diff --git a/notebooks/Customize and Access NSIDC Data.ipynb b/notebooks/Customize and Access NSIDC Data.ipynb new file mode 100644 index 0000000..b1dddc1 --- /dev/null +++ b/notebooks/Customize and Access NSIDC Data.ipynb @@ -0,0 +1,923 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Customize and Access NSIDC DAAC Data\n", + "\n", + "This notebook will walk you through how to programmatically access data from the NASA National Snow and Ice Data Center Distributed Active Archive Center (NSIDC DAAC) using spatial and temporal filters, as well as how to request customization services including subsetting, reformatting, and reprojection. No Python experience is necessary; each code cell will prompt you with the information needed to configure your data request. The notebook will print the resulting API command that can be used in a command line, browser, or in Python as executed below." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import packages\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + } + }, + "outputs": [], + "source": [ + "import requests\n", + "import getpass\n", + "import socket \n", + "import json\n", + "import zipfile\n", + "import io\n", + "import math\n", + "import os\n", + "import shutil\n", + "import pprint\n", + "import re\n", + "import time\n", + "import geopandas as gpd\n", + "import fiona\n", + "import matplotlib.pyplot as plt\n", + "# To read KML files with geopandas, we will need to enable KML support in fiona (disabled by default)\n", + "fiona.drvsupport.supported_drivers['LIBKML'] = 'rw'\n", + "from shapely.geometry import Polygon, mapping\n", + "from shapely.geometry.polygon import orient\n", + "from statistics import mean\n", + "from requests.auth import HTTPBasicAuth\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Input Earthdata Login credentials\n", + "An Earthdata Login account is required to access data from the NSIDC DAAC. If you do not already have an Earthdata Login account, visit http://urs.earthdata.nasa.gov to register." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Earthdata Login user name: tonyzhang\n" + ] + } + ], + "source": [ + "# tonyzhang\n", + "uid = input('Earthdata Login user name: ') # Enter Earthdata Login user name" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Earthdata Login password: ········\n" + ] + } + ], + "source": [ + "\n", + "pswd = getpass.getpass('Earthdata Login password: ') # Enter Earthdata Login password" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Email address associated with Earthdata Login account: zjd0721@uw.edu\n" + ] + } + ], + "source": [ + "# uw email\n", + "email = input('Email address associated with Earthdata Login account: ') # Enter Earthdata login email " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select data set and determine version number\n", + "\n", + "Data sets are selected by data set IDs (e.g. MOD10A1), whic are also referred to as a \"short name\". These short names are located at the top of each NSIDC data set landing page in gray above the full title." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Input short name, e.g. ATL03, here: ASO_50M_SWE\n" + ] + } + ], + "source": [ + "# Input data set short name (e.g. ATL03) of interest here.\n", + "# ASO_50M_SWE\n", + "short_name = input('Input short name, e.g. ATL03, here: ')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The most recent version of ASO_50M_SWE is 1\n" + ] + } + ], + "source": [ + "# Get json response from CMR collection metadata\n", + "\n", + "params = {\n", + " 'short_name': short_name\n", + "}\n", + "\n", + "cmr_collections_url = 'https://cmr.earthdata.nasa.gov/search/collections.json'\n", + "response = requests.get(cmr_collections_url, params=params)\n", + "results = json.loads(response.content)\n", + "\n", + "# Find all instances of 'version_id' in metadata and print most recent version number\n", + "\n", + "versions = [el['version_id'] for el in results['feed']['entry']]\n", + "latest_version = max(versions)\n", + "print('The most recent version of ', short_name, ' is ', latest_version)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select time period of interest" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Input start date in yyyy-MM-dd format: 2013-04-03\n", + "Input start time in HH:mm:ss format: 00:00:00\n", + "Input end date in yyyy-MM-dd format: 2019-07-17\n", + "Input end time in HH:mm:ss format: 00:00:00\n" + ] + } + ], + "source": [ + "#Input temporal range\n", + "# 2013-04-03\n", + "# 00:00:00\n", + "# 2019-07-17\n", + "# 00:00:00\n", + "\n", + "start_date = input('Input start date in yyyy-MM-dd format: ')\n", + "start_time = input('Input start time in HH:mm:ss format: ')\n", + "end_date = input('Input end date in yyyy-MM-dd format: ')\n", + "end_time = input('Input end time in HH:mm:ss format: ')\n", + "\n", + "temporal = start_date + 'T' + start_time + 'Z' + ',' + end_date + 'T' + end_time + 'Z'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select area of interest\n", + "\n", + "#### Select bounding box or shapefile entry\n", + "\n", + "For all data sets, you can enter a bounding box to be applied to your file search. If you are interested in ICESat-2 data, you may also apply a spatial boundary based on a vector-based spatial data file." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdin", + "output_type": "stream", + "text": [ + "Input spatial coordinates in the following order: lower left longitude,lower left latitude,upper right longitude,upper right latitude. Leave blank if you wish to provide a vector-based spatial file for ICESat-2 search and subsetting: \n" + ] + } + ], + "source": [ + "# Enter spatial coordinates in decimal degrees, with west longitude and south latitude reported as negative degrees. Do not include spaces between coordinates.\n", + "# Example over the state of Colorado: -109,37,-102,41\n", + "\n", + "bounding_box = input('Input spatial coordinates in the following order: lower left longitude,lower left latitude,upper right longitude,upper right latitude. Leave blank if you wish to provide a vector-based spatial file for ICESat-2 search and subsetting:')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Shapefile input for ICESat-2 search and subset\n", + "\n", + "For ICESat-2 data only, you may also provide a geospatial file, including Esri Shapefile or KML/KMZ, to be applied to both your file search and subsetting request. Note that currently only files containing a single shape can be applied to the search. \n", + "\n", + "An example shapefile 'jacobshavn_bnd.shp' is included in this repository under the Shapefile_examples folder, demonstrated below. A shapefile of Pine Island glacier ('glims_polygons.shp') is also available, which was acquired from the NSIDC Global Land Ice Measurements from Space (GLIMS) database. Direct download access available from http://www.glims.org/maps/info.html?anlys_id=528486. If you would like to use a different geospatial file, you will need to adjust the filepath in the code block below." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Simplified polygon coordinates based on shapefile input: -119.58908883625021,38.18645781018878,-119.79955181625472,37.955164012608684,-119.42800694711637,37.8656690569239,-119.26039287108506,37.74143900630584,-119.19951408097548,37.8846229968368,-119.30914915468168,37.94606435409836,-119.31077632471995,38.04480313690451,-119.58908883625021,38.18645781018878\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_109/3896187982.py:29: UserWarning: Geometry is in a geographic CRS. Results from 'buffer' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", + "\n", + " buffer = gdf.buffer(50) #create buffer for plot bounds\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# aoi value used for filtering and subsetting logic below\n", + "if bounding_box == '': aoi = '2'\n", + "else: aoi = '1'\n", + "\n", + "if aoi == '2':\n", + " # Use geopandas to read in polygon file\n", + " # Note: a KML or geojson, or almost any other vector-based spatial data format could be substituted here.\n", + "\n", + " shapefile_filepath = str(os.getcwd() + '/Tuolumne Basin Boundary/Tuolumne_cord.shp')\n", + "\n", + " # Go from geopandas GeoDataFrame object to an input that is readable by CMR\n", + " gdf = gpd.read_file(shapefile_filepath)\n", + "\n", + " # CMR polygon points need to be provided in counter-clockwise order. The last point should match the first point to close the polygon.\n", + " \n", + " # Simplify polygon for complex shapes in order to pass a reasonable request length to CMR. The larger the tolerance value, the more simplified the polygon.\n", + " # Orient counter-clockwise: CMR polygon points need to be provided in counter-clockwise order. The last point should match the first point to close the polygon.\n", + " \n", + " poly = orient(gdf.simplify(0.05, preserve_topology=False).loc[0],sign=1.0)\n", + " \n", + " geojson = gpd.GeoSeries(poly).to_json() # Convert to geojson\n", + " geojson = geojson.replace(' ', '') #remove spaces for API call\n", + "\n", + " #Format dictionary to polygon coordinate pairs for CMR polygon filtering\n", + " polygon = ','.join([str(c) for xy in zip(*poly.exterior.coords.xy) for c in xy])\n", + "\n", + " print('Simplified polygon coordinates based on shapefile input:', polygon)\n", + " \n", + " buffer = gdf.buffer(50) #create buffer for plot bounds\n", + " envelope = buffer.envelope \n", + " bounds = envelope.bounds\n", + " \n", + " # Load \"Natural Earth” countries dataset, bundled with GeoPandas\n", + " # world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n", + "\n", + " # Overlay glacier outline\n", + " f, ax = plt.subplots(1, figsize=(12, 6))\n", + " # world.plot(ax=ax, facecolor='white', edgecolor='gray')\n", + " gdf.plot(ax=ax, cmap='spring')\n", + " # ax.set_ylim([bounds.miny[0], bounds.maxy[0]])\n", + " # ax.set_xlim([bounds.minx[0], bounds.maxx[0]]);" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Determine how many granules exist over this time and area of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 98 granules of ASO_50M_SWE version 1 over my area and time of interest.\n" + ] + } + ], + "source": [ + "# Create CMR parameters used for granule search. Modify params depending on bounding_box or polygon input.\n", + "\n", + "granule_search_url = 'https://cmr.earthdata.nasa.gov/search/granules'\n", + "\n", + "if aoi == '1':\n", + "# bounding box input:\n", + " search_params = {\n", + " 'short_name': short_name,\n", + " 'version': latest_version,\n", + " 'temporal': temporal,\n", + " 'page_size': 100,\n", + " 'page_num': 1,\n", + " 'bounding_box': bounding_box\n", + " }\n", + "else:\n", + " # If polygon file input:\n", + " search_params = {\n", + " 'short_name': short_name,\n", + " 'version': latest_version,\n", + " 'temporal': temporal,\n", + " 'page_size': 100,\n", + " 'page_num': 1,\n", + " 'polygon': polygon,\n", + " }\n", + "\n", + "granules = []\n", + "headers={'Accept': 'application/json'}\n", + "while True:\n", + " response = requests.get(granule_search_url, params=search_params, headers=headers)\n", + " results = json.loads(response.content)\n", + "\n", + " if len(results['feed']['entry']) == 0:\n", + " # Out of results, so break out of loop\n", + " break\n", + "\n", + " # Collect results and increment page_num\n", + " granules.extend(results['feed']['entry'])\n", + " search_params['page_num'] += 1\n", + "\n", + "print('There are', len(granules), 'granules of', short_name, 'version', latest_version, 'over my area and time of interest.')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Determine the average size of those granules as well as the total volume" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The average size of each granule is 6.39 MB and the total size of all 98 granules is 626.30 MB\n" + ] + } + ], + "source": [ + "granule_sizes = [float(granule['granule_size']) for granule in granules]\n", + "\n", + "print(f'The average size of each granule is {mean(granule_sizes):.2f} MB and the total size of all {len(granules)} granules is {sum(granule_sizes):.2f} MB')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Note that subsetting, reformatting, or reprojecting can alter the size of the granules if those services are applied to your request." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select the subsetting, reformatting, and reprojection services enabled for your data set of interest." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The NSIDC DAAC supports customization services on many of our NASA Earthdata mission collections. Let's discover whether or not our data set has these services available. If services are available, we will also determine the specific service options supported for this data set and select which of these services we want to request. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Query the service capability endpoint to gather service information needed below" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "# Query service capability URL \n", + "\n", + "from xml.etree import ElementTree as ET\n", + "\n", + "capability_url = f'https://n5eil02u.ecs.nsidc.org/egi/capabilities/{short_name}.{latest_version}.xml'\n", + "\n", + "# Create session to store cookie and pass credentials to capabilities url\n", + "\n", + "session = requests.session()\n", + "s = session.get(capability_url)\n", + "response = session.get(s.url,auth=(uid,pswd))\n", + "\n", + "root = ET.fromstring(response.content)\n", + "\n", + "#collect lists with each service option\n", + "\n", + "subagent = [subset_agent.attrib for subset_agent in root.iter('SubsetAgent')]\n", + "if len(subagent) > 0 :\n", + "\n", + " # variable subsetting\n", + " variables = [SubsetVariable.attrib for SubsetVariable in root.iter('SubsetVariable')] \n", + " variables_raw = [variables[i]['value'] for i in range(len(variables))]\n", + " variables_join = [''.join(('/',v)) if v.startswith('/') == False else v for v in variables_raw] \n", + " variable_vals = [v.replace(':', '/') for v in variables_join]\n", + "\n", + " # reformatting\n", + " formats = [Format.attrib for Format in root.iter('Format')]\n", + " format_vals = [formats[i]['value'] for i in range(len(formats))]\n", + " format_vals.remove('')\n", + "\n", + " # reprojection options\n", + " projections = [Projection.attrib for Projection in root.iter('Projection')]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select subsetting, reformatting, and reprojection service options, if available." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No services exist for ASO_50M_SWE version 1\n" + ] + } + ], + "source": [ + "#print service information depending on service availability and select service options\n", + " \n", + "if len(subagent) < 1 :\n", + " print('No services exist for', short_name, 'version', latest_version)\n", + " agent = 'NO'\n", + " bbox = ''\n", + " time_var = ''\n", + " reformat = ''\n", + " projection = ''\n", + " projection_parameters = ''\n", + " coverage = ''\n", + " Boundingshape = ''\n", + "else:\n", + " agent = ''\n", + " subdict = subagent[0]\n", + " if subdict['spatialSubsetting'] == 'true' and aoi == '1':\n", + " Boundingshape = ''\n", + " ss = input('Subsetting by bounding box, based on the area of interest inputted above, is available. Would you like to request this service? (y/n)')\n", + " if ss == 'y': bbox = bounding_box\n", + " else: bbox = '' \n", + " if subdict['spatialSubsettingShapefile'] == 'true' and aoi == '2':\n", + " bbox = ''\n", + " ps = input('Subsetting by geospatial file (Esri Shapefile, KML, etc.) is available. Would you like to request this service? (y/n)')\n", + " if ps == 'y': Boundingshape = geojson\n", + " else: Boundingshape = '' \n", + " if subdict['temporalSubsetting'] == 'true':\n", + " ts = input('Subsetting by time, based on the temporal range inputted above, is available. Would you like to request this service? (y/n)')\n", + " if ts == 'y': time_var = start_date + 'T' + start_time + ',' + end_date + 'T' + end_time \n", + " else: time_var = ''\n", + " else: time_var = ''\n", + " if len(format_vals) > 0 :\n", + " print('These reformatting options are available:', format_vals)\n", + " reformat = input('If you would like to reformat, copy and paste the reformatting option you would like (make sure to omit quotes, e.g. GeoTIFF), otherwise leave blank.')\n", + " if reformat == 'n': reformat = '' # Catch user input of 'n' instead of leaving blank\n", + " else: \n", + " reformat = ''\n", + " projection = ''\n", + " projection_parameters = ''\n", + " if len(projections) > 0:\n", + " valid_proj = [] # select reprojection options based on reformatting selection\n", + " for i in range(len(projections)):\n", + " if 'excludeFormat' in projections[i]:\n", + " exclformats_str = projections[i]['excludeFormat'] \n", + " exclformats_list = exclformats_str.split(',')\n", + " if ('excludeFormat' not in projections[i] or reformat not in exclformats_list) and projections[i]['value'] != 'NO_CHANGE': valid_proj.append(projections[i]['value'])\n", + " if len(valid_proj) > 0:\n", + " print('These reprojection options are available with your requested format:', valid_proj)\n", + " projection = input('If you would like to reproject, copy and paste the reprojection option you would like (make sure to omit quotes), otherwise leave blank.')\n", + " # Enter required parameters for UTM North and South\n", + " if projection == 'UTM NORTHERN HEMISPHERE' or projection == 'UTM SOUTHERN HEMISPHERE': \n", + " NZone = input('Please enter a UTM zone (1 to 60 for Northern Hemisphere; -60 to -1 for Southern Hemisphere):')\n", + " projection_parameters = str('NZone:' + NZone)\n", + " else: projection_parameters = ''\n", + " else: \n", + " print('No reprojection options are supported with your requested format')\n", + " projection = ''\n", + " projection_parameters = ''\n", + " else:\n", + " print('No reprojection options are supported with your requested format')\n", + " projection = ''\n", + " projection_parameters = ''" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because variable subsetting can include a long list of variables to choose from, we will decide on variable subsetting separately from the service options above." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "# Select variable subsetting\n", + "\n", + "if len(subagent) > 0 :\n", + " if len(variable_vals) > 0:\n", + " v = input('Variable subsetting is available. Would you like to subset a selection of variables? (y/n)')\n", + " if v == 'y':\n", + " print('The', short_name, 'variables to select from include:')\n", + " print(*variable_vals, sep = \"\\n\") \n", + " coverage = input('If you would like to subset by variable, copy and paste the variables you would like separated by comma (be sure to remove spaces and retain all forward slashes: ')\n", + " else: coverage = ''\n", + "\n", + "#no services selected\n", + "if reformat == '' and projection == '' and projection_parameters == '' and coverage == '' and time_var == '' and bbox == '' and Boundingshape == '':\n", + " agent = 'NO'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Select data access configurations\n", + "\n", + "The data request can be accessed asynchronously or synchronously. The asynchronous option will allow concurrent requests to be queued and processed without the need for a continuous connection. Those requested orders will be delivered to the specified email address, or they can be accessed programmatically as shown below. Synchronous requests will automatically download the data as soon as processing is complete. The granule limits differ between these two options:\n", + "\n", + "Maximum granules per synchronous request = 100 \n", + "\n", + "Maximum granules per asynchronous request = 2000 \n", + "\n", + "We will set the access configuration depending on the number of granules requested. For requests over 2000 granules, we will produce multiple API endpoints for each 2000-granule order. Please note that synchronous requests may take a long time to complete depending on request parameters, so the number of granules may need to be adjusted if you are experiencing performance issues. The `page_size` parameter can be used to adjust this number. " + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There will be 1 total order(s) processed for our ASO_50M_SWE request.\n" + ] + } + ], + "source": [ + "#Set NSIDC data access base URL\n", + "base_url = 'https://n5eil02u.ecs.nsidc.org/egi/request'\n", + "\n", + "#Set the request mode to asynchronous if the number of granules is over 100, otherwise synchronous is enabled by default\n", + "if len(granules) > 100:\n", + " request_mode = 'async'\n", + " page_size = 2000\n", + "else: \n", + " page_size = 100\n", + " request_mode = 'stream'\n", + "\n", + "#Determine number of orders needed for requests over 2000 granules. \n", + "page_num = math.ceil(len(granules)/page_size)\n", + "\n", + "print('There will be', page_num, 'total order(s) processed for our', short_name, 'request.')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create the API endpoint \n", + "\n", + "Programmatic API requests are formatted as HTTPS URLs that contain key-value-pairs specifying the service operations that we specified above. The following command can be executed via command line, a web browser, or in Python below. " + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://n5eil02u.ecs.nsidc.org/egi/request?short_name=ASO_50M_SWE&version=1&temporal=2013-04-03T00:00:00Z,2019-07-17T00:00:00Z&polygon=-119.58908883625021,38.18645781018878,-119.79955181625472,37.955164012608684,-119.42800694711637,37.8656690569239,-119.26039287108506,37.74143900630584,-119.19951408097548,37.8846229968368,-119.30914915468168,37.94606435409836,-119.31077632471995,38.04480313690451,-119.58908883625021,38.18645781018878&page_size=100&request_mode=stream&agent=NO&email=zjd0721@uw.edu&page_num=1\n" + ] + } + ], + "source": [ + "if aoi == '1':\n", + "# bounding box search and subset:\n", + " param_dict = {'short_name': short_name, \n", + " 'version': latest_version, \n", + " 'temporal': temporal, \n", + " 'time': time_var, \n", + " 'bounding_box': bounding_box, \n", + " 'bbox': bbox, \n", + " 'format': reformat, \n", + " 'projection': projection, \n", + " 'projection_parameters': projection_parameters, \n", + " 'Coverage': coverage, \n", + " 'page_size': page_size, \n", + " 'request_mode': request_mode, \n", + " 'agent': agent, \n", + " 'email': email, }\n", + "else:\n", + " # If polygon file input:\n", + " param_dict = {'short_name': short_name, \n", + " 'version': latest_version, \n", + " 'temporal': temporal, \n", + " 'time': time_var, \n", + " 'polygon': polygon,\n", + " 'Boundingshape': Boundingshape, \n", + " 'format': reformat, \n", + " 'projection': projection, \n", + " 'projection_parameters': projection_parameters, \n", + " 'Coverage': coverage, \n", + " 'page_size': page_size, \n", + " 'request_mode': request_mode, \n", + " 'agent': agent, \n", + " 'email': email, }\n", + "\n", + "#Remove blank key-value-pairs\n", + "param_dict = {k: v for k, v in param_dict.items() if v != ''}\n", + "\n", + "#Convert to string\n", + "param_string = '&'.join(\"{!s}={!r}\".format(k,v) for (k,v) in param_dict.items())\n", + "param_string = param_string.replace(\"'\",\"\")\n", + "\n", + "#Print API base URL + request parameters\n", + "endpoint_list = [] \n", + "for i in range(page_num):\n", + " page_val = i + 1\n", + " API_request = api_request = f'{base_url}?{param_string}&page_num={page_val}'\n", + " endpoint_list.append(API_request)\n", + "\n", + "print(*endpoint_list, sep = \"\\n\") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Request data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will now download data using the Python requests library. The data will be downloaded directly to this notebook directory in a new Outputs folder. The progress of each order will be reported." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Order: 1\n", + "Requesting...\n", + "HTTP response from order response URL: 200\n", + "Downloading...\n", + "Data request 1 is complete.\n" + ] + } + ], + "source": [ + "# Create an output folder if the folder does not already exist.\n", + "\n", + "path = str(os.getcwd() + '/ASO_50m_lidar_data')\n", + "if not os.path.exists(path):\n", + " os.mkdir(path)\n", + "\n", + "# Different access methods depending on request mode:\n", + "\n", + "if request_mode=='async':\n", + " # Request data service for each page number, and unzip outputs\n", + " for i in range(page_num):\n", + " page_val = i + 1\n", + " print('Order: ', page_val)\n", + "\n", + " # For all requests other than spatial file upload, use get function\n", + " param_dict['page_num'] = page_val\n", + " request = session.get(base_url, params=param_dict)\n", + "\n", + " print('Request HTTP response: ', request.status_code)\n", + "\n", + " # Raise bad request: Loop will stop for bad response code.\n", + " request.raise_for_status()\n", + " print('Order request URL: ', request.url)\n", + " esir_root = ET.fromstring(request.content)\n", + " print('Order request response XML content: ', request.content)\n", + "\n", + " #Look up order ID\n", + " orderlist = [] \n", + " for order in esir_root.findall(\"./order/\"):\n", + " orderlist.append(order.text)\n", + " orderID = orderlist[0]\n", + " print('order ID: ', orderID)\n", + "\n", + " #Create status URL\n", + " statusURL = base_url + '/' + orderID\n", + " print('status URL: ', statusURL)\n", + "\n", + " #Find order status\n", + " request_response = session.get(statusURL) \n", + " print('HTTP response from order response URL: ', request_response.status_code)\n", + "\n", + " # Raise bad request: Loop will stop for bad response code.\n", + " request_response.raise_for_status()\n", + " request_root = ET.fromstring(request_response.content)\n", + " statuslist = []\n", + " for status in request_root.findall(\"./requestStatus/\"):\n", + " statuslist.append(status.text)\n", + " status = statuslist[0]\n", + " print('Data request ', page_val, ' is submitting...')\n", + " print('Initial request status is ', status)\n", + "\n", + " #Continue loop while request is still processing\n", + " while status == 'pending' or status == 'processing': \n", + " print('Status is not complete. Trying again.')\n", + " time.sleep(10)\n", + " loop_response = session.get(statusURL)\n", + "\n", + " # Raise bad request: Loop will stop for bad response code.\n", + " loop_response.raise_for_status()\n", + " loop_root = ET.fromstring(loop_response.content)\n", + "\n", + " #find status\n", + " statuslist = []\n", + " for status in loop_root.findall(\"./requestStatus/\"):\n", + " statuslist.append(status.text)\n", + " status = statuslist[0]\n", + " print('Retry request status is: ', status)\n", + " if status == 'pending' or status == 'processing':\n", + " continue\n", + "\n", + " #Order can either complete, complete_with_errors, or fail:\n", + " # Provide complete_with_errors error message:\n", + " if status == 'complete_with_errors' or status == 'failed':\n", + " messagelist = []\n", + " for message in loop_root.findall(\"./processInfo/\"):\n", + " messagelist.append(message.text)\n", + " print('error messages:')\n", + " pprint.pprint(messagelist)\n", + "\n", + " # Download zipped order if status is complete or complete_with_errors\n", + " if status == 'complete' or status == 'complete_with_errors':\n", + " downloadURL = 'https://n5eil02u.ecs.nsidc.org/esir/' + orderID + '.zip'\n", + " print('Zip download URL: ', downloadURL)\n", + " print('Beginning download of zipped output...')\n", + " zip_response = session.get(downloadURL)\n", + " # Raise bad request: Loop will stop for bad response code.\n", + " zip_response.raise_for_status()\n", + " with zipfile.ZipFile(io.BytesIO(zip_response.content)) as z:\n", + " z.extractall(path)\n", + " print('Data request', page_val, 'is complete.')\n", + " else: print('Request failed.')\n", + " \n", + "else:\n", + " for i in range(page_num):\n", + " page_val = i + 1\n", + " print('Order: ', page_val)\n", + " print('Requesting...')\n", + " request = session.get(base_url, params=param_dict)\n", + " print('HTTP response from order response URL: ', request.status_code)\n", + " request.raise_for_status()\n", + " d = request.headers['content-disposition']\n", + " fname = re.findall('filename=(.+)', d)\n", + " dirname = os.path.join(path,fname[0].strip('\\\"'))\n", + " print('Downloading...')\n", + " open(dirname, 'wb').write(request.content)\n", + " print('Data request', page_val, 'is complete.')\n", + " \n", + " # Unzip outputs\n", + " for z in os.listdir(path): \n", + " if z.endswith('.zip'): \n", + " zip_name = path + \"/\" + z \n", + " zip_ref = zipfile.ZipFile(zip_name) \n", + " zip_ref.extractall(path) \n", + " zip_ref.close() \n", + " os.remove(zip_name) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Finally, we will clean up the Output folder by removing individual order folders:" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [], + "source": [ + "# Clean up Outputs folder by removing individual granule folders \n", + "\n", + "for root, dirs, files in os.walk(path, topdown=False):\n", + " for file in files:\n", + " try:\n", + " shutil.move(os.path.join(root, file), path)\n", + " except OSError:\n", + " pass\n", + " for name in dirs:\n", + " os.rmdir(os.path.join(root, name)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### To review, we have explored data availability and volume over a region and time of interest, discovered and selected data customization options, constructed an API endpoint for our request, and downloaded data directly to our local machine. You are welcome to request different data sets, areas of interest, and/or customization services by re-running the notebook or starting again at the 'Select a data set of interest' step above. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/notebooks/Getting ASO_50m_lidar_data.ipynb b/notebooks/Getting ASO_50m_lidar_data.ipynb new file mode 100644 index 0000000..80174d3 --- /dev/null +++ b/notebooks/Getting ASO_50m_lidar_data.ipynb @@ -0,0 +1,537 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 20, + "id": "2ab16112-ab90-4f03-8947-afaa60ed47d7", + "metadata": {}, + "outputs": [], + "source": [ + "# import elevation\n", + "# import os\n", + "# import regionmask\n", + "import geopandas as gpd\n", + "import rasterio\n", + "import rioxarray\n", + "\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "from shapely.geometry import mapping" + ] + }, + { + "cell_type": "markdown", + "id": "e29454aa-58a4-4ced-910b-af5cbfd6be3f", + "metadata": {}, + "source": [ + "# Data preparation\n", + "eg. 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+       "  * y            (y) float64 8kB 4.23e+06 4.23e+06 ... 4.179e+06 4.179e+06\n",
+       "    spatial_ref  int64 8B ...\n",
+       "Data variables:\n",
+       "    band_data    (band, y, x) float32 4MB ...
" + ], + "text/plain": [ + " Size: 4MB\n", + "Dimensions: (band: 1, x: 1062, y: 1007)\n", + "Coordinates:\n", + " * band (band) int64 8B 1\n", + " * x (x) float64 8kB 2.543e+05 2.543e+05 ... 3.073e+05 3.073e+05\n", + " * y (y) float64 8kB 4.23e+06 4.23e+06 ... 4.179e+06 4.179e+06\n", + " spatial_ref int64 8B ...\n", + "Data variables:\n", + " band_data (band, y, x) float32 4MB ..." + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "68c47fdb-4396-46ff-8cb2-d594775d23d4", + "metadata": {}, + "outputs": [ + { + "ename": "OSError", + "evalue": "[Errno -51] NetCDF: Unknown file format: '/home/jovyan/NSIDC-Data-Access-Notebook/notebooks/ASO_50m_lidar_data/ASO_50M_SWE_USCATB_20130403.tif'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/file_manager.py:211\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 211\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_key\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 212\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/lru_cache.py:56\u001b[0m, in \u001b[0;36mLRUCache.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 55\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock:\n\u001b[0;32m---> 56\u001b[0m value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_cache\u001b[49m\u001b[43m[\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_cache\u001b[38;5;241m.\u001b[39mmove_to_end(key)\n", + "\u001b[0;31mKeyError\u001b[0m: [, ('/home/jovyan/NSIDC-Data-Access-Notebook/notebooks/ASO_50m_lidar_data/ASO_50M_SWE_USCATB_20130403.tif',), 'r', (('clobber', True), ('diskless', False), ('format', 'NETCDF4'), ('persist', False)), 'a7d98c09-f05c-4781-b82c-749232621f08']", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mOSError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[16], line 3\u001b[0m\n\u001b[1;32m 1\u001b[0m path_ua \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m./ASO_50m_lidar_data/\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 2\u001b[0m files_ua \u001b[38;5;241m=\u001b[39m path_ua \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mASO_50M_SWE_USCATB*.tif\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[0;32m----> 3\u001b[0m ds_ua \u001b[38;5;241m=\u001b[39m \u001b[43mxr\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_mfdataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfiles_ua\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mengine\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mnetcdf4\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/api.py:1077\u001b[0m, in \u001b[0;36mopen_mfdataset\u001b[0;34m(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)\u001b[0m\n\u001b[1;32m 1074\u001b[0m open_ \u001b[38;5;241m=\u001b[39m open_dataset\n\u001b[1;32m 1075\u001b[0m getattr_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m\n\u001b[0;32m-> 1077\u001b[0m datasets \u001b[38;5;241m=\u001b[39m \u001b[43m[\u001b[49m\u001b[43mopen_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mopen_kwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mp\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpaths\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m 1078\u001b[0m closers \u001b[38;5;241m=\u001b[39m [getattr_(ds, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_close\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m ds \u001b[38;5;129;01min\u001b[39;00m datasets]\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m preprocess \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/api.py:1077\u001b[0m, in \u001b[0;36m\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m 1074\u001b[0m open_ \u001b[38;5;241m=\u001b[39m open_dataset\n\u001b[1;32m 1075\u001b[0m getattr_ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m\n\u001b[0;32m-> 1077\u001b[0m datasets \u001b[38;5;241m=\u001b[39m [\u001b[43mopen_\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mopen_kwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m paths]\n\u001b[1;32m 1078\u001b[0m closers \u001b[38;5;241m=\u001b[39m [getattr_(ds, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m_close\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m ds \u001b[38;5;129;01min\u001b[39;00m datasets]\n\u001b[1;32m 1079\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m preprocess \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/api.py:588\u001b[0m, in \u001b[0;36mopen_dataset\u001b[0;34m(filename_or_obj, engine, chunks, cache, decode_cf, mask_and_scale, decode_times, decode_timedelta, use_cftime, concat_characters, decode_coords, drop_variables, inline_array, chunked_array_type, from_array_kwargs, backend_kwargs, **kwargs)\u001b[0m\n\u001b[1;32m 576\u001b[0m decoders \u001b[38;5;241m=\u001b[39m _resolve_decoders_kwargs(\n\u001b[1;32m 577\u001b[0m decode_cf,\n\u001b[1;32m 578\u001b[0m open_backend_dataset_parameters\u001b[38;5;241m=\u001b[39mbackend\u001b[38;5;241m.\u001b[39mopen_dataset_parameters,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 584\u001b[0m decode_coords\u001b[38;5;241m=\u001b[39mdecode_coords,\n\u001b[1;32m 585\u001b[0m )\n\u001b[1;32m 587\u001b[0m overwrite_encoded_chunks \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124moverwrite_encoded_chunks\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m--> 588\u001b[0m backend_ds \u001b[38;5;241m=\u001b[39m \u001b[43mbackend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen_dataset\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 589\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 590\u001b[0m \u001b[43m \u001b[49m\u001b[43mdrop_variables\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdrop_variables\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 591\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdecoders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 592\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 593\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 594\u001b[0m ds \u001b[38;5;241m=\u001b[39m _dataset_from_backend_dataset(\n\u001b[1;32m 595\u001b[0m backend_ds,\n\u001b[1;32m 596\u001b[0m filename_or_obj,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 606\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 607\u001b[0m )\n\u001b[1;32m 608\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:645\u001b[0m, in \u001b[0;36mNetCDF4BackendEntrypoint.open_dataset\u001b[0;34m(self, filename_or_obj, mask_and_scale, decode_times, concat_characters, decode_coords, drop_variables, use_cftime, decode_timedelta, group, mode, format, clobber, diskless, persist, lock, autoclose)\u001b[0m\n\u001b[1;32m 624\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mopen_dataset\u001b[39m( \u001b[38;5;66;03m# type: ignore[override] # allow LSP violation, not supporting **kwargs\u001b[39;00m\n\u001b[1;32m 625\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m 626\u001b[0m filename_or_obj: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m|\u001b[39m os\u001b[38;5;241m.\u001b[39mPathLike[Any] \u001b[38;5;241m|\u001b[39m BufferedIOBase \u001b[38;5;241m|\u001b[39m AbstractDataStore,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 642\u001b[0m autoclose\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m 643\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Dataset:\n\u001b[1;32m 644\u001b[0m filename_or_obj \u001b[38;5;241m=\u001b[39m _normalize_path(filename_or_obj)\n\u001b[0;32m--> 645\u001b[0m store \u001b[38;5;241m=\u001b[39m \u001b[43mNetCDF4DataStore\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 646\u001b[0m \u001b[43m \u001b[49m\u001b[43mfilename_or_obj\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 647\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 648\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mformat\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 649\u001b[0m \u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 650\u001b[0m \u001b[43m \u001b[49m\u001b[43mclobber\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mclobber\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 651\u001b[0m \u001b[43m \u001b[49m\u001b[43mdiskless\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdiskless\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 652\u001b[0m \u001b[43m \u001b[49m\u001b[43mpersist\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpersist\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 653\u001b[0m \u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 654\u001b[0m \u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 655\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 657\u001b[0m store_entrypoint \u001b[38;5;241m=\u001b[39m StoreBackendEntrypoint()\n\u001b[1;32m 658\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m close_on_error(store):\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:408\u001b[0m, in \u001b[0;36mNetCDF4DataStore.open\u001b[0;34m(cls, filename, mode, format, group, clobber, diskless, persist, lock, lock_maker, autoclose)\u001b[0m\n\u001b[1;32m 402\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\n\u001b[1;32m 403\u001b[0m clobber\u001b[38;5;241m=\u001b[39mclobber, diskless\u001b[38;5;241m=\u001b[39mdiskless, persist\u001b[38;5;241m=\u001b[39mpersist, \u001b[38;5;28mformat\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mformat\u001b[39m\n\u001b[1;32m 404\u001b[0m )\n\u001b[1;32m 405\u001b[0m manager \u001b[38;5;241m=\u001b[39m CachingFileManager(\n\u001b[1;32m 406\u001b[0m netCDF4\u001b[38;5;241m.\u001b[39mDataset, filename, mode\u001b[38;5;241m=\u001b[39mmode, kwargs\u001b[38;5;241m=\u001b[39mkwargs\n\u001b[1;32m 407\u001b[0m )\n\u001b[0;32m--> 408\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mcls\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmanager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgroup\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgroup\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlock\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mlock\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mautoclose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mautoclose\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:355\u001b[0m, in \u001b[0;36mNetCDF4DataStore.__init__\u001b[0;34m(self, manager, group, mode, lock, autoclose)\u001b[0m\n\u001b[1;32m 353\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_group \u001b[38;5;241m=\u001b[39m group\n\u001b[1;32m 354\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m mode\n\u001b[0;32m--> 355\u001b[0m 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\u001b[38;5;21mds\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m--> 417\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/netCDF4_.py:411\u001b[0m, in \u001b[0;36mNetCDF4DataStore._acquire\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 410\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_acquire\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[0;32m--> 411\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mwith\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43macquire_context\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mas\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mroot\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 412\u001b[0m \u001b[43m \u001b[49m\u001b[43mds\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m_nc4_require_group\u001b[49m\u001b[43m(\u001b[49m\u001b[43mroot\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_group\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_mode\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 413\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ds\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/contextlib.py:137\u001b[0m, in \u001b[0;36m_GeneratorContextManager.__enter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 135\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mkwds, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunc\n\u001b[1;32m 136\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 137\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgen)\n\u001b[1;32m 138\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[1;32m 139\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgenerator didn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt yield\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/file_manager.py:199\u001b[0m, in \u001b[0;36mCachingFileManager.acquire_context\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 196\u001b[0m \u001b[38;5;129m@contextlib\u001b[39m\u001b[38;5;241m.\u001b[39mcontextmanager\n\u001b[1;32m 197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21macquire_context\u001b[39m(\u001b[38;5;28mself\u001b[39m, needs_lock\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m):\n\u001b[1;32m 198\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Context manager for acquiring a file.\"\"\"\u001b[39;00m\n\u001b[0;32m--> 199\u001b[0m file, cached \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_acquire_with_cache_info\u001b[49m\u001b[43m(\u001b[49m\u001b[43mneeds_lock\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 200\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 201\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m file\n", + "File \u001b[0;32m/srv/conda/envs/notebook/lib/python3.11/site-packages/xarray/backends/file_manager.py:217\u001b[0m, in \u001b[0;36mCachingFileManager._acquire_with_cache_info\u001b[0;34m(self, needs_lock)\u001b[0m\n\u001b[1;32m 215\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m kwargs\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 216\u001b[0m kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode\n\u001b[0;32m--> 217\u001b[0m file \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_opener\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 218\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mw\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 219\u001b[0m \u001b[38;5;66;03m# ensure file doesn't get overridden when opened again\u001b[39;00m\n\u001b[1;32m 220\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_mode \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma\u001b[39m\u001b[38;5;124m\"\u001b[39m\n", + "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2470\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4.Dataset.__init__\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32msrc/netCDF4/_netCDF4.pyx:2107\u001b[0m, in \u001b[0;36mnetCDF4._netCDF4._ensure_nc_success\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mOSError\u001b[0m: [Errno -51] NetCDF: Unknown file format: '/home/jovyan/NSIDC-Data-Access-Notebook/notebooks/ASO_50m_lidar_data/ASO_50M_SWE_USCATB_20130403.tif'" + ] + } + ], + "source": [ + "path_ua = './ASO_50m_lidar_data/'\n", + "files_ua = path_ua + 'ASO_50M_SWE_USCATB*.tif'\n", + "ds_ua = xr.open_mfdataset(files_ua, engine=\"netcdf4\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "19957a2f-9435-4157-a340-504d90e8155f", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Getting UA_SWE data.ipynb b/notebooks/Getting UA_SWE data.ipynb new file mode 100644 index 0000000..10d6e4f --- /dev/null +++ b/notebooks/Getting UA_SWE data.ipynb @@ -0,0 +1,5906 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 64, + "id": "9593f596-5ace-49a3-a802-506cddc9dda3", + "metadata": {}, + "outputs": [], + "source": [ + "# import elevation\n", + "# import os\n", + "# import regionmask\n", + "import geopandas as gpd\n", + "import rasterio\n", + "import rioxarray\n", + "\n", + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "from shapely.geometry import mapping" + ] + }, + { + "cell_type": "markdown", + "id": "35d65660-a8ae-4fda-8113-22befee8a49c", + "metadata": {}, + "source": [ + "# Data preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "8769f9a0-a844-4045-8b58-2e9087752173", + "metadata": {}, + "outputs": [], + "source": [ + "ds = xr.open_dataset('./UA_SWE/4km_SWE_Depth_WY1982_v01.nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ff0ef6d7-86c7-4c0c-a2ad-9795d83f9408", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'SWE' (time: 15340, lat: 621, lon: 1405)> Size: 54GB\n",
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+       "Attributes:\n",
+       "    long_name:     Snow Water Equivalent\n",
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<xarray.DataArray 'SWE' (lat: 621, lon: 1405)> Size: 3MB\n",
+       "dask.array<getitem, shape=(621, 1405), dtype=float32, chunksize=(104, 235), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
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+       "Attributes:\n",
+       "    long_name:     Snow Water Equivalent\n",
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" + ], + "text/plain": [ + " Size: 3MB\n", + "dask.array\n", + "Coordinates:\n", + " * lat (lat) float32 2kB 24.08 24.12 24.17 24.21 ... 49.83 49.88 49.92\n", + " * lon (lon) float32 6kB -125.0 -125.0 -124.9 ... -66.58 -66.54 -66.5\n", + " time datetime64[ns] 8B 1985-01-29\n", + "Attributes:\n", + " long_name: Snow Water Equivalent\n", + " grid_mapping: crs\n", + " units: millimeters h20" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "one_day = ds_ua.SWE.sel(time='1985/01/29', method='nearest')\n", + "one_day" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "335bf0fa-93bb-419d-91b7-9d94ed29d026", + "metadata": {}, + "outputs": [], + "source": [ + "poly = skagit_boundary.geometry.to_list()\n", + "one_day = one_day.rio.set_spatial_dims(x_dim='lon', y_dim='lat', inplace=True)\n", + "one_day.rio.write_crs(\"epsg:4326\", inplace=True)\n", + "one_day_clipped = one_day.rio.clip(poly, crs=\"epsg:4326\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cea85591-37e6-4afd-8536-b5cba519035c", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true + }, + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'SWE' (lat: 33, lon: 39)> Size: 5kB\n",
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+       "    units:      millimeters h20
" + ], + "text/plain": [ + " Size: 9MB\n", + "dask.array\n", + "Coordinates:\n", + " * lat (lat) float32 40B 37.79 37.83 37.88 37.92 ... 38.08 38.12 38.17\n", + " * lon (lon) float32 60B -119.8 -119.8 -119.7 ... -119.3 -119.2 -119.2\n", + " * time (time) datetime64[ns] 123kB 1981-10-01 ... 2023-09-30\n", + " spatial_ref int64 8B 0\n", + " crs int64 8B 0\n", + "Attributes:\n", + " long_name: Snow Water Equivalent\n", + " units: millimeters h20" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ua_tuolumne_clipped" + ] + }, + { + "cell_type": "markdown", + "id": "d2c8930b-ca56-464c-ad45-b01d2abaa2c1", + "metadata": {}, + "source": [ + "# Plotting " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "d48f959b-ed98-487c-bdbf-696a7c5f3655", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot for skagit basin\n", + "fig, ax = plt.subplots(figsize=(15,8))\n", + "\n", + "one_day = ua_skagit_clipped.sel(time='1985/01/29', method='nearest')\n", + "one_day.plot(ax=ax)\n", + "\n", + "skagit_boundary.plot(ax=ax, edgecolor='blue', linestyle='--', facecolor='none');\n", + "ax.set_aspect(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "091f1e15-eb2f-4d41-9d1e-7cbd99f58374", + "metadata": {}, + "outputs": [], + "source": [ + "ua_skagit_clipped_mean = ua_skagit_clipped.mean(dim= ('lat', 'lon'))" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "f9b5a8a4-a91b-4665-ad35-12d939e48bc6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'SWE' (time: 15340)> Size: 61kB\n",
+       "dask.array<mean_agg-aggregate, shape=(15340,), dtype=float32, chunksize=(92,), chunktype=numpy.ndarray>\n",
+       "Coordinates:\n",
+       "  * time         (time) datetime64[ns] 123kB 1981-10-01 ... 2023-09-30\n",
+       "    spatial_ref  int64 8B 0\n",
+       "    crs          int64 8B 0
" + ], + "text/plain": [ + " Size: 61kB\n", + "dask.array\n", + "Coordinates:\n", + " * time (time) datetime64[ns] 123kB 1981-10-01 ... 2023-09-30\n", + " spatial_ref int64 8B 0\n", + " crs int64 8B 0" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ua_skagit_clipped_mean" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "d46ecba4-79a6-4503-a112-dd8dbfe6685d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Plot for Tuolumne basin\n", + "fig, ax = plt.subplots(figsize=(15,8))\n", + "\n", + "one_day = ua_tuolumne_clipped.sel(time='1985/01/29', method='nearest')\n", + "one_day.plot(ax=ax)\n", + "\n", + "tuolumne_boundary.plot(ax=ax, edgecolor='blue', linestyle='--', facecolor='none');\n", + "ax.set_aspect(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "9db1f98d-ef6e-4c89-bb48-83d5e2b1da5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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wsLCwsLAIASs0WVhYWFhYWFiEgBWaLCwsLCwsLCxCwApNFhYWFhYWFhYhYIUmCwsLCwsLC4sQsEKThYWFhYWFhUUIWKHJwsLCwsLCwiIErNBkYWFhYWFh0WFoaG5BS2um0N1oEwoqNL3wwgu46KKLUFdXh1QqhYcffthXZuXKlZg0aRJqamrQvXt3nHbaafjwww/d3xsaGnDttdeiT58+6Nq1KyZNmoSNGzdKdezatQtTpkxBTU0NampqMGXKFOzevbudr87CwsLCwsKC4kBjM0788ZOYOHNhobvSJhRUaNq/fz9OPPFE3HXXXezvH3zwAc4880wcc8wxeO655/DGG2/g5ptvRmVlpVtm6tSpmDdvHubOnYuFCxdi3759mDhxIlpaWtwykydPxvLlyzF//nzMnz8fy5cvx5QpU9r9+iwsLCwsLCw8LP9wNw41tWLl5j2F7kqbkMpkMrHgyFKpFObNm4eLL77YPXb55ZejrKwMDzzwAHtOfX09+vbtiwceeACf//znAQAfffQRBg0ahMcffxznnXceVq5cieOOOw6vvPIKRo8eDQB45ZVXMGbMGLz77rs4+uijQ/Vvz549qKmpQX19Paqrq/O7WAsLCwsLiyLEyx/swOR7XgUArPvFhR3SZpTrd2xtmlpbW/HYY4/hqKOOwnnnnYd+/fph9OjRkgpv6dKlaGpqwoQJE9xjdXV1GDFiBF5++WUAwKJFi1BTU+MKTABw2mmnoaamxi1jYWFhYWFh0f5IIVXoLuSF2ApN27Ztw759+/CLX/wC559/Pp588klccskluPTSS/H8888DALZs2YLy8nL07NlTOrd///7YsmWLW6Zfv36++vv16+eW4dDQ0IA9e/ZIfxYWFhYWFhZtRzrZMhNKC90BHVpbWwEAn/3sZ/Htb38bAHDSSSfh5Zdfxt13342xY8dqz81kMkilvCdDv+vKqJgxYwZ+/OMft7X7FhYWFhYWFgrouhu0DscRsWWa+vTpg9LSUhx33HHS8WOPPdb1nqutrUVjYyN27dolldm2bRv69+/vltm6dauv/u3bt7tlONx0002or693/zZs2JDvJVlYWFhYWBQ1KNOUxLADsRWaysvLceqpp2LVqlXS8ffeew9DhgwBAJx88skoKyvDU0895f6+efNmrFixAqeffjoAYMyYMaivr8drr73mlnn11VdRX1/vluFQUVGB6upq6c/CwsLCwsKi7aDEUks8/NByQkHVc/v27cPq1avd/9euXYvly5ejV69eGDx4ML773e/i85//PD71qU/h7LPPxvz58/Hoo4/iueeeAwDU1NTgiiuuwPXXX4/evXujV69euOGGGzBy5EiMHz8egMNMnX/++fj617+O3//+9wCAK6+8EhMnTgztOWdhYWFhYWERBTypKWuFkygUVGhasmQJzj77bPf/73znOwCAL3/5y5g1axYuueQS3H333ZgxYwauu+46HH300fjb3/6GM8880z3njjvuQGlpKS677DIcPHgQ55xzDmbNmoWSkhK3zIMPPojrrrvO9bKbNGmSNjaUhYWFhYWFRfsg6UxTbOI0xR1xjtPU0NyCDTsP4sh+3QrdFQsLCwsLCy2Wb9iNi//nJQDAGz+agJouZe3eZlHEabIIjy/f9xrG3/48nnxbH0LBwsLCwsKi0KC+cq3WENyiEHhlzU4AwAOvrC9wTywsLCwsLPRIunrOCk0WFhYWFhYWHQ7LNFkUFEkLEmZhYWFhUVyg5FJDc/Lc56zQ1IlgRSYLCwsLizijlUhNz723vYA9aRus0NSJYIkmCwsLC4s4gyrkenctL1g/2gorNHUiWJnJwsLCwiLOSHqUIys0dSJYmyYLCwsLiziDykxNLdamyaKASFuZycLCwsIixqAOczZhr4WFhYWFhYWFBlQ912yFJotCIoHjz8LCwsKiiGCZJovYIIkD0MLCwsKieJCBZZosYoLWhHslWFhYWFh0btDNfYs1BLcoJKzMZGFhYWERZzywyMuRuutAUwF70jZYoakT4WBTS6G7YGFhYWFhocXT725zv//30+8XsCdtgxWaOhGWb9hd6C5YWFhYWFh0WlihqRPBxmmysLCwsIgzPntiXaG7kBes0JRwtCbQ+8DCwsLCojhxXF11obuQF6zQlHBQl82UzT5nYWFhYRFjJN3L2wpNCYcUm8nKTBYWFhYWMUYSYzNRWKEp4Whu9eJcWJsmCwsLC4s4I+kmJVZoSjgo05ROWanJwsLCwiK+sEyTRUEh2zRZWFhYWMQZtz+5CpPuWoj9Dc2F7kpBYJkmi4KiuYWEpE+4gZ2FhYVFZ8dvnlmNNzfW408kMnYxobEl2euUFZoSjsXrdrrfEy7AW1hYWBQNdh1oLHQXCoK7n/+g0F3IC1ZoSjje2bzH/Z6xTJOFhYVFItDYnLxktRZWaEo8zjiij/u9xVJNFhYWFonArJfXFboLBcG5x/UvdBfyghWaEo5/LN/kfrcyk4WFhYVFnFFVXlLoLuQFKzQlHP98c3Ohu2BhYWFhEQLWhAJI+i2wQlPC0dDcUuguWFhYWFiEwNL1uwrdhYIj4TKTFZqSDquSs7CwsEgGnlq5tdBdKDiSzrYVVGh64YUXcNFFF6Gurg6pVAoPP/ywtuxVV12FVCqFO++8Uzre0NCAa6+9Fn369EHXrl0xadIkbNy4USqza9cuTJkyBTU1NaipqcGUKVOwe/fu6C+owOheWVroLlhYWFhYaDCwZ1Whu1BwqCJT0oSoggpN+/fvx4knnoi77rrLWO7hhx/Gq6++irq6Ot9vU6dOxbx58zB37lwsXLgQ+/btw8SJE9HS4qmtJk+ejOXLl2P+/PmYP38+li9fjilTpkR+PYXGcQOqC90FCwsLCwsN+nYrL3QXCg9FRkqa13dBqYkLLrgAF1xwgbHMpk2bcM011+CJJ57AhRdeKP1WX1+Pe++9Fw888ADGjx8PAJg9ezYGDRqEBQsW4LzzzsPKlSsxf/58vPLKKxg9ejQA4J577sGYMWOwatUqHH300e1zcQVAa8IkdgsLC4tiQtKjYUeBjCI1NbdmUJogh7pY2zS1trZiypQp+O53v4vjjz/e9/vSpUvR1NSECRMmuMfq6uowYsQIvPzyywCARYsWoaamxhWYAOC0005DTU2NW4ZDQ0MD9uzZI/3FHUmT2C0sLCyKCU02oKXPe66pJVn3JNZC0y9/+UuUlpbiuuuuY3/fsmULysvL0bNnT+l4//79sWXLFrdMv379fOf269fPLcNhxowZrg1UTU0NBg0alMeV5I4fznsLNz+8IqdzrMxkYWFhEV8kTUBoD6hCU3PC2LfYCk1Lly7Ff//3f2PWrFlIpVI5nZvJZKRzuPPVMipuuukm1NfXu38bNmzIqQ/5YOf+Rjz46od44JX1qD/QFPo8q56zsLCwiC+eXbWt0F0oOFT1XFNrsgTJ2ApNL774IrZt24bBgwejtLQUpaWlWL9+Pa6//noMHToUAFBbW4vGxkbs2iXHvti2bRv69+/vltm61e/muX37drcMh4qKClRXV0t/HQWqZmvOYUBZ9ZyFhYVFfPHE295adExt9wL2pHCwTFM7YcqUKXjzzTexfPly96+urg7f/e538cQTTwAATj75ZJSVleGpp55yz9u8eTNWrFiB008/HQAwZswY1NfX47XXXnPLvPrqq6ivr3fLxA1pQoDlMpyszGRhYWGRDDQX6YStXnXShKaCes/t27cPq1evdv9fu3Ytli9fjl69emHw4MHo3bu3VL6srAy1tbWux1tNTQ2uuOIKXH/99ejduzd69eqFG264ASNHjnS96Y499licf/75+PrXv47f//73AIArr7wSEydOjK3nXJqoDXNRubUW6UtoYWFhkTQUq2bAZwhu1XPhsWTJEowaNQqjRo0CAHznO9/BqFGjcMstt4Su44477sDFF1+Myy67DGeccQaqqqrw6KOPoqTE82F88MEHMXLkSEyYMAETJkzACSecgAceeCDy62kX5PBetVibpqLFhp0H8K25r2PFpvpCd8XCwkKDyaMHu9+L1yhcXqcampJ1HwrKNI0bNy6naKDr1q3zHausrMTMmTMxc+ZM7Xm9evXC7Nmz29LFgoDekVw2I9YQvHjx7YeWY8n6XfjH8o+w7hcXBp9gYWHR4agq8zbzSVNLRQV1mTrYlKz8qbG1aSpmUEFS9TQwwarnihdrduwvdBcsLCwCQLUBuTj5dCaoq9QhKzRZ5Iu2M02Rd8UiISheqt/CIjmgLMuOfY2F60gBoWqXDjZaockiT9AxlQt7VKyGhRbA3kPNhe6ChYVFANQ5uqE5WQJDFFBXqaTZ4lqhKYagknhO3nMJG3wWFhYWxQR1jm4qQrsmdZlKmlmJFZpiCDqEcmGPrNBkYWFhEV+o03lzEarVLdNkETlo0LNchPAifP8sLCwsEgOVVWkswklbtWlKGNFkhaY44tl3vfxEVj1nYWFh0TmgztGNzcUnNKmw6jmLvNG7a7n73arnLCwsLDoH1Onc2jQlz4HJCk0xxLC+Xd3vuQhCSRt8FhYWFsUEvyF48TFNauzBpG32rdAUQ8ghB0zllMFnhSYLCwuL2EIVEO56ZrWmZOeFz3vOCk0WUcLkWbB1T4P0v5WZLCwsLOILVRvwyBsfFagnhYNfPVeYfrQVVmiKIeigMqnc1DD8SXPdtLCwsCgm2Cnaqucs2gF0UJkSGquqO6ueK16UpFOF7oKFhUUAkiYgtAeses4icoRlmtTBlrTBZxEdSlJWaLKwiDuss44X3FJs9JJ2T6zQFHOYVG7qb60ZMzNl0XmRtm+yhUXskTD5oH2QvQdio5e0e2Kn2hiCyj0mGYgTkJI2AC2iAWWakrZzs7AoFlhtgGd+IpimpJmVWKEp5jCr57hjyRqAFtEgTYSmYoz9YmGRBNj52SMCSoV6LmH3xApNMcSO/V4oAaN6jpGaLMtQnEgTQ/BmOwYsLGIJ+2p6Nk1pa9NkERV+9I+33e8mGyVusNmdTHGCOs+1FGFqBguLJCBpqqj2gFjTBNOUNDtcKzTFEB/uPOB+N2laOAHJvpPFiVSKMk1WPWcRXxxqakkcuxAV7KaW854rXF/aAis0xRyml4ybd4p1Mip20HFix4BFXHGoqQWf/PkCTLjj+UJ3pSCwQpNn01SSUJum0kJ3wMIME53LvYBJozotogEVlKxNk0VcsXLzHuw51Iw9h5rR0popuqCslgT2M01JW7Ms0xRzmNY/TqCyLENxgo4FOwYs4oqK0hL3e2Nz8UkQlmmCSzWVWkNwi/aAibpk1XP2pSxK0OdumSaLuKK0xGOW1n28v4A9KQy4+TlpTEu+8HnPJez6rdAUc+TqPZew8WcRAZpbWnGoydu1t1gdgEVMQbVxF/z3i4XrSIHAx9br+H4UEmqcpqStWVZoijnMhuBWPWcBPP3uNun/vYeaC9QTCwszkrZARg0+i0Nx3RQvIrgjfiRtzbJCU8yRa8iBpA1Ai/yxv0EWku55cU2BemJhYUaxT0/b9jT4jhWd0OR6zzmfSVuzrNAUc5heqK3MC1hk758FgAbFoHZL/aEC9cTCwoxiExBUbNnjfzeL7ZZ4QlM6+3+yboAVmmIOU8iBu55533csaUZ1Fvlj94Em6f9jB1QXqCcWFmYUu9DEIWlMS74QV2tzz1m0C0zv0yWjBvqOFdsLaAFs3yszjqcd3rtAPbGwMCNh62OHoNhuiWCWSlI2InjOeOGFF3DRRRehrq4OqVQKDz/8sPtbU1MTvve972HkyJHo2rUr6urq8B//8R/46KOPpDoaGhpw7bXXok+fPujatSsmTZqEjRs3SmV27dqFKVOmoKamBjU1NZgyZQp2797dAVeYP0w7sz7dy33HkkZ1WuSP+15aK/1vBWcLi+SgWNk3G9yyDdi/fz9OPPFE3HXXXb7fDhw4gGXLluHmm2/GsmXL8Pe//x3vvfceJk2aJJWbOnUq5s2bh7lz52LhwoXYt28fJk6ciJaWFrfM5MmTsXz5csyfPx/z58/H8uXLMWXKlHa/viiQcxqVhA1Ai+hhhabOg2ff3YYVm+oL3Y3IoE5PxTZWy0v9S24mYUxLvnBDDpQkM7hlQdOoXHDBBbjgggvY32pqavDUU09Jx2bOnIlPfvKT+PDDDzF48GDU19fj3nvvxQMPPIDx48cDAGbPno1BgwZhwYIFOO+887By5UrMnz8fr7zyCkaPHg0AuOeeezBmzBisWrUKRx99dPteZJ4w2TRxEnrSBqBF9LBjoHNg9ba9+OqsxQCAdb+4sMC9iQbqJnDxup3FpU5m4zQV1/sqQg6kU9amqd1RX1+PVCqFHj16AACWLl2KpqYmTJgwwS1TV1eHESNG4OWXXwYALFq0CDU1Na7ABACnnXYaampq3DJxRkuOaVQSNv4s2gFJm4QseKzZ3vkiZqsj8yt/fK0g/SgUhMBweN+u5FhxwQa37CAcOnQI3//+9zF58mRUVzveQVu2bEF5eTl69uwple3fvz+2bNnilunXr5+vvn79+rllODQ0NGDPnj3SXyFg0vey6jnLMhQ9bBqVzgH6GHfs84cXSSLU+YxGsi8GiGf6uy+eTI4V1/v6/rZ9AEgalYTNV4kQmpqamnD55ZejtbUVv/3tbwPLZzIZpFJevH76XVdGxYwZM1zD8ZqaGgwaNKhtnc8TpgFFXzZxKZZlsGhJmjuKBQv6fqthJZKKYp+dhNDYo6rMTSlTbEKTwJrtjvCUtDUr9kJTU1MTLrvsMqxduxZPPfWUyzIBQG1tLRobG7Fr1y7pnG3btqF///5uma1bt/rq3b59u1uGw0033YT6+nr3b8OGDRFdUW4wCeF0rAn3zaR5IljkB9auzQ6BToGBPbsUuguRo9inJ3H5KXg2PcV6T04a5GiIkrZmxVpoEgLT+++/jwULFqB3b9lg8OSTT0ZZWZlkML5582asWLECp59+OgBgzJgxqK+vx2uvebrzV199FfX19W4ZDhUVFaiurpb+CgHTLoR6YvSvrgSQvJgXFvmBV9HaQdAZcLDR8wDuPGxEZ7mOtiFDpCYhNHWeZxsOfbtXAAC6lCcz91xBvef27duH1atXu/+vXbsWy5cvR69evVBXV4d///d/x7Jly/DPf/4TLS0trg1Sr169UF5ejpqaGlxxxRW4/vrr0bt3b/Tq1Qs33HADRo4c6XrTHXvssTj//PPx9a9/Hb///e8BAFdeeSUmTpwYe885wOw9JzD2qL7YuOuAU77IXsBiB7dLszZNnQN/fGmd+725k9CHdmg6SCHlmlQU2z0RQlJ5SUn2/0L2JncUVGhasmQJzj77bPf/73znOwCAL3/5y5g2bRoeeeQRAMBJJ50knffss89i3LhxAIA77rgDpaWluOyyy3Dw4EGcc845mDVrFkqyDwQAHnzwQVx33XWul92kSZPY2FBxhOmFEgJSdZcylNRndy3F9gYWObjHbcdA58C2vV6ess6yGeokl9Em0A1OmjJNRfa+NmelJKEpSZp6rqBC07hx44w3LMzNrKysxMyZMzFz5kxtmV69emH27Nlt6mOhYTKSE+8afQGTZlRnkR8yRN3Rr3sFtu1tsExTJ8GFJ9Rh2Ye7AXQe9jBpC2SUoJeeSnlMU7HdEjGWhdCUtDUr1jZNFuZJRvyWTqWIfrxDumURE9DhUVGWTBsBCx5V5R5b3lmeaee4iraBXjs1BO8sLGJYCKGpojSZ85UVmmIOcxoV57cUvDw+xUb1FjskoalU2AjYMdAZQN/9zrKwdpLLaBPUEDGpIg054Nk0CfVcIXuTO6zQFHOYjOTE2phKpdyYH3bBLC5Q9dzRtd0B2DHQWUA3QJ3FEDxTxFyTpJ5DcWoHMpmMOz+VJTT3nBWaYg6zes75TKe86KrFtmspdtD5pm83x5W3s9i/FDvoYtJp3utOchltgSQwpuBudIvJzovOTRVlWWY8YddvhaaYI0xE8HQq5Qa37DSTq0Uo0Ak3qTs3Cx6UXOosz1S9jMqy4lmCZENwFCXTRMexp55L1g0onhGbUJikcNcQPE285xIW88IiP9AJt7QkmYaVFjyoeq6zPFNVPde9sqxAPSks0qmUm8armDa6lGkqs4bgFu2BP760DrsPNLK/STZNaXEsWQPQIk+Qx12WtmEnOhPoc0zawqKDuCTBMjQX0S5PtmlCUeaeo89bjIGkmetZoSkBmP3Keva4p54rXvfVYgfdubtMU9JmIQsWVFDqLIKwuAqhSu4sBu5hQN9Vqp7rJI82FKjsL8aAVc9ZRA7dmPKCW6bckAOdZUdqEQ6yei67ENkx0CmQ6ZRMk3MdQsBvKqI8ia0S05QqSqaJjumkrllWaEoAdEPKBre0kAzBszraQkzCD7++Cc+t2tbh7XZmUM1V0hYWHTymKZn2LPmAvqtOnKbim7PppSZVaCpoGhWLcNAzTd4P7q4lYQPQIj/EgWnauOsApj60HACwdsZn3MXAIj9QlVynYSNcmyZnjDS1ZJDJZIpizKhPsBjtUOmlliRUPWmZpgSDVc8lbQRa5AVqJ+Ht3DpW5bFjn+eokLRdY6EQxgC6Mwa3FAKCyDsGFI86mU7NVDuQNJuefCDmK8q0JW3NskJTAqCLoisFt7SG4MUJMgYKRXfT9oplAcwHH+9rwCd++hSu//MbxnKS91wnea/FZVSWeXn1GpqLxK7Jxmly70GSU39ZoSkB0M2XXpwmYtOUsAFokR9o2InSAglNrZ3QYLk98dCSDdhzqBl/W7bRWK5zxmlykCbquGLZ6EnecyC55zrJsw0DOl+VJFQ9aYWmBEBH37oJewnLUETvnwUI3Q1vIcqX7clkMvjmg8vwx5fWhipPVUeWaQpGSUj7nc4ojIq5TMxXAJApFqJJYpqK03lHzFdpq56zaE/oFiJq05ROqCeCRX7IuDs3zxA83zHwp0Xr8dhbm/HjR99BYwjVSUsnZETaCy2tGcz417shy3rfk7Yb18FlmtLFyDR5oMEti8qmyVXPkdRfCROardCUAOgWIjm4pXzMojggnjbdueYruPzokbfd73sPNQWWf+ytj9zvzUmbATsYS9btDF2WvsudxRBcXFIJIduSxjS0FfR5FqtNk3upknYkWTfACk0JgG4RzBCmySbsLU4Ie4gUgNKsD3M+KjLVvmJ/Q0vgOa+t9QQByzSZQQ2gg0DvZed5rz31XKrINnqqek6op3SOPp0RglWjNl1JmzOs0JQA6NVzwqbJewGLKJVT4pDJZHDszfPx7WxMoyiRSkVD96tjbX9jc+A5l5862P2etAmwo0FteYLQGXPPuYbASBVdGhHqbg9Q7UCBOlQAUHMCyzRZtBt0Ko9WIrUn1ROhmPB/r23AwaYWzHt9U2R2DNRGIJWSj7WpPmXXe7ApmGmids2Lc1A/FSNyEX6kOE2dZGV1x2YxmhQQd3uguMPESDZNCbt8KzQlADr2iAtuWUzuq0lDdRcvAH9jRJSgEKgd4UbQ/W2HOn8fagwWmug5DU2W6jQhF+FHUs91kveaensWWxoRan8IFLkhOPWeS9gAsEJTAqCLHkyDWybVfbOY0K3CE5oOhLAVCoN5r29yv6cimITVXW8Ypome868VW9rcdjHgYfK8gkDXks7yXtNFs9hSP2UUpimVUO+xfCBrR5K50bdCUwKgNwT3glt67pvJGoDFinsXhouBFITlG3a738VkHCnTFII5oqc8/972PFrv/Fi05uPQZQ8Qe7Kk7cZ18LRz1qZJCI2dRSAOA8q2JdV5yQpNCYDupbLBLZMFuvDNXbwhkjovHDnA/R7FIqROYGEmtITNeQXFhOP6S/+bWEHK2nUaocnd6BWfTQ+1PwQ8pqlILh+AxnsuYTfACk0JgC5GC7VpSuoALCZQe5Y9B4PjH4VBz67lAIDjBlRHpJ6T/z8UQj2nGo+HSUZbrFi4eof0f1hZqLO811RwKDZD8FZFP0diohegN4UBG6cpYdOFFZoSgCbNIkSDW1r1XPxBhd+oDMGFgFRVXuIJTRHUJzD98ZUhzpH/n5eD3U6x4c2N9dL/Jgbp08f088p1luCWREWVTqjLeVtBbVABROLtmjRIsQUT+vyt0JQA6AZVZxiAxQQaOqK6stRQMjwkbxREoZ6T/991IJgRUwWtrXsOtb0DnRz/MWaI9L9JaJJyz3WS91q8AsWaew3wq+eK6/o9oTmp2hErNCUAupfKBrdMFijT9OXTh0ZSp+TG7DJN0XnPheqDckpxLQK5obqyTPrftGB0xpx+YnyVFGGcJrrBAajjRnFcPyB7EJYQm64khV2wQlMCoM8953za4JbJAH2OFaXRvHrUhdedhCM0BA8D9Qxr06SHGqcpNNPUSYQmyo4Xm8s9jVEFFKl6LvuZItoRIFkbLSs0JQC6hYzaNHmqmQSNviJDE1kd7np2dSR1cotQXkOgDeeq43NI7655dKBzo0WREEw2iLRoZ9kMtRB2vHiZJhHcMv9gtEmDtMkjqQSStCmwQlMCoB1QYsFMexNQcoZe8eGn/3zH/R4m/lEY0LATUcRpasvcpa55SbNR6Ej4mCaTeo78pvOgTRpc9Vw6mhAZSYLLsojPIo8ILjNNybkHBRWaXnjhBVx00UWoq6tDKpXCww8/LP2eyWQwbdo01NXVoUuXLhg3bhzefvttqUxDQwOuvfZa9OnTB127dsWkSZOwceNGqcyuXbswZcoU1NTUoKamBlOmTMHu3bvb+eqig14957dpStLgKzZEJShxSKdosMCOtWl6eLnsLdfQXCT6ljbAJ2AapFT6HDuLICqYtbRkCN45ri0I/pADxSU0AvRaveCWgGWaQmP//v048cQTcdddd7G/33rrrbj99ttx1113YfHixaitrcW5556LvXv3umWmTp2KefPmYe7cuVi4cCH27duHiRMnoqXFiy8zefJkLF++HPPnz8f8+fOxfPlyTJkypd2vLyroXiqqnitWT5SkoLGdBAmJaYrARoJbwGhkag7rPz4g/d9e19oZoAq0psWiM+aec2PLpVNIF5kdJlWlA+R9LSL9QIbxngOSNQai8XtuIy644AJccMEF7G+ZTAZ33nknfvjDH+LSSy8FANx///3o378/5syZg6uuugr19fW499578cADD2D8+PEAgNmzZ2PQoEFYsGABzjvvPKxcuRLz58/HK6+8gtGjRwMA7rnnHowZMwarVq3C0Ucf3TEXmwd0u8wn3t4KANi8+xDKs4bFxUT1Jglb6tvHDZ8+7ii8cbjhs2nXQQzv3z10HVZo0kOVfYxCE/kpl0S/cUYLyzQVskcdCU9gcD6Ll2miueeAZDkDxNamae3atdiyZQsmTJjgHquoqMDYsWPx8ssvAwCWLl2KpqYmqUxdXR1GjBjhllm0aBFqampcgQkATjvtNNTU1LhlODQ0NGDPnj3SX6EQRF0++uZHJGN2B3TIImfsOtDYLvVKu9d2YppyVStaoUkPVaA17bDpBihJO3ET5JADxaWe8yXszX4Wj9DoIUUCMgPJUj/HVmjassXJu9S/v5yrqX///u5vW7ZsQXl5OXr27Gks069fP6jo16+fW4bDjBkzXBuompoaDBo0KK/ryQcjD6sx/n7N2Udam6aY4+l3t7VLvbIheP7eONzwaWgOTqWST/liQk5MUyeO00RTP3UW1WMQqLu985k9XkRztuzt6x1P0roVW6FJgLolAs4AU4+pUMtw5YPquemmm1BfX+/+bdgQTYLVtuDEQT2Mv/errixCqjtZKEubx2xbIR43nYSiNgRvytFzyzJNerTVpqnzCE3OZzFGBFeZpmIMOUBjVUlhJxI0CGIrNNXW1gKAjw3atm2byz7V1taisbERu3btMpbZunWrr/7t27f7WCyKiooKVFdXS3+FQtAi+K+3NhddzJOkId1eQhMX3DKP+ri5K1cbqajy6nVG5BKegf7UWV5rOeSAc6xYmJaMatMkjhfJ9QOy0Ax4dk1WPRcBhg0bhtraWjz11FPuscbGRjz//PM4/fTTAQAnn3wyysrKpDKbN2/GihUr3DJjxoxBfX09XnvtNbfMq6++ivr6erdM3BE0nppaMkUZXdZCDpgnBLN8xkAUE7hlmvRQNzVmQ/BOaNNUxIbgBxodtfWOfY59YzHO2er8ksT8ewX1ntu3bx9Wr/YiI69duxbLly9Hr169MHjwYEydOhXTp0/H8OHDMXz4cEyfPh1VVVWYPHkyAKCmpgZXXHEFrr/+evTu3Ru9evXCDTfcgJEjR7redMceeyzOP/98fP3rX8fvf/97AMCVV16JiRMnJsJzDgje6Z9xZG/s3O+8iMW0a0kSjq9rH6bS27lFs3ONYvKyQpMevjx9hltFVRad5a2WQg4UmR3mE2+rNrTRqufueWENpv9rJV77wXj07V4RUa3RwrPrcj6FMbhVz4XEkiVLMGrUKIwaNQoA8J3vfAejRo3CLbfcAgC48cYbMXXqVFx99dU45ZRTsGnTJjz55JPo3t1zf77jjjtw8cUX47LLLsMZZ5yBqqoqPProoygpKXHLPPjggxg5ciQmTJiACRMm4IQTTsADDzzQsRcbEtyCx40nukMde1TfRErsxYSyEvlVO6xHl0jqlW0ExLG2g13AcqzQBrfUw2cIbhAY6LPoLHKFF3IARRen6dNHyw5JUTNNP398JTIZ4NSfL4imwnaAmrTYVc8laOEqKNM0btw44644lUph2rRpmDZtmrZMZWUlZs6ciZkzZ2rL9OrVC7Nnz86nqx0Gds1ijjWTLWpZabpgNk0/++c7yAC4eeJxHdpu0qA+l55dyzQlc4McMC9/9VwU48cKTXr4DcH196ozqud+88z7AICXV3+MbpXO8tNJLi0Qj7+1Wfo/irhqScMH2/cBADbsPAgAibTFja1NU7GCTpRjDu8NgB9Q9JCcQqN9+0dRf6AJf1i4FvcuXItd+9snDlFngbqRiiqXmGsITiOCRxzcMgjCu/OCEY7zRpM1BNdCvb2mWyUWls4EMb7W7NhfdGFS7l+0Xvo/SqYpKe/cjX99U/pf2GEmaQxYoSlmoIOntESv85aFpsKE5KdeUknyfigEVIYhqgjPUsgBt6086stRO/fhxwfwxobdADyq3Y4EPcIagqs2Hp3x9RIsQ5JUM1EiirhqAu9t3RtcKIYQNk0JkfkAWKEpdqBsvbsIsXZO3rEUSMLeAg2+9nGo7zzIJVFrLnAX11Q0aRly3fHNee1D93up2DUW6SIYBj5DcM39Vo8naSceFsXmPaciFcUuJ4ukspKWabLIG1v3eDnKSgyLID2UShVGN1xMuvh8oT6XqOh0yjRFEfcm1/FD48OWZC17kzQBdjTCMk3q0c5yS4UX6f+78Niii9OkIgrHDYHamsoIaul4JJFttEJTzDDuV8+53131XADTVLCYJ6StoCjtxY5c0me0pV7HDDx/up8NbmmosFuF50tSUmTeUG1BWMaxszJNPavKAQB9ulUUPdMUJZI6PkoSaNdmhaYYo9Tduft/yxCiIqoYPblCYrs6rNVkQn0uuaYmCapXMgTPy6Ypt5O7lnuhPYTQZhdBPVR2Vic01R9oUs7rHBBevyVFGKdJRSoCb1eBpKrE0wkMOWCFphjDZFhLJ1/Je64jOsbAEk1miDmhR5UTaqA5IuMzOeRA9lg+3nM5lu9KmKZD2US9xapuCQP1sescKHxhGzrJLRWLY1lJqujiNPkQoXoyQTKHhEKvW22BFZpiDJPOn74k1HuuQ22aMvx3Cz/EMxRBLluiYpq44Jb5GIIzs69JCKNBO0Uk8KRO4B0B9V7qGIISJVdhZxEsBMNakk4XJExKIdG7a7n0fxS5IgWSMj7GHtUXAHDlpw4HAHy48wAAYPvehoL1KVdYoSlmqCLqDtOkInnPFcimiS4AG3cl03ujoyCeS3lWyGiKmGlCqv1smoztk9aE0JQkqr2jod4a3Z1SHQU6yx0VY6M0nSq6OE2fPekwAMB/njkMQDTergIi7EfcIZ71sQO6S8d/+s93CtGdNsEKTTGDtOAY2KNWYssCeCkJOtSmiTT1ypqPO6zdJEIIF2Ul0erwPZkp1W42Tab6qOx39jH9tHVYOFDvjU5g8AlNneSeivhkpSUp4vFbwA51IMQcUF7qTNZRMk0zn/FyuPasiibbQHtgX0MzAM/TVmDbHss0WbQRaqRvQPNSKbYsURoVtgXFsltsK1ymKTthNrVkIlkIaS6nVAQ2ErkuYML25oSBNTisZ5c21VFM8Mdp4supNk2d5Z42tzCG4J3l4gKg5l3zjud//UIYAYBhfbrmXV97YPeBRrz+4W4AXkw3gahsPDsCVmiKGahhqBhWPNPkfIqxVxCbJvI9qgjXnRWqTRMQzUJIbZqiMKrkxo8IfcHhB/PeAgC8ubG+6L2hwkC9N7oFU/Wu7Cx31FPPFS5fZqEhNrjt5TwTlWdu1Fi4eof73W+z19G9aTus0BQzqPGXALNNk3gBC7Fg0R2imozSQoZ4LFRoiiLApcQ0ucfMY8CkGhTjRwQhBLzQF0EIq24pFmaBg3rlYdVzncVaWlbPZVXVneTagkDDgwDtF6Ylrnnoysncd7CxpYA9yQ9WaIoZ6PxhUrdklDKFMASnL+fbH+3B3kNNhtLFDbE4CvUcEK3BtGTTZCj3zLtbccQPHsftT73H/k7H1eF9c6P5PSFf34P5KzbjhB8/iafe2ZpT3Z0F6iPXaSWaOrl6zjEEL3BnOhjEZ8P5bCfvwUY1XEVMUEbmviff2VLAnuQHKzTFGCbDXrFbFwtVIVIS/PfT70v/H0jw7qG9oXrPAUBzBDS6vHsNnoS/NmsJAOA3yrNT65PiPoUcU2FUxN+YvQz7Gprx9T8tCVVnZ0NYQ/AGn/dc55CaBNNE1TNFQjQRT1dhh5o9HvGzbYwp01RZ6nmGf/PsIwvYk/xghaYYI2WwUVGNCqPwnMoV/1j+kfS/dTXXQyyO1D4oCuPHXA3Bywz2SYDHfKRSqZy9e1x1ix0HWqiPRhtyQGWa4rkO5gwvuGU6khAZSQK1P6RfioVpqijzxI3j62oK2JP8YIWmGMNkCC5eQNd7LgZGuHaxNCB7a0pSKXeXHYXxPHUbCCPkBBmJtrpMU+7qAzeCvR0GWhS7IXgT8Z5zycwiGTBa77mI24mrTZMY60N7VxW4J/nBCk0xhtkQ3PkspE2Tiodf31S4xmMOL65WynW3jdwQPAIbCc8rM3eDk2L1hsoFYUMOqMbRnSVOkxzc0jnWOa4sGJ5NU/RhYgb16uJ+jyvT5K1ZyTZms0JTjGFSt3jecw4KYdOk4tca42ILWcgtjTBJpRxyIHtMMwbCeK1lKNOktBGEVAwE97hDFSh1AqZns+j830lkJuI9ly54bLmOhs6kIgpQ9e3+xhb8ecmG6CqPCDqmLWmwQlOMYYq74yZqTcsxPzpywUr64O9IeOpUZ8EAoomn8sy72wAAL63eEWgjEiZ1C90Ne+qTcH0JE/aimMfMoaYWvLp2p3RMyzSReEZA5zMELyVSeWdh0YIhb3TbyxAcAG7865uR15kv1I1+UpGT0PTaa6+hpcXzkFIHe0NDA/785z9H0zMLF6xNk+Ll5BmNd9wERGMOWZhB1V5RplJ5c2M9AGDdxwcCnQHCeOvR9DypHKe3MKxISRFLTX9ZutH9HsgKZmQvs84gV2QyGXfMl6RzdzRIOtrTeScJKvFWZc1KKnJa9caMGYOPP/ZyjNXU1GDNmjXu/7t378YXvvCF6HpX5Ahl06SU7UgvGxoA0cIMGhqgJEKbpvOPrwUAXHxSndeWZhkKJzQ5n1LIgZB9CeM9l04ne8LMB83keYsxoFOZut6W2XJqWpUkgo6LsnQ68bYtucITmmSbpiiQBKFJTf2VVOQkNKm7Ij65ZwIeXkJgUrm5tixKnKaOfHlOOCy5bqMdDbE4OobgzmsXBdPUp3s5AGBon66BO9edBxoD63MZzHTuO+Ew6rkilplQVe7FqRG3SK+ecz4PNTvM/oc7D7Rn1zoE1Fu0hIS+KJYlQ7eZiTIHZZyhOi8lFZHrV4pt99CecCl85mUTjJIoY2Kl2gsJeE9jA3Gv0qmUG6spijhNLe44SBnjegHAzx9bGVhfrhT6iYN6AABuv+xEiGwrJlmwmNVzlWWe0CQECJ2AKbzn4ppHrC2gQlOpVc9FrJ7Lv472BvUgVnFMbfeO7k6bYY1SYgyTCzm1PQHa16jQIn/QBMtunKY8F8RMJoP/e+1DAIqNiGYWXrAyOHUJDW7ptmMYU2XZa6kqLw2VRqWY1XOcK7juVnXG/Hzrdux3v0shB5JAk0QAX8iBCF+FJNxDb+PoHZt4wgAAwAUjBnR8h9qI0lxPeOedd7Bli5M3JpPJ4N1338W+ffsAADt27DCdapEjghZBgAtu2d698pCA9zQ2cG2a4LEt+SYqfW/rPun/KNhG6uESJu4TDYYZRkWcdHuGfPC3ZRt9x3QCaRIDxWYyGTS3ZrQOIjVdytzvTsiB4oLf5d7MDOdUdwR1tDfUjT7gjYkkbfZzFprOOeccaRGfOHEiAGeCzWQyVj2XJ44dUI2Vm/fg6nFHuAIQN3+qapRC2DQlaaAXGm6IiFQqMlUqXZsONrZEEiyQ7gbDqE+o4XgYwb2kiJmmPt0q3O+9u5bj4/2N2nuVCMNeBdfMeR0vvLcdL37vbPSoKvf9Li6pS1ZN2V4Ja+MKNY1KsXnPcXktC5H+K1/kJDStXbu2vfphkcWQXlVYuXkPBvTogo92HwRg9p4TSBfANTlJA73QoPp88azyZRPojn5/Y3MoZlKgTzf/okbPdYSg4D5Qr8BwhuDJEpqeW7UNj76xGT/+7PHoVpHzHlPCwJ5O+ogBNZU488g++MvSjfrgltnjfbqVY8e+YAP+OOCxtzYDAB554yP8x5ihvt895xXn/6IzKdAYQkdx/UmYi1XvQYBGRU/ABWSR0ywwZMiQ9uqHRRY0CKKJPXKZpuy6aVNYxBvUc0TIOvmq56jM1dDc6gUL1JSfcFx/PPmOY9fUVSMAcB4upgmNMk3uGDQIg0kL7fWVPy4GAPTuVo4ffObYvOpqyRqMTTqpDvUHmgDoFzth4D/isBo8t2p7Xu12NHRpPNxFUxxIIMuQD3w2TeJ4kTBNapgcgDo7JQc5TWGHHXYYpkyZgvvuu8+yTu0Eb2ClXCaBi+dD1T3iDHq8I8A1laQdQ0eC2v4Im6Z8jX3pRHmoqSUwLYUQmNRzKaTggyEmNMoehMl/KMV/StBYoUbMbYXwhKNG0MFxmrwpOs73a19Ds/tdN7Y81W/0cYqSAMrK0s9IbJriOzRc0BRNAklU0eYkNH3jG9/A5s2bce211+LII4/E0KFD8bWvfQ0PPPAANm70Gznmi+bmZvy///f/MGzYMHTp0gWHH344fvKTn6CVuGpnMhlMmzYNdXV16NKlC8aNG4e3335bqqehoQHXXnst+vTpg65du2LSpEnt0t8oQI0FhYvyoSa/0LTnoLNTXf+xE7+lIDZNTFOqcbKFjLC2P2GwmKTkaGhuDc0OAfogqDR9R5hFrZWEPBBqx7DqucaYZmNvL3gCaTpwDIiyZQmJZ7Rp10H3uy5MQsZHNWWPt1enYgb1OlMRUm1xFqgFTMnAk8CUCeQkNN18881YsGABdu/ejWeffRZf+9rXsH79elx11VUYMmQIhg8fjquuuiqyzv3yl7/E3XffjbvuugsrV67Erbfeittuuw0zZ850y9x66624/fbbcdddd2Hx4sWora3Fueeei71797plpk6dinnz5mHu3LlYuHAh9u3bh4kTJ0opYeICKo1XlDqPp6HZ388fPSILhoWI08RNd3THaeGBBrcsicim6e7nP3C/NzS1SGtR0DjQtd1MmCavMn091CEhjOBO58t8Qy50JKIwxRL3tiwdfK9amOcQ57tF789tT6xiy3jqKfmcBK2XecEXETxCoi0JzpYq0waY86vGFW2yMCgrK8OnPvUp3HLLLXj66aexadMm3HTTTdi2bRv+8Ic/RNa5RYsW4bOf/SwuvPBCDB06FP/+7/+OCRMmYMmSJQCch3DnnXfihz/8IS699FKMGDEC999/Pw4cOIA5c+YAAOrr63Hvvffi17/+NcaPH49Ro0Zh9uzZeOutt7BgwYLI+hoVqN67olTPNKkRgsMY4XYEdPYMxQ7JpimiZ7XuY28MjBrcM6fUJzp7qlZWPaevzVMTI1A9qKI5CTN9Ftw7mCtEGpWSEupBaWZlSonQVOh324SVm/cElvEWTcWmJ1FLZtvhExqV4/kgzmNDwL3+hHvPtUloOnToEJ5++mncfPPNOOusszBgwAD89a9/xec//3n86U9/iqxzZ555Jp5++mm89957AIA33ngDCxcuxGc+8xkAjjffli1bMGHCBPeciooKjB07Fi+//DIAYOnSpWhqapLK1NXVYcSIEW6ZOIFK4+VZpimMGqMQg49rq9hULmEhGfgLQ/AIhYavn3V4oHruiL5d3e86WxrKNIUxVJW8AkPYaMhMU3LGSu+uvLdhLvBUn6lA+y8h1JZINk15d6Hd8N8L3g8s054RsZMAjmlxjkdQd/5VtDuoXadAUOLqOCIn77kf/ehHePbZZ7F48WIcfvjhGDt2LK655hqMHTsWtbW1kXfue9/7Hurr63HMMcegpKQELS0t+PnPf+4mBRZBNvv37y+d179/f6xfv94tU15ejp49e/rKiPM5NDQ0oKGhwf1/z57gnVQU8BiJVE4DystTV1ibpiQthB0J2cssGqbp3z4xEH9bthEXnViH8tI0DjaamSbqMadjmoSHV2k65Q6qPy1aj3OO7c+WlybCEGOQ2lIlKYBjBUmB0lY0U3uxABZPvEbUpinObIJ6f7iYfX6mpcgMwbOfnnpSqKaKy6aJPveg1E9xRE5C009/+lMMHjwYd9xxBz73uc+hd+/e7dUvAMBDDz2E2bNnY86cOTj++OOxfPlyTJ06FXV1dfjyl7/slvO9nCGCbAaVmTFjBn784x/ndwFtAH2xSgyGtScMrMGbG+vd/8N4LnUEkrQQdiSkiOAhDKZzqXPkYdVwK3d/48p733XPSSzWJekU1mx3jPqff0/v8u6q59LBQTszmQw27SYGwwkaK1HE5BS5BktLgpmmVpdpSoZgQZMRA8D2vQ3oV10pHdPZ9CRhwY8EyvW7hyO4/CS8SjQZuEBK+S0JyEk99/jjj+Pyyy/HrFmzUFdXh5EjR+Laa6/FX//6V2zfHn0ske9+97v4/ve/j8svvxwjR47ElClT8O1vfxszZswAAJfdUhmjbdu2uexTbW0tGhsbsWvXLm0ZDjfddBPq6+vdvw0bNkR5aVrIWeaFa7q/3LnZnf8nh/VyyofI+xU12ETCCRr8HQlZjSUMwfOrs8VlefyGpdyzocf08YGydjfpFA42BjtKUKZJNkT3N7BzvxyksSXmhuD0GqIIytnshhxIhzYEpwFM4/xuqbIdtyFtz4jYSQBV0QPRhhyI89gQ8IfJKUz6r3yRk9B0/vnn4xe/+AVeeeUV7NixA7/85S9RVVWFW2+9FQMHDsTxxx+Pa665JrLOHThwAOm03MWSkhI35MCwYcNQW1uLp556yv29sbERzz//PE4//XQAwMknn4yysjKpzObNm7FixQq3DIeKigpUV1dLfx0Bzys3ZcxRVpKl7Yf0cqIMxyXkgIh6bCHjr0udEBdb9xwKFQQyDFQX3iDvuTBME7Vp0uUQ4/pAhUFd+8s37Fbaircql96jaJgmcW+DvV05pinOC4vKKnEypmrT5Is90MnhDnclTlW+U3ZzS2siBE9ubUqi4NzmvADdu3fHZz7zGZx33nl47bXX8Mgjj+C3v/0tfve73+Guu+6KpHMXXXQRfv7zn2Pw4ME4/vjj8frrr+P222/H1772NQDORD116lRMnz4dw4cPx/DhwzF9+nRUVVVh8uTJAICamhpcccUVuP7669G7d2/06tULN9xwA0aOHInx48dH0s8oIQcLzB5jRpTWqLK9O0j70IFtJR1b9zj2cf9asQXjj+0HIH8Bt1VxS08FCC2S0KRp+7fPOWEM1u3Yj4qyNA42tWTP5dXZcsgB2f5GTck66+V10v9x956j8YaiyKkp2zQJFpm/B+LxUO+5OKswttQfkv7nxrZ3iLd16uzQMW35oiEhHsss0yR+S9AoyFloam1txZIlS/Dss8/iueeew0svvYT9+/dj4MCBuOSSS3D22WdH1rmZM2fi5ptvxtVXX41t27ahrq4OV111FW655Ra3zI033oiDBw/i6quvxq5duzB69Gg8+eST6N69u1vmjjvuQGlpKS677DIcPHgQ55xzDmbNmoWSkvyNO6MG1funXDUONwGpqhnzJNxRiPG8HhukDQxiLlC9USSmiVXPkXMDxsmzq7ajb3cvwWxrBigxsAfUEFyUV3HykJ548f0d7v9xj9NEPUGjUc9RmybnWJBNUzohTNPS9bL5Ayu0q7nnEsgy6HD/y+sw+5X1+Os3TkdNVRlbxs+0ZY/nKTCIjU3cwXvPRcO2dSRyEpo+85nP4KWXXsLevXtRV1eHcePG4Y477sDZZ5+Nww8/PPLOde/eHXfeeSfuvPNObZlUKoVp06Zh2rRp2jKVlZWYOXOmFBQzrnBtX0ANhrlyzqd4AQsx+Li2kqBbLzTc55rnKiiEabGwBqnHKFMRRmBLS0JQBiWMOoULbgnwC0GJslqY1HMPLf4Qf1q0Hvd++VTU1lRqy7UnaPqiSIJbtnh2SkEelF4aFemmJgYmpsnznsseT9KFaSCCDZ916zN4c9p5bBlxlWruuXwv/93Ne4MLxQCqI4DzXfyWnDGQk9BUU1OD2267DWeffTaGDx/eXn0qWuw91IRX1jipMcKr58SC6fzfoTZN1hC8TUgbGMRc4LNpymF9zWTMHqS3TDwOf3hxDWnLvLinUsHqQdVbzqSe+97f3gIAnDbjabz2w3PQr3vHC040UGsUXqFNJJxD0GLBpZxI0rsVJv9gZ2KaBPYc0mdEeCqb+/FAo1MmKpOKMIFF4wDVEB6INsBnRyEnQ/BvfetbGDRokCQw/elPf8KwYcPQr18/XHnllVJsI4vc8LN/rnS/0wB4HCug6sfDhKN/f+tefPm+1/D6h7sMpXJAtrHvnnc0yrNGw3FWIcQBYw7vTXK05VeXayzMeSoZbUoccILAUf27AQCOqe2Oj4idit5g2fn0MU2c0KS4C4ZVz33jgaWhykUNmqQ3CqGJMk1BXkPi/iUljYoKjkX12WEieM7qjLh34VoANFltsajnnE+ZaUqeei4noenHP/4x3nzzTff/t956C1dccQXGjx+P73//+3j00UfdcAAWuWPxei8Ba0k67S2ujBZDVc95x/Wj74r7l+D597bjkt9GGwm9vCSNuh4OE5AkmrUjcUytY2P3lTOGurZBeRuCKxGGg5gmtT1uweYodO5cr7ynnqNB67jyauDTsN5zyz7cHapc1Kju4tmmRGG0/tamegCqTZP5vsrec/F9tyadWCf9b7Rpyv7vDrEYX1d7YH82P6cIXKpLcBwWg7Me1Gce2Se/jrUzqOmJQCGCMueLnISm5cuX45xzznH/nzt3LkaPHo177rkH3/nOd/Cb3/wGf/7znyPvZLGAMgalQUk9FUPwIPbio90Hffnq8gVtKi7BNeMKkXy5rCQVuXrO9Z4j0xE3ZNT2uHGlCmJqW7o+kADiTvtMWWoEDsTfEJwKSi15hkd4f6tnd5JCKpAZltWezrE4rythNm++4JbieDv2K44Q90GE9Mg39ZTunY0beO+55LGNOQlNu3btkgJCPv/88zj//PPd/0899dQOCwLZGUHVF6UlKWNiVy8kvQOT/VNTSytO/8UzkfaVtkUn9iTtGDoSUjyjdETec62y4ByUe24NUTcBGq/M7KfqLaYT8GjQTnoKNw7e3SIbrMY9ejxlxvJlmg6QQKEf728ITJFEU04UInBtrggjkKtHogjjkESIeyOEpnyDvHJqrziCjQiegA2BipyEpv79+2PtWkcf29jYiGXLlmHMmDHu73v37kVZGe9uaREMmrXeyTKvZ288913Zc4oru9dgnJgPeKYpQaO/A0G9zErcRTCiOtPyzp2rm8sJyNrKadS+2sXdFdyCvfdUqDZOFOUhAmu2N6jaJF8Bj6rZJp5QZwwnAsihHMSZcZYx1efNq355RqTYpgzVtCLfzZOnIs+rmnYHm3vO/ZacQZBzRPDvf//7ePHFF3HTTTehqqoKZ511lvv7m2++iSOOOCLyThYjglItaINbMmV3HWj0HYsSNBp0sU2AYUHVWC7TFFXIASXsBOCfgvYzKVE4Y10ulopznO8Dpdxloc1/Qm02anRlWVrqP4cu5YWPodYUIdMk7uuAmkqUpINzz1FbMU+V1/EvV/3BJvzxpbXYtveQsZz6LFlHhOxn1HGKkgZh/+lqEvIcW5zaK47ghOYkMk05hRz42c9+hksvvRRjx45Ft27dcP/996O8vNz9/b777sOECRMi72QxorQk5RqA84ub8+ml0NBPwmFyiLUFdKBb9ZwZXsJez1YtX6HJ9bAKoZ7j2uKDpopv4QzBdRHBueLCALZHl3JsaTpkTNhbWZZG/UHtzx0CGmmZY+pygSfgOvdIEGm6BVOyU3Hfrby6kDMONbXgxB8/CQD4v9c+xJPfHqstG87JwHsHgGQumFHgpguOBRBd4m7OwDqO4MJoJNF7LiehqW/fvnjxxRdRX1+Pbt26+SJq/+Uvf0G3bt0i7WCxoiSVQsYwWfpDDpDflPg77bUBcXeOsIbgQaDqlpKomCZiT0Q/AT/TxE3MnFpAzzQF2N4ohuBceSGEVFU484bJuDpfj6Io8PDrm9zv+YeHcD7Fsw+KCs+Fcuhom6Yn3vYSob+3dZ+xrF9oCsGOJ9AIOB8M6V2F9R8fQM+ujgmLNwbyq9dj8OItNpkM1pPENrbJcKCmpoZNQdKrVy+JebJoO7bvbTDuRNQJSA6Cx5dtL6RSnnGfZZp4UINpYbt217OrI6mzhDFm8NuYMAISI7NoQw5o5BtqV2US2gBPQKgodeYOk2DUEIPYM0f191IxRWV/pgpNQWlUUqnoErvmioE9u4Qum4shuBpyoFimDPU6o0vcnQybJoE0s6FP0hgovLWlBYsTBtYYXyo195ysGgmewKIAbScJHj6FBJ3YXnhvezR1tvonSy/KsGpjou+TXC43pokyaLR9ltlqFUJTsE1THBjLXl2pU0s09mfi/gSl0hFHZaYpry7kjD++tC502TAbNX3IgRg87A5ARhEbo1PPZWuNudDEMU1JZBut0BRTdCkvMe5GM/L7Z0yW2l4LEO2ClzC4fdpKOlzhIsLtoGBqKNPkLkQhmCZTyIFUG2ya6Kc6C1LhoDwrNJnshOLAWNLbY+pOU0srGprNzJi4fmF/FuQM8NibmwE4qZU8L9qOvSf/zPYhDMKp52STAi+4ZVt6F1+8t5XPBefTDkSkplc30HEF6z1nmSaLqNC1vNToxq8a1anJVSnaLR4O2TkWIvddkiBiJEVJoYv0CV0rPNNEnWElHQOmZ6WzO9A9Vl9Ucve40j6pQDBNJo+09hpGG3YewLOrtoUqS++PqTufvesljJnxDA4ZVIri+oWAa4rBRvGrJ9+LLEdZeyKUIXj20/P4jfci31as/5gPIuwKTdn/w46BICTHe8755HPPxXl0y7BCU4xwypCe7vd0OmW0E9LlngPCsQxRIpWyhuAmvC/tPKOb2NZmBbGu5Z7Q5KpyNOq5LmUlrpBl8p5TJ+AgQ3CVaVLbp225QpPBpqm9xuy5dzyPr/5xMZ59N1hwkpkmvj8trRm8s3kPdu5vNCZOVb3nTIIrTRRMz4nzhiSUTZOinnOPt1uvCoMgE4UwsfVyQVIigqusNJBMttEKTTHC2cf0AwCMOKwagPmlUhc3Uzb09mKa6MJYKA+fJGDbXi+J9Z6DTZHUSe8zayOgEZyp954x0rzPpsnch7RCNZlsXMpDME20b8P7ReeRe6jJEUiefndrYFl6fbqe0lhOpiCyPkNwg2pm1str3e/jj+1njMGWCzKZDH72z3fwp0Xr8qpHRWtrBq+s2Skd4+M0Keo5Q9nOCFU9GZV6LmkRwa1Nk0VkEIPq+AE1AMw7THUASjF6lLL5emfoQOnmoKztxQwRzBEwR8HOBXRI9M8GjQTgzsi+MUCEbKEW4Lqis4/gJnZ6yAuwKdfjnk/+F9G+TSEHaN3twbA0NQfXKannNMWp4FdvEIjFpaZ96jl/2c31XiDJC08YEFng2FVb9+IPC9filn+8jb2HohHeAeAZhrVjh7kqkCfQniUMgi5HXL94Bs/n6RiSlIjgHNMY1YagI2GFphjBH0bA+TQb7MrnAAzT1G7ec17jbl8TNPg7ClQNlW9kaQFaC2sjoDHMddS++h2uanfiHjfYPznlhUdUsE1VUMiBMIE580UY4VVSz2nLeL+EsmlyjYD95wsc3qer+71314rI0qgsJAmTv/3Q8sDygvEOwuY9/mjh5pADylgJ1UryoV7/Xc/kF3LErTchNk0mL78kjQErNMUI6oJlckumcX8AxaZJWQ/URYeL65MPUqCLZZKGf8eACpJRCU2SwBLCG4WGJzAnguZ3rVy36fl+polvH/DUc/okwPL/Ud0zClM0cq8flGnSCHjkXTNlqxcClapO5+5BIxEmTx7S0zPuj3BpWbAy2KarS1m4VDbVlf4YybmlfgrVTGKgux71eFSetMmJCO6fW5IYEdwKTTGCN3BUo1p9WT64pXmnHnYyDIJk02RHkhb0/vfoUhYJjS494lA2Tc4njftjWtiAFM4a3oecbyrrj0puYjuF0NSkUc91hE1eU3Mw0xRmIqd9VQ24KW74yxsAgCXrdwEwR4UX9Vx0Yh26VpR6sa/y1OyeMLBHTuXDLmRV5X6hiTtXFfqS6DmVD7wk687/351wdET1IltvvMUm0U/JEFz5LQmwS10M4TfC1duTcCEH1NLqxNwcUTAlKrglwcOnUKBMyVnD+0RCo9OFRjKs1HjPhVXP0d3gMKImMrFSorzcvlKWMF2lWR1Vi0Y9p7ZFbXyiQhj2SjIE17EH5HuDQWhSVZElhh22EJq6ZxkcnUdkrqDX06OqzFDSgclGi4LG2zo6G0U9jPdczNf4NkPkWFSh3pLe3ZzsGScMrMmrvaREBFcN4QFr02SRJ3xhBNIm6lIumzIwTb7/IwpASXX0NrilHkI4OHFQD6RSKdz2uRPyrlNieeD/bvKe27jLyYT7GBe8kCxstF6WOSDHvMTRcnsCNE5RaXZc6wQXrq1te6MVnExG6AKyTVOwgNfQFH7wi/eFswE8lA2UKQzmozIEp9ez+4BZIDrY2IL3t5nzzQkIteQZR/Ymnpn+ct58oRxPznoZCtdnWUUVqvlFVOpJVesQV6gmJYBlmizyhM4Q3LRgUb24LoWFamoRFdNE29UtlhaecCCMgI+vc3aWvbpGk6eRMlc6la7rvUXK/mHhWl9dVLiik5uJlQL8UY516sFUKoXStAg5EE49BwAfagIG5oJ6IijQMBA6hPGeo2UOGgzBTz+iNwDgS6cNBmBWz/3uuQ8AwI37FNW7lQtT9VH9wdBlBYtWVpIOyJcpq6fUqPNJRViWxPM2Vg3ho2EQ424Izhmsp3TUdIxhhaYYQR035ojg/mO6Ham6m23NREOHykxD9ljetXY+iIVRCAtRRE/nBBbnH+dD6z0XMLFS9lDXnnfM+64yTWr7u/Y3AnBUTy7TpFXP+Y9dM+d1Y7/DoKHFE2pEgE0TQqVRIcdXb+PTZwCeHeGIrMBcEiLB9atrndhHURnL0vPLSszjQLBcYSA8EUvTaWO8Nj3TkuxZI1eTu6hDLiQlThPnPaczJ4gzrNAUJ7g6X3kB4tQYHCWrW4w577tojGs9FaFN2KuHmkLDU2W2/V7J6rlgujts1GBaTg5jwfXBO+iPCC7j4dc3ud9LS8zBLTlBYgvj1p4rqKF2GA9SObhlsIBn8kjzCwzhbQB1DHKuoKc3tWSMYRdyaevxtxw174KVW81qeh/TIh1OLMLPeSrTJs7Pr/2kRATn4klFdQ86ElZoihF0EysAvLtFTtGgGoLT8uoA5BandwwpH3IFXWBtcEs/hP2MMICOYrGg58o7N34MqGNLWy8RxktIYT5Ok/ddNQRXF90TB/Vwv3tME79oqyEzosIWYlDeu2tFYPlc1XMmeKop59pLTMJFFvd95RQAZi/aXKAKfgcaDXGlcniRXyTxn8LkNXQXzQS6m3MIe6t86rmonqu7FuRZUTtDXGdanrCc3xI0BqzQFCN4L5UD+hIs+uBjuaxiCE6/h2GaPnf3ojx6mu0DZTsSOPg7CkIN5abQiOBe6Xa3OpWHOmHr64XbR8rGBMVpUkMOqN2rzKqnThzUwxUedbGSuAX3uAHhAi2a8HFWRQh4AqwJVH0YQjtnhKf2dGBSvR/R1/FaFK78Uamx1Nt9oNGU9kX+36Su+8rpQwEA44/tH5DFIPtFVeUmnGsK23+1lE6VnXP7CbFpctchjmlK0BiwQlOM4MXxkBdXwL/z44zqdIvxjX97E4C82ze5R4fvr4MUzLF/ih0triG4/FzzuVf0zHBxT3JXz8lCUwjmAHrBnboblwn1nIZp4tq66MQ6c8dDgCY2DhMR/PcvrPH+0TFNIWkG1e7EmIxbebej8p5TF2cT06T264wje2vLCoF4SO8qo0eYxzQ4n50luGXY/ptSX+UDRhaJJXjtiPOZpDFghaYYYXXWxVdlJABG3cLosTnBZSfZXb+xYXeEvSWTcMraNJnges+5Nk3O8byYJrLmh1LPhZxYKYUuCU0Gu7owgjslGYQRsi6NCieHdKvIPyArdYjQta1DVB5OfqbJX9azgYN0Tr6qb/WZHMxBaDK1TZMRm1ROqjNC5/GeC1ku++lqJzWBaHNuX9lsxxWin/ImKxoVZUfCCk0xwhNvO5nX3QSOZHCpHnDeImResBqavYmRRniOEilYmyYTxGLh2jRFwMpJwS3JcV0gRG68mPqaghplXl82zM6RCm2CadKxPVRtNP7YflL/8wEV/EzRuznka9MkIBgmV2gyOGmoQSDzVuMod1EXhJH2wTtXD1E2KNr8P5Y7zgBLs1HRw9SdBIRWz7lso/yZr0DOMThxBGciYJkmi0ggGCcqket2fpJNEzNhrSYB6u6a/IlI+0l7lLJMkxaeTVN0wQo5e7Lsf2zdoZkmokYKVs+Jst4xz2hZLe8JAqUBQtPC1Z5hcUVW9ZOPp6EAFQRMeeI46FpXu6XNUacsGKaQA+KQX50bvr9sX5VLPmQQHHXMNgdPaPKuj+vr429tkf5P4oLJIffnoth05ftcidZh/LH986usHWFS5ydJdLZCU4wh7fR9Nk1+9VyKmVxpNvOaLsGpE3IBXWCjmtg7I7w4TQpzkMdEIQms9LuW6QmWmujCGMqmiTAMvt8MQlt5lnHTxWmix93EthGMK8rW5iqE6YUh+XhQGAXVM5aLCN6iVefmyzTJ0NmU0T6Eqpeo5wSTpvZ1MxMsM4kLJofwwS2VOdudB/LtgPORTgE/vfh4AMFxuAoBul4IJFFwjr3QtGnTJnzpS19C7969UVVVhZNOOglLly51f89kMpg2bRrq6urQpUsXjBs3Dm+//bZUR0NDA6699lr06dMHXbt2xaRJk7Bx48aOvpTQGNq7CoBqCC6X4e1JxG/+Rac9QHX0OgNgC79NUxQCpirguN+znyZm8stjhgAATh3aU6nT+67aNLGGvWSyds/TLJqeejBYPUcjpXu78fzHFRWUwuSeowjLNOnUfuqCYQo50KIsrlGFHFDHhOkeBKViohD9TadSWjudMTOecb/7Ng8JnzLCdp/Ol85nNOx8a4j7Hwdw8aSsTVPE2LVrF8444wyUlZXhX//6F9555x38+te/Ro8ePdwyt956K26//XbcddddWLx4MWpra3Huuedi714vOu/UqVMxb948zJ07FwsXLsS+ffswceJEtLToDSELiemXjgQgD66Hl2+SyqgZswF+Mf7sSY7XkVgo2wOplDnlS7FDxGkSC2UUNk30GXM7NxXUWPQTQxxhqUxxI5dCCMBb3ICg5L6kfY16hgoNnnqOv36aKDhKmZ+yOstzdIrQPSp1wdMKTWpeSaNrvixkC+Qf3FIRmgz0nd/7UV+vkH0dQ/BsecMy+L3zjwHQecKUhI4rpgjO3r3KD+67RoZLHG8pa3sVEYvakSgNLlI4/PKXv8SgQYPwxz/+0T02dOhQ93smk8Gdd96JH/7wh7j00ksBAPfffz/69++POXPm4KqrrkJ9fT3uvfdePPDAAxg/fjwAYPbs2Rg0aBAWLFiA8847r0OvyYT+1RXYuqfBVaPRsbV2x36pLE91+u1JxECtqoj+UXOMVpLibXQU3EWlRGaa8rJp0oQQ0O40CdPk5X7j2SjRR9kQXC80cR6c6rRNhQbPe45fbYSAdliPLpFGDFYFvxfe245PHdVXW/6Y2u54d8vebPsa9Zzyvy6Uh2oEbAo5oAtRke89yCUHpU7oZcsSJjVM4m4152LS54x84zTle/lU6xCVKrd9IDZZ3pGU9EsyEGum6ZFHHsEpp5yCz33uc+jXrx9GjRqFe+65x/197dq12LJlCyZMmOAeq6iowNixY/Hyyy8DAJYuXYqmpiapTF1dHUaMGOGW4dDQ0IA9e/ZIf+0NT4UiT5Z8WXnn6pTP/kYmLGqk2V5IpXh7qo7A+1v34vt/exObdodPMNrRcCOCC7UE+a3NkxsRgih0O32qHtMli5U88tJyAEjee875lJNGBzFNJE5TgP1POs1vBNoK9VbPenmdsbwUETxEGSCEek55t9mQAxrvuXyZJvV8U9gF09jwlSWMoy4H5WWnDHS/T8qy351FPRfW/ksNOxFVRHApybZoK8862wNiXWI3+nHssAaxFprWrFmD3/3udxg+fDieeOIJfOMb38B1112HP/3pTwCALVscb4z+/WWPgf79+7u/bdmyBeXl5ejZs6e2DIcZM2agpqbG/Rs0aFCUl8bCtxs1CE2cTRM3ubpUfzvYNtGFIKqJPVd8ddZizF28Af81e2lw4QKhudVbVOgn0HYh0xOC5OeqjZNEnpVWaDIwTWwCVkPIAZ/jgvgdKVdo0gkYlMaPcmFVr3fvoabQ5fXqOfn/Ro3KX41Rows50NTSij2HnFAAvgjyxt4GQ30373jqvdBljXGaXKYp2Kbu+xcc4z7/zhKn6coHws09dONCP6OKCJ6CJzXFUQjhbZocxLC7WsRaaGptbcUnPvEJTJ8+HaNGjcJVV12Fr3/96/jd734nlVMXjkwmExiPJqjMTTfdhPr6evdvw4YNbb+Q0JAXIdMVGN29M1y58BPUoaYW3PCXN9xEnEEopE3Txl0Ow/TmxvqObTgHiFvCC01tu2GcETbXpve/N2GVaoQm1aYpOI2Kvw+6BZ56Dgn1nE49RIUxkwt7rlA91Q41mY1RJKFJM62rz09Xp7oh0oUc+HDnAfd7zypZTR+VwbCAiZ31PRpD0zLTxFNNrQbGO0kLJgc17pQOKtsYlcBAhbE4C6LqPAhEN7Y7ErEWmgYMGIDjjjtOOnbsscfiww8/BADU1tYCgI8x2rZtm8s+1dbWorGxEbt27dKW4VBRUYHq6mrpr72hCkJmOYdTz/lVGS0MGxCEhxZvwF+XbsTVDy4zlqPtFDoiuCk3VqHhT1Tq/dbW29VKd5cEuklIYpFCMk2De1X52mP7IBmC8+U59VxQRHBH7Zs9P4KlVbULpIFfVWQyGaz7+AD5X1dO/l8X/8m7H0LlpnsG3v89qsqlslHbNJnge34m9Rxn06Txvkt6Co0o4Gea8qtP8p6Lr8zEmpTEub86xHelAXDGGWdg1apV0rH33nsPQ4YMAQAMGzYMtbW1eOqpp9zfGxsb8fzzz+P0008HAJx88skoKyuTymzevBkrVqxwy8QFOp03B1MeHzoPe5444fux60BjcCHIL3uhbJoEwiRgLRT8alfvtzYzTeKLctncGKDlU6lUOKYpBZx+RG/2N/UYxzSpa6xnCO6p55q06jnKNGXPj2Bc/e65D5R29GXvXPC+9L+u/TWKILZ47U62nKqaKdEIQqJP1Fg6rXmmuUJ9hqa4bb4MBCb1HPH2040/dr4SdSeeawoH/3VGY68nbTJoezGTRlmTkhiHSNAh1kLTt7/9bbzyyiuYPn06Vq9ejTlz5uB///d/8c1vfhOAswBMnToV06dPx7x587BixQp85StfQVVVFSZPngwAqKmpwRVXXIHrr78eTz/9NF5//XV86UtfwsiRI11vuriALmxBUGP/OOc5n3RyVI1Kw0B1RdeBevAVyqZJQHXPjhNU2x9TTsFc61SvOqUh/Wl50b6qHpPDGDjPVaTeCZNYVpyn1kXLplIkRpHm4iXD1nZ8rKax2q+6QvpfV/KWf6yQ/p/xr3fZcuq98oJ28sJJmCTIuUK1nzrt8F7asuqCa4zTxKRRUQUBNhp0Eg1aFJjYShXq5ikqpkn2nst/bmkv8AGZs78laBDEOuTAqaeeinnz5uGmm27CT37yEwwbNgx33nknvvjFL7plbrzxRhw8eBBXX301du3ahdGjR+PJJ59E9+7d3TJ33HEHSktLcdlll+HgwYM455xzMGvWLJSU5J8ENEq0tvoHVVBZKixwKjIxT+YiVOQqgDgLMbJt53RqdIjxO6faldHn29bJQp2ABXQTMWU6dB5O9IBrV+caLPv7wKtc+N2zzEo6n1w0bNqWHKwv+gd85pH6XIyH9egi/a9rP2wOO1k5pw85wKk8owo5oAqyJu859XmbmpbiNGls0DyPSIZlMNQdd5hiXalQN8VRsagZRiCl7cUFJvvauAl4JsRaaAKAiRMnYuLEidrfU6kUpk2bhmnTpmnLVFZWYubMmZg5c2Y79DA6qBOrCYIl4IQmOmGZDDB1COtpFyebJt0CHAeoXiOReM8xLA+gNy6tP+B4ijW1tHoLl1KI3kOPERH95NRz2TZZVkTpL6lXjFndtVNhzIswzpfNBUf3745VW/diUK8u2LDzoC9eENeHIIwa3AMvrf44sJy6y05rhFETI5Pvu/XAK+ul/3VxsoA2qufI81JP4N3NRdH8H25ra0YSyDoKOc07StFc2H8TqKDtV8/Fh4H35gDvmA05YJEfNAshh1ayuxPgWAaODQhCWKaJsh26xbKjkGtajI6EqnKhj6LtNk28ek7HSkzN5iB8b+s+g1s4FZqcT53tDS0vpfLRpVEhQoPO3d5XbxpADmzEoSazqqQp+9J0r3BseUwsgS5tkYrPnnhYiJ7p1XM6g3mevcsPKzfLseZM169Lg8PBVc+FYJq4DVm+13WoqQVjf/UszrvjhZxy5kWB0NHA4X9nPaYpvz5L87DEYscLfAYBB0lSz1mhKUbg4ljoIHY4PNOUIeXk38KAGlWbXmiPGUtp1TIdhY6eLHOBatNE94Ntt2lyPn271RC7d42ttqQe9lSJvO0NbUO2v+GFLMqimqJh03OpN1CQcDn98ZU45ub5eP3DXdoyYoxUlpmDa9KyXv81fVWOdynjVf5uOcE0BannyLGobJpUmCKC+4VGgyqPOJvo2CMuPEVUTMvC93dgw86DWLV1L770h1cjqTMscmGatDZNefZBimsWwdzSXjDaNMWsryZYoSlGoEKIiopS+VG5huCM1E4nV47uD0Jp2mvLKIyQSSCKJLT5IM5CU6u8XkrPoq27TJ0qV6eek8oEeG7RMRVGPccmjfa7z7lt07HCXT+nbgi6Tf/7whoAwK3zV2nLCGalorQk+79JaAinnlKH3QUjatlyGd8Y4O8BZ/cRlU2TisbIcs95mwKdF62J8c73uiqJoLpoTbCqNEq0RZD14jRF81wlBivlPx4XaDd6iB8rZoIVmmIEzrj3v8YdAQD4t5MHSmXNhuC0Tn+5IKzZvs/9bjIWFUjBvLgmFdv3NmBzff7pWWiyXCAqmyZlFc5CFydHKuO2LZfhYnqZhGHWcUFjq0Mn9pKA66esRK67cdNCIYI5hmKaQqqnTGEb5OPOp3g+9B6w6nQyMwcxc22FSWhsa5wmnZNBe0aD7lJeuGVMp2Lm4G50fExTNOo5NU5T3KZi1l4POb7gMYAVmmIEbmKpyu6i6Auwa38j3tpUDyDYpqktIQf+sHCt+10XrA+QX/Yk0qwmLPrgY5z68wUYM+MZrN62N6+6VEYmCpsmNmM4iAylVHviwBoAwJH9uumZplb9gs0zQv4+6BZNuiGg5Z9btc1Xr8Rc5JgXQles/qCXMqXETVhsEBqyfRAxrbQJe0MKV6oqk94DKqCxqWnaKZaNyaYpF6aJ2ivp7HRMceXyNwSPRs3XFohnVxYiTpwuTEjeTBNZNwp3J4Kh5lYFkhlywApNMYK3EyGLEDNpP/XOVvd7kE1TW0IOUJhcqrmFMG4B1dqKL9zzivv9B39fYSgZDL8tA2Va2nq/GJYHemZmYE8nuveU04ZoJ1bOWNdktM0v8HJdXt1eCSqUbd/bYKjXEB4hR+whQlPXCmcjYlLpit+EfZ+upC6Iow5uwl5yD+Rckc4nK1xEtLD82ycc1rophE1TkKejU1YI2yl2DgJ4xjsqpsnEmLU3dJsXDuQVcD4itmlKpeIep8n55GKQxa2vJlihKUZgDWsZtddB4iVEk45yNH5bQg7Q+DUmt2TCMxU8Inh74oh+3fI6n3uu6TxXDI12zmCITQURXsDlmSODek4RBml5Xfsq09TEVCwn7I2GZdm+zxPOjql1UiKZVM8ug+BSbZpyhjQo8nHnUw05AMiqTDaMg0blmSv6dXcCdo4a3AOA+d1WBWjTZkjcxhLD8/IYb+9YKiKpwaci7cBJqJWwokHw3tlobZqkYLC0vZixN6YE3/HqqRlWaIoR1JcK4BetcmIUXtPFizXDvYRtCTlAVXJGoYnSwp3QpklARMVuK0wBC9ts05T9VNWuOlaCxsnxnpVcJ2UMvH6KspxwY5gENeqddEpmG7hUKly9YceVrtSlv33Z/a5LI0Px5NtZNjdgUg+rxjLF6qKMD+c9FxXbJs4XeRpN6jnBMIu5wKiek3LPZY/5GDjTgpnflR1olENNhE0DFQX2ZDetBwPCXXDwbkV+1+/OBZDvb9ym4lZvgSNInobCCk0xghg2HCPBecQBcioErmxbQg40hxWasp8p0nYhx357vXj52xw4nxwj0+Y4TQzV7bShsVdiFi1/1G6OEdP3kzXu1QiD3liRjVW58UWNocPsRHN97kLlZhrbC1Y6QtPeQ83GNnSMmq9c9lNsbOjGZ39Ds/vdFMsm3w2JOF20bWLa/t/Dsko6jCF4Om1Ko+J8sjF68ny//vNPS5S+dhz+vmxTqHJ0/KiG4PnCfW/T8rsVtw2st75ZpskiInCSOKfuEDu7C0bUKhF2/WUzZAEKC+pV1NgcPJypG3khdwztxcpHlVCTz9HWtrq98xSmKfup1krVPjoVGhf7y6R2Ze1v3PZVacITsKjNFCe4SN5AIVQY/373Iv2PWfTp5uWSK0kH1+nrk+b4zx9fKf2vU6GpMWroPaYhPrh7qkuCnCtEH4TQZDKEP3ZAtXKuvl5ZPaRRz7pjixyMSmrQ9KcjMLR3VahytEvqVUe3KVO85/KrNnII9pIazVubJou8wKnnOBdydxFWaAYTK5WTeq45rHrO+x4HmybTIpAP8r0mE4PT1smCY6/o/zpDXOrGr1XPkUpLDO7u5pQfSn/J75JqimE7qDpTG/cpi4ONLVi6fhf7GwVdrMOEZVARtmgw0+SBuzaevZN/ayvE2SIhN6caFfhUViUtvC5NLbshB1IprbelyYs3n8uiNp0C7TQNsOibtRMbcZgqZOovyss9l50D8uwDVelKwS0LZx/PQmzG6SYhqmj3HQkrNMUE9CXj1XPeMW5xA3jBRS0r7HMO79tV2xfKNIVVz0WlQsgH7TVZ5p3mIPsp26o5n1GnUXGHhEY9J+9GVcFK9M3PcnDdpDtcX3ldWaQkYZ8TdL0xq78egT3Koqnz9hTeg6JeQH/vqXp68ujB2ebDPSedgO3eW4bFk20QRR+ZcqF6oMfO/Y6tj9jpc0b4AjT2ktMvfVk5GS+/eTKq5/K4ssff2qztT0dADJWqslLluPJuke/iunX2f7mC3lspcG7cRBFmo+dNRTHrqwFWaIoJONYG0IUREDs7uY408xKqIQcuOrEOADCkl55WpouGKU4TVbnoFsuORHsl7c13EubYg7wNwXVMk7t7lSuWJ1aNeo4JmOp5buXKNPFMlyrlcUyTpJ4LGFfqs3l3yx623NG13QE4caqC7j31tDv3uP5Sn4KgK8fF6OHsANnglhExTQIbdjlBPo0R0TMKK2BUzzmfUnBLjXouDCuZC1yDfYKOzA7APS/nuPw/a9MkfsuzD3QuiHPIAeN8UYD+tBVWaIoJuJ0IoBOEwjNNasgBjxHS94UuZMY4TW67fD87Gi0hope3BVHZHERp0yTgHwNymwIimW1ZSUrLCvLCnfPJCaQ8e6CxaXF/V+owCmP6YIluvcphLiFsa2sGc179EAAwalCPwLFKF92eVeVsOwLdKx2G4Ywje5v7mf2Ud9n+DZEpuGVUskDfbuVufToBQ3QpDNMkp1EBW944VnLrvoSn3/WCo1aV+wMBtzd0c7E/oroHN+RABEKjcz5hZg19KDSI2Oh9i+gedCSs0BQTyOo5bqfvlQ2yaeJsJNTs6qYx2iR5z+lLUpWLO1l2oB7dZzfRbkxTvuczwkhApOnQdSrHPSFDPt6QFX67lJVod3de4lWO6fT3wRx3RT5BRLBf9/F+6Xif7hVQISV3ZdTTXJ8FuNQoVIX3l6UbA+3vxEahe2VpYITlYX0cNXd1ZRnbHwFOlck9B46Qc1mMiMb3sD5e3DGd+j1scM9DTS34cOcBAIJp4sc1F84i6gVTCMztNQ9wCC00sbvi4Dkgk8lg3Y79xthTnkAeb0NwNmGvhhmPM6zQFBO0si+V9zLSiaBFYY/UspJNk7LDC6NHD2/T5M3wnhql4wa/uktuL1o+KlfvsEEjc6nTH6eJX+TE8y4t0QcgdNUskiG4fmLPJRDjGxt2AwDe+UhWnx3WowtTL2Wa+L4K6BgtCmHLIxAU7kGopCtK04HviydcpLXt07bo0/I2GnSTI36LnmkS8alouANd/j3Vk1J3r5as2yXVr9uU8XnHBPJ/b2+ZeJx7PztUPZcd5+oG1m/T5O9TGNXUrJfXYdyvnsOPHnlb3wdqCE7GTVyZJulOWabJoq3g8rjR73RQ6ZLwchO8GnIgjNcW9arhvFNUpJC/ENAWvLh6h/R/u00S+dLn8C8Y+RuC89At8i3uLk+vnuOjNuv7aYoppJb+1FF9AXgJqEXUeW6Bo2M2yHtO7denhvf1laFs6ZDeVaxzBUVDkzP+y0vSgSok0X9hXB1kCM7ZK1LkYieWK8TZktCk2RS5QnZAeAZqy9PQ7AV41DEtfCDUoJ7rMaCmEgBwytCeRgG/vdBCntc3zz7CPe63afK+qzZNpvnlV0+sAgA88Mp6bRlV9ZlvtoH2Asu2Kr8lAVZoignooOESoNI3QMxzKsvA7aBV77kwtjQ0QvH3/vZWyD4H1xs1fv/8B9L/pqz1+SDomhqbW40TtXs7A9SuuYCjugG9zZqbS4wYV+s87MKnUZEFcqe83D8BwVgKd3edGo+2RdUNYZkmziuUCma/n3JyYEwxwTSVS0wT3/67W5xkziLKdlAKjxTzPdCmKaINiWinjEZk16jfVacArdBE+nnsgGrtpowNpRBBellhq1dZVuL2tSPVc+4GNpXCd8492j1uGgfiqsN4RXbJ2mmF6YN4D+Pqxs87A8SzryZYoSkmkHYi5Dhr3M0MPlo2I5VFtmxKW0aFyY6J63OYxa098MqandL/7ZVzylTt1j2HcNT/+xeG3fS4tn3ueUXFNOmcAdRpiKa60KblcJ8n00+WEfL3QTe+9jcIQ/S0sRygGBYHLKw+g2ODYXltdSWOqa0O3Dg8mN3VU0aGE+5WZO20AGBfNqp3kMqvtMR8r/h8fqIP+cF9XumUF3ZAwzSJfgjvOZ2AKVKxHN2/O6rKS7UxwN7fts9pm2HX8rmuQ1lWsLK0JO+NSFtAN7D0/Ta9116cJgemDVd5SfASTW1Lab2mPmQyGRxobNb+3h4QvZEFZ68/SYEVmmICrXou+8ntRlVPIU7toKryghbrj/c1hLYJcF8CIHD33hEohE0TzWm2UuPuzgk4+Qe3NDNNpp0+57VF+8nnPtQLIyzdriyF81530k08/972bL2iDl+1kpAZpJryMWpMOZU1SRsW13e37MHDyz8CALy3dZ9xM3Dp77xn//G+RqnvKg5l86NVlXvxfDjCz3hP83i3JJd3eMKQLv9cq3LPdK+WiLNVVirPLzp1qhSINE+14/6GZjfnW2VZ2p0PO5Lt9pwn5M2jOg65TXEYm6aP6g8F9oEmwwbCzS3Dbnocx93yBBvnqr2gCneAf/5KAqzQFBPo1XP+F6CFmVhpWU495+nRzS/UFfcv4X9g+0wW4gLYNKlor8nSVOum3Qfd76qNmXs+YwQcVe45tUXdROwa9hpYQY85pPWZ1HPOJ0u3K+Wrs675R/brJvWbu35xqESyv/K375QNZpqadUIT0/b2vQ3S/yYjbBqOQ8ewCAjvRcpe8Rsi+Tfa33yGd6syv7j59zS0jBunyfWeMzNNJelgBhEADu/jqU95TjQ8ln3oCWDdK8u88BiFiNOUve4SzfVzm+IwKYJC9UE11wjYGFM71asfXJZf4zmAOg4JRHUPOhJWaIoJdANc6Km5NCo+Q/DsJ11I6g86L4h4mYN2gsuzXk65gCpROnKXV1EqD992E5pC1qvdtbNqLPFbG4Umtx51DPCTEJeJ3pew192xenWGS6PCXZdc9phsLrMzjugjn2NgmlIpj2oKSk/inauvLwzbSlM8HN63KxEgzc/p/BG1TiktI+axZwJpxl6IYyXbkvZF176oO4gZ9tRzQUxTRiqnYwbFdQ/v3909FsZUwAQR5gFwbH/S6fzvU65w4+ApArnPe05impR7lafiVR1bQcnTt+5p4H9oZ7jCHTkW1T3oSFihKSagQ0a2Z/BPLGrASu88/wS3Lbtz3p0VnoJ2xG1BKhWd3UU+aK8NZlhbKeo9RMGp0iILOaAc101CNLaXjj1qZeo0p1Hxj0Odeq5RYVpMggBlsIIWAJ8HoEE95wZ4Ndz7Kx/wmNZ5V58R2lavXzbelJYRy36ynoaM6l1m+2CsOwzofUqlQ9xXV8hOG8u1KEKTJzTI5bpVOEwja88S5gK4trOdGtizS7avHS80qSFduE0uoJ/fAZOTQ7jrUMdWkB1gmZpKooNgfgc6vDtthhWaYgKdek6ATcKrC26ZLUp3O0N7O7R4KmCHqaIu69Ib2OcCuPuqaK/JMuxiJYxSdefL7ubOZ1vvl9amSbMYysbVopLgOsMIN2zuOZ9AJhZhuZ9mmyYSp8lfDIB/R88agvvUc3I7FHsPecaxNV3KjO1/7uSBAIDuFaWucKFTDXFxmriYRpyKNIgdDgPVpiZIna4KQ7onIGyahBpP11eXGSXHguzVguC318xvI5JPHzyWJ9uHVr4cRZBNU2jbUmVsBbHYOjOC9gY7Z8Vgs50rrNAUE6iGmgKsTVOr/JtaVrwsW/Z4RoTCliRoYjnnmH7S/1zEZrfP8F7WQniunDioh/R/lG3LiS/DQcs0MXXmO8FzAgugN/L28hXSAISahY1ZsLkJ3LUVYtRz/txzcn9NggB1oQ5ielSVKLdQtBAhjH5y976vMt5NC7uo95pPH+kKg0HpXriI4JJNk/tO0d14fmos9VwpPU1A/KsgQ/CfPbYSAPDS6o/dutm+KowMkL8RsDoPFtKmSbwDOmN0rke60B9e3eH64H+3zONFnTNMmPf6RnxzzjLXzCMK8GM7OWKTFZpiAmknyOzc+YS9yoKpTPAiF1tVuRfDhFMJUFRm44KMzQYjNE1AbhWpcG6uUaM9bZo4D8QgNOiYJiVWFhCFTZOfuaD1+vrACCK+RysmX9Z7zl+nULlVlBHjZs06oDItpnqpG7fXEzPToZ4rH1MYiWx3N2TTf1BcOHIAAOCyUwZK/eVal+3E9NfDJWt1vvsXN5ZpChDIwkC1aQqyJxLlSwMYZJ/hvEZoaI84TWqd+TpXtAVqzDzRF1OsKMVeO3TgVh1Um6aguTiX0CzffugNPPbmZvxlyQb29zXb9+EH895i3yUV3NgOYtviCCs0xQR00ARlAtfFaVIXohZGuPJ07uaOiMnSKDSJPkKfPqE9EYZlaAvUSSVsvYc0TJOqwgAimOCZCYj+71fPee3qBGcucrkpyrJg1mgsGd1irO6GTQwOH3LAVwwAsOegHGvGZLAuruVA1v1/5/5G7QLSv7pS6ae/DPXKMz1PneqdW9x4BjF/tRM9ldjXa8efK2gGRDoXzPTV2UjvOtUnnSt8fWsz26qwPIIV60C2W52Lde+LrB5V3wG+7rCMmTpmggSRttzvtTv2s8cv+/0izHn1Q5x167OBdbCphNxO5d6nQsEKTTGBZKjJ2b4g4yur2jT5mKZWf7kgTxx3hylcjQ2Dmeqo87XRaQtUliEqVl69N39buoktt/uAnNNMxzQJ4a40TZ9rfioX3SKkS4ApCQ46NoiNXC6fTyGuV2KawJcX/6nG2Nz10zhkQeP1q7MWy9fAqdEUNc4Owo6ojIA/TpJeaKR2P2HiTjl9APnOMU3+hSXIaDsM5Pkl2ABX9Z7TvdciWOmAbA5BHYNoZhnadmGtPvVcdqPXoUyTmC/Tch/UqYAKTS4jZN5oht1QeXOBqD8ciwgAJw/pGaoNXV927Gtkj3Nw+ynNLx2/2c4XVmiKCYL0z3QSCrZpQvZT3mEDVOfOt+cJTVnDVsOLS19Wk2Fve0FNmxIZ06RUs2rrXracYCwEREoHFZ5btl+NlXecptBMk9gRp7QC2/5shOC9xH7BqJ4T6UZKvFQPeqZJCAPyAhdkYB60G1fB7c5V9RydtNV6faoOw67dqzdtdOGXWB4msJ8kNGU/ObY5n/GdIYs4HQM6eHOH2XtOZSd1myc1AKP0W55Mk+vubwiP0V5QQy7owh5IcZrEZ8BGk+4Jh2dtUjn406gE1EuOnzq0l7ZeijCs11f++Br+vmxjYD85wTlJsEJTTMCpRgB+5865etP/RVk17xz9HhSfJWwuLbefBbAn8KnnIpLYcrUlEGho1jBN2dmvhFXPtaWHHCMC6X+TMEDPoONA2KesIVS8GEPrGHqeY5p0oSdUIc8b175qc/Ke053LHRMbhve3eUKwLjSDmpKC64AnNBFbFkMaFwBIkRnXZXvgf7d5j8Q8hCbSRjqEMOqzadLU67tfYly3aspxLEMbL6tFmQfdDWEH7tyaW3jvQWOcppQ8tnS9DcuYqarvIFMJzvEgCJpsOxKeW7Ud3/nzG/p+Zj859Zw1BLfIGerAF2DjNGnVc3JZOql79cl1+PshT5bGF5f0OYqoxbnCzzRFU29ooUmZSHQhB4RwV0aYpnyNe7kJiP6v1soZLTvte2W6K1G7AeB/X1gDAFhC019ksWbHPl8fdJOgP9UDXw7wnmMYmybfuQZDcHHP+3X3wmjoGDGfGpFpy7UZTJtTeMj2LOS7yRCclIvCXlCOCB5sLKyyc/r5wquT9tVXnrkuTmjMBb6QAwGefu0BNTSDPiK4h5TyJYwazXRJqq1Q0LNtZcZbEKLYDHNrnDUEb2fMmDEDqVQKU6dOdY9lMhlMmzYNdXV16NKlC8aNG4e3335bOq+hoQHXXnst+vTpg65du2LSpEnYuFFPIxYCYteu7lC4xVWnnlNpfM7LTgxYfTwZ59NVz4UxBE8FC2PtgeYW1aYpKqYpbDmVaTKr56iatN1yz2meA3W7DwqnMLR3lfvd5Gr83Conj9xvn/uAtK9jurK/K+o5k01TOh3sGn/RiXXS/5yQr4sITn9z/xfvVghvU7op4aJ7e9fjfQ/yoMwov3HX0RaoNpNBtiTiPpQF2Daq41Bn28Wp53IViHV9VBmWjgw54L3bctBWf+45f58Ck1GT6zA9e1UYycWmKeymLYp7ys9ZHb/ZzheJEZoWL16M//3f/8UJJ5wgHb/11ltx++2346677sLixYtRW1uLc889F3v3ehT81KlTMW/ePMydOxcLFy7Evn37MHHiRLS08ItcIfD6Bv9OHvBeLM4F3p+wV54IOUPwEsPkTut20ycYQw54O5x8J8C2wBfYMDKhKeREopTTBrdkhKZ81Zm6xVWnTpIigpPJmravuk+HxelH9Pbad3eO/KKh2FdrDLe9sm55zW36xOAe0v/ceH39w90AvFhCYWyaXDWiYSdM866ZNg26ZNzm4JbRCdhyvfJnkN2LEAa0TBPk/upUiR57SN6BPEMO6NRzHeqMkn1phHAZJiq5+gwAM+Pq/K7vwzubnUThPjs8ndBEpqmwslAkQlP2k/UOTxDXlAihad++ffjiF7+Ie+65Bz179nSPZzIZ3HnnnfjhD3+ISy+9FCNGjMD999+PAwcOYM6cOQCA+vp63Hvvvfj1r3+N8ePHY9SoUZg9ezbeeustLFiwoFCX5MNRJCcTBTeoGhQ9uoA6cVPbEF0ZFR7TFKyeW/fxAbePuoCJ7Yk1ip1NVK7GYW0i1IlOF3KAE3BM3la5tO1nG/nn4ApuKaKbAa/2VYVxHUQsr0tGHea1r3GP9yZMZXFl6qULrC5Yp1rW+99fbtbL66T/6eX54wnJ/TQFlnST2gbEaZJVY4xqglTOumUHjJWDjS1YvG4n/yPkNtzrCqiTXhsQbB/jysKa+YXzCnR/M/ZcD3Xz6NqVdaTQpIs2r9o0ke+qTRNgHl+AXmj58GMvPpJqhxfkIa1rV4Ay56qnclvgtUUF5+B+xA2JEJq++c1v4sILL8T48eOl42vXrsWWLVswYcIE91hFRQXGjh2Ll19+GQCwdOlSNDU1SWXq6uowYsQItwyHhoYG7NmzR/prT4iXon+1HJGYC8t/oMHxcuqazeeklvVsmpxPjuHQTSzihSoLUM/VH/DUNk0tGa0BaHth0+6DvmMdrZ5TjSNbNAl7eTfyPJkmwRwox3WTkLsrT0NRz/kX7HSOswIvCGj661PjcDtsbzEMsnlQBVfNI5DQt5v3junUSH7vOT0jRr3RAg3BOaaJMgpsOX0fAODYW+bjc3cvwm1PvMv+7vQhW6/Stu7OquxoUKTzoGjrzHoZyCIGwR8RvONVPU++sxWAxzrq7Kq4PkmMJ1N3GPXc2x/Vu99VuzIduI0Sh/VEINMlI88FvIrWqucix9y5c7Fs2TLMmDHD99uWLVsAAP3795eO9+/f3/1ty5YtKC8vlxgqtQyHGTNmoKamxv0bNGhQvpdiRFCUbzq492dd3buWl/Jlsy+bmqgUCA4Apxo26oQmaRfSkunwiODvbvYLsUFtP/vuNvzqiVWBTFJbved0gqi3EPoZv7bn3XIrlY5zQkYmk3HL09hHUj3wxk3QpOuWZ4Qsbe4xlZEwMDjUGDusfYZuhw8A4491AjAKj1DJDkon3CniKMsEMHGa2JAD5FCK2WW3ZvyFZUE03MLyP89+oP3NE5plNkJ/X51PL06Trpy8EGqDWzL31ZOZ2vYOqOOvECEHmrKS20fZTZxOeGYFBvq7YfMA6NlvKY+cIujr7sNH9f4NJ4cuZV4okUryva1wbdDIsXAzTbwQa6Fpw4YN+Na3voXZs2ejslKfOFa1wchkMr5jKoLK3HTTTaivr3f/Nmzgw8hHBWpzQsHtng5k4+lUVcgDWTXu5OI0BS3WL3/wsXROGF32yIE1Hb7L29/oV4UFtf3VWYtx17Or8c+3NhvLhbZpUm2qtMb1+h1Wm9Vzoh7fL/7noKqHdGoBNWM7AHx7/FH6PrALIT8OVMHR5D0oxWlSzteVFY4L3LM7oq/jDfiVM4YC8FhUrnwuNk00arYphAS9Rj7aP2UU5N905ThcMKJW+5uknkXw+FO954KGqcv2MEJW8PUHVK6Ban5QCKbpqH6OWcVpWbs+rV2VwvQBik0TUzd9NrrnVE5SSYm0RkH34aoHlpJ69TerkVDpuhh0bQHrPZcgqinWQtPSpUuxbds2nHzyySgtLUVpaSmef/55/OY3v0FpaanLMKmM0bZt29zfamtr0djYiF27dmnLcKioqEB1dbX0157gYioBvHpgxSaHZSn1CVjOZ5g4TTpWRLibi8CNQTYPgJMNPkiFkAt+8/T7OOfXz2HXfn202Y/3NfiOvbR6R6j6P2JUexQqC1fTpYwtp95CNQSCWk5Wz2XbMkwWz67ahvUf8+kLdN5z3HOgwl06LQc25HazVMg+utZZFEYpBte0jTALvF8Y0S/aYuw5tkJ8fWq9Jma0qUW+LqP3nCI4mpKq0oTFYdOocHGKOCcPThANErAbNXHCAC7Ap9yeCjUzQJB9jP+5euV11+8K+Npem6EKTYWIFVdW6rQp8mCKQLirt++TyqmbBkB+xibGFdDP132Iqvmj3Yek38LcBaPQRMaTzl4zF3DxBXONwxYHxFpoOuecc/DWW29h+fLl7t8pp5yCL37xi1i+fDkOP/xw1NbW4qmnnnLPaWxsxPPPP4/TTz8dAHDyySejrKxMKrN582asWLHCLRMHcKwQ4LcRoC8SfWG4si1MnUFBFcXiI4SnfQ3NbDmh464sk11to/D2vf2p9/DB9v24+3m9umHByq2+Y39YuFZb/pE3Pgrdvt8Nnb8odSLTqucYBifoOfzoHyvw1T8uxtjbntP00V8nwBv40uspIYIIIE9WHCNmSuGhprFwztUwTYrg6Kmm/BVvzqoPqruUBTJN4nTX25Opr0Xk/mMigusMyV2bJrd9Thjz6jXldNSlUeHqzqg/wvwMKHRCO+D3NAtm8MTcERARnLCCtH5djCGOaWkry6CGh8jXuaIt0G1273pmtfQ/t3GSHDKYJ0GPhLlH52eZxlzUlKYiF/z3i+73/Q0RCE3Zz6Co+HFHaXCRwqF79+4YMWKEdKxr167o3bu3e3zq1KmYPn06hg8fjuHDh2P69OmoqqrC5MmTAQA1NTW44oorcP3116N3797o1asXbrjhBowcOdJnWF5IcPZHgH9xoRNCbXUlW1a8YBxzEBRUUewuqso91d+m3QdxWDa3lIBqgxWkR28LTDGC1u1wjBQ/dVRfvPCeEy+oT7dybfnr/u9193tQF8PaKoUVrjhWyBTlef3H+3H/ovXmTgqWRznqTkiSyodftNX22QTPisqXgvPO1HllqYKjSYUgVK/9qyuwdU+WUdQ8M3HPy1z1nL8Ml8YmnXLK+gzJFYNe06S+epvDJjQ0txqfpyQ0MPeWFuAE7LDvlkmVrs4FQazMe1udaztE1OCcSYPqPcdtBnSG8EGCWxBUQTDvJNhtgLhOdbP71TOGSf9zQpG0eQlimgJU/327V6BXV2f+M9kL+s8PLgMAyzfsDlcQwN5DTehe6WfnuU2ZgA050IG48cYbMXXqVFx99dU45ZRTsGnTJjz55JPo3t1z4b/jjjtw8cUX47LLLsMZZ5yBqqoqPProoygpyd+4LSpwAg5AbJwEe0RGeYkSckCdCI3qOa2BtyM0iejQAPDcqm2+cmIh8nZ54V9UE6juXM3tRnFSVl10BokRpE5UOgS9oOo1aCcs5bhWPZf95HZY3AR/x1PvGftH+6hOQJw2SVLPpfRMU4ZZAExCtrfAk/bdejNKWXnCNNnWidg3lWUloUNkmGzwvKSqDCOmlH0t67ovhLCw6gM3fQZro8UvFiZWkBYNa5hvcgtXWee096CMoGoZfmGX+8g91yD1ZFvhCzmgHO8ItCqb3TOP7AMAqOvB2+DqDME5ZKRxYS5TTuz0cmPwwt0ruokOwuxXPuRbYppKItOUOKHpueeew5133un+n0qlMG3aNGzevBmHDh3C888/72OnKisrMXPmTHz88cc4cOAAHn300Xb3hssVNGIzhc5OCTAEt8zI53CG4NxLuGHnAdeVv28376XnbCVa3d27frLMFZlMBsfcPN/9/6DJ+DDbDPXquO2JVaHa2azo/lWY8kZR+FQ7QTZNDNPErXMPLw9WJXKCGG2D9pm6C5cowS1pOS+opH9hM4UGCBOIUe2vSZ3rpp0hSd30z0BlmvwFVZsmp59gy+/M2tEJ4d20AA2ocd6RPt0qyPPkJAvRJr/JCWIhVLZZB1O4D3UDFSQMCrX7kX29lDomey2TrZouuKdaR65Qg7GG9TL8x/JNOOMXz+CtjfVta5j2QZm3xRhTXfQ99RwvNLLqbyo05eBkEpR258ITBpB6NYUUmOzlVMxdrBGasp/y2A63KYkTEic0dVboaF4xZap2SlxZdbfr1sna0jAMxwKP4RjUy1PHcS+MP6ibfhEMCzXhrcljo9GNxJv7EN6vsdMSaKt6Ts80cRMbX0dYuKep7IVLzXv1intVkk6hrCQtL1wMyyHnKtQLd7xXoOifZtFIKeUMtkJlJelAt3Q3gr3BYLmJGStBNnjCAN5EyNDrNzGtaowktw/K7/T8toQcMDFN4ie/IThfXoQzKSX3jCuqxrXi5heJaYL/e1unDNX+LKxN07fmLsem3Qfxo0dWtLFl2gfnU9xXHePp/qdhmnibJu9YUER2OdJ69jctOxVcr4rm1kzoqOA0vpPcrvMZJq5bnGGFpphAFyNH9YZqUVgDCi8Gk8xKpZlFkBukB4k6jHqMcUKT6o1jSosRFk1KtMgd+/Tec2p28Vxw7nF6r0nAP+mGVc8FqZDoLtP0HMJoLdQFwzvZ+aDVqmo03WTNjUFzehD4yusWQn88H73Q0tTqPdugSdWvnvOXEeOXumfr4jqJvHuugGVonxrjG73noHm3GdWnOSK4+d1alk0Xw0FlRIJsmlw7MPJ+8Wyj28ts/epx+d5xC2ZbV0xfwt4cbZpMhvNhob4z7jj0bRr0z9X5navb+653MtFvXHT3gc7zudyBRdlwNG2Hvq9J4pqs0BQTcHniAP9OX2KafJOwvHDw6jl+JwR4xn5fHjNEoo5VYUaqO4Rhb1iolPZKJoCl1yenbFkbhKaKMvOw5+13TAuGA51wxannTBPbv31ioLF/gF49xz0HbyGWn5W/HHy/h2FQ5Ng7fHm1vyaVk6ueS6cD6XtXPSdypDHPQDBtFSVUaArqp9xfDl5iYb0BvHRMywr625e958KrMGikfgpfyAG1PU15aivD2zTxwrBk06RRz3H2d7lAVTkGObmoiILdUOdtYa6gCmTsHEDV5EzdUjgQLWvkfObCTOqM9FWom7d/rfDHt6O2r0HgNo+5GK3HBVZoigk8zyX5uKruEBR8KuUXsLxYNa3ZT4Y50Lij7j7QiM31jq1Pz66yF1oDIzT5DcH5enNBE6NeaOaoA3gRyStLczfm1yXWFeCEAdbAOKR6DgzTYMxVFmIH/HQ25MKiNfLujxNGdAEb1XKcXZ1JuOPy3+nKu5O7G71Zv2I2teTONJnUczzTJAQR3eImGBn6m1xWYpoMIQ/UwJJeH+DrA2v7ommfw4EmXvX830+/DwD4cKeXL9JUp5hnyoKEpuynP6ed/5qAaBdM9/77PALDnR+Fx5a6eXSZJs28xTmDAPxzWLGp3mtHa9PkrytovOiejVSmNeP7jUulMnpYb98xHXSx5YAk8UxWaIoNtN5zyi5TyBVqYEtaVix+Ju859YXYssczjr50lMx0nDSwh6+tJmUhimLHwL2UujhRwki8MgevDrfOQ2abJjGZUnsOk1eU+38OE5tJyAyTcPQfGmNxk6GtL2Aj5MnKG4O0n07Z97fJwfoAjU2ThhlSgzaahDEh1FLvOX3us+yz0qhFAF5o8oRL8zXJgUB11+QtmpxwrapHvU7ILDLgtxECwEbZzhUiLIdbZwAbIa6DMrmckKHaa3E2cLqQA54quW0X5rdp0guuHKJgN9Q+lOTANEm/M8eO6Gc2wqfHabVBzKTEbAaoZym4TW0u4o4oKQ2BgPc7jrBCU0yg855TFxexA+TckFW7DqN6ThmkDdmF6rAeXTA4a9ch4h717S4H0QQ89Zig76OICM4JTXsOaoSmrF6+SxtyIq3TRNkW4ART1hA6bMgB08TGCU0htsqMzCy1IU+M2d8MAhVAFwCv4Afb/cKSW17xXqL90qq9BNtliHJNF6Ig9dzvX1gDAHgj6wnFCa6CKeXcsrWMmBAwpd94pimVSrlMV3NrhinHv9sc2WZ6VlHGHzIZ4gPeWJZTzjAFiYoS0BiCM+0CwQbLQfBFBDf1k0F7qOdKDIyjiiCbJlV1bg774Wew9Oxshnzny+w64LcnrWZjL/Hnc1BZXCB/Z4BCwApNMQFnTwL4FxcT0+Q3BPfXqVPPCc81au8jXhJuQAsGSKj0oogIzu1kdAEuBRvRFqHpt8/pI40D3kJChU2zEay+jFOfA5lpChYaTPji6CEAgC+dNlg6zrmxc4bI3KLpjhdy3Q0GVSZnjK7bOaopFIyRxslimOtOlLufrHrOZW/4esUlSQubtp+KgB3AXqltyOoSmZFz6g/PNOkE7vHHOs4PIw+rkdpm7z9RzcjqORPTlMr21V+vVj2no11CQp0zowh7kgsymQw+2O5swMRcobVpYoLRSvZyTJfDhD7hDcHNghunulex8H1/SqrRw3oZ6wqC2ds2dDUFhxWaYgJ9cEvnUwwql2kyCE3ihfUC2pH6yARMJxfh3l9BbITc3TgzEb/9kbOzF0JNFDZNHNOkE5pEPKkosm+rEJdAF0JO7aNOasGG4H6hhU/7EXwPRX/UVDrcYsi5vLvfGaaJ2t74Q2D4wRqhKmX8Xlb6iZ2yLbmGsuDuXWPW/o2zaQoScEyxdOgun94n1XFCtb0R4Ax23TrTtJxcjwm6sSOCE372pDqpUlbIJP0vldRzfqjhNNgFmwpNAWxnLlCzKOTqjJLvOr2J5LDsWeVsMEXamRZdnCZmDnD6wr0HyvzCvSvZzzAerAKt/KORQLULnxzay3ee10dNBRyYeUj8Z9VzFjkjyHvOTY2i2HBQlCgTFptGRWOj4TJNIRYWAFizw9lhHd6nK4Bgj40w4Lz0dh/kww4IVQtdCE2gCWc/fUw/Y1lxDSVk5eIER9+kFhSAjhwz3VuNDamEZl2cKkblw+/w/MINxxzR8RiUhJd+1zFNqjG6SeWQSqVyyqOlK8fF9NKqERX2Qmc0T/9Pp1JSihY/Q+Bn+mgf6FNQhUupryGWea3gLmoNwco89Y6X11FimlgVNV+vjs3gBPe2ThnL1u8CALyZVc12dMJe6lByZD8nrpe4XX6myUEu3FoYJps7ZtqQAfIz1wkrIhvDJwb3ML6DudxrdQzSviZHZLJCU2yg855TJyE1qKRUVgmsxhuCe+XpgBfeaJzQxL1Yj73puJ8Kl9Ow1LjJM0xcG81z9+NH3+HrybYTNrw/tWc50BguuGWZFKPGX86XsFfrF+x88LY/HNMkr057D/nZtmZNyAVTcEEqAPGMlF9wL9EI2fR/eZfLC4PqjljHSDnHPOEtyMFACM1jj+oLQMc0+TcEAL/Aqv+bUphQRowyMuqCyXljOj3w3ysuEGouThZ6tlMW3E0CyxNvb3G/BxmCe89VfHLX5IG3vWnbkvn0u056p1Vb90p9iCD8Ukg4DQmWCfDyG+oEZ21wS6bPfkcTpgfMO8jlNJTrNdcJyN6TYVjhMGBjVbk/hq+n0LBCU0ygU89BmVh9QSUJxPwmFnOaN8otQ86j6rAG4rHkthxiEhI6/TA2Tcs37MaJP34SD7yynv19VzaFBRWEtu9tYMuKawxrFkFf7oMBIQfUBR7gFyPX3d3guSXX5x0zTUSqlvKn//QLjk2tgnGUX+GSlH+BNRqiczZNknrOX48AZ+Css1FQ1wyTOpfayQQZLJ92uOPyfFT/btK5FMbgliojplwHvWc6ASuVkoVLNUwGl26GtsGp51JsuRBMU8AYVNVoXHGanJWOL149I8ZWlmliYiXR75ww2FZ87mTHy3fKaUOctjuYaaKOAAK6Poj/6Dyi82IVUJ+lielh2V7NO6OLoSXX63yWpFNG4/YwbQiIRNxBjHfcYYWmmCAo95wYhK7QxEgLXowQp8yD2cSJ/3zTC0pGbZbo5HjIwDSZJiE35EAAJQwAU/7wKvY2NOPmh/n0BYIS7tm1HBeO9PIjvbRaNkrMZDxD1ZJUCtd++khtm+455LU8GMA0NWTtu0rSKfPirngYBavnqHChFzJVNu5FxijTU88p4yX7+GhfOCHQi+DuHVIjLDvf9YsmZVrc9jUTtk89Z/Ceo0bjpjQucp9FP/0V7soGfCwPE9xS2bmbPJxo2TQZK+o4ePBVZ5OgbgC4d0YVbpzvwfdKgLMLpBWrQiu3uPUj9izUDCBM0maur/Q7q5oxrJirt+3F7U+uYlMfiQ1er2xcuVyDW+YLTsDVsV1rsptLmoRcZpq4eysfC23TFPDO6J6NVIZoKUz1iWMzvzBK7qtSMbX/4lO+JEdsskJTTCAlKSVQJ3dXaGIiYbtGiNnCpwztCQA4dkC1W4Yuss+t2uZ+F0xTBWGawtiTCCEryAiztTWDvQE53zxKOIXj6rw+L83aLrh1kTbSqZRr3EpTv/jaJ+fQiYuDCKdQWZZ2zzNFRRf3NJeI4MY4TSEMzD/OppipqZIDkXKCLh9PSfSNMk3+cnQ4+t3zPeHGq1cjjCj9M3vPeZ0MEtxVGz9dLCXnWigjYL4mcQ+k6OmatkUZwcqo6jld1ncuno4rBHKqXKYO9XqDnAjcoJ2GUA5f+KTnkUlVtUZPT+V+yYIg36dUwO8AMP72F/CbZ1bjhr+84ftNzBfi2Ydhu1/+wL8BaSs4BlHnlXlPNjQGhWwIztSvTDlcfDnWe85Qp9q3oACYqVQQK+xvH/DmUIGHFm/w+seyYsmBFZpigiZNLjV1N2pmmrJls2VEyIAJJNcafcF3k5QLO/b51WBhvFF6ZBftIJumIIEJ8ATHknRaEu78NiLe/84On5+oKKjgZUoEDHihD6g657E3/SkERLdEOb0Rrv+46d6GoeX3ZO2cqD0FrZeyVZ7QZt7ltzALNmWa1G5w6gmdKslnK6RZXOj/csgBsFAT0art0GfSrcJL+aBbYLmdu+4avLLOZ2mA8KyCu1fcQpxSfpP6oDJNAWPQrdZwX8V4PvPIPnI/jTZNsjAsdUNho1yEYJoE/rVii++YO1+UCEFQtK2v8Jo5rwc3FhLcZkjHuL+2bqfvfJNnJuCfBzi2zfRu6zca/HeuTEk65XMwktrPfqrvixo+5phsAmwA6E7iPdk0KhZthpDMVRsVdTdqsmlSA1eajMYBoKyUsk5OxOCl5OXWeaPQBfnnl4yQ2tbaQod4K1ymKZ2SVCmquoouSlSFsycg0rdAINPU7Pe2EvGoKMKq51wVBmU6FAGXq1eAq9eNiK6EXChhGBc2nhJjT6KqpgDVEJwXhMKEHOAWGK5O+q+TCDd7XGt7oTBNGiNsQGZndaoc1f5Ll6ePti2uWdx7lZUUC8ZxhPGldWekZ+VvN+VJLT6EZZpUVVIu7IHHSnLt8/dLtpvJ1qPadOVp0+RGLU+HY7sBYOd+zxs3X5UQG71dMw+eOLAmVF3SsRACMedkEMj6h2GaOPUcU5RjmwFvDvX65HxWlZdIGoEg+6s4wgpNMYHORkXLNBniNIkyeuNyB8KIFvDicpxEXPN1+nm6AzqibzfpN93uJszum2ZWp2rC4IXVu74lzI5ObftgANPUrEzGOnjquTTbT7W/9CmYJiJ1ctzFJGEVbJmae0/cihZpYhTtBzBNTNgLU4BP1Z7FaUNTFqJsSvrUCSKiriCViyguBCJTfWG8/Dz2TP6ULgKyXZ0oU6q8fwKC8dXZ3nGqVHkR9JfzuqQurLwhi3tqtrPvb3UivT/77jZf2VbFIcC4CCsbAo7l4EJuMFXkDHVTqM5/7Q3uvdI9q89kbTTPP75WOu6OrzYKxNy9JWI+2+9wTJMnNJUYzDS88+Wn26AITeLUEXW88GiZJouc0UQEBgp10fACVvofnTpp6Jim04/oLZUDgNrqSgDA8WRQ6+xJqLGpEPKC0geEEpoI20YZDp9rv2KnQhe2DbsO+OpVQwxkMjzDI6BTlaoQ/SgjKTQ4cG7kYXb6Jnj52XjvOTbkACPc0EXXtGA7v/P9DOc9J98DnUBO/6UCcXibJpOA7f+ujyclq3zUulXBHfDeSZ06WWXZzMEt/Tof7haox37w97f8hUDGYPb/j7OMy99f3+Qr6w/wKR9ny2b/Z0MOKMKlgE6VGxYtypzpzn+G+oZkU0RFAe66tA4G2c+uREUMmO2PgsJh0Ha4kAOcfd/0x1di5eY9xjoB6pjEq/xpnaIcxV6F9Vc3IwLWpsmizdAFK0wrb5UnCPnr8DFNyoKiKwd4gkI5GwBQv5sNu8vTCRRcmdISOcKy+kZJbESKn7SkehmPIlN/3soGy3v5g4/dY4f37eorJ6oIrZ5jJra25p47pFHPicWWTnCsnQyrGvIL2bJ6KngS1yULVXfloWyfUpS58jWl9DnNltMxTfqFxW3adw4tKtfrfLrCsxoNWlyOMjbZQJCMMb5JRak+gnUf+zcNtBzzemjLejnd9IKrem2m3HP+6zcwWAouGXWY75iYs9QUJqb3x+QskitM6mmd8B5GcBZQbZi4OYszBNfZii1Zvwv/qxikf0S82iioTZM55ZPzmU6l3M044IX58MqZNw5Jkpqs0BQTcAkyAf8k5KncGKZJKSvqVAUJ0Qad3BsZ9aBONUInpVLFnkA3YYVjmrxJ0JS+gwoEJamUdB2s0MS0berPHxau9R07sl8337EwNk0NzS1u+7Rn5ojg5nuVyWRc+tsnNHE7/ewnZ7AqCQJcnCYa3NAnkPjLuzK+Vj0nn6MTrkRZL5KAjmlyPnVMkypgq9+D1Ij0HM5OxykjbxxUFRm3uAI8y+AuViFUiVz/dchlTVIZRx17SMuqDKIkjDPjX6o3RJ+4DAgu06Rs3BoM6ncqNIXZyJnAna0LO9EWD8Lv/U1mDVmmR9QjbQj4Ov+6ZKPvfBFnz1cvGbNpzbtFy6VSwN1TTnaPN7bIz0B9rwRM1x9XWKEpJnBVQml+UPkigjMyRdqdtJ0yIrK0qmZyDVbJ5C52BmUhAgAKo/VUys806eZwOokMqKlky1BbIio0qVXKhuApafLjmudedp3thwoRNJG3ORBCk15gnPMqcTcPsWjrjlFQewFVPcdN2qaQA7QtLlZYX5LbTucQQJ+VboFVXdODduSiH8E2TXIfdIKdqE/9HqRGpIsRZ1zv1OV86pgOzlhX6g9TLxfFn1XP+Q+x4OxvdPBUKTJ7ZKpXjfTOjWG1miCPW9O5AGXdnXdAeNjdv2i9tp5q4rnFeaPlAlewIK9giWa8ith5WsExxIMMb9PE9+GhJRsQFjT8iE4QBGRhqLqyDMOzm0u/TZNm45DD9ccFVmiKCTw7GvmRqN5I3u7K/+hUDyKd0Ti3yDcx6kExkHVxg6iA575YmtFPBZsKTb446kIsCU0+9sD7nk6nAr1BPBbPqzOssWgYRshkCP7+tn3udy5zfRimSVUp0JAJOqaJi8XCerlRRsoVQLxjIw6rcb/rHAJkoUknjGR/F+o5TZ0qMxRs0+R86m2aZAFb/a4bW6zRPOg99c7xbJrkTYvaB92CEWQIbrL9Ccs0iRkkjHpO7YPREF2WhdmynHra6YtZIJbKMsJei/Jer93BsyZym973Lnkm++bGii6Uxq+feg8A8JelMttjipelgptbt2S9el8ngYp14+UzI2UjdBM49VwYplHMd6+t3Wks5/Y1h+uPC6zQFBOIQVamCDh0l5nJZIiHk78ON/dcRhGalJEqdmY0AJn4TgWaV7N9+p9nV0vne6yYP8JyGPWcXrDyQg4Y1XPKpN6/2mOuuPZbmMChavA1in/7hJOeoXtFKRFa9QuGiGvD0f1U4AljiEyv4bzjnfhax5NAn4BnBF6STvnVuYwxLLdoeV/Jc3F3lzwz6YuB5Bru++vVCbKiLq0hNr2WVEorXME9LgtufvUcrc/77vVTro/P/Sb66m+XluXU3lJZdcFgVJRG+zP4wQU85KDpgqasLOSZBHwR6dlXlhANqhG6QJBAvC+ACVqYzRQg3uVvnTMcgNluibZ0WM8u2nLhwIwVg4DJIge2jZvbXlnj2F1SGyKdkDOwZ3gjeBpywPSc1ITNAncueD9UOdOGIK6wQlNM8F7WBVjsSATUAGgmpqnEFVyc/xuYnFuAJ5jRxLBcJni1bwIc0xTkPUfVYfokkdl6S2TvOZ2qR7RZ06UMw/o4htqqASJtuySdCmUsKoy+PzNygHHRFoJJadqbpFS7Ay6gImBeMES9g7KTnNpXL9yA/1lxXmmtzILJCQJirVeFbB3TwHlncjtHTnBJp/mJ3RcEU8MIqXULFbSZufILImEMdk3xlOjvOpsmLdPC9NFkJ8aNlb8wdiocxJnhmCa5D2EEgb0NTdI5GeaadIbwump//thK9zsXkV9gw07H+F3MASMJO6qCi4DfVnDPKihenQruPdThEJMzc9TgngCAU7PZHwCeGW1uafUZgZtA1cQmLYI4YtjjGsullN+TACs0xQyqwbHs8k2ZJv8odQWXbJmD2SCOKg0tzqVsSyMT0FEHVwgh6q4g7zm6jmgFK2LXJRkgq3UxtLig5v+61L+IUC9CsdDTIHf+vnps3jtZ91zOy0SN0wT4JxYpWSk5bs4953wK+zL1fok8gapqDqAJe/1Mj7wj9gs3nI0GoGcROfUctxBySUp1goCq9gpaWP2559QFW0z+6jWZ22fDKFBWjnE+0AnkgnnS2TRxKlKZldSrRwb3DseWqIl1TfAZd2tUTvT/gT2qpHNamWtSWzZ55QHAiMM8hnW/IV/khCwjmw6xIaLzUL5CE8femVSZHMIIsQJ3P/+B75gYG0N7e969nIp81svrwjcEOeSAyV5VJFk32b0BvO0VPZAgoskKTXEAfXkFxSygZsLmWB4BMXCFUMNR/QC/sDZpgmty+ME8J+EuTcNCd1i8XZE3W+kmKzcqeknKzDQZVJRLlDx1Ttv+xf1vy/Q7dK97XvkZj6+UyjS3tOLRN5zUKtR4Xr22BoY2d76LthimKcArz4vR5BeauIWD2+mzTJPG29ITxvl+8obgfsGFltXbPlEBy2xYTI/LiWXh+65ek9Z7j1XP+QXcRsJ8qLHK6GYkk8lg1da9zu8hVBOuB2OAylNADW6qAzOk9WVVpkk57vaV/F/dpVQ6hw85IJ8fZARcS9Tu1IBboEc2hZAwKXCZdsMKzNmPtRXcZkQn4I7KBg3+xtgjpOOmNCITTxgg/a/m4AR4Zx9vQ+KVU4MQCwh2TgUdA17CXn8nt+xxbKq27fVnTJDr4+eWfKPCFwJWaIoBKPW8+4DMgPiYJs3gA/wqCh0l6i2s/j6oqjwOqpEfoEaO9p/T3Bo8WXmLcJq1k1HP5/LvcfBy2nnlTXQ/lx5B9Qa5+R9vu0HipJQvyrVt20Ny+oVkD8QzLhcG+0oZoZ6rKOPUc/4Fnk2sy9hq6Vzj+YCZGV5oYtRz9NmrIQeCvefAlvP6DF8feMNqVWjyroOrj4+e7pVtJO+LuJeljJBLVSrDlFhfnODGGcyadvEmAYEiF5smvyE4z4pyqk8vyTfTtkZw1T1b+o4OZRZ3VcgPExGc/hSVei6M2leo2vt0kxNscyymQJjesSpyZrzS33928Qhcl41OH05wdMqY5kyTjajTF9E3fuOg9jfOsEJTDEBf3vEkuS7gt2ly7X4MTJMX3FI+LsCFxReDvjyEeo6DFMGbmYzuILZaQQEwS9MpVv0ooLsufXlRr3dtXNwltX7phVbK/N9rXigBOpmoxuAt0sLiHTcxTWr8J3WHp0uhItVLzvGEZ4a9oGpTJfmtWrZFEka830uYeukEOJ2wdH47Gbn/qiu/jhFSy8s58uD77mc5ePaMFzDFb94xoc6uIO+LeCclVpWc1KOLvGCCeVauEMg9K26shFxnPLYn+J1RFzhtDkrlWTmffhabDcAIXsCioAsxH41abjOM0CTZNOW5RnMG7pxNoVPWgT//nugX24CEM47s7SvC2bhyGzIxRx07oBpfOm0Ixh3TL9tP/ibQZ/Z/rzmhCn7zzGq2LACMOdzfNwrteyi1aawiNrBCUwxAvW36kLg4gDx5ZzJgY+MIqJOGdrJiJsFcbJoEvnve0V6d5DTuRaTRtfV2T961cUKhWn9YewA3yGfa86wZZPIkYdiJM46QJ4XDyc73n29u9vrmczf3vtPumtROaqDTtzbVS78L9oJTpXIG+WzMHNG/MEyTYisHyM+wJCAgKo1VpXrPqSsDPS8VYNPU1NKKNdngfLQPXITtMO7+Um+o0MSoUERqnr3Ew8s1BG/h75OqTvaeAWmfeQamqNFhd+c6uyIOuoCVQaygrqwrMCjtBBn5080I64ihzIWmHGlePWZBLBdwql89g8rff5NnpMo+XXRCna8M74zhP/++l9YCgMuOl2g2DblCjROng862UDU/SQKs0BQD0CCTqrBAX8jWTEabTw6g3nNCaPLXQf+nE7rJe06F8C47ZUhPr22NeoSDbq4KzTQZ7gEHuhsTfTdRzdTb7Oj+Tob6C5UJ699OHmhsSyAoThBvCO4cbGaCjwLAg6+uBwC8sVEWpgBvEqK7aE4g5tgTYQ+mS7ujU22UshO2qF++QFFWZwiv2hSZhEtq41EmGYLD911nhO1rn10I5b4BwLIPd/v6wxmCq9HruT7Q/oYNRKqWDwtRr7CdHMi43atMrk6V3MqMbe656uYh03UBsvBp8jJNp+V+hlXP5R0RnGFPgkJp6JgWTnBUD3G95WxcOSH7QKMcoTtINcpdW17llHa9ftC6kiE2WaEpBqB530z0eWsmE45pyg4+HSPDLYK52DSpiTIB+WUIshXQx3LyhwYA9AaoYdVzQmhIgapQ9H30Fu4U6npUZvsgl6fX8PspJ3tuuTmq59g4Tdlj+xu8iY4aHr+XNSzmIB4JnQyF6zZlrFQDzHpi1K9OpClmMaICHR+92im7Y59soxfWey7llufLAbIQKIW0yMGmiS5FQZ6OtAvDGfWusGmiY4vawunUnkEhB8wCtv+YCaJaYZjMxTTSBbdUHwGNEeWxUv7FWMwtH+/nx4LuTaRjnivjzoXCpixtzgGp9osmrm0L6DwhoFP7ur9rDrDXp9zwZmajx9lrcqrvT2RDE0w5bQgAj/UMtutK4ctjnHMuOtHPdHnCoHkuDmMInqcM22GItdA0Y8YMnHrqqejevTv69euHiy++GKtWrZLKZDIZTJs2DXV1dejSpQvGjRuHt99+WyrT0NCAa6+9Fn369EHXrl0xadIkbNwYLr5JR6Cp2S+ECNBBlgFPxwqoahS9AabzKU1szXqVj4pmN6ihN3wkpilgIg+KGk6j0AIetezWn/FPVodrvEAA4It/eBUAsGbHfrfPYZgmU2C3ZmIjcN7xtdoM65K6iYkczN0rMZFRAZa6uH/59KEAgLFH9fWdyyXsXZMNxyDHjHI+RXdFGAMAOGVoL6lOzhCc9tu0y11BBLWTBvXw+qlhL1RWwmQwT8ccrZsTmrQ2TQwjQn+n53KqHTfNDnibpt+/4LmJ6wyhKVoUgYV+NwnYg3o5jJHwKFPhCaPBAoa6wOnYvnmvb3K/CwGWE7Ceemcr2yfO/o7iN097ARJN1+7F/soeN9o0yf/vOdTEFwwBykgLuGyXhibKxaZJPdbIGGHRjaavTkb13r1S9XL0t0u6CwDonTUZofOHWlC0ecLAGgD+6OOt3M0CkCISSFLyz8VaaHr++efxzW9+E6+88gqeeuopNDc3Y8KECdi/3wuXf+utt+L222/HXXfdhcWLF6O2thbnnnsu9u71duNTp07FvHnzMHfuXCxcuBD79u3DxIkT0dKiT+zYkTCpxiT6spU3fBUQp6tMk857jk5WjTkwTV7KFw3TFKSe0zJNHtUcJiI4vV3TJh1v7nQWpYY8cQKUcuY8DQFvAhOZvTl7FtpXUZ/63aRyodHZG8hYFfdatX+jv9F6RR+njvfCWagTK+1Gr66ywTJnYEsFA9ZzJ1tvl3LPWP0v3xjj9VNjf6Kzp+GGlFgwDuvRBVXl3oTOGYKr6l5u48AZNgO87ZGa9wzw3l9qwGxiMzi2zYuVxQjY7MIqb2B075aq9jQJGK+scbxjBTMUaP8FaqvmH39DevP2gyaBGAC27fU8TzkboYzybHMNOQB4cYbaAj7kgPybW1aTxsYTohjhVfmf2+hxm2hOIBJ2kGpy4zDqOe/997evXte5xzqOTDWK04NoRaeipW3GHbEWmubPn4+vfOUrOP7443HiiSfij3/8Iz788EMsXboUgDMw77zzTvzwhz/EpZdeihEjRuD+++/HgQMHMGfOHABAfX097r33Xvz617/G+PHjMWrUKMyePRtvvfUWFixYUMjLc+GmD2GEJjlhaEbKz6bC3eW0KEyTIt6rk0smk3EnetqHGyYcBQDorSyivMeG/3cdwoQcoC9XnZLg141lQ8qIHbZaFgDOP97Z9Vw4coA21QUF9aDSTcTNiuDIsTGAzBAFTWzuOQZPIdoGJ1d6dh3eMWGEf1gPz35FZYREnZz3JCfg0F0+m/IjW1SoRo8dUC3bVGni06gsojFyevYaK0rTWtuIILWAxDQxv3PXBNDAnt4xMRbo4jakl8OAqvHXdPVy6SZMtjqut13aLICox12miTlBpCcRDFEQKyHSDjn9Fv3yCouN2CeHyQxmkF3NaFJebVtyREgpgkBI9RyQn12TyooCegFXVTsLcLaFvnOyZbhsB7xNk1IBPG/fJ91n6hzfub8xcL7m5hRfH7NXVuJuSuXCHnvP1610N9aItdCkor7eofp79XJeprVr12LLli2YMGGCW6aiogJjx47Fyy+/DABYunQpmpqapDJ1dXUYMWKEW4ZDQ0MD9uzZI/21F4R6rpwVhLzvGZDJmmWaFGGIiTfk/C9PLnRnTJmmT2QNvVXmgWOaqKcTb39ChT8N3U4mAHp5JxLVC62fvnBiEeAmQcF2jBrcw51cmgw6RFFDKpXSTsSuh1u2XZ27czMzuTt9z7bF7KA5uxZaLzdZu22k5XrpefuJMajHCMmfnHsVt7jpkkGr7Im3IVDKKYyUQLMm9IUpCKiqzmWZJh/bmm1fwzRRtQH3rFR7GtpnansiIllztkOc7QknEKvPlEIN7qlbd1RVvW68cm3oxuquAzIT5ZT1Cw3N7oaMHwO6NbuWbID2K3no6DlphT0xM03y/7qira0ZPPn2FizQqBYBnj0KjD+mUdHy98A5KBhnjmmiG021Tq7Kd7fslcoAwCNvbPKVo+EUdMmwaRuiOp3NqE6Qpbcj32CjHYXECE2ZTAbf+c53cOaZZ2LEiBEAgC1btgAA+veXYxv179/f/W3Lli0oLy9Hz549tWU4zJgxAzU1Ne7foEGDorwcCXuzenWqyhBQmSZOhy2gToStykQpoO6GqMFluRR3ht+NtigCg659Acpkedfi675ENZtoW85OxaR2o+yFYDt+OG+Fe9995Vu9+nWpGVTBUXftdMclqVx0zBT1SiOLjCw0+a9fwPOec8psJyqOTx3Vh5SD1D4Xn0hAzWkI8AaotF6XwWJYQfq/KruqkemNQUDpeNFMvrqo+HwKE3Id0jX5FyFOLSLGFrU9MUZvZwRHlzli2DtOGFCZJq26RYkppFMn/4WkIZp39enZfvKL8N+XOYvtc6u2kWsS99Ur7XnFyvOFTnAWoK/SnxatV34jmxFVaDIFYPIJTXzZ255chSsfWIr//NMSPP/edr5/LCvo77vTrHz/vfIGgSR7SMzJYZkmLv6XCvo+frTbH82bN1Ewza3O/yUaW7mHljixntTAyKrNbhKQGKHpmmuuwZtvvon/+7//8/2mToiZTCaUNb+pzE033YT6+nr3b8OGDW3reAg0ZBcKapdBQRc4sXAZDcEVmyb1MlWVUxN5GakKxbWRUgUGsWAoO0edGiGsCqqZpASgfVYnVXWhoN+53RhlG3bs84SI+zX5mNzdE/TsWZNih6bb5eomAp3LPT2fPgvOw4obvyVKvfuyiVRrupShX3dv566qx3SqXIAK2X5hRF0I1QVWH5/Ff10ATaWTNSw2BECk0fFTKT56eFB8GJ2QwaWc4QzB6Rjk1HPCpomLZ8OpKDl1oimmjmfTFCA0kUXQVP51EkrhhIE9pHPURbguq+4980hPGOeEBppTkiLQGNnAPEjxr1JyfSam6bV18qKtK/m75zwD/lfXfMyW8Vh/75gu9pR6/73yzqeJxRECt5qVAAiK0+THMbXdff0IUs+J62MT9irzRqlGGK8/2OTrp9oPyzRFiGuvvRaPPPIInn32WQwc6OnPa2sdWxWVMdq2bZvLPtXW1qKxsRG7du3SluFQUVGB6upq6a+9YNrlO8fdLZmXa4gTmlIKNeoKC+pk5Xx66jmPvZIFEX7XoMt/p9vt8oKM/wURu/KK0hJUkGjXOuNqel1lBm8gWv5VstM52MQ7AtCFVo19pfZJzTum9vXYAfy40U2WdGE873jPAyVIhePWK4QMxYNSLevZwMjluH0E5+XkBgxVhZHsp6hX7/LPsxeqo4MpAKKad8vEHmnfAea+qtdlsmmi9XLquU3ZRM8iCKfUB4OKMIzKB/CrSYO8ocTillbniizGH9vP/a7mCVTrPiIb8+wYMsY5YdRlmjSbLJ1wZFpD6U+uPY3GaUOAY17CLNSlmth1nOCs2wyo91/AxKSK+yLSJXFCkyn8C71c4dV25acOBwBsqffYJVX1qfY3jK2YxzSJcSX39bxsUuVLPyHHt6P3IyEyU7yFpkwmg2uuuQZ///vf8cwzz2DYsGHS78OGDUNtbS2eeuop91hjYyOef/55nH66Qy2ffPLJKCsrk8ps3rwZK1ascMsUGu740jBfdIIXEwIX/NHLRi5sY/iFTWUOGjThBnQCgzZytKY8J8hwx9z0IGVpyWhZVW2885Gze6feZYL14myadELpfQvX+co65ZEt7wXZVHdZTa7gKCcL9cU4yn5+9Yyh0nGd0EDbqSovcW1hwto0qQusO/lphAZxv3TqA1qnlEbFnaw1KpdsUc4+i7bvz/0mPyuO5RFw3wVXwJLbluvTsRz+++q0618IJaGJWaw47zkBzqZJXB1vg0UWQUOk6xblOWjZGZVpEnkqNQvhidlF1ukL2Lo55xH6DER5z2mCHyt6QU+/iqqJnYFgFSUXXiDMQn2AESqcdkT7jMDiS88j99Ur73yajPxFuiROPdfMhRxgnlfXrBZDPCsplx/30hPhXaee4+KacQFeSXWMfS1fX5zB64Nigm9+85uYM2cO/vGPf6B79+4uo1RTU4MuXboglUph6tSpmD59OoYPH47hw4dj+vTpqKqqwuTJk92yV1xxBa6//nr07t0bvXr1wg033ICRI0di/Pjxhbw8FybmABAvZUayaeKYJnqspTVDFky5nLojU1VNarlmacHO6F8AzW6X81TjqF43EW1pCdLpFH762eNx8z/e9r2AP3rEicMljBoBc9BKzvYA0DNN1GZICENqd5tV2xvdZJH99O8w5bYEZLVDil0ITMykqiL16HMZuphevHG5/9o8pkkur9r/uGPbtz3jx4orNCgsBzedqownfU/U+tTLUhkxtQ2Z6ZGvhbYtOSMw6jmBTxMGR22DFfIkQ3R+bNHyZW7bGtZGEYp1TJPbF04QUKp+L/v+lWlCj2QyTjVPvL1VKi+gY2XcazPEe+Oelcf28ic2MsfDrNOLFZWewIqP6gHIdjp6VpDflHCqbwDYsPOAa0vlqef88xXH+pvjfzm/USE+6B7oNoTSJiMls30+Q3DN/KLmVk0CYs00/e53v0N9fT3GjRuHAQMGuH8PPfSQW+bGG2/E1KlTcfXVV+OUU07Bpk2b8OSTT6J79+5umTvuuAMXX3wxLrvsMpxxxhmoqqrCo48+ipISv3FmISDGii7CNd1tc2oBAco+tRCmKSi4pZhoK5QYTbz6gNajME2aCUClagF+hysYL2H/wUVY1oEu7Hr2QiOVqn0jO8ggQ3AhaAbtsHyLtmZHqqYn4RZMo00TYRsB3sPHKSff21addAVehaBT0arMECdcOP9n+6cRGlX2iBsvbtm0UpZRd+kma516jhbn7Lw470GPafIeatesc4fqNEH7SxkVTng1qXBEKh3qPfXB9n2+cuo4LFWEZhX0ij3BVS67KhuZfhHJKyl7MTrlF2VtgoRdi1pWt1jOf9szu1BjPXGsoGrPp4K1CdOwWZeOOsz9fgax2aJQjZoBnu2k//uZJr7P/36359kt5mVOPSdSCdHnr76DTvtivPr7wSZDJnUFbQhpmzqHHN1Gj/6fFJumWDNNYei6VCqFadOmYdq0adoylZWVmDlzJmbOnBlh76JDWJumTAZ4MztJci8Q9bhpac14DIumPk9o0jBNAQuLP2Ag/8JwrBKvnpM9jXQ7HAGREgCQF+/WjGycqWMbdKC7ch197jdY1k0susmCvzY5wWuKTUKqM/Cn7XgxuMQvqoCrlGNLef1Q+/avFU6S4i17ZM8b1f5Hb1PELxbe5J6SynFDQCTN3ZkNUMjbNPH3nxNy26Sek4QmwXR4BV1DXSacCBcritvoqM9K4J4X1uCxbLLo1SS1ztL1u3BEXznNCxEHs3VqmCZfL3n7LwphMA5ACtUgyl94wgA89uZmTB49WKmXsgxmx5yuipOMST2nTdOUPae8NO2qunRLTDVhYnQ2TScMrMHS9bvcfJZOXzSsjPg9JOO8dY/nsCLmQ049J4SlQ5Q1Z8arutEK8lqj53qOQ2oZ7hnwcfAySvveefK8nQTEmmkqFrgDWpODnO5ehNC0eps//xjd9apqHrk+eXKhhuAU3ATAea147fvL0P/LSqiHk6/7aHDVc7JHmjqx9+3uRMKmkzDtu5/ZMgulKiiVLCYqdZesxh9yF2ENha1n+9S2M0o5PyOl9oVCfWY61a/nkSULV7xHnvNJr02oXFR47Ank9jVeMzqmSfxuUuF8969vAgBWb9uXbds/XnVqgRJmRywnV6bXxLwHDIPlhRzwHpaOkZPaYNV+XjndQrx8w273+0fEsJdbBdUwFUFxmlIB1w8A/aud93D04b18ZWl5ce1qGg6ZZZD7q6rYVNWULOBm69O8g24bQoBNpdwguFrVIDmuU/cJFdeYw3u7x3QOHu7YUjdPAYIe4DHvnHpOHBt5WA3pg14YF7/RDbaJmzDFqpOZJmUe1NjBcnKxOxckJOiAFZpiANOAco57k5bwgjj3OL/nn5r/TWuwrajRdBM7x7JIOxAN06RjT9KplNZYHKDqOWdn5VG98qTFumVrBEanP85nKpXCVVnvERMolS7i1sxSwhOIgKRezq2ARUhpQ+c59P7WfWw5uhA8+KoT3fe5Vf74MXphSCnnE0j1gqXqaQd4gooKVRjihAB6XeooEN3xec+xrfFtS4tFK/9ucTZwnI0GBe0D5zmlqucymYzW9ou2QfsrvDupR5Mu5MDxh3lea6cO7el+F0Enub6LXmiFfKWc009k25fLiuulZWXDXudTHwjVL2AJqPZH6v/Sgp2SF2ydyrGZzHM6j0CuPzrzgFbm2Xrvq1yWu6+0vIllEZ7EnHZhbzZpMs056MriNP6X8h5K6jlGaqLn6gQ7Lhgsl3/RKZuti3sPRJvJkJms0BQH6FQYAp4k7rEw1ZV+bxxJPZfJaBdMNVmsahvi1qfYx4h63XoCGCz3HDJpiglINZZtac24E6MQmoLiPtHuykwT/3KnUym8RRLI6hCkLgW8SVzN5aSzafIlqtTsSFUPH65ewbSdPKQnVOjiGvlS6SgLjMlOyg2uyGy4BdugthOkntNdvyrom5gm4eH19bOGZdv2l9UyTe599S6KJi3mhAYqNPI2TdkFo0W+pwDPNKnXRhmNhav9dkL+8BTO/13LS3DH509yj9cRz1Md6ELImUGwHmGqIMDcW45p0o4BSZWnCE2KgOD3RiMLttK2Tsih81yQpx2tglOL0TJSeiRN9PYgxtlkiiJCDqj9oLZrXQmLx6mp3fdAcbDQtc2r5zRzG0LYNBk2ZUG2bXGDFZpiAL2HkQM6aTZpIjE751PBoVXLYKl2L7rULLy6g0xWGvZCxzTRPj+0WA4WSqlnIRiKhab+oOzy66lwvPpokEV1p0mFrKBAboBZVSUgVCOiOj0tLdrW3Vu53oONMgXP3VMR+0kk4uXq9XnPaZge1faJu2JTItTrlJxqKtUeaIitrEfqmDXtxA/r6QgHg3o5RsJeyA3KHvHtc0zTF+951Xcduj6YI4K3ZsuQpMaGXJGi2s1ExXbTBcd45bRqb+fzs6MOQ3eyieKGrVhwy5R3S70ujhkty4Hx5RgM3aZM9bTj+itgNELOViMz7f4B48YAS6cChRV6nPO6o/WFUWXq2fxgpkkX3HJa1osYkFWfnJBvcoYxzYiplGd2YY7TJDaPvPOON7/o34OkGIJboSkGCM00ZTLEPoh/dB6DYNhlK6ohMRfqorVK6jny3qpCli64nHiBBJUMAH9atE4q09DknSQmCbGIqJni6eQnQLvuf2G9CSPMi0knwy980rGbOmu450FDJ6N+WaYlyBBcfbK62Dvi/E8f08/tAyBPWKY0Kv44TXz76o5Q52UH8Oo5EVl4cC/Zq0kVhjhWkP6vPg119y76w+321VASvC2HKKNeUzYYKtGjrNnhBaBMsYKAfxGi74CaDPoQGdOqZ6rTX3EdTnm6KFK2SLewUns1mXnxP8S3s7HNhEG1usES4DIOBLmRc0ID/Z27V6LfAuprqQoIRpaDsdMxOZ+kU6lAYYXKh00aponbbOriNOlUlJz6XUVlKR9y4MX3d7jf6fhig7Fq1OSATj2XrQvevdKGpwCj9s3Bpsk1XGd+iiOs0BQDBC3klL7kdrgUMiugYZqUga0LY5C7eg5SvQLzV/hz/KmXvHWvt8sW7S79cJd7jKrzPCHTO58aLJpeWNWriIM4O51Kua7ONAUJnTyO6OPUp0tLEcT0qPeB2l0APMtjErJ9IQc0rJnKSOliWQHeeKEeMUEJe4UQphtbOpsu1U6EnqeycKqRO2eE28QE/wPMcb1UcAssJ1yUKXGaqKCnJiAGwMTocT6pfQptQx1bf83a27W0ykKQeq303RGMhMQ0kQWeC5ZYqom2b2JaALIp06i7OVWewLa9slem3wjZ3zZl80wBdUvSejstUU7kSgOAA9p4btnrkDZvZqbJJzQxmyfVdKFSo547ur8XVocT8mUG0fnkEr337lrhOyap53RzG/XJVNi+sHGanGPZMiHexTjACk0xgGlAOce9cqbgloBsqxKkGhJroC4JcJB6Tm8DJQ/+hWRHxNUDADv2+o1X6QsuXKvpuXo7FXUhhlv++glHu8drqyvBwV0M4N1nOpEdIIt3Wam8uOsWYX2cILk8zb9Hr4kubLr4W84x57NFWbDUouqO0BQRnNs9euoGTe45V2gzC1damyaFaQKAd3yMo9wmp/ITC43K9AS5p3N9lXfu/nfGr/L0hAVWGHUFV7l+taTOI2tzvZOiZcWmesnGsXul7KW2igSVFO7xdDxSpoljcXUpgujmQu2rU5fzqVPPUajX9m+/WyT972NimHulhlxRQcesiWl66h15k0fnHqnPjJOBp/ZT2ta8B9zm4c2Nu6UyQvWqsm+nH+mo5wXrK6BuXAB+zhDv9SCFLZbLG1h0iWlKSXWGjdNEz00KrNAUAwQbHnsLrJfHyayeayYGnmq1JYqxoi41i7oAA34vDAod1TzheL+nn07VcJwmVxtdCMIKAmr5dCqFvt0r8J9nDgMAnDSoB9sWzdlXXip09N6ENSfrvea0qSTs1e3GlSZ0C6FYmES93D01RZBXY1uJs/zqKUVo0jBitCwVCHX5xDxVcravzMLi/C8LVwKqGsE8ocr3lrunOpsm9ZqMMeHca6JCY7ZNysgoqUlMxvWA/llp7d+UsfWpo/oCAL50mqNCFu+OzhECIJ6pOqaJsZkMXggpy+Fnj4I2b4CsmuHzDPICm2SwTqZEbvPisp5pPdPptOU7xIJTz+k3Q+K91myeSJuq6UV1F2fuU5km0YTqSW0OOeAdE+NH7WtDcwv+94U1Tv/I9eViV6Z6z+kYb9ona9NkERpBNk00/hE3qVFQFZlu0lZVM9oEvGRhyygTINe+TnAQRtNURaEKVnRCE6D52mh6CMoccf3V6d5Fl4/o140t59ZPhDLXuLfZK/vaWs+zSU3Yq/ee0yyESheaFK88jr53nytnVEmYRtq+LlGo+hy4McgZAusy16u73BbN2NZNlO74MixqallOlaeW0QvYrVI5wEtu6vXV/6xM9ixqPj/dZkhVz+kdN/SsCODZZ2nj6WT/pfkcdXHNOPujXGyanP7KdXnsFV8OkG0lhf0VAJyTte3zO3f4NyPlJWl3rO5hYpnROE3chlDgZ4+94zvGgZu3deNajDMfO8/0o1xhRYVK1Zc0XNSpZdzpc4WvffddVRnM3Z5qdHP9Ib16jrl3pYytID2XZZo0G6i4wgpNMYBJ3QLIkv6HOw8AcJK5smXJAA+yJXDtWZiJkpYDCHNg6Kvu5RrY06F/PzGkh3vs+DqZUaITmoDwjqJ9pfWHCVjI9VkXS0SA7mK5ss+S+EhqzqXQhuAMe/HYm5sx41/vOn0UwhjHMihCoFyvusDyC7GfFeH76VybPxGtzrZOjX+jVc9pJkr12Zq0Z3pPO/9YUcerKgjQc+798qlyX7OfVN3BqefUMRDENKk2gF4X+L7q45/x7QtwO/hUKuVr3/ku1wWEiZemXpf8bHXvq8mmye2PYMN9C7a4DvmaqrKG7lxMI6om9Prmb5N6MZqgqpLpdz+D6nzqvOdkj0/5XMGy60K5qHVykem5Z6B7B+lct+dQk2+98Or0X4c20rzhXfBssJIhNVmhKQbQ7dgEhDBAY/hwemiAuHy2ZDy9v2bBEGPUm0zkuugL5k5eYdRzynwlQvyfOLAHRg9zogefNbyvVIaze6DG19QmRReiIaznhurlpMJjcrxdH5eElUKX8kWnmuCC69341zfc78IOhbMnaNLsWp12IJXX28korIhhUivLwabJU88FsSfZckpb6rOV7yc/EftjOtH65PYEfDZd5Bx1p+8+O1LG5GXmMiyGdwWgY0a+Hr8qU7TJj2vRblBqFP/7kmUFJLUjJwzybuRBTLYv5IDvHfBfC8B7ffniNLl1yHWa3lfKDoq8eY8r9ko6ZvPZVdv89THPN4hp0od18dcr0K3CsWmiJhe0nBrOorTEv8nhNtBp5n0BgHc2ezZwBxtbyLiWy9H3QVyWLk6TKeSBtylJBqzQFAME0fhicqM6bc6F2SnrDdpApkmZ1HxsABN8TryznBcGt9Ntbc240bQbW1rdGEPqrlXHdokAjs0c1ayZgIKi0Zqy0QOyS38pw7JwCN5hyeU5VoQKQSuzExcnjKk5+rh+iGtwF0wthS+X4yDKNlH1nG7MZD9Ffa7tj1Zo1LEnzu/Ulq1EWfV37HPyc4nD3D3V2zTJHmHSc9AILZyNiCRcaARRrYOHqkrVsJKcF6tznuifLDTp1Ci+9yV73zivSMlWK4StIIV6v/QhTSjT5K8XAPp2czy7/EwTf6/KS/xzpQAbL26JHC+OhomYOt6LQfbgK+t99S1et9PXd12k8bDec5lMBtfMWSaV6Vrhvee0XvcdVO5/mTsXkTmTeQ91nn6Uubp41GGuWnUtCcnR3NKK17LXD3jPIVdVLkDZuWSITVZoigF0QoCAGMN0keeEFsATCJpbWw2Gpc6natOksxECCCtl2DFwu6xNuw+636sry7Su3jpjdJPBri5goborpTm6nHL8zlnAFQzTKZ8buQ46exLVWNnru/NJi1OvvP412fhPzCQscvRVsrF/ZIHY7Y/SAXWBdRdWZkZwd49UPaexaVL7K8qVaQzGdSEaRP9o0Eba1BNvb3FtX4S9Fqfy1Akuuh0xV9atC/T6GaEppBCktqNuSHQCts4OULynQTZ9aj/Ee0CfgXjEpcx1ha1XNVw32bN4jKOfFQGAq8Ye4fRLYVl090owTVxASk5wuXrcEVKZfSR9zcQT6nznUryXTXl0T9Zo2rke82ZAG6cp+/tflmzEuo8PSGVo4Eo5phZfp5lpIkKTJno5nesu/cRhkiC5fa+zUfnNM6vx9T8tcY+rpg+5eM/pwq/EFVZoigF0O0EBMblRPb3OfZcKJTodscpGBLnDAv4dOa8a8r8w9AWcOn64Z3cU0lBQfQllPbpyXURg5PDMuw7F7un8gyL9pnz5xHQIWlh00ZDphDW8nxdD6g//cWr2POd/ek+FurMLY9emCi3iLB97oSywnmzlf66ckBnkPScuvEmjxtNNlFy090G9uvjan/74Sl+bJq8h9dUy2TTphJYMUy9nMO2Fe5DPV+FuXpSboDPa96l+3UVTbl8fEZ/fPNH7yqmRcvGeo/W2KvMLv9Hy31vxdVCvLm7KIHodFOq9cjc5XDBUMs+JZN8ir5uAiAXWpaxEYvNN8bwOkjhO7rWrm8KAOVZc/70L1/rqp0ITndp0Nk2lzPzGmTToWDERaPjTx/RDVXmp9D4IYXTWS3I//UyTLGg9nZ17WZsmt4++n2IJKzTFADo2QEAsuKYUKgKlxF7HnSx9lDDPRqjsFWdzYNoxcOoB8b1HVZkjhGjtI/hrU9VIBxq9naDPVosR2uh3MaFyOzGuLyl4Bvf7G/gAd7p+qnWp4JiWgVnD9+s+fSSOzsZe4e7pISWxMYVL9wfYKvkXWF64oGW5Xa7Wpin7v8tIKcKVxwrJbXHCM/dcaWwan00TVQ/rmCaDTZNe3eQfV1xQyRY395z+ntLyolo90+T9Ltm0EOGe1qeLiK/OL+I9aJXeF2ivS7UB1KlcVOGVS2zrL0tZJG8+ovOBlJkggGlqYJkmr01Xjdciv9dCAKoqL5HGrPoe00Crd3z+ROZ6lLZbzO+LqF/YWlF0zZFpKkv7N3rce6ATxsW9K3cTMnvniPe5XBE2XZsmJhDqCpLvUzBV8rl8P+IKKzTFAEE7UjHBCz29TjVHyza3tnqG4EoZ1WBbtwuScjll69LZqNDyrDeOb2JX0iToVIRKnfSlq1aC+HGLBp04rj/XCWzJ6fwp6PPo1bUcAJ85Xmpb8+IH2jQxO8eBxMifY+8Oueo5xqZJVfkE2MkEBVYE/NGzMxkvXpiqzlMnQFFOjT0jJmKteo5jcMg9GNTTu0fi2YSNT0Pr5JgmFaogSM+ju3xtcEvNJielPFvPtjH4PQT8tm25M03+94ULnivSH23cJauNdEKhKgiI6rnNHm8v5vVPKzRpxrXLDAfYNAkWSbV9EkJTZVmJJOC8RBIoA8Cc17xYbWcf3c/9rhNEdO+L1sg6i0euOUN6d+g98OwK5Updto1uHpj5VWcI/t8L3gMALFojXzPgbTRVm1qT4E7vxRbGM9F9v5IhM1mhKQ4QY0VHIKVdoakl+7++Ls+miRqC84yMF9ySF5rkkAPy4mJSz9GXUN2R66h+L6ijmT0Sp9V0KfOxJ66NhkZoEtnCSw3ec5lMBm9kbaDSKaBHlSM01R9sMob5D/JcCqNyEd1hc1mRasU1iWjkFOpC7HlQyuXEgrt2xz6pn5wwLO6Xx6B4v5WpO+fsp7gsNe6U10+5nIDKnjjn8mk8BOoPNEl9zyVhrxASaM064UJK4io2MBzTFEIQBeCL0aMbXvRecKpvwQio6kEBbf5BwyaHvt+PvPERAOAv2bQtAjp2WDXs9VSu3LVl62IWeKTkccOlEvJ5zylJk9n+pjyhSQ1NIJjsLuUlPjs8ig07PQGSqvg8w26+bZ2amptbjujbFScM7CGtC83MfVLjX3nzGxfp3d+2atO0Y5+zCannYl2J69Dcm1L32Xv9o33+4YXH+s7hbGHjDCs0xQDBNk3ZnUOL99LrQEMOaL3nFDsZXaoJep7faJxr2z8BqLYfJUwMEQB46QMn1UrPLLOj1smlplCRZgQX2o7YsbnecwzT9MeX1mFbls1KpTxBK5Nxyv/Ps6vdsrdfdqLx2gE908Gpp1oZ4ZW/p9nfDGyfqFfHMvxtmbMAPrz8I7l+5r6qTBNl6FR3ZzX2iysMl2gWC9VOh5vcmcWdOhhc8onDsm2LOmh9cr/cfqdlwZkGV/QxJ9lP0dWW1ox7//p088arLmBmkPdcRhnbOvsrp06/4CZUUsHBXXkGS9zXPy/ZgDuyLAMVVi4d5dxfmuuMnhfkRq8Trrg+AbIAz9lV0jrV000G/pTtKdOo6IU9T5eyEm1SdMDZtAnQeVMniDRrBByTwPDBdsdbTZdXM4hpkuM0wa1LIOX+Jrd9Yja465WfOtzXp2ZF9ayCzgfNyprxyWG9WJMCm0bFImeEtX0QuydTDqcyMnG7w9q3YMsLVoMrNKl66pRvIeLUJ2q9ctwXecLUMU1rshNEF+Wl0gUMZBkRpm6xG0+nvLrK0jzTtHHXAfzkn1404PqDTRKT0tKawW1PrHL/P3VoL6afSqc0TA+nx29m4i+Z7qkpJYErZIaMfuIxYn6UKgb264l3jy5zvaeey7JiGqbJtyNn1Ajcc/1XNgn0ecf3d4OnmoJbBjkY0Pvkd82XhRER6gAAPjG4p6+c6jShD26p9gHZ8mo57zu9NhHo1mWalIClAjoVscqO3vjXN319A4BTs7HVhEH+mu378IcX1+htJpUxoAsnQvvQxBktp2RBS1bPZa/JV5+eldyf9YxrbsmQQLjyC9tIvD25BV7gCOK0Ib+v8jUAjgC1O8uGqgKOzhhbBW/6INir/9/euYdXVZz//rv3zs7OfeeeEAgEELAYbgICChb9cShWRUWUYz2orbZqD/RirdpHn9Kf1oJ96vW0arVHVH59hFrAn7VUD1RulhQUYgErl4RrNCGQhFxh57Ln/LH2rDUza9bKRpKdC+/nefIkWXv2rHnXzJp555133pGfghkmRaNk6oJbqvfm1vWRESVZrDZeT467tzX15XSEDKevWZriOk9CdDevbDkMAKhp0vvN8AaqWxKwpRWsUp3NMPnLwrewc6uKiNfjQQdjtuU5fURw47f0Yiu7NnQvNGCcdwQAk4oypOt2pcnt/vaO5bJf/V2SFRDjNOkVN05F3RnpWaszeLEIuqMLALflOUjyAJZ/k+zPA0kmcUB0m7nbt7G7z+bc0pmWpsjzOiX4lamR6VU52zqxNKkRonQDsc6CyBEPg1Z9aaT8HN4BdfegIYOM6v8ivqcjBOuLGqzSbau9WAZ1QuK0lCumFZdeGiNBb50sTU7vjBmiQzNYiW2LOyLz7fhXP71ZTqvkq56n5qbkB+J8aES7tJwm9ls6azcA1DYbddBw1toYIsqkayvc9630+GnzPEx76BOrvbr1s9UNhm9OakAeQnWKSFl1k/l3ZpJiSY/SCTrO60Er9EqTqrRqd89pVgh01m6xLPyVFdtfu8M9TXmk/jIMwKcNmCpCx6gQ5wyfuW4+cFL7OX8JTEuTywBoRrsWBm8nHw3+0oUclueMtMZv9TgDnV+VdvecYsJ3OpKBDxRiFHAxT1vIA80jUDtMXYA7QDBfK2VQZ5bfnTFMnjl1MGQkiXGD3C1CgPOSi+64hQ6lszL+5unsZnG3JUoumpsFSVdO/bKrvOORl2lYTrJtIFQVl39HYimp/iFOTqi6DQGqVYgPmADwyDUXC3nan6lT/Cl1GUdcTlHriu8U4u3pdGTwHZ6TLKVTNzk4TVqs8hq/O9vBqFuiEre5j40cPO20ROzUBkxlVOPbJ7Z7bv0V4/Xo8rHKa/y2BcTVNC7Tt6jNbmkC5KUp8bou2KR4D52lqbrB6GdH5KbYlmc53IrKrXeqUsT5qMxQ1htDstKms3aK8deCQv8BiD5Q7hqDzmfS+RgVu2y6yYOj07q5S9vI59LB6cJn7mOQ6LPF83HyV+XoJju9GVKa+gC8gfIdIW5Kk2lpEg6YtXWWSiNdvesLAPYdTuK9rNmws6ldt9NLnQ05bV9ujjhgJijWLlvkapeBSF0e4dYrAHhw9kjz7ziHDnN3xWnz7/GF6SjMTILXay1RtoXD5tlWahl0M9y//OtL02fIftSE8Vu7jV1jQjd9ykSlSWtpkvN1UtpUnCxigHhgr1wGXRtQn8O/Kw2libcxjlNHaS3lWNd0vjecr48Udy5xWexKu/PZc7L/kS6t6jTMraTqcraltETKYVrvoMXRKqikE5UNXp9cafJ4gOR4efecaj3hDr1qrDE3S5PYtvg7ebZNH3ajs9hDHS4Kuc4hW33HdSEnxPhNIm6WJr4TdsLgDEc3gTORfqiy3vCZG5Uv+3Fxxg1KByCHvjDKLMsAWLGXBmuOvrKsctEpTdKh2Q4WHHO3tTZOk6A0OQS3VN/vOJ8XI/NSIvc3PhuaLU8YzDw9kPpL8Tvq0qT1nUh70X7a+yClqQ9gOYLzl8Q5rd9nf2Ecl+cUHw3dtnp1d8c5hxxQX0CHzorPxtIS5JmY2WEqlhY3SxNP++z6g+Zn98+8yErnEOX7xU3l5t9v3zfNnm+YSQElxTIkRWakzcLMc/FbpebfanF1ZnxracBKbclv/C/2b9o6UJ4Xc0kr8l7kDK4D1fY4MarfiZviGu+wldtJftUq9EbJEVveqkzijDVFCDuhLgsBzstz9jhNzu3Kkqkjkr+9ngD7pgW387bE62bdKnJY6ay/zQlBm2Ud9ijKhaoEbY1YsEVfLPH+OgVDVIgtS5OD0uRgyVZ9mnRKvhlXSZjgqIEYdf3KqPw0AEB2iqw8WbGy5Pa3+cBJfHK0LpLG7qfH4XXHz8b82Tftu73E+0wQrDCA3nrDdx8eq5VDNgD2Jd3Lhmba0gB6ZVC32xYA/HH21QZdv9n5kS/i/WW3CjEQr4gUi49bmjrxaeLFp2NUiHMmXTHdcrzKzMF991wkrThoKcmdlpIWTB5sv7diEWg4wx0anS1NWj8d09Kk92niVjTV2mUPGBgRyVVpCyMcZnhNiForltdawtS/pJcVZUrlEK1j4hKmeLAr9+1xWsJwWnIRn5UYI0a9t6W0Wul17UBURpjgi6YmnT06T/r/rUjcGV2/pSpCTr4UYlp1K/fvF06U/tdttX5pczmO156x5a0u6fJ6y0jym9YDI0/jt9iuneM0yW3ASWEB7NYQp3P3xME9HLZ2rzq9rurSq5OlSbchw4x/JczefQ47x/gzyg8mStfjHBzHVdl4ezzjoDTZY3XxsjI0h9qtOtU920je4vKc6tulU5p4mflOL1MmjXJR39KGO1/bgS0R5dHn8TiHPlHqNkdQynYerbWlU0MIqJb0zhQBJ8uwz+vBH++ZYstXtuJHxgNFeddtdNEvz8n3tvK1W4bUd9BNqjhl956Zn0OYAjpGhThnCoKGH8/Sm8ZoP7eCWzoPVhy+fCRGzlaTq0tufFAQt0+baRVn1ZpmY7aal5ZgT2ua0a1r6hEK/s52rcQ5KE22Wavt9lJoBl04ATWdep4Vnz3dcfkQJb21I0ecLYlKk1PcF446LunM+NyhV9xB6DRrB9yjd/N7Og3E8y4dBMA6ENkNNfaNm7UvoPj/8DKqmwx0g9saYQlPdGJVdya+ETkAevbofDlPjS+Hk1XMfjyPm0yq0qiPPZUsLN22tHV0auXjl9VwHtFMSHQDppMjOK/8K4ZnafPUWpp0SlPruVqagGV/2+eYDhCeregIrhxgrlOGnRVX+4So4awcb8jr9eCL04Yj94bPq6XPOhT/G/Edv/mlEuv+DpHuRXnEILBO2OvV+P3i7ZfiiouyzXS6WGVq4GAzrcaSrj17zsHSpAv7oYa9cVNwzHAOyiTH0dJk3tc5z94EKU29AD7bclqnV0MOuO3q4OcUibtK3JbnGGNmvvFaR3D5pW6NvDQpGgdJ/pKFGcMHn1Vh0/5q00dANbWr4fTbhK2+urLal1F0HTCftXY4HpECyLNDMZ3T0SBiwNA0IT5LvE9Umqx76/inEl1XrdNwmOFExFFV9JtSlYudkSUGwMGvS7gUZtYOSlXD6uz8PRHV0uQWd0dUMAylNHI/5ZnyPMWOXdxldPlwa8CwfFqMtJWRqMKfCDN/wKoP3bKEWlK7RcC47rTDC+jc0iT6451t63Bd8hO/H43SpC698ccmDkRO5ySaO6ccfF90TtNiGXSKjVNaQJwQMKwQHLZ1EfgDwvLc8doWw0IXSWYeAuvTKQzu/jxO518CxrNcWyoH6jTzVdq2rk8U81Qt4wFhwhOKvANuqEtk/LdNEY3cprPo7UaZ5D6DMXEntZCnaeFRrG2KI7hxD7kORL9B9Rmph5x3vnsO2nL0Vkhp6gW0mIqFvlFZzt2dL8+lRnw8GoRormpycxt7ZCbE22rApzsA1vjNO962dmcFS1SI7l2xE3ct/xh/ifjK8M6F3+vAiSbTR6Ip1G52FvFOy3OmRYCXy/4M+BJZS2uHqzIgzg7FjrzNYUYkdsTcXH9xfqq0jb4zS9PPhF1egKV0tkR8oMRdOAMzrGUU7kPFZ/l3Lf/Y/ExrkRCudYSZ486pzs7fE4lXBmN1UJPSCoOgOFipM3JxiVRdHpo2LAu3Ti40/3dycObWMvUe4tK003Kuk6VJ92YFFL8bJ+XG4/FIy5NuS8mAfVmCt0VdtGXVKqVzAg44OGw7vTO6oKEcrdLUHrYNbIbjr7OlSUSM72XlbbTvt3Ycx4xfb8Qja3bbrH5uG0xsliZNYEe13fi8HvzoP0ZCh5qvk9L0eaXh+6dWrbh8H2oPmxshnBAnmoDLbk+NpclJeVc3uojiy5tM5HtzdHG1bNaryFcykvx4b/F01/t37tOkby+9FVKaepijNc2mhcFJGeKDFvcpcFue446xokla7SzFHRviwKY7loO/kGfbjGjYPPij20470Yp0oMroXPigL1pcTkRinWzcZ5nIbT5NqiO4uRvKdnskBSylydXSJAxKYjonk7u404qnvlkZsPmAJTq0itU0LEd2nORKU3NrB8JhJnWGScJslS+NtLR2aAcsFfnoG+fdc52dvyeiWpq2lRt1yI+bEeGDRpjJCqRqvRMHI9WCwU+g56h+XUVZxi6kKYrTrC7KM3fCFQ8NFfOMJjyAKr9uJs6xttB3uLZV8V5mOI+wfbBSy2vuSNNYRflZhKpfnVMU/ziXNiBuaIgX6lSngKg4Dca6pWD+vHYcNqyGf/qkwqZsqpYLo8xOCoNslQTsmxK8Xg8uiizFD0yX/bzUfNVJHMf0YVSWLOO8VmypUHsH5r24Tft9sywOOw2dlHxpyc3pGSibgaQwJUJa3fJsOMywL9Jni83bZvGPXL9lUqEZBNPp/h0u74tRDkTy7BtaEylNPcybJZb52sl8ya0NvCNztTRFBuN6N0uToNmLHYqug+Av7//96JAUDTveVcGyOpKDkSWX71wxFIC8O4QHyxMVJVtEcO4Dpcya9JYmQ/YzbR2uyoBfWp6z0jkuz2lmefYZpryEA1jRon80a4StDOLJ5c2t7UL8E7lj453zmbZ220xM6wwvXAsz5uhX43b+norq3K0GtNSlBWS/Orulyfo/1G7JDtiVIdXSZMUKk/PULc99Hpnpq6fHq3m6hQdQrYi6XXycBCGmkdOzh3nd+K0ebqwGAhXz4G1w8/6TkhzGvS2FTYQx/eCqxj/iEb8BYHeFpWSKoRVsCoh2iVjOl1Oo2XKviw33x+1HI/kY/+viRDktEcdpfJpsip7H42yVU6wiYjsdF4mHZdzf+P21AWnS9yVrY1vYFstLRQ0pogtwC1jtSnSYd1IcrThNGqVJSJqoyfM7b1iWbLEfVGM/MRfLrLn0rixnO/s0yYpjb4eUph5GbEaOSlOkcXMlw83SFIxEnK1rFpQmOHeWrcKApe+sjd+7lZm6qlgYaY3Eul02OWnGstY3xwwwr/EwA7zjmjgkwyabbQu9i58If04NZ9ow97f/sCfg5fR6zAFWVHLaOrE0GVHW9W+2W5A+tWPl6XmH3BRqd9yNIy45RhP8TexrxUju6uNSZ4NuqI7gvKxcEdalBYDmkNUO1A5TTNfWEUaovcMciNR2aFtKcwh7oVuec0IdhCzn484tTW6+R+JSXtQRwaPwaVKtN0+vN86IE7exmwpbu/z+OVkvVL8utU44klVQeba65Wi3AJMqulMItkaivPPyJprvgKWE8+j9Tk7Q0jKW0sbbwmHTKqeWnx9Wy/shj8eDeyNnsIkWYLf4Z9YEqgMZSfbNNSKq4uwUpiJRs4PReZIn16tTmBJumRetipv2WwGWdUF21UmGTmtS+wu3dg1QcEviHBF3vzhp4rwz5GZTtzhN2ZHtxeJaupqtGAm41cGh0fquvkw6Sw5/KXTfmR7ZCeL3eU0lgi/Z8Y4gQxNywe574lwurmBs+PyEtES4cOoQW9qAZlbufJaTVQan11r1e+msrB6Pxzqe4my7eW9ViUk8R6VJCoQYdragOJ2/p0NUGrYcOImXNxvxrHQWJ/H4Cd4Zx3k9msjhluLa2h7Gn3dajrm26OFODtM253Z5ea6+xZo4zJ8oL6emKEeDmEtCNonsVkSnTQuAYmky83SwNJmTF+N/txm5Liq2/d7ceqI4gke+YlMwlDxFRePn14+W7q1uXHBDnEDonpGc1tlqabc0Ge/WqaYQVn58PFJ2+Ts6/7flkd2WnPLqZqGeZAVzw+cnAAAl5ZYbwYSIxVgXLFJn7RXrQY0jpaIepeO0O1hnGbNCf8hp1d1rYr1KShNf+hfy/P7M4ebf4sRbPUXB0pmcJw6tUVqa1BAFvZ0LSml68cUXMXToUCQkJGDixInYunVrTxdJWipyWnVTB6ejp+wOlZxszQ489cXmnVBrR9jsYJ0cHvlLpi7dhTTxiPhL0aQcLRAf51WW4GQfLT646g7IPJdDUPlzEncOZiT58cSNxba0CYrJ//29VTgScVRVLR2ST5Pib8Ex481IkY3dLQ184K5racN1/+cjbRor/lNHVOZr+ZyosGPn5hTcT4fojH7Hazts11V4W+E+a07brv2CZWjnkTrhusPMOcxwsjGEqki+9sGCd74R5UaQLUsJp8F9/5pC7eYuUkD/DvL3hUet5+1bDDHA0VmaOvVp4s7dLrtjRedqp+jRToqAUztUfVq4svnO/74CF+fL1lHTMqu89zrZeDlqmkNmnnPHFWjLrFueM/OGamky5OJ+kgCwepe8C063e+6vkc0onNrmkHnf9jAzn7sY/HOrcKahzl/RbenVtGK2d2C8EvxSxWoDfClNn++5WJrUpTTZp8lKZ4anEfprMcCvGA1dPXbGbTnbbpm1h8cQUScwvZ0LRmlatWoVfvSjH+HRRx9FaWkpZsyYgWuuuQbHjh3r0XKJS0FOgRHFqMeA/bwjkVyN0qR2lqLfEPd9cuq8+Esm+kgBQK0mejjfuafuGFFN+onxcuf+y79+DsCKSi1yLvF0eCcgdph1LW32hLDkPdvegRMNZ3Hff+00P1MVRHHLs5NVmudXUXfGPJvMzdIEWJ3F258ctz1fTqKfx92K3tLE67fxbLt5sKi6Pd9yrjXy5MrZ6vunQSU90VA41DI6+TbxQeZ7K3ZqP+eYnWtHWIrB4xTgdPWuCkx+coPtuvo9MYwDp0OZxYohM041h8x2olPG+XlhXP6miFKuvpeA7P/k5lxulNey3jDG8Ov3DZ9B3YxcVLCclFButREHVsaYYJHQK878OfHNI7r7JwcsxVG0HumaJH+/xfAY4wV/ILnMzkNQY8goz/aIkzh36ncLuSJOcE41hfDdNz+RPr/ioiz8ev5YaYJ2NtI//TKyycVWRp+qBDBzGUtXFNEhX3w+T91sj8PH2+HpljZs3F9tKjBqm1EneYaMfNlLzlMMkcIYc9w9J26c0SH6oKmbBvhEV1cT4nstltPJ0pQSMN6vvqI06U8j7Ic888wzuPvuu3HPPfcAAJ577jl88MEHeOmll7B06dIeK9cBwUF1QNAeMBKQo9J2RjDRvsSlDgRiPJnPvqwH4LxVnlvCgol+Mz4OoA90x/2pOvMp4YN6eXUTKuqcrWaANcM5WtOCiroWHI/4cLgtz0XjS8E7oUMnm/G3PbKylq/UA3/Zy6qbcPBE51uNJzyxHlsfugp7In5gTpYGHqn5bWFpatbXcqU0fAAqq24yIyt3RnZqPI7XnsHuL+rx58hMXH0kfHZaf6YNu47VmR2nLmgpj1SvDtZOSlNyfBxOOyirImmJftS1tOHzygb8d+SMPsA+KKZGjtbhfizifeRyGs9zd0U9KupapPZ61xVFUlruU9bWwXDZk383r+v6de6Xsq+yERV1LeYyuS5WmbmUXtmIFzeVGXk66AX8yKDTLa147R9HTAvap5pdibxcx2pbcM3zloX840dnmX/z9lR67DQq6lrwy/c+x/ufVQl5KMuekf+P1rTgB2+VojGiDOpCHgQT/TjV1IoDJxo7DVPB3++KOqu9OvUvThZuADh4wthEwvuTTftPoqKuBeVCO7hu7ADpO/xdPVrTggff/pfko/PwnItxf2T5SVSoD55oxNaDp8xzIgHg20J74ZOAY7VGH8TDDQD6gZ73r+Unm3A80r/dOmmQ9sSFtESjDf1x+zH8cbs1gVcVIZ5nmdBnWpZJxdIk/F9yqEZ6XpLSFHl/ed/qBn+uNc2tqKhrwarI+Y9rdn2Bh+bI4VT4pHNfVSNSPj+B5f84oi0nJy0y+dhX2YCKuhZkpwS0qw69hQtCaWptbcXOnTvxyCOPSNdnz56Nbdv0W0JDoRBCIctc29DQoE13vqzbY3VqWQ7KUa4ykF05MscxP4/HYw4GbmkS/T6caevAz//7MwAwO0yV7JQADp1qNgcKzuSiTFvadI3CxvOQ0kUGoRc+LMMLH5aZ19Wt5gCQFRkISg7VYPpTG4U87PdKdjiRXAcf8B58+1+2z8SjOQBrEP/Pv1gzUXUACkhOopDK6lQTgzISbdemDpOjNvNnWn+mDd94botDTjJZyQEcrz2DHwhn331zjBw9W3RQFbdF65TuBL8PyfE+NCuKcqJmeQoActMC+OK0NWDedpm9XgFDQTta04IfrvzUWRjoFTnddX4g6uYDJ6XnHx/nxaAMeeeWx+NBSiDOZonUWY94WzvT1iHlq3tWfCB6dsMB85pTJG2e76mmVjwhWDl0Sn9KQhyqI/HPOH6fR4riL/4tlpPjFHLgD5EDZdXrIkZ7ae60rgBLadom+AU5WUnd3tmrL861XVPlUsMY8IFZXF7jiHXr9XqQlxbAiYYQbtKEBXhYUASyko3+62xb2Hb/1/9xBHdMK5Ku8QGf961GufQzJ/WsTU68EjOPp3trxzHzyCMzb6UvSg74zDHgW69ulz4Ti8Et81UNZ21y/dfdU6T/uSL9+82H8PvNh8zrXNEX4X3LS8JSH2A/+5DDx4M3So7ijZKjePM7l7mOcT3NBbE8d+rUKXR0dCAvTz5vKy8vD1VVVdrvLF26FMFg0PwpLCzUpjtf+EzrsWv1B0MCwNcGpGKUEAvjoW+Mcs3zV8JxLE/fMk6b5sYJAyXryJiBQW26ueMLbDPqeRMG4vtXDbelnTIsE0OzkxFM9GNIVhIenD0SryyciI0Pfl1K97+mDkF2SjwCcV7zZ2B6Iu6ZPtSW54yR2SjMTESC32v+pCbEaX0kLhuaiXGDgpJc351hzxMwnMNTA3G25YG//XCGLe314wqQGohDSiAO6Ul+XJSbYnupBygDuJhvcYH+2V47tgAZSX4p7cxR8kBxSUEaLh+ehQS/V+rwFl99EZy4cbxR3rSEOHMp5U6lYw8m+XH9uAJpgJw0JMO06qjcPWOY1A4GZSTicuVYDs5NEwZKaecU5zumS0uIkxTgacPsec4clWMeNcRJDcTZrBSzvpaLEbkpUrsKxHltMbU4N04YKFldAftzAowT3ScNyZDyHBBMMA91FbllUiGCiX4z9Ae/pqMgaDxDdTn48RvsPnjfumwwkuN9Ulv51mWDJSvykKxkc4u7LnyIeCwHYNRLWoL8DgzJSkJRln2bPO8HVOviv34+25Z2TnE+golyu75+rN6nafYl+SjMTJTa4R/vmYL/nHsJHrvOcEb/fz++0vxMfV+vHSNbmmaMyEZeWkC77KceI/M/Jw9Got9nS5ufliBZOoqyk3Ht2AFS/XNevXOS7T63Tio0n2sgzouMJD/+h3LWI2fa8CxbrCgAKB4o+5TddOlA5KYGbG17clGGFAwXMNwJbrtssFRXPq8HN44vkNpL8cAgJirtOhDnxZiBQUwqkpXRWV/LQ3qkrxLl3/DAlVD5zvShuDg/FclKW/lm8QBbWgC4ZdIgs84CcV7H5ezegof1ldjl58GXX36JgQMHYtu2bZg2zfLZePLJJ7FixQrs27fP9h2dpamwsBD19fVIS7NvIScIgiAIovfR0NCAYDDYJeP3BbE8l52dDZ/PZ7MqVVdX26xPnEAggEAgel8igiAIgiD6NxfE8lx8fDwmTpyI9evXS9fXr1+Pyy+/vIdKRRAEQRBEX+KCsDQBwAMPPICFCxdi0qRJmDZtGl555RUcO3YM9913X08XjSAIgiCIPsAFozQtWLAANTU1ePzxx1FZWYni4mKsW7cOQ4bYo0UTBEEQBEGoXBCO4F1BVzqSEQRBEAQRG7py/L4gfJoIgiAIgiDOF1KaCIIgCIIgooCUJoIgCIIgiCggpYkgCIIgCCIKSGkiCIIgCIKIAlKaCIIgCIIgooCUJoIgCIIgiCggpYkgCIIgCCIKSGkiCIIgCIKIggvmGJXzhQdOb2ho6OGSEARBEAQRLXzc7ooDUEhpipLGxkYAQGFhYQ+XhCAIgiCIc6WxsRHBYPC88qCz56IkHA7jyy+/RGpqKjweDxoaGlBYWIjjx4/3m7Po+qNMAMnVl+iPMgEkV1+iP8oEXNhyMcbQ2NiIgoICeL3n55VElqYo8Xq9GDRokO16Wlpav2qAQP+UCSC5+hL9USaA5OpL9EeZgAtXrvO1MHHIEZwgCIIgCCIKSGkiCIIgCIKIAlKaviKBQABLlixBIBDo6aJ0Gf1RJoDk6kv0R5kAkqsv0R9lAkiuroIcwQmCIAiCIKKALE0EQRAEQRBRQEoTQRAEQRBEFJDSRBAEQRAEEQWkNBEEQRAEQUTBBas0bdmyBddffz0KCgrg8XjwzjvvSJ+fOHECd911FwoKCpCUlIQ5c+bg4MGDUpqqqiosXLgQ+fn5SE5OxqWXXoo///nPUpq6ujosXLgQwWAQwWAQCxcuxOnTp/u8XEVFRfB4PNLPI4880qvlKi8vx0033YScnBykpaXh1ltvxYkTJ6Q0sayvWMkU67paunQpJk+ejNTUVOTm5uLGG2/E/v37pTSMMfziF79AQUEBEhMTMXPmTHz22WdSmlAohMWLFyM7OxvJycmYO3cuKioqpDSxqq9YyhTL+uoquV555RXMnDkTaWlp8Hg82jqI5bsVS7liVV9dIVNtbS0WL16MUaNGISkpCYMHD8YPfvAD1NfXS/n0tbqKVq4uqSt2gbJu3Tr26KOPstWrVzMAbO3ateZn4XCYTZ06lc2YMYPt2LGD7du3j33ve99jgwcPZk1NTWa6WbNmscmTJ7Pt27ez8vJy9sQTTzCv18t27dplppkzZw4rLi5m27ZtY9u2bWPFxcXsuuuu6/NyDRkyhD3++OOssrLS/GlsbOy1cjU1NbFhw4axm266ie3evZvt3r2b3XDDDWzy5Mmso6PDzCuW9RUrmWJdV9/4xjfY8uXL2d69e9mnn37Krr32WlsbW7ZsGUtNTWWrV69me/bsYQsWLGADBgxgDQ0NZpr77ruPDRw4kK1fv57t2rWLXXXVVWzcuHGsvb3dTBOr+oqlTLGsr66S69lnn2VLly5lS5cuZQBYXV2d7V6xfLdiKVes6qsrZNqzZw+bN28ee/fdd1lZWRn7+9//zkaMGMFuvvlm6V59ra6ilasr6uqCVZpE1AFr//79DADbu3evea29vZ1lZmayV1991byWnJzM3nzzTSmvzMxM9oc//IExxti///1vBoD985//ND8vKSlhANi+ffu6SRqL7pKLMaPxPfvss91Wdje+ilwffPAB83q9rL6+3kxTW1vLALD169czxnq2vrpLJsZ6tq4YY6y6upoBYJs3b2aMGQphfn4+W7ZsmZnm7NmzLBgMspdffpkxxtjp06eZ3+9nK1euNNN88cUXzOv1svfff58x1rP11V0yMdaz9fVV5BLZuHGjVrno6b6wu+RirOfq63xl4vzpT39i8fHxrK2tjTHW9+uKo8rFWNfU1QW7POdGKBQCACQkJJjXfD4f4uPj8dFHH5nXpk+fjlWrVqG2thbhcBgrV65EKBTCzJkzAQAlJSUIBoOYMmWK+Z2pU6ciGAxi27ZtsRFGoKvk4jz11FPIysrC+PHj8eSTT6K1tTUmcqhEI1coFILH45ECoCUkJMDr9ZppelN9dZVMnJ6sK24iz8zMBAAcPnwYVVVVmD17tpkmEAjg61//uvmcd+7ciba2NilNQUEBiouLzTQ9WV/dJROnp+rrq8gVDT39bnWXXJyeqK+ukqm+vh5paWmIizOOou0vdaXKxTnfuiKlScPFF1+MIUOG4Gc/+xnq6urQ2tqKZcuWoaqqCpWVlWa6VatWob29HVlZWQgEArj33nuxdu1aDB8+HIDhG5Sbm2vLPzc3F1VVVTGTh9NVcgHAD3/4Q6xcuRIbN27EokWL8Nxzz+H73/9+zGUCopNr6tSpSE5OxsMPP4yWlhY0Nzfjpz/9KcLhsJmmN9VXV8kE9GxdMcbwwAMPYPr06SguLgYA81nm5eVJafPy8szPqqqqEB8fj4yMDNc0PVFf3SkT0HP19VXlioaefLe6Uy6gZ+qrq2SqqanBE088gXvvvde81h/qSicX0DV1Fdd5kgsPv9+P1atX4+6770ZmZiZ8Ph9mzZqFa665Rkr32GOPoa6uDhs2bEB2djbeeecd3HLLLdi6dSvGjBkDAPB4PLb8GWPa691NV8r14x//2Ew/duxYZGRkYP78+aYW39vkysnJwdtvv437778fL7zwArxeL2677TZceuml8Pl8ZrreUl9dKVNP1tWiRYuwe/dum+ULsD/raJ6zmqYn6qu7Zeqp+upquTrL46vmc650t1w9UV9dIVNDQwOuvfZajB49GkuWLHHNwy2frqS75eqKuiKlyYGJEyfi008/RX19PVpbW5GTk4MpU6Zg0qRJAIxdS7/97W+xd+9eXHLJJQCAcePGYevWrfjd736Hl19+Gfn5+badTABw8uRJm9YcK7pCLh1Tp04FAJSVlcVcaQI6lwsAZs+ejfLycpw6dQpxcXFIT09Hfn4+hg4dCgC9rr66QiYdsaqrxYsX491338WWLVswaNAg83p+fj4AYwY5YMAA83p1dbX5nPPz89Ha2oq6ujrJMlNdXY3LL7/cTBPr+upumXTEor7OR65o6Kl3q7vl0tHd9dUVMjU2NmLOnDlISUnB2rVr4ff7pXz6al25yaXjq9QVLc91QjAYRE5ODg4ePIhPPvkEN9xwAwCgpaUFAOD1yo/Q5/MhHA4DAKZNm4b6+nrs2LHD/Hz79u2or6937SRjwfnIpaO0tBQApEbdEzjJJZKdnY309HR8+OGHqK6uxty5cwH03vo6H5l0dHddMcawaNEirFmzBh9++KFNgRs6dCjy8/Oxfv1681prays2b95sPueJEyfC7/dLaSorK7F3714zTSzrK1Yy6ejO+uoKuaIh1u9WrOTS0V311VUyNTQ0YPbs2YiPj8e7774r+U0CfbeuOpNLx1eqq/NyI+/DNDY2stLSUlZaWsoAsGeeeYaVlpayo0ePMsYMz/uNGzey8vJy9s4777AhQ4awefPmmd9vbW1lF110EZsxYwbbvn07KysrY7/5zW+Yx+Nhf/3rX810c+bMYWPHjmUlJSWspKSEjRkzpltDDsRCrm3btpn5Hjp0iK1atYoVFBSwuXPn9lq5GGPstddeYyUlJaysrIytWLGCZWZmsgceeEBKE8v6ioVMPVFX999/PwsGg2zTpk3S1t6WlhYzzbJly1gwGGRr1qxhe/bsYbfddpt2e/6gQYPYhg0b2K5du9jVV1+tDTkQi/qKlUyxrq+ukquyspKVlpayV199lQFgW7ZsYaWlpaympsZME8t3K1ZyxbK+ukKmhoYGNmXKFDZmzBhWVlYm5dMT71Us5eqqurpglSa+hVT9ufPOOxljjD3//PNs0KBBzO/3s8GDB7PHHnuMhUIhKY8DBw6wefPmsdzcXJaUlMTGjh1r26pfU1PDbr/9dpaamspSU1PZ7bffrt222pfk2rlzJ5syZQoLBoMsISGBjRo1ii1ZsoQ1Nzf3arkefvhhlpeXx/x+PxsxYgR7+umnWTgcltLEsr5iIVNP1JVOJgBs+fLlZppwOMyWLFnC8vPzWSAQYFdeeSXbs2ePlM+ZM2fYokWLWGZmJktMTGTXXXcdO3bsmJQmVvUVK5liXV9dJdeSJUs6zSeW71as5IplfXWFTE59DgB2+PBhM11fq6to5OqquvJECk0QBEEQBEG4QD5Nv5qRBgAAA1ZJREFUBEEQBEEQUUBKE0EQBEEQRBSQ0kQQBEEQBBEFpDQRBEEQBEFEASlNBEEQBEEQUUBKE0EQBEEQRBSQ0kQQBEEQBBEFpDQRBNFv2bRpEzweD06fPt3TRSEIoh9AwS0Jgug3zJw5E+PHj8dzzz0HwDijqra2Fnl5ed1+QjtBEP2fuJ4uAEEQRHcRHx9vnpJOEARxvtDyHEEQ/YK77roLmzdvxvPPPw+PxwOPx4PXX39dWp57/fXXkZ6ejvfeew+jRo1CUlIS5s+fj+bmZrzxxhsoKipCRkYGFi9ejI6ODjPv1tZWPPTQQxg4cCCSk5MxZcoUbNq0qWcEJQiixyBLE0EQ/YLnn38eBw4cQHFxMR5//HEAwGeffWZL19LSghdeeAErV65EY2Mj5s2bh3nz5iE9PR3r1q3DoUOHcPPNN2P69OlYsGABAODb3/42jhw5gpUrV6KgoABr167FnDlzsGfPHowYMSKmchIE0XOQ0kQQRL8gGAwiPj4eSUlJ5pLcvn37bOna2trw0ksvYfjw4QCA+fPnY8WKFThx4gRSUlIwevRoXHXVVdi4cSMWLFiA8vJyvPXWW6ioqEBBQQEA4MEHH8T777+P5cuX41e/+lXshCQIokchpYkgiAuKpKQkU2ECgLy8PBQVFSElJUW6Vl1dDQDYtWsXGGMYOXKklE8oFEJWVlZsCk0QRK+AlCaCIC4o/H6/9L/H49FeC4fDAIBwOAyfz4edO3fC5/NJ6URFiyCI/g8pTQRB9Bvi4+MlB+6uYMKECejo6EB1dTVmzJjRpXkTBNG3oN1zBEH0G4qKirB9+3YcOXIEp06dMq1F58PIkSNx++2344477sCaNWtw+PBhfPzxx3jqqaewbt26Lig1QRB9BVKaCILoNzz44IPw+XwYPXo0cnJycOzYsS7Jd/ny5bjjjjvwk5/8BKNGjcLcuXOxfft2FBYWdkn+BEH0DSgiOEEQBEEQRBSQpYkgCIIgCCIKSGkiCIIgCIKIAlKaCIIgCIIgooCUJoIgCIIgiCggpYkgCIIgCCIKSGkiCIIgCIKIAlKaCIIgCIIgooCUJoIgCIIgiCggpYkgCIIgCCIKSGkiCIIgCIKIAlKaCIIgCIIgooCUJoIgCIIgiCj4/8nbrqZmPmGoAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ua_tuolumne_clipped_mean = ua_tuolumne_clipped.mean(dim= ('lat', 'lon'))\n", + "ua_tuolumne_clipped_mean.plot()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "181d33dd-a277-4d96-8d9a-fb06eae95502", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}