{ "cells": [ { "metadata": {}, "cell_type": "markdown", "source": [ "# Semivariogram Regularization\n", "\n", "In this tutorial we will learn how to regularize semivariogram of dataset consisting irregularly shaped polygons. The procedure of the semivariogram regularization is described in the study: `(1) Goovaerts P., Kriging and Semivariogram Deconvolution in the Presence of Irregular Geographical Units, Mathematical Geology 40(1), 101-128, 2008`.\n", "\n", "The main idea is to retrieve the point support semivariogram from the semivariogram of irregular polygons. This is the case in the mining industry, where aggregated blocks are deconvoluted into smaller units, and in epidemiology, where data is aggregated over big administrative units. Or in ecology, where species observations are aggregated over areas.\n", "\n", "In this tutorial, we use block data of Breast Cancer incidence rates in Northeastern counties of the U.S. and U.S. Census 2010 data for population blocks.\n", "\n", "## Prerequisites\n", "\n", "- **Domain**:\n", " - semivariance and covariance functions\n", " - kriging\n", "\n", "- **Package**:\n", " - `TheoreticalVariogram()`\n", " - `Blocks()` - described in this tutorial, but this class has been used in **3-5-**\n", " - `PointSupport()` - described in this tutorial\n", "\n", "- **Programming**:\n", " - Python basics\n", " - `pandas` basics\n", "\n", "## Table of contents\n", "\n", "1. Prepare data\n", "2. Set semivariogram parameters\n", "3. Regularize semivariogram\n", "4. Visualize process\n", "5. Export semivariogram\n", "\n", "## 1. Prepare data\n", "\n", "Data structures for semivariogram regularization are rather complex, and that's why Pyinterpolate has classes merging blocks and point support data. There are two structures:\n", "\n", "- `Blocks()`: stores polygon and block data, transforms it, and retrieves centroids,\n", "- `PointSupport()`: performs spatial joins between polygons and points support data and manages internal indexing of those datasets." ], "id": "34dc144e198ac05a" }, { "metadata": { "collapsed": true, "ExecuteTime": { "end_time": "2025-12-21T14:59:21.323077Z", "start_time": "2025-12-21T14:59:08.314980Z" } }, "cell_type": "code", "source": [ "import geopandas as gpd\n", "\n", "from pyinterpolate import build_experimental_variogram\n", "from pyinterpolate import Blocks\n", "from pyinterpolate import Deconvolution\n", "from pyinterpolate import PointSupport" ], "id": "initial_id", "outputs": [], "execution_count": 1 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:59:22.124048Z", "start_time": "2025-12-21T14:59:22.118526Z" } }, "cell_type": "code", "source": [ "DATASET = '../data/blocks/cancer_data.gpkg'\n", "OUTPUT = '../data/regularized_variogram.json'\n", "POLYGON_LAYER = 'areas'\n", "POPULATION_LAYER = 'points'\n", "POP10 = 'POP10'\n", "GEOMETRY_COL = 'geometry'\n", "POLYGON_ID = 'FIPS'\n", "POLYGON_VALUE = 'rate'" ], "id": "e131992ffbddff45", "outputs": [], "execution_count": 2 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:59:23.366140Z", "start_time": "2025-12-21T14:59:22.276120Z" } }, "cell_type": "code", "source": [ "blocks = Blocks(\n", " ds=gpd.read_file(DATASET, layer=POLYGON_LAYER),\n", " value_column_name=POLYGON_VALUE,\n", " geometry_column_name=GEOMETRY_COL,\n", " index_column_name=POLYGON_ID\n", ")" ], "id": "ffae19283f230998", "outputs": [], "execution_count": 3 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:59:23.441394Z", "start_time": "2025-12-21T14:59:23.386898Z" } }, "cell_type": "code", "source": "blocks.ds.head()", "id": "7b5fb7b093edc03a", "outputs": [ { "data": { "text/plain": [ " FIPS rate geometry \\\n", "0 25019 192.2 MULTIPOLYGON (((2115688.816 556471.24, 2115699... \n", "1 36121 166.8 POLYGON ((1419423.43 564830.379, 1419729.721 5... \n", "2 33001 157.4 MULTIPOLYGON (((1937530.728 779787.978, 193751... \n", "3 25007 156.7 MULTIPOLYGON (((2074073.532 539159.504, 207411... \n", "4 25001 155.3 MULTIPOLYGON (((2095343.207 637424.961, 209528... \n", "\n", " representative_points lon lat \n", "0 POINT (2133348.745 558025.221) 2.133349e+06 558025.221156 \n", "1 POINT (1442153.243 550673.936) 1.442153e+06 550673.935704 \n", "2 POINT (1958207.252 766008.62) 1.958207e+06 766008.620010 \n", "3 POINT (2084187.905 556436.412) 2.084188e+06 556436.412055 \n", "4 POINT (2101100.939 600344.125) 2.101101e+06 600344.124709 " ], "text/html": [ "
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" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 4 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:59:26.104721Z", "start_time": "2025-12-21T14:59:24.209347Z" } }, "cell_type": "code", "source": [ "point_support = PointSupport(\n", " points=gpd.read_file(DATASET, layer=POPULATION_LAYER),\n", " blocks=blocks,\n", " points_value_column=POP10,\n", " points_geometry_column=GEOMETRY_COL,\n", " verbose=True\n", ")" ], "id": "7dd8dc1cd8524183", "outputs": [], "execution_count": 5 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:59:26.580899Z", "start_time": "2025-12-21T14:59:26.557491Z" } }, "cell_type": "code", "source": "point_support.point_support.head()", "id": "e8b68807f87e0432", "outputs": [ { "data": { "text/plain": [ " geometry POP10 FIPS lon \\\n", "0 POINT (1277277.671 441124.507) 1866.0 42049.0 1.277278e+06 \n", "1 POINT (1277277.671 431124.507) 2093.0 42049.0 1.277278e+06 \n", "2 POINT (1277277.671 421124.507) 590.0 42039.0 1.277278e+06 \n", "3 POINT (1277277.671 411124.507) 636.0 42039.0 1.277278e+06 \n", "4 POINT (1285937.925 446124.507) 10191.0 42049.0 1.285938e+06 \n", "\n", " lat total_POP10 \n", "0 441124.5068 262794.0 \n", "1 431124.5068 262794.0 \n", "2 421124.5068 89534.0 \n", "3 411124.5068 89534.0 \n", "4 446124.5068 262794.0 " ], "text/html": [ "
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" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "execution_count": 6 }, { "metadata": {}, "cell_type": "markdown", "source": [ "## 2. Set semivariogram parameters\n", "\n", "Now, we must set parameters for the areal semivariogram AND point support semivariogram. It is essential to understand data, especially experimental semivariances.\n", "\n", "The *step size* and the *maximum search radius* parameters depend on the block data. But we should check the point's semivariogram too, just in case. We won't create meaningful results if point support data is spatially independent." ], "id": "56f30192348a020c" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:59:27.861535Z", "start_time": "2025-12-21T14:59:26.878765Z" } }, "cell_type": "code", "source": [ "# Check experimental semivariogram of areal data - this cell may be run multiple times\n", "# before you find optimal parameters\n", "\n", "maximum_range = 300000\n", "step_size = 40000\n", "\n", "dt = blocks.block_data # x, y, val\n", "b_values = blocks.block_values\n", "b_coordinates = blocks.block_representative_points\n", "exp_semivar = build_experimental_variogram(values=b_values,\n", " geometries=b_coordinates,\n", " step_size=step_size,\n", " max_range=maximum_range)\n", "\n", "# Plot experimental semivariogram\n", "\n", "exp_semivar.plot()" ], "id": "ef6b93e2926b9b50", "outputs": [ { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 7 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:59:33.940923Z", "start_time": "2025-12-21T14:59:28.185605Z" } }, "cell_type": "code", "source": [ "# Check experimental semivariogram of point data - this cell may be run multiple times\n", "# before you find optimal parameters\n", "\n", "maximum_point_range = 300000\n", "step_size_points = 20000\n", "\n", "\n", "pt = point_support.get_points_array()\n", "\n", "exp_semivar = build_experimental_variogram(values=pt[:, -1],\n", " geometries=pt[:, :-1],\n", " step_size=step_size_points,\n", " max_range=maximum_range)\n", "\n", "# Plot experimental semivariogram\n", "\n", "exp_semivar.plot()" ], "id": "4be7846e434cc270", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 8 }, { "metadata": {}, "cell_type": "markdown", "source": [ "We see that the point support variogram shows semivariance increasing in a function of distance. However, blocks semivariogram increases slowly and starts from the large nugget. Spatial dependency is low in this case, but probably sufficient for modeling.\n", "\n", "## 3. Regularize semivariogram\n", "\n", "We can move to the next step - deconvolution. We must create the `Deconvolution` object. It iteratively transforms aggregated variogram using point support information. The process is slow, and it is divided into two steps: the initial `fit()` and iterative `transform()`. If you know what you're doing you can perform both steps using method `fit_transform()`." ], "id": "55e3fc3253791543" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T14:59:34.320802Z", "start_time": "2025-12-21T14:59:34.315797Z" } }, "cell_type": "code", "source": "reg_mod = Deconvolution(verbose=True)", "id": "7c96bca3d0f25f06", "outputs": [], "execution_count": 9 }, { "metadata": {}, "cell_type": "markdown", "source": [ "When we `fit()` a model, we have multiple parameters to set:\n", "\n", "- **blocks**: `Blocks` object\n", "- **point_support**: `PointSupport` object\n", "- **step_size**: `float` - step size between lags estimated from blocks data!