0.904502\n53    1.571429\n54    0.939966\n55    1.069091\nName: edge_count_ratio, dtype: float64"},"metadata":{}}],"execution_count":13},{"id":"90000903-a607-43ee-bdc6-90643e02d5ff","cell_type":"code","source":"matplotlib.colors.CenteredNorm(vcenter=1)(grid[f\"{var}_ratio\"])","metadata":{"execution":{"iopub.status.busy":"2024-07-17T22:57:15.593908Z","iopub.execute_input":"2024-07-17T22:57:15.593969Z","iopub.status.idle":"2024-07-17T22:57:15.596055Z","shell.execute_reply.started":"2024-07-17T22:57:15.593962Z","shell.execute_reply":"2024-07-17T22:57:15.595861Z"},"trusted":true},"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"masked_array(data=[0.50747029, 0.5       , 0.47091413, 0.47184616,\n                   0.47257618, 0.50884854, 0.33458647, 0.40350877,\n                   0.54872329, 0.51512929, 0.45175439, 0.5       ,\n                   0.48435277, 0.46140351, 0.45451805, 0.46993927,\n                   0.48446115, 0.45982863, 0.44770798, 0.4801977 ,\n                   0.39450225, 0.48413843, 0.46942413, 0.49323528,\n                   0.46381579, 0.47370081, 0.50285759, 0.48132428,\n                   0.49300528, 0.49723653, 0.49611705, 0.49311907,\n                   0.46421873, 0.53618421, 0.49230123, 0.50137191,\n                   0.49180735, 0.47196539, 0.49404375, 0.44388453,\n                   0.54631579, 0.47309379, 0.48435277, 0.48201199,\n                   0.48042705, 0.47205082, 0.51497278, 1.        ,\n                   0.50544123, 0.5       , 0.73157895, 0.59649123,\n                   0.47235586, 0.66541353, 0.48262165, 0.52      ],\n             mask=False,\n       fill_value=1e+20)"},"metadata":{}}],"execution_count":14}]}