diff --git a/.github/workflows/test_notebooks_pullrequest.yml b/.github/workflows/test_notebooks_pullrequest.yml index a2080f800..8660aa047 100644 --- a/.github/workflows/test_notebooks_pullrequest.yml +++ b/.github/workflows/test_notebooks_pullrequest.yml @@ -37,7 +37,7 @@ jobs: How_to_guides/Land_cover_pixel_drill.ipynb How_to_guides/Imagery_on_web_map.ipynb How_to_guides/External_data_ERA5_Climate.ipynb - How_to_guides/Generating_COG_mosaics.ipynb + How_to_guides/COG_mosaics.ipynb - name: Print changed notebook files if: steps.changed-notebooks.outputs.any_changed == 'true' run: | diff --git a/DEA_products/DEA_Fuel_Moisture_Content.ipynb b/DEA_products/DEA_Fuel_Moisture_Content.ipynb index 61887dda6..b480caea7 100644 --- a/DEA_products/DEA_Fuel_Moisture_Content.ipynb +++ b/DEA_products/DEA_Fuel_Moisture_Content.ipynb @@ -48,7 +48,7 @@ "
\n", " \n", "**Note:** Visit the [DEA Fuel Moisture Content product documentation](https://knowledge.dea.ga.gov.au/data/product/dea-fuel-moisture-content/) for detailed technical information including methods, quality, and data access.\n", - "To explore DEA Fuel Moisture Content on an interactive map, [visit DEA Maps](https://maps.dea.ga.gov.au/#share=s-bbkJz9vaB0EHQ0WEBGo99Fu4Gqn).\n", + "To explore DEA Fuel Moisture Content on an interactive map, [visit DEA Maps](https://maps.dea.ga.gov.au/story/DEAFuelMoistureContent).\n", "\n", "
\n", "\n", diff --git a/How_to_guides/Generating_COG_mosaics.ipynb b/How_to_guides/COG_mosaics.ipynb similarity index 100% rename from How_to_guides/Generating_COG_mosaics.ipynb rename to How_to_guides/COG_mosaics.ipynb diff --git a/How_to_guides/COG_overviews.ipynb b/How_to_guides/COG_overviews.ipynb new file mode 100644 index 000000000..23bd9ab39 --- /dev/null +++ b/How_to_guides/COG_overviews.ipynb @@ -0,0 +1,848 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a416feeb-a8f0-4723-943a-ebb3de1ffd8c", + "metadata": {}, + "source": [ + "# Handling Cloud-Optimised Geotiff overviews \n", + "\n", + "* **[Sign up to the DEA Sandbox](https://app.sandbox.dea.ga.gov.au/)** to run this notebook interactively from a browser\n", + "* **Compatibility:** Notebook currently compatible with the `DEA Sandbox` environment\n", + "* **Products used:** \n", + " - [ga_ls_landcover_class_cyear_3](https://explorer.dea.ga.gov.au/ga_ls_landcover_class_cyear_3);\n", + " - [Landcover continental COG mosaics](https://data.dea.ga.gov.au/?prefix=derivative/ga_ls_landcover_class_cyear_3/2-0-0/continental_mosaics/)\n", + "* **Prerequisites:** It is recommended to first read the notebook [COG_mosaics](./COG_mosaics.ipynb)" + ] + }, + { + "cell_type": "markdown", + "id": "fc893f9f-9d0f-4e73-85a6-213dbc5f4901", + "metadata": {}, + "source": [ + "## Background\n", + "\n", + "Some DEA products are provided as continental-scale mosaics in the form of [Cloud Optimised GeoTIFFs (COGs)](https://knowledge.dea.ga.gov.au/guides/continental-cogs-geotiff-mosaics/). These mosaics enable the streaming of DEA data via GIS software and are accompanied by Virtual Raster (VRT) files, which are lightweight wrappers that automatically provide visualisation styling when streaming the data.\n", + "\n", + "COGs include overviews (or pyramids) that approximate the raw data array at a coarser spatial resolution by applying a resampling algorithm such as `NEAREST`, `BILINEAR`, or `MODE`. This allows data to be streamed at a continental scale much faster than if the entire array were to be loaded at native resolution.\n", + "\n", + "While the main advantage of COGs lies in their use within GIS software, they can also be accessed programmatically using languages such as Python, with packages like `rioxarray` or `gdal`.\n", + "\n", + "Although using the native resolution of the data array is recommended, it is also possible to access the coarser overviews to reduce computational demand when working on smaller machines, or when the data resolution needs to match that of another, coarser resolution product." + ] + }, + { + "cell_type": "markdown", + "id": "e0b22fbc-8c8d-4449-a99a-f5caf63c365c", + "metadata": {}, + "source": [ + "## Description\n", + "This notebook explores how to perform the following actions:\n", + "* Access a mosaic Cloud-Optimised GeoTIFFs (COGs) from the DEA S3 repository\n", + "* Load the data for our ROI using different overviews and observe the differences in Land Cover classes' extent\n", + "\n", + "***" + ] + }, + { + "cell_type": "markdown", + "id": "32919964-e5d7-4848-a82b-9c592690ba17", + "metadata": {}, + "source": [ + "## Getting started" + ] + }, + { + "cell_type": "markdown", + "id": "9d5d18b3-efd0-42d0-b955-e4550e1e126c", + "metadata": {}, + "source": [ + "### Load packages\n", + "Import the Python packages needed for this notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "44d93ba2-3f60-44a8-8fac-1aef66a05c90", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "import rioxarray\n", + "import numpy as np\n", + "from osgeo import gdal\n", + "from odc.geo.xr import crop\n", + "import matplotlib.pyplot as plt\n", + "from odc.geo import BoundingBox\n", + "\n", + "sys.path.insert(1, \"../Tools/\")\n", + "from dea_tools.plotting import display_map\n", + "from dea_tools.landcover import lc_colourmap, get_colour_scheme, make_colourbar" + ] + }, + { + "cell_type": "markdown", + "id": "17e7218b-ebe8-4bb7-8b77-d6bb686c12e4", + "metadata": {}, + "source": [ + "## Analysis Parameters\n", + "\n", + "In this notebook, we will use [DEA Land Cover](https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/) Level 3 as an example, and the analysis parameters below reflect this. You can see which other products are distributed as mosaics here: [Continental Cloud-Optimised GeoTIFF Mosaics](https://knowledge.dea.ga.gov.au/guides/continental-cogs-geotiff-mosaics/).\n", + "\n", + "* `lat`, `lon`: The central latitude and longitude to analyse. In this example we'll define a region around Canberra, ACT.\n", + "* `buffer`: The number of square degrees to load around the central latitude and longitude.\n", + "* `product`: The name of the DEA products to load, in the example we will use DEA Landcover\n", + "* `version`: The latest version of DEA Landcover is `'2-0-0'`\n", + "* `level`: DEA Landcover has a two measurements, `'level3'` and `'level4'` \n", + "* `year`: The year of interest to load. DEA Landcover is an annual product starting in 1988\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5123e65d-ffe8-414f-863b-d83741f92bcb", + "metadata": {}, + "outputs": [], + "source": [ + "# Coordinates for Canberra region\n", + "lat, lon = -35.325, 149.0918\n", + "buffer = 0.5\n", + "\n", + "# Define variables needed to obtain the DEA Land Cover Level 3 data. \n", + "# Currently, the most recent version is 2-0-0.\n", + "product = 'ga_ls_landcover_class_cyear_3'\n", + "version = '2-0-0'\n", + "level = 'level3'\n", + "\n", + "# Set year of interest\n", + "year = 2024" + ] + }, + { + "cell_type": "markdown", + "id": "8d44c4ae-cfe7-478f-b009-52311ecdf543", + "metadata": {}, + "source": [ + "### View your study area\n", + "\n", + "In the default example the bounding box covers the region around Canberra." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1be8aabc-e073-49c9-b0c2-65a82db65d59", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "bbox = BoundingBox(\n", + " left= lon - buffer,\n", + " bottom=lat - buffer,\n", + " right=lon + buffer,\n", + " top=lat + buffer,\n", + " crs=\"EPSG:4326\"\n", + ")\n", + "\n", + "bbox.explore()" + ] + }, + { + "cell_type": "markdown", + "id": "2d148747-a019-4acc-a0c7-5db379fad700", + "metadata": {}, + "source": [ + "## Load data from the continental mosaic and crop it to the ROI\n", + "We will read the COG continental mosaic from the [DEA S3 repository](https://data.dea.ga.gov.au/?prefix=derivative/ga_ls_landcover_class_cyear_3/2-0-0/continental_mosaics/) and see how many overviews are included in the file." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "978cd86e-efb0-401e-b4f5-89b8f4ef435a", + "metadata": {}, + "outputs": [], + "source": [ + "# URL to DEA continental mosaics\n", + "cog_url = f'https://data.dea.ga.gov.au/derivative/{product}/{version}/continental_mosaics/{year}--P1Y/{product}_mosaic_{year}--P1Y_{level}.tif'" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a735fe57-8b37-46ee-9cd9-0d67be3fbdba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Native COG resolution: 30, 30\n", + "Native COG size: 137600 x 128000 pixels\n", + "\n", + "Overview 0: size = 68800 x 64000 pixels, resolution = (60, 60)\n", + "Overview 1: size = 34400 x 32000 pixels, resolution = (120, 120)\n", + "Overview 2: size = 17200 x 16000 pixels, resolution = (240, 240)\n", + "Overview 3: size = 8600 x 8000 pixels, resolution = (480, 480)\n", + "Overview 4: size = 4300 x 4000 pixels, resolution = (960, 960)\n", + "Overview 5: size = 2150 x 2000 pixels, resolution = (1920, 1920)\n", + "Overview 6: size = 1075 x 1000 pixels, resolution = (3840, 3840)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/env/lib/python3.10/site-packages/osgeo/gdal.py:311: FutureWarning: Neither gdal.UseExceptions() nor gdal.DontUseExceptions() has been explicitly called. In GDAL 4.0, exceptions will be enabled by default.\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "# We will use gdal to quickly gather information on the overviews contained in the COG\n", + "# let's add the vsicurl driver to facilitate reading the file with GDAL\n", + "cog = gdal.Open(f'/vsicurl/{cog_url}', gdal.GA_ReadOnly) \n", + "\n", + "# We select the first and only band in the mosaic\n", + "band = cog.GetRasterBand(1) \n", + "overview_count = band.GetOverviewCount()\n", + "\n", + "# Get base image geotransform\n", + "gt = cog.GetGeoTransform()\n", + "base_res_x = abs(gt[1])\n", + "base_res_y = abs(gt[5])\n", + "base_xsize = band.XSize\n", + "base_ysize = band.YSize\n", + "\n", + "print(f\"Native COG resolution: {base_res_x:.0f}, {base_res_y:.0f}\")\n", + "print(f\"Native COG size: {base_xsize} x {base_ysize} pixels\\n\")\n", + "\n", + "for i in range(overview_count):\n", + " ov = band.GetOverview(i)\n", + " scale_x = base_xsize / ov.XSize\n", + " scale_y = base_ysize / ov.YSize\n", + " ov_res_x = base_res_x * scale_x\n", + " ov_res_y = base_res_y * scale_y\n", + " print(\n", + " f\"Overview {i}: size = {ov.XSize} x {ov.YSize} pixels, \"\n", + " f\"resolution = ({ov_res_x:.0f}, {ov_res_y:.0f})\"\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "5876c220-5eb2-45dc-9859-d73ee3982220", + "metadata": {}, + "source": [ + "We observed that there are seven overviews, each halving the size of the array at every level. We will load the data at a medium overview level (`overview_level = 2`) using the argument `overview_level` in `rioxarray.open_rasterio()`.\n", + "\n", + "For categorical data such as DEA Land Cover, the overviews were generated using the `mode` algorithm. This means that pixels at the native resolution representing uncommon or sparsely distributed classes might be lost during resampling." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "efe6bab2-e365-451a-adc8-57994454ad6b", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the overview level of interest\n", + "overview_level = 2" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "28438035-36ea-4fcf-8959-41b0cfde53b5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The CRS of the COG is EPSG:3577\n" + ] + } + ], + "source": [ + "# Read a specific overview of the COG with rioxarray and check what the CRS of the file is\n", + "cog_array = rioxarray.open_rasterio(cog_url, overview_level=overview_level)\n", + "cog_crs = cog_array.rio.crs\n", + "print(f'The CRS of the COG is {cog_crs}')" + ] + }, + { + "cell_type": "markdown", + "id": "ffb4db36-dfc6-437f-9ff8-868d9958a3b6", + "metadata": {}, + "source": [ + "Now we can crop the array to our ROI." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "717da262-082b-469d-a0ee-f2fd76a3ce3e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The CRS of the cropped COG is EPSG:3577\n", + "The shape of the array is (1, 510, 437)\n" + ] + } + ], + "source": [ + "roi_geom = bbox.boundary().convex_hull\n", + "\n", + "cog_roi = crop(cog_array, roi_geom)\n", + "print(f'The CRS of the cropped COG is {cog_roi.rio.crs}')\n", + "print(f'The shape of the array is {cog_roi.shape}')" + ] + }, + { + "cell_type": "markdown", + "id": "0485bbcf-05ed-4284-b4bd-bd305f081ff4", + "metadata": {}, + "source": [ + "Let's quickly visualise the extent of the final array." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "b9da2575-d460-4026-a6fc-b2b6d922041b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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92vHv66+/jsq3bt06q1GjRlZOTg4NyCCIOEJij4EXe1999ZWVlpZm7dy5MypdYWGhNW7cOMuyLOuWW26xMjMzrYKCAuvjjz+2PvroIys3N9fq3bt3LJue9Nid5JNPPmm9/fbb1f4OHTpkTZkyxQJg3XbbbVZxcbE1bdo06+yzz7bOOecc12Jv0aJF1kknnWT16NHDmjt3rjV79myra9euVlZWlpbYq6qqiozKvOyyy6wZM2ZYH3/8sbVgwQLrnnvusRo0aGA999xzlmX9Ohq3Xr161jPPPGN9+OGH1sMPP2zVqFFDKCQ2b95sAbDOOOOMaoH9lnVCMF1yySVWo0aNrD/96U/Wv//9b2vx4sXWtGnTrOHDh0eJSlOxZ7JflmVZn3/+uXXGGWdYjRo1siZOnGgtWbLE+ve//23dfvvtVkZGhtWzZ0/r0KFDUXVs2bLFOuuss6x69epZd911l/Xuu+9an3zyibVgwQLrwQcftE455RTr/PPPj8pjDyRwwh6EcOmll1rz58+3Zs2aZXXt2jVqNC7L//zP/1innXaacGCMZZ0YjWuLpTfeeMMqKSmx5s6daz322GPWkCFDIul0Xlqc6hKNxn3sscesjIwM65ZbbrHmzp1rLV261Jo5c6Z19913Ww899FBU2kmTJkUGVGzfvj2yvXfv3hYAKycnR37gDPjiiy+sxo0bW40aNbIWLFhgrVixIuqPn/6HIIjgILHHwIs9e66vunXrRv1lZGRERriJpoj473//awGwvvjii1jvQtJid5KyP9ui8MQTT1itWrWyMjMzrXbt2ln/+Mc/hJ2jrtizLMt69913rZycHKtmzZrWmWeeaT3xxBPakyrbzJ8/37rsssusU0891crIyLAaNmxo9e7d23rppZesI0eORNLt27fPuvXWW61mzZpZGRkZVsuWLa1x48ZJR7J269bNAiCcssOyTkwf8vTTT1sdO3a0atWqZZ188slW27ZtrVtuucX68ssvo/bbROyZ7pdlnRhFfN9991lt27aNtCU/P9+aPHmycPJdy7KsiooK67HHHrO6du1q1a9f38rIyLBOO+00q1+/ftYLL7xQTSB26dLFatq0qVbb582bZxUUFFi1atWy6tataxUWFlr/+c9/hGkXLVoUudb4KW9s1q9fbw0dOtQ67bTTrBo1alhNmza1+vTpEzVqWUfsOdUlu/bmzZtn9e7d26pfv76VmZlptWzZ0hoyZEi1ufvWr19vAbDOOeecqO0TJ060AFh33XWXtG0mON2z7JQ0BEEES5pl/d8wOAJpaWmYO3cuBg4cCACYOXMmrrnmGnz22WdIT0+PSnvyySejadOmePjhh/HYY49Fre35888/o06dOli0aBH69esXy10giJTl4MGDaNSoEZ577jmMHDky3s0hCIIIDSk/QENFp06dcPz4cezduxcXXHCBME337t3xyy+/YOvWrTj77LMBAFu2bAEAx2BpgiD84+OPP0aLFi1o3VWCIAiOlLfs/fjjj/jqq68AnBB3zzzzDHr37o1GjRrhzDPPxLXXXov//Oc/mDRpEjp16oTvv/8eS5YsQXZ2Ni699FJUVVWha9euOPnkk/Hcc8+hqqoKI0eORP369bFo0aI47x1BEARBEKlOyou9pUuXonfv3tW221MIHDt2DI8++ij++c9/YufOnWjcuDHOP/98/OlPf0J2djaAE9M1/OEPf8CiRYtQt25dXHLJJZg0aZJybiyCIAiCIIhYkPJijyAIgiAIIplJ+RU0CIIgCIIgkhkSewRBEARBEElMSo7Graqqwq5du1CvXj3tJaIIgiAIIlWxLAsHDx5E8+bNcdJJZCdKNFJS7O3atSuy4DpBEARBEHqUl5fjjDPOiHczCENSUuzZC8CXl5ejfv36cW5N7Jm/9HyMLDmxCPwLPZsp044s2V0tzciS3fj24e1BNY8gCIIIGZWVlcjKyor0n0RikZJiz3bd1q9fPyXF3qiVezD10jMwongnRq3cE9k+pbBFtbRpmWkYtXJP1G9pmWnIeuKsyLYRxTuj8lQ8ti+glhMEQRDxhEKfEpOUFHupjkjU2djCzU4zpbAFRhTvxIjinVHb+PQsDe5vLC2fhCBBEARBxBYSeymISKCx222BpxKFNjLrnow5xTnV0vLikQQhQRAEQfgHib0URSXSeKFniz8RuiLPJL1tGWTbaLeBhCBBEARBmJGSK2hUVlaiQYMGqKioSMmYPZWbFTCz7JmKPVFdbuuQiVAShARBEP6S6v1mokOT5RAEQRAEQSQx5MYlhPDWPdaCpmON00VlQdS1LvLQABGCIAiC+BUSe0Q1+BG5vKuU/z1euHEhy4Qgu68kCAmCIIhkgmL2Uiz2gB384CSWeDFnKq506uDr4a15opG7XuME3WC3aXDhhpjXTRAEEW9Sud9MBsiyl8Lwwokf/Wp/FuWzUQkv3by6LmJdS6Ju+9wwpzhHWjZZBAmCIIgwQmIvRZHNdccKMN3pV0zrlYk2EzHnJCRl29zug0iQ8u3QGeVMlkGCIAgi1tBo3BRHZgVzEl72qhpe6tNdycP+E1kC2bJi6d5VTQwt2mZ/nlOcE/XX4P7GkT+CIAiCCAISeymGauSrbh43o2RNV9rgRahK6PEELfpE++9Up2iQC18OK/x4IWhvIwiCIAhTyI2bgugIL1Px4lSf00odqhU6VFY7kfVMJET9HuihW55bUSzLqxJ85CImCIIgRJDYSzGcRrvasPPsebWUmY7IVaXRteyJ9tHLVDE6otSpTXY5bFo/5y/k1x0WDbghQUgQBJF60NQrKTSEnI8LE4kndptfo1p1R/by6EwNIxNyThNCe4039GOZOL5dbieRZhGVqQuNJiYIQkaq9pvJAln2UhhdIeBnDBwv0FjroUzwyEQbbyETuUD9aDtflh+TSQcRVyizEsqEMN8G9mWAz0MWQYIgiMSFxF4K4RRXFtTKGE6xebLBF3Z7dd3AfrmdRe2zRwTz9flZB/vfD5czX7bNmvJhyCueWe2c9GxdyqQaFJVHNL8gf8zJMkgQBBFOSOylGCKREoTIU7lY2XqdhKapqHJyY/o1V2CQ+OHK5Qer5EzrAQDYULQMG7AMUyA67oMwvXwuhmcNivqNb5fM0qpaio4sgwRBEPGDxF6KoZqjTnfwhhNuVsFQxdyJxAUvDk2mdFHNkacTV6grQEVxeSZtNUF0PKI+Fy2LSisfsZwPoBQlW/Ijlr6SLfnK88ZbPkWCe0Rx42rpeUgQEgRBBAOJvRRFJkT8QCbU+I5eZdkTpZHVxcLul+4AD9MRvrI6RULGpD1u0InT07Fw8i7wnq1LIyKvZItZO5zaJ3Mz24JQ1GZyERMEQbiHRuOm0Kgie2oOXSFlp/ELlVVN1BbZdCV+WMic4tpk6Z1EH7/dS2yfqo0qa6TOb6qRwCKXOuveVWE64lqWh22XDiQGCSJYUrXfTBZI7KXQRdvg/sZCIaCKqVPhVsj4ZUX0a/oTXTeuTHyK0vrVPr4cUSykjthjY/fysmYKhaSX86LKLzpuOteOn4NgSAwShDdi1W8ePnwYR48eNc5Xs2ZN1KpVK4AWJQe0XFqKMKc4R7sz141H00XmvvMDO1bMTdmmQkLmJuXLZOMfvey7yMUsEqey+Ds2b860HpFBGmvKhwlH//IxkSbHhxWefDmi46YSqGxev+CXnqM1iQkifBw+fBhnnXUWGjRoYPx3+umno23btmjfvj1eeOGFeO9K6KCYPYIgCIIg4s7Ro0exZ88elJeXG1kPKysrkZWVZZwvlSA3bopcGLI1VWVuTFGcnFtLjyqNkyuZb5Os3ew203a6HVmrGtmsW4bdbhN0XLmy39aUD8OGomVGblvd2E7VMXGbN56wbSI3MJHqxKLfdFtHKvbpppAbN8WRjRYVCSzeXSpzU+oIEFkdLCKXIP+77Ddd2Daophfh26mTT5RXx+2sK46c4Ns4pbAFNjBTsNiuXZ3jKNruJH5N3OthEXr8+bH/2y7fOcU51f4IgiDCDln2UuQtwMmyZxJDpRJnTtYqXZHCj34NUgzoDKowHVygU5eqTqc6dAeX8HXmTOuBDUXLIgM12N9l51dVD9sOp9HTMsIi9EwQWZn5/WfT0ByCRKJDlr3EhmL2UhS+Y1eJK1HnpSrXtozIrEFOYsLUBewVnXJEgw7cuLVN0slG45qWyR7fvKyZQDGwhvvdLo+18sqsvqo2O9Uvy59Igs/pRUhsgc6R7iO5iAmCCBoSeymITHCxiDp+UX43Qk3WJj6/3QaZ1VHX+hUUfo8gVVnYdIWddj3T1G1QCXxZDCW/jd0fJ6twIgk+0XERXas68agAhCOCySpIEISfkNhLEUTWBr5jFrmjRHmdLH1OnTZvqdKNGxRZ11Ti0g+3q1+oOn6ZcDZFVI7MMrehaBlQ7O7YyCyzImGuKtvE1Rsr3L6ssHnt/G72iT2Odn57MnQZZBkkCMIJEnsphCq+y0lsOcWDBeXa5IUl/59PJyLeQk8Ee7ycRLOJaFBZaEVpTOMCVfWKBKbo5YGvV+cFIyj4ffQq8py2uSnfKQ9rGeTvR7IKEgQB0ACNpA/m5F1Eqo7by0AB1Wedutn6ndro1qLoN17duKbngs8nsow5CReV4DdpH99Wp7AAUbu9vCj4ie4x0RHnute4Tlt08ziV4XRfkSAkdKABGokNib0kvzD4t36nDslrx2KXwXfeMoEiS6sSEH5af3q2LkXJlvyo7zbsdhkmwlRldTMRYXxeE0wEnG563kqpK1R1hE0YXPAiTESZjhDm0/qxz07XlEhs88ebXMSEDYm9xIbEXpJfGKbzgHm1TOm6e0VpvVpFTLGFXcmW/KjPJsji13hkFk8/jqGOINKNJ3OyBurmEZWh08YwCjsRJi9MfljrdJG9QKjw2hYShKkBib3EhmL2UgQd95NKhJgKM5026A5MEP0ucgm67bR4655XRJ0tH3ivE1+oEgluRblKKLLxkCKrpInVScfVrIolVJUTb0wt47pxj3bZpudWFYMpO9Zu7l8ZolARuz4SggQRDkjspQg6cUamAfmqctl6bZHD1+fUNv53VfvcigGRyDMRGKZCld0XGTIhpMorc4Wr2sW3SdQ+nfN10f4eaLF2prIOmQBWwQvbsFj+RO1xK9KcjrNTmU5WYFkZMvHHu3TZtLr7yOYVTSvDQmKQIGIDuXGT3ORrP2xNLWdOaXTdfbpxajp1B+HGlYk9L5YzfruoAxVtN4nlMon3k1l2nM6DSDyK8pmIAZMXCb6OsIg9N+gKW5P4Pt16vVgKvdYN6O+D6NqiwSPhgdy4iQ2JvSS/MPxYu1PWubOoLBW6Fie/Y4zsumSizk/3ra7gc0ojKo/N70agqdoqgxdwfrlTZS8ETuWH0Z3Loit0p5fPBQAMzxqkTGtyjZvkidVxNnmJcVsXWQVjC4m9xIbEXhJfGOxkrCqrm24MksiSo8rnxt1qavnRgR1hC3gbkAHILWV825yOucoyp2vpE+Fk8fMzXosvWwcdwaGKMQur4HNCdv+4uVdk14xTPt026rYjKHT2Rec6Jsugf5DYS2xOincDiNggi59iO1ad+CBTgWiCzgPeqeyerUuriTtRGsCd0OPbYB9D9rsuXo6h7HdbOMiOpRvrqeyzDmzsmE67nOIDE1XoqXA6JzpWXNU2k/rdWoKdyhVt5+8dFvZZI0NnX+cU52BOcQ4a3N848mdvI4hUggZoJDG67lKZ4OPFoKnlwC9rko47yEng8XPpeXHfqo6XTl47H3t8RWLHyV2ucg3z58z0+MsErGk5bgQJvx9hdOGaxit6OW5Owtiu2819ypdh5zMNweDLVIk1Vfk66WX7p2NtZ9OOKG7sKKTJVUwkC2TZS3JkFhXdN2edh7voIcvXK0qnarOsXL4OGbb4E4lAldBzKlfWVhlOVkqRC8/EqiHr5NnPbkWSrkVP1zLFW5RF+VXi0G/3sxf442pyvnTSya5DlbWMfznTQXZ9iNrh5iXPCdFzQlWmzrVmeg+ryrGtgiILIUEkEiT2CIIgCIIgkhgaoJHEwZxOb5+6wdhOAfMqdFywovL4fF7cuG5WxdCxZMkC61mcLBKi33XPgVN7vMZfObVDVJfuuZZZNFMFLxZhUX4dF67sfndyqQZ9XnRjV1XPCN0ynOqVXZcmx9VOn2yDQ2iARmJDMXuEoyCTuXn4B62O+NFti41TeX6KPLs+kbB1esib7LtOHJtIVKk6Nj7eykR8yZC5l1UizdRVr5vHy374hZPg1hW5Kty4SnWPpVMer/vnFi9uet49bvoCIbrH+O2qdqnqcxoEkmxikAg3ZNlL4rcAlWXPxKrn9JusHN1YH6dyVMjEnq7Qc9tJ6wo9JxHntaPTiW/0Q4TLytKxJoraxJepIpZWJidUwt+0baKXJf53frsqrRdrYDxEnkn5TseKTWejey2qrmmn61tlBfRCGIUgWfYSG4rZS1F0A8xlAdx8GhY3Ad2iPx1KtuR7GlkrejjzQd66YtDE9ct3cvxn3Q5QF5NjaorK1SZL5xd+iVg/6nXTFlkeE7c/u12F6n6XWbCCFtcm5escK9Pnh6hc3fNoeg2YtM0eEMIODOG3EYQJ5MZNUkxHizlZ8FSuXBVuOgs31gSvEyXz6Ahc1tqgI/hMyhfVxX/mUbl5/RRFvLjj3bumIQEidC1oXsSPG0T77qZOHTe9rgXY5OVK180bS9y8HPJ5/Ub2vNPJ5/e1KMsvE3wjinfSlDFENUjspTg6bsRYu3jclKmK3XNyUznVL5uXT+ZWErl9nESBrlATuZt4oaXaF6/IXNputjkde12rFyuy+WMVC9Fnf3frytX53dQCKErnVYCHAdFxB8zOtcr1yr68ieoT5df9zSSNG9i2Oq2JHkY3MREs5MYlpGLCqaPhceNC8RuZKHOLar4+UfkiC59TR60TX2cqEJ3q1sXUhe+ETtyeiZXUrQXGb5ysbzyiFwX+v64QZPOI0sTL1W0Kez/wf7z1mEfXLS3Lp3PMTY8l22ZVXtk1HMSz1C5TtKII6zImkg8aoJGkwZwmN6yJuGB/k4mNoDtb1tJmOmkyj+oNXrSmLp9X1MmwuHFbqiwPTpik1S2PbZNIxDp1lDILn+q603Fby8rn6wir5crpWlHdXybXmYnICAM651vH9Styi7s5lk5eD9nLmon4F5Wr2wZRelU+p2MiK9s6YuHgM/tpgEaCQpY9QojuG66XuCW3yISem8EaulY4UX1u9lX2lu9GpJm6mLyUp+q4TFyJOmm8umJ1LZ5hsHiprj2VW9/UkuX3tZLI6N7vbHoTMcXX4ZeVzg8rOmvJDKvYJ4KBYvYIISZvlvHoNPwcjMFi77fTWrtO+QHnt2qRUFZ1Hk5uO13LhQyduDA3nZ7KKuiEU/ud3Lg6AjZesB2vqSXS9J7jy+UtyfE+FjYyK5vpde0UgqIqV5VPVpZTfaZlyvLrtM907W/VPR2W64LwDok9QtoJ8GniKeps2FG3/O/2A062D24eXip3sVuc2iD7nT8H/MPfxI3qBZNYKP6zk/tI1Jnpuu1UbYx1qIEpfOwYu91JSOtcT/z14cXFGCQiAaqyJps8r0xeenReTETH1bQuvjynvH4JRv4+FP0P2z3iF2lpaXjggQfw6KOPAgDGjBmDDh06oKioSCt/UVERPv74Y9SrVw+HDx9Gr1698Nhjj6FxY3no1IEDBzBr1izcfPPNfuyCK8iNS0ThNkZKtd0tIiGnEl32b0G2z0T0eXW7sC5ftlPRtSQ4/e5GdOqWJ+oEnToRWX1uOh9eBPvlSosHfrSZ3f94vbj5ha4YU+UXiX+ZeNaxZvPXluhaY7eJxD0fHiNrqw46z3BZWxLxHjHh5JNPxowZM1BZWem6jL/97W9Yv349PvvsMzRs2BBXXnmlMv2BAwfwyiuvuK7PD0jsJSFeR1OprGKxevNj3bS20ONdE/Zvst/9bocTTg9oXRHtJKhkIootQ1S3W0uHF0QWOla0qjptr9cXL/ASWeC4QXQ9yix8YUH0QiN71oiEEv9iJErL18V/F5Xh9LxzaquoTqdjbxrqoGtxFx0nnfwv9Gym1Z6wk5mZiWuuuQZ///vfq/22du1a5OfnIycnB9dffz0OHz6sLCsjIwMTJ07E7t27UVZWBgC44oor0KVLF3To0AHvvPMOAOCBBx7Apk2bkJubi4kTJ6KyshJ9+vRB586d0alTJyxbtsz3/azW1sBrIBIe0dsuL/hi1ZHyosvUvSqLZ2FF3ZTCFphebl6GzDIgcr3K3JIi96eTG4ktl9+uW5asTFnb+H1z0xGqBHGqWBl0UR031TlwcpvL8sUKp5ci/n7htzuhY6XTFUo696FT/WxZQVhYT5Q3SNoWk3bybt2RJbv9bGog8Na6zMxMZGZmVkt3xx134LzzzsMdd9wRtX348OF49dVXUVBQgNtuuw0vvvgi7rrrLmWd6enp6NixIzZv3ozc3FxMnz4djRo1QkVFBQoKCjBo0CBMnDgRmzdvxpo1awAAx44dw/z581GvXj3s2LEDgwcPxurVqz3uvRqy7BGO8C4xe1ssLAR+xMr1bF0a+dMpc3r5XOF2VuDx5fVsXepoPRG5TmTIrAt8RyjLK6vHy3lSWT5kiKxrbjpWnf12KidZBKSOW9HJiuvFDRokIneoLJ3ui4bIgsX++XmPsPWy/0Xl+3XcdY6D6njJLH388+bbh7f70t4gycrKQoMGDSJ/jz/+uDDdqaeeissvvxxTpkyJbKuoqMCRI0dQUFAAALjuuuvwySefaNXLzmD37LPPomPHjrjwwguxY8cO7NmzR5j+3nvvRXZ2Nq688kps2rTJZDddQZY9QpsgHoo6+D3yVlWOkxBUDRCx0T0uOp22znbT8lVv+G47IBOXk9c0Jtddogs7FbzlRVdI83ljiY6lUUeouhH/shdUt1ZDVXonS5moPV5wKsukTh2LflgpLy+PmmdPZNWzGTNmDPr27YtLLrkEQLRgs7+npaU51llVVYWNGzfiT3/6Ez766CP85z//wcqVK1G7dm20bdsWR44cqZZnxowZOHToENatW4f09HTUqVNHdxddQ2KPCA3sQ5MfkKEr9LxaAnkxJ6rXyZUseoP2KlB03K9+IusUvHROpvFKbo+f323RKSvWHSIv7tg2hFXw6Vig7N+dzo9f1jEnwal7XnWtazp1u0HXyu7WKp4o1K9fX3tS5aysLHTv3h1z5sxBbm4uTjnlFGRmZmL16tXo2rUr3njjDVxwwQXKMn755Rc89NBDaN68OTp27Ij58+ejcePGqF27NkpLS7FlyxYAQL169XDw4MFIvsrKSpx++unIyMjA22+/7Rgb6AfkxiUIgiAIIuUYO3Ysdu3aFfk+bdo0jBw5Ejk5OTh48CBuu+02Yb7Ro0cjJycH7du3xw8//ID58+cDAC666CJUVFQgNzcXL7zwArKzswEAjRs3RufOnZGdnY2JEyfi6quvRklJCfLz87FixQrltC1+QZY9IjTYb75urXN+zYVn4i626xyeNUjb2uBkCTK1Kpi413WtUH681euOSlSlCcpiJhtME4u6/ULmzvR63GOBjlXRj+uYzyOqQ9QWt+deZQ0PyoqqU67b/fE7vjAMfP/995HPbdq0wfHjxyPfu3TpgtJSdT8ybdo06W+1atXCokWLhL+9+eabUd/Zep555hllnX5Alj3CN0wC9/l8Nn5OXgyYL6GmWz87QEOGU+yRCtZN5+ZBK8sT9CAFfjAG3xYToRmk2BK1TRW0H1ZUAxpksWjx2jf2unAj1FSDHty2RfXZpCw/XqD8FJhe0vmVjwgXZNlLMrzOsecVN2+v7EOOj5lzwknI6ZTlFKMnS89a84ZnqYUNbz0w6fBEx5Qf3aqy7snqUgWLiyxfXq1xbjozP2Pr+LYkUydmGlvmJq8fmA584PO6GYzC160a3OHFYqh7r5jEw/pxnYqeDaZxnQBQ3tVTM4g4Q2KP8AxrMfDDKmIq+PxEd13JExbDE5/ZffZT6Oi4UIIUQLwlxm3Howqwt8sX1e9FILtpW6JY82Q4DWrReQGINSaDFvwYUKI6Rmz5poNIRGWL8tjpZGWI2usHKoHLwr8AEskDiT3CM0F0mH4IPlF+r9O3yKZa0ekgRJYylehxKxxFeXWtcqK26Obn2+jkSua/O1k7ghR6yYDJ/oRh30WWuyAtrk4xmm7Kkl2fOs8EnTq9Hg+3L5ms5XRE8U5UPLbP0/JiRPyhmD0i6ZGtmOFm9Q0ZOgJM9J/9zMb+uHVVsXlFbdON7xJt1+k4RNY52Wf+z6lcPwkq/suk/mRyI7tBx3rGx8N5tUjqWMlF5Tu9QMmue53rWxbzFy9BnurXZbKSZvEzCaYAlZWVaNCgASoqKrTn5EkU4h2z5xYd4eXGKufWOui0pBpgNpCA7SycLHzsdhlu44p08Mu6Ios19MsV7JYgrYWm7QiDhc1v/BBkOoLL7+tIFsLgBafrXcfSr1OOH+1TWSNty17Q/abbOpK5T/cLcuMSMYWPidONkYslKqHnFpGLx/7s5K4VCSa39evEPfkZJ8Tvg5tgdr+IRedJ6OEUpiALI4iVS9PE0uxk4dMNUzDd5oRpTGCQsbFE/CE3LhEz+LVk+bVq7WlS2D8br1Y90ylYZLBlqISaE27zijoZU6sdX46sfJ3tTjiJSt24Pj/weuziRSK0kcWNu9/PsoNE5m5VuWJ1r3O396YM3ePLtz3ex5gIBhJ7RKjxKtJEotFPUem1I/bDUhG0MBINqODrNo13ckImxvwSPnxsZDKRCPujYzlThTbEO55Nt/0mljXVNp06+fq9kgjXEaEPib0kwo94vaCmPFGNjPXL6mZaP98WN23gH6pujp/bjs8LojpFli9VvSKhp+qodALy2c+s+8uvTl4VZB/rzs00fkwHk4EB8UZ3v2Jp/dXBr3pF12KsBgzx9xe73W4HWfiSC4rZIyLEYm47XlAFKfK8uoFFqGIMdeIPnWJ43MIHWduYCgXWIuE1GJ5tg8hC58ba50cHJLJUirbHGz/2N2z75ITptWtqmTUdDCW7dnVxqsvvZ4FuebKXPdHxqXhsny9tI+ILjcZNopE7Xix7vNDzU4QFWXYs0bEOekGno1MJACdrnSitH0Hruu3hOyInl6/J/iQiQbgk/RjME290X4j8jPvTKUc1cETnODvdD17w+mLGCj1+n2yxR6NxExuy7BFCERPUKNlEFXqA/yt7mFgxdDsjNr1p/JzqbZ//LMuvE+jtZC0RlRO0O1LUEZtY1rxY4dxaYmUkosCzkZ0H9rudjhcnMnTTyNrAb3e6xnXFaqyteSpErlwiuSCxl+LEclmyRBJ6ss7bi+BzEgQ6Ys1tnJdITOnECJl0vrI2sPWp4gJF7tWg486cOnYnIea2bTILkU6dsvKCiO0MA/y1E7QQ0Xnx0rkWYiGY/BBmbBl+hm8Q4YLEXooStMizLYP8RMSJhErw2fh1HJ2sWU6DH9h0ItyIAJWQE9WtwqkMtkPv2boUw7MGCQd+yISjG5zKcHLZ8ZYl0zapLJz8b07uQr+OSVhxcu2bWJxNcZNf977wsl8mlnNTyLqXfNBo3BREV6DwS4qx8+OpyuDnzzOpU0Q8Hjo6D3e/Rt7qdiSqh7uONcLkOIpcVbJ6eMucnU63Tt6SMDxrUOSzqSvaBJmg0mkr2wb2uJh08qw1jv3j6+LrSHZM9tMk1k52PbK/qV6o+GtFFXrgR/t1hJ6XFzBRepnlnkh8AhV7V155Jc4880zUqlULzZo1w3XXXYddu3Yp83z33XcoKipC8+bNUadOHVx88cX48ssvI7//8MMP+MMf/oA2bdqgTp06OPPMMzF69GhUVFQEuSsJhUyEuFkPVlSmPU2JaGJkHr+sYLGI2dJ56APi42hiuWTjjVQWGb6Dd2qXk4tYV4CpBJ2sI3BrOfQqdL2i2geR+PLrOjRxH+uSDNYYp/PsdM2IhLPp9a56AZCVJXshMrlHTPbNbRlOuLVSE+EnULHXu3dvzJo1C5s3b8acOXOwdetWDBkyRJresiwMHDgQ27Ztw/z587Fu3Tq0bNkSffv2xaFDhwAAu3btwq5du/D0009j48aNmDZtGt5//33ceOONQe4KQRAEQRBEQhJozN6dd94Z+dyyZUvcd999GDhwII4dO4YaNWpUS//ll19i5cqV+PTTT3HuuecCAF588UWcdtppePPNN3HTTTehQ4cOmDNnTiTP2WefjYkTJ+Laa6/FL7/8goyM1AxDZKddCWJtV1G5vHVPVk8Y4vVUb6om8T5TCltgevmvv5vsm1fLi1Pcnts3cZE70w+Lm1+WJlF74m158BobGVS8lakLNBGsNzqDBmTHTeR2F8HmF1l42Xp1QiZk9cos+ToWQDeDJ0T1OVkn2XSDCzc41kEkBjGL2fvhhx8wY8YMdOvWTSj0AODIkSMAgFq1akW2paeno2bNmli2bJm0bHtuHZnQO3LkCCorK6P+kh1+HVoeE5GiEorsyhNBDPrQfcD6Ub5OHV7bYfKwZl2u/GfTdjnFI7Hl6O6jrD26rkjdWCmdssKAyXFT7bNp/J+sDp0yTGM544VTHJnTfSJz1bLpRde+17hJVUgEW7/JfcCGgTghckm7eeklEp/Axd7YsWNRt25dNG7cGDt27MD8+fOladu2bYuWLVti3Lhx2L9/P44ePYonnngCe/bswe7du4V59u3bhz//+c+45ZZbpOU+/vjjaNCgQeQvKyvL834lAjKR52aZMpN0flry/ApMV1lSTOJq/BC0bHyPKmBcFLzvxXrHdzKyTlMVyC47VrqiwU/RrhNf6TextIR5tfLpWLPYWM6wwrdR9nLB3yMm16ef+2/6suQWPr/qOaYj/sN+HRDeMBZ7EyZMQFpamvJvzZo1kfT33HMP1q1bh0WLFiE9PR3XX389ZIt21KhRA3PmzMGWLVvQqFEj1KlTB0uXLsUll1yC9PT0aukrKytx2WWXoX379nj44YelbR43bhwqKioif+Xl5dK0yYBoMIVKhDmJMx3xxpcfBtdtmBG5jZw6I77DMnkwm1gDZJi6CIMsX1RfonRUIjEiS+O1DsBslHHY0bVqi4SQSAzxgtfPa8h0YIgXRKEY9naTl8UgjgMRDoyXS/v+++/x/fffK9O0atUqyhVr8+233yIrKwvLly/H+eefryyjoqICR48examnnoqCggLk5eXhhRdeiPx+8OBBXHTRRahTpw4WLlworE9GMi6tcuW0rdW2+eWqNS2LLTPsok8WD8bC/+7H8m8mQk+EykrntkNU1WXXYZrHaZvfBC1aEiXODUistjrBu2T93Denstz+7nTPyOJQ3cTm6ZTP1yVKz29nY/ZoubTExtiy16RJE7Rt21b5JxNetq60Y/NUNGjQAKeeeiq+/PJLrFmzBgMGDIj8VllZif79+6NmzZp49913jYResiIaPBFU2UHnizemnYhb166J+5h/O5fFI6lifrzEv5nGMyaT0Ig3ZGXRR8f67RSqwKNzb4rymlpuRfex15dAWTwg7+6mezX5CSxmr7S0FJMnT0ZZWRm++eYbfPTRR7j66qtx9tlnR1n12rZti7lz50a+v/3221i6dGlk+pV+/fph4MCB6N+/P4ATFr3+/fvj0KFDeO2111BZWYk9e/Zgz549OH78eFC7kxCwsXhu8yYrqrdrpwd8GDpbnfg0nXby1gaZe0cV46hbPxt3qJPfD7yK2rDil2st0Vx0ovaanF9da7LT4AUddPOyrlLZs0f1QidKy5brpc2ymEci8QlsnpLatWvjnXfewcMPP4xDhw6hWbNmuPjii/HWW28hMzMzkm7z5s1REyLv3r0bd911F7777js0a9YM119/PcaPHx/5/b///S9WrVoFAPjtb38bVefXX3+NVq1aBbVLoYWddiUIEsEdq8JpsIHum7tTHSYPR9WbvcpCILNamHQ0qjJl7dG17Mk6TdOO0C2qNrjF5FoJArfHxMl6lAg4hTk4bXeyWvH3UyyOj049uve2aQyvSd2J9FJAOBOY2MvOzsaSJUsc0/Ehg6NHj8bo0aOl6Xv16iUd4EEEY6ELi9Dz0omrYmpUdYkYnjUI08t/tUafGJwitpr5gU58jW4wuEknYmox4PefLcNN5ytD1Z6erUsxojgc16sfxNod7uYFImi8iA5Z/JvsGoql4LPboYtOXK7XY8XWU/HYPtdlEeGD1sZNMoKY6y4suIktkT28nSwGuiLQXsdVlUcXWUfLx+uZwO6nal9VljhdAcYisk567Yx02sOeDz8IqvPXtZ6Y1C1yd6rOu8qFGBahBwQjOmUhBn5do2w9Tr/76aL3A7LoJSepudwEkTKILEt+lNezdXVh4fUhKbOkyawRon2TxTepLJsykekklNny+fbzv7FpROWuKR+GvKyZyvROVpkgCEr0mLqFdd3oNrrHJ9aWQz8wOf9O16romvLTOm/SVpE13c05VdUp+k313CCSBxJ7SYYXl6u9/JmIRI/bsxEJHL8IWoToCDlZJ8Z/lwmyWDz0RQJzQ9EyoLh6WpW1M6j2hRndUAHRC4OOkA8rItHvlJ5Fde/wljXZi5Yp8bK0qdIn2nkn/IPEHuFIUK5hNw9SLw8rkfBRtcstfrpz3eR1csuKrEo6b/si2GOqqlOnHKc6ZG3SsX4kWydnaukT/ZZox0R23cosU7ovBDrXuh/HSveeYu9hlTVbZq13agObLpZWchOGzdiGGrXraac/9vNBAEDXrl2Rnp6OkSNHYuTIkUE1LyEhsUc4EiuLns4D1W0nJXMx+uWu4etxi4nAYutTWXH48lWuHB1LkRtLg9Nx4V1YbD2q30TtNNnuJ7EQT26vf69iPFGQ3d9+lOkVXaHH1637jNJx3cpEcRgFnxtWr15NkypLoAEaRBQqYdezdWmgA0BM4peCDmg2iZXh3UBeEFnlRPU51c0/3EXpdPZRFrPH/snKYOuUWeRk1gmZ+1bUhlSLN3Jz/SejsAPkx0J2zYjQuTbd3ONerK9O178ovZf9I5IfsuwlOH7PsacSc7YQ9Ct+zw8rGPsm78ZtqPPGa2It8tO64zYuzUkMiPbZyRIoym/nc+NOEqVxez5NhLlp2SaoygyjyzSMbTJF56VAlNZtHW6eM6I2+ml5ZNvmJq0oH7tMGpEckNgjolAN0mDTxBu3QojNwwoVnXgYFvs48XPuTS8/MVLXTiNCd21dlcVCNzYnUeBdTW7clKZ1Jdox8oNksnrq7ouT1VomyGTp3WL6zNK17vnpVUim64OIhty4BEEQBEEQSUyalYLLUVRWVqJBgwaoqKhI+GDOIJZK4y1Pw7MGRb1ZxmIaFt7ywr/l6sbb8WXK0ouCoJ2sP1MKW0RZ9WwrH39s7OOpsvQ5HU+RW1k3VolPqxqgIcPUAsoHfpu6OEXn3621LxUteCyy68StGz9emLbF6XoW3RNe7i3d350GRYhCGWT7HlTspmj1jFj0m3YdF7+4zng07vu3d0qKPj0oyI1LVIN35Z4QNO7Fnc6DStSxqwSKyBWn0xmo4s9M2v5rG36dWFkk9AB5rOOvnYCzG1cWj6Q7mo7tNGSCTXZs2O18XU5CzY1QkMVLmoq3sIiUeGIa6B9m/IyFZa8lp8EXvAA0jQPk263zsqUT0+c2llfnPieSDxJ7hDGmVj3Tt3EnYSh6yPEPR5PReLKydEfT8RY+GWycnwheDMoeyioxrCOAnQSbyqqqi8lgC5E1JUwWJTeEpf2m1ibd/GFFd4AO+1n1IsSnF91Lpm0zjRWUtdUU2UtUop1jwh0Us0cIEbkig5x6ReY2cRIJKkuVU/mqclV5RPUNzxqE4VmDqv3Gf1cJZScRLesk2GPgJNLYhz37J0pjCtsGN52J07k2tcoScpwEjk0iHWNeOJm83MVKnLP3lu7zhf/NyzlJpPNJ+AtZ9ggpOiNz/UTXoqTjehHlEdXFuxtVb9peLU+868hN56JrIdN1o4r2V+U+V7VH5xjqoLJG8S44HeJhZYuHtcStm1tlFU8Eq4+T5Y2/7/i0Xi3XJu0TYRKOIctrkp5ITciyR2gRxIAMvlPhRRwrWGTWIpl1yhRRvaJO0LQemZXRrVB0KtepDif3rZNl0rSddpm65TgJPVPiZckw3W8/6mHvIbfESgwEcVxkbZfdtzKLuAo/49tkzxndvCJha/JyJ0M0OINIfEjsJTBBjMT1gp/uBfZBzHecqgeuW6uGKL/IOiarm33Qyjp5XWEqc02zx0ElflR1yKx/Tm1yOtamsVKy33XSmKQPEieLp5uXA6/3kBcrZiytPn7VpXu8ZPejEyoLuRsrnFO9OqEMpvedLC+RWpAbl1DCWvScVtAI4kGia6lwinPhH5Dsg9ckdkaWPghXoaqDErmE3cQA2b/zIldm4XMSkbLfdI+PjiXUTWcXdviwArfwx1rHpZ4MAkC1DzpWblVeUR1eXaeq54jO77JyVXmJ1IYse0So0X0j131blj34ZR2tyoXMtk/nrd1PZO1xSsvCWoJ03VMqCyxbnxuLlqozFVmt4hGLx+LGQuu2LF14a7ioPckiBHgrr/1f5QFwenbouHz9aK8OfDt0hJ7u70TqQWKPiCu8+1O03aQsvhxZGhkmrjC/xAY/CMbJSuF3W9zE5enE94lcxrK6RG5x2W8m7UxmeAu1ad5kQSbWRC8PXkIJRNewbuiBLCRFVo9pSIMKEn4EQGKPCAmsBc2Pjp7F6aGo83AVWRFk7hJToSob/CIqx60rTjcuz23cl+j4iOr1Ek+meyz43+Pd2dnHw8+R7TriRXbMkiF2S3a9qdLqiCPTFy2/j6NTyIN97kQvVvwzLBnOM+EftFxaAi+tErYBGn4hetjJrEa6+U1+V+Vzqtsknaxdfncqpp2cSsy6jQdyiplSucYTFZ1r1mlfvcSbJfrxA9TWYvu76Biy95LqmlUddzaNaX7VPvDl8hZwU4udybNRB9loXFouLbGhARpE6HATj8I/8J3ejlU4xY/pYioq+Qe9bl6+Y5OVzefRKVdnm24b+XaI2sRbIxJZ+Dm5rnX2za3QSxVkx9DtSwmbP2hkMYc6iASiH22maVeSF7LsJehbQDyterLRuEGj0zm6EUu69bop17Q9fosbpzg6meVOJsS8Co2gO1EnS5hsn+OB6Ji6uUa8xKiKygsjTi9gOpZRL9Y5kzqd8vmFar/cnkuV2CPLXmJDMXuEK4JcOk2GKgbFbayZCieLjA4mcWNBdAoiS5lubKLJb6q6nY6BTtC6bhC8U6yTU37TdnlBdVx0Y/G8BPHz+2YSBxdrTO8f9noI6hz6Fc5gksdN7CpBAOTGJVxgL6MWD+ueE0FaxlSWLS9uY1H5ImuPl2MuEgcyi4DXzlEUO8XjdFz58xiUpVPVRl1B5QcmHTjfXieLq8xyqDruYcbr/vDp/H5JFNUnaouOpVx2/ZPAI0whyx6hjW3Ni7VFzxRT94obS11QjCjeiYv29xD+xgs9XVEms1DJRJipdUxlNdPprEQWGVOcLHeqfCrCIH50zptJrKbOPvEvHkFaOE2RXVM6wkmU1zSu1guic2lyPp3aRSKQkEExewnq349HzB4v8kytTEG/QQeBbgB0kPvGx5vJLALTy+dKz4nK4mliDdWxlLDfdax8qrLZ8mRxhKaoYpzCHr/m5hiqLEgiqxN/vE1jCmOFSTwef9/w293UzZajk1bVPlGZXmIEKWaP4CHLHkEQBEEQRBJDlr0EfQuIp2XPaY3cREBkLfNant9WD5n1QGbd81K/qizeuulkqdC1ZMjqdkrP55FZEmXIrDsiS1GYLFlO6Lpn2bSi/XayEIfhmMgsxyyyfRWlcVu/SRlOVlevVnc/IMte8kIDNAhthmcNAgCUbIlzQ3zAz4dnULFMJgM+gugMZO5jWQerK5xNjpdIaJrGavHoxg+GQdTw8MJMFnsmGwwg2r/qoncQhmc5u0jjjZtBC3693LkpRyQ2nQaKxPIapDn2khsSe4Q2YbR+yeoBzNrr5Q06XqLAr3p1LIV8wL69TSQIZOfZKSZJ1T5VO/kyvRyXMAo8FlZkuAni17GiqsoN+/GJJSbxqLpl+WmtJwgWEnsJSFiWSTN14Yb1wRV2ERv04A+2k5eJATcWO5mVzIsb2KkzdBq8kApCRuS21BWI8bYuxYKw7I9OaERY3OZE4kMDNIikw43VI0zI3G2iNLy1zbQeHtmxM4nFY8Wj6bkwtbDyx4otQ/Td1JoYRvyIM3MavRp2RO1kp4Zi/2KxT7rWatGLlZuYVYIwhcQe4Zqwz7cXqw7b7weyaXl+DTBxEk1sh8aLTT6NV9zGzok6UB33JE9YO1nZcWdhBbYq7tOpnEQins8it9eqTtpkOT9E/CGxR2iRiA+dsHbYuqgsd6zVjBdhJuXyowF5t58IkbVO5o7iO0I3g0t0rFA61lCdusKOmxcBVVlejkcQYtFNeU5CL2gh6BQ6IPuu+5KWiM/eMPPHP/4RL7/8cuR7mzZtMGnSpMj3goICbNiwoVq+AwcO4JVXXolJG4OAxF6S4ucDgh42scPuQE1j0VRpdGPg+Pg9p3J0xIJM3Llx8YrKVO2zFzEStmtetj8m8Y1sOX63LV6YCLmg26mKu1MNKnIi0V9QwsZ5552HFStWAAD279+PunXrRr4fOXIEW7duxbnnnlstnxuxd/z4ce8N9gkSe0lKLFyLiTrHXiwx7Vx5IaNzHlXih7XU6bTFJH6IL09khdTdd9G+urX4qcrUJewdrI5renr53Ji2xa+ydK4Zt8s2BiH4nF62ZG51HREY9uswETn//POxcuVKAMDKlStx5ZVXYteuXQCAtWvXIjc3FwMHDkSXLl3QoUMHvPPOOwCABx54AJs2bUJubi4mTpwIAJg4cSK6du2KnJwcvPTSSwCApUuXon///hg6dCh69+4dhz0UQ6NxCUdUD5xEmVg5nqPaTOvVde2YCEEdy4Iq3s3J2sh30k6jP2UuV5NAd1EnKxOafLmJ1ImaxoT9uo/5GJ4ln1DYtA2y4+ylLH7fghBj9vygdh1+oyqTP2ai+0kUAiGzrAd13SbLHHuVlZVR3zMzM5GZmRm1rWXLljh48CD279+PlStX4vzzz8fnn3+OHTt2RL7feeedaNSoESoqKlBQUIBBgwZh4sSJ2Lx5M9asWQMAeP/997F3716sXr0aR48eRY8ePXD55ZcDAFatWoXPP/8czZs3j82Oa0Bij/BEIgg9wL952Nzgt8Dg4+pkgttpX2Vxdm4RCT7Z727cv6Jy+X3UEX6Jhu51I4vDZH9j05rcE6ZWV92ydEQOj6lFTzcWNRY4XZOJ9BISRrKysqK+P/zww5gwYUK1dAUFBVi1ahVWrVqFO+64A5s2bcKKFSuwcuVKDB8+HM8++yzeffddAMCOHTuwZ8+eamV8+OGHWLBgAUpKSgAAFRUV2Lp1KwCge/fuoRJ6ALlxE46wzLHHEvZRuTbx6PCdYtNM2sTG87HlyQS3k5Bzchm7Ecg64tIpzo79zpftp+jwA95N7iavn8jchSZW2VjDntcgYgr5usIk/GUuXllaQk15eTkqKioif+PGjROms+P29u/fj0aNGqGgoAArV67EqlWr8PPPP+M///kPVq5cifXr1+PMM8/EkSNHqpVhWRYmTJiAsrIylJWV4euvv0bPnj0BAHXq1Al0P91AYo/Qho37CtMDUxf7wRrv9utaMpzaKYuhU7nGROJTJvp0rXBsnbL26nTmTmn8PG9+l+U2PtDP61BkxXOyaHmxOsf7PnJLWERTWNqRTNSvXz/qj3fh2px//vn417/+hTZt2gAAunTpgvfeew+1atVCRkYGGjdujNq1a6O0tBRbtpxYH7RevXo4ePBgpIy+ffvitddew88//wwA2Lx5Mw4fPhzwHrqHxB7hiEhA2O7DRInZE6EaPRdkHSZWMpFFpmfr0mriShafxgoRWYyerG4ZsutBJBz43+xtMjHnpj1OeWRWLT+sPG5Fnte8TuUC4tHVMqHPv8iZWpz9wumYuPEihMXz4LRvfr+AEHLy8vLw7bffoqCgAABQq1Yt1KtXD+eddx4uuugiVFRUIDc3Fy+88AKys7MBAI0bN0bnzp2RnZ2NiRMn4tJLL8Vll12G/Px8dOjQAbfddluoRt/yUMwe4YjoARWWB6hbvLpV/Yg9c4sdcK4Serqo9kPHoqdbJx83xZfDd4SyNKadmKxt8bCqBB0vJivfpE77GOu4492U7wUvz5zp5XPj/lLqdO2yXgede0+VRnavuImRTEZq166No0ePRm2zB14AwKJFi4T53nzzzajv9957L+69996obb169UKvXr38aaiPkNgjjJlS2ALTy098jvcD1A0qUeGmjFghskjJLGrsb6KOwcmqqSOGRW5DWT1O1j/RvupaA/0UpUEQr0EBrHDTEQcmxHuggxvi7YXQvU79KMtOI3tpIlIPEnuEMSdcifFuhT+YuDbt352C32OBKvZQ5L6T5WfzyH7z2kbRdlNMLK4q8RiPc6c6F34JUXZfZOKbTxPvGMN4EI/7lT9movtCFO7gB/z5Fr38Jcu0K4QaitkjCIIgCIJIYsiyR6Q8OpYwGbrpg7Ao8G46XUQuYP53kZVIVo6qbfZnNr1OTJhbRPU6DdRwqjsIS5DIkuqndU9WLm/NVR0vwh+cvAeyATN+ngdRCAiRWpBlL4EIyxx7yfaw4DtD1YhEXRGkeqD7iaiD1h00onIhuhkZKBJTpgNhTILH2fLZcxbUsTY9HvwxNB1tbHIOZPGadjn2sRINhPEjhjVo/BoQFkshyx5/2XWtCrMIikQ434T/kNgjjBlRvDMhB2aoYB/Iok6Rx+k3Nj7GzweqKOBalVanbr/apzPa0Amn466Tz+3IXb489j//WYbMUioSobxQ5fOYHAeR1ZT/L7oeeYsPWfX8QXQNsueIIGINiT2CgFhk6AzWEG1jO3cdS6BXVFYPE9HjtaP302Lgti1+Bbp7FT4qC6Wo0xcdO94ypGsNNRlsY2JJtdO4FeTxhl0jNxaIriESekS8ILFHuCbZrHuAuSVHVobfsTEq65BquTSnNvKfVehaOnWEkh/CUiWG3AgSP86XV7ecaeylk6WXF45uXmp02xJ2YvHixden47rXdfX7QSKcJyIYaIAGQSiQvZ3rxqO5fbiq3H6qPDrpZGW6sWQ6lSn7LUjLmSx9LK1RqsEfrMvUqV1uBYqOFdBPgjy2fk7gHqvrwO2AiES0mBKJAYk9wjWpEOOjK7C8lOMkXETWGVU9buO9RO3VcQnK2uxFnOoeV9326Foe/byeTd2uosETflkb+WMtakOy38ux3kfdUI54HvfBhRviVjcRW8iNSxAGyDoMPx7YKqsX32nIRA6fVyU4VOWYoCNqVMHpfnSCTm5bHUugX52un25RlctVtc+y39hzHkS4QZiZXj43sp43D7vdiyVRJdD5eMd4Hne/XiSIxIEse4RrTF1yyWA9MHWt2qg6bz7+jM8jq0/XFShrB1++WyumziAA1q0lym9S5vTyudWC7Z06rlhddypRK0vLflcdJ1E6J3HPu4xF2/1yrZvA7wPbrlggE3S84BueNcjo3uKPs+hcJcNzkEg8SOwlCGGZY0+E7oMr2R5wTlY+nQ5f1LmLtok6RJVwFHU6qrSy/XODTLyy9Yra71S3nb5n62jB56d1yiTuUZTWxP3No+POtsvSccvqtN3+7GQZ9ZtEET0nrIEnBqOp3O/89S4SszrXfZCIniGDC+PSFCIOkNgjCJfoiCT+gS/q4ESCiK/HTeeoirGTCUGV5dJtHJ2TkFUh2gd79HHJluqjlL2KPq9WHLsMnXJUViBVvarvTvlE16OorKAEiako9XNwhhd6ti7FiOJ8x+MmQib6TF4s/CDswpoIFhJ7hCvi4XoJG7qdsx9xOjpxcbyFQSSyeFQdrul5NbEoyayS/GedOlXl6mLq0lblVf0uEtN+WCVNy3XjxtdFdf7chkGEATfnSXXfxtONTaQeJPYIT9DDyRxdS5STW09kQZLFZumU7YSsLJN90HXdmnasbsSZjhjWKUd3u0k6U6GmK5BVLwd8HW4xzRtrC1es0BF6QPQLi1/3KkHw0GhcIoJJB+uX2yyRMXH58S4+mcvPTUfJ/rFliDpRUzegqHwWmeBk2dl5mObeBItbF6gIlavWSdDyrmdVHaJtohg7p9AAWVt069XB9Hg6ub3D4sJlUR0bXde9zJUuOw6ye88LFY/tQ8Vj+3wtkwg3ZNkjItAbpB5urSC67i1ZJy+qW5bHHsAgii1j28OX4Sb+jd8vE/e2qP1s5+dnJyeLFzSNhWTzyepgP8uuF10rqZN7XiQeZS5CVVvcuNGdynLCbV3xYHr5XAD50vPndO5E6WSWVRFBud2J1IDEHuEK0QMrmR4+OsJM90Et6qz9PlZ8G6aXR//mFCMn+82kfpmYarF2prCtOpZHWV2iemTpdNK6QWRBNW2/jnjXdfeqzqno3IiswX5gUlaiPTOirY3V19rlX5h4TIS/6hpItONGxJ80y7KseDci1lRWVqJBgwaoqKhA/fr1490cLcI49Qo9gE7gxv0tyq9yv4ryyDoWe1vP1qXCxd+dOhGv1jSRG5cVfDoxfioRpBKJugKKzefndSs7HzLYcyizwuoKBydkx05UlhdXrl/HM4xuXB72/nI6Tyorquj6dXNN6OLGhRuLftOu4+IX16FG7Xra+Y79fBDv394pofr0WEMxewlA2IVeqok8N52sTqyPLJZL9davcvnaU5TwmFrWRDF5KqsnH2NkCz1VbJKOtVPl9rXzi46fqGyvFi1VDKOs7ToufP4Y6bZPN73qWPhBKgk9wDxOj88j+symd/vy5XeMH5H4kNgjCIIgCIJIYkjsEZ5wilFJRnQsNCw6b9lOMVV8eaI4O1EcpQzeGuaUVtVmXbzGcoksn6ybi48bdNo/t9YPpwB7mWtcdxCFV1QWPrsdQcXrpSLTy+caHUM3gzFMf6dzSvDQAA3CFU6xVamCicByQuWmdKpfJmpU22VtcNPR8G1XCTPRZ9XgAp12qGIhRdtl7XKqT+WCk5UjukZk7jvZvojQjekUtT/V71u/cOtuVsXgia4z2UsfX0Yqh9cQasiyR/hCqnQeIkuOST6nbaLfVDFoJjFDMiEiKp+vR7RdJWycfrc/82LV7/gxWXwgW7+u0GP/y+LeeEujH1Yct8jiP1WCk3CHieiT3bu6Vny+HFl+0TU4pbAFza+XopDYI4xxK3iSAScrjFOnqWONYo+v6AHO/saLDLZsNwMIRGJMJQpMXbMywejmWtJxQ8fK3WU6oMKGt8D5dW/xFlT+T6ddQZKKzw4b1TlWWfyc0jrVmcrHnCCxR7hA9ABKZcuArsvVTmsaQyZ6O5fBi0lZjBYvFPl9Ee2TjoVB9zowsXzZ5fJCRSevqj1erlmTc6hj5bS/q9zBTnXzv/MWTL5N/Hay9PmL6N4D1C8G5GYngoLEHuGaVO0QZNY2XWHh9bh5FTBOsT58WW6tVmx9bixssvrC0hHqWllZeGskf4xNBazuC4IOui5tP0j2Z4eOS1b0Wfelbnr53P9b0QOR/wShgiZVDvkEjGGcYw/wfwRhomLSoYo6ab+sgqI6TOrx8xy6FQ06lkMTN5eb+kWB8jrHS3VenM6b3/ePbl2qayOe93SizLHHws5paSLYVdcWez3y50V0jEq25Avz8Awu3ODYPhE0qXJiQ2Iv5BdGWMWeTaqLPrexZnZe3m0nE0o6HbjbjlpUpxcrj5u8Ivcz+92POnTrZ+v0KvR08/qxL07iNJaC0yuJJvhYoWUSmiBDdk9etL8HgBMTlYuOkWjFHB4Se6kJTb1CuCYs7rR4ohJrJpY62cPdKb/quxeCsDbpumX52EJRftOO0glRJy2KfZO1TxeT60O3DF23bqKQqELPxvS60D2HAPBBw2UATixBCEzC1T/eHdWOki3i8hON1+pORv06NbXTV550FC0AdO3aFenp6Rg5ciRGjhwZXAMTEBJ7hCtM32KTFd0YGxvdoGydDkAkRtyINKc8ugMGVBY4VhTrtpNNZ7JfboWq6njruMNVFkmVi9r0HvLrvgubNS+R0TmWshcKnXjV6tffiSUIe7Y+IfbeOHkSPmDK2tl5GK7+8W6UbMmvFiM6uFB/vxKN1atXk2VPAok9whOp3mGIgu1lb/kyV6nXjtvJDaxyifrlRnQ6DnwaXURWNFFcnd+I2mpiSZUJPS8uYFUeEze/vU3n2AV1fJMFnVg93iLNW4hF6Z3COaaXz/0/S17+/1n5gIv298DOziesfy2KZwKYiZ2dh6HF2pmRMmmOvdSFRuMSrgirVS/WLiAvgfk6ZfMCRyQ42N9kVgL+N5nlSvSd3w+7XaK2BSUMnNzAbHv8ui75YyuqV5SH/011XNj07D74iV1ukG7/VMQWem4ss6LPorL4a8K+lliRabt2P2i4LMrNaws9WV1EahGoZe/KK69EWVkZ9u7di4YNG6Jv37548skn0bx5c2me7777DmPHjsWiRYtw4MABXHjhhXj++edxzjnnVEtrWRYuvfRSvP/++5g7dy4GDhwY4N4QPGF8eLAPwXjBd/i2WNK1oqlchjL3rm6Zdlqn+DneuqAbL8fC77esPbrourZNXMWxwOl8+lU+iyw0wG35YTmWYcGN0GOvA/beUFn8da93261r07P13Xjj5EnKvERqEahlr3fv3pg1axY2b96MOXPmYOvWrRgyZIg0vWVZGDhwILZt24b58+dj3bp1aNmyJfr27YtDhw5VS//cc88hLS0tyF0gCNfI3Jn8Q17XYmWnFW2T5XdqE9sGJ7elDiYWKhNLoMwyKSpDVbdfnZ6sHDZG0caNxVPHLesmntGUIK21iQr7QqlzfGSijv+dv3Z0LbGy+40dvEHnkIjp1CvvvvsuBg4ciCNHjqBGjRrVft+yZQvatGmDTz/9FOeeey4A4Pjx4zjttNPw5JNP4qabboqkXb9+PS6//HKsXr0azZo1M7LsJcrUK2GfdoVQx2OJ0qpi+FTlyuLhnMqQuR91LHtu8SqoZPuoin2UlSFrm9fOTxQn6cbKaqf30i7Z/pscF7/wwwoY5tG4/MhbHv64y16idM+ZzrHkjznvvrXTAO6nXQFiO/XKzuk3mo3G/ekoWgx/LfR9ejyJ2QCNH374ATNmzEC3bt2EQg8Ajhw5AgCoVatWZFt6ejpq1qyJZcuWRcTeTz/9hP/5n//B5MmT0bRpU8e6jxw5EikbOHFBEYQf6D6M7bS6QsjJfaNblqkQkbmUdOuRlaGqSyd+0MmC4nS82HROlhZROSIri8yKZyp4gnSxieIt2d+c2mUq3JLZguQk9ADna1kGe92YHnc+rT0HXxhCWojwEPgAjbFjx6Ju3bpo3LgxduzYgfnz50vTtm3bFi1btsS4ceOwf/9+HD16FE888QT27NmD3bt3R9Ldeeed6NatGwYMGKDVhscffxwNGjSI/GVlZXneL4IwRffBz8Zz6bjzVPjpilW1R2ZB9IpTfKGoY9QRtCauc3u7qBzR93gIHpVId+uKTWbhFk+cwgxMXgpZbIso/58gABdib8KECUhLS1P+rVmzJpL+nnvuwbp167Bo0SKkp6fj+uuvh8xzXKNGDcyZMwdbtmxBo0aNUKdOHSxduhSXXHIJ0tPTAZxwBS9ZsgTPPfecdpvHjRuHioqKyF95ebnpbhMcFPBrju4gB9FvXjtrVSyQKAZQFh8nSq8SgU4dm6mYlcU3BYnbenRjF9l6TMt2Egcm4pwvW6ecVEFmJXO6j2X3stMLB/vfFNkqGl5cuETiY+zGHTVqFH7/+98r07Rq1SryuUmTJmjSpAlat26Ndu3aISsrCytXrsT5558vzNulSxeUlZWhoqICR48examnnoqCggLk5eUBAJYsWYKtW7filFNOico3ePBgXHDBBVi6dGm1MjMzM5GZmWm0nwRBEARBEMmAsdizxZsbbIseGz8no0GDBgCAL7/8EmvWrMGf//xnAMB9990XNVADALKzs/Hss8/iiiuucNUugjDBTUwNay3RicsRBXnLYuHY7W5idUwGfYh+d+t2ckLUHjcB66b1sQRhOZQdL5M4QrYMJ6seawV1e55Mj4MfAzXCgup+crJcs6gs9k7n0ORYTi+fK6wnmVfOIJwJbIBGaWkpSktL0aNHDzRs2BDbtm3DQw89hLPPPjvKqte2bVs8/vjjGDTohOn57bffxqmnnoozzzwTGzduxB133IGBAweif//+AICmTZsKB2WceeaZOOuss4LaHYKIECvXoey7KsjeVOjJBlbIxKVs300GZeiOThSVq4Pb8+N1VCyLqvP2MjCCH2CiI95MxbIf13eyCD1dnESb0298WbJBSipk936qu+CJEwQ2QKN27dp45513UFhYiDZt2mDEiBHo0KEDSkpKolyqmzdvRkVFReT77t27cd1116Ft27YYPXo0rrvuOrz55ptBNTOlcfsQ8BJEnIp4Oc6i+DggemJWL+h2yqoYPZM2yCxaXtpmWp9uPq+xU6qyVbg5J36k85tkeT7YAx28xJ+a4LUcexk1W/ylmvAmxMR0nr2wQPPs+UMyuWqCRGbJMu3UgzrWOu4mr2WqkFnU/L6+TF3nXuviy/I6spot20nou3Vj+3W8vZQVxlGkXpZGE7neVel0yxWhuja8DtCgefYSm5jNs0foE3aRR+hjYslSEaSoNnHX6qBjuXJyc/LxZl7RKcdPoaOLG8FvOpLb77bwbRLlTbWXQB2x5oeLV5Re5qYn7wvBEvg8e0TykmoPdB7deCmvU6mo3uJFrt5Y4sXSISuDHVAQK6HnJ6J2qwZ/xMoV7yeiAUSi6zAZxIZq2hXdARZBIgs3kMXjEqkJiT2CcImpYPPzwctberxYZVRpdISkKI3THGK8BcLL6Nk15cMif+x2+7dYd3TsAAqRxYvfrhqdy+djfxPFbcZKXPH7oTtwJxHp2brU0bWseqEzub7dHi9e8PHXRsVj+1yVSyQP5MYliBjiV+fntRxV3KDK+sYLDlF7dOPJnEacmrhd87JOrAW6pnwY8opnyrIEhsrNyh87WQymSNypzhGfJ9bIrJVeLKkn8uaHMm6vZ+tSjCjOdxTXXgR4Ilp5icSAxB5BpDAy4SETEzoDD5yC0GVWQLY8007LturZn23xx28T/eYVmVWO3S+Rq00mlk0HioRB+Nl4dZmPKN4ZSqFnSizj5XiBTQPnCBHkxiWIGBGGzliErqAQWaJUsWgiESeCL8e0oxKJtzXlwzClsAU2FC2LEoL2b6r266Jys8rEs8z1qWqLbuC/XzGOOqgEfbJSssXZqscS6zhRINqdK3uRS3XS0tLw4IMPRr6PGTMG06ZNU+aZNm0a9u7d61sbioqKsHDhQt/K04HEHkHECJllywk/RKKTMBOlVXXoToNGWGtDLEXuhqJlkc8503ogZ1qPqN/9tOr5FYPpFP8msvrFQ+Dp4PVch92qJ4vDc7pXgjxHsgEahJiTTz4ZM2bMQGVlpXYeN2Lv+PHjpk0LFBJ7IYOmXUkNvEzP4SYGSBbgz6aRDZ6wf+OtBiI3pciVpNPheekMecsdL+jysmZGx/RlzYzLoAZRfab7rRJ+Qe6HjvXVD1FjugJMvHCyxMbymlK9dMT6ZSsRyMzMxDXXXIO///3v1X576aWXkJ+fj+zsbNxwww2oqqrC3LlzsWbNGgwZMgQFBQUAgFatWuHHH38EACxcuBBFRUUATljs7r77bvTq1QtPPfUU5s+fj4KCAuTm5mLAgAGRPPGAxB5BxBA/OkST/DLXq0jQOcXUqerWSWP/prJc2W10i2jQho0oXm9E8U7kTOsRNZrXLap2qwS1Ko8qHlA2kCaIzt30uvXShjAKvuFZg4T3jgrWQu5F4MvQuRf9rC+ZuOOOO/DKK6/g8OHDUduHDh2K0tJSbNy4ETVq1MCCBQswaNAg5OXlYfbs2Vi1apVj2eXl5fjoo48wbtw4XHjhhVi1ahXKyspQUFCA1157LahdcoQGaBBEnOA7jiBjbHTK1hEWvEVMZMmTpZG1w8+AcluwsYMxRCLO3h82PZvfxsTt6yRy2X3X6fy9HpOgAvVl55AlmQWGWyHr9ELkNbxDdL6nFLbwvHJGMnLqqafi8ssvx5QpU6K2r1+/Hg8++CAqKyuxf/9+nHXWWRgwYIBR2UOGDEFaWhoAYMeOHRgyZAj27t2LQ4cOoV+/fr7tgykk9ggiDoisMkF2kKJOQOZyVQkRp0EFbJye2/gyk+OgEmsRd+206nk2FC3DiOKdWANIBaFq9K5KtAY9GlIlFFSueZ12OV2TIsGazMIOOGHV8wOn468TAygrl8/Pfh5caNLK1GHMmDHo27cvLrnkksi2G2+8EQsXLkT79u3xxBNPVLP82WRkZKCqqgoAcOTIkajf6tSpE/k8evRo3H///bjooovw1ltv4f333w9gT/QgNy5BxAgnd048YsdYUaASEaLf2Vg8Pm5INfCD/RzEPrPizW4HG7eXlzUz4rplR+uyos5pmhZ2n00ElZ8xVDJLrNuXCFUMmtO2ZBV8Ji5l/j7gX25kYRJBQvF6crKystC9e3fMmTMnsu3QoUNo2rQpDh8+jNmzZ0e216tXDwcPHox8b9myJcrKymBZFubNmyeto7KyEmeccQaqqqrwxhtvBLIfupBljyBihI4LNWirEFuHyCqnaptsu2l7+frd7i8rxkSWOZFYm1LYIjJCNy9rJlAMrGF+5wdy6KCymrLb2P+yvPHE6ZoIc9v9xhZ5omtfFK7A/676zQk/ymXvLbLsyRk7diymT58e+T5+/Hjk5eWhVatWyM3NjWwvKipCUVERTjnlFKxatQrjx4/HTTfdhGbNmiEnJydKCLKMHz8eV1xxBbKystCxY0ejEcB+Q2KPIAiCIIiU4Pvvv498btOmTdQUKaNGjcKoUaOq5Rk8eDAGDx4c+d6rVy9s2bKlWjp+vr6rrroKV111lWO6WEBijyBSEJl1D9CzVMnS6qBy9craySJyu9qI3K/sZ3bePT6+z6lsHcI21YXuKGlRGj/dzYlmBdTZd91zbbrvsnLDdF0RiQfF7BFECDARQH7VJ4sfFE0Z4dcUEm46Phn2dCm2uGNj9URTqcimVuFdt14nXtYJwndyBfqFH+WbXpup5O51Ilb7zt+ndt0kEAkbsuyFCL8mVE6VUXLJhCymCwh2Khb+u661L0hMrl/RBMqytXH5NGx+ViSKyvWCKO4tiHvUbwEpapvfo6kTDR0Lt9dz6/b8kbAjVJDYSyJk0y+w24j44eQODcoiouNGcxIK/FQtorxO+J2OxVSk8SJvQ9Gyai5et4JP5iJ3mtbGDewIYF2XvF91B/lSOTxrEKaXz/W9XN26/UD3+PgtzvnBXjRAI5qMjAx06NABAFCjRg384x//iBqMkayQ2EsyREP9eRIxhiYZCFLs6Jan6lh0Ox0n16otkvjrT0ck6lybdvlTCltgCk6MrhWNymUnVZaN2r278G6UbMnHmmnuxR2Pyu3p9dyyx8fJvRrk1D7JakVyI/R0585TpfHjeNrXBiv6Kh7b57ncZOOUU05BWVkZAGDOnDl45JFH8M4772jlPX78ONLT0wNsXXCQ2EsidB7uInchCb/wEOZzIRNirFWNF1c6gk7kUtYZJBL5LhmMIZos+e7Cu9ETd0e+TyqehLys6P3gywkTXsSD3y7jZBV8MmTHT/WCovti7cfxZK28qQo/tUlmZiYyMzOV6Rs0aAAA2Lp1K4qKinDo0CHUrFkTU6dORbt27TBt2jR88MEHqKysRN26dfH888/jlltuwbfffotatWrh1VdfRdu2bQPdLz8gsZciOFl0UvkBESaCPhdOI/1MhYJoEISpUOLn3JPV7yQ2AT2RVrIlX7piRtjwK/4riGtKxz3ptt4RxTvRs7WrrHHBVKipQm68tiHVRDhLVlZW1PeHH34YEyZMiNp24MAB5Obm4qeffsL333+P5cuXAwCaNWuGxYsXIzMzE8uXL8f999+PuXNPhBKUlpZi3bp1qF+/Pv7nf/4H48ePR5cuXbB69Wr88Y9/jOvKGLqQ2EtCZB2nzJpCQi/++O1i03EtOdXpdF3w69CK3KY65fIdHy/++Lby9ciWOxNhCz0+TxiFHuDu3hS5emP5QudHXT1bl/rUGn2GZw0yEl9uBJ7I9c7HXOqGXfCIXohSLV6vvLwc9evXj3wXWfVYN+7s2bMxatQoLF68GEeOHMHIkSOxYcMGnHTSSVHLoF100UWRcpcsWYLPP/882B0JABJ7SQgft2cje9gQyYXq3MoGDbi5HkRz08lGwuqIKbazU7nF7LVuNxQtw5pp1UfVsjF9ecW/1svH8+nSs3Wp0bJZ8UIWI6k6nkG3xy0lW/LjIvjcInqx5u8r1UsOf95M3Lp2Wn5gRipSv379KLHnxOWXX47rr78eAPDcc8/hrLPOwowZM/Ddd9/hvPPOi6Rj17sFgP/+978JF7tH8+ylEKn6AEgE3L7NOyGaf4utU+b60b1WRAMieJyEHitGWMsKP2cYED0p8ojinVEib035sBMxea1LIx2ejqgTzcnHEqTQU41+Vv0ugz+W/PGMBYn8nFEdJ9l22YAZ3Zcu1XnROZaysIxUdufqsnz5cvzmN78BcCJ+r3nz5khLS8O//vUvaZ6ePXvi5ZdfBgBUVVVh48aNMWmrV0jshQS/5tgjEh/dB7zOw1zU8cvq0o3vYsWRLbb8noxY1QbR3Hps/SVb8qvlV02ozAtGXsCaWAFNcRrA4hRjaVKPnyKMF+OJju4oXNF+uz0OTueDvx9MvhNy7Ji9jh074p577sErr7wCALj11lvx97//Hd26dZOudQsAzz//PD788EPk5OSgQ4cO+H//7//FqumeSLMsy4p3I2KNPQKnoqLCyOQbJCT2EoOgXSQq64GXOEsn68GUwl+nMDHdRy9z0rHtcxI+a8qHRebD4+tjXX7DswZFWQDZtHx5smXSnFzTsUYUwxivzl3mZvS7PbF04/oxt57J/nu15umUObhwg6tyZMSi37Tr2Dn9RtSvU1M/309H0WL4a6Hq08MGWfYIwoBYdrD227qJe8hNHTa20ONxsmz5IYRk+8SKirysmZhePldYX8mWfJRsycek4kkRMSgavMHvL1DdFc1/31C0LFDrng72deA0ajlWbdEReoli9WMHZnjBdDCFaZk6VkWy7hEySOwRRAhRiR9TnGKN7M8qoacrdvh0fk0HYefXjZ8TiTYZKhd0XtbMiJUwSMFncnzsDj6eHbtO3V7bF8tBMSLxGvTxdQqr4L+zL3+ywTeJIrCJ2ENijyBCgt2Bi0bteYmP0ulURG1hlxIzWYZsTfkw1y4+keVC9JmFjbXjhZttmVO1XxaXZzKli1t04yRteGuvqkz+s9/otCXsiOI7dWNHRWncxFLq1MU+A/x6gUplHnnkEXTo0AHZ2dnIy8vD119/rZ23pKQE5557LgoKCgJsof+Q2COIkMB3Gn4GwMtEJPufdxPbIomNf2PbJsIWXG5dziqLpo6rUCTOdEflsrBCjx2E4jduRbBumX4LMZkr0SlPGHFrOfT7mOoKdz6PaqR1IgvwoFm+fDmWLl2KsrIybNy4EfPmzcMpp5yilff48eN48803MX78eKxatSrYhvoMiT2CCAmi6UacRtO6wXYDOQkyWyTxIod3JbG4EUS8ELPLZi2dMhczL0T5yZZlQo11T7PpNhQtw4aiZdXqi/cADZ3zH0tRxZ4jtn4/rYpBDtDgB2So7gXRsZdZ83TuUVNxphqNHVYhHWb27NmDhg0bIiPjxDTDZ5xxBho2bIgmTZpE0kyePDmy8kavXr1w//3348ILL8SMGTMwa9Ys3H///bj11luxdetWXHDBBejcuTPOO++8yGTLx44dwx/+8AdkZ2ejY8eOmDNnDgBg6tSpyM/PR05ODh566KGY7jeJvRBAI3EJQDzpqt+xWaopPXQEYBDB4CIhxVsudPM5WfBY97RdBuvGzZnWIyIg2fJ3do7vAA0dRJbhIOrgX0BUlkQv129QMXtsuU7Xs9Oo2SDd2Kq2OQ3QIREop1+/ftiyZQvatWuHO+64A6tXr3bMc+zYMXz88ce4/vrrceWVV+Jvf/sbXnrppcgSa2vXrsUzzzyD+++/HwDw8ssvo6KiAuvXr8f69evRp08fbNq0Ce+99x5WrFiBsrIyrFu3DitWrAh6dyPQChoEQRAEQaQE9erVw7p16/DRRx9h8eLF6NevH2bNmqXM87vf/U64XbbE2pIlS3DXXXfhpJNO2NMaNmyI119/HStWrECXLl0AAD/++CO2bt2K888/38e9k0NijyBCAj9/mYnbznQqB12LhMiFG2tYCx9fv2iNW5V1b0TxTvQsvDuyRi5PZH+Lo+fha7HWvRs3FiNn4z06F0iMODHbqseHMrD3npuYRz70QAfZfIWyck3we469ZCMjIwP9+vVDv3790KRJE7z77rtIS0uL/M6uiwtUXy7NRrXEGo9lWbj55ptj7r61ITcuQYQA0xgc01GcNqLAbp2yvLqFnNyrohGxsjx8XJhTfB7PpOJJUYMvROntckV1mhILESSLNYuFO08Wu6caQBAPeLewaOohpzarjqlbl67JfSeLYU0EoR0WNm/ejK1btwI4IcA+++wztGzZEg0aNMA333yDY8eOYeHChVplyZZY69u3L1555RVUVVUBAPbv348+ffpg5syZ2L9/PwDg22+/xb59+3zeOzlk2SOIEKCyXummN7UqXLS/R8RiZYufZgOA3fNPDFSQlctOycJv57fZOAkwmUVuSuGvK3uI4g3ddnI6FkDAn9VB4oHb0dAmiGI+Rd/dtsHPARqi+D+7fJm1j0VkRfe6f04xqSZpwyCmE4Uff/wRo0aNQmVlJQCgS5cuGDVqFLKystCnTx+cddZZkfVynbj11lsxePBgzJgxA3379o1sv/nmm/HFF18gOzsbGRkZePjhh3HVVVdh7Nix6NWrF6qqqlCvXj289dZbaNw4NjH7tFxaCJZWoQEaqYdTp+K1kzZ169miRibk+LQ2vBDy6k7kpzsR4VcdbD2qtXPD4CJl0XH3x8p1zMKKJbZtbtvil9hzGuhhMqVNkMfVqR0mltog3Li0XFpiQ25cgogRum/wXjoTty4zUWyezLXMuk350a1eEE2KLMKeEsbGboNuO0Tl8/XqtMMUkzaqcJqOJyhBIptWhR+h69f0K36MxrXL0BlVqwN/X5jun9vj4XTOCcIJEnsEESOC7oDtOkzcQ3zcHrtdZKlhWVM+DCOKd0atqcu6P00xWbGCtciJXMxuEYk8E4uKKl0QAlJUb1DXmWj6D9lUK6J4uFjDi0WVSDOJW3UrvJwGZMjiLtntYYl/JBIPEnsEESOC6JBl5egKPn6uMNnoW1F5dlrZKFkbJwHWs3VpxGXH5nNy4/HuWB0xxR8P0Vq+poH5fDoTvHTcIsEShBCQHQ8RopGs8XKD29eVSKTZ7eJ/U+2rW9HK1qPKTyKOCBISe3GG4vVSh1h3eCLLg04clCgOjy2TxbasOXVU7Pq1IngrTM/WpY5uPDeWMt4FzCKySonS+ImXayJWozIT1XVYsiU/avAFi6m1zI94PZEVVJVWx9IngqZdIUSQ2COIOBPLN3qdOCgdKwSPqgOSCTLWosdvN4nXUlkOnTryDUXLIulMRjL7idfynEbFBoFTHazFzBZK8bZc9WxdGrUSiqmIdWPZk8UvOgk9N/cgQaggsUcQISCMD3Wd6TtYscT+5wUYa4Wzf2MtL7bo44Ueaw1UjZhV7YNqKhi7Y+XX2FWV5beVy8/yYuUydapDJ77PiSAGd1z9490R0acSoHzsq1+j5PkyVFZz0bkMg2gmEhOaZ48gQkDQ0zq4aYtuWlEeXoCxU6rIRFvP1qWYVDwJwK9z2/EjZP1AVPaGomVAsS/Fa+PH+Q7D9RIUI4p3omdrszyqQRkjin/9rQX3O48fU8eoEI1aZq2gonbolkUQIsiyRxAhIJ5Cj+9cvHQcTh0Pa6WTxfCJRJ1f07uIsDvZIOuQ1esH1NGfQCb0RDjFrvLTyfiNbNAI3wY+j8pCmqixlURsoEmV4zwBY5gGaAQ9fQMhJyxWPS/tkK02oWPVs383KTNWK1sk6ioayQIvzEq25Au3yRBZx/j8w7MGRdKo7gE/71PdsmTPZZmbN6gBGjSpcmJDlj2iGuQWSEz8OGdezj0/3YrT+rM8rBC089sxgaIYQBNEAe92mbI4Q7sNJPSCQfc6s2M72RhPVtzpCD37s/2dz29iybPLMZ1Ghc8jqkc0F6DOyGGK5SOcoJg9IgI7ai4MVibCDC/njD3nbsuRXTf8Umw6RAQWE0ens5SaU/uc6vRSR7zumzDfr6qYMy9WLcD7Chui/E5WPV1EgzB09tdpTj/Z3IX254rH9mm3kUgtyLIXR8LkwuWht8TYwgptNxOvej1ffkw7wpfBD4QwnRdPJg51RCO/nBrbRvs7OyJXll+3vfEUXGEVen6hmvDYDToxqvx2r3Gsbs6RaEQzvYwTbiHLHhFFmEaFpiom8UL8qEFVfq+YdHgqkWRb0JyElL0Mmz0tim4+mRiUWS7Z4+bWahjm+yXo60JWp67IcWqf3y+eIvHIj4blLWhOsXKq7W6QuXDDfJ0R4YYsewRBEARBEEkMWfaIatDbY+xxsl7ouE9jYdEzqUNlidC1oI0o3glw8X5OFg43rle65v1FFlumQmRRE6Vhy3cqTxeZtVfVPlWdXuNnZeXRjAmEW8iyRxAhxSmGT9T5BBFr6VfMkVdsF67Oahd2elskivL4LRjcpPcDnRhPU5HuV1tMR3frTiLsBvZe0Y09Fd1f9v0giqljcTNCVnWvk9AjvECWPYIICU4doyxgm401YvE7xoevw6v1ghdgOjF8tuDT2bcNRctOtBliKw1QfVksG7cWv3h0wn7GunkVenzsqM55Eo1cNb12xZMkD6rWPp36Re1h0+pY9eIRJ0kQKkjshQh6QKQ2ooBxJ2J9rfhVn+2eZRHtsy3u2BG9a8qHIa+4el6n9smmq7CFJ19mmDEVUSb4PcjAKY3MemYzvXxuZNJj0e8y+Hy67XRqt46I9XLsaSYEIghoBY04zrbNTr3CvhWT2EstRFODeJkKJZGvH5XgMy3HZkRx9FJotmC0J2xO5OPlBa/XTizcijrWONXSZ6zYE1mmeWukqB5R3JzK+ifDb1e6qLwg59mL5Qoaa4feiJNr6q+g8ePRo+g86zW0bt0a6enpGDlyJEaOHBlIGxMVitmLE/wceyT0UgNVB6u6BlSdkSy43E3MULwR7bub6VBkcX2shVDmPky0Y+YWr88at7Gcbo6v6jq3Ua2ywaaXxbnyVl/2vlTVzcbwif5k7fUKXx5NqAysXr0amzZtIqEngCx7cbLshXlC5SAgIatG5M4yPV4qK0Wsj73OyEondC16TuWzcX4sqX49erlOnKzRsmvY6VyZWs34snZ2PmHBbbG2+vnm88ji8VQWQNU++BnPqgPfzqDWxLVJBMtevL11YYYsewQRJ0QjaL28/fvhAvYLJyuMTt1G07NwZfPHluJh5cgGqZiWISpTVJ7oupeVw29TWbGBEyKvxVrvsZeyY6J7f3m9j0WfRe2i65nQhQZoEESciFWAt4lb2A2qTkdmbfRzEAAfV8V3llP+LzYvZ1qPyGedtuvWb9cTdkT76lY46KZVle/mpUR13Ypcq7qxbzIXr1NbZedfZp102mendqgE8uBCZdFEikNij4gZ9CZ6ApnL1osAM+kE2f9eYcsWuV1V++WHUHJyE64pHwZME6eXjf50wimoP0i83ENuxZYf6LpCWXQGTZi2QacsVdl8+51Entu4RlF9KlIlzpRwD4k9IiaQyIuG7Qj8EHyqOKegjj3bbnu0Kz/qNcj6WdhO0R5paw/SsOfb49ts6vbm65J9D5JEv4+8vvDpWqlFLzh83SYvPiajY728xIjEoh9tIwiK2SOIGCKyIqhcNqajQ506Plkat4g6I3ZePBlBdVAjindGRtqKplYRuXlNkJ0Pv+Iug0bWfpmICupacSv4TNukaz1ky5Xl8aP9bgjz9UQkDmTZI4gYYTLKjv1v+rCXuSpFv3vBLse25kVGvBaLXZ2xtoiNKN4ZWVfXzfQtynK5z/ESAqbI3N66MXVerFZ+hHGIrimVu1enbqc4OT6dKA+/zQ+BZnKsgh6JSyQ+NPVKHIZpp9q0K8QJRJ1KkC5WUeC53/WyU12IRJWuFckNtshk3bR2nbEQXIk0OMMNMquzF6ucqQCT1ScTqbJzYuKG1U0va5vf6LQlFmKPpl5JbMiyRxAxwMkqoMrjxtUYjwEEzQYAWCv+LYhO0V4JQ2SR4mMHgxCAToNDEh2/LbFuxZTqPtBto+h+UO2PG7EYFLJ7OV6DhIjEhCx7ZNkjkhC/rTKmdcYKVtQBvwpAfhuLX+1MNnHHonLvxrp+wD9hzbtaTSzCTsLKr+Mka4dMrE4pbEGWPbLsOUIDNAgiCZHFkAVpCYi30JNt4/GznclqWTEZqWqC7qAj3molGxTjZmCMbN/4+4T/k5XDt8kLogEjIpwsngTBQ2KPIFIAexSq245hTfkwqZDS6eCCEkU6I391BKBbkqWjVZ0f0SAft7DXoJfRvjpxesCv596OLWXRGYihQjWynt/udj9lx97Pc0KkBiT2CIIgCIIgkhgSe0QU9KZImKAbO+W3Baxn69JI/YCehc/GD0ufblxVomAyWMErrCVOt2zdwUw89lyL9nq5ovCGoK1kXizqQPXjxbqhaZAGoQuJPSKKZHFLJRK2cHGD3xPfxgKn9vZsXVrtj98OIDJ5shOR+f+Yur0KPp2JdxMZXRcli8l17DTlitu5Jdn7QTRSW4Zfgs9rflPxG6vBGUTiQ2IvxtBIXEJG2EWfyHJmYnExDSgv2ZKPki350t/Z48XGFKraOaJ4p5EVMJVhrymdc6Y6V3x5TjhZw0Qjae1ttrWLPcdOgy2c2mKC13uRt9zF0upKJC80zx5BhICerUsdO0sR/MPebTmxQGcqD1vA2fugI4B7ti7F3a1//b6mOFrw+b2Chk0yT70imrbHjzJ1B4K4nXZFZZEU1R82q7isPcl6nRGxgyx7BBFHvFjzRAQh9GyLmW09U43MdYLttHi3rI3XfbDjtGxsoSebvsMtydwBBzWtBz8a16l+k/L47SLXrCo9/98kJo7P5wbWMkkQfkNijyDiBC9yZOInngQV2ybaR9V+y37r2boUw7MGRW2bXj632nGcUtgCOdN66DSZQGyErCjOzmnQi8iqZ7JNNTExv80ELy8Non0nwUf4DblxkxBd91Iyu6ESBdaK5afI8+PcyoSeHy7Rki35UfvrZd+nl8+V/maXO6I4HzBcSYPuj9iiM1KWDQVgz49IMMmEndNvLLwINJ24WQVZ8ohYEqhl78orr8SZZ56JWrVqoVmzZrjuuuuwa9cuZZ7vvvsORUVFaN68OerUqYOLL74YX375ZbV0K1asQJ8+fVC3bl2ccsop6NWrF37++eegdiWh8Gs6AyJx8ePcigYy+Bn7NjxrEIZnDYoI3pIt+RieNSjKAsO7dGWDNuyy2D8WkZjkj9Ga8mHGgxII/5BZuPiYO1505UzrEfljYV2xqomcRRZA0e8y8a9yTasGhdC0KUQsCVTs9e7dG7NmzcLmzZsxZ84cbN26FUOGDJGmtywLAwcOxLZt2zB//nysW7cOLVu2RN++fXHo0KFIuhUrVuDiiy9G//79UVpaitWrV2PUqFE46STyShPhx3YxioQMn8atxcvvTiSIEaxsJ1myJV/oSptS2CIi8Njjwx870RxkvOCzER2bNeXDqsX6UUcce1hLHjuXIi+y2HNtX5d+Xp+sQONFqMoCKdoXlfXOSWg6UfHYPqP0ROqSZlmWFavK3n33XQwcOBBHjhxBjRo1qv2+ZcsWtGnTBp9++inOPfdcAMDx48dx2mmn4cknn8RNN90EADjvvPPQr18//PnPf3bVjlgs6CyDpl4hVCNme7YuxaTiSbi78G7HaUdiPep2Z+dh2D3/1+9+d64sKvecLvYoXH6EL/ubG+y8QY3yDRNBu7J5V6zOiG1RXtW5cHLFuhVcIrewzlREfhxPu5xYir1Y9Jt2HWuH3oiTa9bUzvfj0aPoPOu1uPTpiULMTGE//PADZsyYgW7dugmFHgAcOXIEAFCrVq3ItvT0dNSsWRPLlp146967dy9WrVqF0047Dd26dcPpp5+Onj17Rn6XlVtZWRn1Fw/CIvTIYhFfVCKtZEs+8rJmOgo5/nfZOfXzXNurEAQBb5XjMZm7zE7rVYjJLIA2yS70gOBd2bb4klnGZPPj8VY33irrVKfoM7tNZ347tl26sXcidzI9j4lYELjYGzt2LOrWrYvGjRtjx44dmD9/vjRt27Zt0bJlS4wbNw779+/H0aNH8cQTT2DPnj3YvXs3AGDbtm0AgAkTJuB///d/8f7776Nz584oLCwUxvYBwOOPP44GDRpE/rKysvzf0QSCYpHiR5AjbfkOJAirjO3OtYWOm6lYnFxeNnaZsmkwdOckY+fsY1ffMC1Hto/UWbtHNS+eUx7WquckzLygcsHqiDzViwoJvvjx6quvIjMzEwcOHBD+vnTpUpSW/vq8fuihh/DJJ58AAGbOnIl27dph0KBBeOmllzBzpvzFz+l3AOjVqxc+/fRT850wwFjsTZgwAWlpacq/NWvWRNLfc889WLduHRYtWoT09HRcf/31kHmOa9SogTlz5mDLli1o1KgR6tSpg6VLl+KSSy5Beno6AKCqqgoAcMstt+CGG25Ap06d8Oyzz6JNmzaYMmWKsNxx48ahoqIi8ldeXm662wQRaty4lNziVuBNKWwR1TE7WetEMXS601PkTOsRaZ9smhf2OIn2hd3m5LKlztofnIQVi33MRefFq6Wbf3Hi65dZHFX1idzHIsGo00Z6YffOrFmzkJeXh7lzq4/mP378eDWx98gjj+CCCy4AAEydOhVTp07F3Llzceutt2LYMPmz0On3WGE89cqoUaPw+9//XpmmVatWkc9NmjRBkyZN0Lp1a7Rr1w5ZWVlYuXIlzj//fGHeLl26oKysDBUVFTh69ChOPfVUFBQUIC8vDwDQrFkzAED79u2j8rVr1w47duwQlpmZmYnMzEzdXSSIwPAjzk4WrxfP0X06Ls0RxTujpj/RcaepvqvYULRMOq+effyGZ53oqKeXz8XdrYEpWcui6ri78G5gS3TwP78yBwDkFf86aTN1wuaoBB4vuPgpWHTLFH0XWRVl7eF/c3uf8eKPrpf48P3332Pbtm2YNm0aJk6ciBtuuAETJkzAnj178NVXXyErKwv//ve/kZGRgVdffRX/+te/MGnSJAwZMgSffvopli1bhqKiIlx99dWoqqpCkyZNMGrUKHzxxRe49dZbsX//fmRmZqK4uBiTJk2K/P7SSy9hypQp+Pnnn5GXl4fXXnstZgNLjWtp0qQJ2rZtq/xjY+5YbIueHZunokGDBjj11FPx5ZdfYs2aNRgwYACAE0KyefPm2Lx5c1T6LVu2oGXLlqa7QxAJh45gDNKq5wan+ezY//xvrFVQlE/UxpxpPSLCTDTqmc3LTv+iKpu3ZvIrc1DHbY5KWNm/q1z4fh1zk/AApwEebDkm7SPrcOyYM2cOrrrqKnTv3h1ffPEF9u07MdBl48aNeO+99zB16lTceuutuO+++1BWVobs7OxI3vvuuw95eXmYPXs2Hnrooahyr732WjzwwANYv349iouLUbt27ajfhw4ditLSUmzcuBE1atTAggULgt/Z/yOwSZVLS0tRWlqKHj16oGHDhti2bRseeughnH322VFWvbZt2+Lxxx/HoEEnpkl4++23ceqpp+LMM8/Exo0bcccdd2DgwIHo378/ACAtLQ333HMPHn74YXTs2BG5ubmYPn06vvjiC8yePTuo3SGIhMGkg9nZeZgvAy8iVi4Xgxb4gRlTCn9d7WIDlkV+W4Pqa53q1m+PcJYhE5yqWEI7dnHNtGFYo6ib0EMm4LyIIJHg4kcAs/CjgZ1EIG9t5H8TbTdtrwyadsU9M2fOxFNPPYW0tDQMGDAg4sodMGAAahqMAmaprKzEgQMH0K9fPwBAvXr1qqVZv349HnzwQVRWVmL//v0466yzIoasoAnMfli7dm288847KCwsRJs2bTBixAh06NABJSUlUS7VzZs3o6KiIvJ99+7duO6669C2bVuMHj0a1113Hd58882osv/4xz9i3LhxuPPOO9GxY0cUFxfjww8/xNlnnx3U7hCEK2K99JmppcMPoceLHK9zA0aEXlG0W5X/zrfBng6FbRNvjbMteCaxYU4uQxJ5akymMlGNjnWDKjxAR5w5XScq4SjCKcaPrMPB891332H58uUYMmQIWrVqhZkzZ0YGUNSpU8dT2Wlpacrfb7zxRvzjH//Axo0bMWrUKC0vp18EZtnLzs7GkiVLHNPxgzVGjx6N0aNHO+a77777cN9997luH0HEgljPhQcEu1yeTNiw20u25Fcb0CCaT03UlimFLYBpJ7bxna/KQsO2Y0phC6C4ehtFEzGzZbH/2TSRehRz66Wy4OPdrvY2N5YtFUEJIdV1ydcrm/5F937jyxOVJaqX8I/Zs2fjtttuw7PPPgvghAZp1aoVWrdujSZNmkTS1atXDwcPHtQut379+mjYsCE+/PBD9OvXDwcPHqzmxj106BCaNm2Kw4cPY/bs2bj88sv92SkNaMkJgiAIgiBSglmzZmHgwIGR72lpabj88ssxZ86cqHRXXHEF3nzzTeTm5mLjxo1aZf/rX//Co48+io4dO6Jv3744fPhw1O/jx49HXl4eLr30UuTm5nrdFSNiuoJGWIjHChphmVCZSB3iNXDAXnrMaVAGO0+aU3rT+kUuXRU9W5dWW15tevlcDM8aVC1eL5WteCJEI1Vlrm+355gt0+9BGbzlTjXgQoVuu0xiE2VpRhTHdvUMIDFW0CgvL49qG83E8SuBuXEJgggHfneQMneqLYTysmYCxfqDNuz0fqISZaLfTqy9q57wGUDExUz8iswd7pc482PiYZUA9WsUrK4L1m6H07Hxc6BKqsAvmPDwww9jwoQJ8WlMyCCxRxApgF9WPtHoQ1mZOtY9tly/4IWcPbqXtfbpwu/vGof0qQ4v8Pw8r/y8e6blm4gl02tTZ5CGjmWQ5t7zhsiyR5yAYvYIIoVwGg2oAzs60u3IQnb+NFkAPI/bdo8o3llNAPKrY7Ajge2/ScWTIvmnl8/FlELv6+2KiKXFxo/z74TXiYdVZcYjv5tjppueFa9O1kfd+ySVqV+/ftSfTOw98sgj6NChA7Kzs5GXl4evv/5au46SkhKce+65KCgo8KvZMYEsewSRpIg6Dr+tBmx5JkJIZjmRjVR0arcqDoqfvkW0skbOtB5Cq50tCpt1noQPlC1wTywtObwQSwQrEn99+PGyEsRLhV2217w6MYMjindicKHrqlKa5cuXY+nSpSgrK0NGRga+/fZb1K1bVyvv8ePH8eabb2L8+PGOK4mFDbLsEUQS41fcj1M++3ddN6nIqseXxU9zIesE7fV2ZdNm8J0kL0rZ75GYQ44Wa2dienn1NTQTlViIPL9i9YIq3w1OU7MEXa99ndOEyu7Zs2cPGjZsiIyME7auM844Aw0bNoyadmXy5MmRWL9evXrh/vvvx4UXXogZM2Zg1qxZuP/++3Hrrbdi69atuOCCC9C5c2ecd955+PzzzwEAx44dwx/+8AdkZ2ejY8eOkZG+U6dORX5+PnJycqqtvhE0ZNkjiCTGz1g9p9/dLqXGIrL4OdUdmVuPa4/os53OXs+Wb7NqH/h5+hJ9ZG7YrHqywT82vHA3iW9zWgmDR2dORzfIytCNbSW8069fPzz88MNo164d+vfvj2uvvRZdu3ZV5jl27Bg+/vhjAMCSJUswZMgQXH755fjpp5+wePFiZGZmYvny5bj//vsxd+5cvPzyy6ioqMD69etx0kknYf/+/di0aRPee+89rFixIrJyx4oVK6JWFAsSEnsxgKZdIRIFp05NNT2EqRuXdafaeYMSpXy7+frtNsjEnkjYyfaXguz1UQ3mMJns2KkOr23kP7t1JyeS+zxZqVevHtatW4ePPvoIixcvRr9+/TBr1ixlnt/97nfC7UeOHMHIkSOxYcMGnHTSSZEVMZYsWYK77roLJ510wnnasGFDvP7661ixYgW6dOkCAPjxxx+xdevWmIk9cuMSRJLDTvXg1jXGDmBg07Jz5ZnACy1Vfqf4KpUbl20r+9lp0AaPGwteLAZDJDK6othkBLgsvxdEA05iKdpIGPpPRkYG+vXrhyeffBIPPPAA3n333ailzvhlzGTLqD333HM466yzsHHjRixatEi5/JllWbj55ptRVlaGsrIyfPXVV7j22mv92SENSOwRRJLjxxQndodrD3DImdbDU/yfjnhiRyjKhJgt2kTCit9mizxRu+32sDF7/H9d+PiqRBJ8sRSoppa5WLUtKHHldjQtLzIT6XoKI5s3b8bWrVsBnBBgn332GVq2bIkGDRrgm2++wbFjx7Bw4UKtsiorK9G8eXOkpaXhX//6V2R737598corr6CqqgoAsH//fvTp0wczZ87E/v37AQDffvst9u2LXewluXEJIokJYp6ziPhhJkIWzW3nZmQtu5qGPTeeqHyeiKWQSSfqXPm6+cEZfFv8IFEsM2EWESLrntv8OumcBvvEUsTTlCv+8uOPP2LUqFGorKwEAHTp0gWjRo1CVlYW+vTpg7POOgu/+c1vtMq69dZbMXjwYMyYMQN9+/aNbL/55pvxxRdfIDs7GxkZGXj44Ydx1VVXYezYsejVqxeqqqpQr149vPXWW2jcODZhXrRcWgyWS0vVmD2KXUpNVJZE9prQEVNeBkDY5W8oWhZpi4lgMFluLdGRiZh4379+Th0kKsu0fP446bRHdWzdPiPjMRo3EZZLi+USqIkGWfaIQCHBl3zIRJAq2F4HtjzRKFk3oosVeqbtipXIC8OoXn4wRLLet6JBIKYjdWVlmaRzaxmkKVcIt1DMHkEQRsjmotNxN9mdXNQ6ugyskFS5WHXbKRq44Zd71g/C0hZ+BYewCD3ZFDpuUV2XTu3gXxhkZckGQ7kVlQThB2TZIwjCV3Q6zilFy6Ji/mxUos6rBcyebsVvKxor2HTL9styqYuJ+zosQs8m6Pg4JxGnmt9PdFxVc+mRyCPiBVn2AiaV4/WA8HUcRHDYAsbLiEFVPpEYMnW7BSGoePezylrn9HvQqM5N2O9VL0LJL5HFW/jcDhYRWcFppC0RJCT2CN+hB1bi4sXVlCcZCSsqn//djq1j59/TEUbxEijssRG10W57vMWdjS0wZNPBhFno8deMH5Mkq1ywdl1OU+ewrm9VWfx3U1FHz1PCD0jsEQRBEARBJDE09UrAw7RT1Y1LJDZugvR1XfeyKVHseDrR9CxBj1Y13V+7vTb2ZNP2Z6D6KiH2PthzCYqWiyOq48eAkVhOJyO7D5xiAXUsrvEcjUtTryQ2ZNkjCEK42gT/uxO6MUwbipZVGynLxtOxZcRKBJkKAFF6ewQxL/JseOFqi0Jb4CYKYYgtc+MGdRtjx5bDumJlyOpR3VM0eIMIGhJ7RCDQg8s98Tp2Tp1R0Mhi28Jo9eLbKrLSbShaFhF09nc7X860HtXmtosHbq41r6LJDbpCS4Yf7Q16v9k4Qfs7C82xR3iBpl4hfCNMM+8nKmyH5ma5MbfoWCL8rMvkWnGalmRn52H4oGH05MmxhHXP5mXNPOGmRbRb115P2E4XFhLhPo3ltanC6whz3TaHcb5DIvEhyx4RCGTZM8fpmHmxbIQN3ooBuLfg7Z6PKDEVC2yXLWut21C0DFMKW0RZ80T5bFgrHxEcfgkmN6ENuulZy6WXVWgIQgZZ9ghfSAYBEm9Yixf/mU8XZlLJIsFO0jyieKfQajeieCfwf3F5U9ACGxA9gCOMbuowEy+rntvVPPyaMoYgvECWvQBJhZG4YQjWTkbszoUXfWEUUfz5z5nWQ+mqdHO9OLk+ZUu4BQnrtgVO7Jdt7eMRCfd4tJlvj2xpL7fs7BweF7XfeD1Gqns3rPc2kTyQ2CN8wY+Z5VMV3oUjs+TpunljDTtNii14VCJGd2oWe3+cBFE8BJMojtDer5xpPSKDM3jhF5YXIzYmzM/7tcXa2JyLoI+jzoCQsJxLgtCB3LgEEWd0O1qdNWeDRjVwJK94JqYgemUJN0KMn2vP3hYm2PaI4vB4+Dn46GXIHbFahlH1guVUt+6UKnQNELGELHuEJ/iHIj3AvBOWtTJlyz2JvvPn3a3QY8tzM2rV9Hh5qUOVl7eY2Va+MFm9w9KOREO1fJvTPcP/pnu90rQrhFdI7BGe8LKWKiGH7YidXEpBHXd+FQu+XaopMfywxLkp46L9ZiNb3dThtI/2djYejp1XLyiS/f6L1bPGqWz+3uQ/606qLNtm0haC0IXEHuEap6V9CHeIYh9V0zEEfdxFK1uYEKZ55fxAd3/CZMXzQqwEhyo2LtaeA5nVTRRPK7s3VfeqLBSCIIKCYvYIIuSEQTB4ccuylq6g9yXoAQI6x6Ha2rfTYhNzGOQk2EHATh6s24agRV8QI251RKDs3gjDvU8kB2TZC4hUmHaF8Bev02AEaUFTTRQscjGzbbE/p0LHJVsfV3ZudnYeltTTlchwGuEar3hVJ2usabucplvRSUcQfkBij3ANPaz8xes0GEFaj3Q6LX5QBTsVC6A3sCHseLVu8vveYu3MmE1XEkZ0phPinzNBPmvCPqclQbiF3LgEEVISMSbSFjURUVT862+J0H4nbLHGTqZsf+anY2F/B36dnoYVfGGbUiYW6M4XKVueLMjrSNfl6oVUWmGGCA8k9ghXsA8senD5i1OnlugLpQctcHZ2HuZoLXMruESCThbHJ5t0ORUFninxjF9zuq8S8Z4jCHLjEsawcTX04HOPbDoVp5gh2dQPYaJn61Lf3LUm5UwpbIEPGsrjC/nyTIQX76K2RR6JN310pjWJ9zMlVhM260Jz7BF+QJY9giAIgiBCw5XXN8BJdTO101cdOgLMArp27Yr09HSMHDkSI0eODLCFiQeJPUKbsFqREhmVdVTkzhV9DquF1S+Ll0k5uuvumpZrpydrnjdUVmmTKUniQZjaQohZvXo16tevH+9mhBJy4xLa8C4WevB5hxVs/NQrMpeWaHqKMApxE/crn9YPF7DI7WrjJNbs49mzdamyXMI9Os+PMDxj7PssDG0hCLeQ2COMoYeeP9iDLGTiTjSnl6jTCdP56Nm6NEogyYSRaB4+Xox5FVWq5cyc5razj2nJlvzINvtckFXPnDC/lLDI7je/77GwHwci+SCxFwA0oTLhFrYTcOpkEsGNywsjfjoS9jOflv3ulzXNi1DLmdbDMX8qTpDshJPQC8OgDJtYvUiFZX+J1IHEHuEKeli5x2mtT12BZ38Py7lYUz4Mw7MGKX/nJ1i2EYlCldvVSfzx+e0y7BUr3ExkTELPHJ3RtwRBBA+JPUKbeC1hlGyoYvF0Oz8+1i9e8DFtOdN64I2TJwnT5mXNlIpc3pWrY4HTsfaxU6SwYsxvYWaXl8qrYRAEEV5oNC6hRVhdhYkM77Ll3V387+w2fgH5eJ0fNqat2QBg9/wTfyg0K0fkyhWtTMGnV5VnIhx1kLWFhJ4aem64h+bYI/yCLHuElHhbjZIZ2eTIJm4vfi3deJ+vFmtnotmAE6IPqG6tW1M+DDnTekR9d7LOeRVqdn5ekOkKM6cRvCT05Ph1LdIziCC8Q5Y9QkqivZHbLkXW2hRGdIQda7lzWp8zTMvW7Z5/4v8kTMLdhXcDW8ysck5LxYlG7KpG3dqwYsxEmDmJTRJ5Yvy0NIfhuiaIRIcse0RSEXahB1S3yLH/eXetTlnsWrlhwj4XopG39mfR+rHUuftPPK4NWZ1+Wvzibc0miESBxB6hBXXAwcFbQUTij0VmyQvDOVJZwkxcsqL5ztaUD8OGomXGI3OJ2F8b9rUbtOCz6yIIQg2JPR9pcH9jmmOPMMJJ0IlgLXn857CwoWiZdFUMmTizBZ1s4ui8rJmR7bK5+YjwobMsmiovPxApbNc6QSQCJPYIIsHgLX9BzfLvFluU2TF1/KhYfnUM+/OI4p3YULQMI4p3ak+rwpZLOBMvseTWwhem65ogEhkSe0RS0LN1aULE6wWBanH5eMIKNrtdrPjjP7PoCDgSee6JxTUjcsWL1nMWrQttIgLDdM37CU27QvgJjcYliCQiTFYQ1oLHt4u37tnCTaf9YbfoqUYIxwrZqGbTkIGgYC3SJr+L5pskCMIZsuwRRAoSK2uILXpYgca6c+0/mdtW5O5lyw0Da8qHVVuRw7ZaxmvwSLxEkK5lzmlqFt2pW7xexxT/R6QKJPYIIgHx2kEFIQZkbZIJOlYQOc2Vx6YJk9ADotsoEnfxEHyxFDC6cz7y8XdO80zaaUTp/HDjknWQSCVI7BEEQRAEQSQxFLNHJDSJsmqG3ySiNcLJIieygIXVmseypnwYWmAmkKWeVkZ1zuzrGDhxLXsZcCSamkeUxsbrtaSTXzSPJN8WmTWQ3KwE4R2y7PkEza8Xe+wOMdWEHpD4IxBttywf0wdEx/Ilwn42G4BqMXs87JrAQaMSd7xbNOiXBt7VKhqh6zS9itNvifjiQ8SPtLQ0PPjgg5HvY8aMwbRp05R5pk2bhr179/rWhqKiIixcuNC38nQgsUcQCUqQc6aZlOln7JMfFrxYB907rY/bbMCJ/7r7xlr5vMDPw8huc4qJCwLRVCsm6QnCD04++WTMmDEDlZWV2nnciL3jx4+bNi1QSOwRCUsqWvR4wmDZcONqE62Xy0/CvKZ8mOt9i9Ux4S16tkWy2YBfP7dYW30NYBa/xJ0MlaXPT9Enyq9TppP4C/Iaj/e9I4Pm2AuOzMxMXHPNNfj73/9e7beXXnoJ+fn5yM7Oxg033ICqqirMnTsXa9aswZAhQ1BQUAAAaNWqFX788UcAwMKFC1FUVATghMXu7rvvRq9evfDUU09h/vz5KCgoQG5uLgYMGBDJEw9I7BFEghKmjkrWFnbErcqNyU/Hwm7zqy1B0GLtTEcXrluCEIGsqOJFupfjJosN9FKmLLbPL8J0/xCx5Y477sArr7yCw4cPR20fOnQoSktLsXHjRtSoUQMLFizAoEGDkJeXh9mzZ2PVqlWOZZeXl+Ojjz7CuHHjcOGFF2LVqlUoKytDQUEBXnvttaB2yRESewSRgMQq1sorfg2wiNd8dSp2dq4+v56Nk2vXRiTogrRY825c1VQpXiC3KxFmTj31VFx++eWYMmVK1Pb169eje/fuyM7OxnvvvYdNmzYZlz1kyBCkpaUBAHbs2IHCwkJkZ2fj1VdfdVWeX5DYIwgiUGz3rGry5ERHV9yx+CH0/BBVXoWeqg1Oc+TJ2kFikQiaMWPG4K9//St++eWXyLYbb7wR//jHP7Bx40aMGjUKR44cEebNyMhAVVUVAFRLU6dOncjn0aNH495778XGjRvx2GOPScuLBST2CIIIhJ6tS9Gzdann5cPCKhBtgedG6PmF0yoUuoMgvLZBZ1k2vk7WyqjjUia3K+EnWVlZ6N69O+bMmRPZdujQITRt2hSHDx/G7NmzI9vr1auHgwcPRr63bNkSZWVlsCwL8+bNk9ZRWVmJM844A1VVVXjjjTcC2Q9dSOwRRBIRtEUk1hYXfoqWsPFBw2WBlOvHlEIiESVK4weiKVVEbZHlVc0HSFY+IijGjh2LXbt2Rb6PHz8eeXl5uPTSS5GbmxvZXlRUhKKiosgAjfHjx+Omm25Cr169cMopp0jLHz9+PK644gr07t0brVq1Cmgv9EizLMuKawviQGVlJRo0aICKigrUr1/fc3k0xx4RBlQT9/oZiK9TDrsWrv1d5sblhZzuuqiJTizj9diJlln4qVnYtCZl22Xx29h63JQvSpsK10cYR+P63W+q6jhj4V04qW6mdr6qQ0fw7eXPBNq2RIcsewSRJKjWCvUyfYXMsqJjcRGtfcuLO14AJntHbru3/UZ1nvh59lR5dY6/7nQpXt2xqejODaPQIxIfWi6NIJIE3cmNvVhGTKws7MAMoLqoC6trNmjs5dD8RibinM61U3yd7sCJIKZfIQjCH8iyRxAJDGthUVn2WPzsfJ2EHlB9KbQphS2qTaCcSsiEXhAuXCehpmOhk+UVpWXLong7gggPJPYIIgmQzZemip0yLVuEqjPnV8awv9vl2QMvwir4ghIq8Vj5RXYdqNytovRu6wsbYW8fETyvvvoqMjMzceDAAV/L3b59O2bNmhX5/u677+LZZ5/1tQ43kNgjiCRAtTJCkDiJAN7Cxy+HJvocFoJ0P7KCz4+Rt06YTo9i49U6x1sWwySywtQWIvbMmjULeXl5mDt3rq/l8mLvyiuvxJ133ulrHW4gsUcQCYBTR6kKhg+qUzMp17biieL3/FplI5EJen1cGye3rSqfDT8/ntO0Knz+MIgsE/c0kThUVlZG/ckmMf7++++xbds2PPnkkxFhtmXLFuTl5aGgoABjxoxBXl4eAGDChAmYPHlyJG+TJk0idfXp0wedO3dGp06dsGzZiWmYHnjgASxevBi5ubmYOnUqpk2bhjFjxgAAdu3ahcsvvxwdO3ZE586dsXXr1sCOBQ+JPYJIIPiYKCBa0PGdqp/TVJi4hEWdKD8dix/r4CYDPVuXxsy1qxJnvAhUzX3nVL4ovZcR4SL8sDrS4JHkIisrCw0aNIj8Pf7448J0c+bMwVVXXYXu3bvjiy++wL59+/DHP/4R999/P1atWoWTTnKWRrVr18b8+fOxdu1azJ8/P2K9mzhxIvr27YuysjLccMMNUXlGjx6NAQMGYP369VixYgWaNWvmfac1odG4HqE59oh4wI+8DXqtU9MyZWnD5qqVoTuyOVEwHZUre3lgLWKq8nTme/T6IpIs54bwj/Ly8qh59jIzxXP1zZw5E0899RTS0tIwYMAAzJ07F//973/x//7f/wMAXHvttViyZImyLsuycO+992LZsmVIT0/Hl19+6di+ZcuWRSyJsrYFBYk9giAIgiASnvr16ztOqvzdd99h+fLlGDJkCIATa9t+9tlnSEtLE6Zn18G10wPAjBkzcOjQIaxbtw7p6elRa+LKkNURC8iNSxAJgCi43mnkrQg/Y5R0y0pUV22sLEexcOGa7ovfo3PZfEFanxMdmlA5eGbPno3bbrsN27dvx/bt27Fr1y5s2bIFnTp1wvz58wEgah1bex1cAPjwww/x448/AjgRs3f66acjIyMDs2fPxuHDhwFUX0eX5YILLsBrr70G4IRo/Omnn4LazWqQ2COIBMEpxoh1sTnNm+YFL7FSYRV+PVuX+raknJu6Y4HJOVOtxiE6/05z+IVtJC6RusyaNQsDBw6MfE9LS8Pll1+OSy65BI8++igKCgpQs2bNyO9XXXUVvvnmG3Tp0gXvv/8+Gjc+Ebp19dVXo6SkBPn5+VixYkVke05ODo4dOxYZoMHy17/+FfPmzUNOTg66deuGPXv2BL/D9n4GuTbulVdeibKyMuzduxcNGzZE37598eSTT6J58+bSPN999x3Gjh2LRYsW4cCBA7jwwgvx/PPP45xzzomk2bNnD+655x58+OGHOHjwINq0aYP7778/YpZ1ws81/ihmj4g3Tqsc+Lm2qB/z9vmNX4NQYr3mKi/ygrbwmYhZfi1dnVHdQa/LnAqE2bKXSmvjbt++HUOGDMGaNWs8lxUWAhV7zz77LM4//3w0a9YMO3fujAw/Xr58uTC9ZVno1q0batSogUmTJqF+/fp45pln8P7772PTpk2oW7cuAKBfv36oqKjA5MmT0aRJE7zxxht4+OGHsWbNGnTq1MmxXST2iETFaSSkTn6vHW4qdN5BCz92BC4r+uIx4bIOfqzKkgrXjVdI7JHYC4pA3bh33nknzjvvPLRs2RLdunXDfffdh5UrV+LYsWPC9F9++SVWrlyJv//97+jatSvatGmDF198ET/++CPefPPNSLoVK1bgD3/4A/Lz8/Gb3/wGDz74IE455RSsXbs2yN0hiLjDdpJuYva8dLKiKTSS1TUX9MTUIlEXC6Hn5E71w+Uqc+m6yedneoLQpVWrVkkl9IAYxuz98MMPmDFjRsRyJ8Ie5VKrVq3ItvT0dNSsWTMyYSEA9OjRAzNnzsQPP/yAqqoqvPXWWzhy5Ah69eolLZefbJEgEhmnBedFMVV+rYagW04sOmO/YsFE5cRC0MYqXs9GJ+7T/jN1++rGisqEtKnA9ts6SOIxNejZsyc+/vjjqG233XYbXnrpJc9lHzhwAK+88opjungsqRa42Bs7dizq1q2Lxo0bY8eOHZHRLiLatm2Lli1bYty4cdi/fz+OHj2KJ554Anv27MHu3bsj6WbOnIlffvkFjRs3RmZmJm655RbMnTsXZ599trDcxx9/PGqixaysLF/2jVy4RKxhY6nszzqdnu4aqH7BtzMo/NonVTmp5nIMco5B3ZG4OtZHnbRu2kYkN0OHDo0SWsePH8e7776LwYMHey7brdiLxZJqxmJvwoQJSEtLU/6x5s977rkH69atw6JFi5Ceno7rr78esjDBGjVqYM6cOdiyZQsaNWqEOnXqYOnSpbjkkkuQnp4eSffggw9i//79WLx4MdasWYO77roLv/vd77Bx40ZhuePGjUNFRUXkr7y83HS3CSIU8AvYy8SU3ysV8GU7uXL5Tt1vwRc2K4zX9sRicEa8R8Xqin8noSf7HLZrwpQwx+slE0OGDMG8efMic+eVlJSgffv2eOWVV9C1a1fk5ORErHzHjx/HTTfdhA4dOmDIkCEoKCjAp59+CgCYOnUq8vPzkZOTg4ceegjAiaXSNm3ahNzcXEycONHVkmrbtm1Dr169kJOTgyuvvBI//PADAKBXr14YO3Ysunbtig4dOuCzzz4z2m/jSZVHjRqF3//+98o0rVq1inxu0qQJmjRpgtatW6Ndu3bIysrCypUrcf755wvzdunSBWVlZaioqMDRo0dx6qmnoqCgILJO3datWzF58mR8+umnOPfccwEAHTt2xCeffIIXXnhBaIrNzMyM+WzVBJEseB34Ee/VPGJB2NrD40esJluW6UAhU/esU/luX3BEVstYj8Im4svpp5+O1q1b45NPPkHPnj0xa9YsDBgwAF9++SVWr16No0ePokePHrj88suxfPly7Nu3Dxs3bsQXX3yBnJwcAMCmTZvw3nvvYcWKFZFVOFasWIGJEydi8+bNEYPXsWPHMH/+fNSrVw87duzA4MGDsXr1akycOBGTJ0/G7NmzAQDTpk2LtG/06NG4/fbbMXToUDz55JOYMGEC/va3vwE4oWVWr16NKVOm4JlnnonM2aeDsdizxZsbbIuebHFilgYNGgA4MWhjzZo1+POf/wwAkUkI+bXr0tPTo2a5JohkJ2yWDOo03RGWEbiqeDsTt6mT8GdDEFR1yWIGTcUnn1ZnG5HcDBs2DG+//TZ69OiBBQsW4MiRIygpKUFJSQkAoKKiAlu3bsXy5csxdOhQpKWloV27dhGxV1xcjBUrVqBLly4AgB9//BFbt26tttatmyXVVq9ejQULFgAArrvuOlx22WWR3wYMGADghFFsxowZRvscWMxeaWkpJk+ejLKyMnzzzTf46KOPcPXVV+Pss8+Osuq1bdsWc+fOjXx/++23sXTpUmzbtg3z589Hv379MHDgQPTv3z+S/re//S1uueUWlJaWYuvWrZg0aRI+/PDDqIkSCSLZ4ePMwib+kh2/XIexGKQR9LXBhxfooDt63C6vZ+tSTC+f60mw6R6HZHALE3IGDx6M+fPnY8mSJcjJyUHDhg0xYcIElJWVoaysDF9//TV69uxZLeTM/m5ZFm6++eZI+q+++grXXntttXrYJdXWrVunZZBil1SzLCvqu+2hTE9Px/Hjx432OTCxV7t2bbzzzjsoLCxEmzZtMGLECHTo0AElJSVRLtXNmzejoqIi8n337t247rrr0LZtW4wePRrXXXdd1LQrNWrUwHvvvYdTTz0VV1xxBXJycvDPf/4T06dPx6WXXhrU7hBEaAmL4Es1C4kf+yuaa88J1vIlW83CdPJrN9cNH7upM6kym07H7c/nHZ41qNrAJDdtF63uwf7FcjATEXuaNGmCdu3a4e6778bQoUPRt29fvPbaa/j5558BnNAlhw8fRrdu3TB79mxYloXNmzdHxgX06dMHM2fOxP79+wEA3377Lfbt21dtqTQ3S6rl5eVhzpw5AE4s23bBBRf4ss/GblxdsrOzsWTJEsd0vHIePXo0Ro8ercxzzjnnRA4GQRCxmSZEVTcrQFKlk/Rrn0u25BuJPadpTERuV1X7dC1sbkUV/91USP1axiBp27y0iS+DLHqpwbBhw3Dbbbdh4MCBaNiwIT799FPk5+fDsiycdtppWLBgAYYMGYIPPvgA2dnZyM3NRU5ODurXr48zzzwTY8eORa9evVBVVYV69erhrbfewplnnonOnTsjOzsbv//973HzzTfjsssuQ35+Pnr06CFcUu2OO+6Ist797W9/ww033IBHHnkELVu2xPTp033Z30BX0Agrfs0ETlOvEMQJgpyuI8z4IW5toRfP2L2gz5+b8vljqzOHn2472LxheUEJ+2jcVFpBg+XQoUOoW7cutm/fjsLCQmzZsiVqdpBEIWaTKicbJPSIsBLreKMg5jzzE7/a5HRc3dRjL5sWa6HH70sYxA6P33F4dpn8xNGyQSGmZXsh7EIvlbnooovQsWNHXHHFFZg8eXJCCj0gQDcuQQSNzPUVltGN8SIeHXeYLCQ8frXJqZww7rsM0VJ7Qbbfj+XnVGWYtl/lTmbLYutMpPNL+Ae7elciQ5a9EBNGK0kiEOslqFId3VUR4kGsRqEmMrES6W4GiehYqd3HAFZ3L6tG+tLzmEhkSOwRBEEQBEEkMST2QkwyWA2Cgrfe2XFPqe7CJaIJ2yjLoK3OJvuYCHPJOVnVTC16MkuejvWQIBIZEntEQiMSeCT4CJuwDUII+to02Ud+oAKPn2JQ1x1rp5Vt5+f2M0GVh6/DzVyFBBFmaIAGkXBQTF64COPAjLAH1YdhuhWWoI9TWM6DzsCUMF7PBOEVsuwRCQUJvfARz45R5d4La2B9vKZbkaE6PvEa2c2iWkfXL3TX4SWIRIXEHpFQsJ1kWDpLIn7odPhhs9KYrpgRJCorlh/TpbiFF19BtoOfYy/Wrn+aY4+IBeTGdQFNqBxfwtJREoRbwvCiElZ3pWyJNb/KVVl8+d/CPH8kQZhAYo8gCEJCMnf0Yd6vIOLoTEb2yj4TRKJCblyCIAgJydTRu3GFxnL/RYNqghxoI5tImeL0iGSExB5BEAlNvNYBTiQSod2xEpbs4B1RPGAyCXyCsCGxRxBEQiNb3zRIEkE88ZCI+RXZvIIEkayQ2CMIgtAkniNUveBmXdqw4LdITeY4TIKQQQM0iISCRuISOgTZmSejUAjDPolEWNBCj4QfkSqQZc8QmnYlfpDQI4jkQ7Q0mV9LtamWXmO/xwuaY4+IFWTZSxBS/Q00bMtLEeHEayceDxGQ6vd2kKNhZfGcvDs+lY8/kRqQ2CMIIqnw0nGHYXmwVIMXXUFNZCyy8qW60A4rFUumIi0zTTu9dcQCAHTt2hXp6ekYOXIkRo4cGVTzEhISewkCPZjIqkc4E5b7I9XvVROCXvdWRFjcuIS/rF69GvXr1493M0IJxewlEPRgIojEgO5V9wQRq8cim0yZIJIZEnsEQSQ0NKdecuGHCHMqg4QekWqQ2CMIgiAIgkhiSOwRCQHF6xEibKvbzs7DsLPzMKM8ROrAnnM6/0QqQmKPIIiEZUTxTly0v0fgdRCxIRZCLCznk+bYI2IJjcY1gCZUJojw0WLtTO20osl7bUTzvcVCGNDI0F8J4hjQfHoEQZY9giASFF23LYs9j5vsO582aIJwL/q1+kSyQUKPSGXIskcQBBEHgrLokaj5FbKaEsQJyLJHEElOslp5TNy3Isi9RxBEqkBijyCIlCSZBR65cn8lmc8zQehCblyCSHKSpbMTrW3qtoywLD8YVP3x3i8TgnS1JtJxIIggIcseQaQAiW7psYWZ16Wu7HzxPhbxrj9MqAbJJCs07QoRa0jsEUQKkOgdqmhaFNFnN2W5LYcgCCJRIDeuJjTHHpEMmLguwzqSkbfOmbZPdgxitZ+8UA3b8Y0lNEiGIGIDWfYIIsXQtWCFqfMVtdmttdKO14sXKitlqhGma4wgkhkSewThA4nSYdsCKdEEXxBz0dnHIN7CLyzHOB4kyn1DEIkOiT2C8IFE7LBTvaP1OtjDa92JeM34DR0DgogNJPYIIgVRjUoNswgMc9sId5DwJYjgIbFHEEQUYe543baNRCIRFmjalfiSlpaGBx98MPJ9zJgxmDZtmjLPtGnTsHfvXt/aUFRUhIULF/pWng4k9ggiBQnrSNugiPd+kth0ho4REQtOPvlkzJgxA5WVldp53Ii948ePmzYtUEjsaUDTrhDJip+TLYdhwENYCcuAkDATb0FOpAaZmZm45ppr8Pe//73aby+99BLy8/ORnZ2NG264AVVVVZg7dy7WrFmDIUOGoKCgAADQqlUr/PjjjwCAhQsXoqioCMAJi93dd9+NXr164amnnsL8+fNRUFCA3NxcDBgwIJInHpDYIwiCIAgiZbjjjjvwyiuv4PDhw1Hbhw4ditLSUmzcuBE1atTAggULMGjQIOTl5WH27NlYtWqVY9nl5eX46KOPMG7cOFx44YVYtWoVysrKUFBQgNdeey2oXXKExB5B+EgiWG2CamM8R7cmAnR8xCT6Un5E4nHqqafi8ssvx5QpU6K2r1+/Ht27d0d2djbee+89bNq0ybjsIUOGIC0tDQCwY8cOFBYWIjs7G6+++qqr8vyCxB5B+EhYO3K7Q9XpVKnzDR46vr+S6KNx6VwmJmPGjMFf//pX/PLLL5FtN954I/7xj39g48aNGDVqFI4cOSLMm5GRgaqqKgColqZOnTqRz6NHj8a9996LjRs34rHHHpOWFwtI7BFEEiPriOwOVtTRuu18qdNzJtUGxgRJENebaZl0zScuWVlZ6N69O+bMmRPZdujQITRt2hSHDx/G7NmzI9vr1auHgwcPRr63bNkSZWVlsCwL8+bNk9ZRWVmJM844A1VVVXjjjTcC2Q9dSOwRRJKja80zzcNDAsYZOkbu4dcUDgJ7II2OdZuEe+IzduxY7Nq1K/J9/PjxyMvLw6WXXorc3NzI9qKiIhQVFUUGaIwfPx433XQTevXqhVNOOUVa/vjx43HFFVegd+/eaNWqVUB7oUeaZVlWXFsQByorK9GgQQNUVFSgfv36julpNC6RqPAd0pTCFknTOSXTvsSaRDx2sWwze9/4WW8iz7Fn2m96qaPeXQ2Rlpmmnc86YuHgM/sDbVuiQ2KPxB5BEESokQku1vrmhyALWlCS2NOrg8Se/5AblyAIIRSPRIQF1fJ+YYcGPBFhgMSeA2TVI1KVRHPzEcmLLZhE16Rf12nQAz5I8BHxhMQeQRAEEWpkI8T9mruQhFhqcPz4ceTm5iI3NxdNmzbFGWecgdzcXFxwwQWB1XngwAG88sor0t/XrFmDe+65R1nGhAkTMHnyZE/tILFHEDEk2TuVRNq/RGlrorSTiIbOW/hIT09HWVkZysrKcOutt+K+++5DWVkZPvnkE2U+dp1by7Iic+zpoBJ7x48fR15eHv7yl79ol+cWEnsEEUPYqR2SkURx/SbSaNREaWc88Os+4i2H7PQrJnWo0tN5DJ7KysqoP51JjEtLS3HBBRegc+fOGDx4cGT92latWuGRRx5Bt27dsHTpUjRp0gT33Xcf8vPz8dVXX2HixIno2rUrcnJy8NJLLwEAdu3ahe7du6Njx47IycnBhg0b8MADD2DTpk3Izc3FxIkTsXTpUvTv3x9Dhw5F7969sXTpUgwZMgQAsHLlSnTr1g2dOnVCnz59sHv3bt+ODYk9goghiSAyklWIsoT9HBB6+H0eWbcwX7aT8ON/o2ss9mRlZaFBgwaRv8cff1yZvqqqCmPGjMG7776LtWvX4rzzzotylzZu3BjLly9HYWEh9u3bhwsuuACrV6/Gtm3bsHfvXqxevRpr1qzBlClT8O233+LNN99Er169sH79eqxduxa//e1vMXHiRLRv3x5lZWV44IEHAACrVq3Cc889h48//jiqPe3bt8cnn3yCdevW4aabbsJTTz3l27HJ8K0kgiCUsEIvzB1BmNvmBzQZbmITyxcm2xJvfxa1RZRHRCJPu5IolJeXR029kpmZqUyflpaGDRs2oHfv3gCAo0ePolevXpHff/e730U+165dG5dddhkA4MMPP8SCBQtQUlICAKioqMDWrVvRtWtXDB8+HBkZGRgyZAiys7OF9Xbv3h3Nmzevtn3//v249tprsW3bNvzyyy9o2bKl3o5rQGKPIGIE7yYKq9gIc9v8IJn3LRHREd/xelGy2yaaz0/XAm6nH1zoc+OIatSvX99onj3LstC5c2csWbJE+Du7zi372bIsTJgwAddff321PMuWLcPChQsxdOhQPPnkk8jJyVGWy/LQQw/hsssuwy233IKVK1fivvvu094XJ8iNSxAxJBGsSmFuG5F8OK3FLBJV8Qo1UAk9dj8S4T4nTvDNN9+grKwMwIm1cb/66ivHPH379sVrr72Gn3/+GQCwefNmHD58GN988w1OP/103HLLLbj22muxYcOGauvqqrDX0gWA119/3d0OSSCxp4Dm2COCIJFi4hKprURyEuTcejJk8Xmq+f5YSOQlBieddBLeeust3H777cjJycH555+vJfYuvfRSXHbZZcjPz0eHDh1w22234fjx41i6dClyc3PRqVMnLF68GDfddBMaN26Mzp07Izs7GxMnTlSWO2bMGPzxj39Ejx49pNY/t9ByaQqTL4k9wm+CWnNTVpdp+cnuwiUSm1hZzFQjatl7xP6sc98kesweLZeW2JBljyAIgiAIIokhsUcQMcSO64mVe9S0HrLqEbHCzT0gmg4lFrDxeLxVT9QugggbJPYIIsa4cUW57RiTsROiOMLkwO21aToaVoYqv0jc2XlkcXv8RMzsSF66ZsNDz549q81vd9ttt0UmRvaC09JoNtu3b8esWbMi39999108++yznutXQVOvEEScoPg4d9AxIwDv14FOflkaXev8lMIWGFy4wbhtRHAMHToUs2bNwoUXXgjgxJJl7777Lh555BHPZdti7+abb1ams8Xe0KFDAQBXXnml57qdIMseQcQYN50UCRyC8B9Ta1yswzAI/xkyZAjmzZsXWd+2pKQE7du3xyuvvFJt+bPjx4/jpptuQocOHTBkyBAUFBTg008/BQBMnToV+fn5yMnJwUMPPQQA1ZZGq6ysRJ8+fdC5c2d06tQJy5Yti6RbvHgxcnNzMXXqVEybNg1jxowBAGzbtg29evVCTk4OrrzySvzwww8AgF69emHs2LHo2rUrOnTogM8++8xov0nsEUQcIPFGEPGFnyhZJOREok+0LBrdz4nD6aefjtatW+OTTz4BAMyaNQsDBgwQLn82Z84c7Nu3Dxs3bsSf//xnrF27FgCwadMmvPfee1ixYgXKysqwbt06rFixotrSaLVr18b8+fOxdu1azJ8/H3feeScAYOLEiejbty/Kyspwww03RLVv9OjRuP3227FhwwZ0794dEyZMiPyWmZmJ1atX46677sIzzzxjtN/kxpVA064QQUMdBJGq9GxdipIt+XFtgyjmjhd8OgNCyMqXeAwbNgxvv/02evTogQULFuDIkSMoKSmptvzZ8uXLMXToUKSlpaFdu3aR1TCKi4uxYsUKdOnSBQDw448/YuvWrWjWrFlUPZZl4d5778WyZcuQnp6OL7/80rFtq1evxoIFCwAA1113XWSJNgAYMGAAAKBLly6YMWOG0T6T2COIBIDi+9SIlrMiCCf4EbUy4WayNBqJv/AzePBgPProoxgwYABycnLQsGFD4fJn77zzTtR3e1piy7Jw8803R9y3Ntu3b4/6PmPGDBw6dAjr1q1Denq61kTJaWm/zi9oWVbUd3ut3/T0dBw/ftx5RxnIjUsQCQAJGDXkSkss4m3VE1nu2G389aR7bZHQSwyaNGmCdu3a4e6778bQoUOly59169YNs2fPhmVZ2Lx5MzZu3AgA6NOnD2bOnIn9+/cDAL799lvs27ev2tJolZWVOP3005GRkYHZs2fj8OHDAKBcQi0vLw9z5swBALzxxhu44IILfNlnEnsEkWDEukNJpA4sWS0rNH1H8KgGX9CxTz6GDRuGL774AgMHDpQufzZkyBA0aNAA2dnZ+POf/4ycnBzUr18fHTp0wNixY9GrVy9kZ2dj6NChOHToULWl0a6++mqUlJQgPz8fK1asQOPGJ8LDcnJycOzYscgADZa//e1veP7555GTk4OPP/4YDz/8sC/7S8ulSZZWoZg9IqykukvXaf+TaRF62YhQwhu69xB/LTkto8aTTNOupOpyaYcOHULdunWxfft2FBYWYsuWLUhPT/e1jlhAlr0kgN46U4dUF3qAs9hJJpcuG0+WTPsVb9ysGa0qg0R58nLRRRehY8eOuOKKKzB58uSEFHoAib2kgB4qqQGJesKJMF0jYWqLDNWADP7FSmXVEz2DE2H/CWeWLVuG9evXY+PGjbjkkkvi3RzXkNgjiASBRH1q4sYKFW+hEe/6dVGtkKE7QEO2fBpBhAkSewLCFq+XKA9OgiBiDy/udKxRsSARxY/T8VKN1BVN0kwQYYHm2QsQP9726I2RYKFrgVANQEnW0cixQnT82G38sedFHz2vibBCo3EFI3fCYtmzHxzJNLqQIAj/cLLohe2ZEdZ2AfK28cfYZALvisf2+djC+JKqo3GThZi4cY8cOYLc3FykpaWhrKxMmdayLEyYMAHNmzdH7dq10atXr2oL/h45cgR/+MMf0KRJE9StWxdXXnklvv322wD3IP6E8eFIEIlMolrAZO5C1vqUSG7EMMQYstjt8dquZBJ6ROITE7F37733onnz5lppn3rqKTzzzDOYPHkyVq9ejaZNm6Jfv35Rs03/8Y9/xNy5c/HWW29h2bJl+PHHH3H55ZcbLx8Sdli3AE30SRD+kejuNpGrkXcjhvH5IBKhsRamps9SNk4vjMeUIHQIXOz9+9//xqJFi/D00087prUsC8899xweeOABXHXVVejQoQOmT5+On376CW+88QaAEwsUv/baa5g0aRL69u2LTp064fXXX8fGjRuxePHioHeHIAiCIAgioQhU7H333Xf43//9X/zrX//SWgD466+/xp49e9C/f//ItszMTPTs2RPLly8HAPz3v//FsWPHotI0b94cHTp0iKThOXLkCCorK6P+EhHR2zxBEMkNe9/rjBZNNGLtxlVNt6KbN5Hc5AQBBDga17IsFBUV4dZbb0VeXh62b9/umGfPnj0AgNNPPz1q++mnn45vvvkmkqZmzZpo2LBhtTR2fp7HH38cf/rTn1zsRWwRuWxphBdB+Esi3U/8KFAbncEYibKPsW6nzNWtO/AiUY4rQbAYW/YmTJiAtLQ05d+aNWvw/PPPo7KyEuPGjTNuVFpa9Cgcy7KqbeNRpRk3bhwqKioif+Xl5cZtciLIN9OwzJtFEMlAInXWThYkdjBGoj0b4tVeVujxU6eQxY5IVowte6NGjcLvf/97ZZpWrVrh0UcfxcqVK5GZmRn1W15eHq655hpMnz69Wr6mTZsCOGG9a9asWWT73r17I9a+pk2b4ujRo9i/f3+UdW/v3r3o1q2bsD2ZmZnV2iHD7bQrfjwgRHM3AdEPJXoQEUTyI7MyJar1ToRu2/22xPICWbXUWSIfX4JgMRZ7TZo0QZMmTRzT/e1vf8Ojjz4a+b5r1y5cdNFFmDlzJgoKCoR5zjrrLDRt2hQffvghOnXqBAA4evQoSkpK8OSTTwIAunTpgho1auDDDz/E0KFDAQC7d+/Gp59+iqeeesp0d0JBz9alKNmSL/2dHjgEkbrwbkcvMWeJSBBCTzU9TSK5+QlCl8AGaJx55pno0KFD5K9169YAgLPPPhtnnHFGJF3btm0xd+5cACfct3/84x/x2GOPYe7cufj0009RVFSEOnXq4OqrrwYANGjQADfeeCPuvvtuFBcXY926dbj22muRnZ2Nvn37BrU7geEk9AiC8E4iuDh1YsZk2xNh/+INH/8Y5MAQmmOPCBtxXy5t8+bNqKioiHy/99578fPPP+P222/H/v37UVBQgEWLFqFevXqRNM8++ywyMjIwdOhQ/PzzzygsLMS0adOQnp4ej11wTc/WpUbp6Y2TINyRCPeNFxdtPAc5JCKiJdAIIpmh5dK4pVXCslQaQRAE4Q8iq6nTRMpeSEbLHi2XltjEZAUNojo6Vj164yQI99D9Q5hAo3GJZIYse3G07FG8HkEQyUQQo1j9dBmLXgCCEHdk2fNWxz/nt0eduvphWT8dOo7rB2xC69atkZ6ejpEjR2LkyJGBtDFRiXvMXpiIldBjrXqJHvtCEATBEtTzLKhnJa1MlDysXr2a3LgSyI0bY1ihV7Ilnx4sBEEQCvycMNppEIwfz2MKHyDCCFn24gC5bgmCSEaCenn1s1yZwPMi0shDQ4QdsuzFAdMpVwiCIMJOkFOZxGKaFK9izZ63b3DhBp9aRBD+QWIvAGRijkQeQRDJCLsyRRColjaLN+S2JRIBEnsBULIlP0rY9WxdWk3okfAjCH+gzjb+sGt3h1GQEUSqQ2IvjpDgSy1IlAQDiYvUIWz3UNjaQxAySOwRBEEQBEEkMST2AsJ25drWu5It+TQKN8UhC5QZZDUhWPwYNRsUdG8TYYemXvk/gphQmcQdISKsgeZhg44RkQjQdUokAiT2AoK16MkgMZg6hNEaEVZIDIcP9vqN57mJRd2m15+d3s4zuDColhGEe0jsBQQJOYKFxIs+OseKBGH8CYsADBrZtca/wNEce0SYoZi9GMKPviVrT2pC590bJPRii+h6TVahx84XaO+jbP9Yax7d00TYIbEXMPZDQDTNSjI9JAl96Lx7g45fbOHn0EsFYcPPF5gK+0wkNyT2AoY6JmfoQUoQ4Ub0HPM6gXJY7nveSilrV1jaSxBuILEXA0RWPYrpIwgiUUmml1jTwRii/Ml0PIjkhMReDCBhpyaZH5RkDSCSiVS4nnVi9HhS4bgQiU3Kj8YNYn49HloWjbChwQVEouI0YMGURLoP+LbKLHw07QoRVsiyFyBTCluQ0EtB7JF87Mg+G3b0HlkD1NDxCR7d6zAVzoXJPoqEasVj+/xsDkH4Com9AJG9uQ7PGhTjloSTZO1AbHePLNg7WfebSBxMrXTsiFydshPxGje1NCaSZZKIpry8HAMHDsTZZ5+N9u3b45prrsH+/fuFabdv3468vDwAQFlZGRYtWhT57aWXXsLMmTNdteGpp54yzlNUVISFCxe6qi/l3bhBY6+RS1QnmR6WqnnHWGueyB2UTMfBT9wcF5XIoOP8K0Eei1SZniUV9jEZsSwLgwYNwujRozFv3jwAwKJFi7B//340bNhQmbesrAyffvop+vfvDwC49dZbXbfjqaeewr333us6vylk2QsYXuiVbMmnTicJ4efkYq0brEvXTkvXQDBQEL05OuJMFJLgVGaykwr7mIwUFxejXr16uP766yPb+vfvj48//hhjxoyJbMvLy8P27dsj348fP46HHnoI//znP5Gbm4sPPvgAEyZMwOTJk7Fp0yb07NkzkrakpARDhgwBANx8883o0qULzj33XLzwwgsAgAceeAAHDhxAbm4uRo4cCQCYOnUq8vPzkZOTg4ceeihS1vjx49GuXTtcdtll2Lt3r+v9JsseQfiMbFZ9VQwfEQ1ZPMMDXa/VoReHxGXTpk3Izc01zpeeno5HHnkEn376KZ5++mkAwIoVKwAA7du3xw8//IA9e/agadOmePvtt/G73/0OAPDEE0+gUaNGOHr0KM477zwMGzYMEydOxMsvv4yysrJIm9577z2sWLECaWlpGDBgAFasWIH09HS8//77WL9+Pfbt24d27drh9ttvd7XfZNmLMeTSTW7YpZZEViYSMbGBjrEZquOlMxI1WZHtK11fiYtlWUhLS/O93KuuugrvvPMOqqqq8O9//xuXXXYZAOCNN95Ap06dkJeXh23btuHLL7+slre4uBgrVqxAly5d0LlzZ3z++efYunUrli9fjkGDBqFmzZpo1qwZ+vTp47p9KW3ZO+NPrZCW6f9JZ7Hn2CORl/yQm8s//Dg+dIy9k+ovLew9TcunJQft27ePxOqxZGRkoKqqKvL9yJEjRuX+7ne/w+jRo9GhQwd06tQJJ598MrZt24YXX3wRK1asQIMGDXDxxRcLy7UsCzfffHOU+xYAnn32Wd+EKVn2AmRn52GRPyI1YEfiyn4niESBrtdf4WNxWQYXbohDiwg39O3bFxUVFXj99dcj2xYsWICsrCysX78ewAm36ubNm6vlrVevHg4ePCgst0OHDti7dy9eeOGFiAv34MGDOPnkk1G/fn1s374dy5Yti6RPT0/H8ePHAQB9+vTBzJkzIyOCv/32W+zbtw/du3fH3LlzcfToUezZswcfffSR6/0msUcQBEEQREqQlpaGefPmYdasWfjtb3+Lc889F7NmzUJOTg6aNGmCnJwcPPXUU2jXrl21vL1798batWvRqVMnfPDBB9V+v+qqq7BgwYKIC7djx45o06YNOnTogDvvvBPnn39+JO3w4cORnZ2NkSNHokOHDhg7duz/b+/ug6K6DveBP+tiAAMYFEWIIL4ElAiKIEReFMWIjaPoQNA2JurUapSo0Tq+/FKVqOCAxjhpvm3H1Kg0RiOhKqa2xcTgGxqNSlDTgoBSDaU2voBiBGHv7w+H7e6yr7B3d+/d5zPDDLt79+65h929D+fccw4SExMRFhaG9PR0NDY2Ijo6GsnJyQgPD8f8+fMxevTojh+3IAhCh58tUQ0NDejevTs8l3mL3o3bRrMbl8unEeA8XWHkeCx972lu74zXsekOUtGtA2do2Ws7b9bX18PLy0vU18g7FIpuzyrNft6jxla8kfK9qGWTOqe+Zo/InuR8cuwM3RMp68n6OjuBsLP9TdoCnrPMIUjyw25cGxgTfA7HK6KdvkVP7l+Scj8+W+F8hLZn7nvXmd/jfC+SlDHs2YBmyHOWwGdooXC5YpesdbEubcsa9e0MQdAZjpHkid24JBpn6o6T87EREclBa2srIiMjAQB1dXVwcXGBj48PPD09cfLkSVFe8/79+9i/fz/mzZun9/Fvv/0Wn332GTZv3mxwH5mZmfDx8cFbb73V4XIw7JEoeG0LkfxYMvmy3Oj755XfcdKiVCrVq1ZYEqBaW1uhVD4dMCIIAgRBQJcu5nWM3r9/H9u3b9cb9lpbWxEVFYWoqCjzD6KD2I0rMmecY0/fXFRyPxEQicFYmHC0oGFoDjo5YtBzTA0NDVo/5kyMfO7cOSQkJGDEiBFITU3Fw4cPAQBBQUFYv349YmNjUVxcDB8fH6xatQrR0dGorKxEVlYWRo4cifDwcPzhD38AANTW1iIuLg7Dhg1DeHg4ysrK8M4776iXaMvKykJxcTEmTJiA9PR0jB07FsXFxep1dM+ePYvY2FhERERg3Lhx+Pe//221umHYE9nzFz+zdxFsjsGOyDo0pzvR/XEkjlYea9O3znUbZ5h2RSoCAgLQvXt39c+mTZuMbq9SqbB8+XIUFhbi4sWLeOmll/Dhhx+qH+/ZsydKSkqQlJSEO3fuICEhAefPn0d1dTVu376N8+fP49tvv8XHH3+MW7duYe/evUhMTMR3332HixcvYtCgQcjKykJoaChKS0vxzjvvAAC++eYbbNu2DSdOnNAqT2hoKE6ePIlLly5h7ty5yM3NtVrdsBuXRMfwRyQOewyE0tdqrzk1CZG93Lx5U2uePVdXV6PbKxQKlJWVYezYsQCA5uZmJCYmqh9vWwkDANzd3dWTJR89ehSHDx/G8ePHAQD19fWoqqrCyJEjMWvWLLi4uCAtLQ1hYWF6XzcuLg7+/v7t7r937x5mzpyJ6upqtLS0oF+/fuYduBkY9kh0+taWJCLDND8z5rSa2TNsOVurHrtwHZeXl5dFkyoLgoARI0bg2LFjeh/v1q2b3t8FQUBmZibeeOONds85deoUvvjiC6SnpyMnJwfh4eFG96tp7dq1mDRpEubPn4+zZ89i1apVZh+LKezGJVEw2BF1nqPOf6f5+dacF1HuGPTkp6amRj1oo7GxEZWVlSafM378eOzYsQM//fQTAKC8vByPHz9GTU0NfH19MX/+fMycORNlZWVG19PV1dDQgL59+wKA1tq91sCWPbIZdvMQWYfm58jWnyt94c4ZPtcMevLTpUsX7Nu3DwsXLlQPzMjNzcWgQYOMPu+VV17BlStXEB0dDUEQ0Lt3bxw+fBjFxcXYvHkzunbtiueeew579+5Fz549MWLECISFhWHGjBmIi4szuN/ly5dj9uzZ2LRpE2JjY616rFwb10Zr4zobc78UneEkQUTSZM46wPXZd2xVHLvi2rjS5tTduLfW3UB99h31D3Wepf/5OvIIQ5I+Ob6nrH1MHd2fM3xu+c8oyYVTt+xZ+l9A9//XU8RSSZ/ml76xLg/N6ST0bcsvWCLTeFmEeAyNctYdbOZMjQRs2ZM2p27Zs5RmK6DuD2kzdwSh7rY8eREZp9mapvnZkXMLG5G1jBkzpt38dgsWLFBPjNwZbatlmHLjxg3s379ffbuwsBDvv/9+p1/fGA7QsBJTgY+tgkT2IccWMH3/KFkDp0ky3apH0paeno79+/dj9OjRAJ4uWVZYWIj169d3et/GlkbT1Bb20tPTAQBTpkzp9GubwpY9GzHWKiiHlkFDJ9S2CVf1PcYl1cgW5Pi+amvds9axWbo/Bh+SqrS0NBw8eBAqlQoAcPz4cYSGhmL79u3tlj9rbW3F3LlzMXToUKSlpSEmJgZXrlwBAOzcuRPR0dEIDw/H2rVrAaDd0mgNDQ0YN24cRowYgYiICJw6dUq93Zdffonhw4dj586d2LVrF5YvXw4AqK6uRmJiIsLDwzFlyhTcvXsXAJCYmIiVK1di5MiRGDp0KK5evWrRcTPsERERkVPw9fVFcHAwTp48CQDYv38/UlJS9C5/VlBQgDt37uDy5cvYsGEDLl68CAD4/vvvceTIEZw5cwalpaW4dOkSzpw5025pNHd3dxw6dAgXL17EoUOHsHTpUgBAVlYWxo8fj9LSUsyZM0erfIsXL8bChQtRVlaGuLg4ZGZmqh9zdXXF+fPnsWzZMmzdutWi42Y3roOQcjewuS0Chq7R09dKwO4kovbaPmvWXCZNc5+a1wIa258zfS51j9WZjl2upk+fjvz8fMTHx+Pw4cNoamrC8ePH2y1/VlJSgvT0dCgUCgwZMkS9GsZXX32FM2fOIDIyEgDw8OFDVFVVwc/PT+t1BEHAihUrcOrUKSiVSly7ds1k2c6fP4/Dhw8DAF5//XX1Em0AkJKSAgCIjIzEnj17LDpmhj2JcOQwqBniTH0RWhL0zN0nkbPQnNBYM5x15jPS0TAjx3/IDH0fyekYCUhNTcXGjRuRkpKC8PBweHt7613+7M9//rPW7bbJSwRBwLx589Tdt21u3LihdXvPnj1obGzEpUuXoFQqDS6Tpkmh+N/cv4IgaN1uW+tXqVSitbXV9IFqYNiTCXuGQXO+9PWNuNX3PF7HR2Qeaw7S0Aw05oY4OX42zfkHlKTPx8cHQ4YMwa9//WssXboUvr6+yMnJwauvvgp3d3eUl5ejX79+iI2Nxf79+zFjxgxUVFTg8uXLAIBx48Zh+vTpWLRoEby9vXHr1i24u7u3WxqtoaEBvr6+cHFxQX5+Ph4/fgwARpdQi4qKQkFBAdLS0vDpp58iISHBKsfMsOckrBkGrXViMBXyrH0ROpEcdfbzoW8al7bbzvbZMxXqnHHlDLmaPn06FixYgKlTp8Lb21vv8mdpaWn4+9//jrCwMAwfPhzh4eHw8vJCYGAgVq5cicTERKhUKnh6emLfvn0IDAzUWhpt3rx5mDRpEqKjoxEfH4+ePZ+eZ8PDw/HkyRMMHz4cS5Ys0Wq9++CDDzBnzhysX78e/fr1w+7du61yvJxUmRMwmtQWBE21znW0W8dQ0NPdjzOefEj6DLVg2/u9rHmtnqmpRexdVluwJOgBzhf2nHVS5cbGRjz77LO4ceMGkpKSUFFRAaXS/LI5CrbskUn6vtRSk/73e8FXTy9a7egJwdjADWdbbJ3kR/f6urb7HImxAR/swiRnlpycjAcPHkClUuHDDz+UZNADGPbIClKTyrTCny5zu4hNXSjuCK0hRB3hiJPy6v5DZeqfLbkz9Tdytvqgp9rmxpM6hj0SnVjXCxJJiaMGPmtsIxeO+DcisgaGPbI7R55WhsiaHDE4GZtuxFGvN7Q1hkCSOoY9cni6YVCzy5hBkKhjDE1zZGh0rjNjXZDUMeyRpJkzIo6BkEibuSPgna0Fz9wRyc42Epekj2GPZM/YF3Nng6AzdmmR9Bka/GRshRtDAzrk9P7Xdyz8jJMcMOyRUzO3ZdDQFz5PAiRluoHN1HQr+qZCknroM2cdYEOjlomkgmGPyIS2QKhvehl2EZNUGVqDWveaPd3VbAyFHSm3gFm6rjeR1DDsEXUCRxKTXBhbl9pYi56UmbN6CJEcMOwRiYhhkDrDVq1lusumGXpNc7aRImPHxCBIcsCwR2RHYg4eIekTM1Dpa6HTDTyGunfluLqNvnkFjQ1YIZKSLvYuABERERGJRyEIgmDvQthaQ0MDunfvjvr6enh5edm7OEQdxtY/6dIdANH2u+5jtiyLbnl0y6CvZUvqq2voa800JjWpTOwiOSRbnDfbXiPvUCi6Pas0+3mPGlvxRsr3PKcbwbDHNwbJlCMGwTHB53C8ItrexXA45k5ybI8y6COHblx9AdvQ4BNOqMywJ3W8Zo9IphxtdZExweds9lpSoxusTLUuGWp56whjLYxyZmwwhm5LnzPVC8kTwx6RE7PlABG26FnG1MhQS4OHsefqa0k0tKqGsf1LNQyZE+Y4QIOkjGGPiPTitDG2Zcl1Y9YMVaauuZPzEmKax2HoeHRbPPVNrk7W9dMjlajbOyNes8f+fSJRMAw6FnNa6Ay1cOmbX8/UYA2psDS48po98c6bjx8/Rv/+/VFXV2fxc728vODn54cuXbogIyMDGRkZIpRQutiyR0Si4ByC1qPvurq22+Y819zuV82l0TRvm+rClGLIayPlssuNm5sbrl+/jubmZouf+8wzz8DNzU2EUskDwx4R2ZyjDR6Rio5cs6cvsGmGOXO6jzvSbctBDdQRbm5uDG0iYNgjIofElsGnrDEoQF/LnL5BGfpGBet7rjllkkLIM7ZiiD7O2oVL0sewR0SS40wtg5YM3DB3X+YMADG2PJouU9f6SYWUykpkCYY9IpIlObYMWhrC2rYzth9LHtPcRt/+pRbudBmbaJlIymyyNm5TUxOGDx8OhUKB0tJSo9sKgoDMzEz4+/vD3d0diYmJuHr1qvrxu3fvYtGiRQgJCUG3bt0QGBiIxYsXo76+XuSjICK5qM++Y/TH3sToJjVnShVTZdIcvKH5u+52UmCsC7ft2Bj0SC5s0rK3YsUK+Pv747vvvjO5bW5uLrZu3Ypdu3YhODgYGzduxMsvv4zy8nJ4enqitrYWtbW12LJlC0JDQ1FTU4M333wTtbW1+Pzzz21wNEQkd/aeY9BUyLBGCDG3hdDYAA9T+3bksGQo/DpymYk6SvR59v76179i2bJlKCgowIsvvohLly5h+PDhercVBAH+/v54++23sXLlSgBPWwV9fX2Rk5OD+fPn631efn4+Zs6cicbGRri4mM6vnGePiMTiaF3E+sKcsbVfzdmfuRw5OBkKubotfJohNzWpzKZldCQ8b0qbqC17//nPf/CrX/0KBw8eRLdu3Uxuf/36ddTV1WHChAnq+1xdXTFmzBiUlJQYDHttbz5DQa+pqQlNTU3q2w0NDRYeCRGReZxp8IguqSwpZkkZpXA8RKaIFvYEQcDs2bPx5ptvIioqCjdu3DD5nLZZs319fbXu9/X1RU1Njd7n3LlzBxs2bDAYBAFg06ZNePfdd80vPBGRiOwxeMRYaDFn4uU25ozodfTQZ6rbWt/vjnAtJ1FHWRz2MjMzTQan8+fPo6SkBA0NDVi9erXFhVIoFFq3BUFodx/wtIVu0qRJCA0Nxbp16wzub/Xq1Vi2bJnW8wICAiwuFxGR2KzdMmju6Fpzlj/TvH5PX+gzZ61Ze9ANqxx8Qc7G4rD31ltvYcaMGUa3CQoKwsaNG3H27Fm4urpqPRYVFYXXXnsNu3fvbve8Pn36AHjawufn56e+//bt2+1a+x48eICJEyfCw8MDBw4cQNeuXQ2Wx9XVtV05iIikSoyWQVNr3loyxYojhylHLhuRWCwOez4+PvDx8TG53QcffICNGzeqb9fW1iI5ORmfffYZYmJi9D6nf//+6NOnD44ePYqIiAgAQHNzM44fP46cnBz1dg0NDUhOToarqysKCwu5tAoRERGRAaJdsxcYGKh128PDAwAwcOBA9O3bV33/4MGDsWnTJkybNg0KhQJvv/02srOz8cILL+CFF15AdnY2unXrhl/84hcAnrboTZgwAY8ePcInn3yChoYG9YCLXr16QalUinVIREQOz1irX8FX4Z3at6EpVToy2bOtsfuWnJndV9AoLy/XmhB5xYoV+Omnn7Bw4ULcu3cPMTExKCoqgqenJwDgwoUL+OabbwAAgwYN0trX9evXERQUZLOyExFJiaGpQ1KT/ve7ZiA0d5CFobVzHSVYOfqAESKxiT7PniPifEFERJbTDILmrNnrKGEP6HwAdfbRuDxvSpvdW/aIiEgaNFsGNVsDNTniHIKO1tJIZGsMe0REZDX2mEPQEH3zA3aEs7fqkfSxG5fN0UREDsPagdAagY9hj+dNqWPLHhEROQxrtgxqdt92dIAGB3aQHDDsERGRJFgSBHUngdb93ZxWPgY9kgt247I5mojIKZjTMqg7F9/HSc8bnLLGmfC8KW1O2bLXlm/bJmMmIiL5u7nqusHH+r4bBAB41NgKoenpOeL/xvjhUWMrzxX43/nSCduHZMEpW/Zu3bqFgIAAexeDiIhIUm7evKm1ChZJg1OGPZVKhdraWnh6ekKhUIj2Og0NDQgICMDNmzfZ7C0C1q+4WL/iYv2Ki/VrXYIg4MGDB/D390eXLl3sXRyykFN243bp0sWm/5l4eXnxy0ZErF9xsX7FxfoVF+vXerp3727vIlAHMZ4TERERyRjDHhEREZGMMeyJyNXVFevWrYOrq6u9iyJLrF9xsX7FxfoVF+uX6H+ccoAGERERkbNgyx4RERGRjDHsEREREckYwx4RERGRjDHsEREREckYw56ZTpw4gcmTJ8Pf3x8KhQIHDx40un1xcTEUCkW7n3/+859a2xUUFCA0NBSurq4IDQ3FgQMHRDwKxyVG/X700UdISEiAt7c3vL29MX78eJw7d07kI3FMYr1/2+zbtw8KhQJTp061fuElQKz6vX//PjIyMuDn5wc3NzcMGTIER44cEfFIHJNY9btt2zaEhITA3d0dAQEBWLp0KR4/fizikRDZh1OuoNERjY2NGDZsGObMmYPU1FSzn1deXq41e3uvXr3Uv585cwbTp0/Hhg0bMG3aNBw4cADp6ek4deoUYmJirFp+RydG/RYXF+PnP/85YmNj4ebmhtzcXEyYMAFXr17F888/b9XyOzox6rdNTU0Nli9fjoSEBKuUVYrEqN/m5ma8/PLL6N27Nz7//HP07dsXN2/ehKenp1XLLgVi1O+ePXuwatUqfPzxx4iNjUVFRQVmz54NAHj//fetVnYihyCQxQAIBw4cMLrN119/LQAQ7t27Z3Cb9PR0YeLEiVr3JScnCzNmzLBCKaXLWvWrq6WlRfD09BR2797duQJKnDXrt6WlRYiLixP++Mc/CrNmzRJSUlKsVk6pslb9/v73vxcGDBggNDc3W7eAEmet+s3IyBDGjRundd+yZcuE+Ph4K5SSyLGwG1dkERER8PPzQ1JSEr7++mutx86cOYMJEyZo3ZecnIySkhJbFlHSjNWvrkePHuHJkyfo0aOHjUonfabqd/369ejVqxd++ctf2qF00mesfgsLCzFq1ChkZGTA19cXQ4cORXZ2NlpbW+1UWukxVr/x8fG4cOGC+tKO6upqHDlyBJMmTbJHUYlExW5ckfj5+WH79u2IjIxEU1MT/vSnPyEpKQnFxcUYPXo0AKCurg6+vr5az/P19UVdXZ09iiwp5tSvrlWrVuH555/H+PHjbVxa6TGnfk+fPo0dO3agtLTUvoWVIHPqt7q6GseOHcNrr72GI0eO4Nq1a8jIyEBLSwvWrl1r5yNwbObU74wZM/Df//4X8fHxEAQBLS0tWLBgAVatWmXn0hNZH8OeSEJCQhASEqK+PWrUKNy8eRNbtmzRCiMKhULreYIgtLuP2jO3ftvk5uZi7969KC4uhpubmy2LKkmm6vfBgweYOXMmPvroI/j4+NixpNJkzvtXpVKhd+/e2L59O5RKJSIjI1FbW4vNmzcz7JlgTv0WFxcjKysLv/vd7xATE4PKykosWbIEfn5+WLNmjb2KTiQKduPa0EsvvYRr166pb/fp06ddK97t27fbtfaReXTrt82WLVuQnZ2NoqIihIeH26Fk8qBZv1VVVbhx4wYmT54MFxcXuLi4IC8vD4WFhXBxcUFVVZWdSys9uu9fPz8/BAcHQ6lUqu8bMmQI6urq0NzcbI8iSppu/a5Zswavv/465s6di7CwMEybNg3Z2dnYtGkTVCqVHUtKZH0MezZ06dIl+Pn5qW+PGjUKR48e1dqmqKgIsbGxti6aLOjWLwBs3rwZGzZswN/+9jdERUXZqWTyoFm/gwcPxuXLl1FaWqr+mTJlCsaOHYvS0lIEBATYubTSo/v+jYuLQ2VlpVbwqKiogJ+fH5555hl7FFHSdOv30aNH6NJF+xSoVCohCAIELhlPMsNuXDM9fPgQlZWV6tvXr19HaWkpevTogcDAQKxevRo//PAD8vLyADydvykoKAgvvvgimpub8cknn6CgoAAFBQXqfSxZsgSjR49GTk4OUlJScOjQIXz55Zc4deqUzY/P3sSo39zcXKxZswaffvopgoKC1K2oHh4e8PDwsO0B2pm169fNzQ1Dhw7Veo3nnnsOANrd7wzEeP8uWLAAv/3tb7FkyRIsWrQI165dQ3Z2NhYvXmzz47M3Mep38uTJ2Lp1KyIiItTduGvWrMGUKVO0WlOJZMG+g4Glo20ov+7PrFmzBEEQhFmzZgljxoxRb5+TkyMMHDhQcHNzE7y9vYX4+HjhL3/5S7v95ufnCyEhIULXrl2FwYMHCwUFBTY6IsciRv3269dP7z7XrVtnuwNzEGK9fzU589QrYtVvSUmJEBMTI7i6ugoDBgwQsrKyhJaWFhsdleMQo36fPHkiZGZmqrcLCAgQFi5caNF0TkRSoRAEtlcTERERyRWv2SMiIiKSMYY9IiIiIhlj2CMiIiKSMYY9IiIiIhlj2CMiIiKSMYY9IiIiIhlj2CMiIiKSMYY9IiIiGThx4gQmT54Mf39/KBQKHDx40OJ9CIKALVu2IDg4GK6urggICEB2drb1C0s2xeXSiIiIZKCxsRHDhg3DnDlzkJqa2qF9LFmyBEVFRdiyZQvCwsJQX1+PH3/80colJVvjChpEREQyo1AocODAAUydOlV9X3NzM37zm99gz549uH//PoYOHYqcnBwkJiYCAP7xj38gPDwcV65cQUhIiH0KTqJgNy4REZETmDNnDk6fPo19+/ahrKwMr776KiZOnIhr164BAA4fPowBAwbgiy++QP/+/REUFIS5c+fi7t27di45dRbDHhERkcxVVVVh7969yM/PR0JCAgYOHIjly5cjPj4eO3fuBABUV1ejpqYG+fn5yMvLw65du3DhwgWkpaXZufTUWbxmj4iISOYuXrwIQRAQHBysdX9TUxN69uwJAFCpVGhqakJeXp56ux07diAyMhLl5eXs2pUwhj0iIiKZU6lUUCqVuHDhApRKpdZjHh4eAAA/Pz+4uLhoBcIhQ4YAAP71r38x7EkYwx4REZHMRUREoLW1Fbdv30ZCQoLebeLi4tDS0oKqqioMHDgQAFBRUQEA6Nevn83KStbH0bhEREQy8PDhQ1RWVgJ4Gu62bt2KsWPHokePHggMDMTMmTNx+vRpvPfee4iIiMCPP/6IY8eOISwsDK+88gpUKhVGjhwJDw8PbNu2DSqVChkZGfDy8kJRUZGdj446g2GPiIhIBoqLizF27Nh298+aNQu7du3CkydPsHHjRuTl5eGHH35Az549MWrUKLz77rsICwsDANTW1mLRokUoKirCs88+i5/97Gd477330KNHD1sfDlkRwx4RERGRjHHqFSIiIiIZY9gjIiIikjGGPSIiIiIZY9gjIiIikjGGPSIiIiIZY9gjIiIikjGGPSIiIiIZY9gjIiIikjGGPSIiIiIZY9gjIiIikjGGPSIiIiIZY9gjIiIikrH/D0gX0Q+UFbGfAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 1, figsize=(7, 7), sharey=True)\n", + "\n", + "# Set up the colormap\n", + "colour_scheme = get_colour_scheme(level)\n", + "cmap, norm = lc_colourmap(colour_scheme)\n", + "im = cog_roi.plot(cmap=cmap, norm=norm, ax=ax, add_labels=False, add_colorbar=False)\n", + "ax.set_title(f\"Land Cover COG, overview={overview_level}\", fontsize=12)\n", + "make_colourbar(fig, ax, measurement=level, labelsize=7, horizontal=False);" + ] + }, + { + "cell_type": "markdown", + "id": "c3cd59fa-46fa-4fae-b7c0-ecfbc6f5d8da", + "metadata": {}, + "source": [ + "## Analyse the data\n", + "Now we can include the data in any workflow that involves data arrays. Here we will perform a simple value count as an example and observe the extent of each land cover class in our bounding box\n", + "\n", + "You can see the name corresponding to each class value in the [DEA Knowledge Hub](https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/?tab=description#level-4)." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "ec741fe2-0060-4696-9854-bc9a15783f2e", + "metadata": {}, + "outputs": [], + "source": [ + "# First we convert our array to a 1D vector and mask out NaN values\n", + "data_1d = cog_roi.values.flatten()\n", + "data_1d = data_1d[~np.isnan(data_1d)]" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "fc0495a2-65e3-45c6-8781-e89a1a2a562a", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's count the number of pixels in each class\n", + "classes, counts = np.unique(data_1d, return_counts=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "de465247-9b26-4263-abc4-003e1bc8d426", + "metadata": {}, + "outputs": [], + "source": [ + "# Define the pixel size of the selected overview\n", + "pixel_size_m = cog_array.odc.geobox.resolution.x\n", + "\n", + "# Calculate the area of one pixel\n", + "pixel_area_km2 = (pixel_size_m ** 2) / 1e6 \n", + "\n", + "# Calculate the total area of each class\n", + "area_km2 = counts * pixel_area_km2" + ] + }, + { + "cell_type": "markdown", + "id": "f57a52c5-66bc-4fcf-a5f9-716c359eed59", + "metadata": {}, + "source": [ + "We will visualise the areal extent of the Land Cover classes with a bar chart." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3d64b3f7-a3a7-42f5-897d-9bb061da1251", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x_pos = np.arange(len(classes)) # positions of x axis ticks\n", + "\n", + "plt.figure(figsize=(8,4))\n", + "\n", + "# Plot area in km² per class\n", + "plt.bar(x_pos, area_km2)\n", + "\n", + "plt.xlabel('Class')\n", + "plt.ylabel('Area (km²)')\n", + "plt.title(f'Area of DEA Land Cover Classes in Region of Interest\\nCOG overview level: {overview_level}')\n", + "\n", + "plt.xticks(x_pos, [int(c) for c in classes], rotation=90)\n", + "plt.grid(alpha=0.5)\n", + "plt.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "id": "e0cebbee-c1a6-4e32-a496-73a568129447", + "metadata": {}, + "source": [ + "## Compare different overviews\n", + "\n", + "In this final section, we will quickly explore the differences between overviews to raise awareness on the caveats associated with using a resampled product, particularly when representing categorical data." + ] + }, + { + "cell_type": "markdown", + "id": "8d3bbe58-c5a0-414c-b641-069808efe40c", + "metadata": {}, + "source": [ + "### Load two different levels\n", + "\n", + "> Note, To load the native resolution, we need to remove the `overview_level` parameter" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6859cd67-a80b-4702-828e-e2d023d2ab31", + "metadata": {}, + "outputs": [], + "source": [ + "# Get the data in the native resolution (30 m) and crop it to our ROI\n", + "fine_cog = rioxarray.open_rasterio(cog_url)\n", + "fine_cog = crop(fine_cog, roi_geom)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "25109851-3564-465e-93d7-2d3cb1fbc425", + "metadata": {}, + "outputs": [], + "source": [ + "# Repeat for a higher overview \n", + "coarse_overview_level = 3\n", + "coarse_cog = rioxarray.open_rasterio(cog_url, overview_level=coarse_overview_level)\n", + "coarse_cog= crop(coarse_cog, roi_geom)" + ] + }, + { + "cell_type": "markdown", + "id": "54e6dc1b-0a8f-4f0d-ba8b-1589da8cb84f", + "metadata": {}, + "source": [ + "### Plot side by side\n", + "\n", + "We can observe how resampling to a much coarser resolution has changed the pixel values and the look of the landscape." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "6728b0b1-d331-41ed-bbf1-2ded005921fa", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 5.5), sharey=True)\n", + "\n", + "# Set up the colour map\n", + "colour_scheme = get_colour_scheme(level)\n", + "cmap, norm = lc_colourmap(colour_scheme)\n", + "im = fine_cog.plot(cmap=cmap, norm=norm, ax=ax[0], add_labels=False, add_colorbar=False)\n", + "ax[0].set_title(f\"Land Cover res={int(fine_cog.odc.geobox.resolution.x)}m\", fontsize=12);\n", + "\n", + "im = coarse_cog.plot(cmap=cmap, norm=norm, ax=ax[1], add_labels=False, add_colorbar=False)\n", + "ax[1].set_title(f\"Land Cover res={int(coarse_cog.odc.geobox.resolution.x)}m\", fontsize=12);\n", + "make_colourbar(fig, ax[1], measurement=level, labelsize=7, horizontal=False)\n", + "\n", + "for a in ax.ravel():\n", + " a.axes.get_xaxis().set_ticks([])\n", + " a.axes.get_yaxis().set_ticks([]);" + ] + }, + { + "cell_type": "markdown", + "id": "e949f9dc-5d63-48e6-b2a1-dbb9c06c3d03", + "metadata": {}, + "source": [ + "### Find the area of each class" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "34908166-0ac5-4bea-ab3e-d06bb5fbadf1", + "metadata": {}, + "outputs": [], + "source": [ + "# Finest overview (30 m spatial resolution)\n", + "fine_data_1d = fine_cog.values.flatten()\n", + "fine_data_1d = fine_data_1d[~np.isnan(fine_data_1d)]\n", + "\n", + "fine_classes, fine_counts = np.unique(fine_data_1d, return_counts=True)\n", + "\n", + "fine_pixel_size = fine_cog.odc.geobox.resolution.x # effectively this is 30 m for the overview level 0\n", + "fine_pixel_area_km2 = (fine_pixel_size ** 2) / 1e6\n", + "\n", + "# Coarsest overview\n", + "coarse_data_1d = coarse_cog.values.flatten()\n", + "coarse_data_1d = coarse_data_1d[~np.isnan(coarse_data_1d)]\n", + "\n", + "coarse_classes, coarse_counts = np.unique(coarse_data_1d, return_counts=True)\n", + "\n", + "coarse_pixel_size = coarse_cog.odc.geobox.resolution.x\n", + "coarse_pixel_area_km2 = (coarse_pixel_size ** 2) / 1e6" + ] + }, + { + "cell_type": "markdown", + "id": "8200e4d1-19e7-4c84-b233-8a5e1a3e43bd", + "metadata": {}, + "source": [ + "### Plot the difference in class areas\n", + "\n", + "Finally, by plotting the class extents of the two overviews side by side, we observe that the extent of some less common classes decreases at the coarser resolution, while the area of other more widespread classes increases.\n", + "\n", + "Class area is expressed as _relative_ to the total class area in the fine resolution overiew (i.e. the 30m resolution dataset). This means all the values for the `Native resolution` columns will be 1, while the coarse overviews will be either greater or lesser than 1 depending on how the area has changed following the resampling.\n", + "\n", + "It's important to note that a high _relative_ change does not necessarily correspond to a substantial shift in _absolute_ extent. Rare or sparsely distributed classes with very small _absolute_ areas can show large _relative_ changes at coarser overviews, even though their overall contribution to the landscape remains minimal." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "fe0eba98-c402-43a2-a661-85534c0f15c6", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Combine unique classes from fine and coarse overviews into a single sorted array\n", + "# Remove no-data counts\n", + "all_classes = np.union1d(fine_classes, coarse_classes)[0:-1]\n", + "\n", + "# Create dictionaries to store counts by class\n", + "fine_dict = dict(zip(fine_classes, fine_counts))\n", + "coarse_dict = dict(zip(coarse_classes, coarse_counts))\n", + "\n", + "# Align counts to all_classes. Use 0 if class not in one overview\n", + "fine_aligned_counts = np.array([fine_dict.get(c, 0) for c in all_classes])\n", + "coarse_aligned_counts = np.array([coarse_dict.get(c, 0) for c in all_classes])\n", + "\n", + "# Convert counts to area (km2)\n", + "fine_aligned_area_km2 = fine_aligned_counts * fine_pixel_area_km2\n", + "coarse_aligned_area_km2 = coarse_aligned_counts * coarse_pixel_area_km2\n", + "\n", + "# Normalise area to the fine resolution. This allows to visualise the relative change of the classes' extent\n", + "coarse_aligned_area_km2 = coarse_aligned_area_km2/fine_aligned_area_km2\n", + "fine_aligned_area_km2 = fine_aligned_area_km2/fine_aligned_area_km2\n", + "\n", + "x_pos = np.arange(len(all_classes))\n", + "width = 0.35\n", + "\n", + "# Plot bars side-by-side comparing fine and coarse overview areas\n", + "plt.figure(figsize=(9, 5))\n", + "plt.bar(\n", + " x_pos - width / 2, # -width/2 shifts bars to the left\n", + " fine_aligned_area_km2,\n", + " width,\n", + " label=f\"Native resolution\",\n", + ") \n", + "\n", + "plt.bar(\n", + " x_pos + width / 2,\n", + " coarse_aligned_area_km2,\n", + " width,\n", + " label=f\"Overview level: {coarse_overview_level}\",\n", + ") \n", + "\n", + "plt.xlabel(\"Class\")\n", + "plt.ylabel(\"Area (relative to area at native resolution)\")\n", + "plt.title(\"Comparison of Land Cover area by class\\nnative vs coarse overview levels\")\n", + "plt.xticks(x_pos, [int(c) for c in all_classes])\n", + "plt.legend(loc='lower right')\n", + "plt.grid(alpha=0.5)\n", + "plt.tight_layout();" + ] + }, + { + "cell_type": "markdown", + "id": "b1f379fe-3e92-45b3-a10a-e92f5689c6b5", + "metadata": {}, + "source": [ + "***\n", + "\n", + "## Additional information\n", + "\n", + "**License:** The code in this notebook is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0). \n", + "Digital Earth Australia data is licensed under the [Creative Commons by Attribution 4.0](https://creativecommons.org/licenses/by/4.0/) license.\n", + "\n", + "**Contact:** If you need assistance, please post a question on the [Open Data Cube Discord chat](https://discord.com/invite/4hhBQVas5U) or on the [GIS Stack Exchange](https://gis.stackexchange.com/questions/ask?tags=open-data-cube) using the `open-data-cube` tag (you can view previously asked questions [here](https://gis.stackexchange.com/questions/tagged/open-data-cube)).\n", + "If you would like to report an issue with this notebook, you can file one on [GitHub](https://github.com/GeoscienceAustralia/dea-notebooks).\n", + "\n", + "**Last modified:** October 2025" + ] + }, + { + "cell_type": "markdown", + "id": "583eb9ce-b2b5-478c-a220-a384c8157d2d", + "metadata": {}, + "source": [ + "## Tags\n", + "" + ] + }, + { + "cell_type": "raw", + "id": "c735cca0-a4e0-4119-9c02-7ad9990e93a7", + "metadata": {}, + "source": [ + "**Tags**: :index:`sandbox compatible`, :index:`Landsat`, :index:`COG`, :index:`mosaic`, :index:`continental`, :index:`overviews`, :index:`pyramids`" + ] + } + ], + "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": "3.10.18" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": {}, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/How_to_guides/README.rst b/How_to_guides/README.rst index 2849ea782..34af45281 100644 --- a/How_to_guides/README.rst +++ b/How_to_guides/README.rst @@ -10,6 +10,8 @@ A recipe book of simple code examples demonstrating how to perform common geospa Animated_timeseries.ipynb ARD_overpass_predictor.ipynb Calculating_band_indices.ipynb + COG_mosaics.ipynb + COG_overviews.ipynb Contour_extraction.ipynb Continental_scale_animations.ipynb Detecting_seasonality.ipynb