\n", "- **max_range**: `float` - maximal analysis distance\n", "- **nugget**: `float` - nugget, for initial experiments it is better to set it to zero\n", "- **variogram_weighting_method**: `str`\n", " - equal: no weighting\n", " - closest: lags at closer distances are more important than distant lags, thus error weights are increased at close distances (semivariogram fits better the first lags)\n", " - distant: opposite to closest\n", " - dense: error weight is inversely related to the point pairs within lag. More point pairs - decreased error weight; less point pairs - increased error weight" ], "id": "3de7df5d7ce8857c" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T15:01:42.575901Z", "start_time": "2025-12-21T14:59:34.439200Z" } }, "cell_type": "code", "source": [ "reg_mod.fit(\n", " blocks=blocks,\n", " point_support=point_support,\n", " step_size=step_size,\n", " max_range=maximum_range,\n", " nugget=0,\n", " variogram_weighting_method='closest',\n", " models_group='safe'\n", ")" ], "id": "2f92cfa0ce301808", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Initial fit of semivariogram\n", "Regularization fit process ends\n" ] } ], "execution_count": 10 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T15:01:42.736848Z", "start_time": "2025-12-21T15:01:42.609201Z" } }, "cell_type": "code", "source": [ "# Check initial experimental, theoretical and regularized semivariograms\n", "reg_mod.plot_variograms()" ], "id": "ce8056fe85a16c13", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 11 }, { "metadata": {}, "cell_type": "markdown", "source": [ "After fitting, when we check how initial semivariogram behaves, we can perform `transform()` operation. This function has few parameters to control the regularization process, but we leave them as default with one exception: we set the `max_iters` parameter to 5.\n", "\n", "This process of fitting and transforming takes some time, so it's a good idea to run it and go for lunch..." ], "id": "b3de1fffd52aa4f7" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T15:06:19.576020Z", "start_time": "2025-12-21T15:01:42.768993Z" } }, "cell_type": "code", "source": [ "reg_mod.transform(\n", " max_iters=5\n", ")" ], "id": "e7696f22183da5d4", "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Transform procedure starts\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 5/5 [04:36<00:00, 55.36s/it]\n" ] } ], "execution_count": 12 }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T15:06:19.806865Z", "start_time": "2025-12-21T15:06:19.623703Z" } }, "cell_type": "code", "source": "reg_mod.plot_variograms()", "id": "6e81a21286c7f6be", "outputs": [ { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 13 }, { "metadata": {}, "cell_type": "markdown", "source": [ "## 4. Visualize process\n", "\n", "The process is automatic, but we can check how it behaved through each iteration. We can analyze deviation change (the most important variable, the mean absolute difference between regularized and theoretical models) with the built-in method. Still, if we are more interested in the algorithm stability, we can analyze weight changes during each iteration." ], "id": "9a28849c9d5d802d" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T15:06:19.973139Z", "start_time": "2025-12-21T15:06:19.850762Z" } }, "cell_type": "code", "source": "reg_mod.plot_deviation_change(normalized=True)", "id": "5c79a5f00360dcd2", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 14 }, { "metadata": {}, "cell_type": "markdown", "source": "**Clarification**: The deviation between a regularized semivariogram and a theoretical model decreases with each iteration. However, a significant improvement can be seen after the first step, and then it slows down. That's why semivariogram regularization usually does not require many steps. However, if there are many lags, optimization may require more steps to achieve a meaningful result.", "id": "3901226b6652a0" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T15:06:20.139892Z", "start_time": "2025-12-21T15:06:20.009802Z" } }, "cell_type": "code", "source": "reg_mod.plot_weights_change()", "id": "9e977ab165eee749", "outputs": [ { "data": { "text/plain": [ "
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" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 15 }, { "metadata": {}, "cell_type": "markdown", "source": [ "Weights check is important to track problems with algorithm, especially if average weight oscillates. This may be a sign of problems with data, or modeling assumptions.\n", "\n", "Weights are smaller with each iteration. It is the expected behavior of the algorithm. The general trend goes downward, and small oscillations may occur due to the optimization process.\n", "\n", "The most important part is to compare semivariograms! We have plotted those before, but now we do it again.\n", "\n", "Regularized Model is different from the initial semivariogram. It is the result of regularization, where the areal aggregates are distributed along population point support blocks." ], "id": "53b4c4cebf3b6395" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T15:06:20.329675Z", "start_time": "2025-12-21T15:06:20.173906Z" } }, "cell_type": "code", "source": "reg_mod.plot_variograms()", "id": "d4432e25e31b4646", "outputs": [ { "data": { "text/plain": [ "
" ], "image/png": 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cE7Vs/fr17v369euXYmAkGntbrVq1qG2ecMIJLohLibat8j388MNue3rs0w9f/TCLJzbAOOqoo9wPxVgDBgyI+vHeqlUrr0qVKvvttx/Mxwbt+vGuH4mp2bhxo9uHESNGuG3MnTs3y46z9rNRo0beypUrvfRSEFKyZEmvffv2qa6X3mPmB+0K9CLpR6eWv/vuu1HLVRmgH8qRP361XrzroFOnTu6ajg28RRUE+gGpY6vjpe3GXn8KJGLFXpt++e+77779Krp811xzjavw0rmMV2EQGSTqsfJMxNIxv/XWW73sCNpffvll9766xuSWW25xFTpr167d77Ou6+3qq6/O8Ocy9jMVSefHPxfHHXec22ZmgvaDdZxVTlVUKsD06XpObbx3avt4IEF7Rs5Vek2cONGVR0FnLH1/6Ll4OVB8qpDQOvFyojRt2tSNdU+NKiL0t0YVlbHbVDAfGSj65zZ2bHPs+Gedr4suushLi8aSa3uffPJJmuv+888/rtKgdu3aaa6rSm1tV0F8Svr27evWSSm4TG/QHkt/YxS463susuJS3486Lo8//rj366+/uvOuc6Mx4dr+ihUrwutqjLg+L9oPVY7df//97rtVFWKRMrLN2HwuDRo0cBUwaf09Sul8qpLJr4yLrPBRhYvev0uXLnG3R9CO3Ibu8UAOpi5u6o6srtbKDNy3b1/XjVrdEP0us+quqN+66hqvaVwib+pOre6UkdQNMnZaGnUVVNe/2GUSO/4ylrriauz8lClTwmPx1a1fyyNpmhx111O3OI3TV/n8seKxYzljsxWnRN191c02lrqa+s/7/2v8eex+x8sQLBq/HS8L88KFC12XZR0bdbHUPqjbd7x9OJDjrC7B6j6uddXtVV3Q1aU3NcrWrDGOGgecFcfMFzl2NrK88fZDXSxjj4Ouy1gaf6tuqv40VRrjqa7u/nhnda3UsdXQjnjjfNNzfairs7pUq9ustte+fXs39jZyvK32VV2D/QzLGT0WommR0vqMZJbfbdcvnz7rOh4ayx/7Wde6kZ/19H4uY+kcqhu7zlvkuVB+hvSMuY7nYB1nlVXZsSO7yKtrvMZvawhHdu5jrIycq/TyuzfHGzPuj5FObXx1Wq9Pa2x2kyZN3HCfyBwc/mvOO+881z3dpzwZ+pxq2Es8GsOv71J9X8Vmc4/12GOPuRkJlD8kNkN8POr2rqEIGmYVOZ1YPMptonHZ8aY0E/1tVRd2lVPdubOS/sYof4O+5/TZ9CmHi/ZTw6L0d0td8pV/QcdY/OOsvwkavqChSvpbqu84TZemIVXKT6CZAjK6zdix/xqiot8a2p4+wylJ7Xz614iGTET+3dDnXNdUStcIkNsw5RuQBPTHXQG8bgp49INE00LpD7R+gCo41B9ojduMFfvHON46qS2PTRQVS3/0Nc5NP5Q1Vlr/60eQP37UDxj0Y1rzs2oMqf5wa580XY5+PMeOp8toYp+sFu/99cNKgaCCdY3n1Q8fjZ9UcjiNjY/dhwM5zhdddJE1bdrUjXHV2Ez9aNV4cVXgaKz9wZTV10s8mt5JAbXGvitRmioAdE3rB2O8sZbpuT70ev3Q1BjusWPHunH5+nGrfApaFu9Halqycp/Twx/371cu6VgoCNT42ngUEGbkcxmPphHTGFkdKwVJCoD0Op2bgzWN24EcZ10z+n7UeGrlpNB+67sncoztgeyjrqt45YhN1JaRc5VefoKxePNga5n2I7UpDyNfH1vppmWqIEyLXqdg2OcHcrHJ0ET7H6+iRZVJyjehz7n+BsRW5sSOAdf3q/Jc3HPPPWmWL7KcftJBP1llPH7iS60XjyqbVamk3B7ZIbKcPh0XJT3VXOvKyaFcMLrpc6zrxk9Oq2OjyhblZoik3AN+2f2/F+ndZiRNzabp23QNKydMStI6n2ldI2nlYgByC4J2IMn4SYT8H24KHvUjUq0aCugPNrWc60eDKhEUkKtlSwFnZK28gia17nz00UdRrWgHmoRGPzoif0BG1vr7z/v/q7VAxymy9Vtz2qaXMmYrEZYC58hkREr4lR30A1vJuHRTq9zxxx/vksalFLTrh5cqFNJKXJjeY5ZVYhNXiRLYKaD0AxcF15oTWAG1Tz9GY5MoZoZa/HTTsVOLmZLRKVHf1Vdf7fZVLWzqvRL5QzO7jkVGqDVWlTb6Ue+3SOuzrvKqxSqtiov0fC7j0blQ692rr74atVznIjLwje1FkpqDeZw1v7QSwGl//WtNPZQys4/xqMU/Xq+X2N4CGTlX6aUeQPrMaFaEWOqJlVLCTp//vF4fGaArSZg/x3patO+RFQ5KxilKghZL21VFbSR9hyrA098DJZv0KxLiUZCpz6l6ScRLWJpWOdNTOaJrUnODp7SeAlZd6507d87Q+2dFOfW30v976bfGq/eQT0lF9TcttsLIT3KoLO0Z3aZPCRRVkapErEoKm5L0nE+16BcoUCDFayQzFVhAMqJ7PJBDKaCN16KjmmzxuzjrB41aptQNOHZ9Pc6OzNax1OVWf3zVLU7TJsV2wfVbziLLp66j+lFwINTdTz9W/S7AomzsL730kpt+Sdl5Rdma9YNBlQaRQaG6XKZXvH3Q9ELqipiV9AMstouuWiMUbKU0lZKopVABiypI4v2o98ud3mOWVfQ+kVPVqVVGP8b1Q88/pvo/9tpVtveUpplKD7XwxW7TD1r846hjoffwpwLzqfeHfqhnpleDuv0rGFUgkFkaLqCs/2p900wHfoCsHhgqr1qHY+kHemwlR1qfy3jinQsF/rE/uFUpIOmpWMmO45wStRjq864WdlXOqEePPheZ2cd4FIzr/PpDO0THVq2akTJyrjIy5ZsCLLV+Rk7nqWBJlRORvSgUuGmbka3yms5LQbQ+65GfLc1UofOgYVe+yP2L/NujIE8zAPj0d6h+/fruMx15zauHkMqoGUAiv2d0Leg4a1vxhs74NC2kek2oglSBc+zUYqmVU9t/7bXXXHd2P4jU9328mVB0fnQtRO5T5DHUdaEu3PGGbGREvHKqPAqKVVHkV36kRBVPum7Und+nSnqVPXbGBE1PJw0bNszwNkU9uzQdo7rc9+jRI8XXp/d8qqJO66kbvF9RJxq+p2WR1wiQm9HSDuRQ6jKsAEDjxPRDSwGi/sCpBUnBlbqA+j8i+/fv7/4Aq9ubfqDqj6RagNVSp9YTjWPLTvqDrPfU++gHcWzNvQI0/XhWl121gqkVUQGzgtF4XT3TS1N56QeKfvRrKjd1D3399dfdvms+Y/+Hnt5TAYNaDPQjRD/k9ENQ3dvT22qoboRqZVOLsN5Lr9E0V1ndNVo/5NSdUz+g9WNY3bjVYqfpuyJbouNRt1/9WFY3fp13tdDq+OqHp6ZmUkCT3mOWVTTGUUGU3ktdd/1Kjsj5tNUirGOp7pWqNFCgr31Wt9XM0j7pvfT50WdEx1XXnHoj+ONidT2qxVWBsT47Ot46fgpA1FVar8soVYhomxq6olwEadEP3hEjRrj7+lyoR4jOl6ZY0rzmunZ9Oq96rK666v6tz5VasNSbQa/RtGKRgVdan8t4dC40/EPfL7rmNRWaPiux877r2Oh6euGFF9x7KIjXeOd4+Qay4zinRpUTyjWh869rL7brb3r3MR51qVfPBW23W7durheMjoEC4si5vzNyrvQ9rbKoElPzvadGgZRer+Op7zJdMwqy1Jrp/03wryt9/vV9pW7UPq2r7tMqj4Ji9czRd6NatP0eHaLjoqBPPbv0uVTFmwJh9fzwc5FEVr4o8FJwq31W5YOOkYLK66+/Pryeerno86FjqIAtcgpLfc/5lSvqtaAy+hUJ2t9ICsb98eWa3swffqWKTV1fGr+tgFLH2KfPk/ZHfwP81n8NmVGwqYBd48Fj6XlVesfOzR5JZdV3l/iVpfp77PcgUeWbqKeAphnUZ0EVAPpe1vFUd3W9PjKHiqYC1XnR50n5GPQ6fV60XQ2R8+laUXCtY64u5roGdZ5USaf7/rz2GdmmrkUdUwXguh787yafzrPfzT2959P/26TKJXWz198C0fSe+vsTez3pelSllj/9oiqi/dwE+l3k51UBkk7QmfAAZM5nn33mXXXVVW5aMmUbVubVI444wrv55pvjZv99//333VRYxYoVcze9TtlsFy9eHF5HWY9js5enlsVaXyGRU4/Fyx7vUwZYPReZrT6SpuNRdmZNHaOpxQYOHOi99tpr+20vtYza8TJdKxOuMuUqA6+2rczosVP9yG+//ea2W6RIEZeBX9Nh6Zjp/SOnYEvpGMnkyZPdtEjaRuXKlcPT8MXLQJ/Z46yMwpq+SZl9S5Qo4c6l7kdOC5QaZeTV1G/aR2XB15R62nZkNvz0HDM/+3pslnj/GoidSk1Z2rVc2bFj90vZzzW9j8rTsGHDqGMlynh95ZVXugzUutY1ZZGmI4o93ym9d+Rz/rU0e/ZsNz2dpn/S+1aoUME799xzo6afE2W8VlZynU9lMlY5NTVWbOb/2M+CL7aMkVnr0+JPb6ibsnIrQ7quG2XKVlbolLz00ksuE7yuQ10jxx57rLsW402vldbnMt6Ub/psaBopbV/TYSkTeLys6ZrFQtNwKWN0ZJbn2Ezq2XGcU6Ms9Sp7ZOb9SOndx3jZ40Xb1OdK38nKrK3vgHj7nN5zld4p3yKnINM0Z8oSrs+wzvHq1auj1vHLHu+YaQo3lVufi6pVq7ps+7EzLCjru9ZRBnydL32Orr/++v3ex/fll1+670Z9n5QtW9ZND7Zq1aoUr/fYW+Sx8z9DKd0iP1uaVvP0009333e6DvUdohlXZs2atd93zKWXXur+huq4ad/1WdMMJinNLnHxxRe7fVc2+pSkVtbIa0lTuGoWk0MPPdRtU+dN5zDeVHf6LtZ3sq4XlVXH9Z133on7/n/88Yf7naAZV3Q96prW90fk93BGtul/j6d0i/zuTu/59Omc6HtIf9NUDs10smTJkv3WS2278X57AMkij/4JuuIAABKRuiaqa6Bq8TVeFFlLLWU33njjft2iAQAAsA9j2gHgv3HCkTTGUd0o1Q2QgB0AAABBYUw7APyXsE9jCZWMTGMuNVZPSXFSmpIJAAAAOBgI2gHgvwzyStCjIF2Zk5XwTNml05NRGwAAAMgujGkHAAAAACBBMaYdAAAAAIAERdAOAAAAAECCYky7me3du9f+/PNPK1GihJuCCAAAAACA7KSR6ps2bbLKlStb3rwpt6cTtJu5gL1atWpBFwMAAAAAkMusWLHCqlatmuLzBO1mroXdP1glS5YMujgAAAAAgCS3ceNG13jsx6MpIWhXCv3/usQrYCdoBwAAAAAcLGkN0SYRHQAAAAAACYqgHQAAAACABBVo0D5x4kQ777zzXLY8dQn44IMPop7fvHmz3XTTTW5QfpEiRaxu3br2wgsvRK2zfft2u/HGG61cuXJWvHhxu/DCC23NmjUHeU8AAAAAAEiyoH3Lli1Wv359GzJkSNznb7vtNhs3bpyNGDHCFi1aZD179nRB/EcffRRe59Zbb7WxY8fau+++a99++63LBN+hQ4eDuBcAAAAAAGSPPJ4mh0sAamkfM2aMnX/++eFl9erVs06dOlm/fv3Cy0444QRr06aN9e/f3zZs2GDly5e3N9980zp27Oie/+mnn6xOnTo2ZcoUO/nkk9Odta9UqVJueySiAwAAAABkt/TGoQk9pv3UU091reorV650E8+PHz/elixZYmeddZZ7ftasWbZr1y5r2bJl+DVHH320Va9e3QXtKdmxY4c7QJE3AAAAAAASTUIH7c8884wbx64x7QULFrSzzz7bdaU//fTT3fOrV692y0uXLh31uooVK7rnUjJgwABXo+HfNDceAAAAAACJJuGD9qlTp7rWdrWqP/HEEy7p3FdffXVA2+3bt6/rguDfVqxYkWVlBgAAAAAgq+S3BLVt2za766673Dj3c845xy077rjjbO7cufb444+7LvGHHnqo7dy509avXx/V2q7s8XouJYUKFXI3AAAAAAASWcK2tGusum5580YXMV++fLZ3795wUroCBQrY119/HX5+8eLFtnz5cjvllFMOepkBAAAAAEialnbNw/7LL7+EHy9dutS1pJctW9Ylk2vWrJn17t3bzdFeo0YNN6XbG2+8YU8++aRbX+PRu3Xr5qaG02uUce/mm292AXt6M8cDAAAAAJCoAp3ybcKECda8efP9lnft2tWGDRvmkslp/PkXX3xh//77rwvcr7nmGjc3u6aIk+3bt1uvXr1s1KhRLit869at7bnnnku1e3wspnwDAAAAABxM6Y1DE2ae9iARtAMAAAAADqakmKcdAAAAAIDcjKAdAAAAAIAERdAOAAAAAECCSth52gEcuB9//NGqVq0aHiOjGRrmzJljlSpVipoWcdy4cbZ161Y744wz3EwMsmLFCpsxY4ZVqFDBmjRpEl73yy+/tE2bNlnTpk2tfPnybtmff/5pU6dOda/VNnzjx4+3devWuffSe8qaNWts8uTJbvxOixYtwutOnDjR/v77bzvxxBNdmeWff/5xs0YUK1bMJZn0ff/99y5RpaZ9VIJKWb9+vX3zzTdWuHBha9u2bXjdadOm2cqVK61+/fp2+OGHh2euUIJLTRl53nnnhdedOXOmmzKyXr16dtRRR7ll27Zts88++8wlv7zgggvC62qmi99++83q1KnjbrJz5077+OOP3f3zzz8/PGXl/Pnz7eeff7YjjzzSjj32WLdMU1d+8MEH7v65555rBQsWdPcXLVrkbrVq1bKGDRuG32/MmDGmFCRt2rRxM2rIkiVLbMGCBW62jUaNGoXXHTt2rJsys1WrVlaiRAm3TGVVmatUqWInnXRSeN1PP/3UJfQ888wzrXTp0m7ZsmXLbNasWVaxYkU77bTTwuvqmOnYaWaPcuXKuWV//PGHTZ8+3Q455BA7/fTTw+tqKk6Nz9LrtR3ROdO50/vo/SKTkirZqGb9qFy5slv2119/2aRJk1z5tR++7777ztauXev2V/stusZ0rRUtWtTOPvvs8LpTpkyxVatWueOo4+mPHfvqq6/c8dZx9+la1zV/3HHH2RFHHOGWbdmyxT7//HM31Wj79u3D686ePdt+//13q1u3rh199NFumRKhfvLJJ+5+hw4dwuv+8MMP9uuvv1rt2rXtmGOOcct2795tH330kbvfrl07y58/9Kd44cKFbtpSXae6Xn2jR492/+u61vUtP/30k/t816xZ044//vjwuh9++KHt2bPHfV70uRHN0jJv3jz3udLny6fyqtwtW7bkO4LvCL4j+I7gO4LviKT6jkg6SkSX223YsEHJ+Nz/QLL44YcfvIIFC3oLFiwIL3vppZfctd6+ffuodWvUqOGWT5s2Lbxs5MiRblnLli2j1q1bt65bPn78+PCyMWPGuGWnnHJK1LqNGzd2yz/++OPwsi+++MItq1+/ftS6zZo1c8vffvvt8LLJkye7ZUcccUTUum3btnXLhw4dGl42Z84ct6xSpUpR6/7vf/9zy5999tnwsiVLlrhlpUqVilq3a9eubvnAgQPDy1auXOmW5c+fP2rdG264wS2/7777wsvWrVvnlum2Y8eO8PLevXu7Zbfffnt42c6dO8Pr6nU+bU/Lrr/++qj3K1CggFv+xx9/hJc9+uijbpnKHUn7peWLFy8OLxsyZIhb1rFjx6h1K1eu7Jbr+Pl0XLWsTZs2UeseeeSRbvl3330XXvbOO++4ZTp/kXR+tfzzzz8PL/vkk0/cskaNGkWte+qpp7rluo58ur60rE6dOlHrtmrVyi0fMWJEeNn06dPdMl3HkXSda/mLL74YXrZw4UK37JBDDolat3Pnzm75U089FV62dOlSt6xIkSJR63bv3t0t79+/f3jZ2rVrw+czUo8ePdyyvn37hpdt2bIlvO7mzZvDy++66y63TK+J5K+7Zs2a8LKHHnrILbv66quj1i1atKhbrrL7tE9adskll0StW758ebec7wi+I4TviBC+I/bhOyKE74ic+R2RbHEoLe1Akrr99ttdja1qRf3ae7/G06/R9al2VbXSxYsXDy9T7bfW9V/rU61tmTJlXA23TzXjWlc1y5FUK62aX63vU+2o1vVbKnyqOVbrgmpafard1bp+jblPrReqfVXtvU+16FrXr7n1qfVCy/0aelEtupZF7q+oBlvLVYvsU/m1TC0pkQ477DC33G/JEa3j1yj701KKWjq0XP/79Ly/buS2tT0t92vzfaeeeqo7Pn5NuqicWlfljqSaZtVk+y0uomkwta7f6uNr3Lixa5nwW1xEx1Xr6jhHUouEnvNr3UXHW+v6Nf++Bg0auOPr17qLroN4ZVDLlY6H3zojur5ij5noelRrjt86I3ofrRs71aeuc+2bX0Mval3QupHlErWIaHlkDX2hQoXcMv0fSddu7HWplrDI1gSfWjq03G/JEbWc+Ov6rSiidbTcb/Hz+euqRcen9473OVJrlFo8IsusfdK6fquPT61RallS66OP74gQviP24TsihO+IEL4jQviOSPzviGTDlG9M+YYkpe6Bffr0sUceeSTqBwkAAACA4DFPewYQtAMAAAAADibmaQdyISU7UdIXAAAAAMmBoB1IInfddZcbh/TYY48FXRQAAAAAWYCgHUgSmvpD013o/9ikLwAAAAByJsa0M6YdSUbzp0bOywkAAAAg8TCmHcilCNgBAACA5EHQDuRwn332md1yyy1uXloAAAAAySV/0AUAcGDZ4rt3724rV6608uXLW79+/YIuEgAAAIAsREs7kIMVKVLEXn31VWvRooXdfvvtQRcHAAAAQBYjER2J6AAAAAAABxmJ6IAk9vfff7sPOQAAAIDkRtAO5DDqHNOtWzc79thj7fvvvw+6OAAAAACyEUE7kMP89ddftmDBAlu1apUVL1486OIAAAAAyEZkjwdymAoVKtgPP/xgkydPtuOOOy7o4gAAAADIRrS0AzmQWthbt24ddDEAAAAAZDOCdiCHGD58uI0dOzboYgAAAAA4iAjagRzgt99+s+uuu87atWtnX3zxRdDFAQAAAHCQMKYdyAEqV65sN9xwg82dO9datmwZdHEAAAAAHCR5PM0flculd1J7IGi7d++2/PmpawMAAABySxxK93ggwT/IkQjYAQAAgNyFoB1IULt27bLmzZvbRRddZP/880/QxQEAAAAQAJrtgASledjnzZtnS5cutZ07dwZdHAAAAAABIGgHEtQZZ5xhU6dOtTVr1lilSpWCLg4AAACAABC0AwnshBNOCLoIAAAAAALEmHYgwbz++uv2559/Bl0MAAAAAAmAoB1IIJMmTbIrr7zSjj32WNctHgAAAEDuRvd4IIGUK1fOGjZsaPXr17eKFSsGXRwAAAAAASNoBxJI3bp1XfI5ssUDAAAAEIJ2IAHs2bPH8uXL5+4XKFDA3QAAAACAMe1AwNavX+/GsL/yyivmeV7QxQEAAACQQAjagYA999xztmjRIhs4cKBt37496OIAAAAASCB0jwcC1qdPHytUqJCdfPLJVqRIkaCLAwAAACCB5PHoj2sbN260UqVK2YYNG6xkyZJBFwcAAAAAkOQ2pjMOpXs8EADVlY0dO9b27t0bdFEAAAAAJDCCdiAAb775prVr187atGlD4A4AAAAgRQTtQAB27dplRYsWtSZNmljevHwMAQAAAMTHmHbGtCMgS5cutapVqzInOwAAAJALbUxnHEr2eCAgtWrVCroIAAAAABIc/XKBg9iy3rJlS/vpp5+CLgoAAACAHIKgHThIevbsaV9//bX16NEj6KIAAAAAyCHoHg8cJEOGDLF8+fLZ448/HnRRAAAAAOQQBO3AQaKkc6NHjw66GAAAAAByELrHA9lo9+7d9uOPPwZdDAAAAAA5FEE7kI0GDhxoDRo0sEGDBgVdFAAAAAA5EEE7kE08z7N58+bZrl277JBDDgm6OAAAAAByIIJ2IJvkyZPH3nrrLZcxvkuXLkEXBwAAAEAORCI6IJsD9zPPPDPoYgAAAADIoWhpB7LYlClT7K677rIdO3YEXRQAAAAAORwt7UAW2rlzp3Xt2tV+/vln18r+0EMPBV0kAAAAADkYLe1AFipYsKA98sgj1rBhQ+vdu3fQxQEAAACQw+XxlOI6l9u4caOVKlXKNmzYYCVLlgy6OEgC+lippR0AAAAADiQOpaUdyAKbNm2yrVu3hh8TsAMAAADICgTtQBbo2bOnNWjQwKZPnx50UQAAAAAkERLRAQfo33//tc8//9z+/PNPMsYDAAAAyFIE7cABKlu2rC1YsMDGjRtnTZs2Dbo4AAAAAJII3eOBLFC6dGm7+OKLgy4GAAAAgCRD0A5k0tixY+2bb74JuhgAAAAAkhhBO5AJq1atsiuuuMJatGhhn376adDFAQAAAJCkAg3aJ06caOedd55VrlzZTZH1wQcf7LfOokWLrF27dm7+umLFilnjxo1t+fLl4ee3b99uN954o5UrV86KFy9uF154oa1Zs+Yg7wlymxIlSthFF11kxx9/vLVq1Sro4gAAAABIUoEG7Vu2bLH69evbkCFD4j7/66+/WpMmTezoo4+2CRMm2Lx586xfv35WuHDh8Dq33nqr66b87rvv2rfffusyeHfo0OEg7gVyI1UQPf/88/bdd99ZgQIFgi4OAAAAgCSVx/M8zxKAWtrHjBlj559/fniZEnspIBo+fHjc12zYsMHKly9vb775pnXs2NEt++mnn6xOnTo2ZcoUO/nkk9P13hs3bnQt+dpeyZIls2iPkIw0pVuhQoWCLgYAAACAHC69cWjCjmnfu3evffLJJ3bUUUdZ69atrUKFCnbSSSdFdaGfNWuW7dq1y1q2bBleplb56tWru6A9tcBLByjyBqTnmmzbtq1dddVVXDMAAAAADoqEDdrXrl1rmzdvtkceecTOPvts++KLL+yCCy5wXd/VDV5Wr15tBQsWdNNtRapYsaJ7LiUDBgxwNRr+rVq1atm+P8j5VBE0fvx4e/vtt8mbAAAAAOCgyG8J3Kop7du3d+PWpUGDBvb999/bCy+8YM2aNcv0tvv27Wu33XZb+LFaTQnckZbTTjvNJU9UIsQjjzwy6OIAAAAAyAUSNmg/5JBDLH/+/Fa3bt2o5RqvruRfcuihh9rOnTtt/fr1Ua3tagXVcynRmGTGJSMzlBgRAAAAACy3d49Xt3dN77Z48eKo5UuWLLEaNWq4+yeccIJLVPf111+Hn9f6agk95ZRTDnqZkZw++ugj+/vvv4MuBgAAAIBcKNCWdo1Z/+WXX8KPly5danPnzrWyZcu6ZHK9e/e2Tp062emnn27Nmze3cePGuendNP2baDx6t27dXFd3vUYZ926++WYXsKc3czyQmvnz57uZCXR9zZw506pWrRp0kQAAAADkIoEG7QqCFIz7/HHmXbt2tWHDhrnEcxq/rsRxt9xyi9WuXdvef//9qC7KTz31lOXNm9cuvPBClxVemeafe+65QPYHyUe5FTR+/fDDD7cqVaoEXRwAAAAAuUzCzNMeJOZpR2q2b99uW7duda3tAAAAAHAw49CETUQHBEl1WXny5HH3Cxcu7G4AAAAAcLAlbCI6ICjbtm2zpk2b2ltvvRV0UQAAAADkcrS0AzGGDBlikydPtl9//dXOOeccK1GiRNBFAgAAAJBLEbQDMXr06OFmNtCUgwTsAAAAAIJEIjoS0QEAAAAAEjQOZUw78B91iacOCwAAAEAiIWgHzGzcuHHWpEkTa9eune3evTvo4gAAAACAQ9AOmNmKFSusYMGCdvjhh1v+/KR6AAAAAJAYiE4AM+vevbuddtppVrNmzaCLAgAAAABhBO3Af+rWrRt0EQAAAAAcgD17zCZNMlu1yqxSJbOmTc3y5bMcje7xyLXWrFljHTt2tN9//z3oogAAAAA4QKNHm6njbPPmZp07h/7XYy3PyQjakWvdeuut9v7779tll10WdFEAAAAAHIDRo806djT744/o5StXhpbn5MCdoB251gMPPGBnnnmmPfvss0EXBQAAAMABdInv0cMs3uzN/rKePUPr5USMaUeudcQRR9jXX38ddDEAAAAAHIBJk/ZvYY8N3FesCK13xhmW49DSjlzF8zxbtmxZ0MUAAAAAkEVWrcra9RINQTtylRdeeMGOPvpoe/7554MuCgAAAIAsUKlS1q6XaAjakata2b/88kvbvn277dq1K+jiAAAAAMgCTZuaVa1qlidP/Oe1vFq10Ho5EUE7co08efK4bPG63XTTTUEXBwAAAEAWyJfPbPDg0P3YwN1/PGhQzp2vnaAduS5w79Chg+XNy6UPAAdKWXgnTDAbNSr0f07NygsAyPk6dDB77z2zKlWil6sFXsv1fE5F9ngkvYULF9qHH35od9xxh+XPzyUPAFlB891qep3IbL36YaSWjpz8wwgAkHN16GDWvn0oS7ySzmkMu7rE59QWdh8RDJLa7t27rWvXrjZr1ixbt26dPfbYY0EXCQCSImDv2HH/+XBXrgwtz+ktGgCAnCtfvpw5rVtq6COMpJYvXz7r0aOH1apVy2699dagiwMAOZ66wKuFPTZgF39Zz550lQcAIKvk8ZRSO5fbuHGjlSpVyjZs2GAlS5YMujjIphZ3usYDwIHT2PXmzdNeb/z45GvpAHDwqQIw2bo6AxmNQ4likJR27Njh/i9UqJD7n4AdALKGfjhn5XoAkBJyZwAhdI9HUurXr5+deOKJ9sMPPwRdFABIKmrpysr1ACC13BmRAXtk7gw9D+QWBO1Iym4mb7zxhs2bN8+WLVsWdHEAIKmoa6paumLnwfVpebVqofUAIDPInQFEI2hH0tF4EAXsQ4YMsXbt2gVdHABIKhpLqq6pEhu4+48HDWLMKYDM0xj22Bb22MB9xYrQekBuQNCOpFShQgW74YYbgi4GACQljSXVtG5VqkQvVws8070BOFDkzgCikZ0LSWPixIlWsGBBO/nkk4MuCgAkPQXm7duT1RlA1iN3BhCNKd+Y8i0p6NzVq1fP/vzzTxszZgzd4gEAAHIojVWvWTOUdC5epKKhOOrZs3QpFYXIHXEo3eORNJo3b26HHXaYtWjRIuiiAAAAIJPInQFEI2hHUlANlTLGT58+3YoVKxZ0cQAAAHAAyJ0B7EP3eLrH52h79uyxfFSzAgAAJG1XeXJnILfHoSSiQ46l+qZLLrnEDj30UHvkkUesaNGiQRcJAACkggAMGaXr44wzgi4FECy6xyPHmjlzpr377rv2/PPP25IlS4IuDgAASMXo0aHkYs2bm3XuHPpfj7UcAJAyWtqRYzVu3NjGjRtnP//8szVo0CDo4gBA8tJIul27zLZuDd10v0aNfc/PnGm2fn3KzWSKznxz5pj980/K79Wy5b778+aZrV2b8rrart9Mu2CB2erVKa97+ulmBQuG7i9aFEpLnZImTcwKFw7dV6Xw8uUpr3vKKWZ+LpVffw2ls07JiSea+d0ff//d7JdfUl73hBPMypQJ3V+xwmzx4pTX1d/AQw4J3dd+af9ScuyxZhUrhu7reOm4peSYY/bNqfXXX2Y//JDyukcfHRpsLP/+azZ7dtTTal1//gGz2q7F6EhbbqHrZ9MfG+z5C2dY+XtDre77Oeyw0E02bzabOjXlMuiaPPLI0H1dp99/n/K6KqvKLDt2hAqYksqVzerWDd3fvdtswoSU19Wx1TH2Pzdff53yujpnkb9fvvnGbO/e+OvqWtA14fv229DnMB5dY7rWfNo37WM8unZ1Dft0zHTs4ilSxOy00/Y9njbNbNOm+Ovqs6bPnI/viNB9viNS/I6Ios+x/zdmwwazGTNSXjel74hatcwOP9yShsa053YbNmzQuH73PwAAOcrOnZ63fr3n/fmn5/3yi+fNm+d5U6d63jffeN6kSdHrvvKK5z34oOf17et5PXp4Xvfunteli+d16OB5V18dvW7r1p5XoYLnFS/uefnyKfzYd6tSJXrdJk2in4+86fWx201p3bx5o9dVuVJaV7dt2/ate+mlqa/799/71r322tTXXb5837q33Zb6uosW7Vv3nntSX3fmzH3rDhiQ+roTJ+5b9+mnU1/3s8+iz3Fq67733r51R41Kfd033ti37kcfpb7u88/vW/err1Jd9zZ7PPzwJJuS+nYfeGDfdnVtp7Zunz771tVnIbV1b7pp37qrVqW+7lVX7Vt348bU1+3Uad+6u3envu6550Zf74UKpbzumWdGr1u2bMrrnnRS9LpVq6a8br160evWrp3yuocdFr3u8cenvG7FitHr8h0RwndEur4jvMcf37fulEx+R9x7r5dMcSgt7chxJk6c6FrWSRoIIKGpxSxvxCi0hQvNtmzZ11rt37ZtC7WAXHjhvnVvuy3UauE/H7n+UUdF9ydWS4JaWeJR66De1/fEEym3sFSvHv143br4LVjap8j98sug1pB4YvONqPXDb4mMt+1IamlJad3YuaCqVUt93ciB00pHndq6BQrsu69WpNTWLVQofitrSi2VvvLlU1838riVK5f6usWL77uvlrfU1o3821mqVOrrli69736JEqmvW7ZsdHki1t28xey33/Y9/bf91+KnBnEravMstK4ay4rHTgBTocK++2rZTK0Mhx66775aTFNbV63nvvz5U1/Xbx30r9HU1o39HKW2rsYGRKpXz2znzvjr6nMT+9lO6TMX27qoFk6/RTbWEUdEP9b3i9/anNpx8F+bUmu/rtnYMvEdwXdEZHlSW9fvFeDv57GprJvSd4TfWyBJkD2e7PE5ytKlS+24446zsmXLuuC9RmT3TABID3VvjQyY9QPVn1NIWbI++CD6+ciAWT9+r7lm37ZatAh1x4sNxHXTc+PG7VtXf19S6kp66qlmkyfve6zy/Pln/HX1g0RdQn0qk7pF6sesftxE3vSj+sMP963br5/ZmjX7r6cfivpB1anTvnV//DF0PCLX0f/6sRo7cTKQilGjQmPY0/Lmm2aXXHIwSgQAiYHs8UhK69ats/Lly1vVqlXdDUASUR1yZAAcGzCrZey440LrqsV68OD91/FvSjXcu3doXQXKtWtHj8eOpED1rbdC9xWMduyYchnbto0O2jV2LqXxn7HL1QKn/YsXMNepE71u376hMaixAbNusS1mGuunigfd0gqmH3zQ0s0fwwscIH/Ia1atBwC5DUE7cpTjjz/e5s2b52qlmJ8dOEjU2qqu3n53QAWjaoWNFyzrpuQ4fkYpzeukADSldS+/3Ozee0Prqot3ar1nFCy/+GLovgLau+9OeV116YvsLqdyxFKAq2A48rtErdVnnhnqLhsvYI4NZEeODL0+NhDXLbI7oqSWzCfWTTelf111SQQSmL4OVM+u/Ffx+nfqo6jn4yaiAwAQtCPnKV68uLsByGIadNqjh9lPP0UH1hpjqRbae+4Jradsto0bp7ydO+7Y9+t7+3az119Ped3ITL6R4/PiBcGRvWv0HdCtW/z1/G7hPlU2KBtx7DoaZxivZTq1bM+xzj8//esCuZTqtdQxRp1Y9JGLDNz9j+CgQczXDgApIWhHwtu9e7d17NjRrr76ajv33HODLg6QnJSsTOOqN25Mu6u3WnbV1TuyBTry5ndh95PJDByYcnDtjyX3E+nofdQynlY3b3UFf+WV9O8f00ICgerQwey990L1gn/8sW+56uIUsOt5AEB8JKIjEV3CGzJkiN10001WunRpl4hO/wPIhi7wrVqFup0/9FCoe3lsV+/IDLgAkMmvGk0brhErGsOuTjm0sAPIrTaSiA7Jolu3bvb7779b/fr1CdiBrKRu7mrmUsu2fjW//34oOI+cygYAspC+apQnEgCQfrS009IOIDfS3ErXXhtKBDdkSNClAQAAyHU2pjMOzXtQSwVkwIKMZFoGkD4aM3711WZduoSmH9NYdnWJBwAAQEIiaEdCmjp1qusO36lTJ9sVO6cygMzRNG0nnmj26quhRG/9+pl99RVj1QEAABIYY9qRkObMmWN58uSxggULWgHG1wIHbtgwsxtvDLW0V6xoNmKEWcuWQZcKAAAAaWBMO2PaE9bMmTPt8MMPtzJlygRdFCBn+/tvs6OOMlu3zqxFi1DAfuihQZcKAAAgV9tI9njkdI0aNQq6CEBy0FzpQ4eazZ9v1rcv8ysBAADkIIxpR0LVNHXv3t1WafJWAJmnDlQvv2z26af7lrVvb3bPPQTsAAAAOQxBOxLGHXfcYa+88oq1a9fOGLUBZNLGjWadO5tdc01oOrc1a4IuEQAAAA4A3eORMG6++WabNWuWPfHEEy4JHYAMmj3brFMns19+Mcuf36xPH7Py5YMuFZLYnr17bNLySbZq0yqrVKKSNa3e1PLlpTcHAABZiaAdCeOYY46x6dOnE7ADGaWeKUOGmPXqZbZzp1n16mZvvWV2yilBlwxJbPSi0dZjXA/7Y+Mf4WVVS1a1wWcPtg51OgRaNiQuKnqQUVwzyKhkvGYI2hG4v/76y8r/1xpIwA5k0K5dZhdfbDZ69L6x66+9Zla2bNAlQ5IH7B3f6WieRQ9lWrlxpVv+3kXvEbhjP1T0IKO4ZpBRo5P0mmFMOwL1zjvvuGndhiqzNYCMK1AgFKDr/0GDzMaMyXTArprpCb9PsFHzR7n/9RiIpetCP4hiA3bxl/Uc15PrB3EreiJ/SEdW9Oh5IBLXDDJqdBJfMwTtCNSbb75pmzZtsqVLlwZdFCBndYffvHnf46efNps2zaxHD3VXydQm9Yes5uCa1vz15tZ5dGf3vx7n5D9wyB7qchj7gyg2cF+xcYVbDxAqepBRXDPIqD1Jfs0QtCNQ77//vssYf4+mogKQtn//DXWBv/BCs717Q8uKFDFr2DDTm0zmmmlkPY0RzMr1kPyo6EFGcc0goyYl+TVD0I5A5cuXz7p162YFCxYMuihA4vv+e7MGDczGjjX79luzOXMOeJPJXjONrKekPlm5HpIfFT3IKK4ZZNSqJL9mCNpx0C1fvtyeeeYZ2+u3EgJInT4rAweanX662YoVZkceaTZ1qtkJJxzwppO9ZhpZT1l4ldQnj8UfiqHl1UpWc+sBQkUPMoprBhlVKcmvGYJ2HFQK1K+88kq75ZZb7Pbbbw+6OEDi++svs3POMbvzTrM9e8w6dzabNSvU4p4Fkr1mGllP0+YoC6/EBu7+40FnD8rx0+sg61DRg4zimkFGNU3ya4agHQeVpnTr2LGjm+Lt+uuvD7o4QOK76CKzcePMChc2e+UVsxEjzEqUyLLNJ3vNNLKHps3RtG5VSlaJWq4fTEz3hlhU9CCjuGaQUfmS/JrJ43lKQ5y7bdy40UqVKmUbNmywkiVLBl2cXGHr1q1WtGjRoIsBJD6NW7/qKrPhw83q1cvyzWusurLEK+lcvHHt+kOnQGxpj6U59g8dso+uHw2dUE8MVeyoBYPrBBmZP1ktX/ohTUUP4uGaQbJfMxvTGYcStBO0HxR79uwxXWr58+cPuihAYlu1KjRe/YIL9i3T13Qmp3LLSPZ491YRgbtfM03LKYCsQkUPMoprBsl8zaQ3Dg20e/zEiRPtvPPOs8qVK7tu0x988EGK61533XVunUGDBkUt//fff61Lly5uJ0uXLu0ykW+OnL8YCeGJJ56wJk2a2OLFi4MuCpC4vvwyNFa9UyezGTP2Lc/GgF3o6gzgYNEP5zNqnmGXHHuJ+z9Rf0gjcXDNIKOS8ZoJtNlzy5YtVr9+fbvqqqusQ4eUfxSOGTPGpk6d6oL7WArYV61aZV9++aXt2rXLJTm75ppr7M0338zm0iMj51lB+9q1a23y5MlWu3btoIsEJJbdu83uv9/s4YdDrerHHpul49bTQ4F5+9rtc0zNNAAAQG4RaNDepk0bd0vNypUr7eabb7bPP//czlEG5QiLFi2ycePG2YwZM6xRo0ZumaYSa9u2rT3++ONxg3wcfMWKFbNZs2bZ0KFDXaUKgAh//BHKCD/pvynVrr3W7KmnzIoUCaxmGgAAAIkjb6JPD3bZZZdZ79697Zhjjtnv+SlTprgu8X7ALi1btrS8efPatGnTUtzujh073PiByBuyV9WqVa1fv35uiAOA/3z6aag7vAJ2tayPGmX2wguBBOwAAABITAkdtA8cONAlLtOc3vGsXr3aKlSoELVM65ctW9Y9l5IBAwa4Af/+rVq1alledijp9Rz74Ycfgi4GkLgWLjT75x+z4483mz3b7OKLgy4RAAAAEkzCBu3qTj148GAbNmxYlrfO9u3b12Xo828rVqzI0u3DbNu2bda5c2dr3Lixffjhh0EXB0gckRN29Opl9txzZt9/b3bEEUGWCgAAAAkqYYP2SZMmucRl1atXd63nui1btsx69eplNWvWdOsceuihbp1Iu3fvdhnl9VxKChUq5LLNR96QtbZv325HH320lStXzmWNB2BmqsBq2lTZGUOP8+Y1u/56fSkFXTIAAAAkqIQN2jWWfd68eTZ37tzwTYnlNL5dSenklFNOsfXr17tWed8333zjxsKfdNJJAZYeZcqUsdGjR9vMmTNd4A7kajt3mvXsaXb++WaTJ5s9+WTQJQIAAEAOEWj2eM2n/ssvv4QfL1261AXnGpOuFvbYYK9AgQKuBd2fMqxOnTp29tlnW/fu3e2FF15wU77ddNNNdvHFF5M5PiCe54WHM+j/KlWi530Gcp3ffgvNuz5zZujxbbeZ9ekTdKkAAACQQwTa0q5W2IYNG7qb3Hbbbe7+vffem+5tjBw50nXDbtGihZvqTV2xX3rppWwsNVKj6fnuvvtul6EfyPXee89M328K2MuUMfvoI7MnnjArWDDokgEAACCHyOOpaTSX05RvyiKvpHSMb888ZYpvoOmr/stJwFh25GrPPqtarND9U08NTedWvXrQpQIAAEAOi0MD7R6P5FK/fn177733bP78+QTsQIcOZv37m115pdkDD2h8jyW6PXtCU8avWmVWqVIoZ16+fEGXCgAAIHejpZ2WdgBZRd3gGzXa93j9erPSpS0nGD3arEcPsz/+2LesalWzwYND9Q8AAAAIJg5N2OzxyDnmzJljW7duDboYQHB0/V99tVnjxqFx7L4cFLB37BgdsMvKlaHleh4AAADBIGjHAVm7dq21bt3aJRD8TVmygdzmxx/NTjzR7NVXNWVCKFt8DqIu8Wphj9fnyl+m2eq0HgAAAA4+gnYckOXLl7up+AoVKsT0bsh9hg0Lta4vXGh26KFmX31ldscdlpNoDHtsC3ts4L5iRWg9AAAAHHwkosMBadSokS1YsMD+/vtvF7gDucLmzWY33mj2xhuhxy1bmo0YYVaxouU0SjqXlesBAAAga9HSjgNWpkwZO/LII4MuBnDwqNlZAXvevKEM8Z9/niMDdlGW+KxcDwAAAFmLoB0ZpgkHrr76ahs/fnzQRQGC0aaN2YMPmk2YYHb33aHgPYfStG7KEq/h+PFoebVqofUAAABw8OXcX5oIzBtvvGGvvvqqnXvuua5bPJD0Nm40u/ba6MHf99yTFJGs5mHXtG4SG7j7jwcNYr52AACAoDCmHRnWoUMHmzJlih199NF2yCGHBF0cIHvNnm3WqZPZL7+Ebko2l1KzdA6ledg1U128edoVsDNPOwAAQHDyeOrrnMuld1J7ALmIvhqHDDHr1cts506zGjXM3nrL7OSTLVlpWjcN11fSOY1hV0cCWtgBAACCjUNpaUe6LVu2zGoocAGS3fr1Zt26mY0eHXp8/vlmr72mrIuWzBSgn3FG0KUAAABAJMa0I10WLVrkusN369bNduzYEXRxgOyzZIlZw4ahgL1AgdCAb91P8oAdAAAAiYmWdqTLN99844L1VatWWcGCBYMuDpB9NJC7WDGzww4ze/tts0aNgi4RAAAAcjGCdqTLjTfeaMcee6wdccQRlifJknABtm6dWalSoanbihY1++gjs3LlQssAAACAANE9Hul2+umnW+XKlYMuBpC1Jk82O+44s0cf3bdMrewE7AAAAEgABO1I0fbt2+2OO+6wf/75J+iiAFlv716zAQPMmjULzXM2fLgZ+RoAAACQYAjakaL777/fHnvsMWvRooUxMyCSytq1Zm3amN11V2ies86dzaZONStUKOiSAQAAAFEI2pGijh07Wp06deyBBx5gHDuSx4QJZg0amH3xhVmRImavvGI2YoRZiRJBlwwAAADIuqB9+PDhdtppp7kxzpq/WwYNGmQffvhhZjeJBNOoUSP74YcfrF27dkEXBcgaf/1l1rat2apVZnXqmE2fHpqPnUopAAAAJKhMBe3PP/+83Xbbbda2bVtbv3697VH3UjMrXbq0C9yRs23evDl8v4DmqQaSRfnyZo89ZnbllWYzZpjVqxd0iQAAAICsD9qfeeYZe/nll+3uu++2fPnyRbXMzp8/PzObRIL46quvrGbNmva25qcGksGXX5rNmrXv8Q03mL32WmgudgAAACAZg/alS5daw4YN91teqFAh27JlS1aUCwF59tlnXbb4SZMmBV0U4MDs3m12zz1mrVubXXSR2YYNoeV0hQcAAEAOkj8zL6pVq5bNnTvXatSoEbV83LhxLnEZcq53333Xnn76abvuuuuCLgqQeZrCTRnh/cqnVq3MChYMulQAAADAwQnaNZ79xhtvdPN4ayqw6dOn26hRo2zAgAH2ijIxI8fSGPZevXoFXQwg8z791Ozyy83++SeUEf7ll806dQq6VAAAAMDBC9qvvvpqK1KkiN1zzz22detW69y5s8siP3jwYLv44oszVxIE5u+//7ZPPvnELr/8cqZ2Q87uDq9515VoTo4/3ky5GY44IuiSAQAAAAc3aJcuXbq4m4J2ZRuvUKFC5kuBwKinxPXXX2/vvfeeG/Lw1FNPBV0kIHPy5jXzE2HefHMoeC9UKOhSAQAAAMEkovv555/d/aJFi4YDdi37/fffD6xEOOhOPPFEK1asmF166aVBFwXIuL179wXtb7xh9uGHZk8/TcAOAACA3Bu0X3HFFfb999/vt3zatGnuOeQc6g7fu3dvW758uZ1wwglBFwdIvx07zHr21Hid6HnY27ULslQAAABA8EH7nDlz7LTTTttv+cknn+y6WCNndIvf67dQmlnZsmUDLQ+QIb/+aqbvoMGDzYYOjZ6HHQAAAMjtQbtaZzdt2rTf8g0bNtiePXuyolzIZq+++qq1bNnSli1bFnRRgIx5991QkjkF6qpsGjvWjF4iAAAASFKZCtpPP/10N71bZICu+1rWpEmTrCwfsoGm6uvXr5+NHz/e3n///aCLA6TP9u1mN9xgdtFFZhs3hlra1bPn3HODLhkAAACQWNnjBw4c6AL32rVrW9OmTd2ySZMm2caNG+2bb77J6jIiixUuXNidr2effdZ69OgRdHGA9NFY9S+/DN3v29fsgQfM8md6AgwAAAAgR8jjaXBzJvz5558u6Pvhhx/cnO3HHXec3XTTTTlybLQqG0qVKuW695csWTLo4gCIZ9w4ZcE0e/11s9atgy4NAAAAcFDi0EwH7ckktwTtv/zyi0s+d9RRRwVdFCBtW7eaLVxo1rjxvmVbtpgVKxZkqQAAAICDGodmum/p+vXrbfr06bZ27dqoLORy+eWXZ3azyCa7d+9287DPmzfPRo0aZe3btw+6SEDKFKxr7PrKlWazZ5sddlhoOQE7AAAAcplMBe1jx461Ll262ObNm12NgLLJ+3SfoD3xKNu/zlXBggXteGXeBhKROv4MG2Z2441m27aZHXqo2dq1+4J2AAAAIJfJVPd4da9u27atPfzww1a0aFHL6XJL93id6iVLlrgEgkDC2bzZ7PrrzUaMCD1u1cps+HCzihWDLhkAAAAQWByaqSnfVq5cabfccktSBOy5iXpBELAjIf3wQ2iudQXs+fKZPfxwKPEcATsAAAByuUwF7a1bt7aZM2dmfWmQ5f7v//7PBgwY4Ma0AwlLGeGXLDGrUsVswoTQlG55M/X1BAAAACSVTI1pP+ecc6x37972448/2rHHHmsFChSIer6d5lNG4H766Sd74IEHXKLAE044wc4666ygiwTEN2CAuoKEgvVDDgm6NAAAAEDOHtOeN5UWMHXB3rNnj+UkyTqmXad2+PDhNnXqVHvuueeCLg6wz6xZZs8+a/byy2b5Mz2JBQAAAJBjMU97BiRr0A4kHH3dKFi//XaznTvNBg40u+OOoEsFAAAAJFciOiS2n3/+2XYqIAISybp1ZhdeaHbLLaGA/fzzzbp3D7pUAAAAQELLdL/ULVu22LfffmvLly/fL0BUZnkENx97q1atXI3NBx98YLVq1Qq6SIDZtGlmnTqZLVtmphwYjz9udvPNoXHsAAAAALI2aJ8zZ46bp33r1q0ueC9btqz9/fffbgq4ChUqELQHnHxO50R5Bw4hoRcSwRtvmHXrZqYZDA47zOztt80aNQq6VAAAAECOkKnu8bfeequdd955tm7dOitSpIhLdLZs2TKXofxxtaAhMI0bN7YFCxbY6NGjrUSJEkEXBwjNv67W9f/9z2z2bAJ2AAAAIAMylYiudOnSNm3aNKtdu7a7P2XKFKtTp45b1rVrV9fam5OQiA7IYqtWmVWqtO/x4sVmRx1Fd3gAAADgYCSi07zs/rRv6g6vce2iN1yxYkVmNokD1LdvX9fjAQjU3r2hOdeVS2Hy5H3La9cmYAcAAAAyIVNBe8OGDW3GjBnufrNmzezee++1kSNHWs+ePa1evXqZ2SQOwJgxY+yRRx5x52KVWjgBM9uzd49N+H2CjZo/yv2vx9lq7VqzNm3M7rrLbMcOsw8+yN73AwAAAHKBTCWie/jhh12WcnnooYfs8ssvt+uvv96OPPJIe+2117K6jEjDGWecYZdeeqnVrFnTKkV2SUauNXrRaOsxrof9sfGP8LKqJava4LMHW4c6HbL+DcePN+vc2Wz1arMiRcyGDDG74oqsfx8AAAAgl8nUmPZkkyxj2vfu3RsetoDcHbB3fKejeRb90c5joe7p7130XtYF7nv2mPXvb/bAA6Gu8XXrmr3zjtkxx2TN9gEAAIAkla1j2pEYNM1eJAJ2qAu8WthjA3bxl/Uc1zPrusqPGWN2//2hgP2qq8w0bIaAHQAAADj43eOPP/54+/rrr61MmTJuTHueVJJKzda0TshWSv5Xv359NzRh4MCBVrhw4aCLhAQwafmkqC7x8QL3FRtXuPXOqHnGgb/hhReaXX65WatWZpdeeuDbAwAAAJC5oL19+/ZWqFAhd//8889P78uQTT766CNbv369TZ8+3WXzB2TVplVZut5+du82e+ops2uvNVMXHlXevf565rYFAAAAIOuC9vvuu8/9v2fPHmvevLkdd9xxbo52BOOmm26yI444wg477DDLly9f0MVBgqhUolKWrhfljz/MLrnE7LvvzGbNMnvrrYxvAwAAAECGZHgQtALEs846y9atW5fRlyKLnX322XbUUUcFXQwkkKbVm7os8X7SuVhaXq1kNbdehnzyiVmDBqGAvUQJsw7ZkIEeAAAAwH4ylblMc7H/9ttvmXkpDoB6OWg+dmUZBOLJlzefm9ZNYgN3//Ggswe59dJl1y6z3r3Nzj3X7J9/zE44wWzOHLOLLsr6wgMAAADImqC9f//+dvvtt9vHH39sq1atckFk5A3Z48knn7S+fftakyZNXAAPxKPp3DStW5WSVaKWqwU+Q9O9qTt806Zmjz8eenzLLWaTJ5sdfng2lBoAAADAAY1pj9S2bVv3f7t27aKyyGvKdz0moMweJ510ktWqVct69uzJOHakSoF5+9rtXZZ4JZ3TGHZ1iU93C7so8eSKFWbKXTF0qDJQZmeRAQAAAGRV0D5+/PjMvAwH6PTTT7cFCxZYkSJFgi4KcgIvn9nvZ5gpUbzyzlVLZ3b4/P99LZQvH5qHvUIFs5o1s7u0AAAAALIqaG/WrFlmXoYsULRo0aCLgBxg9GizHj1CPdx9VauaDR6cSg65X34x69TJ7NZb9825fuKJB6W8AAAAAOLL46lPeyZt3brVli9fbjt37oxarungchKNwy9VqpRt2LDBSmruaSCHB+wdO2q4SvRyfyTLe+/FCdzfecfs6qvNNm0yq1HDbMkSs4IFD1qZAQAAgNxmYzrj0Ey1tP/111925ZVX2meffRb3eca0A8HQR08t7PGq4rRMgXvPnmbt22v6RjPbti3Usv7ii6GVmjQxGzWKgB0AAADIydnjlQht/fr1Nm3aNDe+ety4cfb666/bkUceaR999FG6tzNx4kQ777zzrHLlyi6B3QcffBB+bteuXdanTx879thjrVixYm6dyy+/3P7888+obfz777/WpUsXVzNRunRp69atm23evDkzuwXkeJMmRXeJjxe4K7ec1rPFi81OPjkUsCuav+suJawI9aMHAAAAkHOD9m+++cZNP9aoUSPLmzev1ahRwy699FJ79NFHbcCAAenezpYtW6x+/fo2ZMiQuF3vZ8+ebf369XP/jx492hYvXuwy1kdSwL5w4UL78ssv3RR0qgi45pprMrNbQI63Sknn0uHfn9aaNW5sNm9eKOHcuHFmDz20LwkdAAAAgISQqV/oCrYrKKO0mZUpU8Z1lz/qqKNcq7gC7PRq06aNu8Wjvv0KxCM9++yzduKJJ7px9NWrV7dFixa5Vv4ZM2a4CgR55pln3JR0jz/+uGudB3KTSsoSnw5lj65gdsMNZtOnm40cmf4XAgAAAEj8lvbatWu7Vm9RS/mLL75oK1eutBdeeMEqZeOPfw3QVzd6dYOXKVOmuPt+wC4tW7Z0rf/qup+SHTt2uEH/kTcgGTRtGurd7iedi1THfrSa9rtVqxZaz/r3N1PFGAE7AAAAkFxBe48ePWzVf/1w77vvPpeQTi3fTz/9tD388MOWHbZv3+7GuF9yySXhzHqrV68Ot/j78ufPb2XLlnXPpURd+NWS79+qKYoBkoCSy2laN9kXuHt2hQ21mdbI3rJONvixnaEkdOoK7+4AAAAASKru8Rq/7jvhhBNs2bJl9tNPP7nA/ZBDDrGspqR0F110kWl2uueff/6At9e3b1+77bbbwo/V0k7gjmSh6dw0rZuyyK//Y5M9ZzfYZTbCPVezfik76awtZkZ2eAAAACBpW9q/++67qMdFixa1448/PlsDdlUMaIx75Px1hx56qK1duzZq/d27d7uM8nouJYUKFXLbibwByRa4/z5vo62ueYoL2L28+Wxv/4et4uxxSkQRdPEAAAAAZGfQfuaZZ1qtWrXsrrvush9//NGyix+w//zzz/bVV19ZuXLlop4/5ZRT3NRzs2bNispsv3fvXjvppJOyrVxATpCvV08r9vtC1W5Znm8nWN67+5rlzdRHHgAAAEBAMvULXnOl9+rVy7799lurV6+eNWjQwB577DH7I7UJouPQfOpz5851N1m6dKm7r+zwCtg7duxoM2fOtJEjR9qePXvcOHXddu7c6davU6eOnX322da9e3ebPn26TZ482W666Sa7+OKLyRyP3G30aLOhQ0MD2995x6xJk6BLBAAAACAT8ngaKH4AFGi/+eabNmrUKDeu/fTTT3et3ekxYcIEa968+X7Lu3btavfff79rzY9n/PjxdsYZZ7j76gqvQH3s2LEua/yFF17oEuIVL1483fugMe1KSKfs9HSVR1JYtEjJJ8xatTJ75JGgSwMAAAAgk3HoAQftolZwZZDv16+fzZs3zz3OSQjakZT+65FiBUk6BwAAAOTUOPSABriqO/oNN9zg5mbv3Lmz6yr/ySefHMgmARyIf/7Zd1/BOgE7AAAAkKPlzeyUaeq6roR0Gn8+ePBgN9Z8+PDhbow5gAAsXmymISX9+mkahaBLAwAAACCoedonTpxovXv3dpnds2OaNwAZtGtXaAz7pk1mU6eSJR4AAADIzUG7usUDSCAPPmg2c2ZoDvZhwwjaAQAAgNwWtH/00UfWpk0bK1CggLufmnbt2mVF2QCkx5QpZg89FLr/wgtmVaoEXSIAAAAAWSTd2eM1nZrGrVeoUMHdT3GDefKQPR44WDZvNmvQwOzXX826dDEbMSLoEgEAAADIwjg03S3te/fujXsfQIBuvTUUsFerZvbss0GXBgAAAEAWY+ArkJM1bmxWvLjZG2+YlS4ddGkAAAAAJEIiOpkxY4aNHz/e1q5du1/L+5NPPpkVZQOQlmuuMfvf/0IJ6AAAAAAknUwF7Q8//LDdc889Vrt2batYsaIbx+6LvA8gGygNxZYtoRZ2IWAHAAAAklamgvbBgwfba6+9ZldccUXWlwhA6l5+OZQtXl3imzULujQAAAAAEm1Mu7LHn3baaVlfGgCp+/nnUPK55cvNZs0KujQAAAAAEjFov/XWW23IkCFZXxoAKdu92+yyy8y2bjU780yznj2DLhEAAACAROwef/vtt9s555xjhx9+uNWtW9cKFCgQ9fzo0aOzqnwAfOoSP22aWalSZsOGqctL0CUCAAAAkIhB+y233OIyxzdv3tzKlStH8jkguylYf/DB0P3nngvNyw4AAAAg6WUqaH/99dft/fffd63tALKZMsWrW/yePWYXX2zWuXPQJQIAAACQyEF72bJlXdd4AAfBzp1mxx5rtm1bqJUdAAAAQK6RqUGx999/v9133322VQmxAGQvzcP+3ntm06czJzsAAACQy2Sqpf3pp5+2X3/91SpWrGg1a9bcLxHd7Nmzs6p8+I96Rk+aZLZqlVmlSmZNm5rlyxd0qZCtduwwK1jQTDkjdNOJBwAAAJCrZCpoP//887O+JEiRkvH36GH2xx/7llWtajZ4sFmHDkGWDNnG88w6dTIrXDjUJb5s2aBLBAAAACAAeTxP0UHutnHjRitVqpRt2LDBSpYsaYkWsHfsGIrhIvkJ+9VrmsA9Cb36qtnVV4da2tUtvn79oEsEAAAAIIA4NNMTPa9fv95eeeUV69u3r/3777/hbvErV67M7CYRp0u8WtjjVav4y3r2DK2HJPLrr6ETL/37E7ADAAAAuVimusfPmzfPWrZs6WoFfv/9d+vevbvLKD969Ghbvny5vfHGG1lf0lxIY9gju8THC9xXrAitd8YZB7NkyDa7d4emd9M0b6efbnbbbUGXCAAAAECAMtXSftttt9kVV1xhP//8sxXWmNv/tG3b1iZOnJiV5cvVlHQuK9dDDvDII2ZTppipe4wqv8g2CAAAAORqmQraZ8yYYddee+1+y6tUqWKrV6/OinLB0p8snKTiSWLmTLP/+7/Q/WefNatRI+gSAQAAAMiJ3eMLFSrkBs3HWrJkiZUvXz4rygULTeumLPFKExBvXLuS0el5rYcksH27WcWKZqeeanbppUGXBgAAAEBObWlv166dPfDAA7Zr1y73OE+ePG4se58+fezCCy/M6jLmWuoZrWndIrPF+/zHgwbRgzppNGliNn++2Ysv7n/CAQAAAORKmQran3jiCdu8ebNVqFDBtm3bZs2aNbPDDz/cihcvbg899FDWlzIX03RumtatSpXo5WphZ7q3JEo+5ytTJnQDAAAAgAOdp/27775zmeQVwJ9wwgnWokULy4kSeZ52n6Z1U5Z4JZ3TGHZ1iaeFPQn8/bfZaaeZ3Xmn2RVX0MIOAAAA5BIbs2Oe9ilTptjHH38cftykSRMrVqyYPffcc3bJJZfYNddcYzt27DiwkiMuBeia1u2SS0L/E7AnAdWXKaHjkiVmjz1mtnNn0CUCAAAAkGAyFLRrHPvChQvDj+fPn+/maG/VqpXdeeedNnbsWBswYEB2lBNIPq+/bjZ6tFmBAmYjRyrDY9AlAgAAAJCTg/a5c+dGdYF/66237MQTT7SXX37Zzd3+9NNP2zvvvJMd5QSSy9KlZrfcErqvad4aNgy6RAAAAAByetC+bt06q6gpqf7z7bffWps2bcKPGzdubCtWrMjaEgLJRgkKLrvMbNOmUMb4O+4IukQAAAAAkiFoV8C+VC2EpuG3O2327Nl28sknh5/ftGmTFVBXXwApe/RRs8mTzUqUMHvjDRIUAAAAAMiaoL1t27Zu7PqkSZOsb9++VrRoUWuqNOb/USZ5Tf0GIBVbt4ayxD/9tFmtWkGXBgAAAEACy5+RlR988EHr0KGDm5ddc7K//vrrVrBgwfDzr732mp111lnZUU4geTz4oFmnTmbHHBN0SQAAAAAk4zztmkdOQXu+mG69//77r1seGcjnBDlhnnYkAX3UmIcdAAAAgGXTPO0+bTg2YJeyZcvmuIAdOCi+/NLslFNCc7IDAAAAQDplKmgHkAH//mt2xRVm06aZPfdc0KUBAAAAkIMQtAPZ3SX+uuvM/vzTrHZts4cfDrpEAAAAAHIQgnYgO40YYfbuu2b584fuFy0adIkAAAAA5CAE7UB2WbbM7KabQvfvu8+sUaOgSwQAAAAghyFoB7LDnj1ml1+ulJChBHR33hl0iQAAAADkQATtQHYln9u61axYMbPhw0Pd4wEAAAAgg4gkgOxQvrzZ99+b/fCD2eGHB10aAAAAADkULe1AVmeL9xUowDh2AAAAAAeEoB3ISnfcYXbXXWY7dwZdEgAAAABJgO7xQFb5+muzxx8P3W/Z0uzMM4MuEQAAAIAcjpZ2ICusW2fWtWvo/rXXErADAAAAyBIE7UBWuOEGs5UrzY480uyJJ4IuDQAAAIAkQdAOHKg33zR76y2zfPnMRowITfMGAAAAAFmAoB04EMuXh1rZpV8/sxNPDLpEAAAAAJIIQTtwIBYsMNu9OxSs33130KUBAAAAkGTIHg8ciLZtzX74IXQ/Px8nAAAAAFmLKAM4UIcfHnQJAAAAACQpuscDGbVjh9m555p9803QJQEAAACQ5GhpBzLqnnvMPvnEbOZMs99+MytaNOgSAQAAAEhStLQDGTFhwr552F96iYAdAAAAQLYiaAfSa/16s8svN/M8s6uvNmvXLugSAQAAAEhyBO1Aet10k9mKFaHEc089FXRpAAAAAOQCBO1Aerz9ttnIkWZ585oNH25WvHjQJQIAAACQCxC0A+nx1Veh/+++2+yUU4IuDQAAAIBcguzxQHoo6dw554RuAAAAAHCQELQD6ZEnj9n55wddCgAAAAC5DN3jgZQsXGjWpYvZP/8EXRIAAAAAuVSgQfvEiRPtvPPOs8qVK1uePHnsgw8+iHre8zy79957rVKlSlakSBFr2bKl/fzzz1Hr/Pvvv9alSxcrWbKklS5d2rp162abN28+yHuCpLNzp9mll5q9+abZbbcFXRoAAAAAuVSgQfuWLVusfv36NmTIkLjPP/roo/b000/bCy+8YNOmTbNixYpZ69atbfv27eF1FLAvXLjQvvzyS/v4449dRcA111xzEPcCSenee83mzjUrV87skUeCLg0AAACAXCqPp+bsBKCW9jFjxtj5/40bVrHUAt+rVy+7/fbb3bINGzZYxYoVbdiwYXbxxRfbokWLrG7dujZjxgxr1KiRW2fcuHHWtm1b++OPP9zr02Pjxo1WqlQpt3212COXmzjR7IwzdBGajR5tdsEFQZcIAAAAQJJJbxyasGPaly5daqtXr3Zd4n3aoZNOOsmmTJniHut/dYn3A3bR+nnz5nUt8ynZsWOHO0CRN8DRtXD55aGA/corCdgBAAAABCphg3YF7KKW9Uh67D+n/ytUqBD1fP78+a1s2bLhdeIZMGCAqwDwb9WqVcuWfUAOdMstZsuWmdWqZTZ4cNClAQAAAJDLJWzQnp369u3ruiD4txUrVgRdJCQCZYn/+muzvHnNhg83K1Ei6BIBAAAAyOUSdp72Qw891P2/Zs0alz3ep8cNGjQIr7N27dqo1+3evdtllPdfH0+hQoXcDYiipHPz55t99ZXZaacFXRoAAAAASNyW9lq1arnA+2u1fP5HY881Vv2UU05xj/X/+vXrbdasWeF1vvnmG9u7d68b+w5kWOnSZh07Bl0KAAAAAAi+pV3zqf/yyy9Ryefmzp3rxqRXr17devbsaf3797cjjzzSBfH9+vVzGeH9DPN16tSxs88+27p37+6mhdu1a5fddNNNLrN8ejPHA/byy5q+wKxbt9D/AAAAAJAgAg3aZ86cac2bNw8/vu2229z/Xbt2ddO63XHHHW4ud827rhb1Jk2auCndChcuHH7NyJEjXaDeokULlzX+wgsvdHO7A+myaFEo+dz27WZKatiuXdAlAgAAAIDEm6c9SMzTnkvt3KkxFmazZ5uddZbZZ5+FktABAAAAQDbL8fO0A9nu//4vFLCXLWs2dCgBOwAAAICEQ5SC3GnyZLNHHgndf/FFM3IgAAAAAEhABO3IfTZtMrvsMrO9e80uv5xs8QAAAAASFkE7ch/Nw/7772Y1apiRtBAAAABAAgs0ezwQiAsuMJs4MTSGvVSpoEsDAAAAACkiaEfu1KRJ0CUAAAAAgDTRPR65g2Y2vOMOs8WLgy4JAAAAAKQbQTtyhxdeMHvssdC87EpEBwAAAAA5AEE7kp9a13v1Ct2/916zEiWCLhEAAAAApAtBO5Lbrl1ml15qtm2bWYsWZrfcEnSJAAAAACDdCNqR3B580GzmTLPSpc2GDQtljAcAAACAHIIIBslr6lSzhx7aN6a9atWgSwQAAAAAGULQjuQ1eLDZ3r1mXbqYdeoUdGkAAAAAIMOYpx3J6403zI4/3qx796BLAgAAAACZQtCO5FWggFnv3kGXAgAAAAAyje7xSC5r1pj93/+Z7dwZdEkAAAAA4IDR0o7k4XlmV19t9vHHZj//bDZiRNAlAgAAAIADQks7ksfLL4cC9oIFzfr0Cbo0AAAAAHDACNqRHNSyfuutofsPP2x27LFBlwgAAAAADhhBO3K+3bvNLrvMbOtWs+bN9wXvAAAAAJDDEbQj51PL+rRpZqVKmb3+ulleLmsAAAAAyYHoBjnbhg1mgwaF7j/3nFm1akGXCAAAAACyDNnjkbOpdX3mTLNRo8w6dw66NAAAAACQpWhpR8532GFmd98ddCkAAAAAIMsRtCNn+vJLs6+/DroUAAAAAJCtCNqR8/z1VyhbfMuWZh9+GHRpAAAAACDbELQjZ/E8s+7dzdasMTvmGLPWrYMuEQAAAABkG4J25CyvvRZqXS9QwGzkSLPChYMuEQAAAABkG4J25By//mrWo0fofv/+ZvXrB10iAAAAAMhWBO3IGXbvNrv8crMtW8xOP92sV6+gSwQAAAAA2Y6gHTmDusR//71ZyZJmb7xhli9f0CUCAAAAgGyXP/vfAsgCHTqYjRhhlj+/WY0aQZcGAAAAAA4KgnbkDHnymHXpEnQpAAAAAOCgons8EtvQoWZ//x10KQAAAAAgEATtSFzjxplddVUoS/z69UGXBgAAAAAOOoJ2JCa1rl95Zej+hRealS4ddIkAAAAA4KAjaEfi8Tyza681W73arE4ds4EDgy4RAAAAAASCoB2JR1O6jR4dyhSvjPFFigRdIgAAAAAIBEE7EsvSpWY33xy6/8ADZscfH3SJAAAAACAwBO1ILHfdZbZpk1mTJmZ33BF0aQAAAAAgUMzTjsTywgtmJUqY9e1rli9f0KUBAAAAgEARtCOxlCpl9tJLQZcCAAAAABIC3eMRvG3bzN58M5Q1HgAAAAAQRtCO4N15p1mXLmbXXBN0SQAAAAAgoRC0I1hffmn29NOh+x06BF0aAAAAAEgoBO0Izr//ml1xRej+DTeYtWkTdIkAAAAAIKEQtCMYGr9+/fVmf/5pVru22WOPBV0iAAAAAEg4ZI9HMEaONHvnHbP8+c1GjDArWjToEgEAkKvt2bPHdu3aFXQxACBpFChQwPJlwTTWBO04+DZvNuvRI3T/vvvMGjUKukQAAORanufZ6tWrbf369UEXBQCSTunSpe3QQw+1PHnyZHobBO04+IoXNxs71mzIkFDmeAAAEBg/YK9QoYIVLVr0gH5YAgD2VYhu3brV1q5d6x5XqlTJMougHcE49dTQDQAABNol3g/Yy5UrF3RxACCpFClSxP2vwF3fs5ntKk8iOhw8CxeaLVoUdCkAAMB//DHsamEHAGQ9//v1QHKGELTj4Ni+3axTJ7Pjjzf79NOgSwMAACLQJR4AEvf7laAdB8ddd4Va2kuWJPEcAAAAAKQTQTuy39dfmz31VOj+a6+ZVagQdIkAAACy1RVXXGHnn3++5WQ1a9a0QYMGWSK5//77rUGDBgethfSDDz6w3JicslWrVlasWDGX+TzRDBs2LMPl+v333935nDt3ruVEBO3IXuvW6a9W6P6115qdc07QJQIAANlgzx6zCRPMRo0K/a/H2R0U60d47O3ss8+2RDB48GAXXCSCZAo+b7/9dvtaDUIHwapVq6xNmzaW2zz11FNu3xXgLlmyxHKrKxKo4o3s8cheN95o9scfZkceafbEE0GXBgAAZIPRo8169Aj9yfdVrarA1axDh+x7XwXoQ4cOjVpWqFAhCzojv4LkUqVKBVqOZFW8eHF3y047d+60ggULurm1s5v/Xonk119/tRNOOMGO1O/3LKREbAUKFMjSbeYWtLQj+yjhnKrbNbXBiBFmxYoFXSIAAJANAXvHjtEBu6xcGVqu57OLAnQFVpG3MmXKuOcmTJjggqFJkyaF13/00UfdtEtr1qxxj8844wy76aab3E1B9iGHHGL9+vVz8yv7duzY4Vp3q1Sp4roLn3TSSW7bsV11P/roI6tbt64r0/Lly/drpdN73XzzzdazZ09XxooVK9rLL79sW7ZssSuvvNJKlChhRxxxhH322WdR+7hgwQLX2qtAVa+57LLL7O+//47a7i233GJ33HGHlS1b1h0DdSGP7OIuF1xwgatM8B8rMGvfvr3bprbduHFj++qrrzJ0/HUcTjzxxHA36tNOO82WLVsWfv7DDz+0448/3goXLmyHHXaY/d///Z/t3r07/LzK8+KLL9q5557rMmzXqVPHpkyZYr/88ovbL2331FNPdWWN1z3+iy++cNvWtIWRevToYWeeeaa7/88//9gll1zizp/e49hjj7VR+n0awb8OdG50DbRu3TpuD4X58+e77WoaL02ReM0119jmzZvDz2vfdC50LPR8nz59rGvXrvtdB/He68knn3Rl0z5Xq1bNbrjhhqht+9fZxx9/bLVr13b70rFjRzcP+Ouvv+7Oq64rvb8qjlLz/PPP2+GHH+4+H9rW8OHDw89pO++//7698cYbbv91HcczY8YM14Ve+6DPTrNmzWz27NlR6+j1eq927dq5/XrooYfSdV2kdSzSY/r06dawYUP3Ho0aNbI5c+ZEPa9j1K1bN6tVq5Y7nzoO6h0TeZ3puKqsfi8e/3Ov83rUUUe5c6Dy6zvjQDLDp4sHb8OGDfpmdv8jC+3a5XkPPOB5/fsHXRIAABDHtm3bvB9//NH9nxm7d3te1aqKcOPf8uTxvGrVQutlta5du3rt27dPdZ3evXt7NWrU8NavX+/Nnj3bK1iwoPfhhx+Gn2/WrJlXvHhxr0ePHt5PP/3kjRgxwitatKj30ksvhde5+uqrvVNPPdWbOHGi98svv3iPPfaYV6hQIW/JkiXu+aFDh3oFChRw60yePNltZ8uWLfuVT+9VokQJ78EHH3Sv1f/58uXz2rRp495Py66//nqvXLly7vWybt06r3z58l7fvn29RYsWuX1o1aqV17x586jtlixZ0rv//vvdNl5//XUvT5483hdffOGeX7t2rfudq3KuWrXKPZa5c+d6L7zwgjd//nz3unvuuccrXLiwt2zZsvC2deyeeuqpuMd2165dXqlSpbzbb7/dHRddR8OGDQu/XsdL5dKyX3/91ZWnZs2arpw+latKlSre22+/7S1evNg7//zz3TpnnnmmN27cOLfNk08+2Tv77LPDr7nvvvu8+vXru/u7d+/2Klas6L3yyivh52OX/fHHH+6czZkzx5Xj6aefdsd92rRp+10Hul50/nTzyzdmzBh3f/PmzV6lSpW8Dh06uGP29ddfe7Vq1XLn2de/f3+vbNmy3ujRo935uu6669wxiL0O4r2XjvM333zjLV261G27du3a7nrw+deZzr+ug2+//dZdK2eddZZ30UUXeQsXLvTGjh3rrvG33nrLS4nKpu0MGTLEHfMnnnjCHQ+9t3+96Hhrm7pe9NmJR2UcPny420+dp27durnjvnHjxqjzW6FCBe+1115zx17XRnqui/Qci1KlSqW4j5s2bXKfm86dO3sLFixwx+Wwww5z5dF1IDt37vTuvfdeb8aMGd5vv/0W/uzrWvS3oWOgY6HjoNuOHTvcc/rs6rOu8n300UduvwcOHJip79n0xqEE7QTtAAAglzrQoH38+JQD9sib1stqCpYUbBQrVizq9tBDD4XX0Y/sBg0auB/fdevW9bp37x61DQVQderU8fbu3Rte1qdPH7dMFGToPVauXBn1uhYtWrhA2g8g9DtSQXBs+WKDtSZNmkQFlyrvZZddFl6mwEDbmjJlSjg4UFAWacWKFW4dBVzxtiuNGzd2++GLDD5Tc8wxx3jPPPNMuoL2f/75x213woQJcZ/XMXr44YejlinIU+AbWS5VFvi031r26quvhpeNGjXKVSbEC9pFFS4K8n2ff/65q1RRhUdKzjnnHK9Xr17hxzqGDRs23G+9yOOmipUyZcq44N33ySefeHnz5vVWr17tHit4UwVB5DmuXr36ftdBvPeK9e6777qg3OdfZ6og8V177bUu0FSA6WvdurVbnhJVLsV+Dv73v/95bdu2DT9WeSMrI9Jjz549rlJKAbJP5e3Zs2eGr4v0HItSqQTtL774ols/8nvt+eefjwra47nxxhu9Cy+8MEMVg6JzfsIJJ2Rr0M6YdmS9iRPNTjpJfdaCLgkAAMhGq1Zl7XoZ1bx5c9f9NpK6iPvU/XfkyJF23HHHWY0aNVyCrVgnn3xy1DzKp5xyij3xxBOu+6y6Q+t/dYWNpC7z6v4c+T56j7RErpMvXz63DXUD9qmruqxdu9b9/8MPP9j48ePjjuFWl3G/XLHvXalSpfA2UqLuxuoC/Mknn7ikY+qevG3bNte1Pz10nNV1Wt271U26ZcuWdtFFF7n39ss+efLkcJdo0bHcvn2769KtrsWxZff3P/aY6DUbN260kpo6OEaXLl3cOfzzzz+tcuXK7nyfc8454ezies+HH37Y3nnnHVu5cqUbQ67z57+/T2O4U7No0SKrX7++67Lt03CAvXv32uLFi103bA270HCByHOs7WqdtN5LQxMGDBhgP/30k9tXnY/YY6X/1a098tioO3vk9aFlqZ177Ye69UfSfkR2DU8P7es999zjuozr/XScVdbY60dd0yOl57pIz7FIjfZR15XOSeTnOtaQIUPstddec2XWta9rIz0zE7z99tv29NNPu8+gPkcqX7xrMysRtCNrzZ9v1qqV2dFHm33zjVnEHzQAAJBc/ovPsmy9jFIApXHgqfn+++/d///++6+7RQZdadEPcgVes2bNcv9HigyUNCY2MvBPSWwSLr0mcpm/DT/I0/ufd955NnDgwP225QfHKW03NlCMpXH6X375pT3++OPuGGofNEZagUt6KQmgxlCPGzfOBTIK4rRNBdEqu8Yqd4iTiTAymIq3/6kdk1gai69A9q233rLrr7/exowZE5W1/7HHHnMBqaau88dJazx57H5m5Lo4ULHvpenINK5f5VcwqwqR7777zo25Vjn9QDWt6ye95z4raKy+8gXo2KpCTLkcFBindVzTui7SeywOlK4XfQZUQadyK6eErpVp06al+jrlXFBFkfZBFVYaz69taTvZiaAdWWfHDrNLL1UaTLPq1VUFG3SJAABANmraNJQlXknnInK3hSne0vNaLwhqCbv11ltdwjcFlQo01IqXN+++XMyxP9KnTp3qsmYrSFciK7UCqiWxaQA7oWRdSgqm1tT8+TP/s12BXWxyMrV2qqVcCer8YEoBU0bpGOnWt29fF/y8+eabLmhX2dUCnValSlZQEKUW9qpVq7pzq5b2yP1Uwr1L9Rv1v+Bf05gpaWBGKEmeKgOUONAPRLVtvZ+SmCl4Uyu3ErSdfvrp7nkdcyVnS6v1VpVCKpcCP//aVM+A7KD9ULn1WfDpcUaPh17z3HPPWdu2bd3jFStWRCVITEla10VWHIs6deq45HpqnfcriPS5ji2/khwqyZ0vMuGh34Mm9nOjSkBVUtx9993hZZHJF7ML2eORdfr1M5s3z6x8ebNXXgn9pQYAAElLjc9+r9rYP/v+40GDQutlB3VzXr16ddTNDxz0Y1uBmlrDlJ1drcLz5s3br0VMXWNvu+02F0goq/gzzzzjso+Lup8rILz88stt9OjRtnTpUpeVWl131a08u914442ud4CynysYVFDx+eefu/1JK0N4JAX9mttcx2fdunVumSomtE+ai1tdljt37pyhFlodCwXqanlU0KJM7j///LMLmOTee+91GcjVIrlw4ULXZVktkmqNz2o6RwqO1TKr3gKR0/5pP9X6r2BLZbj22mvDswdk9D0UACrYVUZ/DVvQbADK5u9369djXRvKOK7rSdeRjndavTAUwCr7uK693377zQWcL7zwgmWH3r17u8oHDSvR+VKmdl0HanXOCB1XlVPHVBVfOj7qrZGWtK6LrDgWnTt3dse8e/fu9uOPP9qnn37qepTEln/mzJnu86RKHGWA12cs9nOj7wydS32vqFx6nb4zVGZ9HtVNXr07shtBO7LGt9+a+R8GBez/fXkBAIDkpl6u771nVqVK9HK1sGt5ds7Trm7Z6iYeeWvSpIl7TgGcgklNKSZ67qWXXnLBgYJUnwJyjWfVWGQFyQq0Isf8KtjXOr169XItqpq+Sz/uq6tXYTbTGG21CCpAP+uss1z3bnXt1njtyN4CaVFFhQJXTZ+lVnFRsKYpwtTaqC74qtxQK2h6qZuyxhxfeOGFrnJDx0zHT0GxaHuankzBvLqwq/VdOQXUSpnVFOjp/CnAUvAYSedb+6XyaLo1TYkXOQVbRvZXAZ4qUbQ/qhxo0aKFPfvss+F1NBWYKlh0vajXgYZQ6H0jhwPEo7HyOh8aBlGvXj3Xa0DBf3bQvqtLu4LYY445xn0+dI3r2GTEq6++6iokdGxVcaFhEppOMS1pXRdZcSyKFy9uY8eOdTkpdL2rVTx2iImuU3XR79Spk5vGUV39I1vdRUG/PvMal1++fHn3WdT0deq9o2n71INClUEK+LNbHmWjswSlLyglyBgxYoSrGdQXl7rx6MPn11ip+Pfdd5/r9qQ5GpVIQTVHqgVJLyU4UJeWDRs2ZHsSgaS0YYOyiKiq2uzqq81efjnoEgEAgHRQ91G1mGqu4rQCi7So4VdToivpnIZbqzd5drWwZxUFKvrhrfHOQFZTzwX1PFCCvgcffDDo4iABv2fTG4cm9Jh21YgoANfE9qoJUhcGdQfSjqk2Rx599FHXLUHr6ECopkM1OOoKcaB/fJBOvXuHAnZls4yTlRUAACQ/BegZbKwDkoo/TKBZs2Zu6IZa4RWsqbs2cCASOmhXdwMljvCTSWhcgcYaaSyR38qumlG1vGs90RgJjSv54IMP7OKLLw60/LlG375mP/9s9vDD6o8SdGkAAACAg05DFjReXOPDFaeoe7cSH/rj/IGkDNo1xkZjj5QcQGNlNP5IKf81zkFUc6Vu85oX0qdWeI1LUFKMlIJ21XzpFtktAQegVq3Q9G4kngMAADmI5pgGsopyBmjcM5CrgvY777zTBdRHH320m3ZDY9yVVMRPMKGAXfyMjT499p+LR8kMlLEQB0DZRWfONDvxxNBjAnYAAAAAyF3Z4zUnnzIGar5HTeOgcevKdKj/D4Smp9Bgf/+meQUT3Z69e2zC7xNs1PxR7n89DtQzz5iddFKoazwAAAAAIPe1tGseQbW2+93cNc2FEjyopVxzJGrKBtFci5rGw6fHygSaEs3dGDl/Y6IbvWi09RjXw/7Y+Ed4WdWSVW3w2YOtQ51snEclJT/+qDktQverVTv47w8AAAAAuURCt7Rv3bp1vzko1U1e0yeIssUrcP/666/Dz6s7/bRp09zciMlAAXvHdzpGBeyycuNKt1zPH1Q7d5ppeIJyArRpY3b99Qf3/QEAAAAgF0nolvbzzjvPjWGvXr26m/Jtzpw5LgndVVdd5Z7XXO09e/a0/v37u3nZ/SnfNJ/7+eefbzmdusCrhd0zb7/ntCyP5bGe43pa+9rtLV/egzQR6n33mc2da1aunNmrrzKWHQAAAABya9D+zDPPuCD8hhtusLVr17pg/Nprr7V77703vM4dd9xhW7ZssWuuucbWr19vTZo0sXHjxiXFHO2Tlk/ar4U9NnBfsXGFW++MmgdhYtRJk8wGDgzdf/lls4ghCQAAAACAXNY9vkSJEm4edo1j37Ztm/3666+uVb1gwYLhddTa/sADD7hs8du3b3dzIWp6uGSwatOqLF3vgGzbZnb55WaeZ3bllWYXXJD97wkAAJCF9Lvxgw8+SHWdK664IkM9Nn///Xe33bnqiZhgDmbZMnrcksn999/vZq9Kz/UVhMyU64wzznA9mpEYEjpoz+0qlaiUpesdkCJFzB5+2Oz4480GDcr+9wMAAMjiIHHVqlXWRjl5UgloBw8ebMOGDcvSsi5dutQ6d+7seo2qN2jVqlWtffv29tNPP1l2zxuufa5Xr55lt+w4bjnBokWL3FTSL774YtT1ldtMmDDBfZ7U8xm5rHt8bte0elOXJV5J5+KNa9eYdj2v9Q6KSy4x69TJLCY5IAAAQE7gzzyUmlKlSmXpe+7atctatWpltWvXttGjR7sZj/744w/77LPPsj3AUQLn9OzzgdizZ48L1rL6uKX2XrGJqoOknsCiShiVLavs3LkzqncxcrfEueKxHyWX07RufoAeyX886OxB2ZuEbs0as7/+2vc4gb4kAQBANtqyJeXb9u3pX1dD7NKz7gFSd95bbrnF5TsqW7asC1bVbTmlbsJKYCwNGzZ0y/X6eC34ypWknEmlS5e2cuXK2bnnnhsO1NJj4cKFbv3nnnvOTj75ZKtRo4addtppbsinHvtWrFhhF110kXsflV9BoHoD+PxyPfzww64rttbTENHdu3e7aZL1GrXgDx06NPyayN4Emn1Jzz///PNR5VOiZwXBGo4qSvqsaZaLFSvmWuqVW2rz5s3h9dWarvf+6KOPrG7dum4a5eXLl+933Hbs2OHOR4UKFVzvAh3DGTNmRL23tqFk0nq+efPm9vrrr0e11qb0XtqOKkIOOeQQV1nQrFkzmz179n7nWq3fOl9Fixa1OnXq2JQpU+yXX35x51r7d+qpp6Z5LufPn29nnnmmFSlSxJ1/5dHyj4euLyXOFh3DlIJ2VTZ069bNXXPajipw1DMhkn/8lIRbPTK0Tnqui/Qci7QoP9jll19uxYsXd5VKTzzxxH7rDB8+3Bo1auSGL+uzpZ4jyjkmKo/On5QpU8YdB+1PVnx+EEIEluA0D/t7F71nVUpWiVquFnYtz9Z52jV+vWtXs2OPNfv22+x7HwAAkHiKF0/5duGF0etWqJDyurHdhWvWjL9eFlDQp2BM0/8++uijLqj98ssv4647ffp097/yIalbs1rBUwpobrvtNps5c6abZljB2QUXXBCegjgt5cuXd6957733XPCWUmt869atXUA0adIkmzx5sgugzj77bNfi6vvmm2/szz//tIkTJ7rg+r777nNBkAIl7fN1113nkjarJT+WynDJJZfYm2++GbV85MiRrhJBlQn+ek8//bSrbNDx1HuqIiR2WuaBAwfaK6+84tZTYB5Lr3n//ffdNhREHnHEEW4f//333/CQgY4dO7pA9YcffnDlvvvuu/fbTrz32rRpk3Xt2tW+++47mzp1qgv827Zt65ZHevDBB10wqkqLo48+2gWaep++ffu68+l5nt10000pnjude5VZx1fB8bvvvuuuF/81t99+e7iSRNeQbvH4FSZ6/Y8//uiSat911132zjvvRK2n62vx4sXumv3444/TdV2k91ikRpU+3377rX344Yf2xRdfuK7usYG/yqLjqXOlii8F6n5grsodnWtR+XUc/EqJA/384D8evA0bNqjvufs/Ue3es9sbv3S89+a8N93/epztnn1WYbvnFS7seT/+mP3vBwAADqpt27Z5P/74o/t/P6Hq+/i3tm2j1y1aNOV1mzWLXveQQ+Kvl0Fdu3b12rdvH37crFkzr0mTJlHrNG7c2OvTp0/ELpk3ZswYd3/p0qXu8Zw5c1Ldbqy//vrLvW7+/PmpbifSs88+6xUtWtQrUaKE17x5c++BBx7wfv311/Dzw4cP92rXru3t3bs3vGzHjh1ekSJFvM8//zxcrho1anh79uwJr6PXNG3aNPx49+7dXrFixbxRo0bFLZv+z5Mnj7ds2TL3WNuqUqWK9/zzz6dY9nfffdcrV65c+PHQoUPdNufOnZvicdu8ebNXoEABb+TIkeHnd+7c6VWuXNl79NFH3WOdl3r16kVt4+6773bbXrduXarvFUv7oWM7duzY8DK97p577gk/njJlilv26quvhpfpOBXW79wUvPTSS16ZMmXc/vg++eQTL2/evN7q1avdY11PmQmpbrzxRu/CCy+MOn4VK1Z05z0j10V6j4V/3cfatGmTV7BgQe+dd94JL/vnn3/ce/To0SPF8s+YMcNtV6+X8ePHR5279H5+cvv37IZ0xqG0tOcQ6gKvad0uOfYS93+2z8uuxCi33x66/+ijZnXqZO/7AQCAxKIuwCnd/mtVC1M32ZTW/eyz6HXVtTfeelnguOOOi3qsrr5+F97M+vnnn10L9WGHHWYlS5a0muopYOa6aafXjTfe6GY6Uqv2Kaec4lpcjznmmHAvALVeqtu2WlTVkqqbukJrZqTIrsR6TeR4bnWTV1f2yDHs6oKc0j43aNDAdRP3W9vVuqp1//e//4XXUUtyixYtrEqVKq48l112mf3zzz+uxdunsdaxxzqSyqyWWbXg+woUKGAnnniiS9zmt8g2btw46nV6Pla891qzZo11797dtSqrS7jOi7qsx56TyNfpWEnk8dIyHeONGzfG3Q+VtX79+q73hk/7pFZilT8jhgwZYieccILreaHz+9JLL+1XXpUtchx7eq6L9B6LlGg7arU/6aSTwsv0Hn73fN+sWbPcUIDq1au78qgbvqT1Plnx+QGJ6BCPuttcemlovNpZZ+kvTdAlAgAAB1tEoBLYuhmkwDCSxtYeaDdcBSrqOv7yyy+7scbanrKxR3ZbTw8FOtqWbhrPrm7P+l/jkRVkKaBTUB9LQV5q+5fRfe7SpYsL2u+88073v7paK9AXdXlWd/vrr7/eja1W8KZu1xqPrf3V2HDRuOysTLqWmnjvpe7gqkhQF2ydG411V2VI7DmJPDb+NuIty+6u2m+99ZbrSq+x4iqnroXHHnvMDWmIFFk5IOm5LtJ7LA6EP0xAN5VF762gW4/Tep+s+vzkdgTt2N8DD6g6TZkkzDROh+RzAAAgyfgtmimNMxcFQ2pRVcDRtGloth4FsQdKwaLGWH///ffu8fHHH29vv/22G6+t1sjspHHd99xzj2s51Tj7F154IfyclimoUnDpt+jHjrtOj8MPP9wdX43B9sfKq+Vd48L9ub/Vkvvpp59GvS42UV1KtF0l9tPYbT9Z299//21ZTb0SlAxPQasfUOu9dWxiW6LTKq+S3impny89ydjSc10c6LHQuVJFhioQ1Iou69atsyVLloRb0zU1oT4LjzzyiBu/LhqjntbnKbs+P7kR0RiiTZ1qNmBA6P6LL5pVrhx0iQAAALKcAiG14iq7tboYb9iwYb91lIBMrdDqyqxuykrKpqRaGaEkaMr4rQBZSci0nVdffdVee+01t9xv/Vb2bz1WwjElaVMyMGVfj5dU7kCoe7ICSLWeK8Bq165d+Dkli1Nw/cwzz9hvv/3mMoZHBvXppQBXrfVKcKbjq/1WF251sdf7ihLCKRjs06ePCxBVOeDP855WK766gqts6r6uYFPHT+cyq2m7ymyv1uwFCxbY+PHj7eabb3ZDBvzu9umh8irI/fzzz92+9uvXL10VFOm5Lg70WKjLvc6JzpWub+2nEsxFDsNQMK+g3L8ulM1fSekiqXJG500J9P766y/XSyArPj8IIWhHtLp1zS67LHSLGN8EAACQTPLnz++ypGtaMHXb9QPoSApc1LVZLdDq0nvrrbe6bs0ZoazhCpT/7//+z40bVuupujLrsZ8tXd3OlRFewVGHDh1cC68CKY1dzo6WdwV2Gi+tLN6RAZ7GbysrvbK1a3/VFXqA35iTQWqVvfDCC12Aq31W0KagVYGcaPozVWQoa7/GnmsqOv94qIt3alTpodZgbVfb96eWy2o6LyqzMt5r/L2y3Wu8/7PPPpuh7aiCQue1U6dO7hpQC3Rkq3tq75/WdZEVx0LXtFrC1ZW9ZcuWboo2dcv3qTu8KlSUi0FT7+ncPv7441HbUA4EXdMadqEKDWXYz4rPD0LyKBud5XJKPqHEDaphze4uSTnG7t36axZ0KQAAQDbSj3+13imAUosiECSNo1fLvrp4A7nhe3ZjOuNQojKELF0amjfV745EwA4AAIBspLHYasFWF2qNzVYrbGrzpgO5FZEZzFav1hwbodvw4ZrnIegSAQAAIMlpOjBl0Ff3c3UB79Wrl/Xt2zfoYgEJh6A9t9PoCCUEUZbJlSuzdRoWAAAAwPfUU0+5G4DUkYgut1OGeE23oYQfI0aE/gcAAAAAJASC9txsyRKzXr1C95UZtF69oEsEAAAAAIhA0J5b7dpldumlZlu3mrVoYdajR9AlAgAAAADEIGjPrR56yGzGDLPSpc2GDdNEpEGXCAAAAAAQg0gttzr7bLPDDzd7/nmzqlWDLg0AAAAAIA6yx+dWJ59stmCBWeHCQZcEAAAAAJACWtpz45zsPgJ2AACQRfbs3WMTfp9go+aPcv/rcW5xxRVX2Pnnn3/A2xk2bJiV1tDFBCvrwShXdsuTJ4998MEHQRcjYcoRacKECa5c69evD7ooSAFBe24ydqxZrVpmzzwTdEkAAEASGb1otNUcXNOav97cOo/u7P7XYy3PzuBTgYZuBQoUsFq1atkdd9xh27dvt5yqU6dOtkSz+ySBmjVr2qBBgyxRrFq1ytq0aRN4RUVkOX7//Xd3/c6dO9cOljPOOMN69uwZtezUU0915SpVqlS2v//KlSvt0ksvtXLlylmRIkXs2GOPtZkzZ2b7++Z0dI/PLdauNbv6ajP9IVu2LOjSAACAJKHAvOM7Hc0zL2r5yo0r3fL3LnrPOtTpkC3vffbZZ9vQoUNt165dNmvWLOvatasLggYOHGg5jfZBQYxuyHqHHnqoJXM5dP2o8iozChYseFCOz7p16+y0006z5s2b22effWbly5e3n3/+2cqUKZPt753T0dKeG3heKGBX4H7ssWb9+wddIgAAkATUBb7HuB77BeziL+s5rme2dZUvVKiQCzaqVavmuny3bNnSvvzyy/Dze/futQEDBrhWeAXD9evXt/feey9qGx999JEdeeSRVrhwYRdMvP7661Fdhe+//35r0KBB1GvUgqyW5JSMGzfOmjRp4lpq1aJ47rnn2q+//hp+3m9hffvtt61Zs2buvUeOHLlf667ew+9NEHnzrVixwi666CL3mrJly1r79u3dtn179uyx2267LVwO9UTw9LswDSpH9erVrWjRonbBBRfYP//8E/W89kXvVbFiRStevLg1btzYvvrqq6jW3GXLltmtt94aVWZt55JLLrEqVaq4bauVddSoUWmWReVXl3L/PLVu3drte6Tnn3/eDj/8cBeA1q5d24YPH55it3T/+I8ePdqdc5VF18aUKVPC3cWvvPJK27BhQ7j8ug7kueeeC5dD+9+xY8c0j2dK5dB1KQ0bNnTLddx8r7zyitWpU8e9z9FHH+3eN63rJ63jq94p3377rQ0ePDi8X9pWvO7x77//vh1zzDHuM6br8IknnojaDy17+OGH7aqrrrISJUq46+Wll15Kdd9VmabPqiraTjzxRLf/Z511ljtvSB1Be27wyiuhrvEFC5qNGMFYdgAAkCUmLZ9kf2z8I8XnFbiv2LjCrZfdFixYYN9//70L2nwK2N944w174YUXbOHChS6IVNdcBS6ydOlSF3Qp4P/hhx/s2muvtbvvvvuAy7JlyxYXLKvb79dff2158+Z1wa8qESLdeeed1qNHD1u0aJELRGPNmDHDdVvW7Y8//rCTTz7ZmjZtGm5Z1WsUME2aNMkmT57sAmj1Pti5c6dbR4GWgt7XXnvNvvvuO/v3339tzJgxqZZ92rRp1q1bN7vppptct20Ftf1jGnw2b95sbdu2dfs2Z84c957nnXeeLV++3D2vYLhq1ar2wAMPhMsvGrpwwgkn2CeffOLO1zXXXGOXXXaZTZ8+PdUybd261R566CF3LrWfCi4vvvji8PPaJx3HXr16ue3qPCroHj9+fKrb1bm+/fbb3X4eddRRLuDdvXu36y6uipmSJUuGy6/1dD5vueUWt1+LFy92lTOnn366ZZa/36rw0HvouIkC8Hvvvdfts64NBcf9+vVzFUqpXT9pHV8F66eccop17949vF8KomOp14oqg3SM58+f7yos9P66liLp+mrUqJG7Bm644Qa7/vrr3XFJiSrItP7//vc/q1ChgqusePnllzN9/HIVD96GDRtU5ej+Tzo//+x5xYqpTtXzHnss6NIAAIAEsm3bNu/HH390/2fGm/Pe9Ox+S/Om9bJa165dvXz58nnFihXzChUq5H7L5c2b13vvvffc89u3b/eKFi3qff/991Gv69atm3fJJZe4+3369PHq1asX9fzdd9/ttrVu3Tr3+L777vPq168ftc5TTz3l1ahRI6os7du3T7Gsf/31l9vm/Pnz3eOlS5e6x4MGDYpab+jQoV6pUqXibuOWW25x77l27Vr3ePjw4V7t2rW9vXv3htfZsWOHV6RIEe/zzz93jytVquQ9+uij4ed37drlVa1aNdWy6ti0bds2almnTp1SLJfvmGOO8Z555pnwY5VVxykt55xzjterV68Un9cx0bGaOnVqeNmiRYvcsmnTprnHp556qte9e/eo1/3vf/+L2g+tP2bMmKjj/8orr4SfX7hwoVumbfvvG7vP77//vleyZElv48aNXmbFK8ecOXOi1jn88MO9N9+M/sw8+OCD3imnnJLq9ZOe49usWTOvR48eUeuMHz8+6prv3Lmz16pVq6h1evfu7dWtWzfq/F566aXhx7oOK1So4D3//PMplkWfU9369u3rzZ4923vxxRe9woULe8OGDfNy6/fshnTGobS0J7Pdu80uvVTVveqnZHbbbUGXCAAAJJFKJSpl6XoZpVZgtZKqdVjj2dW6euGFF7rnfvnlF9dC26pVK9cC7d/UWut3VVeroLp2R1K33QOlcbpqtT3ssMNca63fld5vifap1TE91O341VdfdS2VGgcs6hmgfVRLu79v6iKv1lbtn7p2qyX1pJNOCm8nf/78ab6nWm0jXyNqnY1taVfLs7pvq+u63luvi92/WOqu/+CDD7pu2yqrXvf555+n+TqVO/I8qbu43lfv6ZdZY6Uj6bH/fEqOO+648P1KlULX6FoNJ02BrqUaNWq486oWbLWI6xrLSuqlofOn3g6R1616O0QOsZDYc5nZ4xsrpeOp61rvEe/4qXu9hqqkdvzU0+T44493PQfUyq6eAGr1V08YpI5EdMnu3HP1V8tM3WnyUkcDAACyTtPqTa1qyaou6Vy8ce15LI97Xutlh2LFitkRRxzh7qsLuMYlK7hVwKPAUtRVWGN8I2mcbnqpa3vsOHB1TU+NuooruFPX38qVK7tgpV69euFu65HlT4u6eN98881ubHJkkKT9U1doBY6x/MA+uyhgV+6Axx9/3B1/5QvQMIPY/Yv12GOPuS7a6nquwFL7r0zmab0uu0QmbvPH3ccOYYikCpLZs2e7MeBffPGF68KuruMaxpBVmeb961bXTmzlSb58+VK9fg728Y1NfKdjmNrxU8VI3bp1o5ap4kfj55E6orhklj+/2T33mP32m1n16kGXBgAAJJl8efPZ4LMHhwP0SP7jQWcPcutlNwXXd911l91zzz22bds2FxwoOFcrowLLyJs/jlcJy2Knm1IAFhsAr169OipwT22KLiUDUwu+ytGiRQsXlChrdmaoJV3BsParQ4foDPxqsVTLp8YGx+6fpu7STUGSeiH4NF5b45VTo/JGvkamTp0a9VjjypXUTOP0FRyqhTUyAZ4ot0Bkq6z/OiWwU14BVbCoxTo9U9yp3JHnScdX49pVVr/M2nbse8UGiBkRr/x+q78SHj766KM2b948t9/ffPNNpt9DIt9Hye1U0fPbb7/td179xHUpSc/xTWm/IqV0PDXuP7biICPUWh875l3lUwUXUkfQnoy2bQtN7eYrWTLI0gAAgCSm6dw0rVuVktGt2Wphz87p3uJRgisFFUOGDHGtomoRVvI5JfBS12K1kj7zzDPhhF5KWPbTTz9Znz59XPDwzjvvhJNt+S2vyuj9119/uSBN29C2NV1VSjR9lTK1q0u7gm4FdEpKl1GqeFCLvd+NWBUH/k26dOlihxxyiAvSlIhOSfXUAqxEaUpaJ0pS9sgjj7hs5dpPJQuLzBAej16vBGtqRVelwLPPPuseR1L2dCVNU+WFuul37tx5vxZWDQmYOHGim5f777//Dr9OLfRKGKgu2Dr+a9asSVeLrnobqDJBlQ6qMFBSPn8oQ+/evd15UwZ5lfnJJ5905dP5zyyVX63eSran8qsb/Mcff2xPP/20229lx9dQC+23Kn8yQxUu6qWg46vjoCEN8n//938uiaLeS9elksEp47r2KzXpOb7aLx1HVTZov+K1jCuhn/ZbXe31/vq86Do4kOMp+iyqAkjd4/XZePPNN93n5MYbbzyg7eYK2TjmPsdIukR011/vecce63k//BB0SQAAQBInoou0e89ub/zS8S7pnP7X4+yUUvK3AQMGeOXLl/c2b97skmMpWZcSthUoUMAtb926tfftt9+G1//www+9I444wiXIOuOMM1wiLf0ujDwmWlatWjWX9O7yyy/3HnrooVQT0X355ZdenTp13DaPO+44b8KECelKQBaZ/MxfJ97Nt2rVKleeQw45xL3XYYcd5hKy+b9plXhOSceUPK106dLebbfd5tZPLRGdvPrqqy5hnZLanXfeed7jjz8elZRNZWvevLl7Xsfl2Wef3S/B2ZQpU9y++0kC5Z9//nHvXbx4cZe07J577kmzPP4xURI47Z+217JlS2/ZsmVR6z333HPueZ3no446ynvjjTeink/r+CsJm5YpKZvvuuuu88qVK+eWKyHhpEmT3H6WKVPG7bv27+23344qa1rhVWQ55OWXX3bHUEkUtW3fyJEjvQYNGngFCxZ073f66ad7o0ePTrH86T2+ixcv9k4++WRXfm1D24pNRCdK6KjEczqe1atX9x6LSWgdL9GgEjbqOKVm7NixLvmjzuPRRx/tvfTSS16y25YFiejy6B/L5TZu3Oi6EKl2S8lCcrRPPzU755zQfc1T2rJl0CUCAAAJSknL1EKrbrea6xnmptlSYqzYecARDLWga1x2Wj0EEsF9993nphNUjwcgPd+z6Y1DSUSXTP76y+yqq0L3e/QgYAcAAEjDc8895zKTq0u7xu0qmZfmKAcySsMm1I0cyGoE7clCHSauucZM41aUdGPAgKBLBAAAkPA0BlrTaf37779WvXp1N563b9++QRcLOdD06dODLgKSFN3jk6V7/GuvmXXrpkwd+sYwa9Ag6BIBAIAER/d4AEj87vFkj08GmtJN3eHlwQcJ2AEAAAAgSdA9PlnmYz/hBDNN2XCAUzEAAAAAABIHQXsyqF7d7OuvzTS3Y758QZcGAAAAAJBF6B6fk23Zsu++gvWyZYMsDQAAAAAgixG051Rbt5o1bmx2882h+wAAAACApEPQnlP16WO2aJHZ+++bbdsWdGkAAAAAANmAoD0n+vxzs2efDd0fOtSsXLmgSwQAAJB07r//fmtwgLPy/P7775YnTx6bO3euZZdhw4ZZ6dKlM/y6K664ws4//3zLSTK7rymZMGGCOz/r16+37JQTj3VuNSwT15iuoQ8++CDbykTQntP884/ZlVeG7t90k1nr1kGXCAAAIBArVqywq666yipXrmwFCxa0GjVqWI8ePewf/V7KoHg/um+//Xb7Wsl+D0C1atVs1apVVq9ePQvKwag4yA41a9a0QYMGRS3r1KmTLVmyxHKawYMHu2AwkQLBRHCwKk1yOoL2nMTzzK691mzVKrOjjzYbODDoEgEAAATit99+s0aNGtnPP/9so0aNsl9++cVeeOEFF2Sfcsop9u+//x7wexQvXtzKHWCPxnz58tmhhx5q+TVFL5ydO3dm+rVFihSxChUqWE5TqlSpLO0hkAx27doVdBFyDIL2nOSNN0Jj2PWlP2KEWdGiQZcIAAAkqS1btribp0aDiGBLy3bs2BF33b1790b9INey7du3p2vdjLrxxhtd6/oXX3xhzZo1s+rVq1ubNm3sq6++spUrV9rdd98d1WL74IMP2iWXXGLFihWzKlWq2JAhQ6KelwsuuMC1+vmPY7vH+12cH374YatYsaILwh544AHbvXu39e7d28qWLWtVq1a1oRq+mEIrt7ahx7E3tTiKjq1a+FVGlfWkk04KP+dTi632t2jRoq7MafUsqFWrlvu/YcOG7r3OOOOMqOcff/xxq1Spkqug0HGNPB/pKc/7779vxxxzjBUqVMgduyeeeCLqef/4X3755VayZEm75ppr3PLvvvvOmjZt6gJx9Ui45ZZb3LUhKuOyZcvs1ltvDR8jf99jg9+xY8da48aNrXDhwnbIIYe4Y+IbPny4q9wpUaKEqzzp3LmzrV271jJC7/3888+760tlPeyww+y9996LWmf+/Pl25plnuud1HLWPmzdvTrF7vPZP+3vHHXe460Zl0/UWecziXZOx9Jm86aab3PnT/qu3yYABA1LsYaEW7cjrzW/p/uSTT+y4445z2zj55JNtwYIF4df4x1yt/kceeaRbp3Xr1q6nSyQdo8MPP9x9LmvXru2Ofbzj2K5dO3ctde/e3Zo3b+6eK1OmjHtexymeYf+V4eOPP3bb1rXfsWNH27p1q73++uvu+GgbOqZ79uwJv27dunXuutNzeo3OoSr6Mvp5+vDDD+344493+67z/3//93/uc3/QePA2bNigv0bu/4T23nueV7q05/XvH3RJAABAEti2bZv3448/uv9j6beRbmvXrg0v69+/v1t29dVXR61btGhRt3zp0qXhZU899ZRb1rlz56h1DznkELd8wYIF4WUvvfRShsr9zz//eHny5PEefvjhuM93797dK1OmjLd37173uEaNGl6JEiW8AQMGeIsXL/aefvppL1++fN4XX3zhntc+qkxDhw71Vq1aFd7n++67z6tfv354u127dnXbufHGG72ffvrJe/XVV93rWrdu7T300EPekiVLvAcffNArUKCAt2LFCvcaHROtM2fOHPd4/fr17j38W48ePbwKFSq4+6Jje+qpp3oTJ070fvnlF++xxx7zChUq5LYtU6dO9fLmzesNHDjQ7cvgwYO90qVLe6VKlUrxeE2fPt2V4auvvnLvo+Pn70/JkiW96667zlu0aJE3duxYdy4jz0da5Zk5c6YrzwMPPODKo2NYpEgR979Px1/v8/jjj7tt+LdixYq560Tbmjx5stewYUPviiuuCJ/jqlWruu36x0q03ch9/fjjj925vPfee921PHfu3KjrQufo008/9X799VdvypQp3imnnOK1adMm/Pz48ePdsVm3bl2Kx0/PlytXznv55ZfdPt5zzz3uPfV+snnzZq9SpUpehw4dvPnz53tff/21V6tWLXd8I6+d9u3bhx83a9bMHZP777/f7f/rr7/urum0rslYOh/VqlVz5+f333/3Jk2a5L355ptxrz3RfmqZ9jty/+vUqePee968ed65557r1axZ09u5c2f4mOuabtSokff999+7c37iiSe668I3evRot86QIUPcMXriiSfcMfrmm2+ijqOu9ddee82dD5X3/fffd8v1Gu2nPh/xDP2vDK1atfJmz57tffvtt+6cnHXWWd5FF13kLVy40F2/BQsW9N56663w69q1a+f2TcdH14Y+q0cccUR439LzedJrda6GDRvmyq3jpOOjcxe5b2PGjMnw92x641CC9pwUtMuff3rerl1BlwIAACSBnBq064d2aj+Sn3zySff8mjVrwkHj2WefHbVOp06dooK3eNuLF7RrW3v27Akvq127tte0adPw4927d7tgdNSoUSkGTj4FLIULF/a+++4793jZsmUu0Fm5cmXUei1atPD69u3r7l9yySVe27Zt99uX1IL2lMrg74/K7Pvf//7ntpfe8uj8KpCK1Lt3b69u3brhx3qP888/P2qdbt26eddcc03UMgWcCqD861Gv03UUKTZoVxDepUsXL71mzJjhjsWmTZsyFLSrYiPSSSed5F1//fXh61eVRArefZ988onbl9WrV6cYtDdp0iRqm40bN/b69OkT9b4pXeO+m2++2TvzzDPDFVSRMhK0Rwa6qjBRxcvbb78dPuZaR587nyp5tGzatGnusQJ4VZZF0rUUea1q/Z49e0atk57jH1kGVfb4rr32Wvfd459LUVCu5aLKEL1GFUK+v//+2+3bO++8k+7Pk6732ArC4cOHu4qagxW00z0+p6lUKdQ9HgAAIBupa69u6m7sUxdwLXvWn8XmP+purOXqYupTN2ste/XVV6PWVZddLa9Tp054WUpdYtMS2XU/LRrnHvt4kabPzSB1A8+bd99PaHWTP/bYY6PGsKt7dFpdsOfMmWOXXXaZO5annXZauIu1uvYeddRRbjy9f/v222/t119/deuozOqintq+ZXR/VGafuln7ZU9vefzy+/RYXZAjuymri3qkH374wXVLjtyuulxr2MTSpUvTXX51/W7RokWKz8+aNcvOO+88d22qi7yGUsjy5cstI1K7fvR//fr1XZfvyGOgfVm8eHGK21R39EiRxz699NnRMVCXcXUN13CRzIjcP3XX1/YiPx/KyaAhCL6jjz7adVePPAbxroPYz1jsdZARRYsWdd3vIz976havaydymX8M9d4qd+TnRZ/NyH1Lz+dJ16qGwUReq+rarwST6p5/MBD9AQAAYD+RAYhPY1V1S8+6BQoUcLf0rpsRRxxxhBv/qh/ckeOXfVquMazly5e3rBZbVpUj3rLIMfuxVq9e7cb1Xn311datW7fwclVmKIBWoBkZSEtkYJKVUit7VpYn9rxr29dee60LNGNFVv6kRWPIU6Lx8aoI0G3kyJHuelCwrscHkgwvq2T0uolH46xVyfHZZ5+5fA4XXXSRtWzZ0o259yuXIiu3gk7+Fu/zfzA/e5mha1Vj2Dt06LDfcxrjfjDQ0g4AAIAcRa1lrVq1sueee862bdu2X0CsAE1Tg/nJy2Tq1KlR6+lxZGu/fvxHtgxnFyXma9++vWupfPLJJ6OeU6I4lUEthaqYiLwpUZmozNOmTdtvX1LjV7RkdP/SW57JkydHvU6P1TofG+jHBps//vjjftvVzS+v/k+rzGqtTmlavp9++sklFXvkkUdcwjsd84y2ZKfn+tH/ao31k+j5x0BBs1p1Myu916SS++l6f/nll+3tt992iQE1e4JfaaUWYV9K0/5F7p+St2lavcjPh5KuzZw5M/xYPQiU1C7yGMS7DurWrZst12Z6qEwqd+TnRdeDyu6XKz2fJ12rek28azWy1012ImgHAABAjqNu5cpsrlbTiRMnukzW48aNc8G8Mp0/9NBD+wUQjz76qAtGlDn+3XffdXO6+9TNVsGfgn4FLdlFrcsq69NPP21//fWXez/d1PKrQLdLly4u2/Xo0aNdC+r06dNdNnBl9xa1TGs/lfFdXdB1HPQ4NZoiTS3SWm/NmjW2YcOGdJU1PeXp1auXO27KDq9jq0zeKpMyzqemT58+9v3337vM5woktS/K0K3HkedE51azAfz9999xt3Pfffe5Kf/0v3pYqEv/wP+mRVaLvYLCZ555xk0R+NFHH7lyZoaul9dee83to95Lx8Evq46RWly7du3qsq6PHz/ebr75Zjf8Qd21Mys916QqfrT/qqBQ2VROVaio67rOuTLBq9JCx0bDGu65556421H3b72Xyq8u9xoWE5ntXhUI2icFuOp5oXW07RNPPDE8dEbDHZQdXudS5dI1k9Z1oGz3qlxTVnh9HiIz7h+oI4880lWQqSu7ZipQxcqll17qvh+0PL2fp3vvvdfeeOMN19q+cOFCdyzfeuutFI9ldiBoBwAAQI6jH+Rq+dP0S+oSrLGummZLU0hNmTLFjcuNpOBS66v1uH///i6oUMDv0zRlX375pZt6TOtkFwVOavlUS5/GMPs3BbCi6eIUJKu8aqVV4DRjxoxwl3EFSmpRHTx4sBtHrTHMaQUPGterSoIXX3zRKleuHA5Y0iOt8qgV8p133nFBTL169VyAowAwrTwFaiHXsVCgqVZwHXO9VuXzaTvKgaBzm9JQB02dpkBVAbmm59O0awqoRa9RIKnndbwVvCo4ywwFbNpHlVsBnAJlv7VWY60///xz17qtcd+aikzj7GNzP2RUeq5JjdNXZZTGiuu9dbw+/fTTcAuwKhrU2nzCCSdYz5493bUfj46NKrG0nioJNI1e5FAY7aMqWjRlnsaqa3iEWvV9ui50Ter4Kk+CrjVdO7HTC8ZSAK1je+edd7oKjshKm6wwdOhQt0/nnnuuG6uuoQI6Pn63+vR8nvQ9oUoFPadjrNc89dRTrsLhYMnzX7a7XG3jxo1WqlQpV+uo7iUAAAC5gbpqq/VU83gfrLGZQVCLpQIW3YCMUkvwmDFjolqek4XmaVdFl1ry1Tofjyo+9NlRd3hk7fdseuNQWtoBAAAAAEhQBO0AAAAAACQousfTPR4AAORSuaV7PAAEhe7xAAAAAAAkMYJ2AACAXI6OlwCQuN+vBO0AAAC5lD/t0datW/+/vTuPrar4Ajh+2lI2oS3I0hZaFssSZREEoYCggpRFZNGASBTUiBRIcAEqKJtbCSImGjQYA/yDmtawGLbIqkAQ0bCDRBCsIFBCZUtbaOn8cibem/faQh/+Ku+27/tJno/bO72dNsd5c+7MnQl2VQCgUsr9p3112tt/o0o51gcAAAAVSEREhN3mKTs7292LWbe3AgD8/yPsmrBr+6rtrLa3/xZJOwAAQAiLjY21707iDgAoP5qwO+3sv0XSDgAAEMJ0ZD0uLk4aNGggBQUFwa4OAFQakZGR/9cIu4OkHQAAALZjWR6dSwBA+WIhOgAAAAAAPIqkHQAAAAAAjyJpBwAAAADAo3im3WfD+8uXLwe7KgAAAACAEHD5n/zTyUdvhqRdRK5cuWLfExISgl0VAAAAAECI5aPR0dE3PR9mykrrQ0BRUZH89ddfUrt2bbvtCQK7K6Q3Of7880+JiooKdnWA20L8oqIjhlGREb+oyIhflCdNxTVhj4+Pl/Dwmz+5zki7PtgfHi6NGzcOdjUqJG2saLBQURG/qOiIYVRkxC8qMuIX5eVWI+wOFqIDAAAAAMCjSNoBAAAAAPAoknb8K9WqVZNZs2bZd6CiIX5R0RHDqMiIX1RkxC+CgYXoAAAAAADwKEbaAQAAAADwKJJ2AAAAAAA8iqQdAAAAAACPImkHAAAAAMCjSNpDxOzZsyUsLMzv1bp1a/d8fn6+TJgwQe6++26pVauWPPnkk3Lu3Dm/a2RlZcnAgQOlZs2a0qBBA5kyZYoUFhb6ldm6dat07NjRrqiZlJQkS5cuLVGXhQsXStOmTaV69erSpUsX+emnn/7D3xwV1Q8//CCDBg2S+Ph4G68rV670O69raM6cOVPi4uKkRo0a0qdPH/ntt9/8yuTk5MioUaMkKipKYmJi5MUXX5SrV6/6ldm/f7889NBDNh4TEhJk3rx5JeqSmZlp/3/RMm3btpW1a9fedl0QWsqK3zFjxpRok/v16+dXhvhFMKSnp0vnzp2ldu3a9rN+yJAhcvToUb8yXuozBFIXhJZAYvjhhx8u0QaPGzfOrwwxDE/R1eNR+c2aNcvcd9995syZM+7r/Pnz7vlx48aZhIQEs2nTJvPzzz+brl27mm7durnnCwsLTZs2bUyfPn3Mnj17zNq1a029evXMtGnT3DK///67qVmzpnnttdfM4cOHzSeffGIiIiLM+vXr3TJff/21qVq1qlm8eLE5dOiQeemll0xMTIw5d+7cHfxroCLQGHvzzTfN8uXLdYcLs2LFCr/zc+fONdHR0WblypVm37595oknnjDNmjUzeXl5bpl+/fqZ9u3bmx9//NFs27bNJCUlmZEjR7rnL126ZBo2bGhGjRplDh48aL766itTo0YNs2jRIrfMjh07bBzPmzfPxvVbb71lIiMjzYEDB26rLggtZcXv6NGjbXz6tsk5OTl+ZYhfBENKSopZsmSJjam9e/eaAQMGmMTERHP16lVP9hnKqgtCTyAx3KtXLxtPvm2wtqkOYhheQ9IeQkm7dv5Kc/HiRduJy8zMdL925MgR29HcuXOnPdbGKjw83Jw9e9Yt89lnn5moqChz7do1ezx16lR7Y8DXiBEjbOPpePDBB82ECRPc4xs3bpj4+HiTnp5ejr8tKpviSU9RUZGJjY01H3zwgV8cV6tWzSYuSj9A9ft2797tllm3bp0JCwszp0+ftseffvqpqVOnjhvDKi0tzbRq1co9Hj58uBk4cKBffbp06WJefvnlgOuC0HazpH3w4ME3/R7iF16RnZ1tY/H777/3XJ8hkLoAxWPYSdonTZp00+8hhuE1TI8PITrdUadqNm/e3E651Gk/6pdffpGCggI7JdKhUykTExNl586d9ljfdVplw4YN3TIpKSly+fJlOXTokFvG9xpOGeca169ftz/Lt0x4eLg9dsoAgThx4oScPXvWL5aio6PttDPfmNUpxZ06dXLLaHmNuV27drllevbsKVWrVvWLWZ1G9/fffwcU14HUBSiNTqvUKZetWrWS1NRUuXDhgnuO+IVXXLp0yb7XrVvXc32GQOoCFI9hx7Jly6RevXrSpk0bmTZtmuTm5rrniGF4TZVgVwB3hnbA9Dkb7RyeOXNG5syZY5+DPHjwoO2waadPO4i+tKHSc0rffRsu57xz7lZltIHLy8uzncgbN26UWubXX3/9T35vVE5OzJUWS77xqAmRrypVqtgPbd8yzZo1K3EN51ydOnVuGte+1yirLkBx+vz6sGHDbPwdP35cpk+fLv3797edtIiICOIXnlBUVCSvvPKKdO/e3SY2ykt9hkDqgtBWWgyrZ555Rpo0aWIHs3RtkLS0NHvDc/ny5fY8MQyvIWkPEdoZdLRr184m8dpYZWRk2IWHAAB3ztNPP+3+W0dztF2+55577Oh77969g1o3wKGLY+nN/e3btwe7KkC5xvDYsWP92mBdiFPbXr2Jqm0x4DVMjw9RekevZcuWcuzYMYmNjbVTeC5evOhXRleu1HNK34uvZOkcl1VGVz7WGwM6BUlHkEor41wDCIQTL7eKJX3Pzs72O6+rvuqK3OUR177ny6oLUBZ9bEnbSG2TFfGLYJs4caKsXr1atmzZIo0bN3a/7qU+QyB1Qei6WQyXRgezlG8bTAzDS0jaQ5RuG6R3E/XO4gMPPCCRkZGyadMm97xOEdJn3pOTk+2xvh84cMCvE7lhwwbbMN17771uGd9rOGWca+j0H/1ZvmV02pIeO2WAQOiUYP0w840lnY6mz/r6xqx+COrzYo7NmzfbmHM+nLWMbs2lz5P5xqw+RqJTiwOJ60DqApTl1KlT9pl2bZMV8Ytg0bUTNdlZsWKFjbnij2B4qc8QSF0QesqK4dLs3bvXvvu2wcQwPCXYK+Hhznj99dfN1q1bzYkTJ+wWQLqFhW5doStqOttN6HYYmzdvtttNJCcn21fxrS/69u1rt8/Q7Szq169f6tYXU6ZMsStfLly4sNStL3RV4qVLl9rVkceOHWu3vvBdnRNQV65csdus6EubqgULFth///HHH+42VRo7q1atMvv377crcZe25VuHDh3Mrl27zPbt202LFi38tszSVVt1y6xnn33Wbg2j8akxXHzLrCpVqpj58+fbuNadGErbMqusuiC03Cp+9dzkyZPtysDaJm/cuNF07NjRxmd+fr57DeIXwZCammq3ANQ+g+92WLm5uW4ZL/UZyqoLQk9ZMXzs2DHz9ttv23jRNljbvubNm5uePXu61yCG4TUk7SFCt6CIi4uze0U2atTIHmuj5dDO2fjx4+32QdoADR061DZwvk6ePGn69+9v9wHWhF9vBBQUFPiV2bJli7n//vvtz9EGUPfJLE73sdTGScvoVhi6BzFQnMaSJjvFX7pVlrNV1YwZM2zSoh+IvXv3NkePHvW7xoULF2ySU6tWLbtNy/PPP28TJl+6L3WPHj3sNfT/DU1gisvIyDAtW7a0Mavbu6xZs8bvfCB1QWi5Vfxqx1E7gtoB1AS6SZMmdu/e4jcviV8EQ2lxqy/fz3Mv9RkCqQtCS1kxnJWVZRP0unXr2jYvKSnJJt6++7QrYhheEqb/CfZoPwAAAAAAKIln2gEAAAAA8CiSdgAAAAAAPIqkHQAAAAAAjyJpBwAAAADAo0jaAQAAAADwKJJ2AAAAAAA8iqQdAAAAAACPImkHAAAAAMCjSNoBAAhBYWFhsnLlymBXAwAAlIGkHQCASmTMmDE2IddXZGSkNGzYUB577DFZvHixFBUVueXOnDkj/fv3D+iaJPgAAAQPSTsAAJVMv379bFJ+8uRJWbdunTzyyCMyadIkefzxx6WwsNCWiY2NlWrVqgW7qgAAoAwk7QAAVDKajGtS3qhRI+nYsaNMnz5dVq1aZRP4pUuXlhg9v379ukycOFHi4uKkevXq0qRJE0lPT7fnmjZtat+HDh1qv8c5Pn78uAwePNiO5NeqVUs6d+4sGzdu9KuHln3//fflhRdekNq1a0tiYqJ8/vnnfmVOnTolI0eOlLp168pdd90lnTp1kl27drnntd76O2i9mjdvLnPmzHFvPAAAEApI2gEACAGPPvqotG/fXpYvX17i3McffyzffvutZGRkyNGjR2XZsmVucr579277vmTJEjt67xxfvXpVBgwYIJs2bZI9e/bY0f1BgwZJVlaW37U//PBDm4hrmfHjx0tqaqr9Gc41evXqJadPn7Y/f9++fTJ16lR3Gv+2bdvkueees7MEDh8+LIsWLbI3Hd57773//O8FAIBXVAl2BQAAwJ3RunVr2b9/f4mva6LdokUL6dGjhx1N15F2R/369e17TEyMHb136A0AfTneeecdWbFihU2+ddTeoYm9JusqLS1NPvroI9myZYu0atVKvvzySzl//ry9EaAj7SopKcn9Xh1Vf+ONN2T06NH2WEfa9edoYj9r1qxy/usAAOBNJO0AAIQIY4xNyktbvE4Xq9NEWkfM9dn3vn373vJaOko+e/ZsWbNmjR2B1ynreXl5JUba27Vr5/5bf7Ym/tnZ2fZ479690qFDBzdhL05H3nfs2OE3sn7jxg3Jz8+X3NxcqVmz5m3/DQAAqGhI2gEACBFHjhyRZs2alfi6PjN+4sQJ+8y7Ppc+fPhw6dOnj3zzzTc3vdbkyZNlw4YNMn/+fDs6XqNGDXnqqafs8/G+dAV7X5q4O9Pf9XvKujGgo+3Dhg0rcU6fcQcAIBSQtAMAEAI2b94sBw4ckFdffbXU81FRUTJixAj70uRbR9xzcnLsKLgm3jrC7UtHwHWEXheocxJsXa3+dugo/BdffOH+nNJuJujz775T5gEACDUk7QAAVDLXrl2Ts2fP2kT73Llzsn79ersavE5714XdiluwYIFdOV6nqoeHh0tmZqadxq7PsStdlE4XnOvevbtdmb5OnTr2GXhd1E4Xn9PR8xkzZvjtAx8IXTVeV5cfMmSIrZ/WQResi4+Pl+TkZJk5c6ats646rzcStG46Zf7gwYPy7rvvltvfCwAAL2P1eAAAKhlN0jUB1mRbR8x14TddIV63T4uIiChRXrdjmzdvnl3lXbdu0xHztWvX2iTZWQFep8InJCTYxN5J9DV579atm03cU1JS7Mj47ahatap899130qBBA7tgXdu2bWXu3LluHfWaq1evtmW0Xl27drUL2fkulAcAQGUXZnRVGgAAAAAA4DmMtAMAAAAA4FEk7QAAAAAAeBRJOwAAAAAAHkXSDgAAAACAR5G0AwAAAADgUSTtAAAAAAB4FEk7AAAAAAAeRdIOAAAAAIBHkbQDAAAAAOBRJO0AAAAAAHgUSTsAAAAAAOJN/wNXh3zzD/ZHnAAAAABJRU5ErkJggg==" }, "metadata": {}, "output_type": "display_data" } ], "execution_count": 16 }, { "metadata": {}, "cell_type": "markdown", "source": "The regularized model works well and follows general trends. It assigns smaller weights for closest lags, and for more significant distances, weights are bigger. It is related to the semivariogram weighting method - we penalize more poorly performing models for the shortest distances from the origin.", "id": "c840474f720aaba3" }, { "metadata": {}, "cell_type": "markdown", "source": [ "## 5. Export semivariogram\n", "\n", "After so long process we shouldn't forget about saving modeling results! Thus, we will export regularized semivariogram and store it for Poisson Kriging." ], "id": "bf504ae38dadefac" }, { "metadata": { "ExecuteTime": { "end_time": "2025-12-21T15:06:20.365378Z", "start_time": "2025-12-21T15:06:20.361801Z" } }, "cell_type": "code", "source": "reg_mod.export_model_to_json(OUTPUT)", "id": "d296cbc143ebd686", "outputs": [], "execution_count": 17 }, { "metadata": {}, "cell_type": "markdown", "source": [ "## Changelog\n", "\n", "| Date | Changes | Author |\n", "|------------|----------------------------------------------|----------------------------------|\n", "| 2025-11-07 | Used **values** and **geometries** parameters instead of **ds** in the experimental variogram initialization | @SimonMolinsky (Szymon Moliński) |\n", "| 2025-05-21 | Tutorial has been adapted to the 1.0 release | @SimonMolinsky (Szymon Moliński) |" ], "id": "2e864205a7efbc8d" } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }