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ricardobarroslourenco/slow_notebook.ipynb

Created March 15, 2023 18:06
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Very slow notebook, especially when writing down the netcdf file
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slow_notebook.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "891d0454-cee6-469e-8965-d12fbf2531c6",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-e79c2a12-c352-11ed-840c-509a4c71acba</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
"\n",
" <tr>\n",
" \n",
" <td style=\"text-align: left;\"><strong>Connection method:</strong> Direct</td>\n",
" <td style=\"text-align: left;\"></td>\n",
" \n",
" </tr>\n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
"\n",
" \n",
" <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"http://127.0.0.1:8787/status\" }'>\n",
" Launch dashboard in JupyterLab\n",
" </button>\n",
" \n",
"\n",
" \n",
" <details>\n",
" <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Scheduler Info</h3></summary>\n",
" <div style=\"\">\n",
" <div>\n",
" <div style=\"width: 24px; height: 24px; background-color: #FFF7E5; border: 3px solid #FF6132; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <h3 style=\"margin-bottom: 0px;\">Scheduler</h3>\n",
" <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Scheduler-24b1230d-a524-454e-9b94-65869fed4fc3</p>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm:</strong> tcp://127.0.0.1:44533\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Workers:</strong> 8\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard:</strong> <a href=\"http://127.0.0.1:8787/status\" target=\"_blank\">http://127.0.0.1:8787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads:</strong> 32\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Started:</strong> Just now\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total memory:</strong> 187.50 GiB\n",
" </td>\n",
" </tr>\n",
" </table>\n",
" </div>\n",
" </div>\n",
"\n",
" <details style=\"margin-left: 48px;\">\n",
" <summary style=\"margin-bottom: 20px;\">\n",
" <h3 style=\"display: inline;\">Workers</h3>\n",
" </summary>\n",
"\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 0</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:42195\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:38924/status\" target=\"_blank\">http://127.0.0.1:38924/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 23.44 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:42686\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /tmp/dask-worker-space/worker-xdkn7dq3\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks executing: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in memory: </strong> \n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks ready: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in flight: </strong>\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>CPU usage:</strong> 2.0%\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Last seen: </strong> Just now\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory usage: </strong> 108.20 MiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Spilled bytes: </strong> 0 B\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Read bytes: </strong> 256.76 kiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Write bytes: </strong> 149.97 kiB\n",
" </td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 1</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:44559\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:35375/status\" target=\"_blank\">http://127.0.0.1:35375/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 23.44 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:40798\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /tmp/dask-worker-space/worker-5sjqm9dc\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks executing: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in memory: </strong> \n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks ready: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in flight: </strong>\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>CPU usage:</strong> 4.0%\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Last seen: </strong> Just now\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory usage: </strong> 107.94 MiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Spilled bytes: </strong> 0 B\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Read bytes: </strong> 256.95 kiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Write bytes: </strong> 150.09 kiB\n",
" </td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 2</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:33207\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:45160/status\" target=\"_blank\">http://127.0.0.1:45160/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 23.44 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:36015\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /tmp/dask-worker-space/worker-vx67mkxu\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks executing: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in memory: </strong> \n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks ready: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in flight: </strong>\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>CPU usage:</strong> 2.0%\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Last seen: </strong> Just now\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory usage: </strong> 106.93 MiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Spilled bytes: </strong> 0 B\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Read bytes: </strong> 256.87 kiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Write bytes: </strong> 150.03 kiB\n",
" </td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 3</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:45381\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:38787/status\" target=\"_blank\">http://127.0.0.1:38787/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 23.44 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:37857\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /tmp/dask-worker-space/worker-qy_18jgi\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks executing: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in memory: </strong> \n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks ready: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in flight: </strong>\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>CPU usage:</strong> 4.0%\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Last seen: </strong> Just now\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory usage: </strong> 106.93 MiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Spilled bytes: </strong> 0 B\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Read bytes: </strong> 258.37 kiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Write bytes: </strong> 151.53 kiB\n",
" </td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 4</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:37239\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:44236/status\" target=\"_blank\">http://127.0.0.1:44236/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 23.44 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:35489\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /tmp/dask-worker-space/worker-f9nkqsn3\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks executing: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in memory: </strong> \n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks ready: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in flight: </strong>\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>CPU usage:</strong> 4.0%\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Last seen: </strong> Just now\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory usage: </strong> 106.93 MiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Spilled bytes: </strong> 0 B\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Read bytes: </strong> 256.86 kiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Write bytes: </strong> 150.03 kiB\n",
" </td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 5</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:45106\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:40875/status\" target=\"_blank\">http://127.0.0.1:40875/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 23.44 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:43190\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /tmp/dask-worker-space/worker-oyod11b1\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks executing: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in memory: </strong> \n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks ready: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in flight: </strong>\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>CPU usage:</strong> 4.0%\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Last seen: </strong> Just now\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory usage: </strong> 107.96 MiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Spilled bytes: </strong> 0 B\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Read bytes: </strong> 256.95 kiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Write bytes: </strong> 149.94 kiB\n",
" </td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 6</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:37828\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:43179/status\" target=\"_blank\">http://127.0.0.1:43179/status</a>\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory: </strong> 23.44 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:35723\n",
" </td>\n",
" <td style=\"text-align: left;\"></td>\n",
" </tr>\n",
" <tr>\n",
" <td colspan=\"2\" style=\"text-align: left;\">\n",
" <strong>Local directory: </strong> /tmp/dask-worker-space/worker-gn4zfhwi\n",
" </td>\n",
" </tr>\n",
"\n",
" \n",
"\n",
" \n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks executing: </strong> \n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Tasks in memory: </strong> \n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
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" <strong>CPU usage:</strong> 4.0%\n",
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" <td style=\"text-align: left;\">\n",
" <strong>Last seen: </strong> Just now\n",
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" <td style=\"text-align: left;\">\n",
" <strong>Memory usage: </strong> 108.16 MiB\n",
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" <strong>Spilled bytes: </strong> 0 B\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Read bytes: </strong> 257.08 kiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Write bytes: </strong> 150.16 kiB\n",
" </td>\n",
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" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
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" <div style=\"margin-bottom: 20px;\">\n",
" <div style=\"width: 24px; height: 24px; background-color: #DBF5FF; border: 3px solid #4CC9FF; border-radius: 5px; position: absolute;\"> </div>\n",
" <div style=\"margin-left: 48px;\">\n",
" <details>\n",
" <summary>\n",
" <h4 style=\"margin-bottom: 0px; display: inline;\">Worker: 7</h4>\n",
" </summary>\n",
" <table style=\"width: 100%; text-align: left;\">\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Comm: </strong> tcp://127.0.0.1:36169\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Total threads: </strong> 4\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Dashboard: </strong> <a href=\"http://127.0.0.1:44510/status\" target=\"_blank\">http://127.0.0.1:44510/status</a>\n",
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" <strong>Memory: </strong> 23.44 GiB\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Nanny: </strong> tcp://127.0.0.1:43256\n",
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"\n",
" \n",
"\n",
" \n",
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" <strong>Tasks executing: </strong> \n",
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" <td style=\"text-align: left;\">\n",
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" <strong>Tasks in flight: </strong>\n",
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" <tr>\n",
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" <strong>CPU usage:</strong> 4.0%\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Last seen: </strong> Just now\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Memory usage: </strong> 106.93 MiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Spilled bytes: </strong> 0 B\n",
" </td>\n",
" </tr>\n",
" <tr>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Read bytes: </strong> 257.05 kiB\n",
" </td>\n",
" <td style=\"text-align: left;\">\n",
" <strong>Write bytes: </strong> 150.14 kiB\n",
" </td>\n",
" </tr>\n",
" \n",
"\n",
" </table>\n",
" </details>\n",
" </div>\n",
" </div>\n",
" \n",
"\n",
" </details>\n",
"</div>\n",
" </details>\n",
" \n",
"\n",
" </div>\n",
"</div>"
],
"text/plain": [
"<Client: 'tcp://127.0.0.1:44533' processes=8 threads=32, memory=187.50 GiB>"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from dask.distributed import Client\n",
"\n",
"client = Client(\"tcp://127.0.0.1:44533\")\n",
"client"
]
},
{
"cell_type": "markdown",
"id": "8b8683ec-ce00-4747-b0c2-5fe4bd7ee848",
"metadata": {},
"source": [
"# Assignment 04, Notebook 01 - LAI x Temp"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "86a5d77f-b156-45e6-9783-bf46a0800402",
"metadata": {},
"outputs": [],
"source": [
"import xarray as xr\n",
"import numpy as np\n",
"import pandas as pd\n",
"import scipy\n",
"from scipy import stats, signal\n",
"\n",
"import rasterio\n",
"import rioxarray\n",
"import pickle\n",
"\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"id": "87cacb22-df66-4aeb-9afb-1085cbdf93b2",
"metadata": {},
"source": [
"## Load LAI and Temp arrays and put them in a common dataset"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "5d8687b4-ad4d-45ea-8b78-32f57644adde",
"metadata": {},
"outputs": [],
"source": [
"lai_array = xr.open_dataset('./data/GLASS_LAI_82_15_montlhy0_5deg.nc4',\n",
" engine='netcdf4',\n",
" chunks={'band':408, 'x':54, 'y':54},#-1,#{'band':408, 'x':50, 'y':50},#'auto',\n",
" cache=True,\n",
" decode_times=True, \n",
" decode_coords='all'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c4e44c8b-6467-4e42-9b8c-ef8915fdf741",
"metadata": {},
"outputs": [],
"source": [
"lai_array = lai_array.rename({'__xarray_dataarray_variable__':'lai'})"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "15dda5e4-4860-47d7-9f8e-db285f29e403",
"metadata": {},
"outputs": [],
"source": [
"nc4buffer = './data/temp_buffer_not_important_when_not_calculating.nc4' # make a str to save the resampled data\n",
"\n",
"def dump_to_disk(objecttodump):\n",
" xr.backends.file_manager.FILE_CACHE.clear()\n",
" #objecttodump.close()\n",
" objecttodump.to_netcdf(nc4buffer, format=\"NETCDF4\")\n",
" return\n",
"def refresh():\n",
" objecttorecover = xr.open_dataset(nc4buffer,\n",
" engine='netcdf4',\n",
" chunks=-1,#-1,#{'band':408, 'x':50, 'y':50},#'auto',\n",
" cache=False,\n",
" decode_times=True,\n",
" decode_coords='all'\n",
" )\n",
" objecttorecover.persist()\n",
" return objecttorecover"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "30db368f-1cc9-487d-ae3d-5ec9eaa5654c",
"metadata": {},
"outputs": [],
"source": [
"# dump_to_disk(lai_array)\n",
"# del lai_array\n",
"# lai_array = refresh()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "f3b47026-a668-40e4-b5bf-76d42fb02d1e",
"metadata": {},
"outputs": [
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".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (band: 408, x: 720, y: 360)\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 1982-02-28 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 ...\n",
"Data variables:\n",
" lai (band, y, x) float32 dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d52d22b0-3609-4b48-8c6b-7a901832245c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d52d22b0-3609-4b48-8c6b-7a901832245c' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 408</li><li><span class='xr-has-index'>x</span>: 720</li><li><span class='xr-has-index'>y</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ed393e03-f98e-4cff-a709-4df55c3fab46' class='xr-section-summary-in' type='checkbox' checked><label for='section-ed393e03-f98e-4cff-a709-4df55c3fab46' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1982-01-31 ... 2015-12-31</div><input id='attrs-834bdc1c-187e-4689-b301-7a7a6ee65110' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-834bdc1c-187e-4689-b301-7a7a6ee65110' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65666a36-06d9-4751-9362-69c04ea5064e' class='xr-var-data-in' type='checkbox'><label for='data-65666a36-06d9-4751-9362-69c04ea5064e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1982-01-31T00:00:00.000000000&#x27;, &#x27;1982-02-28T00:00:00.000000000&#x27;,\n",
" &#x27;1982-03-31T00:00:00.000000000&#x27;, ..., &#x27;2015-10-31T00:00:00.000000000&#x27;,\n",
" &#x27;2015-11-30T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-ee36a90b-56d4-413d-9138-11c0d9fdb62c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee36a90b-56d4-413d-9138-11c0d9fdb62c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b3c070d-2626-4828-ae19-c1be0aba888a' class='xr-var-data-in' type='checkbox'><label for='data-9b3c070d-2626-4828-ae19-c1be0aba888a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ..., 178.75, 179.25, 179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.75 89.25 88.75 ... -89.25 -89.75</div><input id='attrs-af3a334c-8d1e-4a6d-a5d4-aafad98d3c3c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-af3a334c-8d1e-4a6d-a5d4-aafad98d3c3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e25f7cc-4e8d-4356-b160-65f7b6a8c59f' class='xr-var-data-in' type='checkbox'><label for='data-5e25f7cc-4e8d-4356-b160-65f7b6a8c59f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 89.75, 89.25, 88.75, ..., -88.75, -89.25, -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6d9d7b0c-6347-4548-9356-788c7230b41d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d9d7b0c-6347-4548-9356-788c7230b41d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9d112bd-6cc2-49b4-8c1a-c944f5e56ce8' class='xr-var-data-in' type='checkbox'><label for='data-c9d112bd-6cc2-49b4-8c1a-c944f5e56ce8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.0 0.5 -0.0 90.0 -0.0 -0.5</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7344ce61-8848-40ec-8fbb-eaff1b74d8dc' class='xr-section-summary-in' type='checkbox' checked><label for='section-7344ce61-8848-40ec-8fbb-eaff1b74d8dc' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lai</span></div><div class='xr-var-dims'>(band, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;</div><input id='attrs-314e58a3-3c56-420e-99cf-dea2f8005082' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-314e58a3-3c56-420e-99cf-dea2f8005082' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d77082a8-e89f-4ff7-bad5-e616749e8622' class='xr-var-data-in' type='checkbox'><label for='data-d77082a8-e89f-4ff7-bad5-e616749e8622' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>540</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>87.72932166302</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>0</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>134.37581892842</dd><dt><span>bands :</span></dt><dd>408</dd><dt><span>band_names :</span></dt><dd>Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band 7,Band 8,Band 9,Band 10,Band 11,Band 12,Band 13,Band 14,Band 15,Band 16,Band 17,Band 18,Band 19,Band 20,Band 21,Band 22,Band 23,Band 24,Band 25,Band 26,Band 27,Band 28,Band 29,Band 30,Band 31,Band 32,Band 33,Band 34,Band 35,Band 36,Band 37,Band 38,Band 39,Band 40,Band 41,Band 42,Band 43,Band 44,Band 45,Band 46,Band 47,Band 48,Band 49,Band 50,Band 51,Band 52,Band 53,Band 54,Band 55,Band 56,Band 57,Band 58,Band 59,Band 60,Band 61,Band 62,Band 63,Band 64,Band 65,Band 66,Band 67,Band 68,Band 69,Band 70,Band 71,Band 72,Band 73,Band 74,Band 75,Band 76,Band 77,Band 78,Band 79,Band 80,Band 81,Band 82,Band 83,Band 84,Band 85,Band 86,Band 87,Band 88,Band 89,Band 90,Band 91,Band 92,Band 93,Band 94,Band 95,Band 96,Band 97,Band 98,Band 99,Band 100,Band 101,Band 102,Band 103,Band 104,Band 105,Band 106,Band 107,Band 108,Band 109,Band 110,Band 111,Band 112,Band 113,Band 114,Band 115,Band 116,Band 117,Band 118,Band 119,Band 120,Band 121,Band 122,Band 123,Band 124,Band 125,Band 126,Band 127,Band 128,Band 129,Band 130,Band 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" <tr>\n",
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" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
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" <tr>\n",
" <th> Bytes </th>\n",
" <td> 403.42 MiB </td>\n",
" <td> 4.54 MiB </td>\n",
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" <text x=\"20.000000\" y=\"100.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,20.000000,100.000000)\">408</text>\n",
"</svg>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d1f0bf8e-2135-4f98-b32c-ccd95dfae1b7' class='xr-section-summary-in' type='checkbox' ><label for='section-d1f0bf8e-2135-4f98-b32c-ccd95dfae1b7' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5dc1bf4b-af66-4cdf-a722-ee94ccbca609' class='xr-index-data-in' type='checkbox'/><label for='index-5dc1bf4b-af66-4cdf-a722-ee94ccbca609' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1982-01-31&#x27;, &#x27;1982-02-28&#x27;, &#x27;1982-03-31&#x27;, &#x27;1982-04-30&#x27;,\n",
" &#x27;1982-05-31&#x27;, &#x27;1982-06-30&#x27;, &#x27;1982-07-31&#x27;, &#x27;1982-08-31&#x27;,\n",
" &#x27;1982-09-30&#x27;, &#x27;1982-10-31&#x27;,\n",
" ...\n",
" &#x27;2015-03-31&#x27;, &#x27;2015-04-30&#x27;, &#x27;2015-05-31&#x27;, &#x27;2015-06-30&#x27;,\n",
" &#x27;2015-07-31&#x27;, &#x27;2015-08-31&#x27;, &#x27;2015-09-30&#x27;, &#x27;2015-10-31&#x27;,\n",
" &#x27;2015-11-30&#x27;, &#x27;2015-12-31&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;band&#x27;, length=408, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e93abfb6-6794-440f-ad76-539b815410c8' class='xr-index-data-in' type='checkbox'/><label for='index-e93abfb6-6794-440f-ad76-539b815410c8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75,\n",
" -176.25, -175.75, -175.25,\n",
" ...\n",
" 175.25, 175.75, 176.25, 176.75, 177.25, 177.75, 178.25,\n",
" 178.75, 179.25, 179.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b37498e9-b0fd-4452-9802-022ac234d211' class='xr-index-data-in' type='checkbox'/><label for='index-b37498e9-b0fd-4452-9802-022ac234d211' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 89.75, 89.25, 88.75, 88.25, 87.75, 87.25, 86.75, 86.25,\n",
" 85.75, 85.25,\n",
" ...\n",
" -85.25, -85.75, -86.25, -86.75, -87.25, -87.75, -88.25, -88.75,\n",
" -89.25, -89.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df122bc1-9a05-484a-9bae-b873314e710b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-df122bc1-9a05-484a-9bae-b873314e710b' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
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"<xarray.Dataset>\n",
"Dimensions: (band: 408, x: 720, y: 360)\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 1982-02-28 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 ...\n",
"Data variables:\n",
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]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lai_array"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "2e95f3be-355c-40c8-9943-e1211395b119",
"metadata": {},
"outputs": [],
"source": [
"temp_array = xr.open_dataset('./data/CRU406_temp_1982_2015_month_mean.nc4',\n",
" engine='netcdf4',\n",
" chunks=-1,#{'band':408, 'x':50, 'y':50},#-1,#{'band':408},#'auto',\n",
" cache=True,\n",
" decode_times=True,\n",
" decode_coords='all'\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "57dd2825-5730-47de-8cd7-6799c6a8287b",
"metadata": {},
"outputs": [],
"source": [
"lai_array['temp'] = temp_array['__xarray_dataarray_variable__']"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "b27be708-1e13-4764-be53-7a037ee564c0",
"metadata": {},
"outputs": [],
"source": [
"# dump_to_disk(lai_array)\n",
"# del lai_array\n",
"# lai_array = refresh()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "09f7ef5f-428b-475d-84f1-deede17c0154",
"metadata": {},
"outputs": [
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (band: 408, x: 720, y: 360)\n",
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"Data variables:\n",
" lai (band, y, x) float32 dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;\n",
" temp (band, y, x) float32 dask.array&lt;chunksize=(408, 360, 720), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f534c373-ad1c-4288-9e58-50fc720e930b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f534c373-ad1c-4288-9e58-50fc720e930b' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 408</li><li><span class='xr-has-index'>x</span>: 720</li><li><span class='xr-has-index'>y</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a72d4215-18c1-49bd-b848-d7533146a2d8' class='xr-section-summary-in' type='checkbox' checked><label for='section-a72d4215-18c1-49bd-b848-d7533146a2d8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1982-01-31 ... 2015-12-31</div><input id='attrs-5e25b6d8-0563-4641-b8b2-03d9e792b41d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e25b6d8-0563-4641-b8b2-03d9e792b41d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cb89d80-53a1-4e12-897d-9e6ab41aa003' class='xr-var-data-in' type='checkbox'><label for='data-8cb89d80-53a1-4e12-897d-9e6ab41aa003' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1982-01-31T00:00:00.000000000&#x27;, &#x27;1982-02-28T00:00:00.000000000&#x27;,\n",
" &#x27;1982-03-31T00:00:00.000000000&#x27;, ..., &#x27;2015-10-31T00:00:00.000000000&#x27;,\n",
" &#x27;2015-11-30T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-819fdb83-60b5-48d5-aedd-75592146169e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-819fdb83-60b5-48d5-aedd-75592146169e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79a8d18f-13df-4b8f-bdb6-fe5f5a008dfa' class='xr-var-data-in' type='checkbox'><label for='data-79a8d18f-13df-4b8f-bdb6-fe5f5a008dfa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ..., 178.75, 179.25, 179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.75 89.25 88.75 ... -89.25 -89.75</div><input id='attrs-3135b20a-c5fe-4074-8970-8b94dafb6567' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3135b20a-c5fe-4074-8970-8b94dafb6567' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0ea511e-8705-4d5d-9785-630bda4409d9' class='xr-var-data-in' type='checkbox'><label for='data-a0ea511e-8705-4d5d-9785-630bda4409d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 89.75, 89.25, 88.75, ..., -88.75, -89.25, -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-38b33c85-9203-4be3-9c12-e8d460fdca95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38b33c85-9203-4be3-9c12-e8d460fdca95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1850354e-fcd1-420c-bc1b-d4042550fcc9' class='xr-var-data-in' type='checkbox'><label for='data-1850354e-fcd1-420c-bc1b-d4042550fcc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.0 0.5 -0.0 90.0 -0.0 -0.5</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a8ee2de3-8440-4e21-bdba-878502d85a98' class='xr-section-summary-in' type='checkbox' checked><label for='section-a8ee2de3-8440-4e21-bdba-878502d85a98' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lai</span></div><div class='xr-var-dims'>(band, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;</div><input id='attrs-314dd5ee-25f8-4475-bdd5-2f07b8977912' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-314dd5ee-25f8-4475-bdd5-2f07b8977912' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a46a62b-4eeb-46a1-ae3c-e5fa76c628e7' class='xr-var-data-in' type='checkbox'><label for='data-8a46a62b-4eeb-46a1-ae3c-e5fa76c628e7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>540</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>87.72932166302</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>0</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>134.37581892842</dd><dt><span>bands :</span></dt><dd>408</dd><dt><span>band_names :</span></dt><dd>Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band 7,Band 8,Band 9,Band 10,Band 11,Band 12,Band 13,Band 14,Band 15,Band 16,Band 17,Band 18,Band 19,Band 20,Band 21,Band 22,Band 23,Band 24,Band 25,Band 26,Band 27,Band 28,Band 29,Band 30,Band 31,Band 32,Band 33,Band 34,Band 35,Band 36,Band 37,Band 38,Band 39,Band 40,Band 41,Band 42,Band 43,Band 44,Band 45,Band 46,Band 47,Band 48,Band 49,Band 50,Band 51,Band 52,Band 53,Band 54,Band 55,Band 56,Band 57,Band 58,Band 59,Band 60,Band 61,Band 62,Band 63,Band 64,Band 65,Band 66,Band 67,Band 68,Band 69,Band 70,Band 71,Band 72,Band 73,Band 74,Band 75,Band 76,Band 77,Band 78,Band 79,Band 80,Band 81,Band 82,Band 83,Band 84,Band 85,Band 86,Band 87,Band 88,Band 89,Band 90,Band 91,Band 92,Band 93,Band 94,Band 95,Band 96,Band 97,Band 98,Band 99,Band 100,Band 101,Band 102,Band 103,Band 104,Band 105,Band 106,Band 107,Band 108,Band 109,Band 110,Band 111,Band 112,Band 113,Band 114,Band 115,Band 116,Band 117,Band 118,Band 119,Band 120,Band 121,Band 122,Band 123,Band 124,Band 125,Band 126,Band 127,Band 128,Band 129,Band 130,Band 131,Band 132,Band 133,Band 134,Band 135,Band 136,Band 137,Band 138,Band 139,Band 140,Band 141,Band 142,Band 143,Band 144,Band 145,Band 146,Band 147,Band 148,Band 149,Band 150,Band 151,Band 152,Band 153,Band 154,Band 155,Band 156,Band 157,Band 158,Band 159,Band 160,Band 161,Band 162,Band 163,Band 164,Band 165,Band 166,Band 167,Band 168,Band 169,Band 170,Band 171,Band 172,Band 173,Band 174,Band 175,Band 176,Band 177,Band 178,Band 179,Band 180,Band 181,Band 182,Band 183,Band 184,Band 185,Band 186,Band 187,Band 188,Band 189,Band 190,Band 191,Band 192,Band 193,Band 194,Band 195,Band 196,Band 197,Band 198,Band 199,Band 200,Band 201,Band 202,Band 203,Band 204,Band 205,Band 206,Band 207,Band 208,Band 209,Band 210,Band 211,Band 212,Band 213,Band 214,Band 215,Band 216,Band 217,Band 218,Band 219,Band 220,Band 221,Band 222,Band 223,Band 224,Band 225,Band 226,Band 227,Band 228,Band 229,Band 230,Band 231,Band 232,Band 233,Band 234,Band 235,Band 236,Band 237,Band 238,Band 239,Band 240,Band 241,Band 242,Band 243,Band 244,Band 245,Band 246,Band 247,Band 248,Band 249,Band 250,Band 251,Band 252,Band 253,Band 254,Band 255,Band 256,Band 257,Band 258,Band 259,Band 260,Band 261,Band 262,Band 263,Band 264,Band 265,Band 266,Band 267,Band 268,Band 269,Band 270,Band 271,Band 272,Band 273,Band 274,Band 275,Band 276,Band 277,Band 278,Band 279,Band 280,Band 281,Band 282,Band 283,Band 284,Band 285,Band 286,Band 287,Band 288,Band 289,Band 290,Band 291,Band 292,Band 293,Band 294,Band 295,Band 296,Band 297,Band 298,Band 299,Band 300,Band 301,Band 302,Band 303,Band 304,Band 305,Band 306,Band 307,Band 308,Band 309,Band 310,Band 311,Band 312,Band 313,Band 314,Band 315,Band 316,Band 317,Band 318,Band 319,Band 320,Band 321,Band 322,Band 323,Band 324,Band 325,Band 326,Band 327,Band 328,Band 329,Band 330,Band 331,Band 332,Band 333,Band 334,Band 335,Band 336,Band 337,Band 338,Band 339,Band 340,Band 341,Band 342,Band 343,Band 344,Band 345,Band 346,Band 347,Band 348,Band 349,Band 350,Band 351,Band 352,Band 353,Band 354,Band 355,Band 356,Band 357,Band 358,Band 359,Band 360,Band 361,Band 362,Band 363,Band 364,Band 365,Band 366,Band 367,Band 368,Band 369,Band 370,Band 371,Band 372,Band 373,Band 374,Band 375,Band 376,Band 377,Band 378,Band 379,Band 380,Band 381,Band 382,Band 383,Band 384,Band 385,Band 386,Band 387,Band 388,Band 389,Band 390,Band 391,Band 392,Band 393,Band 394,Band 395,Band 396,Band 397,Band 398,Band 399,Band 400,Band 401,Band 402,Band 403,Band 404,Band 405,Band 406,Band 407,Band 408</dd><dt><span>byte_order :</span></dt><dd>0</dd><dt><span>coordinate_system_string :</span></dt><dd>GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.017453292519943295]]</dd><dt><span>data_type :</span></dt><dd>4</dd><dt><span>description :</span></dt><dd>L:\\NDVIseason\\LAI\\glasss\\GLASS_LAI_82_15_montlhy0_5deg.envi</dd><dt><span>file_type :</span></dt><dd>ENVI Standard</dd><dt><span>header_offset :</span></dt><dd>0</dd><dt><span>interleave :</span></dt><dd>bsq</dd><dt><span>lines :</span></dt><dd>360</dd><dt><span>map_info :</span></dt><dd>Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,WGS-84</dd><dt><span>samples :</span></dt><dd>720</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 403.42 MiB </td>\n",
" <td> 4.54 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (408, 360, 720) </td>\n",
" <td> (408, 54, 54) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 98 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
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" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(band, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(408, 360, 720), meta=np.ndarray&gt;</div><input id='attrs-5c61dce6-08e1-40cc-b740-b53f7080b25a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c61dce6-08e1-40cc-b740-b53f7080b25a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b98ed138-e2b4-4777-b66f-f6a29c8958d1' class='xr-var-data-in' type='checkbox'><label for='data-b98ed138-e2b4-4777-b66f-f6a29c8958d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>33.700000762939</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>1.#SNAN</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>-53.799999237061</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>1.#SNAN</dd><dt><span>bands :</span></dt><dd>408</dd><dt><span>band_names :</span></dt><dd>Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band 7,Band 8,Band 9,Band 10,Band 11,Band 12,Band 13,Band 14,Band 15,Band 16,Band 17,Band 18,Band 19,Band 20,Band 21,Band 22,Band 23,Band 24,Band 25,Band 26,Band 27,Band 28,Band 29,Band 30,Band 31,Band 32,Band 33,Band 34,Band 35,Band 36,Band 37,Band 38,Band 39,Band 40,Band 41,Band 42,Band 43,Band 44,Band 45,Band 46,Band 47,Band 48,Band 49,Band 50,Band 51,Band 52,Band 53,Band 54,Band 55,Band 56,Band 57,Band 58,Band 59,Band 60,Band 61,Band 62,Band 63,Band 64,Band 65,Band 66,Band 67,Band 68,Band 69,Band 70,Band 71,Band 72,Band 73,Band 74,Band 75,Band 76,Band 77,Band 78,Band 79,Band 80,Band 81,Band 82,Band 83,Band 84,Band 85,Band 86,Band 87,Band 88,Band 89,Band 90,Band 91,Band 92,Band 93,Band 94,Band 95,Band 96,Band 97,Band 98,Band 99,Band 100,Band 101,Band 102,Band 103,Band 104,Band 105,Band 106,Band 107,Band 108,Band 109,Band 110,Band 111,Band 112,Band 113,Band 114,Band 115,Band 116,Band 117,Band 118,Band 119,Band 120,Band 121,Band 122,Band 123,Band 124,Band 125,Band 126,Band 127,Band 128,Band 129,Band 130,Band 131,Band 132,Band 133,Band 134,Band 135,Band 136,Band 137,Band 138,Band 139,Band 140,Band 141,Band 142,Band 143,Band 144,Band 145,Band 146,Band 147,Band 148,Band 149,Band 150,Band 151,Band 152,Band 153,Band 154,Band 155,Band 156,Band 157,Band 158,Band 159,Band 160,Band 161,Band 162,Band 163,Band 164,Band 165,Band 166,Band 167,Band 168,Band 169,Band 170,Band 171,Band 172,Band 173,Band 174,Band 175,Band 176,Band 177,Band 178,Band 179,Band 180,Band 181,Band 182,Band 183,Band 184,Band 185,Band 186,Band 187,Band 188,Band 189,Band 190,Band 191,Band 192,Band 193,Band 194,Band 195,Band 196,Band 197,Band 198,Band 199,Band 200,Band 201,Band 202,Band 203,Band 204,Band 205,Band 206,Band 207,Band 208,Band 209,Band 210,Band 211,Band 212,Band 213,Band 214,Band 215,Band 216,Band 217,Band 218,Band 219,Band 220,Band 221,Band 222,Band 223,Band 224,Band 225,Band 226,Band 227,Band 228,Band 229,Band 230,Band 231,Band 232,Band 233,Band 234,Band 235,Band 236,Band 237,Band 238,Band 239,Band 240,Band 241,Band 242,Band 243,Band 244,Band 245,Band 246,Band 247,Band 248,Band 249,Band 250,Band 251,Band 252,Band 253,Band 254,Band 255,Band 256,Band 257,Band 258,Band 259,Band 260,Band 261,Band 262,Band 263,Band 264,Band 265,Band 266,Band 267,Band 268,Band 269,Band 270,Band 271,Band 272,Band 273,Band 274,Band 275,Band 276,Band 277,Band 278,Band 279,Band 280,Band 281,Band 282,Band 283,Band 284,Band 285,Band 286,Band 287,Band 288,Band 289,Band 290,Band 291,Band 292,Band 293,Band 294,Band 295,Band 296,Band 297,Band 298,Band 299,Band 300,Band 301,Band 302,Band 303,Band 304,Band 305,Band 306,Band 307,Band 308,Band 309,Band 310,Band 311,Band 312,Band 313,Band 314,Band 315,Band 316,Band 317,Band 318,Band 319,Band 320,Band 321,Band 322,Band 323,Band 324,Band 325,Band 326,Band 327,Band 328,Band 329,Band 330,Band 331,Band 332,Band 333,Band 334,Band 335,Band 336,Band 337,Band 338,Band 339,Band 340,Band 341,Band 342,Band 343,Band 344,Band 345,Band 346,Band 347,Band 348,Band 349,Band 350,Band 351,Band 352,Band 353,Band 354,Band 355,Band 356,Band 357,Band 358,Band 359,Band 360,Band 361,Band 362,Band 363,Band 364,Band 365,Band 366,Band 367,Band 368,Band 369,Band 370,Band 371,Band 372,Band 373,Band 374,Band 375,Band 376,Band 377,Band 378,Band 379,Band 380,Band 381,Band 382,Band 383,Band 384,Band 385,Band 386,Band 387,Band 388,Band 389,Band 390,Band 391,Band 392,Band 393,Band 394,Band 395,Band 396,Band 397,Band 398,Band 399,Band 400,Band 401,Band 402,Band 403,Band 404,Band 405,Band 406,Band 407,Band 408</dd><dt><span>byte_order :</span></dt><dd>0</dd><dt><span>coordinate_system_string :</span></dt><dd>GEOGCS[&quot;GCS_unknown&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]]</dd><dt><span>data_type :</span></dt><dd>4</dd><dt><span>description :</span></dt><dd>C:\\data\\Course\\Grad_course\\2023\\Greening_climate_project3\\data\\CRU406_temp_1982_2015_month_mean.envi</dd><dt><span>file_type :</span></dt><dd>ENVI Standard</dd><dt><span>header_offset :</span></dt><dd>0</dd><dt><span>interleave :</span></dt><dd>bsq</dd><dt><span>lines :</span></dt><dd>360</dd><dt><span>map_info :</span></dt><dd>Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,WGS-84</dd><dt><span>samples :</span></dt><dd>720</dd></dl></div><div class='xr-var-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 403.42 MiB </td>\n",
" <td> 403.42 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (408, 360, 720) </td>\n",
" <td> (408, 360, 720) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"220\" height=\"150\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
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"\n",
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"</svg>\n",
" </td>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-14330b1c-588e-49b0-b93d-cdb174033c85' class='xr-section-summary-in' type='checkbox' ><label for='section-14330b1c-588e-49b0-b93d-cdb174033c85' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-045fd46a-6afb-4234-85cc-4c68237662c4' class='xr-index-data-in' type='checkbox'/><label for='index-045fd46a-6afb-4234-85cc-4c68237662c4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1982-01-31&#x27;, &#x27;1982-02-28&#x27;, &#x27;1982-03-31&#x27;, &#x27;1982-04-30&#x27;,\n",
" &#x27;1982-05-31&#x27;, &#x27;1982-06-30&#x27;, &#x27;1982-07-31&#x27;, &#x27;1982-08-31&#x27;,\n",
" &#x27;1982-09-30&#x27;, &#x27;1982-10-31&#x27;,\n",
" ...\n",
" &#x27;2015-03-31&#x27;, &#x27;2015-04-30&#x27;, &#x27;2015-05-31&#x27;, &#x27;2015-06-30&#x27;,\n",
" &#x27;2015-07-31&#x27;, &#x27;2015-08-31&#x27;, &#x27;2015-09-30&#x27;, &#x27;2015-10-31&#x27;,\n",
" &#x27;2015-11-30&#x27;, &#x27;2015-12-31&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;band&#x27;, length=408, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5f3507a2-99ad-43c2-a070-d7c02ce0c639' class='xr-index-data-in' type='checkbox'/><label for='index-5f3507a2-99ad-43c2-a070-d7c02ce0c639' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75,\n",
" -176.25, -175.75, -175.25,\n",
" ...\n",
" 175.25, 175.75, 176.25, 176.75, 177.25, 177.75, 178.25,\n",
" 178.75, 179.25, 179.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7c6f6b78-2a1d-46aa-a1d4-a4f67606255f' class='xr-index-data-in' type='checkbox'/><label for='index-7c6f6b78-2a1d-46aa-a1d4-a4f67606255f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 89.75, 89.25, 88.75, 88.25, 87.75, 87.25, 86.75, 86.25,\n",
" 85.75, 85.25,\n",
" ...\n",
" -85.25, -85.75, -86.25, -86.75, -87.25, -87.75, -88.25, -88.75,\n",
" -89.25, -89.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-22c47f2a-8419-4a5b-8724-f8cc4ba1d8fe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-22c47f2a-8419-4a5b-8724-f8cc4ba1d8fe' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (band: 408, x: 720, y: 360)\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 1982-02-28 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 ...\n",
"Data variables:\n",
" lai (band, y, x) float32 dask.array<chunksize=(408, 54, 54), meta=np.ndarray>\n",
" temp (band, y, x) float32 dask.array<chunksize=(408, 360, 720), meta=np.ndarray>"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lai_array"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "7d75ae4c-b7b3-47ed-a9bd-307120acb564",
"metadata": {},
"outputs": [],
"source": [
"del temp_array"
]
},
{
"cell_type": "markdown",
"id": "e6282987-6443-43c1-8fd2-4c99b7dd1477",
"metadata": {},
"source": [
"## Clip Global north (Item 1.0)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "a68efafa-240a-4452-b7c0-e4ea2adf1741",
"metadata": {},
"outputs": [],
"source": [
"min_lat = 0\n",
"max_lat = +90\n",
"\n",
"min_lon = -180\n",
"max_lon = +180"
]
},
{
"cell_type": "markdown",
"id": "111a961b-3e35-4ba3-a769-fe00e09cde02",
"metadata": {},
"source": [
"For LAI"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "44a3b2f3-defb-450b-8be0-ac0c5de64695",
"metadata": {},
"outputs": [],
"source": [
"lai_array['lai_north'] = lai_array['lai'].rio.clip_box(minx=min_lon, miny=min_lat, maxx=max_lon, maxy=max_lat)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e3b4b7d0-7b52-40f2-be03-2a81e4dcd05c",
"metadata": {},
"outputs": [],
"source": [
"lai_array['temp_north'] = lai_array['temp'].rio.clip_box(minx=min_lon, miny=min_lat, maxx=max_lon, maxy=max_lat)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "2e940f64-60b8-4758-9ce1-900dfdf41246",
"metadata": {},
"outputs": [],
"source": [
"# dump_to_disk()\n",
"# refresh()"
]
},
{
"cell_type": "markdown",
"id": "7446ff46-8138-46fa-991e-e1b43501b18a",
"metadata": {},
"source": [
"See if crop worked"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "3445c353-575a-4fe2-b7ac-067dcbeaf07f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x2b1a5bbe9a80>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lai_array['lai_north'].sel(band='1982-01-31').plot(cmap='cividis')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "7216e613-8fc0-488a-b3cd-989996bd5f8e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x2b1a64561420>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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d1hXsvvvu69XGzjBo0CDsvffeAIBXX30Vzz77LBYuXJhqHwD8+Mc/ThHHcrmMV1991ZIlxr333ovLL78cF154Ie666666boGtt966xrLBOO2003DttdfiJz/5CQ4++GAARAIeeOABHHzwwbY93QFbeboKtmz927/9W7ePtb4YNmwYLrnkErz22mtYsmQJ1qxZg8bGRvv7gw8+iJaWFvudn6/Onm2tNe68886aYxWLxS5bhoQQNe/LL3/5S/zoRz/CTjvt1LWT6yYaGxvxmc98Bj//+c+xzz77oFAo9Nq+n3rqqS7l8FnXc7bjjjvioIMOwgMPPIDx48dbK+2Pf/xjvP766xg9enRvNDdHjl5BToI2Ezz66KMIwxDHHHOMjQ7bd999cfbZZwOg8NUtt9wSV199NaZMmYIoivDggw/itddeW+9jXnzxxVi8eDEuvvhizJo1C7vuuiu+9a1v4Tvf+U6Xti8UCt0epDvDT3/6U/z5z38GAKxcuRJaa3z9618HABx44IHWSvDCCy/glVdewT777AOtNV5++WXMnTsXxx9/PK677jq7v8MOOwwHHnggpk6dijVr1uDTn/403n//fSxfvhxvvPEG7r//frvu1772NXzhC1/Av//7v+Oqq66qsZDtt99+dSPSfFx22WVYsWIFzjrrLMyZMwfbbbcdbrnlFrz++uv47ne/m1r3zTfftASSEzzyuX7kIx9Z53W9/fbb8f3vfx/HHnssdtppJ6xevRrf//73sXz5chx66KE1CRN533/605/stR40aBAA4Mwzz7TrTZ06FdOmTVun5e7ggw/GySefjH322Qdbbrklfve73+H++++3GYgZhUIBCxcuxKpVq3DggQfa6LATTjgBhx12GADgmGOOQaFQwHnnnYeJEyeivb0dt956K957772a4+6999549NFHceutt+KTn/wkpJQdXquTTz4ZM2bMwJQpU3DEEUfg9ddfx/Tp07HLLrt0OapxfbB06VIcdthhOPzww3HNNdfgIx/5CD744AP84Q9/wFNPPdXtLOQMJvy9gblz5+KYY47BWWedhWuvvRbvvPMOJk+ejL322qvG3ZsjxwbFhtNk5+gPcHTYz372M33KKafoQYMG6cGDB+vzzjuvJoHfSy+9pA855BDd2Niot912W3355ZfrV199tSbaZ8SIEbqpqanDY/n43//9X33GGWfY455xxhk2mqm/E+pxNFG9j9+WH/7wh/rggw/WQ4YM0cViUe+11156wYIFulKp1OzzX//6l77hhhv0Jz7xCd3Y2Ki32247feSRR+pvfetbXT42AP3GG2906RzefvttffHFF+utttpKl0ol/f/+3//TzzzzTM1699xzT4fHGjFixDqP88Mf/lCffPLJeocddtCFQkE3NjbqfffdV8+YMUOvXr26Zv3Ozs3HuHHjtBBC/+53v+v0+JMnT9YHHHCA3nLLLXWxWNQf/ehH9ZgxY/Q//vEPuw4/h7/85S/1kUceqRsaGvRWW22lr7nmGr1q1arU/p566im977776lKppHfccUc9YcIE/e1vf7smsvDdd9/VZ555pt5iiy20ECLVfmQio8rlsh4/frzecccddalU0vvvv79+/PHH9YgRI2oizLLbdgXoJLLrjTfe0JdddpnecccddRRFetttt9WHHnqonjlzZpf30dd4+umn9f/7f/9Pl0olvdVWW+mLL764btLQHDk2JITWXVDC5ciRI0cv4KCDDsLOO++Mr33taz3e1yWXXIKvf/3rWLVqVS+0LEeOHJsjcndYjhw5+gUrV67Ea6+9ZrMe58iRI8eGRk6CcuTI0S8YMmRIXjwT686Cvq6MzDly5Og95O6wHDly5OhHrCtZ4IgRI3Dvvff2T2Ny5NjMkVuCcuTIkaMfsa60D9tss00/tSRHjhy5JShHjhw5cuTIsVkidzznyJEjR44cOTZL5O6wDJRS+Otf/4rBgwfnhf5y5MiRI0en0Frjgw8+wA477NBngvb29nZUKpVe2VehUKip+7g5IydBGfz1r3/ts5T3OXLkyJFj08Rbb71VUyqnN9De3o5ddh6Ct9+p9sr+hg8fjjfeeCMnQgY5Ccpg8ODBAOiBHjJkyAZuTY4cOXLk2JixcuVK7LTTTnbs6G1UKhW8/U4V//PTf8eQwUGP9rXygwQfPuAXqFQqOQkyyElQBuwCGzJkSE6CcuTIkSNHl9DX8okhg4Mek6ActchJUI4cOXLkyLGxQ2v69HQfOVLISVCOHDly5MixsSMnQX2CPEQ+R44cOXLkyLFZIrcE5ciRI0eOHBs5ckNQ3yAnQTly5MiRI8dGDq0ltO6Z8yYvEFGL3B2WI0eOHDly5NgskVuCcuTIkSNHjo0cWotesASpXmrNpoOcBOXIkSNHjhwbOZSWUD0kQT3dflNEfkVy5MiRI0eOHJslcktQjhwbGAcc0wKlAQEgNtZqIQBpEtBKAQRSI5DAS99evsHamSNHjg2H3hFG53aPLHISlGOzwb6fbcFrzy3Dvp9tQRQAP31mWZ8e79ATmqE1kBhikyiBQFJ0htICQmgkSiBRAMdsKA0EwoSyCiJGWgNxQusdcEwLhNCQZp2Xn85JUY4cmwNyEtQ3yElQjgGNnT81GgCRBx9SAIHQ0IZsFAIAENj906MghECiNT55dEsN+QDI6qK0WC+S9KkTmsmqI4j8SEEEJ1G0cw0WOAKxElDmXym0PX4ogcSQJ51pmxBAIAQUACk1Dj6uOZX7IydFOXJsmqB+o2f1yXq6/aaInATlGBDY+VOjLdFRGoi1QChoQey92GVFBKKsBbaNFGItDMkAClIjlEApIJJTThx7CASQgEiLMvs76NhmvPz0chxyPBEb35KjNRAGxlID+rea0LJAOhJkiZAwri5DfEIJKGMhCk27lAZCQdvSvxoC9F1rOo70+jD+W8EcE8ARJ1Nbv/8fORnKkSNHjnVhwJCgOI4xdepUPPjgg3j77bex/fbb45JLLsEXv/hFSEkmPq01pk2bhjvuuAPvvfceDj74YKxYsQJ77rnnBm59ju7io4eNAuBmLkp7HwhIaLQnwhKBqgYiARQlkY1BAREgpYn8DIo0CoFGFACFQIMLPmtLrMhiBBCZePnp5Tj4uGYcfFwzAskuKm3JCCM2JEcIIArot0psLDYS1m2VKGr72qpA1YtSlcblVQh0an2A9ldNyJQUSG32I6w+iNsfStcmdr0ddmKzPYavLxKC1hcCeObxnCh1B8eeRtf06cfcdTv6c835tczRL8jdYX2DAUOC5s6di9tuuw333Xcf9txzT/z0pz/FpZdeiqFDh2LUKBow582bh0WLFuHee+/FbrvthpkzZ+KYY47B66+/jsGDB2/gM8ixLnz8iFHQhuj86QdL8W+Hj4ICubSkYCsN4IZ8ba1ARQmERitTCoBIaijDJiLpSAlAREJ4FpUo0AigrfWmmgjr1lJKQGkNZbbRhpDQfmhfyiNPgSEkSSJQTRwpAcjtVUkEKhkSJAVZrwLTRrYMVWJqpDDnLUAusEDCXietARgrEhOqLISGcZ/RtfDdazk6BpMewBFTJjtHf64ZUgLffcJ957/7A6w3+9F/umMecrxrr788x6YBpYW1UvdkHznSEHqA5NE++eSTMWzYMNx999122RlnnIHGxkbcf//90Fpjhx12wOjRozFp0iQAQLlcxrBhwzB37lxcddVVXTrOypUrMXToULz//vsYMmRIn5xLjjT2OHKUHdAT7Qbp//7+UgDALkb3I4RGYl5iab47YkQDPEDf2dOVaIFKQgShIdQohdoQGaAUEqEoRs51FSfO/VQIHdEox8JaohhMPPxlUrCWh4TMiSUrpOOJVe369mPOKRDOukTERlsLTihdpJjfncWK2sltp+vl3GSBJFddGKQtGZsjPndOc+pakU7LXbf/+Dpdn+NOJ1LBv2lNRFKp9HVmDdizPSBBR55Cx3rhqe7v45Djm+m5Uo40R4HOiVA/oa/HDN7/X179NIYM7pndYuUHMXbc/3v5+OZhwFiCDjvsMNx22234r//6L+y222547bXX8IMf/ABLliwBALzxxht4++23ceyxx9ptisUijjjiCLz00ktdJkE5+hZ7HtkCAPjNCyQ63uNIdnvB6m5iTRqafzt8lHEV0bZkLaGRh11HACzZqCQC7QktUxCWpISC3GHFUCOSQDHSqCZORHzEyTQASePSApwgmZeXIo04oZkUEw4mQMq43fxBkbdP6rjzuH0SOi3oFoDUAgk06YzMdRBCIIC24fPsMgsDdzwpYFlR1QitQ0MKAwl8byPUCJ12brMRjbOVq7ZIZCABKQW+8VBapH72hfQcPfJA98XrT3x1OU49Oy0olwJ48mt0jU48sxkCwHceXY4Tz2xOEWMmQL3tAlsf8gOkrT8ArIWzmuQz/k0NGr3gDstTA9ZgwJCgSZMm4f3338fHP/5xBEGAJEkwa9YsnHfeeQCAt99+GwAwbNiw1HbDhg3Dm2++2eF+y+UyyuWy/b5y5co+aH2OHDly5Mix/sijw/oGA4YEffWrX8UDDzyAhx56CHvuuSd+8YtfYPTo0dhhhx0wYsQIu54Q6Zusta5Z5qOtrQ3Tpk3rs3bnqI89j2zBb15YBgG27AgI1vsosugUICBBGhlp3EHas84kykViCaPVKYHcToFQiGRaYyMAFEJdk3DwxW+67585hVwL7BrjkHY/Mq0zB7JvBaon6gbICsT/Sk9mbS06MJYR687SJjTeRa9ppZEosgbxeqwbaiqS1UrKDRsldtYFLgWBFHQ9AaCaOF0T4Nx6bAXi60T3ViAIave9PhYgH08+0vF1+dbXl9f9GyCdUG+5Ez9jXGDP99AKZPVgUttIRaUFPnk0Wct+9t2+zYeVI8dAxoAhQRMmTMDkyZNx7rnnAgD23ntvvPnmm2hra8OIESMwfPhwALCRY4x33nmnxjrko7W1FWPHjrXfV65ciZ122qmPzmLzwV6faUl9//Xz1BGzG4zxmxeWYZ/PtkBpbXL7UKRUMRRWIxQrgWLo3GA+cQJcCHqigWJI+4m8CCqB+gMBR/YAhqQoWDEzaywcoUlHhfFA3REhYg1QNn9RZ/AzRLMmyCdA7ALkKDZ7fE9XxO394beX20F2Q0FIQCVAojSkEIgTjTAQqMaAkkTSAkn3ke6/m6xoreu6wTY0elNP9fxTPbtHQrgM4gcfR67FaiLMs6KhIFLPJ0c6ZgXVOQYGcktQ32DAkKA1a9bYUHhGEARQJtnKLrvsguHDh+OZZ57BfvvtBwCoVCp48cUXMXfu3A73WywWUSwW+67hmzhY4wOkCc6vnydyA9CgzKSIB/pfPud+/+VztN0+n22BQloXEgggCHRKIKw0JxeklUgLxFYXbaxERJwEgNfM/g8/qdmSBcDoPJQTRdMgUkt+gHRGZ0ZnFiHBgp4MVN34Lb9NHe+ULT2+YZND7wuhE0FrEMFbXwtDbyGUAgIaQSCgFRAGAnEClAqURgAApKCou0gKfO3BWsJz9kUteOT+jYsI9SZ6co98i+ZPvkN///tR6clHoon8WHG+pmjLA49txit5Ys0BhTxEvm8wYEjQKaecglmzZuHDH/4w9txzT/z85z/HokWLcNlllwEgs/no0aMxe/Zs7Lrrrth1110xe/ZsNDY24vzzz9/Ard/0sMeRo6xFBkgPzFnIDn7jxf9+VAt+8eyyFBkCXC4fKRxpItN/LVEIhLMk+S4wmSEMsU67j5SGFTyz5Slr9WF0x6oDULSXqlmma9xgHW4var/715ldHxwaz1FgAPo1XLszFCIBpQAtDUHVAJRAKaLfAwk0NUisWqtw9kVG7OyRnk2ZAPUFfvFs7fU6+LhmQ57pnZLSCfIPOrZ5vbOj58ixKWDAkKDly5fjxhtvxLXXXot33nkHO+ywA6666ircdNNNdp2JEydi7dq1uPbaa22yxKeffjrPEdSLYMsPj8UdkZ99P9tiSQ1/Z/Dw/9pzy+zM1Z/B/tJbzp36vx/VkrLiAJ4uSGgUAoHARERxMsBsx87uJM70DJBFIgpogFDGDVZNyK1QrxSHD7ZaMYQJc8+GwNfspwsESGk6QGBCwRJzQCWM9cQcLzRJFsPAWLa8ffR37posHrpvqf374stGoZq4CDDSsDiSc85FLTZs/eyLWlCMBO7/0tKOdp2jizjoWHL5+uVUDj2h2cYI5WVWBg607rk7a2AkxOlfDJg8Qf2FPE+Qg+/OAugF8l1evovLJzwAkR4h6s9Ms9j/6BaUY6AYAq8a7c7+RztS9Op3l2H/o1uQFQ6zJQRweYEC0bEQ9NMnNdvcPyyoBkyJi4SWVxMiRnEiOqQq/vVIa4aEdU9R6LfTBWVzDPmolyuIrVq+NUwK7aw+5vdipK0rzM8PBPQsb01f4IJLRiFOtC3/0VSSuPuOJQCAcy8m4lOuakRhToA6w6EnNNeI+xkHHUsao3rk5uDj0vqjvABv76C/8gS98ZNjMGRQ1LN9rapil4Ofycc3DzkJyiAnQWR1qScE/vXzyyzxYaEzgy09r3lkKGvN4XV4kP7Fs8tSZCcLn/QwgYhVWiSsLeHwS0LoDvUOx5/ejEQBUejKWdhIMO20QUDtrClLfjhqzC90qnQ6SWJHeYJ8+BYuLvzKZMfXL/HvrI0qBkSCShFZgnwt1cZayuHCSykr+IP3bnok5+jPNdvnKZA90/usD5gA8SPmvwMHHttsJw4APVt+UV9+ZpnMKw38/sWN+x7tk7E2byj0Fwn604+PxeAekqAPVlXx0f/39GY9vmWRk6AMNncSxMSlI12MD+6AfFfXa5lOyXdzZS0VUpCV55MeEap3zKzrjQuLMhERQtsOPZS6S7PbU8820TQxuWi4fUJQlJj2vsOsxxYfDp1nLZFPhGgwcfqirliD6pEg/ts/f9YEBcKRoJKxBEVhrWvyO49ufCRoU8Dxp3NJFXfP2cXHEAJ47sn+u/4HMgGq4y6pJ7ZXSljrKT+/sUpvq7TL2t4fyIb0H3BMy4DQKuUkaGAjJ0EZbO4kyCc0PtjSkQVbhLJWHiY4QG3ECsNfx+/wDjimJUVAspYdrpsUm9pdUUAN4wiZ7uKUs5vxVCd5Yz53jpvhs9XI/04uMOENJkSE3OyasmDXlN0wAuksCWKXGH8HavMGsSUsCjQKAZX+4HXDAPjPnAD1Ok44ozl174GMhU70nwvykOObLSkHnLDfLwXSkXqE32XOfB4bQsSWy2xJmj/9oG+J0J5HtiAKakm/X5rEb3dXXOz9if4iQX/80XG9QoI+dsh3NtvxrR5yEpTB5kSCPnl0S5cSqe37WZf0zndRAagxsQPpUG3AER0gTYisy0e6R/CVp5fjgGOcNSpr/eHviRK2DldoosEC2bf5T844vwVxom0uoWrsCBEPKNyuWNUOLtm6Yf518EkQUEuEnAtQ2+KnfC0KgUZTkUtMkNBbGpL0xFdzMtQTfP5cIj72PhtxNz+LfeFyPPKU5rplND51QrO1UDKh1ob8ZN9BBpOh7CTGT/3AVqBKImw0Iz9/Wgu88cMlvXZuHWGfz7bYa5olQ4yqci5wbjtjQ7rF+osE/eFHJ/QKCfq3Q769WYxvXcWAiQ7L0fvoaibZ17zQ9V8/v6xDa9Frz9Fvnc3W/MzLbEU64JiWVNg7d75ScgV5kwlXuBk3W3+qibOQ9DWCAIhCgaAKxIkjZcJkQtYANDQCmzxAUzgX0u6SroTa80wcoMGAQ/79wqnCiKXDgI4tvSgxgYFBgK69bixuuXnRhm5Gp/AteOx27G1X42dOcXmdsgTosBObU4Ve2QrFEN6Mw5+sAG4i4ZMHJs8+MQokUPBq2bEFsz8IEODlDdMUdcmJP/lZ9/uqPY9sSb3v3U1dkSOHj9wSlMHmZAli+JYXH4nXkWa1PvWE0N3F/ke3WCuRL9ysJ2o+8NhmCmVHuhzEYSc24wff6r/B/oJLRiFRGpUqUK6SVYitBJxzqJqkrUFa0yw2MS6zODOI1XOHAZT1V4AGqKIJhQ+kRtEMxDwQMAlk608YoFP33saEq0eOxW0rNk4SdPp5zvIHOPKQLaXRFzji5Ga8+M3lOPyk5pTg3VmAnCYuG6XoI6sTy1qA6G9XAFhpoKIo3USiBd7sJxKURW/0L/2F/rIE/fdLJ/aKJWjXQ7+1WY1v60JuCcqBnz6zDAcc0+JmiJ55nTvNbKfUUefE0V71ZnBZbRAP/ETChLH2aCvyFHAhvIXACZh5uyNPoTIAR57SjGpMbWZi0Fc1s6SkquZK0+AYsEDWFzYbC5Yy5MbObL1ZbmezVwWfCDlXnwC5utiqVAhdTS6/hlhndbE2NmysBOisC1qgtQvnB+ieffsbfXdtjzi52Wl6NKV0YGT1MYB5x4Sxl+j0s8btzW6TtRTZ5Rp4/XtL8W+Hj0IkXaABAOx4yOjUNn/78ZK67d/6oDEAgATAv15e3IUzJnz0sFE1uiO2AnEi1RxMeDtSenV9HznSyC1BGWyOlqBPHt2S6uyBtAug3hPSEQnKEh3uwOqFwrPlx38thXBaGLYIfeoEGgzY0sGDfTVByk2gNFCJKVqsIQK+10dEaMTlowGtsWqtRjXWZPHxdUKK2sHWIA6bZ2sQW4L4+nYkjOY6akIApdBZgaKQiFchSrsBC2ZK8/hXBg4J2ljBViC+R0Ksn3XtM6c0W+LBbq4jT3Ekn11gXEMsbeWh7bIaGH7W/eVsEeLv2XQOQJr4cCi8f8xs8d84Y33yS77wvv7+E0d2tj5oDIzRzFqRAcrSvvanXSdFexw5KpUTK5BpXeHGhv6yBP3XD0/qFUvQbp/6j81qfFsXcktQDgCGkGQ626zFggmLEC6XkD9Dfe25ZSnC5BMlP5qGB3x/f7yvLAE69IRmp73hdf32mR2xLoZz6PQ5sxcCpYIhLYm2s3cpAalZ0yOMBUhD2L87LiPCBMh3gwGwQmhfE8TRYQAXIc3JT2/hTM8KtL7kB0gTICAtePYJEOP5p5bjiJM51L2+9afehCRLgPx1s6u79TonQJzWAQDijPWASVECYAu2/nTwwsXr8SL+9oWl+PgRoxAIsoDGauPJCbQhkVuC+gY5CdrMwVYgJhiA6xR9ZDtXhr8eu8zq9Xssmub8OQwhgF+YjNBaA68+0/mA88JTy/HZU5vtthLkiuJdBtKFMB92IumIejtpHRFGTVXQA6C9SsuldO4x323FtTS0EIDUUEqkouyy1p/AWrzoeyhdPqBEmXXhyA+3KUfPcdYF9Axnxce9DX5efaJ05CnN9h0MvAmIT4bqusWMS8zXDdWzAHVm/fHdXwpIRTJm/+3I2pNknsJYA2E3H8yPHjbKWoB4FsbXhBO1psPmBX77wtKa/WyKyElQ36AfYmpybOzwXwvfRVOPDAEcBVVnlplZtk8miqyeC01rIk+Jqj3Wwcc118xm2ZVQbz+csJBJhBDUmR/1Odrm+NPrb7teEDToBFKgGAk0FKgoaBS44wfSkRgWMjMnYhdY6Fl/fAIUSm3yAAGFkAhQQwEYVCK3lxDAmrLJkh0IiM4q2OboEs68oCXlhgLWn1xmrUAMfn5feGo5juxgHWTa0JlggV1hjOw7ZAmRdgJoTulQ9TKdA0R+bIi8IPcXW4HY8lOPAGURa6CqyDpaTTSq2Wi2/cemvm9x0Bhse/AY7HjIaPzpB0uRKJFKPKoyH3fu+TOfo+fINUEZsP/1xNOvRBgW+izM+NjTmk3G4dqQ2A0FP0mhn6l1r8+01OQGYnT08PidcbbEBpA2b/tkSYp0eP0hxzcTYWAC4REcoJao+ZE0fj4Xbn8h6p3onkuvGG0JWrVKs9VqorG2rNFe4fpjpFtSOh0t5s+yrUvLc235uYAoiZxGQ4FIUCEEVpdp+aAGYYvIag08+vCm5S5oGT0Oy5Ys7Ndjnn4ePYtKayLmCviPbjwvnz21ua5rqqvIWlkdgUmvU08T5Ov4/H+ZOPBEI0m9Lx1nNI85Aah37ETXurgSkH6I2mPsylp7PnYB/fPFAACx/xizlSACL4Ci0CiYyUIoXPLQwLwH9UrH+OfI1+H176UtQh8/gqxKfWkpOv70ZiRxBd996o4+1wT97nun9oom6BOffjLXBHnISVAG/MAdfeqVeOaJ23ttvyef1Zzq1HhGxgMY48VvbhyEyIdfKJXBf/oPT71kbAB1XkyE9vpMS4oU7WPyCvH6bAnxidCnTmiuITQ+CcrCX+S7+XhbDicX6N4Al8WIy0ejXFGQQqAQ0ez1g9UKaysa5SqRICeadkSIs0kD6Q7eL5IK0KAQBWQFGlQi4hOFAu0Vyg3UVCJDbqw0lAK+/uD6kaCNJVfP6DHjocwNTcxITUSE/hYCiEKJm5f1PTH63DmuNAbQ9ciwnpCg7LPs9xf+/vg7Wz7rhcnz3yzKp9QMwmrusuSHCQ+D9YDWAuSRn3puL9qnthZJbRsR89kAkIAMvC3pRY5kmghJUE6sbB09ble9dz/bdiD9TnEx4t6ONDvhDCJBzzzZ9yToNy9+rldI0J5HPJGTIA85Ccqgt5X+nz+32dYYUp4FgLPP1g1j1bX5PYQgixFHkayPzuWgY5ttyPmBxzZ3WGS0HrKurXrWoKz2wK/DzloGvwo9Y88j0/sOZdp1dugJzSkhMFBfXCxEnesGU3RVpouMSqOniYL1Typ40WWjUokLhRBYW1ZY3U4kiHMIcf4gnwj5pnwWc/vRMJwJuxhqlIwVqFQQSBINKQWi0OxbAaEJlf/KlwemJahl9DgEUqBi/CZxopEksBXnYQbXMCCyuWpNgi9vgCrzxxl36roSJfI72pWONav18TU/9Sw+AE+ehMtS7ll7BLStXVfP6sOJSFn3A7jIr9gj5j4Jqph9ZHU/sddOtur4y6AVkJQBVTEvXQTIEEYkR/8KAYgAQgoUDUELBVCSQEGmrUIArGWItUw+fJ2dgJvw+GVmukOCDj2hGS99e919Q39Fh/36hc/3Cgna68jHcxLkISdBGfTmA336eS1QWtsOqTMS5BOfeiTId/H4d0wKGmz9hIEHH9eMn3xnOQ46thnKdHxZzsCd5k+fWWbz8jAEUFOElEtn+G6cbAmNdIedHuQZPglic7XLryysJSSStRmtOYGcH2JsjyHSEWT+csARH9u5G2JUjNafBF1y+WjEiUYxcpqc1e2JdYn5JChRxjXGg5ffdji9kF8mRPLMWBpXgakRxucVSKAQCspkHYgNQgzWF2PGjjfvhUa1SmQnjjUSoyWJE01ZuQ0pCKRAGAoUQrY00LZfumNJv7bbL57K0HA1w9ZFgHzS09Fv/t8atS6xSkwbV2zJFmH3GSuXeiIxlp/EvOtMJPyknbEWCIW2JCg0KlF+z6u6vhWI3V98Hr4AOtaAVhpI2okAJWUiPzKClaHqGJAFQEi3zBAiQENIiSGBRiTIRVYIUFPTLPH0Qb64NTBleJgI+Xm2QpNwlCcavK+ffGc5Dj6u2VqaXvr2cnzqBLrXhdC8n8K55APpLIM5CRrYyElQBr31QJ9xvtEWKG3N0dx5slUoRYJQG6EFuE6Q10uUiwiKlZu18YsLkI4myQy0/iywKxCCopi4Da9+d1nKGuR3IFkCR9uIFGECiOywhSjVgQny2+9x5CgIaFtMMZBUOJVJHcMXnXKHz7qqY09rxtOPLcdxpzfXaAl4fSYlQsDk3RH4xkPdt6JccfUYxIlGFJK+IY412iukCypXNSpxOpO0bxHi68pt4vpp7tzTFivfDUBiaWBwIz0IA4n8TJgwEdVY0TOtiOisLStUY22fa2VIkCUa1q0hEEXCpVhgYmAewPvuWtLt9pxxfoshGRqPdTHFwHGnNyNJ3HvsP19cOb4jYXRHqBcSz+fnkyBer1x1denKlhQJVFQ6tUWBr6nZJ5MFv6Cvv36cEVlXdMeRXx2RoKoCWYGqHwDxGvo7KJoGRIBOiOyIEM5NZixEZodRSIHLjVKjyElSjZssFLCuPD/DOiPwahHyBCP0ghSkidD0rzdvw2TnpW9Ttm5/H0BaZxiaIIiH7p7VLyToV8+f1iskaO/PPJaTIA95iHwvg0NsGRr1O7JsOCuvK731gbR+iDscMoOTyZtFi1KQu4tnOLSPdIcmvTBaH/UsUEo5+02iaqvL1+QQ0o74ZPVAvN7vX1yGPY4cVRPVoTRZhWi/gvQEgnIFHXRsc2qQOfyk5pT1CSI9K89W9eaZm7c6BHeesmfZle+8bTGuGTmWRLS2dIY22oi01c7qfAKA6Y9PgvxzLITAkEagXKV9NhTIitRUJKuPFAJhMLDIDwBMnDgRSmkorVGpasSxRjWhf+OECLqGsZx6Wjl+hrTWkAnIJ2IsrPYaam3dk4kCHry39tqM+MIoxEn6Nx4IfQJ0ytmk6/nm15bjxDNd2Qp/0JTSESD//fXd1fWIUEfZwn3dWkfvKE+CwsCQZkOmOS1EIDWkeeelsQq1J5RtPYtQ2M0tYgWE0DYpoguJd9afem1mAhRAo6x5WgKy8gjzguqYSE+ylv7Vil5EwFzMiiFLJUArVFUAIYjoZS1UMdzkj+GTPLaA+WRZmXYFcP0ipxaoJmRV4rQEUaDxKeOC9+usMRJFTY5NstYzLpxQe4H7ABs6RL6trQ3XX389Ro0ahSVLlpj9aUybNg133HEH3nvvPRx88MFYsWIF9txzzx61sz+Rk6AOcMFlE1EoFLptIRCGxTCZkSY3DAdL8NtkRYfK5STh2b/fSfJsh/UszB9iRYOG79cPpQASF2nkv7h8rFCma1eFsn6nq5EhOZnf/ciuXxqhM2V6pQ7Jt1gBwO9fpIGHIzV2/zSRHmHM1coTZrK7iEt5CJA42ieB/r7rZYbuKKIEcKRDaxrwokCsd2QVE9ZEaRrgfUKGDBEzv4V1BiUeZDn3j9bA1kOkHWjD0A14/e0C6ikmTpwIKaV9zuJYo72srOuLk1smKk0OFZNyb4QjcuS2A9z2/jYXXTYK92dIIpPUsy8iQv/I/cusmPzUs5trJh8nntmcsvQkyqWO8Mltop1VSJtlnzml1sXcVctQ/VxA1KdENimoOz6/P9COkMTaves1bmvUWk+yFiFnGfL2J9LLgNo8QJpnfapKbrCkSguSKsDPvTZiaWFawcSIW5aspdVkhFgEpAPSpFsKzVX0iREy5wbQofxzEqb/TRRps/1JRxQY17XmTPQChVDb686knP/myQ33kfVI7aaGV155BXfccQf22Wef1PJ58+Zh0aJFuPfee7Hbbrth5syZOOaYY/D6669j8ODBG6i13UNOgjqBEMDZF7VAAPjq/V0bJB+5fxnOuagFEObFk8b6a3pBAdiXNmslYPhuAb+T8Tt7gDoCNoULj2HFgI2G4PX4ePzisgg30elZls50gv5s2wdHjCktsOeRLda64yeZ8yOgmPRkoTV1OgwFj8RpABBQ0Pjht5enqmlnSRbDF636OqEnvrocnz+32ZJRO2gqoKI1PndOM6JQQEq6h12F1hpJYgiQtVzQEezxeRCS5m9vApy1EBZDoLEINJYEAgk8cM/AsvbUg5QCc+bMwbjxE1CpKpQritzEXux1zfzUzPR5TOXHW2ltI6/tv8JMNgz4ml546Sho7Sw/KVeTeTb5mfB/z/7r7mv6vfCRskqJNFHx/33uyeU1BInX9XdpT8cjexx5zsJo3if/TO+xtlZfJgouDYNzh3uvqUWskLICVTRZgWyeH1FLgJgcBdA1LjN7kSGMuwtp8x7/LiPDNCpAvBoIG2iZStCuzJWRhtgIl2KCKJN2ZAjuXBMtEElt+7pYATHIQhQwOfK0VEwYK4m5jrEAQo0QbnLip9rgU5UC6KFxpsvYUJagVatW4YILLsCdd96JmTNnevvSWLJkCW644QacfvrpAID77rsPw4YNw0MPPYSrrrqqR23tL9QZRnIAnu/X/H3+iFG44JL6A3kWX71/mXPHCCAIRE1oti+04/XY4sO/2crg0lkIbN/oJTFrT7hjEOY3oOpFIvmoJC5yxJ89+x8O2+d1sr+53437Cy5HST3BZz34A0tn23CfeegJbrDia8v7qJdA0SdA7PIS3rZ83dmsvaodWN1OlpxzLmqp2V9HiL3aYSzk5ePXawtrCQohfYoR1QLj+yuN2LmhKHHf3QOfAAHAnDlzMW7cBCilUa4qVGKNSqzB/7EFyH/W/cHfPfOwbkf+TqVJhCOThigpzS421w7fgvM1YwHKvgt2XXjjNdy/fhQXr5d9hq3VF0j1A7JOb8vPhd1/nd/99TS4n9BWRyYEExz3jGctOoCpW5d5132Qvia9kAgQUdFqZgOfENUQIHc1vD+9v1VC1iJebs1ta4HKe/SbjgEVo73SjpWJwBqvP6tkbxjv1juvqiLSUPUIY6yETRbJ90VrF0VXCNyk0NdVcnSpNP/yh9/Z/gA/kz36mH2tXLky9SmXyx0ed+TIkTjppJNw9NFHp5a/8cYbePvtt3HsscfaZcViEUcccQReeumlPrgCfYPcErQOUOdDFgIh3OwSqK87YARS4MF7l+Kci1pMRmDqmbSxCPAMhf35/HCGAQ2oMtOp+u2hzl+baCrHZJX2O1QzsAj3GyNWwlhRdGr/HekR6rXDR70w/86wLvOx7Q+1a7/fT/rrAPWTTX7za+llnz+3ORPO7vYZFAAZk1WI8tMInHlBCwK5bgtgENDQlAj3rHA0nj84Cu0RYE/8zO4NZXQGUSDQWBIDzuW1LihNAuhyhUTQUjiZPOeuob/JLWatnhmCIswyZf7WQqem4qwpstYcs+/zRrTg4fuW4eyLWqAVRW6yWw1IE6R1wX9PBIw7TKcnKdmBkdfnki/Z5an91/zhjsv9BVuV2aASSDOQaxdGzhYQ3xoCZFxj2pGmWpE0N5LbQhtmXWCBd4eqMGzN5gcSZAWyVp/AuzmKhNNBARAhrGYIGojXkphaayBZC139AO1hE5JiE4YGGo2BI2xZ+QDgLGDs+lPeddGg6FMG66ucvlK7SYm55kHgBSv4xJaZ6QDDTjvtlPo+ZcoUTJ06tWa9r3zlK3j11Vfxyiuv1Pz29ttvAwCGDRuWWj5s2DC8+eabvdfYPkZOgjpAYGZuwnvgbacjnLndd1dcfNmoVATHBZeMwlfvX2Y1CAy/w/X3TSZaZwlii4vObMvw+yIbZSYo+ipluu0irHUGzgLEx0zqmFF9d4GdMXmdaFf6Br5WWe1O9mjcQf/Qi9qQomvJJZkA2b7cgxSG4Bo3ggYRoUIkuuQCVQpY3a6s/jN71jx28D2msHaeWQqUihT2Xa7SdlEocM+dS9Z5XB/jJ0yEVhoLF863y8aMHU/nJyl8P5ACc+fO7dZ+O8LkyZMwZ07X9jVp0iQoxWJnbcWkTE6Y8ATSifalIBco3zN+p2D+lshYXgyZAohIWcGt2Y4te0yAWGTtAgjS++ftUkLoDiYIrBMSnVgDbL9hvvuC+bokiAk6nKXJ/ua1hx83LjIaCKpPRwM+RWAy+fFz/6wLodCIIRzZMf9G0hGeevogXqeqBCALgKySRccSIgBaEvlRVXfRVEInxuupGKiuBFQJCJuAoAEQFUCVUS0D/4iasFWoEUqRslxVvJp8Srk8QwrkStNgK6LrIwEgMH+wBchaeISz3IbSWeECb1yQQkB11fzdQ2hqdY/3AQBvvfVWKjqsWCzWrPvWW29h1KhRePrpp1EqlTrcZ7Zkj580cyAgJ0EdgGYCwpqxJfdkMJ2T15uM+MIoQAir+2HfPUAzUAETAKHTg740/YFvWid/vtuesw7zC8nEqJKY+joa8CMhhPGR+x04m3o10kUW63WKPFPyrTuxEnbAYLLmQ3v7qyqn6ekIKZFyHbLG3234v7fssBObu03u/NXZ6mJJGyjT8pkXtCA0v0tz77uCYkFAaUnRewDaKypFbLPnGYVAqUiDU7EgKSt0JBEE1KI7bl3crXObNGmSPclx4yaktE52sNUaCYDW1sloa5vTrf33FInSSBKFSlUhNg+i/8ixxcS3nCRJ2lInUF/DwgSGra5Z2MmHrt3GThq8dzKrF+sIfpoLjXU/74wsGQK8d1/XWjp9t5q/nIk19R0a0qzF1iAJ+jfg3zzNHyNlQUZaRK3gyJMwB2PdTyjofLPaftYGJXwthTAKZGnyBGVCu4VEzdnJEKiuMR1d1f0uC7S9NBajpIp3EWFoqFE0RCib8Zr1lLEGIunK0ChNbefrJEARtmQlchYg7nPYVc19exT6EydniesP9KYmaMiQIesMkf/Zz36Gd955B5/85CftsiRJ8L3vfQ8333wzXn/9dQBkEdp+++3tOu+8806NdWhjRk6COgCTlAfuWWpJDs8GpQS+dJfrdO+7eylGXD7aEiGhte0MqBNyUUOWKHnwc8D4oZ8apM/LEiCekVRNzhnOccH6HEDbqBUKMRUpvzdgQmS1y+rskxGruYCoIUk+4eFjcrt5Oa/jdzxZTuF3+Dxz9b+HppMhqwEt961AGsD3OrACnXK2S3pWr8vwLQtxonHmBS34+oPLcPaF5ALrTublFcsXobllHMpVRdmbA2HLPGTPl91dpUhASoGmBokVyxd1+Vj1oJRGIIWNfGIXD2lt2P1GD269dq0PumoF4vZx4kMb6SZFmuQIslj5eX4uvHRUyi3mPR41+hulNc4b0WIJDVvwtMoQDm99JkH1hM6WZGSsQQARtIoxWHASS25Tikx1NlbxbMI7h7q6Iu/Yqf177eVBLZQk6g0ECZSti8vsz+9Xso8Bv3tSmjxDIEtQUaZXLhpxsm9VKgn3ricaJuuzQJXzAMmIfM180r5VCPCsQTGRoKiRvsdrgaRiSFQ7EDYCOiarUDQY0ArvxxLbFWqfaSmAUqChQARIa+Hl4dL2+kppsi2AXGK+xUcIqjPoFz9mwp21/NUL0NgUcNRRR+FXv/pVatmll16Kj3/845g0aRI++tGPYvjw4XjmmWew3377AQAqlQpefPHFXrM69wdyEtQB7rptnmPKQtgHXQrg7jp6jfvuWuKKagpHTBIFCO3pHkxHFEgzWzcvFM/c/fIQSrtwaiZBSnNeC+44tRX+BSbywR/8mbgl2kWU0L4pC6sjKsJoimjgibNJRDwwyeH9s5UoS3SYdLH/3SdD3KFnrUK8T58Y+rvVpi/VGvj0SZTLo14JEe0dSwgB5Y1kzEN5IEwSjbMuaLFi2e4iDAU0JEWJaYFqnBZIZ10eUlL5h54SIACYP38eJk6caK+RMmSIwTmL2KrZn5gwYSLiRNk6YIAbTADPwor6A4mfXqLe4yjsv+7+apD7y7/m9clDmgCliEuGjPj/2jw9/vOZIXS8rB7scXi/mXWFt6wemeLn2k2ytCGJwi733WLSOx++xNL0G/C+U1CDRkFSSQ1+WSORftd98TS5mtyMLRAkWA6gURWCSBBAHZxW9G/c7i4Qh84GRbeOjAAoihDTij5BgVtqb4SQkiw9yvWR3L7QltggC1ACFyXGpAfCRdBKoe2kSwgSR1uBPlw/JczDKqS7T4Fct+Ww19ALlqDuhLINHjwYe+21V2pZU1MTtt56a7t89OjRmD17NnbddVfsuuuumD17NhobG3H++ef3rJ39iE2Uw/Yu7rtrCb50xxJjBu34IbrnziW4964lCCRsZM8D9ywlU78wZtQOOkobfSBcpJAvzOMswT44GkyCXWjCahSklypeeB2ZEJqSqgmXcl9DmGrnJoKkAwLka4b8ZUwo/IgUNt1ydWtf5Jyd9fr+eXZ/CdNWKXRqXX/Ayu7H7c/723dhamdJ87fjiLfuRIX5CAJpjxmFwswWnZjeH1ulBApR589RdzFv3jwEgUQgBaJIolQIUCoEiEIJKelYhULQLQtOT8FaICKGjuS7ApvmPpr2+SHuI74wyv7NYun0TfWuqSDSJwRZ8FIE11vHub50DSn1n6Xs85RdbiM1A2fd89ert7+smyv1b+aTWsaDb533xv+N1w0lD8o0aYukeYfgOnr//Q+YCMD9HUiNQHBmZk0fSfqaUkCfUDrhsV/1nfdZlEBJaiCISNMTDSLrjYyA0GhL6OYTAZKR+QQmKowZXkT7SCpm2yb6NykjFMDQgI7dnlAfY2R1qZB5OpSzjPHvkaRzLYQaxbA22suej7cv/56xVffh+5bh7tvnoT+gM/3q+n16t00TJ07E6NGjce211+KAAw7AX/7yFzz99NMDJkcQgLxsRhZdLZtx5TVjAHRdw3HORS2mXhjXRiKzOs9Q2LTOHTZbKbgQJ2eCbq+SRaccA+XEhX9KGAGg0BhS1CgEZJWIFbC2Srk+tCYLlUDaAuMLmQOvk/WRKJ5l1lp8fCLExRmz7DqUbvaYIn8i/fhZ4meInDDLfvSfy1MJE4VwJSa40/pPkyPoc+eQyyxbE+z081qQsDgXjmiymTsMu5cjyMeYseNRrigTAUVlM6qxtpF/SpGeYHCDxNZDQ0AAy5f2XjX066+fnNYBga5RHCvMm9c/nTQATJ48GUmijMCcroefRJKya2tL1oOASONdty/GpVeMpnWUc1f5bkv/SeGBn5+HrLg56wpjYTbgSpnY7bx98vZMptjyouHq/VnrLGq3XRey1qh1rq/T55EotvY5tzVnkI+982Idn59MFXDvFpC2xPrXg9/nqkq7w0PhLEj8nvPxeR+szUkAlBVQ1gJaGYuODXtPACjnGpPG0sN1xoSErTafVB1ZYhIkZE1tsYrpBwvS5Q0qBk5cTyUzXEkerifG/RFPNMPAXZfA64/DgMg4R3ZKI3148N6l/VY77JXvnI1BTYV1b9AJVq2u4MDjHsnLZnjI3WHrCX45rrxmTF0i9IUrR7uOM9a2Y7WdK8zgjXRobWziNANJpCYydyhWRJqYwPhEwgocNUySNHrZyZIiPOKga7bnTp4tOcLbZ3YdoGMCZL/DETJ4f/M6HMZriVhmlmzbIFBT5Z4TJgoBfN/LEn3c6c2WANlrUmdUevThZfjcOa60SKJoNidtx1a7TVchhUCxQFmRy5UkPePX1LmWIoGGouh1AgQAs2enBc9fvKEVM2e19eox1oXW1snQWiM2o3Olqqw1yLcAQQgvTQARoCuvGWO0Te5pYpLDJFWtgzVo+z/nXam3DltPspFf9p3yLZ6ZZ8K30vjvReoYOv3uZLdH9nh11vOX+YeQAuQVUrXb19PphdIkFjVEjglC4LnntebfhU1WSoYandIR+kEKidYukMBsp3gmJIgMBUZTVJaS3DhCAjoyoVoJbM8lhCE/AjZ3kAhhhTccPg8AMkBJAoBGgyE8TICcOwz2PFivFAWe9cvof3xLuxAsevaeO3PtmQBJ8y4rcF8qMOILo7B88Yzam5djwCAnQeuJ22/p2AJ05TVjqNPXrqORgnQN0jAOG4HA7zkcOSpFwpYUAACENIEqhtShVRKNQAjbwdnoMNMZKC2MVUA7MbfpIANJHSA3j5OcSTgyxH2ZP2NMa2zcuWYtPlrXJ0J8jnY7j4hl9SDcAdXDD76VLqQqpavg7bfhMc8KdMb5Lbb8CUd9cYh0nGhEQiAIyfWyvuDw9OZR4xAGrB0RRqxMfzcUiSj1NgGqh/4mQJMnT0Y1ThDHlBARAKpVZS1hUrhnEeABiAgQg/VLEAIio1Qm/Yu5d2BrBP8s7LLUi+SBBjZTlw6OjNRzTQsBF8jA24IHP7duR0nysvuqEVj7JAm1hAiZZVnBNZeA4FPlNkL718TZqKJ6ZVogID0iRO++ThE8DQHpB3TA9RG8S6WJVEDRRfIDJAByjZUTcyUFYBmcjLxlhiDFq8nio2MSRocNsOLqgNxogwKgMXDh7xJAMVIom6zzkXR9YSgpTUAotUmCSOTHWoMCd01tVFiGvHL5M+6v2NIaeiGBV103qfYC9wE2dO2wTRW5JqiXcfXIsQDSnYbt0OwsgwhMFDrfugZZJQY1CJQKwg7W1kwrYTUmkZnFhFKjGGqb5ZT+Jh0Nu5J88CyOiBAtI/+/RhTQv/5szx+HanRKZlDzOw7qQN1GRLa0XeZrB3z4FigBOu6r3123W8rX25x4ZjNOOrPZLj/17GZ87pxmnHp2M5TS+Py59NujDy/Dow8vw2NfWY4wELYC/MP3LcND9y3FxZeNWudxO0MUSjQ2BCgVpQmvFQhNxnAqvNi1TmjsuP4pyuhj2tRWTJ/a2u3tJk+ejGo1wQerY6xem6BSVahWlXHTuOeB//LTT/gQQjhLpRT0Mfc48J5/zhLN2iurvbE7cv+kuJRwbgz+vaMPH8t/vqXXhtTx4LXRe8btvrx3igdS3ofs6OOfr9cGPg9/4KbjplkfLw8lZ5cx/YLw//Zcz4KtJO7fbNLAUOrUh/smOndPSyQBaSJUAwBNUht9El84z58IQYETQricQOFgoDCEGIiMPCJUREkCjYFG0UR5FYxGKZIUvh/Z9tPvobEWRQERoCIblCTwzOPLEQQubxdfD61dJC7Ablu2FgkUI4H7v7TU3E+B22/uH61dz/VAvSCs3gQxoCxBf/nLXzBp0iR8+9vfxtq1a7Hbbrvh7rvvtnkMNoaKttSBUpi0lALaWH10QvNVzi9h/y+AclUj8kyukglSIFCJtdWTFASwtkyuMTLvClSTdOcnBPu/kdIIcCftw0Y/+I2HtokRsybz+udL7UjrgBzpscfKdOL+b0wCGa891zVdznNPdp4okdvMrr7Tzm1OVQtn69B5I0gQnU1+uT7gyK84FkBRomrUmrHSKBW6HqHV1fV6E1II3Dhldre2+eINrZQHKKYUAXGirbuRoZUmQ4EQpuyAqHEhu1k2vTP8LGl4pMUTNfPuOexeQpjs0Z5OR8AWMk4dBwK2gBvqu68EaAzW7L41v8fe7/yvbxHKPstZMDHrzNrpn6C/Hlt7fAsWtG9l1gghUi4swzPSx/IIXOpdNP8qc86cO0dl9scuNUdwacNEAYEmm1wkTXoOAYRaoKAoV9XKhK12tJMo0ychbIS94FqTeywoQAiBLQONgqSJnhBAKVSQgvRPhYAuRmAy6UMRQYsCbSeR1t0uyAp04pnNllzbc4ITSGvt7q9P2r9sCvNy8MvKlSvr3cEcAwQDxhL03nvv4VOf+hSiKMK3v/1t/Pa3v8XChQuxxRZb2HW4ou3NN9+MV155BcOHD8cxxxyDDz74YMM1PEeOHDly5OghcktQ32DAWILmzp2LnXbaCffcc49d9pGPfMT+vbFUtOXZWnZmSbNF0vrIgM3pwsxKSMwsBRXPFFJYTQI7/oPYWVe0BtaUgdVlsiD5YeSsNeI5mhQ0G+I8QDaEXrjfbdV23k6lw9KlcEJDf0apdG24sT8TTrkLBAtc+Tcvl463XletQFl86+sdW4V4JpsosgYFgcDXvZxAD9+3zLaxp1gwfx4mTJgIAKhWNYJGsgq+vyoxLrKuzTsWLpi/7pV6GesTKGotFd6mPNvmC88uYCnT7igfvihaer4mXygN877ce9cSjPgC1fDLRsNZob89KfPuGa2a0yS5pJbsomRNkp/yX0PbMjkCnmsrY0X1NTvZF8BflFrXNdE7Xhq8njbHVCbKIPWOSzJs8fFDz3KrvTZkRc68vMYtaf5nrW2Z7dKWY0CY4q1SANVEWD2hNBmrpTbmKwUMCTTWKGEzTBeNtTkWJjkjgAQSQwONVYlAY1SgxIea3P6RpHvEOh+A/qZEkV6mbEGuspe94IqjPtds+6DvPLocp57d7J4ZKVKWcqVdDqAwqJ+RvL/hR0D2ZB850hgwlqAnn3wSBxxwAM466yxst9122G+//XDnnXfa3zeWira33Lwo1ZGSe0vYTic0IcGkFaFEeyTWI9GskML8TXWlmkoSjSWJIU0Sgxql1QMVIqAUkQ4oCujvUgQ0FFyqd2k6olByp6GNSdjV2LG5QaSnEWC/ukeQbNQEMloHwS4xWo9dT5wbiLf1BZt+bTMmTb7gtCc4+SynC2LYNgsnRD37QnKB8b8AcP+Xeqejmz9/HhqKIZoaQ0ShQBgKDG4KUCoGWLSw/8lNV3HT1Ppi6ulTWzHNaIVuurFWM6SVNtdVp3U70v/wMvq3edS41D44V5CUnsZHkAYjCoV1E/NAdd/dS0kzBE+zY3RCvovHdyGl9DZm8hEELp8Tt9V/FqV07yvn8vLLKghvf9lagz5h8N8bbpfwtvcnBLLObz754uV2P3DaQXqP6X0WAva9JuLgPv47wfm4+MP9gZ9PKKVzsm0j7aG/Xw5DdznKaB+hIFdWUQKNUqNJagyWVAi1JIGtQ41GCTRKYNtIYVCosU1BY1ijwqBIY+sGhS2KCoMKGoMKGg0R6RgDo2cshdSmQkB94uBimgD51/aZx5fjlLObrTaNdT5SkvaHnjnYXEAbAwHK0XcYMJagP/3pT7j11lsxduxYXH/99Xj55ZfR0tKCYrGIiy++eL0r2pbLZZTLZfu9J/7dltHjUhEcAJGhQJqK79qr8WU61cBYfaRi8ScltCpEtDwIWF8koTWXSNBY006JvjjRn9VGCFeJ3s0Wdcoy5EdvSNNpKQ1TD02kko2xPgBwM03eB+sRIMgqpISwrNpPjsggEkILrd7I/BYI4GddEEOvC9/82nKbJ6geqVoXz+oNXRCAVNr4ceMmGI1X/8w5pk9t7ZDQrC+mmP1Nn5He74yZbRg/YaLTwLGlJWBtHH33SQUP4KNGj8fSJQvQMnocDdhBOj5PGYskR2JecfUYaE3pJ6yYXgqoxEU7EunhCvXImCPpYyo7WNNnVp/jvyt2mQB04sgTwJYjt5Iwyzp6yHxLETfBbiuc1cbmJvIsSr5WSUoAyr2HvvWJtSxKk9XEPyPOvcTnYy+JJZHpdyZRgIJOuVCkKUMhvMAI0iW6gqtac2i9gNRwliCYfkJTJmpLjgJnuYsMqWIhNlt7hHA1EW2QCFxfpqFtepEwqH8LvmuiSE8+yxAg87wFAfeZAg/dtxQXXDIKkbnHX+6liVFvoDcLqOZwGDAkSCmFAw44ALNnk3Bzv/32w29+8xvceuutuPjii+163a1o29bWhmnTpvVqW1csX4RrrxtrIrxMp6G0zTcBcMQBzUKUCW2/dcUiXDNyLAkLQ+qZlda4bYUbBO716itdcMkoSEli1GoMW59Jw83auJPgQaMh0jbhIs+cwsDlzeFU8kJoKCXSrgWkBwbPa0EDgKlbxOQoG2JP6+n0dtxx9sJ1Xxf8x4BN/I884IjXuRevX8bodWHhwvkYN35Cr9XuWhd6u4LzuggVDcyuPhPn9RHCJQeldrFFQdjvY8dNwLIllDLgupZxlBdP0zvE7wLAaSdoGy6wysfyrYluEuIGXR+cfFB429iTAJyrScPVh2KipOl9dq4wYS2L/tWwZMjfb7Z9mTbVdaPxe2M4hP9dmuskQRMeaUiUf368K9vnaGcJTbyXOu2m9tYPKFEr5wCia+1C65lwUaoOWlYMef/CVLIHkAgrRE+UQGOobVHoULhEqhRppk1UG1l0SpFfy9BFxgpBf2vTjkQB1ZiWP/1Yx67xU89upnJEwk3g+Hl86D4iPBur5ScPke8bDBgStP3222OPPfZILfvEJz6Bb3zjGwCA4cOHA+h+RdvW1laMHTvWfl+5ciV22mmn9Wojd+YAucUYV14zBlEoUlEoZKoWEEIgity2YUAm+aVLFtTs/87bFqe+88t6yeWjEQYa5Yrp0AJgbdm4vkyWW+V1vlIClZg6mwIlLyaLUkAzKjZ9J9C2hEaK/Jjv3DmFEq7j1hQtlqhMNJo3a+VBK6zT8fYG6uVd4eUQsFYLH+de3GKsYRQtxjqh3kJ/any6G+HVEbpqUeIZNeudlNJUtBU0UPv3Qxp2HhjXk08Mb162EGPHTYDWCs0t4ywxuHrk2NS6nINLA4BOk2reG4/xgTmw1VNkyrDQ/lxeIGsh8Ym6dGRLmHQH9kcvaWGaiIm07ofzE4lawu+To6whKWsh0nBkRwSOnNjfdK2Gh8G/aVA0WbY/AtJaJw0iLVXlWVzMO+TXNBSAjXpnN1isNFAV0MqE6ZuSDUGgUfVSRRQCzpnm3GoAUIy0TSNSEC5LNp8O9yHf+vpynH5eC7TWNu/P8acT0cmSoc+f22yvURgIPPLAMlxwyaiNlvTk6B8MGBL0qU99Cq+//npq2X/9139h5513BgDssssu61XRtlgsolgs9l3DwS6udNdna0uB5o6jx4zHksULsHxZ9xPpsXXo4stGWT2SFBrtFe0qcZvDc6KzYugISRQSUYqV6+xZ55AVUltkOlASZwvKlw9NM3qPBNF+3E7YOuW73g48ltxY2UzR3cEpZzdbC1i2ySIzIJx+XgsefXgZzrqgxWYYThQg6mQa3pwwfWprjbumc5C2hq0uUgosW7IQo0aPr1UdGgLEebCWLHZkf9z4CYZgC1sPzGxS8xy7/YkUAeJtAu1Cu631xVpzaomyb8XKkgNr1PG0QHYfRjzrogoMqdKZbbMPn9dm3kaACA4/v94lA+CSNTLh4WAFwMtrI5zFx79/LCC3wRGZFXgi4r/nSeJ+I/cUbE1CSwrNhyc9nIhQVYEwcAfh9Aax4vqJ2iZu5TpnrGOSwtVK7Myqw3j04WUkcg6Apx6pv/7p57VYC1/B6H0uvHRUr06++hq5JahvMGCE0WPGjMGPf/xjzJ49G3/4wx/w0EMP4Y477sDIkSMBUCfDFW0fe+wx/PrXv8Yll1yyUVS0vW3FItx+y2JIIXD7LYudINP0fEuXLEAYSowb37MEeV/+0lIUIoliQaCpQaJY4OReNHOLQqAYuRo53IlxNftQevVzwEJUtgylRZPOtQGbht4mVxMZ8Si4k08nY/QJkJ9ksSd46pHlnbrWeABJFEWcnH6eswDRrJq2Pn/EqF5pz0DCjGnXY+b06wHAulW7iiiUiAKJMJDWqimlcSlZk4oTTC9cOB8LF85PJYXk96HewOS70LgQayAzBYnh/SuEE0ILN8CzKyX7kULAj9b0kzH6wmh259UUyBVw7jOkRdMic05ZHRF//HPw28778gsis0icNYCpulfSnaf9G15WZK99IrN/PrZrq3vXYwWUY+Pm5Jp4Ot1edrUxmUw0UEkEyrFA2USO2X7G6H/IJe9KWfD5PPN41ydDTz6yvEMCdMb5LXZyqDVQjAQeuX8ZHrhnaa/o//oL1uLXw0+ONAaMJejAAw/EY489htbWVkyfPh277LILlixZggsuuMCuM3HiRKxduxbXXnutTZa4MVW0vXUFucg4AkUKgSWLF2D8hImmc+s5S7/ztsUY2TwWCIDBIkB7WaG94lLEJ0oDIQmx2cTM87UwcDPGOHFia56tcefIZIEEkq7z4yR5IkOtE5D1SWc6dsDMKM0svLfmKE89QtEf/L77rgYAVrSuNQCpbe0jhlaAlptnb6GUNqVY6PpMndIKKTrXBc2ZM6dm2fgJE7F40QKMHjPeiGtpue9mmjhxIgpetjyy5AgsWTwfLaPHQSpht6XthF2TyLtzhbEFxb/nECLlBgZAiRhFxsXkWYoAYZ5Jdy7+wOETGgFqHzdeAykdkW8Z8p8mdpd1hKx7jI5F75yfEkADaYGz91z752Q/AHTiyJHyXHn8TvrRnbycrU28vJq4tnIABrebi9P65XdI/0hS9aq1Ljn3Fyc05H1I4UTMPcVZF7TYc1dwpPT8EaOsBmigILcE9Q0GDAkCgJNPPhknn3xyh78LITB16lRMnTq1X9rT2joZbW21A0BnGDV6vJ3VLlm8ABMMAarnMltfFAuB0b1oE8mS2MzS1ZjD9U1uD7MNzwQDuCKuGulZtJ09atiBUmQ0FlLo1ICkNUWNVBPXobL7zKc+VGCRlhx0bLMlXT/89vp1hn4Hz995YMsOSInW1j3DvytFOqGvfLl3tUEDAVo7V0gQuIG2q+A8SRMmTCTdm+Yiuoa0mGstpcCcOXNT2/A7IIVAYopUEhnQdpCHENZyaCcP2um8mBxppDOVJ/4zwYO8DTUXVmPE1kK+FgxuCxMR0u+451YAqW1T1h/zP63TgQZZgpX9N+1FE9bl5Ueh2V0oc68Ev3tpAsiTFKnSx2X3t+8+9IXWsXL70RpYWxVojCgaiy1OSiMVCUgTI2GjvMg97fKacVZ77j80yEXPE7HeQopQmoLJQtBznSMHMIDcYRsjukuAxo6bYDtRftHnz5+HIBCYO3cu5s2b1yvtWrRwPqQUpjYW1bJqLEoUIqpnVYgoKWPWNcCmaDarA84/75vW/XpF2eKMvpuNZ3nCrOdHnvl5RGD+Nf237VQ1gE+d0IxPn9TcpfM+7vT0ejZfkXKDAmAsYMppJADnHrNCUyBFjDZ1zJpxvU3RQNef74sLj+8KJk2iYpIC9GwL6ep7sXupI2hz4cd5LjIefIWoTbIoTWABDWouvw/9LZzbSrBbC6ncRcJu77MVV68s1baMc5DLLfgJIH2LS8rP5X1895M9v+wHzo1s3cnwJiHCP3fnqku537xt+Th0jfl80iTL1++xBSdRpOHhdWLzvRSSW4z7C99d5l+lwLi7AJdrSJhgCz5e6LnoSgXgPx9d3mni0+7gvBEttp+txhpxohGG6RQhAwm5O6xvkJOgfsL4CRPBeX4WL1qAxYtIEDp58iQ7G+5NhIFAGBDxaSwFRIACgWIkjSbJiRitpUc6MsSaHSZFgCMo3MEy/H6eSVNK78P/Ckd2/JpmPvnhtgAeadHoEhHiyBDAkR7b6ZtOPfE66uzAoDw3EOOiHhZUHSi44cbZzgUizfMTAlOndS/fUA1RMQM2R4P57KLmuc8M0D5h8p8PeH93xKn8AqSuLen6fEFmHbuqR4TcZEDUHM8/tJ/hyG6b+fBGKZKSIT/2PI2equbvFBFKa358ZNfzr2vqkntuN+sKU/5ExyVCLJokrBVjAYqVC8/3NUF8/GxhV9ZkcY4w5b13hZAmZr0JvmesbysVBOIYCH0WOoCQl83oGwwod9hAxOTJk2xobxBILJiftvb0BQECqI7a5MmT7PdSUUIIZTt9rWHEwE4D4vcNUQgEXgFWIG1NqdeHcLSbJRlw60rUhiZn9+MfX5rfNB9YAEecTGnuX3iq45nifz5Kv2WjfHiZJTmizjK7HuVI0nLzmjl98abZmDXjekghoKWGVqQJ6q41iK/55MmTrKVGAzXPfhb1LrX/DJDFhoqrKs9N5t9DGvhFajmQ1tbw7/w9RQ7MNrx/LiGTfZZS7avT8Lrrmnawu9g/abt+lpCh/jvlHzL1Tur0987Gev+chCCSozQQw5GiQGpUE2EnLlSVnpaXIkfapKB31k4o4N7hUALlmJazxaqSACVzvJIJ3Hj04d51PUsJxFV6RhqLEkJQ3iopgfu8fGs5Nm/kJKgPccMNrYDpTBfM6xuy0xmCQNqcLXYwKCsoSdEYcUKzr2qi7eCgtLZm7kQAMDmGosBpBWKdTq4GeDNZO6rAajikmc36g4Xy91Fndp3tvBPlotY+c0pzqvN+1ogoOZz2hDOc1Sg7QKXJjhuU/GMrDRNmTOLviy4bhUCKVKLKTQ2zZ1xvXYH+wE5Zy7uzJ3oA2MVkCQRICN2Ry1cjPdinthXudyBNYNhqA7gBlrxqGVG13RetLD2aEQiaDAjQs6BUmhxJuBIw/A5w+1LiZz5Wxi6TdaXxyXhcvIb8yMz7IuBqn2W1Pr6QGd4yAQo9rw3Xz+zb7CgMqCZhbM6fy2twtJdv8SkEtS4lAfes+ElbKcJUWH2RL4b23Yq9DZ5URaY0UWJ0gUEgatYdN24CFm7EJW0AZwnq6T5ypJGToD7ADTe0WnFmvciZ/kIogQQk+JRSmo5SIQwFqgnVx6nGGkUpbMcYJ5SUrhBSBtbA64ytxUjS3w0Fp5/Jziqz2gMpTJ4TpCNPfHQ2a81acOrh2NNcuYx6q2WtAol25+f/5qcOYPKmlMalV4zGPXcu6bgBAxQcFu+Di0cmCZ3/tKmtXbIG0fMh4Nss2BLKBGjc+AmWASxcMB8TjKsYqCVCPHjCFOlkoh1k7mV9K42zFtVrZ8q6o4hcWSLP+xaUF5qjqRJOtOltawdwVf/hZFLE58F/C3PC/mPtXHNsQfPcSUiLqgHW7tCEppo4cTLvS2XIhXPvubxDAu49jhVNeDjvjwZlbtYAylUmtjpFWnhb6V1T+w5KM8iYEj+AsKV6imHanXfyWc345td6Rw9E7aSnqaEoLJErFTIMkM8BwJix461MYWOEQu39X5995Egj1wT1Mm78IhWYbGubg9mzNxwBAoBpM9ps0sPZs+cYnQcVaOXaOaTVoPW5UGUQ0DpNJYFBJYGGgnD5hST578OAhIxR6EiDFWd6ViHpdbIM1hmk9BjCM6sLryP1xhUhvPDbzGwYcJYgpWpnxlkCxO2wx4Z/TDNowQimN+GeY86s1kxeGOPC8GbL3RFUtrXNQTb8my2R4ydMxLjxE4w+i8jRxIlUd0xZYbqucWUBnnXFkCzSe4jM8+GW8bMEoO66fK4MaUXhjrBkKU3CpW+Qtfykr509vveMO2Igatfx/q555s1/vp7JPq/2fXPaF8rlQ4QoTmgiYy6b3d5/T3l/fI+tdca0weqmYCw6ntiZwRMHLtzsL+dTYa1gKdJoiKgPqcRAueoyz3/za8tx0pnNOPFMZ8k9/bz1L2XDtRkhqL+LQqCpQeK+u5fWrLto4fyNmgDl6DvkJKgXceMXWzFjZhtmzerd4pU9BROCIJAohNKRH0Hma47aoY5MoBCSKDYM0gnhpHQdXSFMC6dtEVbtZsb+QMQkya+CbTtmiRRxApASk/JMUXmzzI7GZH8Q0dnvGVdYdsAB2PKgrUuE1+H8J5dfNWa97kF/Y8Gc2mrvnYEHxECSSPr6G2dbgsz3cfrUzvd5ww2tuP76yTXLU25Q5SxDgIvKcxYdUbONIzWODKCGfAhvffd3ijR0YGrk0Ho+jn/ebCnUGkglZxTOegpkSbS3LLM8S4T849YQnoyrKDARn7wNdMfHrRo3Nuf7YksNnxO/txz0UCq4wICqF/rOV4zfJdteZCJFvfea98sh+XSf3TsEEDlbW3X3I1Gw5EeArEJ0b+resi7BFuvVZL0rRNIW4x2Q6A1RdO4Oq0HuDusF3HRja2oA35gwfUYbvngDDV5SAFEkIRKNKHTmjSTRgOnEooh6dqUFqrF24aRSQGrK86E0udG4Q2XzeblKLiY/Jw/g5RQCDyBu8EjpK+rBIys2Dwn/ZPwIx3y+Gc88vrwmRB7wiJl3HD9izM6CzX6FieDLtstto3H1yLG4bcUibMwYP7lrRJwGrnSlcIA0Qnzd/ISaWUyd0mqzbQuzQypaTL9nLUjsohBCpO+jh6xr1Rcy0woCMHmmRI0Ch+DnDOL9EEF3omm/fhzXJrMWHrgTFsLpgtzv6UYzKYH7J/Ws2XX47878unDPfDZSUgtnbeF7opUjMPyOSeGSGiptLLeRp8+DSxMReCkx2CrD+0i0Izx8ijz5qXuO3h/SuMISBURw72KinO6Inxs+LhMqpYDTzm2uq9/pKqQ0qUACUdPOgYhcE9Q3yC1BvYDpM9owbUYbps/YuCxADCmBKTeRlSoKJYqFAMWCNKU7aB1tZo2hcZNJQZagKBC2Dlgghek8tfXlA2kzOyc7446T9uPcX7b0gMwOCsbaJLz0/sKJoblz56iVehae7zy63M7auYNn+Blstb+sTseYHTjT1q2+7UTq6XP6EtloYc7bRNdK23UYHVmDnDUjS2iFJTK8nm/N8UkuXWudsZKw+0m4TOvSWVMCP5FOneeJq9n7Vg0uhZHdjI/JZTlcqQ53XB+pZ0dw3iH3W9KBW7YrH37fpDQldmTajQaQXqsaa5SrGpXYTT788PZKTFmaK7HLjxOaulz8vgbSWYs4HN6fBGTzg1kiKJzrOVFGP+Zdf/8+cB8hhNMQkrvZvYf2vVRseerZuyaFsCTq9lsW445b188KNHGiS/6ZY9NDToK6iJtubLWfgYbpRhsEADNmtiGQQKkYohhJRJG0HYXfyXPn6+sDnAvLDRBMnmLlOlQ2h6fyh8D77rnC/I5VeOsLwRllXafP6/mm+uyg6+srfPiDYM26wuuItRn0JK/TeX6Y3sYXb+qdCvBdhXNDkd5DCtIJMazOprOd1CE/WUuOI0HCWWA8FxgLoO3194/r3WvW90jpCI7vJrO1yrwPb2OfZyb6svY5ybafa4RBcPV4kTk3EPkxh8uaNLPPTT3Nm08QnGbHnaOf6JHvmVIU7s1uLyFc3h6AllViPgfaxidC/nvKbjN/Gbed12XyxH87y5o5nkqTG7u9d415G7ZQsTbQJ44aaZ1UT9411nn5Fr/1wbx589DaOhnz15Hioa9hr29PPhv0DDZO5CSoE9z4xVZMMaRnurH0bKzWnnXBF7pOn9FGYaMhZ5UWKERmdizcssgIqQuh6/ilBMIQ1j3BnaoToboZqd8JZqNG/I4/O8P117H/esTIiq2R3o7dYfX2B14faaLkR7YIbznDnYPLygsA17WM67V7syHhD3STrm/DxOvbnD4L/jWsza6ctQr5bsZ6YnLeL7md6At17Okg8tS9tyYJ9xFwVoI0kUhniLZNTT13vsjY7YPP0be4ZLMvZwlOSmTvPRtdGXJF9iPgHZsnH0afZq4PC8oTQ4D4mnI7OHt7NXHuJcClo/BFy6wBZNdfFKbfV2v9CTITFbiB1D82kM7Ezr9nxdfVxBV7JaE17TMIXJJGf5ueYMXyRdYi3VN0Vh1g+vSpPT9AF5AnS+wb5JqgTjBj5sAkPPVww41pC4MAEAYSiJwLIrY6H5o1x4kLSwaASkx6GaU1VCCgYm3q/dC6dubpkR7e3obaC5NLpI7pnLcF3MCmqOhUjbVBiPSge/zpze73zHTHP07NzNwz70szAHF72QrgZs7CltK4ednC7lz+jRZC1OqHJl7fhjmzWhFCuMzewt3TGdPquOwED4Lu4vvPAO3D6IUyM3P63dMRga0qtYeRgvL/CGMxYiLMpDylJzLKHW3yBUGAtvM0NdYaBbeMdUt+u6X3nQmJd+rmWRKA0tBS1OQGqnn26pybMD/4v2k4QlmNHfGRoSALbOLO29e+JeYYgSlZwwSJBcphoFPavUSlL7c/GbBWBK4nJ9PWU7uMJz7C/QvhJkrMy1ivxC45Jle2yLOmv7/+4DKcdcH6R4cBZPHr63f1ppumYuHC9XO1dQdap5+79d1HjjRyS9BmCg2gVJAoFAKUjEao4LnGBEDuspBcZmEoLAHS2pGCYkSJyAqRsKHz/qxTmNk+L7eRNp67LAi8Gag3s0+RHqRnqv5snYs/anhFIP1z5QFCpmeYPIAG0li+QkF11SJhyo44QhgGAnfcurhu6YGBjM4E1Gn3DF0/312nAWhDUlO6ZU1h7kopJIn7+JFhqbxA1g3m7rvLEF0LKUh/k7Xw+OHxqfNAxl1mlrE1KGU1zJx/Z9cG8EhAIFLPV1aq5KxSGZFuHQsb4M7dupnMcquB4utslifKWYAS5aw0oQSltwjpX99qa91bGbeg4HuOtBaIrUJ28gD3Tvm6IXufvPeUtXzsfgskueBse822ico8SwDOvnD9iFBzy7hNZrKSo++QW4I2U7CVRUqBYjGArAoASY0bQwgKm1dKQIVAe4Wyv8oAgDffFYZkaGUy8Jr9cPQHh8tyyC4LqP1IHB4seGDk49dzNTC5EsITSvvtRp1tvP1D8IBASSP90GMphTeY0eB2x62L0TJ6HOkYernG0caGBXOoVMbkG2r1SbNmkBWII6wAUzgzrRKGENqIZomdOlGzQKJY/EwWE3+g9PUbHK2VhbX2mR81XIV53zqUbSc0Hc8mYITbJkWbzQ7YQqp1Zh2z0LdKAvQMKU7rLMgOZS1TwumqmLBrDUijDPetLtlz1Z7pKZ3Nmm1djrgkCjZaj0kJW1ykoJIY/r7ZsuYfi9+tQuiOxWTMbgsiRdxeDmBgcsTvPV8bSUZnxMqdJxOfyKTb8PsKriMm7P+6h+taxqFeksyBjDw6rG+Qk6DNFNOmt2Ha1FaUK9rofwIIQeHy1UTZumLsmyhEVHtMQ9rZfDESqMQaiTWT0wDHCCQABWg7+3YzRQ6Dreeq4pm0HzbLy30XmLUcpAa09D4AWDs8L+PVw8DpfB64ZykA4LIrRwMAvnTHkpprRpoF2unoMeMRSLHRp9pfH2StQ/PbWjGhtQ2zZ9S6wZxBg0iDlIJSLoAGdsUx1jCDv7UE0f8SpT3RpoZkF5a1OLLFR7jjeS4sJgO++4mfB9+15RpcYybyrE5pIuQZa2rcu1lLo30W2arkRRgyAeJnng9luY3wXE724tDkQwiBckWlND4sCK/Gyk4CrIXVkJ1KTPvivF6c+4gSUhqiouAIkP0f7acYuWcd4PdGp0maOW/pRYRa0uNdOzZeSQkE3v4iL9eYEIBKnGXqkQeWYX0xavR4hIHAksWblhXItwj2ZB850sjdYRs5upv0rjvgEHju2ErFAKVSmDK5Q7ikiVFIlej9zNJSUEr6itETsRWHZ7YcVcIDhdUTZIq2+tob39rD23NCRht1JtOdq28xykbg+CZ9c0qUNTug2Wwhkhhx+Whccvloq4248poxNdeL27U5RVrw8zd3diukKZ7psiu72TwNgO65YCsKRzdZY4z5l3iCyRKttB1gk8R8z1pEOmifgDu2dSuZhe45MlaFDFGxSQtr9sHrdDJr9nbE5IhdOR1lGNce8claV3wClCigUtVWYyWksBFbGto+55EJbHB6NqTuiS+98jU7kcmbw6HtGml9D7uB/feT3yN+//zvTGaybmo+VyHSkWWcdd7PTs0EKpSZbOUwVd+7AQ2NJYvz7M85uobcErSRo7N+uKe44cbZmDn9esQJdbhRKCASbWf1UciuC57JaoShQJK4wSqwugJhyYlVQApv9ixdh89WHiloYp5yfQk3WWeGzh2jkEC1qlFRGW1PhpBIj+ywa4StCjb3jXSDgVYuKWQYCtxdxwrUMjodDcaD9vgJE9dZHX0gYuHc1pQQk/42ZML8TwqBhLU9IusGoavPpCI2pgwb5s0RYtCGsBApTZSGSszzZC2Awoql682GmcQozcczx4C2+iJ+WOhZ8NxsWgDSCwCw67L42h0n9bzWsT5y6Q/vQNYKlLJi8v6Y8GsNbXaSKBddSQTRXYNAakAJ+/6krJ7GysL3LPJq/7GIOYSz7kjhxNO+DqcQ1vY5/v0WUiNGmujZ90gjlf2bS9wImHbAy/slXNuCQFjRN6cGAIDzRrRASuCh+5aiO1i2ZNOyADFyd1jfILcEbeQYN6lvI9S+eNNsW2WZZ5c8m6OBgV4aFkb7lgAGiYphrUahLxKFm4VmrTw88/OTu3HnGBkrVTGi8P0wpGVSsobHWIGEZxnyxdXmOFZ8bZ70MKCQ/8jQ/zghAsQJ6TqCb83wsSl3Kb41hu6N0+/wQJYiP8IRAU44yJ8wkDSIwmwrXbJDV57FPGPSizIT/vFTBkr7YTB510obouMsQ/52fi0xel7Swmkmcb41yBIOvh6p503Y6+VnNWfLWDYXEeeu8ZMhunOAy2MkhNXHsHCfi5o6a5K2yUjZwsIBChz2rsHvgEgJl/n+8d9Zt7N/j/k83ISo1vJEFltXkidl5ZVcPT77vjuSGEqgEDnLz8P3Leu2FWhThrVA9/CTI42cBOWwHXa5om3HlM1WyzPIQiRp0BLkHuMZuxQCofnbRlVJ7riFJTs+UQHSpnYbhSRM7TLbmbuBshgJlArC6nl815gf+ZJN0sbtd244UaPzCGRaCzSyeWzqOgXe4MV/b6rws3/790xKnSJDABEHdnHStr7wmKLEtHF7cRV2JiMBkyORLo1h9TLwibOw32lwNUQq8BMYCtgCq952nNDQd5VlyRDX7mIi5B/L7scQGyZgqXxE9njummUJo7V+pMi/K2rM6zpC4rkdPVc0a6Co2LEjPcWIJg6+65jvEU90ArMvJk5MTnxxtrVuef7L7ONuCxkD1rVsXXHSkSE7qTFEjvsP7ju0uY7FAk1QfMsPuwFz5Ogr5CQohymWCRQLwnRsXr4g6ZIoAmbGFkgUC9KFxZpwctLtCNvRhqHX0WcGptRM0pASNzumjjXk43uzS0eKzKzT/MYzUEtQBM+k/cHQuMFC4YS4QthZtY+rR47FiuWL0GySIi5ZvABLl9AnCiWCQKaEo5saRk9oq7Gq+QO6D3a3ACZCSpNotxorlCsJ4sQIoOtsy8REpPYnjJtGpwTEfkkNfh6lZDLrrEx+JJrffv8gfoi9TbDoWXaoVIyzBlmyz8+yTCdW5OvjLCHuX+E9i6ln1E4yOPzc7ZdCxznVgC++FvYa8bXnyUbBvBeR0dtEgXM9c/CC/5wTeXTn5euGgDQB4vuUciWa4wvPmua++5MTYUPsHTkUNSSXk7TmqI88WWLfINcE5QAAlkGQb14T0QkCaTt5pfzIH39mSx2vUjQgVIxv32oDzAAZGH2A1M4aY3UYHhnyTfdOLOuIlK8xEEJbIbNv5s3Wa+IXn/VLHMHG9Ig7dQ0SREspcNuKRRg1enyn14qLbm6qGDW+DUvmG2F+RuoipYZOaDCvxmRBjBON2MRAK6UteWaLiYJGFNK8i11ASlFEmDJsh/UwJLjXViycLX/AWaeZANE++VffOmNE2tx4wFqcrPDZDO6LFzox7egx45EIY7XiZ1lmCrl64MKsWdeXT5BSlkeRdgXxOnxdAknvYjXWKEXCuwXa/s7CZrYacci6FTXHRmNlCFU1MYQVsBF0UgISfF3S+rysNSz1m+ZABVeKhPZBL4eStI4SLkGmPVfThjCAPUgkaV++Hu/yq8Z0uYDquHETNslITR+94c7K3WG1yC1BOQAQoeCXTEqBMJQoRDQzc8THzXytZsEzc3PhVTZ/84w3lJ5JX6bdWIDrbP33kwkQRd24fCs2wsTMfIuRM7H75vjAm9lyLhI/WSOdDzz3nee2ALnClGFcY8amydC8efMwf/48zJkzFzI76m1iGD2hzV13z12ilEcgNRGg9nJM1pCQrGRRJFGIAjSUQgSBRBhIQzzcoMuw1pLAszqCLQ9uOBZCYM6cuant+D7OmTMXc+bM9SwmnkXIswimtEkAFsyfh4UL0gMoW4j8IZjJHFtsrCVTOuuMr/exz3/gLEFpi6VnObIuQvecsmWJz8+2y1iQWDfH1pUoEChZC61L/FmMBBqK5OorFSQ96+bjJzv03XTS/K6Rfi/9jOpseWIrrfCumzQXl89BehYzmONYC5N2WkIfYShwz51LOngy09jUCRCQW4L6Cpt2D56jy5h8Awmwg4BC5f3q21prcm9UlU2ApzW7wagDDwPqHNlFRUTKucl4xkjagbQwNKX1QNplYmfJvo5EClvtPrQfF47Ng4ATbDptkhXBwmkwrKtOZDL3CrfPjjBnTsc1hQYqli1Ip2VgIsRXQSlhI3+YkMaxQhRKNBQDlIoCxUIAKQSaGiQaSgKNpWw2ZyYM0pJnHhTZ2hKY8G96JtLbz5s3zyNAArNnu/tgLSHSbQek3XlZAuNj3PgJ9jniqCjfHcb7cS4vTwTs/R3470IHfzMZ4fauLStUqhqr1iRor3ilOjyhMru/QjP5KEaOQHCuptDU/isZAlSw9QFhJyYuOstzX/u6Hr4fyllX+bx40mHF1sK/5o5AsquUwvjd9WKXGd+HMADuun1x6j7ctmLROp/V7AQlR47uIneH5QBAlcOFEKTBMLPQakydMGs8tBf6biKbUa4qO4tlzQdrZbTxhykNE/FD2yRw+xW8P8CLzDGuKuE0O/5AyI3wZ5exLRpJx9QaNgeSMDtl15XtrCVbrniAE17H7EjaQJ9l3rJkMq4d3XWy1jK+NiJRCgDGMhAEGtXYkQcOCx/UGFgSGYZp7QgNmrLGZUQk2+hgEtIP+aL2dFFRgZmzqG033NBqf5cCuMkUOiZSC8BYqhKdzjnkkx4hkLIqAcDEiROt606YZ8yvVQZwmHzGpcb79P6wz7GBNvu0pTvMT1oD7VWFNWsTEyKvUSxI44JOr8t/SykQAtDSpJtQMDmDqF3lqjZWXUdUbEJLcy/ht83T//B3beTXStHqPInh1dzEht4zBaqxBulc3kICSDxyZNrB1l3AlNyJaufjo8eM7zDfz9hxE7Bo4XwsXrT55APKWuXWdx850shJUA4AHpmIeWCi70lCkT3aVK72NQScX0hrnfLdFyJjKTCkSRqXFlMlIajgqmQyZTZl8mRn7jDRI9p1oNk286BGEfpGhARYHQlAZCsKBKpaQxnLhU9+ChG5ELRyRE4KsckkXOvEkNX9fcG5TeIECCQ9A+wW1TqdOJP/DQKNYgSsaXe5gwAXSZaYpIAF6VIRsJWuXk2wWYYM3fjFVvt8TJ/Rhik3tUJop4/xJVvuuLQj34rX2jrZuoIV17UA7HMB753wCREy15YXu5pkwlstXdIDALkSlcbqtQnKFY3GErmh2fKUDtXXtn2Crx0M+QHl7uL1GyRlc4fWLhmi0pa0CI2UP9JGeglO4uhyJ/nRnO6+wLrkAEcU+Q3kd4hziTEZlJIupBDCCr6LkcSdt6WtQIzr6tT/GjN2/GZFfhh5nqC+Qe4Oy2HB5IctP4nJoaM1EQeeFXKmX45q4cgddo0JAWt2Z/M5DwrcubvZqbMe+QOdH5bN7pJ6s2GBtPskNNqIQlQbvSWEsFoi0jDRcW9ettBYntzMuS+lPgvntmLR3L7LBJ7FNaN67rJz7iO+XxpRqG3x3DCQpkCmtnWf/AF/0vVtCMx9UUqbZJtOpE5uImlC5h234EE9DARmzKy1UCkNzJzVhukz6Ldp09tsGQcm6a79ArCDcXo/aQtRmoy7yCdhI8dshJiojfzyCZB9Tnl96aLFpGGISmsMaQowfJsIhUigXNVYszbBytUJqlU6AQEXEeba7LneJFAsSPsOBibHFr8TpBGi7dkl5fsFpUDq/eXz50hNzgPFk4dCJK0eKe1mpHOl+0+BC0yU2M3Gx5OSIlI7eteU1ogTbROVXtcybrMlQDn6DjkJygEANsIqURrlSgKttRdFRdFiTnfAHXxag8Mmcu6ErWbIJxeChZ1eYsWUm8t1tjwu8TaszWDSxASJIaVAqShtFXgbyWNdFyTm9DVJtxrdAYfaL1uyEEsWL+jTjpYHjI0Jyxe1YsXiybh5Mf1bA6+9PPCWCkSCi5HG0EFAQ0GjVKCUC4MaqP4UWxfmzm7FDTfORmOJ69ABlEPIudPs/qUjD4ATxdcDW4R8TJnaBikEKlWFSjXB2nKMNe1xWgskBG78YmtqP74o21kla8PhU+H09pMOe/fzUfk6oTAQtn4aJf4kHVVjKSQReTHAkMYAUShQqQL/XJlg1ZoEa8sKSmtUqspMVNJ6oUqVJiVRRNYZFlwXI4kw9DM6Ezlk4sL3k/Nw8XcpvCSWnnsSAEpFkSJA9hHJTlRAOb3YLcf75TZzQMUdt9a3AgEm+aYCmkeNQyGSmzUB4qCOnn5ypJG7w3JYUDi6ooy7KV2P0SlEEhWhUu4yfqcSpa3FJgxotq8FucBMZQJYYzkrarUmrVDKZYFUOHNquRlMOBw/5XqQbvDk3DKhpsEoMetxziEpgFtuTosuF/Wj7mdsH2cB7y5uXtyKOAaU5twtAksXtKZCo9m9yde9ENlbiCikQa55rDuvQAI6cB0vE9rJN7Rh3uxWlKsB1pbJ2hjHGhxllyTKWmVSlpRuksYbp8zGlJtasWp1jHZznGIUpIhOxlBoBLsuf41Np2BdY561KHtAYwXhVaKSNNmzfY0cWyMlqjGRmjVrE2stXbM2se4tgGvuCbRXNCpxAq0lGorSFlMlty6RvSAQiGONQiSRGL8Uu9PiCkdaauN+ZBLGhNy968JciMDogXhyw+4xP2u8bzHiqDEuo5KNwLQTGgmEGtCyvg6IMXrMeDs5ohI+svaab2bIQ+T7BjkJyoEFc1pNtI9Xz8mAdRIc/VMsBNAm8y+HzvvrusgcFkuaDlnA1gQLJOVeYTIDlRbRQqQTHPqdLQCrE+JSFn46f3jn4YcZ82w0DESNxiAHu0CBxNSmihNts/UKAZQr5KYhtwvQUACax9Unc8sXtiIMDXmSGnEiUkQ3DEjMWyoItFcoDxRpUbQd9LQhzVJSSPe06d0njtOmt2Hu7FaEoUSlmuD9VVUTui8QBRJRFGDa1FZMmUr7njGzDTfd2IoQArFJLug/t07Jz9fFkQdnpfTTSEiUK4nNlF2pKkomqYBKVWFtmSYUxYJEpZ2ITLmqUa4orGlXdl+BpCi7RBHJiUJp9HlEaBqCgMhk4mqX0WSC3g9OQEj1xowGK3HWKcAFLXDOoRgCEuzSpHeVZUSxokiy1Pmb7RIFo/lhaxVZlApROvEk0HH0F0d8cZ4ptvYO9ACFHBsnchKUI0eOHDlybOQgS1DP7GG5JagWA1YT1NbWBiEERo8ebZdprTF16lTssMMOaGhowJFHHonf/OY3G66RAwTjJ7fZqBC24tAszs02AylM9lmv/IGnK2BLjK9V8Iup+jl8aoTRnmDUF03a+knwrUEZNxmcHoNFphxm7Nf3YitQIAVGj8lzi9RDojgRnjYZi4Wx7gHFgsagBlMWQ9UvJsuoxhRRxSHVYaBRqQosnkcanLGT2tD6xdkoFYDBjdJqhOjZk9AaKBQCRKFEmNGjZDFtaisWdiIyn3R9G7YeKrHV0AK2HFJAQ4msJmvLCSV3zKw/fUabtR6yFcLXsvllMFjr45e7SOXAEkC1qvD39ypYuSrG6rUJ3n2/in/8q4r2ClmFGksS7WWy/Lz9zxir1yrEicaWQwKEIdBUEthySIChgyMMHRRCKQpaECDXId8Izh2kvYrwvtDfJW2EOS/3G8PPk8UuLwD2fWTXVmgsr8rTNvFryS4bjvyqJtpmmw8DysTOn7r3a9IkRCFZuxYunI/GUoBlSxZu1logRq4J6hsMSBL0yiuv4I477sA+++yTWj5v3jwsWrQIN998M1555RUMHz4cxxxzDD744IMN1NKBAwEmQa4T5+VciTtJtNUNBIE0NbS8+ly2I3QEimts+QOEE6i6zLnsrrLZpL12sI6DdQ2AJxky+9LesMw/sRbBZZTmOk0C4ydM7PuLOoCgFFAINcLAaTqk1NZNFkjSATWWNJpKteUhfAQBucpaxrdRDStTzTxOkIqKm3h9mz0e4FIVBIEw2iBAmqKfs2dcnzrGnFmtmD61FdUqsHotuXQ7wr9Waby/KkZ7OUFTQ4httiwiDAXWlhOrr/HhD/qAsOQsW/2d3hUT0cZESKRrcFGVd3r+hjSFVIA4pBQRa9oV/v6vGGvaFaJIYEiTxDZbhBi2NRGeHbYpYOstIpQK5Je0GikhnMsXrNkilxtPXjj1hJ+XiN8pSh4qbYg9v1e8ve+6ZNLEUZ0cYcYTEync+8rwBejQ5CK7bcUirFi+7uSHc+fOxbx58+z3/tTq5dg8MeBI0KpVq3DBBRfgzjvvxJZbbmmXa62xZMkS3HDDDTj99NOx11574b777sOaNWvw0EMPbcAWDxw4MaOwmhufEPmRWNyR+jodTrTI9YoYfqFInjmTZoLDe4UVabpZtSeq9ISlnc1mLLHKFrGUwkS3ydT55CAIaFsuwddfKeW0WSxuDgKgUNApEbSP25dPRiFyN6hlfBuiCGgqaXs/l3oZqcOMRaJYoIgmLtAbmlw0QgDz21qxYE4r5sxqRWKzVhP9LRtLE1uFFs5txdzZrZhyYyvaywnWrI1RrioS8Etg2y2LGDoowspVVcycniZYMiCLkEuVkA55Z4tJNt+RHwXG6SMAYOjgCFsMKdB6gcvwzO9MGAhsOTjEdlsV0FgyFrCAM2kLV3cLjuz4yTzpfqXrqHGm65oEjUiTFNbzhKHLOs0kSKl0mRvOvM7pMvy0F7Qv1hC5iU0YUIRYdyHyV7QOBHQPP3Uk/Zs9BhwJGjlyJE466SQcffTRqeVvvPEG3n77bRx77LF2WbFYxBFHHIGXXnqpv5s54MBWmEGN9Eg4cbO27jCehVIekHQ+EZ+YVKsacaJsJJZPpsIwQ1ICt18b/h64ztV28vAixKzrDHZbV+3eW4cTuoUSkXG5LFwwH/PmzcOC+fOQg+BnaAZgS2JEoUaiBKqxQLlK97h5bBuubuk475BSQLVa29GOmdiGMADKVVpn+cJWLF/YmnJ7Fkx4d7FAA2fk5Ydq/eJsAGRNKleB9rKmsHAr9NVY3S7wwRpykb27EvhgNfDBmti6baJAYNWaKtaWaeAfMihEU0OERGm0zXRESACYOqXVDP6uNEyaCPkuonS4uDTbCVAJmqaGAIVIoLEhRCCBVWvI/ZUojWJBYHCTNHm3RCpayzYGhlxIepbDUNqaYoAjM+zuDYN0jiz33sKz/PBvzirEKS24xAaTomKB0k40FKUhaC49hV9fTQjY8HwhOb8TMKixe9JTzgbe2lonVcNmjNwd1jcYUCToK1/5Cl599VW0tdXOQt9++20AwLBhw1LLhw0bZn+rh3K5jJUrV6Y+myMSkxU3CnjGzmTI0wl4s1JA23wjNo+KeZo00q41Rk1SOW+G6ZMjTrRoXQsCKeIE+Ob4dNI6m7wuEG42LUXdIpk5CP6gHhrrXDFiqx0nzBQIw3QPevvy2kFKeXWmGNeNofe1EFGW8JDSe5OGS7GlkDOS06f1i7MRBvR3IDXmzW5FNRZIlHBWKbOdVhTtxMSoXNGoVBKsbY+RJGQ5kZIKugLAqtVVlCv07DaWmDx77lTPmmKvjWfx8BMlWm0b2Arqot00gGIhsNXgV6+JsbasoKFNYVOJLQaHaCgGKEaBPb7WvnM3e6+crkd47xNbOX0rD78zMG0T/sQF9O5Qlm+j7REud49NahqQZTcMBBqKgXHtSfv+QgDLly00qRLoPZYShkhJDB0U2nxjXQV3GfmAnUZOgvoGXaLo+++/f7d2KoTAk08+iR133HG9GlUPb731FkaNGoWnn34apVKp02P7SFehrkVbWxumTZvWa+0cqBDgUFrK+FuuKvjh8tSJwtavUUp7Sd9cB6ugbf4YSvEPAC7/iDDfKX2+O37gHSNVogCwVJ0tTgKosUxx23go4f10Vvx0IOCuFZNw+ci5616xB9DauW64PpyzFFAyxCiErT92yxIqMRHW6T060wpdO3oOlsxv9QZijTET5xDBqVISPqXIAgWQZmjOrFZDlkhHwxoeGQBI3L5jZYTCSFsDkRh9DOiZbSiGWLWmikpV27BtKVwBYYByDM2cfj0qVW1Jlr1WHpHg6yMAsjp613PVGoVqnGD12gRJQuHxAJGkhqJEUymw1snUMyrYtVV/tGJLkRBGowdQXi/Q9/ZKYu9dqShtWRJ7CO3uEYXQO42d78JmTZ/yCAyTP6U0lIQVK48aPR6R0QpJAShJ5zmkKLF86fqlo1AaEPmInaMf0CUS9Itf/ALjxo3DoEGD1rmu1hpz5sxBuVzuceN8/OxnP8M777yDT37yk3ZZkiT43ve+h5tvvhmvv/46ALIIbb/99nadd955p8Y65KO1tRVjx46131euXImddtqpV9s+EGAHHNBgVK7yLwJKKdsxa5MrJdGwpvrQFMYMAon2ckL5TEJp8wjZGl42H5ApWQHX2dv9a5dBOJtJ2tZN4oGDZ8RwBClbf8w//kBEnPR92+l6kuuLv1NdMJdQj0nS7cupGOstSybjquZat1hH5IgRhRrlikgljAwDLp9A953dY0vmt6IYmeK42nFm6VkchXkOk0SZAVsjigJoTStJL/libCKVSoUAxcgM8MaSk4UU5J6LE22fcwlAi4yqImUtovWrsTZES7nnUAisWqPsBGHroUGt7kUj/eCac9OZ90EpjXI1sbXulNJYW1YIJAmm+b1b064wqDFAqUDvom8ZAoxlQPn5ttLWsEASMSpG0ojWhWuWR5jIbeYKDTePGmesSuvnaOD3eCC/t32BvHZY36DLztoJEyZgu+2269K6Cxf2fjK6o446Cr/61a9Syy699FJ8/OMfx6RJk/DRj34Uw4cPxzPPPIP99tsPAFCpVPDiiy9i7tyOZ9LFYhHFYrHX2zsQEUiuEaS92SiZ3RMTcu4qsVP6fzbDSyGQJJTMrVJVVBGeLTc8iEgBKNgq3C7ii2fUNMJIcLVrr9M1+/DrkLG1CeD0+q5+ma9Zamvree2sDQVff3P3LZPwhWt73yqU1XTxMqXJtcPWkNuXT4Y0bqOOqtKTLqT+DP7WpeQ+KxXTv4+d1IbF81qxpiwQBeR+W7aAq8QDUlMBVg1TBFf50VsOidJGwwOEUpBrKpDeYJogThTCQGJNe2KjDus9H9ffOBuzZ1xP10UbyxPquxOsRdKYI9vLiRV3J4nGex/ElBXbNENpjZWrqxjUGFpLjY+UFYj/NNtSksXEPvus1+Oq89UqvZtVU8R21ZrEupOTRJsSGsJGgwUBESdwIkVzOKUUChGlKygVg1RQRFZPJ6XAfG8Zk7E4URg3fkK33dBsxeMkjzkIrNXr6T5ypNElEvTGG29g22237fJOf/vb32KHHXZY70bVw+DBg7HXXnulljU1NWHrrbe2y0ePHo3Zs2dj1113xa677orZs2ejsbER559/fq+2ZVOFlAAUoEA+/TXtKhWdxZ2TYmsQ5+KxeglhazSx3oBm3zQ6kEaINQ/sPhPWpQBhJ780Q2WCo/2MvLB5Rwi8jnIuELgK3JsS+vJshADde80zcVqeJKAw90jXtfxkkS3WevvyybbdVJ1cIJDpnvjmxa1EXIwejXVJgXBtiRMXjp0oQCWwGrQ4BgCni1Emo3EUCQQBaXLiWKVIM0CWoSgUmN/WigmttTpDKYlUr16rgCrph1ykYtpqwo+a0kBDKUSpQBOH9z+oukrrmiuzC7SXFbSOscXgKE3mDONnF5fwlq+tJFjTnliXGlnoaI3AuLPi2EXh8eSCrUSR0elowJJFIQR0AoDvCZfkCAQESAwdhtIjahqTJk2yE8uJEyemCNC48RPoPdV8b7o+6k65qdVYIXky0+VNc+RYb3TpMdt55527NaDstNNOCIJg3Sv2MiZOnIjRo0fj2muvxQEHHIC//OUvePrppzF48OB+b8tAxLhJFMEjABQKjvgIAYShCS83YmPAWWKklNZCVI2dxoDT9DMhcf9KS5o4osUXoJLGwolQrbXJEhxphaFMqqhTl3YZk7NNiQZdlrEC3VFHmNwTcKHMMEQqdxPQudanMzDhKVcFKlUiQNnosuvGtEFIIEmETbAIQbmGhKC2hEHa8udbB4UAmhokSkV6TkpFinCSAihGFG0WhtK6isKQnlcB2HxE9cCRaYWInikqZeHSNvjV5vnZlZISHNK5C+sSCoxuzlqM2LVltVfpRIUMDSIFq9bG+GB1jHLFXM8K1R5jHY8wk45iQZj2+pMT2OKrfrFajuxy0WzpYrFBIOx1rtf9T548CVIKTJ7snsOFC+bbfkIIsghdf33XnlMmbix8nzpt46qxt6HR0/B4Fyafw8d6lc3417/+hZdffhnvvPMOlK+mA3DxxRf3SsO6ghdeeCH1XQiBqVOnYurUqf3Whk0NnOsjDDSKhQDtlQSNkTTCY2HUnzS75rBagKLJtAaaGgLqoBUJNTnzLrsTEuMKy/qmHcnWdoDKWnvY/O+v74pBurb4hSynzdx0O9Io7PosuzPwVY5CGlQpLB5orwg0lTQKhXVbgZYvakXBWItuWzbZ1B8Drh1NxO3WpbQsayliXDemzdawax7bhluWUEV7IUlArbUwrlrSD5WrdO5JQskYtxqi8P4qiWIEl/ARLPgHAimhVIS17TGRbjvYCyQJ5RUa5+mUFs5tRRAAlbUUBcncoVzRaCgad6yAsXI6cE4cdi1TIkUgiWmQp+g5beqHkeuMIrJcdJfxQkMlVEfs/VWJtYYy2QKIlEWhsFYhGQg0mozYYUDBDUxkk4T+LVeUDXMXKfc2f0z7g8Bq9NL1BOn3yZMnu9QDkcQXb2jFzFl0/RbMn4fJkyehqtMu9JtubMX0GfXfx+lTW6EUtY/uXT5YZ9Eb0V251rwW3SZBTz31FC644AKsXr0agwcPTlmIhBD9SoJy9D6kBIQJ8W0sCSgtKUFeSBoLthAA1IkLowXi3D5RGNj+UggmVN7+zSyYa1H6s2HWINHfzs3gNENpd4Y201TWMKWy1ko3WGyq8MO6ewINsr5c1TwHty+fjEqV7llDUaNYTA9Hty6dbC0nTJgAIh5a0+/aaGiiUOO2ZZNxdcscXDOKyFFnCKQTVUsJ6ERj5Jg5uHXpZJSK9Hy0VwS22ULh/94lt5cUwFZDaZtCpFOV7gH3DDQUNQIpAYSUbRlUfqIQSaxuF6hUNREf79xWrWESRQ9yuUrbWBJh2s1uYQ755+dVKW1JOhEiAWme1YqxmP7rgxhSUM4iKYFiFEDDaH/aFVauSTxFuIYEuZjZNQg4t15gJhtOSyNRTUyKvJAEzhqUI0mYCQVbXZkxSmGCD6RLmMrw84YBaeFy9knk91GEZL0F0CEB4uvTXiZxO2mQOlw1R45eRbdJ0Lhx43DZZZdZvU2OTQujJ7RhfluryftBydy0pgFm1VrKHwMA5UraReJmjdSp2tICcGZuwBM4sysDJqw2GxkDJzblbTlSiS0C7HIjN5jbzronNnEWdMnVvZPw0bfyCBBhuLKO5ee2ZURw4pgsIKwf0hqosg4LRIA0NK68Lu2+6yzJIkCCaTYsX91CEWgAWY9uXz4ZSSRQjDSuGUWh9u0VoFQgK9KS+a1ECqRPQshaw27RUkFDN0msadcpoW+5QuHyf/tHAgFg6y0CrGl3z3cYEPEoRmSBShRZl/i55GvA+XAqVdjIqn/+q4JKbErNSKBQDFCuKqiKJpG3mQy8+36MUlEgKVKuo/dXJ5bk8fkkCYBAA4kAG6ASpU1uIoVS0bmliwXS/sQxvcMketaIjGtZw2nrpBA26o3fGxJSw77D/IJpberyBc4NPmtWfXLj8ip1etsxfWorVq9JIAQwdHDoNII5UtDoBUtQr7Rk00K3SdBf/vIXtLS05ARoEwabzwHYkgWARkPBE6kGQKCF7aQZZA2S1q/PegPOR1IvQlMIQCrnvhCBS4jnb2PJjUnsmCS0fhh6uVzMOpzVNkf3cGXzHNx586Sa5bcvn2wGejM48kBliCgAE2EE6IRyC3GOI/53XdFt7Cq7delkXDNqjo1A4+8+Rk9ID7yUbDFjBZJO18MDa0NRo5rA5ABiKwX93dQQIEmAf32gUCxICqP3NCoJyL1F14GOwftur2i0t8fg4sMAvQeVmPJuDW4K8cHq2NYO0xporygkMbddY+VqjZWrjTvIE6fz/MAWHM4+15rC8hOTPkIpEkNHoUA10ShS3CWSREMKbcLhSbejlUYQShutqZRCEFA9D3YX+t4wsgaZRKroOPIyDAQqNLOpKVMzY9r1uHHKbEyb2mraRZrDakyWoGKh/vOxuSMPke8bdJsEHXfccfjpT3+Kj370o33RnhwbAQohUDazz8C4OTgsOQh8Macx89soLhhztiMxNt+I5orkjjH5sxJhzN9s6fFFuYCbDWtNri7+HoZmGAvcMq7wnaP7uGP5ZJSrErcunWwGXW2JDyUsNJY90DMRRXTdy1VHOMh15bIw86Dd1fD+OBG4eTGFyIcdhNvfsXwyqkZnc82oOQikNoVFHSnnKDMmQJzQcFADUKkGUEobYTQRm0ENfPzAuPc0IglUY0/QnGjr8gJI39Ne1qjGCpUqVYAHqBisUgmGDgpQKgTk4pEC5WqCONZoLElEocBKY/Gh1BBAJXZh/iZjBALBJMglPvTdxDDPu0mTBADQSqNcNe+TAhLzgzSuO7LQCiT2/ggjAqcXkSysJiO3dvdQKReRNrMDCxDDFZut/W3KjXR/g4A+jaFEe8Vkqxa6brRejv5HW1sbHn30Ufz+979HQ0MDDj30UMydOxe77767XUdrjWnTpuGOO+7Ae++9h4MPPhgrVqzAnnvuuQFb3nV0iQQ9+eST9u+TTjoJEyZMwG9/+1vsvffeiKIote6pp57auy3M0e9oHkfuhTXtbM52ocuUEh+oGEtLe0Wnygb4sz4bRQZ/BpvOi6KUn5MINryWhaAMDodmlwO565xuiE37LkpHY9L1eUfaXQihEQbA2nZB7iktqSyFchmFnfsHCDXQUKKM0tWY7pez+xG6m9uoeWxbXesPY35bKwY3ESkLAn5uHDFTmp5RBhNqIQAZEKkjKwtZNZygnj4F6XRQpGkjl1MQ0PNdiemZryZUokNrIlNCAsIcv1xV2HbLIpoazHsjKGjgnXc1AIX2skIYCDSVJFatVXS9BGnv4FlerBzIs24xAfKtpcKQKKWJAHEW9TjRphwJJzWEzQ/EeZJsgeSU5UkgDE35G+/adzVKUEogRLryPADMmnE9lKZ8RXy8ONEoRBrFqPd0bpsiNoQw+sUXX8TIkSNx4IEHIo5j3HDDDTj22GPx29/+Fk1NTQCAefPmYdGiRbj33nux2267YebMmTjmmGPw+uuvD4jI7C6RoM9//vM1y6ZPn16zjESySc3yHAMPgQQGN5Jgob0MVKoCBRONJI1othLTPa9Wdco9AJjZI1yHzWCxNC+XlijxAEruL6Ez5QkAm9+EEsSZEF5rsRLeQNG/BOieWyfh0mv6trRFf4BC7o3A3cxtSP9Df/M9Y7dTGJBlT8BEYHFUlgak0D0q99ERAQJQ10qwag2F4IcBETKliRT40VSCRcGCCA1ZiMh9pZUwVg5YHZtPAIjw03k2FE20leDyFZRRXYDeG9b0lisJmhoCFAtEpKJYYMshEcoVjWq1bMlIwWiNoNPvjbW+8ODnvVtKGUuXdC5p322nlLZJEnmiwaJx1i9FwmXpZpdyNeEgA9/S5F1o712eclMrpk3vSA9E1+KmqW2YPYOK086Z1Uqur4AtUS5KNJ+wrBsbggT953/+Z+r7Pffcg+222w4/+9nP8OlPfxpaayxZsgQ33HADTj/9dADAfffdh2HDhuGhhx7CVVdd1bMG9wO6xOuVUl365ARo04GQZBFqHtuGpgaNYsFoCQRlmwgDoKkBaCxqFArC6hWkgE10Rh05J2/T9jsXyZRSI/CKZAbme2g+gXT/BoF2BVWNcFOAj6dtsU0p+z+4VsqBP3u98+ZJVH9KAeWKsMQX4Lw4jgAFkhIo2npjYIIKcx9p+d231GqL+gpau2SLaUuj+UM4os0WnSh0xXoBYNVaOufEuLuUdueuNAuk0yQlMexfgELFuUgrBEytM2GJQxBoNBa1CTSQNqcWAJRsPiPPhWdcYIVQ2KrxdA+0bRPX72NrKODEzFQmIx0gwELlQhQgCimSjF16sXL5tZxrOd0e917T39Omtta9H1OmtuGmqURsrr9xNubNboXWdM2Vgo1wC6TOM0N3Eb2ZJyhbNLyrZa7ef/99AMBWW20FgBIpv/322zj22GPtOsViEUcccQReeumlXr4CfYNup0D78pe/XPeCVSoVfPnLX+6VRuXY8LhuTBtWLJ6MW5ZMtp1vIdIuYibUpgMDCqEhQgFZiViHADgiBMCQHVj3GYf5Zj92Ri3hiI/pMN0+HIkiiwR9Jl3fhon9PKsccVXvRGn1Fb58x8ROf79rxSQoLVCNBeJEoL1Cy5kM2A9gr3XKUpEqvEkLlRb9SkY5oWK2bVkRMUc98qdgoh8To3lrr2jECZEdXp+fwcDohKqJMBokoKEkUSwECEOJhiLl1hESKBWoNlhTiYk+66U0mhoEthgcYXBjaPdvxcqGnEUZ4uMHGnAiyERpW0qBsmQL6/orFiiM39cQsV6OitQKO2FpLEmUigKhdBqdYsHdP5H98P6ADu/xLGP9AYC5s1uRGD2ZEPT+NpaoXuHE69vQ+sXZqW2XL6pPrHL0HnbaaScMHTrUftra1t1naq0xduxYHHbYYbZKw9tvvw0ANfU5hw0bZn/b2NFtYfSll16K448/vqaO2AcffIBLL700zxO0CYFztFwzak6qlhMLZqsxm9PJVcbCVI5kkWag8HtKf3DiCJhspI1Aeh32BFBlac4n5GajfIyaqJkc+NItk3DZtZ2TNN/MztcwkC4klwXwnE2c68JJoe098901fBs0yBrE++7MPXb78snkjpKoG57fGe5YTnmEPlgjEAVeyoVOMKhBI0kE+OlavVZRqZeIButSIe36k5JmjFXpCvZGIRHwJKGM1QBFj3FIOsNazMw1HDpIIVESQwZFWLUmtsJmLnzKVk6tNRScNQaA26+mOn1aURZstqZwtmhOWcHLOXu1Br2nQQAUTHFarrknQhcAQe+bI1H+v7DvX8cv3A03OmJDqQs0AhPdFIUa4yd3POg2j81dY/XQm+6wt956C0OGDLHLu1I/87rrrsMvf/lL/OAHP6j5LfsscLThQEC3LUEdndz//u//YujQob3SqBwbD1ibIQMgjtOze2ey53+dW6pYQM0sNPvY+CUZmMzYsHpZb/9OzxEEzkLE62fDpnPUltqoB62FJTVxTAJnDXJ5caLLyIjimQAB9e8p4GQj3GkrJZAogS914B67a8WkVDTgHcsn464VXXelCaGhtLDC/Wz76rUxDIGiSQY5pNGI802bSwWNakylPpQ3+PPzBtB1iYzrqrFEpAkABjUGKESUt6qhKGxWb3JP0TpBQBmuhzQBjQ0BCuyWM+4raG2F/lwQmEXMUlDaCtbA2XUEC8Cdi4+ImrCCaL6+hUjaY5IrWaTcbEzGOnaDudB5IdJWn3qY0NpGofCK+og88mv9kLLK9uADAEOGDEl91kWCmpub8eSTT+L555/Hhz70Ibt8+PDhAFBj9XnnnXdqrEMbK7pMgvbbbz/sv//+EELgqKOOwv77728/++67Lw4//HAcffTRfdnWHBsQUmhrzmZ9D3WYwlljkHVf1dcVpMzpIk1+nNnedb6BR4qkqCVLUgKjxucd6/qAiYnWwJp2gQ/WCJQKGoWIiK8MSCQtU+HmpC5YF6oxiZVZR9MZbN4hOBLVEWnKLldaYPutKyiE3nPjE6CMJdJ3MQHk2m0okpuIMzm3V2jAThIi/oXQucRIe8bWIBaFExFi0W8YmHBvjpzMnEMQAE0NGttvU0SpKFE0WiLf9aiMuwvmFApRep/++xEERHYoSzVre4wVKBAIjUVIQBg9EJ0DtZ9cVEkCVCqa0iF4Am1ul71PmXe1K/N9tpz1t6s6R8+gtcZ1112HRx99FM899xx22WWX1O+77LILhg8fjmeeecYuq1QqePHFF3HooYf2d3PXC112h3GE2C9+8Qscd9xxGDRokP2tUCjgIx/5CM4444xeb2COjQMcTaO1wBXXzcGXbpkElsH7pQq4gyYhKmykTdaUy51rNuSWO17OBcSQMPsL6qw7MKyuGyUonFtgbTt9LxW0GfDJuiKNnislNvaW+64SXs+6TeCTXhrQ77mVCIwfTce/s55ImP0owCZuFILcaUyAeD+kWxL46z8KJGTOtEfWeT5SbYNH1k1EWJwQEaRSFxplE3UmJGVM9/VCfAzWFPn7lpLD8bUlQkJocPbPMKDrXSoEeHdtBVEgsNaQMK7wTm0TtgK9f27+PdQJucfYOkQCbYUwFIhjbVxklHqiWODyHaatEhBG78e5hSyJ5GPA1QLke8ToSmAAT2RyrD82RHTYyJEj8dBDD+GJJ57A4MGDrcVn6NChaGhogBACo0ePxuzZs7Hrrrti1113tdUkzj///J41tp/QZRI0ZcoUJEmCnXfeGccddxy23377vmxXjo0MVzXPwfJFra6MojCRQIGfCNGFvwOwRTQ5VDnbefPAk10GGGuD2UZxTn8zO7W1yaRbL8f6QZlEiABspl6be0fURltpiLr3sfNjcDi5s2wwmbns2rnewM6ES5iPvxfKQG2PZX7TmiwYSeLaEQQu43lXzBRCaAwdpPHu+/S9ElN29DghrVucAB+sFdh6qIJKhA1j5+ZxpBZPBmRALispdJr4e8fjtkaRwLCtA7SXA6wtKzQUJeKEMkCT1dUQoUzKCCmplAdbbOg6axSK0v4dRRJxQkSIEltKFKXAoAadImtp0sgV5NPkV0JAiWz2aEeE585u7TTMPTRWxRzrjw2RMfrWW28FABx55JGp5ffccw8uueQSAMDEiROxdu1aXHvttTZZ4tNPPz0gcgQB3dQEBUGAq6++Gu3t7X3VnhwbMVjfcPctlBeHdRxBABvay24qq9fxOtlUBBi7swSl8ucQef7O2h/JRAfeb5IjzTgJ3Aa+MAMYYUCJ6sLAu96euwNIW/k4RYJParMWPq1rlyujDVLKJTRUOh1GX58oMxlgfZH7TcNEofEz5h3Td9ECaRdedjYsBEU4Dh0EFCOK0Gqv0LWJjbvXJzhaEwHnZ9yPIONjBoFxKZr2u0/aJdxQVGgsaeyyYwFNDYG5J1QZPgjIipMkpBFSXrReNSarTGTSUUShwODGkNbxrlEYSEuAkkRhyKCgrms69rKb8PmwRYiTjxYi7e5lnXe6M8QJ5ZzKMbDABXOzHyZAAFkqp06dir/97W9ob2/Hiy++aKPHBgK6HR229957409/+lONbzDHpg92kfAgQvlgnLUgSQTKJqEcJXIDIuEGDX+As8TI0PCsC8X/d+SYdLQQF9a0BTs3gTw9GxKVqkBDyeX3qXc1/XviExFe5ltGfPiESAs3ExWWrOgaMsUpFZSpTec0PERjhNaphJsdWhTXMTATidfWzcb6Hq1ddJnSJhQ94QSFLjBEmvchqTp3jwbQVCK9T5wIFKK0pdJZOmH/KES0n49+KML/vE1RXP94r4KiiVRjVxu0yfRsRM8NJYlKlcTsrClqLAVIEo1SKUR7ObbXNEmUzc2TIrfg34VNP+FbePg6Cf5uEhza30AEqLNoLwAYOynXAvUUGvXfse7uI0ca3fbSzpo1C+PHj8c3v/lN/O1vf6tJupRj08UV180lImQifaQ0uVZMzpUwIPN8nMCWuQDPnhWsxScyOYZ8i4IQFIl2zag5uLpljpld1k98eO1oKq7Js+x1zUJzdIxqQuUxolDXdLJZS5D/6QxKee4oDy5azHz3lqPO375uRSlhk3VqTcRNgFxWYZAZ3JF2Q9VDVl9x2bVzEYUaDaX0OXOWdICSSPLyapVcg5xVmgkkh/kXI46UTO+P2ufA1paGokIh0vjQdiEaSxINRWnz+RQLRHxKRYmmhgANpQBRKCAg7O9CAg2lkGp/CWBtOxEgDounyDKXCNIXoldj2GvICSf5k7of0onDw0CnLG/rg9uXTzZZynN0Bb0ZHZbDodsk6Pjjj8drr72GU089FR/60Iew5ZZbYsstt8QWW2yBLbfcsi/amGMjAofVcicZehme+ffUIKlhRbSAm7FzmDvv56pMbpgrm+fgiuvmdpozhkjZugflzRV3Z0TE92eSJt5/x0Ryh4XazfYzH5kZuLPWumynWk8Az4MpkyMWQgvU75ydJsi5z7gNvi7C16fBuGyYfPvPHLvBshYZmXl2rLtPUKQXW3eKETCoEahUab0wgCVkxYLJpu654YoFnXIfuXY4i0r2E0iNhqLC4CaFLQYBWw4toKHkuucgIA3Q2rJCuZKgGmusLSeoVOnilAoBtt0SGNIEDGoIMbgxNAVQOQGjRFNjaPRG5p4AqMSgrNZwy/x6ZFYrJN3zEAa9M5hKE2maI8eGRLfdYc8//3xftCPHAADXyLrv9olIEoERV83Dg3dNAKAhYQouaqAQmNk4aIbKug0/XJjdWEJ0nkRvXQiCXBjdEbSmnDthADxw5wQwlXno7glGl0ORS4kSlpgwlCIhs9Ys8BU1+7aDuLec73PWTeULl8ndJCypoe3qD4Y+4ckeJ0kEGooKpYIiy5AAoLznoYOROmWV8UmQYAJD4f2lIj3HcWKyoQsKH2fiXY0FGktUNywINNrLAo1F7fIBGaspvGtU71lNCaVDjUGNCkpLbDWkgFVrBf7yf+1IlBNJK0WaoFJRUuSXALbdMjCEjDQa732gEYYSUlBIfCGiJI5KUbRbFGpUqunrwMENrD3iSUpWXwVPSB3KdbvC7rx5Eq64rvYdr7csRyfoDUtOzjlr0G0SdMQRR/RFO3IMADABEmbWfd/tE1EIgVhRUrlipCC9GHYmQoH0M99qL7Fb9yuMZ8GD8AN3TsCFV8zv2QluYggkcPlIsqQ9cOcEXHTlPEuAOPpKeVYARtqSkw5dh3ADonVnwVhetMselLWyBJ71gC07Lt+Nrhv67cORiLQmpRBqXHD5fNxz6ySsWiuNdcEJmf39pqwvdY5x6TVzce9tE5EIIgkk/NW2XAxnoq7GQNXUVdOGzLGGKAzqaKZQe26ORHoWK48IbTE4QaIEmhqAQQ1F/GsVsLZd4b2VFUShwFZDIzQY3U8hClAq0rUIQ41CKCCERKIo31GxEEBp894J0lmVq65tNRoguNI2yFwrjkbTGlZMvy7kZKd3sCGiwzYHrFfmhn/9619YuHAhLr/8clxxxRVYvHixLayWY/NAYGb7531hvhWpBhJoKGWtBNpFkHlutJrZ5XqCB9HcGlQLPxdPPYJo9SrZj3DX1P3N+iuqhRUErOtKExSBepFZaX0Pa2nWqdtJtVV7BEvbiCVeViwo636yz5ekY6Wex5RLjNZndyEAXHL1PBQiZbOflwoaTQ0KhZDOPww0ipEr1hsnHPlkrEXSuYa5LY6EcaJRL0dPnXYB9C4VQiJgTQ0K222p8aFhErvtXMI2WxaxzRYRthoCbDU0ws7bk6YoCjXimCYkDUWNYuSKGRcjEmA3FJ0LSilh9UG+gN3XV/nXzkbBBS4qNH/t+g+6lz450ug2CfrpT3+Kj33sY1i8eDHeffdd/OMf/8CiRYvwsY99DK+++mpftDHHRoQRV83DxVfOS1kILr6SalMFgUYxUqYaOf0WhTDCUU1RLsJpC3pDD8DuMB50v3rPeHz1nvE93u+mDiYsHQ3Czk3lPn7qA9+6oXTarSWQdl9pmFxPZhuZIVjumOk2SOFIDmuW7D4MEfKtLoMaEkRhev+B1Os8xywuuXoeSgV6lkkwrE3SRIkkoUKzgZdSQEoXQh6GPEFw14P/TZWCMUSyIzDpDCQRsUGNCoMaFLYeqvDRHRPssG2MrYcm+NB2MYY0JWgsKaPx4RIc7j5R4AIRI35XFCdxFGmiAzgSy/+mlpsPlyhpWUem9o6yfufIsbFAaN09L+Phhx+Of/u3f8Odd96JMCRvWhzHuPzyy/GnP/0J3/ve9/qkof2FlStXYujQoXj//fdTBeZy1OL+OyZCCG2tDA/dPQFryxIrV0mEIVl+OJqE3RMcRROY6JKLruxZBfavfGm81bacc+mCHp7Rpo2H755ABDUxrhykRcnsGrOlGthagbSOC6BBPlFEdjkiKlFewkxzTM4n48MOuqivBWK9ENcE84/PZMIfwC+5mp6hr3xpPMpVesAqVZHaFuDIMpFqH5Mptmx15FL9ypfGo70iSTOjnTtXedcgCo2+CkgRHF6XxdrcLv8e+C7Gjq6L1iKVUsC/lkoBq9YGKBUUpCTL1Oq1EqvbSeTNQQxryyLVJv4boHW4xElkhBLVmJJoWgLquTkDqW1twc0ZfT1m8P5vXdyChoZ1FzrtDGvXlnHNmGX5+OZhvSxBkyZNsgQIAMIwxMSJE/HTn/60VxuXY+NGEKQFszzrDLjGBVy0SWBnwb0bEXLuZQtSrhbGI/fm1qCOQJYZ+rvWReSS36VcXWCXSFqXwIJnymcjUIldHin/LttBvwu33q3ryAJbhZybxlmW2PJH9bvILVSItCVtnIjT3z/tS9e0kwTkDl/50nh7HApjVyhGJMaOjGssCilCjELjlSVUtp4eJwX1ItfY0sP7BnzrnFue/jidktZAuSLRXpFW5A4Q0eEJR5y4SC4BWCsVW3EKJp8XF4ONAvoUIt9iZbRb5sP3kBOV5ug/5CHyfYNuP8ZDhgzB//zP/9Qsf+uttwZMmuwcfQMhNAqRwqBG8lFIE07vC2LZTdabL2O9zjjoRaK1qcAOrmCio1MuJp+kyk7cSL6VgoWxbJXRpvZWFil9j3BEwO2zVq/ju5IApIiFjT4TzoJ11ogFqcg0zn2TThCoLdnoCphsnXPpApxz6QKrhZGSXbEaUaBRCBWiUNkUEkT6HWlxVheXa8gKw/1r4113/9q4a6ftNWSyVY0l2svSrhsGGsWCMtfMTUDKZXfs0OiXONt7YMpaFAqe+1PAFKVNZ3LnnGCc1uLu3OWVYwCj29Fh55xzDr7whS9gwYIFOPTQQyGEwA9+8ANMmDAB5513Xl+0McdGCp61PnT3BJz/hfk497IF+Pp949BYSrB6LXXK2tj3BZwrDOgdUbTbl8aZIxba7489MBZnXLyo9w6wiSFtiaP0BrpmOf0GuIGb/gaAdF0v3z3C67BbJ2sNYj0Mo172aZgkmspEHQqkC7bCEiB2L/nHoCNzMr/Ei4Tz25oiYJnzYDx89wQkmfadNaJzl+sj946H1EzEBKQJDBDZdqZIWVqYzKDrXp8oMlEFWOAsUYgUOLM2oG0maiGInMoAQEL3hBM5aq0tIdOaLHsU4UYHiiJnWZKSi6i6Nt1z66SaCc2Dd03ABZfnkZq9jTw6rG/QbUvQggULcPrpp+Piiy/GRz7yEey888645JJLcOaZZ2Lu3DwUcnPCeV+Yb/UI7EbgmaiQ1KFSgU734vFMd13C0O7AJ0BZPP7g2F45xkDHI/eOT7suhftXmigv32pS64bhGl4ZVw1gExqylSQK64eJM+pZAbOh7Eyk/PZmLVWAG7y/ft84APQslArK5LlxpUCEt64TTaetRIyH755g3WAA/n97dx7eRLX+Afw7kzRp2tJaKHaRsqPIjkWxoAIKFAQEURbhYiuLF7GWsojyuFCQArIvXmVREVQUFFB/INCiLCKLsl1BEESWcqEVwUqh0DTJnN8fk5nMJGmbpEmbtO/HJ49kMpmcSdrOm/e85xzVv8syMGWuapZo2wSMthmZlV2NyiJk+/edVwSVgGNbpSkOpOBKOlcp+6rVilkbDuLvWpCWgVesD6fVSLO3q4/P82LwExQktVk8jlggLu438sW3sXr5JOuyImKjpL8BlRkAfb2m6v6+U3eYb7idCdLpdFi0aBFmzpyJP/74A4wxNG7cGCEhIb5oHyGEEEKIT3hc2hYSEoKWLVuiVatWFABVY0NHWrNB1izDU8/OA8+LBapy/Q+g6lYwmzlr3YRvvpZI35a/XjPe4Vt+dSVmQKTaDjitBZLIWQq7miBlV4yU2eA4pirMlR6XR4lJWQ9FjY/6mymnOrb0eoxxctG11Gb7LJW8BIvdxIxa69p0tqUsmKIdUK15JWfClFkowDaKjAOKzTw+/3Ai1q+e4NJ7PSB5ru31FO+zlP10eK8V9TbK91t6f6TRcMrMqXI/QeDk3y/eWotkttiKyuXiZkD+vZNn7ZazU8y6JpgtQyR2mdm6v6Qav+HWCU6lLGCIXnxxf5istO+QqtsNzrx0I2puZ4IKCwsxa9YsfPfdd7hy5QoEu7z32bNnvdY4Ehh4DlD+FDAGhAYLMJltf5w11p806WLAcwyCF6Za2/jJeDDGof8wW5dYv6G2P4Qc/drLI+VUtSewm9CQk7ZaH5fqUZh4NZe7vqB8jriUg7jEhPoCrdEwVYG0dDzlJIzKQEQ6vvLY4rpX0gXYNseNfAzYRnkpg6C+Q+bjq0/Ho/C2WLXNcwCsS1jw1tfjFa/jrD5NOhvpZ/tWkQbBesFxxxIoa36Uo8IAgEmFTfb7KmqpwIkF5mIxOFPtJwWQ0vssTk3AyefCmLikB88zMOuQfd4a+Gg4cYoEXRCzTnPAgVN0G0oTmkrdifLnJIjHk77YSJ+JyczTjNAVxBvdWdQd5sjtIGjkyJHYtWsXhg0bhtjYWHD0Vbva4zjxm6PkqWfnYf3qCdAF8dZ1jmzf9u2/kXvq6zXj5eUfAGDDxxPA80wVAEnfCjd9Ng69n1lQjlcLfMqAQxoybcvMcHD8NVZnK5RBgZQhAcRMgLQKvfR5SBdiee0s2AIVaU0yiUWwLUiqbJ/0usrsjxQASe0oTZBGAKCR56jiwUGAMuvFqQIU+3l5lIfneYZgvVg35WrR78CUuappGuwzavLoNh6qjJXZIv5i8ByTJxq1/d5IwRAnz/PDQczemMzSDM7ipI5ajRiwSPVB0vvHAISHCrAoF6a1vifKAnIp6yQ9T6pPShltC3h4DgjW0VWVBDa3g6AtW7Zg8+bN6Nixoy/aU6Vt/GQ8nvxX1UvXDh7uOGKG58XVyaVMkPztFdYuFMaVK0sjLsfBgTHbH3nGxEJonmMwW3hVdqi6BkLK2bPljARvv1aXej0u5WO2oly7oMgafIqfs1RczUFgimwPU2c8eA6wKI4pFvbajqku1obqucogTeqyAiB37ZgtnOr3SxpOz3FMzrxI3WxSu22vq860OGSkFN1vPOf6z6yGF3/O7QNQDW/LvEjdUmKbAC2s3VXgwAm2TIw8ytLJlwdpxmrOeo7iXEGc/DyB2YqmdVoBAuPERWfNvOqzkD43qStT+V4862RSU4HBrewYKR/KBPmG2zVBkZGRqFmzpi/aUqqZM2fi/vvvR40aNXDnnXeiX79+OHXqlGofxhgyMjIQFxcHg8GAzp0749dff63wtpaE48SL9MZPqu4IBgBYv3oCOE78AynVa/C8bYiwNGLF9dlagG/WjFPd5zlAw4mZnyDrH32NostEGQD1fmYBej+zAJs/T/fG6QUch24nu6wEYKtfUQYj4uOOa10p8bxUgyP+X6uxBVS2uiHFTOG8+LhYx8JBq7XV5djmJ1IGKNY2wJatUGazlOelJHUVOS7/Yb9NPS+PfQAk/ezKtUlu1LHZ6pmkmh/na4aJ74+4j9QdxXO2mjlbBk1qo7iPXHukeFw6FmDrztJIQZL1fdVpBcVris+VlkTR2q0LN+z52SXO6j505BzVcHniW9IQ+fLeiJrbQdBbb72FN998E7du3fJFe0q0a9cuvPjii9i/fz+ys7NhNpvRvXt3FBYWyvvMnj0b8+fPxzvvvIOff/4ZMTEx6NatG27cuFGhbS2J1FXDcbYhvVXRU8/OQ7+h862LStoKK6VvpWYLp/qGXJIt69Lx7dp0OXhRBjG9n1kg/1Hv/cwC9B82D08MWSBnl5TPk+73GrzQW6cYcNTdKXD4v2qSRIfHnN1sC6rKszkDUA4Pt+/GkpaoKLbWiplMnFx867DqvBTQ8kx1TGm5FWVtECDNosypPnPx50xRPyO/jq2mSP3+SP92DFh4u2DJFf2HzbO9B9ZZq3neVhwt1TnprLNMKyephPW9lIKjYL0FHCf+bj2dPE+er0jKANmG/lt/16zviTw0Xspk8WLmSRA4uZsrSCvOfi2vbwYxwHFlSZuBKbRUTUWhwmjfcDsImjdvHrZt24bo6Gi0bNkS9913n+rmK1u3bkVKSgqaN2+O1q1bY+XKlcjJycGhQ4cAiFmghQsX4rXXXkP//v3RokULrFq1Crdu3cKaNWt81i539Rs6H/2GzpezQlWZ8gIEiKtYSwtQ3rylcekY6gup+jFnQc0TQxbI3T0Aqm32RyIGDsr30Zol4B1HJNku+s4XV5UCBeVnKgUvGml1dEAVSNnPPs1bX9ds4cSlGKAIMuTn2PaVXlM5+7IySJFmY5ayQ7bzZgg1WNQZFM4WqCmPaZ/5sn//lI8pa85ce/+ZnJHhObEoWa7n4cSsjP2yL/Zt0GrFc1RmNyVP/mu+OBpTL6jmcLJYOLn7ThqVJr3H4mg+W6aO58U5v/41ag54juGZEZU/youQiuJ2TVC/fv180Az3Xb9+HQDkrrlz584hLy8P3bt3l/fR6/Xo1KkT9u7di3//+99Oj2M0GmE0GuX7BQUFPmy1jVQ8XFV9s2YcBCFIXiBSKpwVGGAxi2lZs4XDF6smljoLr222WtsoGVdYrN90BSYGQtU1CzQwRZzFW3mx51WjwByDS7m7SLFNLpxVPA+wZmAU1V0WQV20LBXjKoUECzBbOBSbONvF2ZrFEATb0HTl69smHVQEZ7DNemxrp/j/7k8vxrYvxuImrw62pRFv9iPKpKDI/jjlpawB3PTZOPnf8lIyUBebS0GS2MWlmESxlO/wHMfkLi6pbkTZTSUPibceRWAcNIr3XLnwMMeJowndyfCsXz0BTz1b8oSlxDukv6XlPQZRczsImjJlikv7ffbZZ3jiiScQGhrqdqPKwhjD+PHj8dBDD6FFixYAgLy8PABAdHS0at/o6GhcuHChxGPNnDkTU6dO9XobXVFYpMHXa8ZXybktnhiyABs/GY8iowbFjIOxWPxmalb8kS+8zSMkuOTCSvtZh3sMXOTSazMmBkFBOgFaMPQYuAibP08HY+JFV/yWzPD4oIXlOMPAoczCOFv2wnFfqfuSc/y3cu0ruaDYukSFdWSYfCw4/pvjxDuMwbroqHJdLXWQomyTFADJXWNQd20pa2IkSQMW4Zs143D9ZpDd8dTHtg+ASlLeQQ3yyDbeMaiR7ksjtMSsDid3d5XVro2fjJd/rqX5fJQF0Yxx8ogx+2ye5MtVE8B7sJ4NBUAVgwqjfcPtIMhV//73v9G+fXs0bNjQ68dOTU3FL7/8gj179jg8Zj9kX1wbp+Rf7MmTJ2P8eFu3VEFBAeLj473X2FLw1onmqiqdVhDrGop5BOvEC5WR53Dztka8iHGcami90pZ1YtAiMFt9SEmyvkxD96cXy/edZX16DV4ozh1TpEFosAVA9cgQbfh4ghzYSEECY2LGRRAgF6hL769ypJ06W2L7nOzrcZTbmXXkH68MnmALAOSuUWn+H86WZZJGPwmC87mEpOBBWZckDTHneds5Kem04nxVUpegcs4d+wBI+R4oSa9ZXspjKIf7S3PvKLt8LRZO7kZ7YsgCl46tnhxSHK3HKTI+gGJNMK2tuFoiPsM750pIoPB4xuiyMB+FnC+99BK++eYb7NixA3Xq1JG3x8TEALBlhCRXrlxxyA4p6fV6hIeHq24VJdRgqZJD5iW9Bi+U0/Q1QsyIrFGMGiEW3BFmRtQdZuiCxHqIb9aMU43+2rIuHYC1pghcCfPY2CgDIADYum6s6v63a8UCa6119luzPHeN+Pjmz8XHqyKxOFeqrXH8nZS2SzfeLkti/3/5AqvslpIfF7ebzer31/Zc66g+a5GwNAuyNMRbqlmR5hriYNtfbqciq6U8thgwc6rJ/KTHtFp1gFRaAOSMfYbJU8pjKNfckt4LZXG6YJ0p2tVpJKTpBpSvoaw9khZM1QUJMOgtMFs4h+5lKbtGmR3/RGuH+YbPgiBvY4whNTUVGzZswPfff48GDRqoHm/QoAFiYmKQnZ0tbysuLsauXbvQoUOHim6uS6pyACTpOXAhDDqxQNVo1iBYZ0FYiAV6nTh8XroQWAQO36wZJwcj9vOXuIOBUwVV0nwoRcU8grSCHJhxnBggmQUegsDJwVPWl2nlP3E/4uwCrrxAKhfvtJ+2QFVUzCmCGd4+ALL9WyrktWWglEXMTH6+uBgnU3V1Scs0lLhgK6f+eZCDN57J9UnbvlAHweLcOFDNYF1SAKT8uVO+B86Kkj0lBpvWzJli2DLHAVretqyFlhdcygIB4t8SZSZHKsTWKN4XaQi+NOqMMajqEoOCmNvnufGT8VV60VJ/QkGQ6PTp01i+fDmmT5+OadOmqW6e8Fl3mLe9+OKLWLNmDb7++mvUqFFDzvhERETAYDCA4zikp6djxowZaNKkCZo0aYIZM2YgJCQEQ4YMqeTWV2+9n1mA7za+hNtFWtwyauVvoAITZ5M26G21HoxZuwnt1pTqOXChy6/Hc+JEjRLliJ7Nn6c71AJ9uzYdjNlqjuwzS4Gu39D58khEZfeXchSdXFcDsftYuV35fyU5S8HZ6rzk+h3YPR/qIl+HLBNTTyzIK76eKScpdDZnkVRYrNUyBGkEFJlsxdBJAxZhy7p0aDUM/9zUysGBsy4w5RwqYhedGCx4KwDqOXChPEeY9N7LXb6cLeAXBB4GvcXtrlp53h/GoAsSUGTU2D5ns3UfaU4tjXVZDkXduLu1iWLGlqelaUiFWbFiBV544QVERUUhJiZGVerCcRzefPNNt48ZMEHQe++9BwDo3LmzavvKlSuRkpICAJg0aRJu376NMWPGID8/H+3bt0dWVhZq1KhRwa0l9h57cgm2b0gDxzGYLDyKTWJ2xmzhYDRpoAsSwMDBaOLFuUs0gjikWNFt4KqkAYtKfMzZhSVQC6Q/++Bll4Yz20/FwDkJAqTRSLZuIsX+ivmFGLNli+R9FFW2PMegHJCl4WxF1coCZ/uaIsaAYiMvz2/jNOjiHP8t1fFq5Bokcf4hJQ0vIMzAUFCohTTzsvJ1xf9z8mSeUkAIOJ8NvTyk2aChGNlmsfDy+yEI4pIfnnS/aTUCpBmuASBIK84/pOEYBJ6zLmtiawcH8ffvy1UT8HSyZ4GeNEkp8T2p07q8xwhk06dPR2ZmJl555RWvHTOgusOc3aQACBAjwYyMDOTm5qKoqAi7du2SR4/5ilS/QsrWtf9iBOstMOgsqBFiRqhB7BITZ/9lMJvFi+WNQi2KzeKPJgdxCQxJRXVVBcL8QjzPsH71hDJnIFcWzQK22g/l46rsjLL42MkEi8ouLOV+HJhqTiL7AmZbd5jivuI4QdY5c6Q1sKS2yG3k7NoIZXbIdoICU3eJSdkQaQFQ5Yg3qZZIKsCXLui6IFZi0X55aDUMWl6wvr76/ba11bPAXLnOmoZn0GptXYUl1T7xJVwBXPm7xhhQbAqYS0jAo+4wID8/HwMGDPDqMX32E1yvXj0EBQX56vB+w51uGiJmhJIGLIIuyIIaBhM0GjF1H6yzoEaICYB1Bl3eVg+iD7JUeDt7DV7o9wHuoOfm4qln55VaW/bNmnG2+XTAybUoykBHUtYsycqiZPslLuTHYav5UdUQOZvjR6pbsQZDUp1QSd0r6rbZlodQBltytkpxiG5Pid2bysBG7gYD5FoZrYZBrxOgDxIwIHmuT2ZD5jkxEFO2UVxo1raPpxcq5aKnUsZT+gIRpLHVaWmUs0NzYjbIfs4yV/6ucZz4u6r8kkKILw0YMABZWVlePabb3WEXL14Ex3HyyKyffvoJa9asQbNmzfD888/L+x0/ftx7rSRVjtQ9VrNGMXieyXU4mz9PB8cB128GITzMDC0vdg18uzYdRhOPYB2PrevGQqNh8sXNV6pKrYPYZSSei1SLwoODRVEELKjqYRwzMEoO9Tz23VSKgiJnmRwpAFIFXQywCOIaZFJAIE4WyMnZDPFcbOchHtex60yrsRVIJw1YhG1fjAXPi8POOY5BG2QL5qQuIt5apO/r0VG8tTtMWfDPK7Kh4LhyfbHiOFsAw3MMFsU6buKSI9YiaV6cioDngCKzBnqd2e3XEgQgJNgCI2WDKgYDyv0nKQD/pC1ebPs737hxY7zxxhvYv38/WrZs6ZBoSUtzv6fA7SBoyJAheP755zFs2DDk5eWhW7duaN68OT755BPk5eV5VJhEqqeu/R2DmGC9BTf/0cJk5lB4W4NgnTgBnNa68KbRxEPLM3CcgOz1aT4PhPzdho8ngOfLXs7BvggaYNAAMAucdaJBdUZH+TyH7jRFl5R0R7mP9G/74fZSAKSc90naJsh1RrbCbGkVdvtlMXhOfWz5NWGdDVnDcOOWFhGhYmax4FYQ7ggTg22TmYMuSHyuyWwr6IaFQRfEoNX4fuIuDVfylag8AZD9cwVmv2SIrYuR5wDw4iSmWg3zKJsjzgEluL2UCPGQN7qzAjAIWrBggep+WFgYdu3ahV27dqm2cxznURDk9k/+8ePH8cADDwAA1q1bhxYtWmDv3r1Ys2YNPvroI7cbQIjSY08uQa2IYtVQ6WITD6OJVy2hUREExjnMOeRvCm9rSiyilaYJcDbMXLkoqXrOHdvwdFuhtPP6H1u3mvr4gLp4WpkFUk4I6PTYiscFZltgVZrDSJpbSHlsZbccY2LGp9gkBnbZ69PkCQGlFe7NFluRMMcBBr0FYQaLvAivL3V/erH8/tqWJ/HNBIVy1xdsw+QBWzE5BwZDsIAgrYBiE+f2UHfpc1BOR0F8h3npFmjOnTvn0u3s2bMeHd/tIMhkMkGv1wMAtm/fjieeeAIA0LRpU+Tm5nrUCELsxUffRpB1Ph8pI+CLC0Vp7Gtm/NGw52c7DG2WLkpPDFmgKpZVZXBgDUzsinKVN6mwVklZywMoi3Eda4Ns9xXdYMqADLbaImUWiePUw/ilgM1+0kTV6ylugmLuHSnDYbFw8lISUgZIF8QQEixOWNr7Gd8GP/ak0WiC4DjBo7doOPXnZ19IrqwN83SElxRYVdXJRol/mTZtGm7duuWw/fbt2x7PE+R2ENS8eXMsXboUP/zwA7Kzs9GjRw8AwOXLl1GrVi2PGkGIUtf+i8FzDOGhJgRpBIQEWxBmMMs1HIJdYamv9By4sNTh9v5Kmc2Q5ruxp7z42WdjxOfZdWcpsz+KbBGgHvYuUU+W6Fg3pK4VUqxqb92u4Zl1yLei+0Y+tmNXmHR8k4WXs0VSILR9Q5ocbIXoBRj0AsIMFoTozZXSlaPhxVodBg4WxsEs8DCZvR8I8bztfeM4cdJIabkMcZv4uMZaFO4u6WdCKjb3ZSC0fUPVmsDUEzQ6DJg6dSpu3rzpsP3WrVserwHqdhD09ttvY9myZejcuTOeeeYZtG7dGgDwzTffyN1khJRXl37v4LEnl6DX4IUI0grQB1mg0wqwWDh5OQ2pmNpXfyClrjB/7xIri/KPnzpDI/5fqrGRl89QbbfdpKUd1JkX24XWfrJBZ0XLytdWd5WpM0yAdcg6x+SRguogTR2ISefJgeH6TS04iIXOWo34MyMdr0aIGWEGM7QaodLWjdNqxekgxIDeuk4Yb+uucldJP58cJ9Z8ScdVdlHaukNt0xS4S2AczBbeYbkOX3B31viqiIIgcaocZ2uB/ve//0XNmjU9OqbbQVDnzp1x9epVXL16FR9++KG8/fnnn8fSpUs9agQhpenafzEYIC61oVFnBaTHfUGaQdrV1ev9lX1hs0Qxx6FDACLV+qhvjl1PymyP/Wva1xGpH3ecv0a5LpmUndAohnaLNUGCfN/2OuIxLIJ4UQ4LscAi2M5RHJLOQR8kQKsR8PighWV+pps+822di7KuyVc1blKhtPR/DkwVDKu7D92/QD4+aKEcTPYcuNChWN2bHntyic+OTfxfZGQkatasCY7jcPfdd6NmzZryLSIiAt26dcPAgQM9OrZHM0YzxnDo0CH88ccfGDJkCGrUqAGdToeQkBCPGkFIWR57cgmy16fBoLPI6yBJtm9IcykQ+m7jS/LsxdIQfV8FUP7CWSBSbOblgEdgdiu5WykLmKXjqI6rWDLDdl/KNDCnz1EW5No/Lo0mYxwHzjprsuPoMtuq8UrMehE3CzwsAgeLRZzETxA4mK3f84wm3q2iZ8GH35i79l+Mrz4dLwd29u+1t2xdNxZaa8pH6tbd9Nk4VVZNKiS3CBx0nHsj47K+TEOPgbapLbQa2ySLNH+a93kjkxOomaCFCxeCMYbhw4dj6tSpiIiIkB/T6XSoX78+EhMTPTq220HQhQsX0KNHD+Tk5MBoNKJbt26oUaMGZs+ejaKiIsoGEZ8Sv0EzeVkDwL1MkHSB/m7jS+jav+p/u3x80EJsXTdWvsgKjIPJzFsnzJP2Uv9l1HC27ifAsQtNNdReuWQGrw5u5PXEFMPiVSPIFJkoZRAgBUClZZlsy11YsxkCh2ITD7OZQ1ExD4sgBjJa6zp09tlDe1+vGQ8Nx+QCaV+PEgsOsqDIpBGH9FvnwvJF4GDfjaTRiNME6IOY/OYFaQWYLZoSjlC2bV+MtU1CydRzThHv8cborgCNgZCcnAyzWZzLqmvXrvI8hd7gdnfY2LFj0a5dO+Tn58NgMMjbn3zySXz33Xdeaxgh9ro9tdijb0Pfrk3HlnXp1nlNRGYz77DauDOu7BMIlMPexWyJMoNjq+dRFtIqa4TUs0Qrjyv+X2NXsyMeS1lMrThmKWuDScO57YuqlTNUK1ejF9turU8xczBZxIu6NEO2NMpMYI5LrmxdNxabP0+XC3p9PUdQ9nrb62u1DPogAcFBFrk43VPudNf2HLgQOq14nsoA1aC3uBX4ZX2ZJtfkJQ1YBMbEGj3p/Q50VIjtf7RaLcaMGQOLxbsrCLidCdqzZw9+/PFH6HQ61fZ69erh0qVLXmsYIc4oZ/51xfYNaQjSAgWFQQgPFcA4oNikkSfjy16fhn9u6uS5ZLZvSINZMVIn0OuBAPEcpABAORxaXEyUg8Bso76kfZSrtgOKWY3l/RTZHwFgHCcu1GktWpcuroyJk12aLWJNjnR8Z9md0nCwy2owDuAYeF4MfqS15qRlKSwWTq6BEeyGoG9dNxYMHG7cCgJjQLBeUGWlfEU5sSfHMblo21ev22PgojJHbEmfhSejwwBbMMRxzPqFoQpEQPBdnWF5VOfuMEn79u1x5MgR1KtXz2vHdDsTJAiC00jsf//7H63WTnyO4xh0QRbcMmrLXEz1+40vyRPshQabrfPR2P5Imy0cbhvFFeyzvkxD9vo03CrSgIFDj4GLqsQ3Won0rZ3jAH2QOEEeYP3DCvUK6rxdMbRYiKzIxPDOipgFKEeKSZkhKQCSJr50JXiVlpVgqhmPmV02ismBkdnCg4O4Bp046zNDsN52fgITa4SKijXYsi4dZguPYjMvj8oCIGdHKspjTy4Bx4mzWQvM9aDeXc6KlZ0FoJ4s2ArYfq7kY3mhw+W7jS+V+xhVEY0OA8aMGYMJEybgnXfewb59+/DLL7+obp5wOwjq1q0bFi5cKN/nOA43b97ElClT8Pjjj3vUCEJcJZahcNDyDDdvB5UZCEm/84K1K0zDCygq5nHbqLEtmqkVg3rbXCoCtm9I89mFqbJ0f3qxNViwyN/8xUwJrP+2ZXfs63c42N579QzR1pmcFTVG0pBpqdhZGpUlvUZpf4zlAAicPGrJ2eegGhVmrfkRR4eJgZPZzMmZqGITD5NZ/Mxv3tbCaBIDoGC9Rc5wCQJX4XNCcZy4YKvGuqq8t7tet30x1iHgAcTuK+WoP3drkbauG+sQ/EjZth4DF4ED8+hcKiL4+Z4CrIA2aNAgnDt3DmlpaejYsSPatGmDtm3byv/3hNvdYQsWLECXLl3QrFkzFBUVYciQIfj9998RFRWFzz77zKNGEOKqR61DZbO+TENhkUb+45v1ZZp8Ebe/mEnZCAbxIlxk1CCyRjG0WgFaiBdqQQ4AxOdIF2ll7UNVoCxEVnYVKecAUj4uLi5qW+ZB6jaSsmpyNxUTR+zxGgYL04jZJF7dxSZNcglroCMPE4e16NP6eiYLLy+cK2eEFEXXtsCIA68Y8QaIAZjJbPtuV3DL9idOCsp4ntm6BGGti/GwO6i8NKr6KO+2QawB8346s8R6Lo7h27XpeHzQInmUmCd8ORz+0QAeak/dYeISGt7mdhAUFxeHo0eP4rPPPsPhw4chCAJGjBiBoUOHqgqlCfGl7k8vxtZ1Y1Fk0uCbNeOg03LyBTLryzTbjMWwdfkwxuHy1WBERxqh1Qpy9QInXYVh6/phVXSEC88zWCycNbhRZIHAwX4ZDWkf2/tk7Qqzvj8Cs5bmWB8TZ2kGDNYVyRnjYBHEyfSUNSdSYCWP7GKK7I/1oi1OdKieidphyD0vXuSV64BpNQwWQZzF+JZRK3dzOZsoUAr8BIHzSjeOux57cok8cq9r/8Vyl6y3FgUuqzvXk+A+68s0JA1wfF7SgEXIXp8GxsRh+J7UGNFcQKWrzqPDJN6sBZJ4NE+QwWDA8OHDMXz4cG+3hxCX8TwT5w1itgtij4G2P8awXthvG7UwFvMI0vDgOQatXf2HlCEAANgFQIxx2PbF2IBcPsMZQbCNmNLyAgSel4MAVfeXtehYXMdL0RUGaR9mDaI4gIe8XVlgLAUnPG8bNm0fbEjda2azGCxJtUli0Oq45Id9ATPHifVeBYVB1ueLXWLSkG1bN53482FbUFQMtKRlIyqr/svXiwHzPHOazfQ0u1nS+5T1ZRosAg+zwIMxIMijKwspDWWCRH/88QcWLlyIkydPguM43HvvvRg7diwaNWrk0fHcrgkCgI8//hgPPfQQ4uLicOHCBQBiN9nXX3/tUSMI8UT3pxdb6xvE+9JILmnRTLNZrCsRBLE+5OZtLWrfUSw/39nSDtJF1GIRZx+W/mgohzcHsqQBi2xLZCiGn0sjugB1IARIdTycqo5EyrIBUmBlyxQp64kYEycqLLYGOcqlIWxz/diyNPJaZnZD99X7q9vH82KRu8Xa3aYPEnDzlm3eG51WkG8ajskr08tdgGCVFuR2f3qxPBJJ6m4sq87NVcppBLyltIsob52CQdm9XBpv1ufs+CrVa8ci/mvbtm1o1qwZfvrpJ7Rq1QotWrTAgQMH0Lx5c2RnZ3t0TLd/Rd577z2MHz8ePXv2RH5+vjxSLDIyUlUwTUhFsb+ASRfQ64VBKCzSIkgrIFhngSDAae2HuLQCD7OFh8nEwyKIvxaqoeRViHK+H631QqmxW4NL/D+nqimx7yJUjthSPi4VW5stHIqKxekIzGZONT+R/b4ObZQDKVstkj1b8CQGNmLhuy3IMRZz4rnJ9USKeYasowalpTn8hUXgPeqKdRakS91LHOe9IN5ZBmnrurHgefH3MDTYDA3P5BotbygpWHI18KkqxdDSl5Hy3gLZq6++inHjxuHAgQOYP38+FixYgAMHDiA9PR2vvPKKR8d0+yd1yZIlWLFiBV577TVotbacZ7t27XDs2DGPGkFIeSn/OEsZCJOZw+0iHrogC4wmDW7etmUHlPVAxSYNTGYexSZezogoMxbS8arKBGripJNSFxiDlhcUI8KYXIwM2LJCUqbH/g8pz9neKSlQEWdv1qCoWCNPXKjV2t5T5RB4QVAfj1lHevHWVeVLYh8UaTQMhmALgrS2bJRex+TZr7UacdSfVGekHGrvT0oaDecpW5bJOxkmZ8GURsPk15GCzuCgsie0c7VIuaT9uvR7x+m/7fnXJ+w5GiIPnDx5EiNGjHDYPnz4cJw4ccKjY7odBJ07d87pUDS9Xo/CwkKPGkGIN0mZIV2QVMDLwWS2ZQ0k0kXfZOZRVCzOIyMFQNIoI4sPRtf4A+k9UnZfAeq5eQB1l5hE+cfU2ezRAhOXrjDoxdmQtbzYDSUvxyF3dTn+UbbvolQeU5mVUnVhCuJEjNLnZbZwCA22wKCzWOcvgjwdgkbj/Pj+IkgreNSm0oqpvVV3tHXdWDmTJ/lu40uq9hab1GuV+QNlwbW7WaGqkkWqKmrXro2jR486bD969CjuvPNOj47pdvlagwYNcPToUYcq7S1btqBZs2YeNYIQb5Mm/RM4wFiswfWbWkTXEuuBpNFM0sXQZK3/MZs5hASLXTZGE2+tG5FqVQDGGLZvSFN8q6r4uWW8qcfARdi6bizAlTw6StnlBDh+K7fvkpC6t6Tia1twxeQCdGX9kfQc+8yb8v/KtjjLlDAG6HUWFJt56+fCW4uxxSyFIAC8xvqZA+B5W5aLsdIDiIrE86zcbSlpdBnHibVyW9eN9XgW9GIzjyAtU70GY5xqdmVpCoLKsuOr1FKzQq5mn77f+BIefXKJXw2pp9FhwKhRo/D888/j7Nmz6NChAziOw549e/D2229jwoQJHh3T7SDo5ZdfxosvvoiioiIwxvDTTz/hs88+w8yZM/H+++971AhCfKWwSOwC0wUJCA02y9ttI5jEBUX1OgGCdWSLdJEVBGtwZGHyc+2zF4E+ckwcVWe7X1pXjHRxkS4Q0rbvNr6kytKIE1EKcvG1FOS4kuFwVsgrjQhjyn8rmqmx1gPptAIEgYPFwhAUxORuNQ0vLqUhzWVkUUz46E81Et4IxiwWcTSjfUAlBad8WSvJluC7jS+B57TysbLXp8lD+5UCZZkZ5c+wM/4U/EhodBjwxhtvoEaNGpg3bx4mT54MQJy2JyMjA2lpnnX3uh0EPffcczCbzZg0aRJu3bqFIUOG4K677sKiRYswePBgjxpBCCGEEFIajuMwbtw4jBs3Djdu3ACAci/X5VYQZDab8emnn6JPnz4YNWoUrl69CkEQPO6Lq07K+uZBvEvKGBSbOHDQoNYdJlWWQ5lRkEYvBWkZTNZ1qKS1tSyC1K1jKxi2P04gZ4OkSSeVlBkEqRhcWVdh/3OsHL3FcUCowSIvk2GxKJe/sO6jGIovPw9M7pZTdpfZD6MXADDrArqCNbMTpBUXItUFCTAW85CWPwHEbJ5WI0DDQ84GKdvtbzVB3mA08fI8SYC12NxasOxJjZvYBcypRgF2f9o/f95L6wpTCsS/xZQJUvPWWqVuFUZrtVq88MILMBqNAICoqCgKgFwUiL90gcwi8Lh5W4PomiYUmzlVV5hEurCGGczy/ECAbdkFrYYhWCcgRG9WPMc2VFW5zT6QCCTSYrFJAxY5DIHu2n9xmStqS2uFdX96sXXkma0YVzkSTL6vnCtInhVIMU+R3eg8QDm6RSp0F6czMFqH4AOKofIc5FmxNYpRYIzZ5jNSnl9F8nWhLc8z6LQCis28HMB27b9Yfo9KmjeopJFj2evT5DmzAOcLspKKwcCstXXluAV4VdCff/6JYcOGIS4uDlqtFhqNRnXzhNvdYb5Yyr66oGxQxRH/cHMIM5hwJT9I9RgDrBkB8Q+CXicAsAU6umBAqxFUBZ6McXJWg1csLSEer3KWXfCm8mSy7AOnYJ0FDFCNJBIYwDHHYmees80K7Szwsd/fYhFnfeZ5cQoEcQFUcRszcwjSikGrwDjr8Hxb1kl97MpJAfn691/K7JlMHJjeVigtB0EeBDFSJk8qei6rdqmy19v7fuNL4DjmclaIBI6UlBTk5OTgjTfeQGxsLDgvpHLdDoKkpez/97//ISEhAaGhoarHW7VqVe5GVVUUAFUck0WcbwYA4qLEzKVjV5a4A6+YRVgu6uVscwlJBbnKi7L0b6lLhoi2rEtHz4FLSlwR3NmyFwLjVUGkKjCSusEEdfclIA4nlwrbg/VmOeOjZeKSGxpF942zboCKzgJVlB4DF+HbtekwW3h5hfruTy92mu3ZviENXfsvdhq0SIsSMyaODAsOsrjUnVLZCw57++9sWSPOKgqNDgP27NmDH374AW3atPHaMd0OggYNGgQAqkpsjhP/6HAcJ88gTUhlyV6fhiKjHsE6AbeMWhh0tp9JKRBikCbmE/8sBGmFMv9AiAut2rp5wNQZoUCuDfKWngMXAlB0f8kZCKk2xX54u7obTKIMeMSRYeLFWMwEWWCx2Op5LBZp0kXb56ucJVraLn3WAuPk4KCqkbpltRoGs0Wc/VwKdMSsmXoYfUmBoBQAAZDf2+rKHwIgAIAXaoICPQqKj48H8/IPo0eTJdrfzp49K/+flI0m4PKd7PVp4jBpATAEW2Ay8/KK5U6XXlD8nzGxOFpag0p+zEkmQXnhdmWdpOrGWVcWp8jwSPedDclXBj/KzBtjYobPtvSG+LhF4OSMkCCvc+Y435BtNfqq21UiDVHv/vRi8ByTJwn9ztpF5E7vAWPWSSqttVWCB4Xk274I3Fo5f0MzRgMLFy7Eq6++ivPnz3vtmG4HQfXq1Sv15g/effddNGjQAMHBwUhISMAPP/xQ2U0iFYQxcc2q20ZxdfQaBpOTfWyFzVKxrMV6ceXAVN1htq4w2wR7qgVCXZz/prqRCpMB28zc0gSWysVRy6rNsQUukJ8jve/ifevrCTyKTRrcLtJCwwuQ1jVTBkDSXEOPVfFuaSkQ0mhsi8S6Int9mnyT5n2SMmZiZskWUJVky7p0VeBT3TOjVYE/XU8HDRqEnTt3olGjRqhRowZq1qypunnC7e6wb775xul2juMQHByMxo0bo0GDBh41xhvWrl2L9PR0vPvuu+jYsSOWLVuGnj174sSJE6hbt26ltUuJaoN8Q0rh/12gQ6hBnBzRNimerYvFWZGs2cKLF2jOcQRNyc+DPCGguF+Af83yki3r0gFw8ntjn9VxNWhUBZ/WEWM6rSBn96Qh9QWFQQgVzCgq1oiTXlqL10s7XlUnzgztvA4IKHlmaeVnxZg4UlJazFZZYyURa8AWOn2NbV+MBWMVP4Gitweg7Pw6FZ37+kHmsBKKgvzteuqLRdrdDoL69esn1wApKeuCHnroIXz11VeIjIz0WkNdNX/+fIwYMQIjR44EIL5p27Ztw3vvvYeZM2dWeHtIxRD/4IorlRcU8qgfWyR/++c4sa5BGRBJVMW2HBSjidSDSZ3WstgV+BKRhhcLlqXZqKUCciXVvEF2M0ADjiPupBFKJmstkBbSKvbiKDBpigM9xM8aGunYTHXRrqrF0CXhOEUdnBsBoPTzblH83uiC1CMmpd+5b9emg+fEGiSNxpb9EZ9X8UGnt79k+kUAhMopjPa362lycrJL+82aNQujR4/GHXfcUea+bneHZWdn4/7770d2djauX7+O69evIzs7Gw888AA2bdqE3bt349q1a5g4caK7hy634uJiHDp0CN27d1dt7969O/bu3ev0OUajEQUFBaobCUwMHAqLtDDolYWx4mPq4e62i670b2k4tdR1ozquXRbIvuhXydkq29WBmP0RcRzkYewaDbMLFtX/LwsDJwdQxWbeVgxtDXgtFg5aLUOxiYcgADcKNaqV6ZX1XFW1DsgZKfvC8wxms7rbkeOcL9GhzNYBkIvPxa5h25pgUnei9HujC7IVmSuzQs5+lwLVzq9Ty94pgNhf86S5/5Q8uZ76ixkzZuDvv/92aV+3M0Fjx47F8uXL0aFDB3nbY489huDgYDz//PP49ddfsXDhQgwfPtzdQ5fb1atXYbFYEB0drdoeHR2NvLw8p8+ZOXMmpk6dWhHNIz5EtQeVS3nx85cFSYl79U/uZsnK6uaqSj8H/pAN8uaM0fHx8artU6ZMQUZGhmqbJ9dTf+HOCDK3g6A//vgD4eHhDtvDw8Pl0WFNmjTB1atX3T2019hPoCR10zkzefJkjB8/Xr5fUFDg8ANCCCGEVCZvBkEXL15UXcf1en2Jz3HnehqI3O4OS0hIwMsvv4y//vpL3vbXX39h0qRJuP/++wEAv//+O+rUqeO9VrooKioKGo3GIUq9cuWKQzQr0ev1CA8PV90IIYSQqsr+mucsCPLkehqI3A6CPvjgA5w7dw516tRB48aN0aRJE9SpUwfnz5/H+++/DwC4efMm3njjDa83tiw6nQ4JCQnIzs5Wbc/OzlZ13xFCCCGBhHnp5qrqcj11uzvsnnvuwcmTJ7Ft2zacPn0ajDE0bdoU3bp1A28dW9yvXz9vt9Nl48ePx7Bhw9CuXTskJiZi+fLlyMnJwejRoyutTYQQQkh5SIuglvcY7qgO11O3gyBA7CPs0aMHOnfuDL1e71f9g4MGDcK1a9cwbdo05ObmokWLFvj222/9ZiJHQgghJBAE6vX04YcfhsFgcGlfjrkZGgqCgMzMTCxduhR//vknTp8+jYYNG+KNN95A/fr1MWLECI8a7S8KCgoQERGB69evU30QIYSQUvn6miEdf9y49FILmF1hNBqxYMHCgL6+WSwWbNy4ESdPngTHcWjatCn69esHrdajnI77NUHTp0/HRx99hNmzZ0On08nbW7ZsKdcEEUIIIcR7aO0w4Pjx47j77ruRnJyMjRs3YsOGDUhJSUGTJk1w7Ngxj47pdhC0evVqLF++HEOHDoVGo5G3t2rVCr/99ptHjSCEEEJIaSq6NNr/jBw5Es2bN8f//vc/HD58GIcPH8bFixfRqlUrPP/88x4d0+380aVLl9C4cWOH7YIgwGRyXKySEEIIIaS8/vvf/+LgwYOqJbkiIyORmZkpT9HjLrczQc2bN3e6iuwXX3yBtm3betQIQgghhJSMusPE0el//vmnw/YrV644Tc64wu1M0JQpUzBs2DBcunQJgiBgw4YNOHXqFFavXo1NmzZ51AhCCCGElKwyhsj7mxkzZiAtLQ0ZGRl48MEHAQD79+/HtGnT8Pbbb6vW/nS18NvtIKhPnz5Yu3YtZsyYAY7j8Oabb+K+++7D//3f/6Fbt27uHo4QQgghpEy9e/cGAAwcOFCemkcK7Pr06SPf5zgOFovFpWN6NKYsKSkJSUlJnjyVEEIIIW7y5tphgWrHjh1eP6ZnA+sJIYQQUmG8MbYrwGMgdOrUyevHdCkIioyMdHlW6L///rtcDSKEEEIIcaaoqAi//PILrly5AkEQVI898cQTbh/PpSBo4cKF8r+vXbuG6dOnIykpCYmJiQCAffv2Ydu2bZWyaCohhBBS1VFhNLB161Y8++yzuHr1qsNj7tQBKbkUBCUnJ8v/fuqppzBt2jSkpqbK29LS0vDOO+9g+/btGDdunNuNIIQQQkgpqD8MqampGDBgAN58801ER0d75ZhuzxO0bds29OjRw2F7UlIStm/f7pVGEUIIIYQoXblyBePHj/daAAR4EATVqlULGzdudNj+1VdfoVatWl5pFCGEEEJsaNEM4Omnn8bOnTu9eky3R4dNnToVI0aMwM6dO+WaoP3792Pr1q20gCohhBDiA1QTBLzzzjsYMGAAfvjhB7Rs2RJBQUGqx9PS0tw+pttBUEpKCu69914sXrwYGzZsAGMMzZo1w48//oj27du73QBCCCGElI7mCQLWrFmDbdu2wWAwYOfOnapR6xzHVUwQBADt27fHp59+6slTCSGEEELc9vrrr2PatGl49dVXwfNuV/M45dJRlOtxuOLGjRseNYYQQgghjmgBVaC4uBiDBg3yWgAEuBgERUZG4sqVKy4f9K677sLZs2c9bhQhhBBClFi5/wv00ujk5GSsXbvWq8d0qTuMMYb3338fYWFhLh3UZDKVq1GEEEIIIUoWiwWzZ8/Gtm3b0KpVK4fC6Pnz57t9TJeCoLp162LFihUuHzQmJsahcYQQQgjxDBVGA8eOHUPbtm0BAMePH1c95urSXvZcCoLOnz/v0cEJIYQQ4iUBHsSUly9WkfdedREhhBBCiI+dOXMG27Ztw+3btwGUb/4jCoIIIYQQP0czRosLuD/22GO4++678fjjjyM3NxcAMHLkSEyYMMGjY1IQRAghhPg5acbo8t4C2bhx4xAUFIScnByEhITI2wcNGoStW7d6dEyPJkskhBBCCKlIWVlZ2LZtG+rUqaPa3qRJE1y4cMGjY1IQRAghhPg5Gh0GFBYWqjJAkqtXr0Kv13t0TI+6w3744Qf861//QmJiIi5dugQA+Pjjj7Fnzx6PGkEIIYSQktGM0cAjjzyC1atXy/c5joMgCJgzZw66dOni0THdDoLWr1+PpKQkGAwGHDlyBEajEYC4VMaMGTM8agQhhBBCSkaF0cCcOXOwbNky9OzZE8XFxZg0aRJatGiB3bt34+233/bomG4HQdOnT8fSpUuxYsUK1YSIHTp0wOHDhz1qBCGEEEJIacLCwnD06FE88MAD6NatGwoLC9G/f38cOXLE4wma3a4JOnXqFB555BGH7eHh4fjnn388agQhhBBCSuaN0V2BPjqsQYMGyM3NxdSpU1Xbr127hjp16sBisbh9TLczQbGxsThz5ozD9j179qBhw4ZuN8AV58+fx4gRI9CgQQMYDAY0atQIU6ZMQXFxsWq/nJwc9OnTB6GhoYiKikJaWprDPoQQQkigoZqgkoO4mzdvIjg42KNjup0J+ve//42xY8fiww8/BMdxuHz5Mvbt24eJEyfizTff9KgRZfntt98gCAKWLVuGxo0b4/jx4xg1ahQKCwsxd+5cAOLCar169ULt2rWxZ88eXLt2DcnJyWCMYcmSJT5pFyGEEEJ8a/z48QDEQug333xTNULMYrHgwIEDaNOmjUfHdjsImjRpEq5fv44uXbqgqKgIjzzyCPR6PSZOnIjU1FSPGlGWHj16oEePHvL9hg0b4tSpU3jvvffkICgrKwsnTpzAxYsXERcXBwCYN28eUlJSkJmZifDwcJ+0jRBCCCG+c+TIEQBiJujYsWPQ6XTyYzqdDq1bt8bEiRM9OrZH8wRlZmbitddew4kTJyAIApo1a4awsDCPGuCp69evo2bNmvL9ffv2oUWLFnIABABJSUkwGo04dOhQicPnjEajPMINAAoKCnzXaEIIIcQD1XmeIGnh1Oeeew6LFi3yalLD48kSQ0JC0K5dO681xB1//PEHlixZgnnz5snb8vLyEB0drdovMjISOp0OeXl5JR5r5syZDkVWhBBCCPEvK1eu9PoxXQqC+vfv7/IBN2zY4PK+GRkZZQYgP//8syrYunz5Mnr06IEBAwZg5MiRqn05jnN4PmPM6XbJ5MmT5f5GQMwExcfHu3oKhBBCiM9V50yQL7kUBEVERMj/Zoxh48aNiIiIkIOTQ4cO4Z9//nErWAKA1NRUDB48uNR96tevL//78uXL6NKlCxITE7F8+XLVfjExMThw4IBqW35+Pkwmk0OGSEmv13s83TYhhBBSEWiIvG+4FAQpU1CvvPIKBg4ciKVLl0Kj0QAQq7PHjBnjdj9dVFQUoqKiXNr30qVL6NKlCxISErBy5UrwvHp0f2JiIjIzM5Gbm4vY2FgAYrG0Xq9HQkKCW+0ihBBCSNXn9jxBH374ISZOnCgHQACg0Wgwfvx4fPjhh15tnOTy5cvo3Lkz4uPjMXfuXPz111/Iy8tT1fp0794dzZo1w7Bhw3DkyBF89913mDhxIkaNGkUjwwghhAQ0WjbDN9wujDabzTh58iTuuece1faTJ09CEASvNUwpKysLZ86cwZkzZ1CnTh3VY1J6T6PRYPPmzRgzZgw6duwIg8GAIUOGyEPoCSGEkEBFNUG+4XYQ9Nxzz2H48OE4c+YMHnzwQQDA/v37MWvWLDz33HNebyAApKSkICUlpcz96tati02bNvmkDYQQQkhloSDIN9wOgubOnYuYmBgsWLAAubm5AMSlNCZNmoQJEyZ4vYGEEEIIIb7gdhDE8zwmTZqESZMmyRMLUs0NIYQQ4jveqOmhRJAjjydLBCj4IYQQQioE9Yf5hNtBUIMGDUqdfPDs2bPlahAhhBBCSEVwOwhKT09X3TeZTDhy5Ai2bt2Kl19+2VvtIoQQQogVJYJ8w+0gaOzYsU63/+c//8HBgwfL3SBCCCGEqFFNkG+4PVliSXr27In169d763CEEEIIIT5VrsJopS+//BI1a9b01uEIIYQQIvFCdxilghy5HQS1bdtWVRjNGENeXh7++usvvPvuu15tHCGEEEKoJshX3A6C+vbtqwqCeJ5H7dq10blzZzRt2tSrjSOEEEII8RW3g6CMjAwfNIMQQgghJaFMkG+4XRit0Whw5coVh+3Xrl1TrSxPCCGEEO8QR4eV9z9iz+1MECshlDQajdDpdOVuECGEEELUKBPkGy4HQYsXLwYAcByH999/H2FhYfJjFosFu3fvppogQgghhAQMl4OgBQsWABAzQUuXLlV1fel0OtSvXx9Lly71fgsJIYSQao4yQb7hchB07tw5AECXLl2wYcMGREZG+qxRhBBCCLGhGaN9w+3C6B07dlAARAghhBAAwPnz5zFixAg0aNAABoMBjRo1wpQpU1BcXKzaLycnB3369EFoaCiioqKQlpbmsE9FcykTNH78eLz11lsIDQ3F+PHjS913/vz5XmkYIYQQQhT8NJXz22+/QRAELFu2DI0bN8bx48cxatQoFBYWYu7cuQDE2uFevXqhdu3a2LNnD65du4bk5GQwxrBkyZJKa7tLQdCRI0dgMpkAAIcPH1ZNlkgIIYQQ3/LnmqAePXqgR48e8v2GDRvi1KlTeO+99+QgKCsrCydOnMDFixcRFxcHAJg3bx5SUlKQmZmJ8PBw3zSuDC4FQTt27JD/vXPnTl+1hRBCCCE+VlBQoLqv1+uh1+u9+hrXr19XrSe6b98+tGjRQg6AACApKQlGoxGHDh1Cly5dvPr6rnK7Jmj48OG4ceOGw/bCwkIMHz7cK40ihBBCiA3z0g0A4uPjERERId9mzpzp1bb+8ccfWLJkCUaPHi1vy8vLQ3R0tGq/yMhI6HQ65OXlefX13eF2ELRq1Srcvn3bYfvt27exevVqrzSKEEIIITZSd1h5bwBw8eJFXL9+Xb5NnjzZ6WtmZGSA47hSbwcPHlQ95/Lly+jRowcGDBiAkSNHqh5zVkrDGKvUEhuXh8gXFBSAMQbGGG7cuIHg4GD5MYvFgm+//RZ33nmnTxpJCCGEEO8IDw93qQYnNTUVgwcPLnWf+vXry/++fPkyunTpgsTERCxfvly1X0xMDA4cOKDalp+fD5PJ5JAhqkguB0F33HGHHPndfffdDo9zHIepU6d6tXGEEEIIqZzC6KioKERFRbm076VLl9ClSxckJCRg5cqV4Hl1R1NiYiIyMzORm5uL2NhYAGKxtF6vR0JCgnsN8yKXg6AdO3aAMYZHH30U69evVxU86XQ61KtXT1XwRAghhBDvkHpiynsMX7h8+TI6d+6MunXrYu7cufjrr7/kx2JiYgAA3bt3R7NmzTBs2DDMmTMHf//9NyZOnIhRo0ZV2sgwwI0gqFOnTgDEmaPj4+MdojxCCCGE+IY/zxidlZWFM2fO4MyZM6hTp476Na2Bl0ajwebNmzFmzBh07NgRBoMBQ4YMkYfQVxa3V5GvV68eAODWrVvIyclxmO2xVatW3mkZIYQQQvxeSkoKUlJSytyvbt262LRpk+8b5Aa3g6C//voLzz33HLZs2eL0cYvFUu5GEUIIIcTGnydLDGRu92mlp6cjPz8f+/fvh8FgwNatW7Fq1So0adIE33zzjS/aSAghhFRr3hwiT2zczgR9//33+Prrr3H//feD53nUq1cP3bp1Q3h4OGbOnIlevXr5op2EEEIIIV7ldiaosLBQng+oZs2achV4y5YtcfjwYe+2zgmj0Yg2bdqA4zgcPXpU9Zg/rlBLCCGElJc3Z4wmNm4HQffccw9OnToFAGjTpg2WLVuGS5cuYenSpfLYf1+aNGmS06H40gq1hYWF2LNnDz7//HOsX78eEyZM8HmbCCGEEF+i7jDfcLs7LD09Hbm5uQCAKVOmICkpCZ9++il0Oh0++ugjb7dPZcuWLcjKysL69esdCrP9dYVaQgghhPgnt4OgoUOHyv9u27Ytzp8/j99++w1169Z1eWZJT/z5558YNWoUvvrqK4SEhDg87ukKtUajEUajUb5vv7ouIYQQUtlodJhvlHvGw5CQENx3330+DYAYY0hJScHo0aPRrl07p/t4ukLtzJkzVavpxsfHe7XthBBCSHlRTZBvuJQJGj9+vMsHnD9/vsv7ZmRklLne2M8//4y9e/eioKCgxJVuJZ6sUDt58mTV+RUUFFAgRAghhFQDLgVBR44ccelgpQUbzri6Qu306dOxf/9+6PV61WPt2rXD0KFDsWrVKo9XqNXr9Q7HJYQQQvyKNwqbKRXkwKUgaMeOHT55cVdXqF28eDGmT58u3798+TKSkpKwdu1atG/fHoD/rlBLCCGElBfVBPmG24XRlaFu3bqq+2FhYQCARo0ayYu1+esKtYQQQkh5+fMCqoGsyiwFL61QGxwcjI4dO2LgwIHo169fpa9QSwghhBD/FBCZIHv169cHc5LX88cVagkhhJDyou4w3wjIIIgQQgipTigI8o0q0x1GCCGEEOIOygQRQgghfo4yQb5BQRAhhBDi52h0mG9QdxghhBBCqiXKBBFCCCF+jrrDfIOCIEIIIcTPURDkG9QdRgghhJBqiTJBhBBCiJ+jwmjfoCCIEEII8XPUHeYbFAQRQgghfo6CIN+gmiBCCCGEVEuUCSKEEEL8HNUE+QYFQYQQQoifo+4w36DuMEIIIYRUS5QJIoQQQvwcgxcyQV5pSdVCQRAhhBDi56gmyDeoO4wQQggh1RJlggghhBA/R4XRvkFBECGEEOLnGAMECoK8jrrDCCGEEFItUSaIEEII8XPUHeYbFAQRQgghfo5Gh/kGBUGEEEKIn2OMA2NcuY9B1KgmiBBCCCHVEmWCCCGEED9HNUG+QUEQIYQQ4ueoJsg3qDuMEEIIIdUSZYIIIYQQPycwgCtnKqe8ky1WRRQEEUIIIX6OaoJ8I6C6wzZv3oz27dvDYDAgKioK/fv3Vz2ek5ODPn36IDQ0FFFRUUhLS0NxcXEltZYQQggh/ixgMkHr16/HqFGjMGPGDDz66KNgjOHYsWPy4xaLBb169ULt2rWxZ88eXLt2DcnJyWCMYcmSJZXYckIIIaR8qDDaNwIiCDKbzRg7dizmzJmDESNGyNvvuece+d9ZWVk4ceIELl68iLi4OADAvHnzkJKSgszMTISHh1d4uwkhhBBvoJog3wiI7rDDhw/j0qVL4Hkebdu2RWxsLHr27Ilff/1V3mffvn1o0aKFHAABQFJSEoxGIw4dOlTisY1GIwoKClQ3QgghhFR9AREEnT17FgCQkZGB119/HZs2bUJkZCQ6deqEv//+GwCQl5eH6Oho1fMiIyOh0+mQl5dX4rFnzpyJiIgI+RYfH++7EyGEEEI8IBVGl/dG1Co1CMrIyADHcaXeDh48CEEQAACvvfYannrqKSQkJGDlypXgOA5ffPGFfDyOc1wXhTHmdLtk8uTJuH79uny7ePGi90+UEEIIKQexJogr543Yq9SaoNTUVAwePLjUferXr48bN24AAJo1ayZv1+v1aNiwIXJycgAAMTExOHDggOq5+fn5MJlMDhkiJb1eD71e7+kpEEIIIT5HNUG+UalBUFRUFKKiosrcLyEhAXq9HqdOncJDDz0EADCZTDh//jzq1asHAEhMTERmZiZyc3MRGxsLQCyW1uv1SEhI8N1JEEIIISQgBcTosPDwcIwePRpTpkxBfHw86tWrhzlz5gAABgwYAADo3r07mjVrhmHDhmHOnDn4+++/MXHiRIwaNYpGhhFCCAloNFmibwREEAQAc+bMgVarxbBhw3D79m20b98e33//PSIjIwEAGo0GmzdvxpgxY9CxY0cYDAYMGTIEc+fOreSWE0IIIeXDWPm7sygIcsQxRm+LUkFBASIiInD9+nXKIBFCCCmVr68Z0vEbPvhv8Nry1a8KZiPO7l9G1zeFgMkEEUIIIdUVzRjtGxQEEUIIIX5O8EIURKPDHAXEZImEEEIIId5GQRAhhBDi5xjjvHLzNaPRiDZt2oDjOBw9elT1WE5ODvr06YPQ0FBERUUhLS0NxcXFPm9Taag7jBBCCPFzgp8coyyTJk1CXFwc/vvf/6q2WywW9OrVC7Vr18aePXtw7do1JCcngzGGJUuWVEDLnKMgiBBCCPFzgVATtGXLFmRlZWH9+vXYsmWL6rGsrCycOHECFy9elBc6nzdvHlJSUpCZmVlpo9WoO4wQQgipRgoKClQ3o9FY7mP++eefGDVqFD7++GOEhIQ4PL5v3z60aNFCDoAAICkpCUajEYcOHSr363uKgiBCCCHEzwnMOzcAiI+PR0REhHybOXNmudrGGENKSgpGjx6Ndu3aOd0nLy/PYR3PyMhI6HQ65OXllev1y4O6wwghhBA/583usIsXL6q6n0paRDwjIwNTp04t9Zg///wz9u7di4KCAkyePLnUfTnOsTCbMeZ0e0WhIIgQQgipRsLDw12qwUlNTcXgwYNL3ad+/fqYPn069u/f7xBMtWvXDkOHDsWqVasQExODAwcOqB7Pz8+HyWRyyBBVJAqCCCGEED8ngANQvoyJ4Obzo6KiEBUVVeZ+ixcvxvTp0+X7ly9fRlJSEtauXYv27dsDABITE5GZmYnc3FzExsYCEIul9Xo9EhIS3GqXN1EQRAghhPg5ASh/d5g3GuJE3bp1VffDwsIAAI0aNUKdOnUAAN27d0ezZs0wbNgwzJkzB3///TcmTpyIUaNGVeo6ZlQYTQghhBCf0mg02Lx5M4KDg9GxY0cMHDgQ/fr1w9y5cyu1XZQJIoQQQvwcY+Wf54dV0Nph9evXB3PyYnXr1sWmTZsqphEuoiCIEEII8XMWlLciiFaRd4a6wwghhBBSLVEmiBBCCPFzFgZwAdIdFkgoCCKEEEL8nJmCIJ+gIIgQQgjxcxZw4MpZFcTKXVVU9VBNECGEEEKqJcoEEUIIIX6OusN8g4IgQgghxN8xLwQxFAQ5oO4wQgghhFRLlAkihBBC/B5D+VM5lAqyR0EQIYQQ4u8oBvIJ6g4jhBBCSLVEmSBCCCHE71EqyBcoCCKEEEL8HWMAE8p/DKJC3WGEEEIIqZYoE0QIIYT4O+aFiYIoE+QgYDJBp0+fRt++fREVFYXw8HB07NgRO3bsUO2Tk5ODPn36IDQ0FFFRUUhLS0NxcXEltZgQQgjxFsFLN6IUMEFQr169YDab8f333+PQoUNo06YNevfujby8PACAxWJBr169UFhYiD179uDzzz/H+vXrMWHChEpuOSGEEFJOTPDOjagERBB09epVnDlzBq+++ipatWqFJk2aYNasWbh16xZ+/fVXAEBWVhZOnDiBTz75BG3btkXXrl0xb948rFixAgUFBZV8BoQQQgjxNwERBNWqVQv33nsvVq9ejcLCQpjNZixbtgzR0dFISEgAAOzbtw8tWrRAXFyc/LykpCQYjUYcOnSoxGMbjUYUFBSoboQQQohfoUyQTwREYTTHccjOzkbfvn1Ro0YN8DyP6OhobN26FXfccQcAIC8vD9HR0arnRUZGQqfTyV1mzsycORNTp071ZfMJIYSQcvJGTQ8FQfYqNROUkZEBjuNKvR08eBCMMYwZMwZ33nknfvjhB/z000/o27cvevfujdzcXPl4HMc5vAZjzOl2yeTJk3H9+nX5dvHiRZ+cKyGEEEL8S6VmglJTUzF48OBS96lfvz6+//57bNq0Cfn5+QgPDwcAvPvuu8jOzsaqVavw6quvIiYmBgcOHFA9Nz8/HyaTySFDpKTX66HX68t/MoQQQoiveKM7i7rDHFRqEBQVFYWoqKgy97t16xYAgOfViSue5yEI4oeamJiIzMxM5ObmIjY2FoBYLK3X6+W6IUIIISQg0TxBPhEQhdGJiYmIjIxEcnIy/vvf/+L06dN4+eWXce7cOfTq1QsA0L17dzRr1gzDhg3DkSNH8N1332HixIkYNWqUnD0ihBBCCJEERBAUFRWFrVu34ubNm3j00UfRrl077NmzB19//TVat24NANBoNNi8eTOCg4PRsWNHDBw4EP369cPcuXMrufWEEEJIedFkib4QEKPDAKBdu3bYtm1bqfvUrVsXmzZtqqAWEUIIIRWEaoJ8IiAyQYQQQggh3hYwmSBCCCGk2mLMC5kgKoy2R0EQIYQQ4vdoskRfoCCIEEII8Xc0RN4nqCaIEEIIIdUSZYIIIYQQf0ejw3yCgiBCCCHE31EQ5BPUHUYIIYSQaokyQYQQQojfY9ZbeY9BlCgIIoQQQvyeF7rDaIi8A+oOI4QQQki1RJkgQgghxN9RYbRPUBBECCGE+DuaLNEnKAiyw6w/JAUFBZXcEkIIIf5OulYwXwcYgtk/jlHFUBBk58aNGwCA+Pj4Sm4JIYSQQHHjxg1ERER4/bg6nQ4xMTHIy8n2yvFiYmKg0+m8cqyqgGM+D18DiyAIuHz5MmrUqAGO4yqlDQUFBYiPj8fFixcRHh5eKW3wFTq3wETnFpiq8rkB/nF+jDHcuHEDcXFx4HnfjDUqKipCcXGxV46l0+kQHBzslWNVBZQJssPzPOrUqVPZzQAAhIeHV8k/XACdW6CicwtMVfncgMo/P19kgJSCg4MpcPERGiJPCCGEkGqJgiBCCCGEVEsUBPkhvV6PKVOmQK/XV3ZTvI7OLTDRuQWmqnxuQNU/P+J7VBhNCCGEkGqJMkGEEEIIqZYoCCKEEEJItURBECGEEEKqJQqCCCGEEFItURBUiTIzM9GhQweEhITgjjvucLoPx3EOt6VLl6r2OXbsGDp16gSDwYC77roL06ZN8/06NmVw5dxycnLQp08fhIaGIioqCmlpaQ6zovrjuTlTv359h8/p1VdfVe3jyvn6q3fffRcNGjRAcHAwEhIS8MMPP1R2k9ySkZHh8PnExMTIjzPGkJGRgbi4OBgMBnTu3Bm//vprJba4dLt370afPn0QFxcHjuPw1VdfqR535XyMRiNeeuklREVFITQ0FE888QT+97//VeBZOFfWuaWkpDh8lg8++KBqH389N+J/KAiqRMXFxRgwYABeeOGFUvdbuXIlcnNz5VtycrL8WEFBAbp164a4uDj8/PPPWLJkCebOnYv58+f7uvmlKuvcLBYLevXqhcLCQuzZsweff/451q9fjwkTJsj7+Ou5lWTatGmqz+n111+XH3PlfP3V2rVrkZ6ejtdeew1HjhzBww8/jJ49eyInJ6eym+aW5s2bqz6fY8eOyY/Nnj0b8+fPxzvvvIOff/4ZMTEx6Natm7yWoL8pLCxE69at8c477zh93JXzSU9Px8aNG/H5559jz549uHnzJnr37g2LxVJRp+FUWecGAD169FB9lt9++63qcX89N+KHGKl0K1euZBEREU4fA8A2btxY4nPfffddFhERwYqKiuRtM2fOZHFxcUwQBC+31H0lndu3337LeJ5nly5dkrd99tlnTK/Xs+vXrzPG/P/clOrVq8cWLFhQ4uOunK+/euCBB9jo0aNV25o2bcpeffXVSmqR+6ZMmcJat27t9DFBEFhMTAybNWuWvK2oqIhFRESwpUuXoyTRFAAAEwRJREFUVlALPWf/N8KV8/nnn39YUFAQ+/zzz+V9Ll26xHieZ1u3bq2wtpfF2d+/5ORk1rdv3xKfEyjnRvwDZYICQGpqKqKionD//fdj6dKlEARBfmzfvn3o1KmTarKwpKQkXL58GefPn6+E1rpm3759aNGiBeLi4uRtSUlJMBqNOHTokLxPIJ3b22+/jVq1aqFNmzbIzMxUdXW5cr7+qLi4GIcOHUL37t1V27t37469e/dWUqs88/vvvyMuLg4NGjTA4MGDcfbsWQDAuXPnkJeXpzpHvV6PTp06Bdw5Aq6dz6FDh2AymVT7xMXFoUWLFgFxzjt37sSdd96Ju+++G6NGjcKVK1fkxwL93EjFogVU/dxbb72Fxx57DAaDAd999x0mTJiAq1evyl0teXl5qF+/vuo50dHR8mMNGjSo6Ca7JC8vT26nJDIyEjqdDnl5efI+gXJuY8eOxX333YfIyEj89NNPmDx5Ms6dO4f3338fgGvn64+uXr0Ki8Xi0Pbo6Gi/bre99u3bY/Xq1bj77rvx559/Yvr06ejQoQN+/fVX+TycneOFCxcqo7nl4sr55OXlQafTITIy0mEff/9ce/bsiQEDBqBevXo4d+4c3njjDTz66KM4dOgQ9Hp9QJ8bqXiUCfIyZwWY9reDBw+6fLzXX38diYmJaNOmDSZMmIBp06Zhzpw5qn04jlPdZ9bCYfvt5eXtc3PWPsaYantFnZsz7pzvuHHj0KlTJ7Rq1QojR47E0qVL8cEHH+DatWslnot0PhVxLuXl7HMIhHZLevbsiaeeegotW7ZE165dsXnzZgDAqlWr5H0C/RzteXI+gXDOgwYNQq9evdCiRQv06dMHW7ZswenTp+XPtCSBcG6k4lEmyMtSU1MxePDgUvexz26448EHH0RBQQH+/PNPREdHIyYmxuHbjZQatv8mWF7ePLeYmBgcOHBAtS0/Px8mk0lud0WemzPlOV9ptMqZM2dQq1Ytl87XH0VFRUGj0Tj9HPy53WUJDQ1Fy5Yt8fvvv6Nfv34AxOxIbGysvE+gnqM06q2084mJiUFxcTHy8/NVGZMrV66gQ4cOFdvgcoqNjUW9evXw+++/A6ha50Z8jzJBXhYVFYWmTZuWegsODvb4+EeOHEFwcLA87DwxMRG7d+9W1Z9kZWUhLi6uXMGWM948t8TERBw/fhy5ubmqduv1eiQkJFT4uTlTnvM9cuQIAMgXIVfO1x/pdDokJCQgOztbtT07OzugLyhGoxEnT55EbGwsGjRogJiYGNU5FhcXY9euXQF5jq6cT0JCAoKCglT75Obm4vjx4wF3zteuXcPFixfl37WqdG6kAlRaSTZhFy5cYEeOHGFTp05lYWFh7MiRI+zIkSPsxo0bjDHGvvnmG7Z8+XJ27NgxdubMGbZixQoWHh7O0tLS5GP8888/LDo6mj3zzDPs2LFjbMOGDSw8PJzNnTu3sk6LMVb2uZnNZtaiRQv22GOPscOHD7Pt27ezOnXqsNTUVPkY/npu9vbu3cvmz5/Pjhw5ws6ePcvWrl3L4uLi2BNPPCHv48r5+qvPP/+cBQUFsQ8++ICdOHGCpaens9DQUHb+/PnKbprLJkyYwHbu3MnOnj3L9u/fz3r37s1q1Kghn8OsWbNYREQE27BhAzt27Bh75plnWGxsLCsoKKjkljt348YN+XcKgPzzd+HCBcaYa+czevRoVqdOHbZ9+3Z2+PBh9uijj7LWrVszs9lcWafFGCv93G7cuMEmTJjA9u7dy86dO8d27NjBEhMT2V133RUQ50b8DwVBlSg5OZkBcLjt2LGDMcbYli1bWJs2bVhYWBgLCQlhLVq0YAsXLmQmk0l1nF9++YU9/PDDTK/Xs5iYGJaRkVHpQ8jLOjfGxECpV69ezGAwsJo1a7LU1FTVcHjG/PPc7B06dIi1b9+eRUREsODgYHbPPfewKVOmsMLCQtV+rpyvv/rPf/7D6tWrx3Q6HbvvvvvYrl27KrtJbhk0aBCLjY1lQUFBLC4ujvXv35/9+uuv8uOCILApU6awmJgYptfr2SOPPMKOHTtWiS0u3Y4dO5z+fiUnJzPGXDuf27dvs9TUVFazZk1mMBhY7969WU5OTiWcjVpp53br1i3WvXt3Vrt2bRYUFMTq1q3LkpOTHdrtr+dG/A/HmB9Ov0sIIYQQ4mNUE0QIIYSQaomCIEIIIYRUSxQEEUIIIaRaoiCIEEIIIdUSBUGEEEIIqZYoCCKEEEJItURBECGEEEKqJQqCSJXTuXNnpKenV6nXTUlJkde48lT9+vXlhV//+eefEvf76KOP5GVZiPelpKTIn8NXX31V2c0hpFqjIIgQL9mwYQPeeust+X79+vWxcOHCymuQE9OmTUNubi4iIiIquylV3s6dO50GnIsWLVKtIUcIqTy0ijwhXlKzZs3KbkKZatSoIa8yXtlMJhOCgoIquxkVLiIigoJQQvwEZYJIlZefn49nn30WkZGRCAkJQc+ePfH777/Lj0vdP9u2bcO9996LsLAw9OjRQ/Vt3Ww2Iy0tDXfccQdq1aqFV155BcnJyaouKmV3WOfOnXHhwgWMGzdO7voAgIyMDLRp00bVvoULF6J+/fryfYvFgvHjx8uvNWnSJNivbsMYw+zZs9GwYUMYDAa0bt0aX375pUfvz0cffYS6desiJCQETz75JK5du+awz//93/8hISEBwcHBaNiwIaZOnQqz2Sw//ttvv+Ghhx5CcHAwmjVrhu3bt6u6e86fPw+O47Bu3Tp07twZwcHB+OSTTwAAK1euxL333ovg4GA0bdoU7777ruq1L126hEGDBiEyMhK1atVC3759cf78efnxnTt34oEHHkBoaCjuuOMOdOzYERcuXHDp3Ms6r/nz56Nly5YIDQ1FfHw8xowZg5s3b8qPX7hwAX369EFkZCRCQ0PRvHlzfPvttzh//jy6dOkCAIiMjATHcUhJSXGpTYSQikNBEKnyUlJScPDgQXzzzTfYt28fGGN4/PHHYTKZ5H1u3bqFuXPn4uOPP8bu3buRk5ODiRMnyo+//fbb+PTTT7Fy5Ur8+OOPKCgoKLWeY8OGDahTp47c/eRO98e8efPw4Ycf4oMPPsCePXvw999/Y+PGjap9Xn/9daxcuRLvvfcefv31V4wbNw7/+te/sGvXLtffGAAHDhzA8OHDMWbMGBw9ehRdunTB9OnTVfts27YN//rXv5CWloYTJ05g2bJl+Oijj5CZmQkAEAQB/fr1Q0hICA4cOIDly5fjtddec/p6r7zyCtLS0nDy5EkkJSVhxYoVeO2115CZmYmTJ09ixowZeOONN7Bq1SoA4ufSpUsXhIWFYffu3dizZ48cpBYXF8NsNqNfv37o1KkTfvnlF+zbtw/PP/+8HHSWpqzzAgCe57F48WIcP34cq1atwvfff49JkybJj7/44oswGo3YvXs3jh07hrfffhthYWGIj4/H+vXrAQCnTp1Cbm4uFi1a5NZnQwipAJW6fCshPtCpUyc2duxYxhhjp0+fZgDYjz/+KD9+9epVZjAY2Lp16xhjjK1cuZIBYGfOnJH3+c9//sOio6Pl+9HR0WzOnDnyfbPZzOrWrcv69u3r9HUZY6xevXpswYIFqrZNmTKFtW7dWrVtwYIFrF69evL92NhYNmvWLPm+yWRiderUkV/r5s2bLDg4mO3du1d1nBEjRrBnnnmmxPfFWXueeeYZ1qNHD9W2QYMGsYiICPn+ww8/zGbMmKHa5+OPP2axsbGMMca2bNnCtFoty83NlR/Pzs5mANjGjRsZY4ydO3eOAWALFy5UHSc+Pp6tWbNGte2tt95iiYmJjDHGPvjgA3bPPfcwQRDkx41GIzMYDGzbtm3s2rVrDADbuXNnieddkrLOy5l169axWrVqyfdbtmzJMjIynO4rrYaen5/v9HHl+0MIqRxUE0SqtJMnT0Kr1aJ9+/bytlq1auGee+7ByZMn5W0hISFo1KiRfD82NhZXrlwBAFy/fh1//vknHnjgAflxjUaDhIQECILg1fZev34dubm5SExMlLdptVq0a9dO7hI7ceIEioqK0K1bN9Vzi4uL0bZtW7de7+TJk3jyySdV2xITE7F161b5/qFDh/Dzzz+rMiQWiwVFRUW4desWTp06hfj4eFWtkfK9UmrXrp3877/++gsXL17EiBEjMGrUKHm72WyWa2YOHTqEM2fOoEaNGqrjFBUV4Y8//kD37t2RkpKCpKQkdOvWDV27dsXAgQMRGxtb5rmXdV4hISHYsWMHZsyYgRMnTqCgoABmsxlFRUUoLCxEaGgo0tLS8MILLyArKwtdu3bFU089hVatWpX52oQQ/0BBEKnSmF0tjXK7ssvEvkCX4ziH59p3sZR07NLwPO/wPGW3nCukwGvz5s246667VI/p9Xq3juXKOQiCgKlTp6J///4OjwUHBzu8l6UJDQ1VHRcAVqxYoQpSATHIlPZJSEjAp59+6nCs2rVrAxBritLS0rB161asXbsWr7/+OrKzs/Hggw+W67wuXLiAxx9/HKNHj8Zbb72FmjVrYs+ePRgxYoT8mY0cORJJSUnYvHkzsrKyMHPmTMybNw8vvfSSS+8HIaRyURBEqrRmzZrBbDbjwIED6NChAwDg2rVrOH36NO69916XjhEREYHo6Gj89NNPePjhhwGIGYMjR444FDkr6XQ6WCwW1bbatWsjLy9PFTgcPXpU9VqxsbHYv38/HnnkEQBiZuTQoUO477775HPS6/XIyclBp06dXDqHkjRr1gz79+9XbbO/f9999+HUqVNo3Lix02M0bdoUOTk5+PPPPxEdHQ0A+Pnnn8t87ejoaNx11104e/Yshg4d6nSf++67D2vXrsWdd96J8PDwEo/Vtm1btG3bFpMnT0ZiYiLWrFlTZhBU1nkdPHgQZrMZ8+bNA8+L5ZPr1q1z2C8+Ph6jR4/G6NGjMXnyZKxYsQIvvfQSdDodADj8DBBC/AcFQaRKa9KkCfr27YtRo0Zh2bJlqFGjBl599VXcdddd6Nu3r8vHeemllzBz5kw0btwYTZs2xZIlS5Cfn19qBqR+/frYvXs3Bg8eDL1ej6ioKHTu3Bl//fUXZs+ejaeffhpbt27Fli1bVBf4sWPHYtasWWjSpAnuvfdezJ8/XzXXTI0aNTBx4kSMGzcOgiDgoYceQkFBAfbu3YuwsDAkJye7fF5paWno0KEDZs+ejX79+iErK0vVFQYAb775Jnr37o34+HgMGDAAPM/jl19+wbFjxzB9+nR069YNjRo1QnJyMmbPno0bN27IhdFlZYgyMjKQlpaG8PBw9OzZE0ajEQcPHkR+fj7Gjx+PoUOHYs6cOejbty+mTZuGOnXqICcnBxs2bMDLL78Mk8mE5cuX44knnkBcXBxOnTqF06dP49lnny3z3Ms6r0aNGsFsNmPJkiXo06cPfvzxRyxdulR1jPT0dPTs2RN333038vPz8f3338vBdb169cBxHDZt2oTHH38cBoMBYWFhLn82hJAKUGnVSIT4iH2B8t9//82GDRvGIiIimMFgYElJSez06dPy4ytXrlQVAjPG2MaNG5ny18NkMrHU1FQWHh7OIiMj2SuvvMIGDBjABg8eXOLr7tu3j7Vq1Yrp9XrVsd577z0WHx/PQkND2bPPPssyMzNVhdEmk4mNHTuWhYeHszvuuIONHz+ePfvss6oibEEQ2KJFi9g999zDgoKCWO3atVlSUhLbtWtXie+Ls8JoxsTi4zp16jCDwcD69OnD5s6d6/B+bN26lXXo0IEZDAYWHh7OHnjgAbZ8+XL58ZMnT7KOHTsynU7HmjZtyv7v//6PAWBbt25ljNkKo48cOeLw+p9++ilr06YN0+l0LDIykj3yyCNsw4YN8uO5ubns2WefZVFRUUyv17OGDRuyUaNGsevXr7O8vDzWr18/Fhsby3Q6HatXrx578803mcViKfF9cOe85s+fz2JjY+Wfm9WrV6uKnVNTU1mjRo2YXq9ntWvXZsOGDWNXr16Vnz9t2jQWExPDOI5jycnJqtcGFUYTUuk4xjwobCCkmhMEAffeey8GDhyomiXan9WvXx/p6ekVsqTIjz/+iIceeghnzpxRFZwTG47jsHHjxnIvh0II8RzNE0SICy5cuIAVK1bg9OnTOHbsGF544QWcO3cOQ4YMqeymueWVV15BWFgYrl+/7tXjbty4EdnZ2Th//jy2b9+O559/Hh07dqQAyInRo0dTtxghfoIyQYS44OLFixg8eDCOHz8OxhhatGiBWbNmycXLgeDChQvyqKaGDRvKxb7esHr1arz11lu4ePEioqKi0LVrV8ybNw+1atXy2mu4q3nz5iXOHL1s2bISi7F97cqVKygoKAAgTsWgHDFHCKlYFAQRQqokZdBnLzo62mHuIUJI9UNBECGEEEKqJaoJIoQQQki1REEQIYQQQqolCoIIIYQQUi1REEQIIYSQaomCIEIIIYRUSxQEEUIIIaRaoiCIEEIIIdUSBUGEEEIIqZb+Hzo+C1cr7CxRAAAAAElFTkSuQmCC",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lai_array['temp_north'].sel(band='1982-01-31').plot(cmap='cividis')"
]
},
{
"cell_type": "markdown",
"id": "8694578d-656c-4dfa-8744-21e7bafac0a1",
"metadata": {},
"source": [
"## Define growing season (Item 1.1)"
]
},
{
"cell_type": "markdown",
"id": "15c1cef5-c88d-4b73-b7a7-de72b2d88c6b",
"metadata": {},
"source": [
"### Aggregate all north, across all years, using median values, to be robust to outliers"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "6c16b16e-530d-4181-9561-2784c14b0569",
"metadata": {},
"outputs": [],
"source": [
"lai_agg_time_xy = lai_array['lai_north'].groupby(\"band.dayofyear\").median(dim=\"band\").median(dim='x').median(dim='y')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "47c42d7f-3dc0-4c3f-9f71-4a3546f0bfd7",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x2b1a64df3c70>]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lai_agg_time_xy.plot.line()"
]
},
{
"cell_type": "markdown",
"id": "a41887fb-3dfb-473f-9fca-0907a5f2fd94",
"metadata": {},
"source": [
"Plot LAI values"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "2f21fc24-5f0a-4f81-9558-fe1a8ba3d417",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 15.5 , 16.5 , 16.5 , 19. , 20.5 , 30.25, 31.75, 53.5 ,\n",
" 53. , 90.5 , 90. , 113. , 111.5 , 116.5 , 116. , 78. ,\n",
" 78.5 , 46.75, 48.5 , 23. , 23. , 18. , 17.5 ],\n",
" dtype=float32)"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lai_agg_time_xy.values"
]
},
{
"cell_type": "markdown",
"id": "8401bffd-583a-44b0-b138-e50966d0cb3e",
"metadata": {},
"source": [
"Plot DOY (regarding the LAI vector)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "b41f01bf-1f2b-4e4a-9f13-a2a47ab5637a",
"metadata": {},
"outputs": [
{
"data": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;dayofyear&#x27; (dayofyear: 23)&gt;\n",
"array([ 31, 59, 60, 90, 91, 120, 121, 151, 152, 181, 182, 212, 213, 243,\n",
" 244, 273, 274, 304, 305, 334, 335, 365, 366])\n",
"Coordinates:\n",
" spatial_ref int64 0\n",
" * dayofyear (dayofyear) int64 31 59 60 90 91 120 ... 305 334 335 365 366</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'dayofyear'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>dayofyear</span>: 23</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-6525d46c-7921-475e-ac9c-694fdb79291a' class='xr-array-in' type='checkbox' checked><label for='section-6525d46c-7921-475e-ac9c-694fdb79291a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>31 59 60 90 91 120 121 151 152 ... 244 273 274 304 305 334 335 365 366</span></div><div class='xr-array-data'><pre>array([ 31, 59, 60, 90, 91, 120, 121, 151, 152, 181, 182, 212, 213, 243,\n",
" 244, 273, 274, 304, 305, 334, 335, 365, 366])</pre></div></div></li><li class='xr-section-item'><input id='section-79f2452f-5623-4890-a85d-23392ac14b98' class='xr-section-summary-in' type='checkbox' checked><label for='section-79f2452f-5623-4890-a85d-23392ac14b98' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-0668e86c-8ac0-4fb0-b363-60b3346f98b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0668e86c-8ac0-4fb0-b363-60b3346f98b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3b9bde2-7802-411c-9aee-928fb452290e' class='xr-var-data-in' type='checkbox'><label for='data-c3b9bde2-7802-411c-9aee-928fb452290e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.0 0.5 -0.0 90.0 -0.0 -0.5</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dayofyear</span></div><div class='xr-var-dims'>(dayofyear)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>31 59 60 90 91 ... 334 335 365 366</div><input id='attrs-c3db1476-326c-4ae0-914f-9bee0e79488c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3db1476-326c-4ae0-914f-9bee0e79488c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e510835-b575-4672-81bb-a75ee41ff424' class='xr-var-data-in' type='checkbox'><label for='data-6e510835-b575-4672-81bb-a75ee41ff424' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 31, 59, 60, 90, 91, 120, 121, 151, 152, 181, 182, 212, 213, 243,\n",
" 244, 273, 274, 304, 305, 334, 335, 365, 366])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f9793fbf-f535-4aec-9fa7-1c6d06a52c98' class='xr-section-summary-in' type='checkbox' ><label for='section-f9793fbf-f535-4aec-9fa7-1c6d06a52c98' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>dayofyear</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-46967338-f08a-4cc5-bcb9-30724526fa99' class='xr-index-data-in' type='checkbox'/><label for='index-46967338-f08a-4cc5-bcb9-30724526fa99' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([ 31, 59, 60, 90, 91, 120, 121, 151, 152, 181, 182, 212, 213,\n",
" 243, 244, 273, 274, 304, 305, 334, 335, 365, 366],\n",
" dtype=&#x27;int64&#x27;, name=&#x27;dayofyear&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b950e130-d201-4486-baaa-8b607475e0bb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b950e130-d201-4486-baaa-8b607475e0bb' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'dayofyear' (dayofyear: 23)>\n",
"array([ 31, 59, 60, 90, 91, 120, 121, 151, 152, 181, 182, 212, 213, 243,\n",
" 244, 273, 274, 304, 305, 334, 335, 365, 366])\n",
"Coordinates:\n",
" spatial_ref int64 0\n",
" * dayofyear (dayofyear) int64 31 59 60 90 91 120 ... 305 334 335 365 366"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lai_agg_time_xy.dayofyear"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "868923c7-a33f-45b3-8c4d-99f25f653bd9",
"metadata": {},
"outputs": [],
"source": [
"del lai_agg_time_xy"
]
},
{
"cell_type": "markdown",
"id": "26e9b18b-f739-4690-8484-22f7b07eaa51",
"metadata": {},
"source": [
"Interpreting the curve, it is possible to define that the **growing season** (in DOY, for all north) is where the **LAI is greater than 30** and:\n",
"\n",
"- **Starts** in DOY = 90\n",
"- **Ends** in DOY = 334\n",
"\n",
"We will use these parameters to threshold all datasets."
]
},
{
"cell_type": "markdown",
"id": "abb6c20d-b472-4842-9ed5-e2aa9ffc9af5",
"metadata": {},
"source": [
"### Create mask for growing season on LAI"
]
},
{
"cell_type": "markdown",
"id": "de63297d-86a8-4351-b467-cc2214b6da11",
"metadata": {},
"source": [
"Only retain values above 30, in all data (lat,lon,band)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "a328c687-934b-4ee8-8569-e755bdfd9d93",
"metadata": {},
"outputs": [],
"source": [
"lai_array['lai_north_masked_30'] = lai_array['lai_north'].where(lai_array['lai_north'] > 30)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "3e7f90cf-16a2-434e-a464-37b84567a292",
"metadata": {},
"outputs": [],
"source": [
"# dump_to_disk()\n",
"# refresh()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "486977da-a989-4177-aee1-099c9b43bd35",
"metadata": {},
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".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (band: 408, x: 720, y: 360)\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 0\n",
"Data variables:\n",
" lai (band, y, x) float32 dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;\n",
" temp (band, y, x) float32 dask.array&lt;chunksize=(408, 360, 720), meta=np.ndarray&gt;\n",
" lai_north (band, y, x) float32 dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;\n",
" temp_north (band, y, x) float32 dask.array&lt;chunksize=(408, 360, 720), meta=np.ndarray&gt;\n",
" lai_north_masked_30 (band, y, x) float32 dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f5cec3c4-1455-4a25-8b08-d64980513e0a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f5cec3c4-1455-4a25-8b08-d64980513e0a' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 408</li><li><span class='xr-has-index'>x</span>: 720</li><li><span class='xr-has-index'>y</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-61ee9238-7f9f-4c55-8ca5-8a0b8f4f0900' class='xr-section-summary-in' type='checkbox' checked><label for='section-61ee9238-7f9f-4c55-8ca5-8a0b8f4f0900' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1982-01-31 ... 2015-12-31</div><input id='attrs-a3e3f9a1-ccf7-4943-9873-ba47e84ea272' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a3e3f9a1-ccf7-4943-9873-ba47e84ea272' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c5edb64-4bc9-4e7e-b9f0-e6d0d41ae565' class='xr-var-data-in' type='checkbox'><label for='data-2c5edb64-4bc9-4e7e-b9f0-e6d0d41ae565' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1982-01-31T00:00:00.000000000&#x27;, &#x27;1982-02-28T00:00:00.000000000&#x27;,\n",
" &#x27;1982-03-31T00:00:00.000000000&#x27;, ..., &#x27;2015-10-31T00:00:00.000000000&#x27;,\n",
" &#x27;2015-11-30T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-022b04f5-8741-4fa3-ad68-96f18cd7e61a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-022b04f5-8741-4fa3-ad68-96f18cd7e61a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ffa3658-36c9-4fcf-a24d-a7b78afc586b' class='xr-var-data-in' type='checkbox'><label for='data-9ffa3658-36c9-4fcf-a24d-a7b78afc586b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ..., 178.75, 179.25, 179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.75 89.25 88.75 ... -89.25 -89.75</div><input id='attrs-037223df-b078-49d0-8642-f3d4aaa7cf10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-037223df-b078-49d0-8642-f3d4aaa7cf10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92349d9e-71e8-46c0-a4af-8131f383a3d1' class='xr-var-data-in' type='checkbox'><label for='data-92349d9e-71e8-46c0-a4af-8131f383a3d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 89.75, 89.25, 88.75, ..., -88.75, -89.25, -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-66b4efb0-9e54-405e-b2e8-418af0ec5e53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66b4efb0-9e54-405e-b2e8-418af0ec5e53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19c7de89-9541-4365-8d1b-09b3119d1150' class='xr-var-data-in' type='checkbox'><label for='data-19c7de89-9541-4365-8d1b-09b3119d1150' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.0 0.5 -0.0 90.0 -0.0 -0.5</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-36b0e295-45e6-4087-884e-774c9d6bfe90' class='xr-section-summary-in' type='checkbox' checked><label for='section-36b0e295-45e6-4087-884e-774c9d6bfe90' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lai</span></div><div class='xr-var-dims'>(band, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;</div><input id='attrs-0c739c25-c778-4cac-92bb-b35294dea378' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c739c25-c778-4cac-92bb-b35294dea378' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a88fd52-2882-40f9-9f25-c37d87232f74' class='xr-var-data-in' type='checkbox'><label for='data-6a88fd52-2882-40f9-9f25-c37d87232f74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>540</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>87.72932166302</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>0</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>134.37581892842</dd><dt><span>bands :</span></dt><dd>408</dd><dt><span>band_names :</span></dt><dd>Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band 7,Band 8,Band 9,Band 10,Band 11,Band 12,Band 13,Band 14,Band 15,Band 16,Band 17,Band 18,Band 19,Band 20,Band 21,Band 22,Band 23,Band 24,Band 25,Band 26,Band 27,Band 28,Band 29,Band 30,Band 31,Band 32,Band 33,Band 34,Band 35,Band 36,Band 37,Band 38,Band 39,Band 40,Band 41,Band 42,Band 43,Band 44,Band 45,Band 46,Band 47,Band 48,Band 49,Band 50,Band 51,Band 52,Band 53,Band 54,Band 55,Band 56,Band 57,Band 58,Band 59,Band 60,Band 61,Band 62,Band 63,Band 64,Band 65,Band 66,Band 67,Band 68,Band 69,Band 70,Band 71,Band 72,Band 73,Band 74,Band 75,Band 76,Band 77,Band 78,Band 79,Band 80,Band 81,Band 82,Band 83,Band 84,Band 85,Band 86,Band 87,Band 88,Band 89,Band 90,Band 91,Band 92,Band 93,Band 94,Band 95,Band 96,Band 97,Band 98,Band 99,Band 100,Band 101,Band 102,Band 103,Band 104,Band 105,Band 106,Band 107,Band 108,Band 109,Band 110,Band 111,Band 112,Band 113,Band 114,Band 115,Band 116,Band 117,Band 118,Band 119,Band 120,Band 121,Band 122,Band 123,Band 124,Band 125,Band 126,Band 127,Band 128,Band 129,Band 130,Band 131,Band 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"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lai_north_masked_30</span></div><div class='xr-var-dims'>(band, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;</div><input id='attrs-aae1a4cb-3d9e-4672-89f3-d5d6ad079014' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aae1a4cb-3d9e-4672-89f3-d5d6ad079014' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f22ed8d-5592-435a-a87d-109ad87fcf7c' class='xr-var-data-in' type='checkbox'><label for='data-6f22ed8d-5592-435a-a87d-109ad87fcf7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>540</dd><dt><span>STATISTICS_MEAN 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97,Band 98,Band 99,Band 100,Band 101,Band 102,Band 103,Band 104,Band 105,Band 106,Band 107,Band 108,Band 109,Band 110,Band 111,Band 112,Band 113,Band 114,Band 115,Band 116,Band 117,Band 118,Band 119,Band 120,Band 121,Band 122,Band 123,Band 124,Band 125,Band 126,Band 127,Band 128,Band 129,Band 130,Band 131,Band 132,Band 133,Band 134,Band 135,Band 136,Band 137,Band 138,Band 139,Band 140,Band 141,Band 142,Band 143,Band 144,Band 145,Band 146,Band 147,Band 148,Band 149,Band 150,Band 151,Band 152,Band 153,Band 154,Band 155,Band 156,Band 157,Band 158,Band 159,Band 160,Band 161,Band 162,Band 163,Band 164,Band 165,Band 166,Band 167,Band 168,Band 169,Band 170,Band 171,Band 172,Band 173,Band 174,Band 175,Band 176,Band 177,Band 178,Band 179,Band 180,Band 181,Band 182,Band 183,Band 184,Band 185,Band 186,Band 187,Band 188,Band 189,Band 190,Band 191,Band 192,Band 193,Band 194,Band 195,Band 196,Band 197,Band 198,Band 199,Band 200,Band 201,Band 202,Band 203,Band 204,Band 205,Band 206,Band 207,Band 208,Band 209,Band 210,Band 211,Band 212,Band 213,Band 214,Band 215,Band 216,Band 217,Band 218,Band 219,Band 220,Band 221,Band 222,Band 223,Band 224,Band 225,Band 226,Band 227,Band 228,Band 229,Band 230,Band 231,Band 232,Band 233,Band 234,Band 235,Band 236,Band 237,Band 238,Band 239,Band 240,Band 241,Band 242,Band 243,Band 244,Band 245,Band 246,Band 247,Band 248,Band 249,Band 250,Band 251,Band 252,Band 253,Band 254,Band 255,Band 256,Band 257,Band 258,Band 259,Band 260,Band 261,Band 262,Band 263,Band 264,Band 265,Band 266,Band 267,Band 268,Band 269,Band 270,Band 271,Band 272,Band 273,Band 274,Band 275,Band 276,Band 277,Band 278,Band 279,Band 280,Band 281,Band 282,Band 283,Band 284,Band 285,Band 286,Band 287,Band 288,Band 289,Band 290,Band 291,Band 292,Band 293,Band 294,Band 295,Band 296,Band 297,Band 298,Band 299,Band 300,Band 301,Band 302,Band 303,Band 304,Band 305,Band 306,Band 307,Band 308,Band 309,Band 310,Band 311,Band 312,Band 313,Band 314,Band 315,Band 316,Band 317,Band 318,Band 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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-27db4c24-c1d3-4021-8331-c8375190a7f1' class='xr-section-summary-in' type='checkbox' ><label for='section-27db4c24-c1d3-4021-8331-c8375190a7f1' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f41bdd0f-f12b-49bc-826d-0a0f3c75a8e0' class='xr-index-data-in' type='checkbox'/><label for='index-f41bdd0f-f12b-49bc-826d-0a0f3c75a8e0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1982-01-31&#x27;, &#x27;1982-02-28&#x27;, &#x27;1982-03-31&#x27;, &#x27;1982-04-30&#x27;,\n",
" &#x27;1982-05-31&#x27;, &#x27;1982-06-30&#x27;, &#x27;1982-07-31&#x27;, &#x27;1982-08-31&#x27;,\n",
" &#x27;1982-09-30&#x27;, &#x27;1982-10-31&#x27;,\n",
" ...\n",
" &#x27;2015-03-31&#x27;, &#x27;2015-04-30&#x27;, &#x27;2015-05-31&#x27;, &#x27;2015-06-30&#x27;,\n",
" &#x27;2015-07-31&#x27;, &#x27;2015-08-31&#x27;, &#x27;2015-09-30&#x27;, &#x27;2015-10-31&#x27;,\n",
" &#x27;2015-11-30&#x27;, &#x27;2015-12-31&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;band&#x27;, length=408, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6c0540ba-6381-4a0c-97e1-27992bf5cee4' class='xr-index-data-in' type='checkbox'/><label for='index-6c0540ba-6381-4a0c-97e1-27992bf5cee4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75,\n",
" -176.25, -175.75, -175.25,\n",
" ...\n",
" 175.25, 175.75, 176.25, 176.75, 177.25, 177.75, 178.25,\n",
" 178.75, 179.25, 179.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-72019a68-0237-4b84-b3ce-a3c8fe98275d' class='xr-index-data-in' type='checkbox'/><label for='index-72019a68-0237-4b84-b3ce-a3c8fe98275d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 89.75, 89.25, 88.75, 88.25, 87.75, 87.25, 86.75, 86.25,\n",
" 85.75, 85.25,\n",
" ...\n",
" -85.25, -85.75, -86.25, -86.75, -87.25, -87.75, -88.25, -88.75,\n",
" -89.25, -89.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-19e58803-2fd7-4e95-a6c3-36db817f2752' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-19e58803-2fd7-4e95-a6c3-36db817f2752' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
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"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 ... 2015-12-31\n",
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" spatial_ref int64 0\n",
"Data variables:\n",
" lai (band, y, x) float32 dask.array<chunksize=(408, 54, 54), meta=np.ndarray>\n",
" temp (band, y, x) float32 dask.array<chunksize=(408, 360, 720), meta=np.ndarray>\n",
" lai_north (band, y, x) float32 dask.array<chunksize=(408, 54, 54), meta=np.ndarray>\n",
" temp_north (band, y, x) float32 dask.array<chunksize=(408, 360, 720), meta=np.ndarray>\n",
" lai_north_masked_30 (band, y, x) float32 dask.array<chunksize=(408, 54, 54), meta=np.ndarray>"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lai_array "
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "24300ce2-d840-434d-a77b-c978f6232a3c",
"metadata": {},
"outputs": [],
"source": [
"lai_array['temp_north_masked_30'] = lai_array['temp_north'].where(lai_array['lai_north'] > 30)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "7d06c762-3e9a-43b8-b0db-93978c7ad670",
"metadata": {},
"outputs": [],
"source": [
"# dump_to_disk()\n",
"# refresh()"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "b27b5f46-5d1f-41df-9a6a-2982e774ff03",
"metadata": {},
"outputs": [
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".xr-text-repr-fallback {\n",
" /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
" display: none;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (band: 408, x: 720, y: 360)\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 0\n",
"Data variables:\n",
" lai (band, y, x) float32 dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;\n",
" temp (band, y, x) float32 dask.array&lt;chunksize=(408, 360, 720), meta=np.ndarray&gt;\n",
" lai_north (band, y, x) float32 dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;\n",
" temp_north (band, y, x) float32 dask.array&lt;chunksize=(408, 360, 720), meta=np.ndarray&gt;\n",
" lai_north_masked_30 (band, y, x) float32 dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;\n",
" temp_north_masked_30 (band, y, x) float32 dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e1e1c87a-3417-44e1-adca-69f82eb39255' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e1e1c87a-3417-44e1-adca-69f82eb39255' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 408</li><li><span class='xr-has-index'>x</span>: 720</li><li><span class='xr-has-index'>y</span>: 360</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4afa39a9-0725-45b4-95ce-f224c7bb044c' class='xr-section-summary-in' type='checkbox' checked><label for='section-4afa39a9-0725-45b4-95ce-f224c7bb044c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1982-01-31 ... 2015-12-31</div><input id='attrs-aa135902-5468-4654-b62d-e4e61a671cef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa135902-5468-4654-b62d-e4e61a671cef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f97f13c6-651e-45b5-8882-b3055baeb5f5' class='xr-var-data-in' type='checkbox'><label for='data-f97f13c6-651e-45b5-8882-b3055baeb5f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1982-01-31T00:00:00.000000000&#x27;, &#x27;1982-02-28T00:00:00.000000000&#x27;,\n",
" &#x27;1982-03-31T00:00:00.000000000&#x27;, ..., &#x27;2015-10-31T00:00:00.000000000&#x27;,\n",
" &#x27;2015-11-30T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-b382dda0-679c-4235-b304-306078f4fde6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b382dda0-679c-4235-b304-306078f4fde6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66becb58-eb6c-4ff3-b821-513b94325958' class='xr-var-data-in' type='checkbox'><label for='data-66becb58-eb6c-4ff3-b821-513b94325958' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ..., 178.75, 179.25, 179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.75 89.25 88.75 ... -89.25 -89.75</div><input id='attrs-b081ccee-c3fa-4011-9784-39ac7f8dc142' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b081ccee-c3fa-4011-9784-39ac7f8dc142' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cbead5db-c507-4c3a-9d55-0813325bb105' class='xr-var-data-in' type='checkbox'><label for='data-cbead5db-c507-4c3a-9d55-0813325bb105' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 89.75, 89.25, 88.75, ..., -88.75, -89.25, -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-56e355af-24a9-4f0f-81fa-e125c68483aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56e355af-24a9-4f0f-81fa-e125c68483aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01934437-94e7-43f3-9558-25cea3e76b60' class='xr-var-data-in' type='checkbox'><label for='data-01934437-94e7-43f3-9558-25cea3e76b60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.0 0.5 -0.0 90.0 -0.0 -0.5</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-db6de668-f7bd-4684-9b2b-c3c6ddb480ce' class='xr-section-summary-in' type='checkbox' checked><label for='section-db6de668-f7bd-4684-9b2b-c3c6ddb480ce' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lai</span></div><div class='xr-var-dims'>(band, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;</div><input id='attrs-09da6ede-a404-496e-930b-b298e1f803a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09da6ede-a404-496e-930b-b298e1f803a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e50a513-cd6d-4491-aa59-b8e9f40a38e4' class='xr-var-data-in' type='checkbox'><label for='data-2e50a513-cd6d-4491-aa59-b8e9f40a38e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>540</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>87.72932166302</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>0</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>134.37581892842</dd><dt><span>bands :</span></dt><dd>408</dd><dt><span>band_names :</span></dt><dd>Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band 7,Band 8,Band 9,Band 10,Band 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"</svg>\n",
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"</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-77295c7b-e9e9-41a5-a12b-347485bdbb6a' class='xr-section-summary-in' type='checkbox' ><label for='section-77295c7b-e9e9-41a5-a12b-347485bdbb6a' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5a360a20-b2c4-468f-9fad-e1d6b8eb8e9a' class='xr-index-data-in' type='checkbox'/><label for='index-5a360a20-b2c4-468f-9fad-e1d6b8eb8e9a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1982-01-31&#x27;, &#x27;1982-02-28&#x27;, &#x27;1982-03-31&#x27;, &#x27;1982-04-30&#x27;,\n",
" &#x27;1982-05-31&#x27;, &#x27;1982-06-30&#x27;, &#x27;1982-07-31&#x27;, &#x27;1982-08-31&#x27;,\n",
" &#x27;1982-09-30&#x27;, &#x27;1982-10-31&#x27;,\n",
" ...\n",
" &#x27;2015-03-31&#x27;, &#x27;2015-04-30&#x27;, &#x27;2015-05-31&#x27;, &#x27;2015-06-30&#x27;,\n",
" &#x27;2015-07-31&#x27;, &#x27;2015-08-31&#x27;, &#x27;2015-09-30&#x27;, &#x27;2015-10-31&#x27;,\n",
" &#x27;2015-11-30&#x27;, &#x27;2015-12-31&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;band&#x27;, length=408, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-623fe773-0f11-46bc-9f6b-bfeade3cfc75' class='xr-index-data-in' type='checkbox'/><label for='index-623fe773-0f11-46bc-9f6b-bfeade3cfc75' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75,\n",
" -176.25, -175.75, -175.25,\n",
" ...\n",
" 175.25, 175.75, 176.25, 176.75, 177.25, 177.75, 178.25,\n",
" 178.75, 179.25, 179.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-14c6052b-5abd-498b-ac0c-54a9b6f1000f' class='xr-index-data-in' type='checkbox'/><label for='index-14c6052b-5abd-498b-ac0c-54a9b6f1000f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 89.75, 89.25, 88.75, 88.25, 87.75, 87.25, 86.75, 86.25,\n",
" 85.75, 85.25,\n",
" ...\n",
" -85.25, -85.75, -86.25, -86.75, -87.25, -87.75, -88.25, -88.75,\n",
" -89.25, -89.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e50c0ac-b53a-41dc-970d-f40d389d6c44' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6e50c0ac-b53a-41dc-970d-f40d389d6c44' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
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"<xarray.Dataset>\n",
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" spatial_ref int64 0\n",
"Data variables:\n",
" lai (band, y, x) float32 dask.array<chunksize=(408, 54, 54), meta=np.ndarray>\n",
" temp (band, y, x) float32 dask.array<chunksize=(408, 360, 720), meta=np.ndarray>\n",
" lai_north (band, y, x) float32 dask.array<chunksize=(408, 54, 54), meta=np.ndarray>\n",
" temp_north (band, y, x) float32 dask.array<chunksize=(408, 360, 720), meta=np.ndarray>\n",
" lai_north_masked_30 (band, y, x) float32 dask.array<chunksize=(408, 54, 54), meta=np.ndarray>\n",
" temp_north_masked_30 (band, y, x) float32 dask.array<chunksize=(408, 54, 54), meta=np.ndarray>"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lai_array"
]
},
{
"cell_type": "markdown",
"id": "1ff7ddbb-b047-46c1-92b6-ab5466a92fa0",
"metadata": {},
"source": [
"## Calculate annual averages (Item 1.2)"
]
},
{
"cell_type": "markdown",
"id": "d2f64fb8-6b19-4c57-8169-2373addaabbd",
"metadata": {},
"source": [
"### For LAI"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "f47da793-bfd9-4450-8203-31a4eb20abf5",
"metadata": {},
"outputs": [
{
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" lines: 360\n",
" map_info: Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,W...\n",
" samples: 720</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lai_north_masked_30'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 408</li><li><span class='xr-has-index'>y</span>: 360</li><li><span class='xr-has-index'>x</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-8ce19834-6ae5-44e2-9840-4f86a01eb623' class='xr-array-in' type='checkbox' checked><label for='section-8ce19834-6ae5-44e2-9840-4f86a01eb623' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 403.42 MiB </td>\n",
" <td> 16.64 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (408, 360, 720) </td>\n",
" <td> (408, 198, 54) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 56 chunks in 16 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"220\" height=\"150\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
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"\n",
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"\n",
" <!-- Text -->\n",
" <text x=\"110.000000\" y=\"120.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >720</text>\n",
" <text x=\"190.000000\" y=\"70.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,190.000000,70.000000)\">360</text>\n",
" <text x=\"20.000000\" y=\"100.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,20.000000,100.000000)\">408</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></div></li><li class='xr-section-item'><input id='section-f57e2a17-ec9f-49e5-842d-fb6579d96a9b' class='xr-section-summary-in' type='checkbox' checked><label for='section-f57e2a17-ec9f-49e5-842d-fb6579d96a9b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1982-01-31 ... 2015-12-31</div><input id='attrs-049b0ef1-7e9c-4298-aa50-14154340237e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-049b0ef1-7e9c-4298-aa50-14154340237e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfdb065b-e804-4931-b079-8de85a98e829' class='xr-var-data-in' type='checkbox'><label for='data-bfdb065b-e804-4931-b079-8de85a98e829' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1982-01-31T00:00:00.000000000&#x27;, &#x27;1982-02-28T00:00:00.000000000&#x27;,\n",
" &#x27;1982-03-31T00:00:00.000000000&#x27;, ..., &#x27;2015-10-31T00:00:00.000000000&#x27;,\n",
" &#x27;2015-11-30T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-4a235530-1744-4cc4-b581-7895f60d69db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a235530-1744-4cc4-b581-7895f60d69db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-400d7da5-16cd-43b9-9742-856a917d94b5' class='xr-var-data-in' type='checkbox'><label for='data-400d7da5-16cd-43b9-9742-856a917d94b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ..., 178.75, 179.25, 179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.75 89.25 88.75 ... -89.25 -89.75</div><input id='attrs-3b9c6357-f6ec-4f06-9138-ab2638d36de3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b9c6357-f6ec-4f06-9138-ab2638d36de3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-523f9586-2ae6-44a2-a08d-ab533668abbe' class='xr-var-data-in' type='checkbox'><label for='data-523f9586-2ae6-44a2-a08d-ab533668abbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 89.75, 89.25, 88.75, ..., -88.75, -89.25, -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-9b00f6b4-c6ba-466b-b29f-e2c954b7d13d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b00f6b4-c6ba-466b-b29f-e2c954b7d13d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45e64860-775c-4785-8974-71f59c07b003' class='xr-var-data-in' type='checkbox'><label for='data-45e64860-775c-4785-8974-71f59c07b003' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.0 0.5 -0.0 90.0 -0.0 -0.5</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-adefbd8c-0529-49c3-ae87-9769b9aa6635' class='xr-section-summary-in' type='checkbox' ><label for='section-adefbd8c-0529-49c3-ae87-9769b9aa6635' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b6c1afa-fcc2-427a-b556-43e0f1e8813f' class='xr-index-data-in' type='checkbox'/><label for='index-9b6c1afa-fcc2-427a-b556-43e0f1e8813f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1982-01-31&#x27;, &#x27;1982-02-28&#x27;, &#x27;1982-03-31&#x27;, &#x27;1982-04-30&#x27;,\n",
" &#x27;1982-05-31&#x27;, &#x27;1982-06-30&#x27;, &#x27;1982-07-31&#x27;, &#x27;1982-08-31&#x27;,\n",
" &#x27;1982-09-30&#x27;, &#x27;1982-10-31&#x27;,\n",
" ...\n",
" &#x27;2015-03-31&#x27;, &#x27;2015-04-30&#x27;, &#x27;2015-05-31&#x27;, &#x27;2015-06-30&#x27;,\n",
" &#x27;2015-07-31&#x27;, &#x27;2015-08-31&#x27;, &#x27;2015-09-30&#x27;, &#x27;2015-10-31&#x27;,\n",
" &#x27;2015-11-30&#x27;, &#x27;2015-12-31&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;band&#x27;, length=408, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f914e50a-5449-41b1-a237-a166176bbba9' class='xr-index-data-in' type='checkbox'/><label for='index-f914e50a-5449-41b1-a237-a166176bbba9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75,\n",
" -176.25, -175.75, -175.25,\n",
" ...\n",
" 175.25, 175.75, 176.25, 176.75, 177.25, 177.75, 178.25,\n",
" 178.75, 179.25, 179.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f080f315-ec20-4859-886d-c789dd62f063' class='xr-index-data-in' type='checkbox'/><label for='index-f080f315-ec20-4859-886d-c789dd62f063' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 89.75, 89.25, 88.75, 88.25, 87.75, 87.25, 86.75, 86.25,\n",
" 85.75, 85.25,\n",
" ...\n",
" -85.25, -85.75, -86.25, -86.75, -87.25, -87.75, -88.25, -88.75,\n",
" -89.25, -89.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70d49d2b-ac90-4c99-a0f0-a7fb3ad52825' class='xr-section-summary-in' type='checkbox' ><label for='section-70d49d2b-ac90-4c99-a0f0-a7fb3ad52825' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>540</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>87.72932166302</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>0</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>134.37581892842</dd><dt><span>bands :</span></dt><dd>408</dd><dt><span>band_names :</span></dt><dd>Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band 7,Band 8,Band 9,Band 10,Band 11,Band 12,Band 13,Band 14,Band 15,Band 16,Band 17,Band 18,Band 19,Band 20,Band 21,Band 22,Band 23,Band 24,Band 25,Band 26,Band 27,Band 28,Band 29,Band 30,Band 31,Band 32,Band 33,Band 34,Band 35,Band 36,Band 37,Band 38,Band 39,Band 40,Band 41,Band 42,Band 43,Band 44,Band 45,Band 46,Band 47,Band 48,Band 49,Band 50,Band 51,Band 52,Band 53,Band 54,Band 55,Band 56,Band 57,Band 58,Band 59,Band 60,Band 61,Band 62,Band 63,Band 64,Band 65,Band 66,Band 67,Band 68,Band 69,Band 70,Band 71,Band 72,Band 73,Band 74,Band 75,Band 76,Band 77,Band 78,Band 79,Band 80,Band 81,Band 82,Band 83,Band 84,Band 85,Band 86,Band 87,Band 88,Band 89,Band 90,Band 91,Band 92,Band 93,Band 94,Band 95,Band 96,Band 97,Band 98,Band 99,Band 100,Band 101,Band 102,Band 103,Band 104,Band 105,Band 106,Band 107,Band 108,Band 109,Band 110,Band 111,Band 112,Band 113,Band 114,Band 115,Band 116,Band 117,Band 118,Band 119,Band 120,Band 121,Band 122,Band 123,Band 124,Band 125,Band 126,Band 127,Band 128,Band 129,Band 130,Band 131,Band 132,Band 133,Band 134,Band 135,Band 136,Band 137,Band 138,Band 139,Band 140,Band 141,Band 142,Band 143,Band 144,Band 145,Band 146,Band 147,Band 148,Band 149,Band 150,Band 151,Band 152,Band 153,Band 154,Band 155,Band 156,Band 157,Band 158,Band 159,Band 160,Band 161,Band 162,Band 163,Band 164,Band 165,Band 166,Band 167,Band 168,Band 169,Band 170,Band 171,Band 172,Band 173,Band 174,Band 175,Band 176,Band 177,Band 178,Band 179,Band 180,Band 181,Band 182,Band 183,Band 184,Band 185,Band 186,Band 187,Band 188,Band 189,Band 190,Band 191,Band 192,Band 193,Band 194,Band 195,Band 196,Band 197,Band 198,Band 199,Band 200,Band 201,Band 202,Band 203,Band 204,Band 205,Band 206,Band 207,Band 208,Band 209,Band 210,Band 211,Band 212,Band 213,Band 214,Band 215,Band 216,Band 217,Band 218,Band 219,Band 220,Band 221,Band 222,Band 223,Band 224,Band 225,Band 226,Band 227,Band 228,Band 229,Band 230,Band 231,Band 232,Band 233,Band 234,Band 235,Band 236,Band 237,Band 238,Band 239,Band 240,Band 241,Band 242,Band 243,Band 244,Band 245,Band 246,Band 247,Band 248,Band 249,Band 250,Band 251,Band 252,Band 253,Band 254,Band 255,Band 256,Band 257,Band 258,Band 259,Band 260,Band 261,Band 262,Band 263,Band 264,Band 265,Band 266,Band 267,Band 268,Band 269,Band 270,Band 271,Band 272,Band 273,Band 274,Band 275,Band 276,Band 277,Band 278,Band 279,Band 280,Band 281,Band 282,Band 283,Band 284,Band 285,Band 286,Band 287,Band 288,Band 289,Band 290,Band 291,Band 292,Band 293,Band 294,Band 295,Band 296,Band 297,Band 298,Band 299,Band 300,Band 301,Band 302,Band 303,Band 304,Band 305,Band 306,Band 307,Band 308,Band 309,Band 310,Band 311,Band 312,Band 313,Band 314,Band 315,Band 316,Band 317,Band 318,Band 319,Band 320,Band 321,Band 322,Band 323,Band 324,Band 325,Band 326,Band 327,Band 328,Band 329,Band 330,Band 331,Band 332,Band 333,Band 334,Band 335,Band 336,Band 337,Band 338,Band 339,Band 340,Band 341,Band 342,Band 343,Band 344,Band 345,Band 346,Band 347,Band 348,Band 349,Band 350,Band 351,Band 352,Band 353,Band 354,Band 355,Band 356,Band 357,Band 358,Band 359,Band 360,Band 361,Band 362,Band 363,Band 364,Band 365,Band 366,Band 367,Band 368,Band 369,Band 370,Band 371,Band 372,Band 373,Band 374,Band 375,Band 376,Band 377,Band 378,Band 379,Band 380,Band 381,Band 382,Band 383,Band 384,Band 385,Band 386,Band 387,Band 388,Band 389,Band 390,Band 391,Band 392,Band 393,Band 394,Band 395,Band 396,Band 397,Band 398,Band 399,Band 400,Band 401,Band 402,Band 403,Band 404,Band 405,Band 406,Band 407,Band 408</dd><dt><span>byte_order :</span></dt><dd>0</dd><dt><span>coordinate_system_string :</span></dt><dd>GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.017453292519943295]]</dd><dt><span>data_type :</span></dt><dd>4</dd><dt><span>description :</span></dt><dd>L:\\NDVIseason\\LAI\\glasss\\GLASS_LAI_82_15_montlhy0_5deg.envi</dd><dt><span>file_type :</span></dt><dd>ENVI Standard</dd><dt><span>header_offset :</span></dt><dd>0</dd><dt><span>interleave :</span></dt><dd>bsq</dd><dt><span>lines :</span></dt><dd>360</dd><dt><span>map_info :</span></dt><dd>Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,WGS-84</dd><dt><span>samples :</span></dt><dd>720</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'lai_north_masked_30' (band: 408, y: 360, x: 720)>\n",
"dask.array<where, shape=(408, 360, 720), dtype=float32, chunksize=(408, 198, 54), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 1982-02-28 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 0\n",
"Attributes: (12/16)\n",
" STATISTICS_MAXIMUM: 540\n",
" STATISTICS_MEAN: 87.72932166302\n",
" STATISTICS_MINIMUM: 0\n",
" STATISTICS_STDDEV: 134.37581892842\n",
" bands: 408\n",
" band_names: Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band...\n",
" ... ...\n",
" file_type: ENVI Standard\n",
" header_offset: 0\n",
" interleave: bsq\n",
" lines: 360\n",
" map_info: Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,W...\n",
" samples: 720"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lai_array['lai_north_masked_30']"
]
},
{
"cell_type": "markdown",
"id": "78c6248e-7445-4df5-9fc3-08cf2d34dbe0",
"metadata": {},
"source": [
"Test averaging and select a month for display"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "9d26860c-3b33-40fd-86a7-84330776f6a0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x2b1a64eab7c0>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lai_array['lai_north_masked_30'].resample(band='1Y').mean().sel(band='1982-12-31').plot(cmap='cividis')"
]
},
{
"cell_type": "markdown",
"id": "19bee172-1f3d-45ca-9454-67e61130a9e6",
"metadata": {},
"source": [
"Same period, but now displying standard deviation"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "ee0c3fac-c28c-436a-922c-176d14704167",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x2b1a64ca1a80>"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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C+wEeeughLrzwQs466yzuu+++Lq1i+fn5XVruVoeUknfffZecnJy1+pyDw8bEET5/YlpaWvjLX/7CGWecwdZbb00wGOSzzz7jP//5zyoXzfz8fC677DLmz5/PlltuySuvvMK9997LZZddlsngOvroo5k8eTJnnHEGF198MQ0NDUyaNKnLi8z222/P448/zhNPPMGAAQPwer0ZC8bKHHPMMTz00ENsvfXW7LDDDnzxxRfcdtttnS4SG4K7776bI488ksMPP5zzzjuPnj170tjYyA8//MD06dN56qmn1mm9u+22W7eMT9d1Jk6cyNlnn80ll1zC6aefzs8//8xVV13FYYcdxhFHHNFpeSEEBx54IO+8885vrru0tJTjjjuOsWPHUlJSwrRp03j99deZMGECfr8fUN/LM888w+WXX85JJ53EggULuPnmmykpKeHnn39ep326+eabOeKIIzjssMMYOXIklmUxYcIEAoEAjY2NazTu0tLSddp2Vzz99NMA/PLLL4Aqb5COrznppJMyy6Vdw3369GHp0qU8+eSTPPfcczzyyCOdBM7QoUN59NFHOeywwxgzZkwmxueWW26hqKiIM888M7PsSSedxKuvvso111xDfn4+n3zySea9UCjUycq3OubNm5cR2rNnz+60T/369cv8Fp966ikuuOACdtppJy655BI+/fTTTuvZeeedf1MsHn/88ey4447stNNO5Ofns3jxYh566CHeffdd7rjjjk6xQGs6LgeHjcLGja122JDEYjF56aWXyh122EGGQiHp8/nkVlttJW+44YZOmTsHHnigHDx4sHznnXfkbrvtJj0ejywpKZFXX331KhlPDzzwgNxqq62kx+ORAwYMkFVVVZnsljlz5mSWmzt3rhwyZIgMBoOdsmy6ysxqamqSF1xwgSwsLJR+v1/ut99+8v3335cHHnhgp8yw9cnqWl1G1tdffy1POeUUWVhYKF0ulywuLpYHH3ywvOuuu9Z4HRuaxx57TO6www7S7XbL4uJiWV5evkrWT1tbmwTkaaed9pvrSxcwfPrpp+XgwYOl2+2W/fr1k5MnT15l2fHjx8t+/fpJj8cjt9lmG3nvvfd2WWwQkEOHDu1yW+eee26n11544YXM/vTp00eOHz9+oxQwlPLXM/BW5MYbb5QDBw6UHo9H5uTkyCOOOEK+9957Xa5z+vTp8q9//avs1atX5jy58MIL5fz589d422ta/PPBBx9c7TpWPO7nnnvur25vxXN3dUyYMEHuvvvuMjc3V+q6LvPz8+Xhhx8uX3rppXUel4PDxkBIuVLkmsNmx0EHHUR9ff161YVx2Li88sorHHPMMXz99dertaw5ODg4ODi9uhwc/hS8/fbbnHbaaY7ocXBwcPgNnBgfh02W36qQ3B31TjYVVk5LdnBwcHDoms3jquDwq7zzzjubnJtr7ty5uFyuX33cdNNNG3uYDg4ODg5/MByLj8MmSWlp6W+mjXdn9o+Dg4ODw58DJ7jZwcHBwcHBYbPBcXU5ODg4ODg4bDY4rq6VsG2bxYsXEwwGu7Xfj4ODg4PDnw8pJW1tbZSWlm6wZIpYLEYikeiWdbnd7i6bDG9OOMJnJRYvXkzv3r039jAcHBwcHDYhFixYsEGqzcdiMfr3DVG3LNkt6ysuLmbOnDmbtfhxhM9KBINBQP2IQ6HQRh6Ng4ODg8MfmdbWVnr37p25dnQ3iUSCumVJ5n++E6Gg/tsf+BVa2yz67PYViUTCET4OHaTdW6FQyBE+Dg4ODg5rxIYOjQgF9fUWPg4KR/g4ODg4ODj80ZFSPdZ3HQ6O8HFwcHBwcPjD4wifbsNJZ3dwcHBwcHDYbHAsPg4ODg4ODn9wHINP9+EIHwcHBwcHhz84UmpIuX5OGqdRg8JxdTk4ODg4ODhsNjgWHwcHBwcHhz84UopusPjY3TSaTRtH+Dg4ODg4OPzBsaWGvZ7CZ30//2fBOQoODg4ODg4Omw2OxcfBwcHBweEPTvcENzu2DnCEj4ODg4ODwx8eR/h0H47wcXBwcHBw+IOjgpvXrx/Y+n7+z4Ij/xwcHBwcHBw2GzYZ4WOaJtdeey39+/fH5/MxYMAAbrrpJmy7Iz1PSsnYsWMpLS3F5/Nx0EEHMWPGjI04agcHBwcHh/Un7epa34fDJiR8JkyYwF133cXf//53fvjhByZOnMhtt91GbW1tZpmJEycyefJk/v73v/PZZ59RXFzMYYcdRltb20YcuYODg4ODw/phS9EtD4dNSPh8/PHHHH/88Rx99NH069ePk046iSFDhvD5558DytpTXV3NNddcw4knnsh2223Hww8/TCQS4bHHHtvIo3dwcHBwcHD4I7DJCJ/99tuPN998k59++gmAr7/+mg8++ICjjjoKgDlz5lBXV8eQIUMyn/F4PBx44IF89NFHG2XMDg6/B2L7izb2EBwcHDYwkm5wdW06l/wNyiaT1TV69GhaWlrYeuut0XUdy7K49dZbOf300wGoq6sDoKioqNPnioqKmDdv3mrXG4/Hicfjmeetra0bYPQODg4ODg7rjpPV1X1sMvLviSeeYNq0aTz22GNMnz6dhx9+mEmTJvHwww93Wk6Izl+slHKV11akqqqK7OzszKN3794bZPwO644YeDxiq1M7v7bVqYiBx2+kEf2xkN/eu7GH4ODg4LDJsMkIn1GjRjFmzBhOO+00tt9+e84++2yGDx9OVVUVAMXFxUCH5SfNsmXLVrECrUhlZSUtLS2Zx4IFCzbcTjisE3L288iZT3R+baXnDg4ODn9m0haf9X04bELCJxKJoGmdh6vreiadvX///hQXF/P6669n3k8kErz77rvss88+q12vx+MhFAp1ejhsGsjZz6/3OsQ2Z3TDSBwcHBw2LE46e/exycT4HHvssdx666306dOHwYMH8+WXXzJ58mTOP/98QLm4KioqGDduHIMGDWLQoEGMGzcOv9/PGWc4FzeHrpE/OBl/Dg4ODpsTm4zwqa2t5brrruPyyy9n2bJllJaWcskll3D99ddnlrnqqquIRqNcfvnlNDU1seeee/Laa68RDAY34sgdHBwcHBzWDynXPzhZym4azCaOkNI5FCvS2tpKdnY2LS0tjtvLwcHBweFX2dDXjPT65/zvMEJZrvVbV3uS/nu+vtlf3xyHn4ODg8NmgBh8XiYT0olt2/SQ3VC12QluVjjCx8HBwWEzQM54CDn7ecTg8zrFtnVVFkJsffrvOTQHh9+VTSbGx8HBwcFh9YjB5yFnPNTxfOcK8lyCRlNArB6kDcl25KznOn1Ozn4eMfD4zF+HPyZOAcPuwxE+Dr876UnWwcFh3RDbnAG2lalnJba/CGyT/D2G0xhpBVcQEs00ijzk9MkZ15ac9Rxim7OQP0xDbH8Jwh1AJsKQPUAt4w6C0EBsnEuDtusIDAHJptnOHLESkvVvOeG0rFA4wsfhd8eZ0Bwc1g0x+DywTXDngxVRz2NN4M0HUKJHGBBbBpobrCTQuWyD/GGa+vvt3YjtL1F/dxmB8BcjE20btRK4/cXkjbZth80HR/45dEIMPB6x5ckbexgODg5doXlAaAjDBe5sIHUjEWtQgibZDnYSdB948pBf13b6eMleFSutz63WMX0y9heTnfYnf2Ccys3dhyN8HDKk/fvyp6dW8fWv3CvL4Y+FI1Y3E+wkCA2ZaINYPXLGQ8rq4+uBtusIRFZPXL4ccAWVi2vnCgDE9pcAsDSppnzfbsMRO1xGoc8x+m8qOMKn+3B+9Q4dePPBTgCruqOc3lh/bORPT23sITh0A+7dRpCMtShLjBnG5e9B4vPJaLuOwCMkaC71njRBs5WgceciNIFMhOnh91OfBJeukb/HcOSX1YByawV3Hw4IxA6XgeFDfnPnKtvfYv9hGBq0JQQ+QzLr/an03LsCnyGZ3WqDNLv8XBqxy3CQIL+csoGOkIPD+uNYfByAVOwANiTaNvy2UtYjJ4PEwaEzyURU/aN7kd//k2xd4tttOB4h8WhKwMiva1UAsisEmptePkm+IUFz02IJit2qJq0F9N+3gvw9hgPQbgl6elRvQzm9I5Zmi/2HZZYRKNHj1iDLLcnfYzhLEhr1cY2BIU0JL1LB1Cshdq6g0A0IgRh8/oY5QJsxjsWn+3AqN6/E5li5WWxzFmgG2Ikue1c5WVgODmuP2GUELg1MCQFN0vbZ2llBivYazrIEYMZA2ng9fmLxCIUBP0s/mULvfSqwJcQsQUJCnlsy78NqcvYYTksSvDpEP1fbzN9jOI2myAge924jANCR5BgSnyFJ2gKPri4H4aS6QHp1aE0KQi5JxBL4dUm7KWgwBSFd4hJgCIlbV+OIWGp/09tdXwbtP4zGuEbDp39cC9LvVbn5x/ePJbielZvb2pNsvf+Lm9X1rSsci48D8odpqv7HalJYHdHzx8Wxmm0YxJYnrfex7eOzScbDyES4k+jpu2/FKsuW7NVhmUmz9JMp5BmQ5fHi8gTwaIA0WdbagNjuAtpMgZ26bXULaEwIxC4jaDFFRvTk7zGcnD2GE7Ghh0tSsOdwivYaTtKySSaixGKt1LW3sSSmsTBsIVCiJ+CSeHVI2FDgszE0SQ+vRa7PZvn/plDgkrTETerjFqZUbjGAIq9NnktmLD6991l1X9eEbQ8axi6HlgOsInr67lvR5TF0cFhTnBgfhwzy+39u7CGsMWLr00H3gBkFK75KUbbNBUeUbhjkT0+vl/DZ+sBhtCc1lSq+c2dBY9qrLr/kk2pAWWJ0JNHPp1CyVwURWxCzIduQtMQTYASQX9WoOB2U+0oXELchZgtcGiQ+n8zWBw4DIGKDR4McQ1IXV4IoFm8H2yLLn01PvzsldGxMW8O0BSG3xGNILFvikcoKFJOCuCWQwOCDygm6BKZtkOWSBFw2DTF1D21LyHJJ8GSv9tgM2n8YP78/FVCCL2oLmlcSN9+/M7XT8/2OKuODV1SG2rwPq9foO/iz4RQw7D4ci89G4s/qA1/xYrE2F441WVYMPg+x7TkqRkjoqp6Jt8cqosexgjh0B+sjKpviGhYqfgaU2ypNwlYXn/77VqQCjjvwCEkodTu65JNqYjbI6VOwJBmR79ttOBg+WkxlYYmnhJScPhmPkJTsVYGUULDncLJ0JXzaLYHQBLFEjDxfFsXBEFm6pC6qETEFM1uVwAl6bLLckoaoEkFZboklIWEJjNTVIm4J3Drkemw0QCAp8luUBCxCHknCEsjpSsgsjAm0XUd02ke3Ltn2oGGU7FVBwCU7iZ6tDhjGoNQxW5G06FmRbQ8aljm+K7PVAcPY9iD1+LOwvn260g8Hx+Lzu9Nz7wpilgBX1irvuXcbQeLzTa+Al9jq1EzWV6eLhTuwyvu/up7fiCVasRz/r+FYQRw2Nks/mULRXsOZ9f5UxC7DWRbveK/dUn/nxTRyU8HLMVtQ6JaEbRVHkybbUO6iuNTI8isrSlwKhABDCFoSGn4NDE0tl6WDJqA+pmFosCxqqkxNaYLQyPZlkeexiVmChS2tZPmzlUDLknzfatDHZ+PVJQV+G11AzFTrzvXZJFLjDriU1cqjg8stkVIFRPtdEk2Aoanx73poOV5NJ2bZiB0uY8cCF25dkrA0PIZEE0ps7XRIObYEn0uS7QUJ7HRIOYYGQkhaYxoz35vK4IPKmfFODQP2G4bfJVPjkuxzZBmtMcF3b9cw+KByTFvgNWRGqP1ZcCw+3YcT3LwSGyJQbddDyzF0iUtXpmAp1d9wQp2swd2H0yOViTHnw2r671tBxBIEXTbZHolpq4nllw+mstth5cRM+O7tmvUa0+5DyvjstVXvotaVFUWL2PYc5X5yUuAd1hKxYxmFPoMmU7BttokuQAiIJiFqqnNgUyRnj+EZy4Z7txGUeGzak4I2W2QCjPN9NuGEhqFJdA1+fLdjX7fYfxizUu4h327DiX4+Rbl7kxHw5iO8OeTqkjZbkLSh0C2JWJClS5pNQax1IfhL6OXXyPbYLI3o1CeVayxblxkxFjJUsLKhQY7Hpi6i0ztoEU2CSweXLrFTFquYqb4bTaj5LOSVfPSqmlO2OkBZWn5q1yG6jKysAoq9NgG3JGYK3LoKiE5Y6rNuHdriAo8haYlreHV1DEIeG4nKNouZgoQlaIkLegZtmmOCHK+kJabWN+OdGvruW5FxY8zpBpdYurL1r/F7BTd/984J3RLcvN1Bz232wc2O8FmJ7v4R731EGUlLEEmqOyK/WyIEmJY66d26Wi5hqZPblurktyVYNrTGNSwb6mIa7VZHVoZvt+HETItSn8aij6vXeDy7HVaO1yVJmGDagulvdBZQYvD5qgrsb1hNxODzId6YcTOJLU5QfYC2uwD53f1rPJ51JVNs8bfG6WSkbRKI7S6gOCtIUcAmaamLrKGBrkmSVuff6R/BMjpo/2G0J1XMDKz7RTZ/j+H08HYE/bQmBKZMZ1RJvLrMxMMM2n8Ys1pN5Nd3rLIesc0ZkAir2Bp3DgidYo8KUI6l5pmWpCBoSPpkW7SnREZTVCPbK3HpkrgpMvFHhqbmnxVxGxIpBUJIwgkNbyqgOWFBtlcSSwq+SH1P+xxZxswGg8ZwC2g6/bP9JG1Bod/G0CWWLdCEEjetMUG2T5IwRUbg5vvtzDiSluDz1zu+/60PHEY4KQi6JZqQfPd2DdseNGyVuKBfY4eDy4mbgtZUvaIstyTglnz8n843g3sMKWNBq06+zyaSVEJrQZuOLTuCrn8v4fPt23/tFuGz/V+edYSPI3w6010/4l1TGQkSaI6pAERNQLa34w4GwJP6HceTatmAR/1v2dAY1Zjdaqp4FinBTqimglJlaHh1Sb9ci2hCTWJtccE3b3VtCdr2oGFke21sCaal7grTy6YvJGLHofQKuFjYuBQ0F/L7fyK2u0Dtx+8gZiAlaLy5EGtyBMufHLHz8Eyhu50OKc8I/s9eq2XvI8qwJfzvvx0XohUDXHc7rBwpYX6bjt+Qv3vAa8leFbh1JU5curporizQ0myx/zDilmDBR12Psf++FQgh0QU0JzT8hsStKbdR0iZj5Rp8UDn1MZ0mU3QSfmLg8cqtLAz1cGcjNEFAk/h16JVlYUlIWuA1lJi0bIGuKSu0aau5KW6qeSQtfqRUywBYtiBhqeXSFh5DA02o9w0dPnq1lgOOLsOy1VxW16YxJyxw6RoDAhaGJokklahJW3s0oeYjIdT4PIakPSWqvK6UsDLVtqNJkZk70xaerQ4Yxsz31PHZ4eDy1c5/K7PvkWWA2vdfmg2yDImuSQoCdifxc+AxZbTHBPPbdNotyNKhPtyq6igB6D5a3rvldxE+37x1YrcInx0OfmazFz5/Mi/oH4N9jyzDTJ38bXFBQcAm6JH0CNh4XFAf1mmMarTGNeY26TSENZaGderDOjOX63yyzOCnZoPZLQmyPW68hs4exbBHqZt9ipLsU5RkUJ7JgDyL91+u5fPXa9A0yPFJtvtLOXseXrbKmL5/Zyqmre6eDF12miASn09mi/2HsWW2QXNSqMJowkBsdwHZ/hClwSBARgRtUIRQsTzuYMdLv8d2HX5/Wn8BlFU05FV3/y5dnT+leYIVb8l2O6ycWLIjPiFmwhdv1LBVvsm8D6vZ58gy1YZhx1V/+yuyx5Bff39l9j6ijH1SF8mdDinP/F3ySTXzPqwmz2+nBAA0RDX67luRuagW7DmcAfspF9WCj6oZsF9HoO0W+w/LpHrP+bAalwbtSUHPLIug28adElQhT8dBmPFODUs/mbKKtUvOfh68heDJB3c2eS7IN1TdILcmiacEhc+ljplEiR8hSP2v1iNQriu3rkRQQ0SjLa5lUuY10bGcS1exPWn3UySh3kxaoGnQGNFYEtMylaM1AX6XJOhWYivgTa8H/B6pYnniaiBeQxJOCMJxwUev1rKgTeebt2poTWrMen8qPQI2PhfseHB5RvQAXYqewQeVZ25C0/PifkeVYeipsQrYKt9EpGoRLQ9rbH3gMHY7rJx9jywjloTpy5LUtzcRi7dTH43RKxRUPdGEAdIie8fT1ui35PDHwRE+q6H/wWM6ZWKsDR++WktpriTkU2JnYauyhy9p00iYkOezSdpqYk/YAoG6wwt5VEzPVkGbHh4br9vLVrkmg3Mt3IaaJIRQVqL0ZJsmPWkFPRK30TFJ73BweWYZlw5el+R//63NTAZpZr0/lZnvTaXtsyl4PX7kjAfAFSJiC5YnVfn7gbmrBmR3hdhxaEasrFybZJVlU9ltYuDxiB3LOjK0Em0qVoh1tzZ1VV3W4Y+DnP08Ox1Sjq5BXhBCPsjyQk5AXYw/fa2WE05TFysVDCszwiUd49YYUVPYR6/Wsl2eyfb5gu3+Us7uqxE47tWkc+w+pIwDju4QOaBEj5ZySwNk+5QC+OrNGvY5soy9jyjj4//UUhhS1p4lKZGgaeoim++1aUl0TLErxifleCXFWVbmebbXZot8C59L4jXAkoJ4SlSsTO99Otf8KdmrQvXv+nIK8sspeHVJfdyiaK/h5Ptsvnu7BtNSbqTdh5RlYnTSbqZ4UtUDiplkrDUeQ9IjYOE1JFkeZdnxpQKKQz6JxwCPS5LlkWT7JF+9qb4PV8r1Z2gSQyir3g65Jnl+Wwkej8S0lFVb15Qh2+9R85mZint06ZLikI3HkOx5eBkFPnWcln4yJTNuVypmcsUMsK4yuHwuNefteXgZpiXY/2hlSYwm1DwaTQosGwoCNuGkoCmuMbOhnS/qony0yFQWRyuqmr4CmGEWLp2D/PFfkGgGM0rL1493/aPqZpzKzd2H4+paibRZ0djpcoTuoafHZm67tUqX47Vhz8PLcKWyLSxbTYytUUFrXMMGCgMWmlAntK51TLSWrSaS7ABs0ctNOGKh64J77pzCeRdWsKjeIhwHvxtiSWVuDvmgKQzvv9z1ePc5soyPXq1lp0PKM5NVmgH7DeOXD6bi3m0EfX0WLl1ZitJ1N3ruXUFjUnSqyip2uEyZfa1YRqCIHYcCGmg6QtOQ7YvA8KnO0vFGcAXBDIMZ6wiITgdqplidm+uP6ArruXcFRQGbmCnWKs5gc0bsVI78Sv3+9juqjGw/eFzK5eHzCFrDNi88Wcv/nVGOZUuaw5A01WdXjCVJs/uQMgpD8PLTtZm4OilZZbmV2f/oMkwLPv5PLX85toy3X6xlnyOVuyZ9PkYTyuWja+oGQ6Iu1LZUQkpKcLtgbr1GfsDGbajzW0u5b0xbWWtWHKuUKu7v+3emsv/RZZnzdbfDynHpktaYYMY7NaucpysGOe8+pIxlYT3j5kv31Mr2SBa3a9gIivxKTKVFhhBKJO52WIdrsSWmXEsxU8Uh6pqy5nhdHW4wQ1fHX0/F/qStRB6Xmntao4KkLcjz24jUPPd9g0GLCf18kny/jUvvGIemdcxvAS9E4jC7QccQKiapX46aBNPWprgp8LnUeNPj0QSE48oNtqK1Z7fDytE1idelvh/TUt9Del0uXW07HUsmpbopzfHKjKsyZ4/htEQjmR5noVTw+bfLo6qsQLJdfTBQClYCvn9gg7u6vnrz/wgG1tPVFU6y0yH/3uxdXY7wWYn0j4x+RyLnvKIsEnYC+cO09V73PkeWEfCoySKeVBNG+uT3udWJ6DagOawmnNwA9C8xqG+28LoFSUuSHzK4847JnHHuMOYstQl4OianhNlxoguhzsv3UhPqkf9Xxqv/Xnvxtsuh5Ux/Q/nSXbqq79FsKvEjBp+P8OYQ0iU+TbI0qWF/MZne+1SwsD1Ood+buUsDZd2RMx5Qlhyhqc7SW5+O/PFfiC1OUI0Tf/zXeh/n9SE9HofuR2x7TqcimbsdVs7nr9dw1Ell+NwCI2UtkBKenFbD8acqV8N/n1EC6N+PrV7EHHtKGS8+qX7fBxytrDbJlKBZ3XKgRFf64tvQps4bUOdRwoS8LGhN6fF0fInPo85Rl64EQHtM0J4Q9AjYfPjqb59juw9RwszQyIif3YeUIVDr+/g/tex/dBkJs0OsrBjrBB1um/a4oD2pZcRPyV4VLPmkGrHzcHYrMDPBwem4Hq9LkkzdWKUtxun3Y0mB25AIlNDwuiR+N7RElRXGY3RYhKSEkB8SKdEjBHz+eg2HnaCE5IJmjflRnaAm6ReycBvKQuNawXLdFoW2mIor8rogbsLMJoOWaISt8rxke5WI/Ha5kcmIS4vEdFbqLoeWY9nKqhM3BX63JBxXwdJpgZa0OoRaOqbJpav5Ny1kY0nBkna1UL7PxpaCOe0avXzK7ZjrV8dnRpOOX4PGeAKsGOh+PJpF/Ks7HeGzCeEIn5VYMbg5e+8KwIZYE3jzkTMeoP++FcyNqhNkxUZ/a8KQv6o7yfSda5YPQn7BsmaZuYOxbGWGLcoRWKlbFLchcLsE0x6cyv+dUU5JDxfNbSZCwLylKhIxPWEbupqwgcwdaiypnvvc8NYLagLd76gy3EbH8zWhZK8Ksj0Sjy75+q0axM7D6eeXxCwwpTKXR2wo8drMCauNpwuZQUfm18qI7S6AaH1n648Z7dZqzGKXEdC+YIN2MV/d/jl0IHYZ0em8OflMVcPl34/VcPKZ5Tz1aA1nnDuMhCnRNHjykQ6xc/RJZbz8dO1qn68Jhx5fRl5QkDQlhi546tEaTjitjKSZCtjV4cUnazn6pDKVRp/ocMtYtrppSZrqoi8lLGlWAc17Hl7WSZwceIxyqaxoed3z8DKkVBfadNo6qJiakE9ZM955sZZ9jyzjw1eV+PGkBJgQ8O5Lal3pi/5Bx5YRT0J9WEMTKjYmbglCHnXhNm1UFilpi4fKpPK41L6ks7bSFpRwXBA3IZDJPBX43CoDNG39+uCVWvY7SokuIyX8GtqVde3rt2rY90g1r0jUsZvZZODRYFCempQKswWRuFpna0pQmbayoM1qMvAbkvlhm/5ZyvqU65dk+2FZi3J9QodVLG292vVQZeHJ8qq5Lr0/8dS8J0SHJd1OlRJJB3NrQgmhdNbtt00apV7wGSrmpy0h6J1tYabE01f1BtvlmfzcbNASbsHlyyHZthh+emKDC58v3zypW4TPzoc8vdkLHyfG51cQ3hwVaGt4cflyAOWHltMnr1b0HHp8GYceX8b+qbvOIX8t4/ATyzj4uLLUnQn0CAmyA6lELVv5uF26OjGjCTKBnW5DYOiChQ2SuUttTjy9nJawZF5dgnDUpjVsE0uqO6X0CZ+WsWlPbsJU6/e4OlxooCawt15QJu81Jd+nZsqvU2Zl+eUU5nxYzZJPqqlPBZ5GP59C2NTw6kr0iJ2HI7Y6FbHj0K5Fz/YXqUF7c1WczxYnQDLS/QJiBdEjtjw50yG+W3H5u3+d60inCto7lXcKrN1YFO01nL0Kkxx4TEccTdKSuAy4dOgInnpU/a4ee3gqTz9ag7Th9HPLOfVs9RtdWeSsieg58fRyTjy9nJPOVOt44/laNA2efVz9BSUAvG5BcZ6Oz61+x36PIJ6E/sUGH7xSy7sv1dKrhyDbL8jNUtahJc1K7B90bIfoOfaUMo44sYyAV52PR5xYxl+OVfFD//tvLblZHYG+PpfElYpXsVJW2/2OUqJnv6PKyA8qS5TXDQXZguNPVcfts9dU9tQ7L9aStJR7aeZ7U3Ebkmyvjc+tYnL87o7aYbqmRJCuqbkinhToGniMjhYaLl0S8sqOGMKUFUjXUplVJhx+Yhk5AdhjGy9FOYL8kCDLI3EbHdldK9589fRbLGtrIZJQx6o1Iokl1VwU8MiMxRugf7bJ/NYoxV6NOS0xZjRBU0Qwv0FVHD7gaBVXZdrKIuc21PFOxz0GvGoqcaW2n5ullvG51f57XClrE7CwVUcTKuO2KabRltRoiWtskaUCsP0py1jfHEsVSsxSAqqnz8ZOtdgoDobo47PAm7cWZ8G6szFifKqqqth9990JBoMUFhZywgknMHPmzJXGJRk7diylpaX4fD4OOuggZsyY0WmZeDxOWVkZPXr0IBAIcNxxx7Fw4cL1PibriiN8fgX7CyVu5MwnSMbbEDsOZWFb9Fc/88bztRlT8m6HlWf84q5ULEDQLzAMcOkCn1vQEpG0x9QkXpij8cErakJ+5l819C7yEPBphHzqpKtvTdXfsSCSUH5xl65ifFy6miQa2gXRhLrbW9Ss0RTV+G6pgWkps/hOh5Sz4woBz5+/XsNfji3LZKL8Gt+9XdOpqNqKyOmTafh0CtseNAxvqvLsVgcMI9uljp/8+g4lalJk6vB8ey9yxgOpTK4ActZzHZafFZZfb+xk5/+teLe3tuiqs/1GQ3dn/k3H0mxs+oZMXLqyXJx8ZjlHnFiGoauJ+K47Vr2R8Ps0TBOeeKSG089dc4G+Ioahzr8eOSqq+dSzy7FtOOf8YeRnG5x/cQV+j+Dfj9VgS0iakr9dVEHAp1GYo9HUZnHmecM4/MQy2qOSZ/5Vw5ImmN+i4zEkAwpVQHYaAWQHBEGfoChHYFrKOhT0KVETjpEJ8vWkUsvTcSbheEdrhiwvhPwaHpfgtWdricSVheqSy4dz4DFlGbGSG1DJDPsdVYaRquAcjotUnZzUcfSoMdipQOKEKQj61HY1Tc0fXpd6pG+eXIZ63W2o9ffuIdlzazcDSlzstGUW0Zhyv/vcgg9eqc1YoN59qVbFabnVuhpjGrhDyn2WFMSSqeSMlLXI51bHLGEKYqZg9xI3fbIt8nx+slw637cazG/XWR7RmLHcoC2u3FntMSXElrWkA9+hParWa0sVIO9KibBIPBVXJNQ+6hoEU4UU02VGAobNgDyLwiybuqhGQ1Qj12dT16YRTblbpVRWtXTcWFHAZlFUo+X98ev029wUePfddxk6dCiffPIJr7/+OqZpMmTIEMLhcGaZiRMnMnnyZP7+97/z2WefUVxczGGHHUZbW1tmmYqKCp599lkef/xxPvjgA9rb2znmmGOwLKurzW5wHFfXSvxaHR+xzRnrfHH7vzPKWdgg6dND+cOfnFbDmecNI2lK8rINmlpNcoI6HreGy9BIJG10LWX5cWnMXRyjNSppjagT2+tS8QfNYWgMC3oEVUBiS0QQt2BJWM/UAlkcjlMa8LC4rY3irCBZLklzQmPbApN3X6rl0OPLeOP57qviXLDncAp9FlFTNTZc9HE1JXtVkLDFKp2W064PMfg8kLaqHZQqoog3X/2FVYOgDa9TGXol9jmyjI8X02Ug/sYs5JiO5QEyv7VTzy7PxMz0LPQw+fbbVvnc2BsqGXtjFQBn/W0Y0x7sEN1/Pa0MXRcIyFiKuuLiy4bT0m5RmOvCMASLlyd44pEaRowchcvQWNYY58F7qzntnHIe/2cNw0dcSWOLEsmRmMTjFsQTkvpWSUG2oD2mMpuyfBqxhGrkKQS4XSLjljvj3GFIJK1hmXGttEfVzUvaEmLayq2UF1BWj49SVp4PXqnl8BPLyParc98wwDTVTc/bL6rjtrS5w1Ji2R2ZapbdsQ0VlN0RfJ12pacDrtPCw7JTaeUGxBJkiqumxx3yqWUGlBoYuiCRVPOMz6NR15Aky6/RFrFZ2iwJeOA/z3T89vY5soxv6w1ChsStqbYXv7Tq9M2y8LokpiWwpGpV0RjRcOnK0pKwRKZCdF1YoyRL1R9LpOa1PI9Nnt/OZIRledU+pcefjsNyG8rdFY6pmz5dU2JTEzCnSQVSG5okZgmKAjaNUVWg0JMSe/Xtghyfqgytiw5L98r8XgUMp79+Clnr6epqDyfZ5bAn13msy5cvp7CwkHfffZcDDjgAKSWlpaVUVFQwevRoQFl3ioqKmDBhApdccgktLS0UFBTwyCOPcOqpytK+ePFievfuzSuvvMLhhx++Xvu0LjgWn7Vgfe7o22OSnEDqDlSHo04qQ9NSGV7tFoYumFtnsmR5kvlL4ggBdQ0JEkkbW0q26hdgUC83pq3ukly68qPnBQW982UmqFnXlLm6b8gi16vMtn2CHpXRoHupiyaVqd2QRBLKFbCy6Dn0+N+2/mz3l9XfgS//3xRMW9CWVOm9OXsMZ8kn1WxbYOLebUSnZdMuQznjoUzgq5zxAHL28x1/V7xgay7lUkoVEBNbnrwGR78DscUJ3WtJ+gPx0au1q80+3JgZcAGPZMeDyzny/5ToOetvw3AZyoXSt8TbpeiZOqlD9ACdRM8Jp5Xx7OO16JoSPf93xqq/xVPOLufcC4Zxz51T6FPsxe3WkBIK81xUDL+SZNJG1wU5QYPhI66kME9dUGwbPO6OaVEIdb4WZAt65BgkTRjU28tjD08l6BdkBzTyQzohv84Jp5Vx7gXD0DQlVvJCapv/faaWkF+dtwlLqOafqTTwWJJMm4e0tcdjKCEVDGgZ8RNKeVEtG/oUaHhdynKSttDkBAQBr/qbDrrO9qeK/aXcQukECCHUfqb3Nx2P0yMkMq6qdMygZcPrz9ViWdCz0EtBnodexV6CWS4G9vbjMgRtUcn7L9ficam4q7RL7qNXa9mxwKRfjkX/fAu/W7JdD2X1m9+i/FshrxJxQY8k29tZ9Pg9koF5FtMbO2oQDMgxsaRKQ9c1FW8Vjqn3YqlYrKBP7WskDu0x9brfLVkW1jMV8TWhiiAWZNkUBWzchhJdyyMapq2sZj2yZOY4ZKXqKW2x/zAGH7Ru1sf1RcrucHepdbW2tnZ6xOPxX994ipaWFgDy8pR7b86cOdTV1TFkyJDMMh6PhwMPPJCPPvoIgC+++IJkMtlpmdLSUrbbbrvMMr83jvD5nfAY0COkEY1L4gnI8qk7yXBMEo7ZJE3Jf59RZuKlzZLvfkmwpFFy5x2TuX3SbXz4TTt/r7mdLUoEwdRdWCyh1u1zC0J+Qa8eGluUaPTMUwGX25RKtimxKc6y8BmSbXN1QOOnxnaWxQW2LTAtyQmnlXHKWepkPubkNbP+/FavsIQlyPPYhNwSHei7bwXvv1zbZauBor2GZ2r2/Bby+3+qwOdU5tXaBivLWc85Aci/I7scWs67L9Xy9Vs1FGSr6Wbag1PpU+Jji94BJk6cuMpnJo2vZNiVSvSMGaPuIkeMHJV5/7nHaznlrHKeSFlYusr2evKRGnJDLq65phJd1zBNidejowmBpglsKXHpAo/bwJ0KMhl5pdqGyxD0yHHjMgR5IReaEDw5rQZdF5Tma2ia4KJLh+NxaxTmewgGDO67ewrPPV5LPCmxbSXIHnlgasYC8uq/a3n9uVq+erOGLI/kvZdr+ejV2k798o4+SQmG3JDOtAenEk+oRp7/ergGPeW3CvhUC5uiXFUTLO3aaY+l3eAqyDcvS2TcOjmBjriOLB+U5AlyswT9ig227G2wy5Y+tuxlUNzDhW2r9QV9yqoMympWWuhFCCjMFeRkwbhxyr1TlO9lu/5eLrl8OEV5Bi8/XcvzT3Ts0wev1PLhq7Vs09fFdv1d9C/W6J0v2abQIj+oAps9LnXD1h5XLvqEmc4yU3WGdsgxMW1VdVoT0JLQEEAkoWodSZRlx+eGkjyNloiaG9MXedMWJC3oGbIQwOJWHZ+hRFZjRKM5JoglBb1yVW2lpKWy27xuFdvjNZTQ2umQcoqybHL9crV1ojYkaSvc+j4AevfuTXZ2duZRVVX16xtHxfKMGDGC/fbbj+222w6Auro6AIqKijotW1RUlHmvrq4Ot9tNbm7uapf5vXGEz++Eoau7KQH4vQLLgoQp8bgEA3p68Hs1jjuljCen1fDey7U0RwQNYVVwq2DP4bz9oppMflqksk4KcgQBr8BlqLonPo/6Kk1bIjQ45axy4kk1edgScr2SqCkYGNIozAoRizYBML9BBVCnJ4mXnlLbOfg4FUi4rsz5sJqZ702lMa6R5fr1lgJLP5mC/P6faLuOWO0yK+IIl02HdF2US4eO4J8PKKvNxHGVeN2Cm29ZdbK9/rpKrhxTxY1jKwEYP34CwCpWoSenrSp2TjunnHPOV0HcV111FTkhF36vYPz48bhdGm3hJFOrJ2HogmDATXvUxOsRCCEwdA3LkkytnkR20I3LpfHYw1OZWj2J3sUqiMfr1nEZGoahEQoY9Mj1YJo21VMmAXDtNZWUFnjoU+LlwkuGZ8ayIiecVkZRTtcBpkV5OhddOpyQX+fcC5RVzOMWnHZOeSbV/+H7qonGJaYlKcnTKMrVyEoFUrsNCHg1CnMNPG5BblBjcH83rpTFx9Bh2/4++pb42XXbLPJzPAQDLiSgaYJE0uaN52t558VaXnm6lp239LLDFl4G9PKSk6XESXM7tIRh5MhR5IUE8YTFouVx2sI29c0qXuPyK0YwatRVjL+1MrNvd9RO5q47JvPIA1MZ2NvHWy/Ukpsl6NVDUJwjCHpVvNK8dp26iI4tBbkB6JkrKcqWmLbIBGlvW2CSTKXmewypMvJSLrumNhtjpTo9pq0qMrfGBPNbdHwuNRfaqOVKQjY+l6QlAsU5yjqkCZjXoDG/XpCXpSpJt8YFC1t1PqwzqGvXETsPX/kr3GRYsGABLS0tmUdlZeVvfuaKK67gm2++4V//WrXchxCdf9NSylVeW5k1WWZD4QifDcyBx5RxxrnDeOZfNZmU2faoTTgmKcw1kFK5tJY3W7ywQn2RwpCkZ65yYe1YbHLMyWWcdGY5vfKVQknHE2hCIDRludE08Hs0SvLdFOW5Mumq2T4wdEmeTxU38+sShE7cgta4+uHpeudxB7ydgzbXlBVdYFvsP4zl/5vSpejJ6aKis0zb338nxNan/67b25xIZzICHHZCGXUNKm5m/K2VxJOC68euKnoqK8cQT9hce00lN3Tx/m9h2/DPB6Zy1t+GkTRtsnxgWZKRV47C7dYpyPMwevRoZQGybGJxi6aWJNlZGqEsA8PQUuuRBHw6N994NTffeDUTJijxddttE9U55tXI8hu4XYLsoIvrrlUXjVturSLoN9B1jaRpZ4Teijz3eO1qY5IeuKeaSMzGtCQJU+L1aLgMQXO7BAFnp4RU7yI3hXkuWsI2y5ptwvGUCwuIxG2WNJgsabSJJyUt7Sb9Sn1s3c/HoN5ufB4dj0tZm4N+DWlLLMumKN9DLCG5dGjHzYffa1DSw0BKQWtE9evK8nWMd35dAtOSuA1BYZ6LYEDj0qEjaG6z8Pt0fB71fadF7CWXD+f66yrJCynX1ROP1LBFbz9+n4YQqk9h2BYU+Cw+f72G9hgsa1Wuwjy/KhAKSqwUBVUNs5yAyoItyFYZZqGUdSueOh7tMejbQ7X+yPZKSoMWhUGbPjkW8z6s5uf3p5IwldUpNwua2lXdosWtGsujGm0JQXssXWU61cQ1GWHBR9WZPnO/F92Z1RUKhTo9PB7Pr267rKyMF154gbfffptevXplXi8uLgZYxXKzbNmyjBWouLiYRCJBU1PTapf5vXGEzwYkneUQT0rOOHcYjz08lfxsHbdL4HHB4noTy5Z8Ox8+XWCw+5AyTjunnPMvruCt+S5em7GMprDgjedreempWpraZSczcktYuckiURtdgwXL1aQ5tXoStTW3ZxqgSpSICXgkLakgv2x/iBnNOo0JjbqmDl82wN8uquDFJ5Vpfm1Z0QWWrjDbFX1S5fqDu3cIoHRfn98Lp1Bh9yIGHk/BnsMZfFB5Jq4ElMuhtIeL66+rxLIFHlfX+RSRqIlp2txy69qLHuiwAk17cCpF+S6uHKPWE/AZBLyCsTdWYRgabkNZKgBcLg3TUinjOUGDysox+Lwa190wTgUEa3DD9ZWMGnUV115TyT13TlHZSF4VQHvjTVXcfEsV426+GoAbb67i1lurWNeUkUcfmsqdd0zmXw/XcNcdk6lrMOlVYNC70MOAXn5uuelqplZPwu91MXiAn312yCKehL5FBpatii0aOhTnavg86mHbEq9bJ8vvoj2SJGml6oZpEp/XwNA12sImeSGDvGyVDXjtNZX4vYJEUtAelbgMgdcticYFfg94vQamJQn6DfJzXEgJ2Vk6oYDB1v39CAFL6iW/LIwRjqhj7TY0whGTpCWZOK6SW266mvxskakz1BqFQ/sk8RrqxujjuU18vkhlD31Y58KlqeKEKr0elrUJXn66ltaopCWsrGCxuMzUKEqY0J7QaGpXliNQQikd45Pmf/+tzVTOT5jwfYuaFxvDLcypm8cny1zMateZ29LGoo+rkd/evW5f7nqyMdLZpZRcccUVPPPMM7z11lv079+/0/v9+/enuLiY119/PfNaIpHg3XffZZ999gFg1113xeVydVpmyZIlfPfdd5llfm82KeGzaNEizjrrLPLz8/H7/ey000588cUXmffXpJ7A78k7KffUvx+r4bGHp3LK2eU0tFhIG55/ohavW91Jffd2DfWxBJ+9VksooON1ayQ+n4yc+USnbs+BlUT5G8/XsrxVxRM8fP9U3n2plsUNNmeeN4yLLh3OgnpBfZsy95bkaQS8Sow0xTU8GhzQ02SfPia6pjJYAIaWjSCRVP+ffGZ5Ju6gu0j31mmKqZ9e22cb7q5J7HCZqi79G40rHboJ3Y0hJAML1cWnR7bG6eeWkxcSeNwa8YRqW3DV1V0Lm6nVk7i9i0DnNaV2ciVVtygBEo3BzTeq/wOpmLhrr6nE7QIElPTwUFLgJcuvo6Xu5g1dkhsUaEJw09hKdE2SNCWxuLJE6ppKg5d0BBVff12Hi6Bi+JWZ/4vyf/0OekUuunT1LpMXnqwlP0e53gxdiQ9Q7nKXoRGL2+yxjdqP/JDGFj0NivMN8nMM8kIu8rI9aJrAlboJUp+xsGxobLGIxEyicYvckIto3MbrFgwtG4FpqX3P8km8bkHd8jgJE7xuJZq8bkHPQi9C63CT+b0usvw6zW0mTS1JTMumd7GPiRMncunQERTkucnyG6qoagKyA5KRo6sI+FTMTn4QGttVcHPfLIvSnB7gDpG/x3CK3aoZrBAdtXnSN1lLmlVqeySuSgKkO75rmmq588Ertbz8tIo1MjRVHDZdDBJUHFpbTImeSFKwbbZFqyUQ3hyQEmkm1Xy8jj0DN2WGDh3KtGnTeOyxxwgGg9TV1VFXV0c0qsq6CCGoqKhg3LhxPPvss3z33Xecd955+P1+zjjjDACys7O54IILGDlyJG+++SZffvklZ511Fttvvz2HHnroRtmv1bTs++PR1NTEvvvuy1/+8hdeffVVCgsLmT17Njk5OZll0vUEHnroIbbccktuueUWDjvsMGbOnEkwGFz9yh0cHBwcHP7AdEeT0bX9/J133gnAQQcd1On1Bx98kPPOOw9Q8XTRaJTLL7+cpqYm9txzT1577bVO19wpU6ZgGAannHIK0WiUQw45hIceegh95RiL34lNxuIzYcIEevfuzYMPPsgee+xBv379OOSQQxg4cCCgrD3V1dVcc801nHjiiWy33XY8/PDDRCIRHnts4xaWO+jYDotDWxSaw5LDTyzj6Udr8KbqzMmv7wDgnjun8I+/d10V+oUna1dJI/9opd5AugbhmGoUKISq8toSgZmLbJY2qx99gc8m32unCnqJTJXZkSNHcUftZHoWerj8CuXr/7XquMeeoqrSrg0/p9xfCz6qzry2QaooA/KbOwEbzLY/bQr7Hwn501Ms+aQagFeerqW0wEN2lnLTuF06EyZMWK21J82KAbFrwtRJKywvVUmHqluuJuCDUEAS8CnrgipiqBr+5oUglpD4PRK/J1VE0JAkkiqtO008qWJhJkyYQEGuTlNbAp9Xw9AlbpeqfZOTpZa9+rpxmSBnUPFAZ/1t9dWy05bU8oqR3HvXqlbPESNHcUPKmpRIWiSSNomk7Og5ZQjysyE/W2NALwOvx6B/Lz952W626uulR46b/BwXsbiJlBIr1b8vGDDQNJXQIISgPWJiWpKAVxLw6miawOPWU7WJlBVE06C4wINpiUxmUG5IEgqo+J78bBfbDvDgMgR+L/QuMphaPYlJt03kuhvGATCwl4egH3KCEPRDXjY0purbFeZ7CMdVfI1LlyRMQV5A0sNns3O+zZZ5Jv1zVV2wdK2y+nbBHkPKOOjYMryGJJr67hY1CepaNX5q0bts1ByJw9MrxVh92aAzt8Vg+iJlBygISvINyd6FSQj1X68G1d2JSmdf/8fabVN2+UiLHlBWn7Fjx7JkyRJisRjvvvtuJusrjdfrpba2loaGBiKRCC+++CK9e/fuhqOybmwywueFF15gt9124+STT6awsJCdd96Ze++9N/P+mtQT2Fh4XdB/3wq+X5jqnyUg4BGccnY5r6xlr6HfSiMH1eVa0wSD+yih06dAEPKReX7w9jq7DoCePTSyszSSJhTkGoSCBudeMIyJEydiS4gnZSbN/dwLKzpt47prK3nxyVoiK1wo1iQLrKvWCRuyEKGc8ZCqCi2lKpLosEFIVwPf76gyckPqLm7ixIn0LvZy++23ZQKEf4t0rNlNY9dMAAngjiljADAMyXU3jCNpKnfaipWLLVsFP1eMqsJlKEHkcauUZdV5XIkBTahUd8sGWyrXV/VtlWgauF06bkNTLRBSGZojRq9eyK1YewhUWnh5xUiuv66SXoUGo0ePpqb69lU+d8N1lRTkurjxZrVuv89FIqni95raVIacLWFJvZ2pGZSfLfC4BLkhjVhCkBtU7mu/zyAvW8fvTVWL90iy/DpSSvw+jZygi9ICVedrUF8dr1vSI8dFdpaq/mzoSoz43JJrrhuHlKp2TjIJbpckO8tG0yRlI1Ws05Vjqhh+lRr3lImV1N5eyd+nVGa6sqvvSX0+HcDe1JIkYQmaI6radJZXEvKr30K/AvWZ/CDkB2wCHonXJcnPkthSsKhZXcKSlhI8XkPV51mdCz2dHbsiXh0GF5o0mILv3q7hnRdryffa/Nhg/O5xhw6/D5tM5WavV6UYjRgxgpNPPplPP/2UiooK7r77bs455xw++ugj9t13XxYtWkRpaWnmcxdffDHz5s3jv//9b5frjcfjnYo3tba20rt3726pwrnjweV8/VYNuw8p4/PFEeR399N33wqKsyx6BCE7oFJmu5tB+w/LWFbSDD6onH230cnyqwqs8YQq4GZZqr6Jadr0KjKob4FbfyO4dOSVo2htNwkGjC6Lz62OFSv4gqrtsyCmZVqDbEjE4PNA90Gi2Qlq3kBou47A/mIy5RUjV7mg3zi2El0TXHu9sgBMGl+ZCT5eH+6qGUMyKUiaq4qQh+66CssWROMampBYKYtFJKYEgM8ricUF8YSKCTEtic+j/k93M48lJKGAsiQ1tdokkjbbDtCIxjXKRqzb+G8cW5lp1XHNdePW6nORqE1Le5K77pjMLTddjZTQI8emNSzwuiGeuhHRU20wPG6QNuipNjKaBomkaqXRFlHZmz6vWsZOWQSa21P9xLxKLKbX5TJg2JVVTJ5Q2elYTxqvRGq6k3wsIYjGZarchrLgqL6E6lh63JJwVGS+/5EjR7FwWZyWsBKc2anMrCWNkpK8joay8aSqVZSuSK0JFRANKv6xJdLRJf63SHd57w5+r8rNH718BlkB929/4FdoDyfY5+jHnCalG3sAa4pt2+yyyy6MGzeOnXfemUsuuYSLLroo44NMs7b1BKqqqjoVcupO81u6xPnnc5dkKg3P+7CavJR5fEOIHrHlyZ1Ez1Epk3qffMnMBSbRmMqwMFKiZ2r1JDxujZ6FBsOvqsLQ4ZabrmbMmDGZddxwvZrYrrmmkjFjxnD7pNu4964plBasXYjYyhPSvA+rsb+YzLYHrd4l0F3IGQ+lXF8OG4q0gF1R9NxWpX47HheZoHlgvUXPv/85ktrbK/F5bNxuSU5w1VIIbpfqTH7F8CoCqQa7oYCN36uKDEaiKqA54JO4XcoK5HWn2kC4wO9VQmjk6CrcBiRNm9yQi6QpaGxVlplbbrp6rcZ941iVqh+LKwvKjWto2QJlIZkwYQKlBZ6M2PC4JD/OMTNd411GR/dx0yIl6iAcFcQTgmhMEI6qzKiiPGXxkjaQqubcFlEFCkMBZQVrbpf8NC/G59+HmTk3znXXVmLZ8PcplYy/tZKJ4yqxbbVsNK7cg4auRE/ApxrQ9sixaY/YLG9KqIrKMSV6Jo2vZNL4SrICBpoGuUFBTkAJHI9LZKxxuiawbEk4pr6b91+uxU5VlP7ff9XjrRdqcRsyU6Dv1xjy1zI+2Hj9MdeZjZHV9WdlkxE+JSUlbLvttp1e22abbZg/fz6wZvUEuqKysrJTIacFCxZ088hhx96lyG873HJuAwK+DfMDXLmScVZqO5E4/FSvE0vYNLclEanqtaDiBSJxweQJlWQH4Nrrx9G7SF20JoyrpEeOEkPTf2xn/HhVsXX4iCv5dlY0E4ewPnz/TvcLwK5Id34X25zxu2xvc6OrRqKjKlMVmK+pwuNeu998+uKe5smHVNbUM4+MxLQEwYCFz6METVNb56nsntoxtIZ13C6b+/8xGk0oS4Nlg9cj0XXlcrFtgWWJTONMTZN4XCqWpqFFWURAWTFCWeoCrWuQF4TCPMm114/rHGfUBZWVHTcR6SOQLt64tvWKRowcxfVjq2hpV+epLaG4hxvTUqInEhdEY+qvSKVo21Kl3idMcLshGFBCr7lNtcQQKfe7EErspe8TbQmffR/mfzMtPp8jmLNE1WJKmoJwFIJ+SdISmeXDURsrZTXzeWRG5CQSIlX/SKXcW5Zyg6VjTgxdUJzvVn3DNOWSUy42ePyfNTzzrxqee7yWt1+s5c1UVfmuXFYf/6e2UxZsVxx/ahmvz4r9YeJ2HDYOm4zw2XfffZk5c2an13766Sf69u0LrFk9ga7weDyrFHPqbr56s/PJ+PwTtZkChGKHy7p9eyvy5CM1nH3+MMJxwZJPqnnw3mo+mWmxaFmMljbVMfG6G8axvDFBOCYI+CX3/0O1CRhVWcXoq6swdBX8+eq/a7n+OnWXF4lZPHxfdSYOAeCIE1Ww4T5r0Ok9zdEndRS66y7Edhd0fp4Knk53fv+1nmvd3bF9c+Gok8r418M1XHtNZxGwokVjbdw6d9eOydShAiV20hfYpKkupElTEE9oXFI2nsJcK/O7BQj4LNyGKuoZClicffFEvJ50Wroky2/j96pyv263shQE/TYSQTAgCfhsfB5B2ciqzGf8HtWt3OO28XpsckMWNZMqca+mLlF6PFVVHd27rx9bRdUtV3P2+cM4/+KKLj938WVdp7ePGDmK3JCLW266miy/Wn9JDwsz1eBaoI7xjTdXEQqotPPhV6m4m7KRVYy5poorhldhWRCLC7L8ar+lrWJuYgm1DivV8PPrn9rxuKA9Icj12eSHNJKmRKIEi5XqqwWweHmcRNLC64b2qGBZk6Q15XpKmKhiiVLSFlHWNCFUj63WsLLKa0JQkGOwuEGyrEUye4ndqeFpd/H8E7UUBrK6fb2/B47Fp/vYZGJ8PvvsM/bZZx9uvPFGTjnlFD799FMuuugi7rnnHs4880xAZX5VVVXx4IMPMmjQIMaNG8c777yzVunsG9pfu7E4+qQyQgHB4gblJ48kBAMKJfPrBV+k7pKuvaaSUKDDzB+LCzwelRHSHlEnzFVXV/H3KZXMW2xx220dfZZOPrOc5a3KhSCEultbk55fBx9Xxlsv1HLUSWVrHej9a4itTwfbRP70FGLnit8MUkx3LxfbnLFezWj/LBx0bFmmDtXGZMK4SkavkAn22tPlDDmphqcevhK3y+b4Mybz6H2jCEd1IjEVHFt+ZRXPPToCTUgiMZ3Tzp/Ew3dfRXObruJVbAgGLBJJjYYWjSyfZNaCpLpIJ22yg24KcgSL6y3ysw0qrx3HhHGVCCDgUxaRUMAiGtdwuyRtER2Py+aiKyZQWTmGqqrxXHdtZZctOUC11uhZqDLebAljb6zilpuuplehyXmXdpxTE8dV0qLq9yGlxLYlPQvUvWrAZ+N2qcKlp1/QOc7u9gmVjBytYnHao0ps1d5eSXtUUHntOCaNr1QF+1KZxIkkeD0Qi6s+WQKob06yrDFBXaNNwKvO6aZ25Wraa7CPj76LMrBEI56Q9Cv1IqWq5yOE4Ic5YRY0CLbvp6nCqsssfG5V+6e0wE1JgYvGFpu8bGVxamlXl6DZCyLUt0qSFt0Wf7MiFcOvZOrrTao2z4wHum29v1eMz/svnNUtMT77HzftT3d9W1s2mTo+u+++O88++yyVlZXcdNNN9O/fn+rq6ozogTWrJ7C5kk5LP/aUMl58spYDji5jZp2WcTP137eC0w7wYktBUxs0t6sKrULrMGsPHT6eW266OtM3CNRk0tiiLhrL2wQ+15oFF6Z56wU1Lk3A/51RzoxFgh/fXX/X14oBzPLLanruXcGij6uVuyvQk61yDGa2SAYGBbPen9rRvTwRJrj78A1aWHFToDtFz3XXVqb6yak7/q7aVazMkw9dyeLlLoJ+yUN3XcV5l07kpX8N55jTa/j3P0fi91gcfVo19/59dKaNQsWoKiaOUxamlnaDn+fr9MiBO6eOIRSQxBJQmGvT3KZS0oWwAY1lTRCOWmRnGeSE3Ng2LG200TUlFADq6pMU5btSQdAgpY5lCSIxFUvkMtTFu2ehukFYUfRMmVjJ8KuU+CgbWcXA3gGaW+P072mwtEEJlWuvV0Kl6parM3FIV109gZvGVpLlU+6gLL/EMCyCfpNITCcc1TF0mQni9rpt2iI6AZ9yE3rckKND9W2VKbeXGmP6/E2mmpx63Ur8tIZhfl0Er1sQSZXE2HlLHwuWxvC6NRY1qgae02dGEcCcOpu4CfFklMZ22Lq3wezFJg3tgrglmLPEYsZyg78MEuRk6fg8GtG4RWOLSpdvapXousDv1VhQF0XXVeDyx//pXtEzZszoVDkAG/nd/exy6Mbprr6+bIw6Pn9WNhlXF8AxxxzDt99+SywW44cffuCiiy7q9P6a1BPYnDng6DJe+kng2204DWFBSbbNSWeWc96FFQQ9Ng3NcWIJFeSY5VOWn2WNEI6qu8ObxlYSi0tMSxDw60ydVEn1lEmZyWvGOzWZyRWU1eCAo9fMjfXSU7VYtqRnTvf161qxYvOij6tVU0FfEfLrO2hLCORXXQi0QCn6Zj43pGtFnXZO91wgpFR1bsIxQSK5Zp9pj+hUjFLB9uddOpHa2ysJx9QV27QESxtVZeSg36K5Xc8EtabdYedeMhHDUGLosmHjOfPC2/B7VaCv2yX55mdBXYPOLwujNLbE2bKvl4G9dfJCgp4FgobmRCYFf8yY0XjcBn6P6nk3cnQVTW0aLWFBJCZoCwuWN6XGZsK0e0cxeYJK5QaVSQVQNrKKsTdU0q/E5o7ayVwxXKWAj0xlSEUTgqI8i+wsi79dNoHJE5QF1uuRhGMCXZe4dEnS1PB7lX8rYQoSSU1lOIV1PC5J0G9RmGeRSKoMrXQsTbqGUjql3+tRr9up7K8vZ7bT0GJR32yRNCXN7TazFkRpbJMsbrD47LVavm7UmN2gM79Fpz6ske1T8YMhHzS1WdhSNfnsnWvz/vwEA3JUZfho3MayJT6PTnskmcrysgl4VdZWa9hicYPsdtEDEI6aGLpgcX2SPQ8vY59tXb/9IYc/NZuU8HFYP+Y1q7zUQ/qryejN52tpjUgeuq+ab96qIRKzKchR2S3RuKCuQYmQaNwiEhXouuCWW6tImpKAV2WAjLv5ah68t5qH7qsGVN+bv11UASirwXtrYbI2dLFOjVFXh/y6lv77VnS80PoLxBvpuXcFi8OqhEG6n5h7txGIXUaA4aWlvaH7BrEJ8t3bNex3VBnRePd4wdO9t9Kp7Ktd7qarM20mwlGNZ6eN4OKy8dROVpaSUEDFpOlaRwBuNK5j2xBPqBdGVVYx/lYVlB+L29w4VmUO1U6upLlNEIlpuAzoXaSTSAp6F3sJBlx8OiOMECr7zDAkHrfgmmsqmTqpkoJcjWBAw07FtaT35bobxtEatvG4Icsvqb5NCZ3GFoOWMMSTqm1GY4vKjBx7QyXhiMU3P5uZc2TFDu71TYmUqFOG+BVjaSIxSSyh0dyu0xbRSZoaPo9NLC4QQhJJZWvVNegsbVDNjwtyLYJ+JX6S6tAxZWIld04dg0Rgmun0dmhuE7h0ZQGKxCWNbZI5ywXPzYghJZTk68pSYkYpzrJY2NxIYZZN0Ad5QUE0AQvqJUU5KjDZ0GHvXm4KQ/DJLxrRuGT+0iRzFsd59UuLpQ1xXC6dcExS3xRn/nLZqY1Ed3HdtSqI+t2vo3y5QOd//63lx3mJ3/7gH5CNUcDwz4ojfDYj5n1Yjfy6FpcO37xVs8od/bQHpzJ0+HgG9EzS0JzAsiTzl0SYdNtEogmRCU7VU5ktoO7kV+bBe6spKx+51uN7+tEann+iNpOCD3DMyesX+Jzn67AgydnP0yO3l2o0uFJqe+Lzycjpk5HTp4C3x3pt889A0qJTQ9y14Youvvt/VKvMpl9rQLq8MUFza5LLho6gPSpobHUp91CqXk5rWAmCk869HSEk0+4dha7LVLyLpOoWJZwCPijKUxdXUEG09U0wc24YTYOLy8ZTNrKK624YRygA0ZhF32IPy5sErRHBZcPGs6Te5NZbq1jaKMnyS4I+yXvTw7RFBf+oHsOYMSpwuak1QUMLzK+DOYuStEUEc5eYtIVNrhxTRVvY5Mabq7jxpioWL4vR3JakNWzhMlJV1PPc3DS2krLykUipxjZydBXVt1WyvMmmuV3V4GlpT7C0QaWjL23Q+eArFWSdE7RobtOwU8UG26MSlwuyfBYuXRUY7FkgKcxTFqLhV1VhWoJIVAmz9qhKG1+yPMan83RMC+rboCBbI8cn2bHQR25QFUfcppdgtxI3jRGNvKx8WmOqUGoooLP7Nn76FggaWiX9izRaIpAbUFmlA1LbfumpWtpjMKfNpD1q88MvYeYvifDpTHOVCvTdwbXXVOJxC2YtTJAfhBN2V7+fNYk9/CPiBDd3H47w2QwZ0NPLBRdX4Pdq2Ct5lq67tpJoXGOLPi76FOt4PRo3XF/ZKSPHslWxuMaWJJPGd13LpLZm1Yq0v0W6xP8rT9fy19OU4HnpqfWbpL5YKb21JGBl/t/tsK5dOXL65C4rTG9O5ATW/bM+z6r9dy6vGN/Fkp0pyHPjcqkimwDLm7RMVtWtN1/NqX9TLSGee3QE5106kbMuug2XLjnv0onMr1Op6KCsF3MW6fTr6SMas1XdnITN9oP8/DQ3wfXXVXL11WO47tpKKkZVMbV6EhKob05kfufP/KuGMWPGkOVTWWO6Ltmit5e8kM3lFeMZP165we66YzLxhE1V1XhcLo2G5gSTb78tU9hz4sSJXHzZcE47p5zmdouEKQn6dRpaLP5ybBm3T7qN72arCObqKZMYNeoqRo8eTTimiv4tWR5jebNgxy0N+hRbjBhdxdXXjSMv201Tm0E4qqk0dJ9NYyv0LlLp+MubXei6RNNkqiFpx3diWemCjYK2sE19M8QTkkUfV+P3CgpD4PdqxE3I9kmmzxUsazLJCers2N9gYIFNsd/i2L0D7LldgN0He/D7NPqVetl+oIfsoEHfQkFRnk5xvpuWqOCt2Tpb7D+MeY0apX4XLWHJ8lblTlubmMC1oaU9wXPvt9MahYN3y8Lj1hC7rL4hrMPmgyN8NkOWNSXIy3aTE3TxxvPKNXXhJWpCeOGjMF/+qNRQ2cgqeuR6ufGmznfp6aqzBbkurhxT9ZsujJXpyiIAnUv8P/v4+gme1aXJf/NWxyT7RYOu4n6AHQ4uZ9cVgh7ntG/eNmHTUu0nhvx17S1uK2b7rS2RWBLLlqofVir1YuwNlbiNju+jpd3gqYev5NlpIxBCMnVSJVv0luQEJaUFFlJKvB7BDWOrcLuUcO9VqNHQbOHx6Fg2bD/QwusRGcvk5Ntvo6b6ds6/uIKRI0dx/XWVhCMqs2nKxEpyQxaxhJWxdKYZWjaCCRMmcPkVI4gnrE79utLnVHO7hcclaItITFOytCFJPNnhrnvykRoaW83MsTNNm9Z2i6Rpc8+dU/j4m3a+/slkeZOesTRJCZeUjWfo8PFcOaaKnCyTLB9kBywiMcGiZYLmNiNVhZpMG4mH7rqKWAJchsRtSPJCqZ5fOe7MWOY1CL6YpWJ63n2plgO2ERTnu9hpKxc7bumlX4mLIbu46Vtip+oDqUSI/BydfqUuCnLdDOoTYJdtfGzVz+CE/QKctrvg4O00dusv2b7EoihXo2+hxotPbjjrS59iNzl+VZOprsHC7dLxbsJXPMfi031sMlldDt1HwKezpD5Oz0IVUPPgvdWZ9/bcSmd+XQIhVDxAvxKbx+4fRUOzwbezYmzdT6WujhkzJlPMcG35+xpYgy64uIL776le7ft/ObaMd+az2kJkLz9dy2En/PpFW07vyNxaURD5dhueCXw+8JiyDRJ78EfjkOPLMsXhgMz/J55ezklnlq/S2HFDkEhKvG5lmYgnRSYeobk1SX62Ckgdd/PV9MhRLSOK8hLEEhp9iuN8OdNDSQ/JnDqNeMJC1zUmT6gkGrMo6aGztBH8Pp3ljXGCAZ3F9SoOJm2ZvPnGq5m9IILbJWgNm7RHTUIBFy3tSWzpQgCDersJ+pVAGVo2gjtqJ9PQYnL6ueW4XRqGBqecXZ6p0RVP2hx2QhkJE959qYYTTy9n5iKJ3y0x7c7xFkKofng5WQbhqMV9d09hzJjRjL+1kpBf8OXPCXYYZPDdbOW+S9+M3F07hsXLBK1hQWt7lG9nSUp6CPw+ncX1guyAwOuRTJlYSTCgSjSns7r8Xkljq4bHLWltV1Hnuw8pozikXJ3HnlLGrttksef2XoJ+i/aISBVKFPQrdRGJgsslaWjR8LolBdk29c0amiYozbO5tFzND6NHjyY/x0N2AGYvjFFaYBCNqcarG5In325HSsFRewX4cU6YX5ZC9PMN/zveUNipx/quw8Gx+GyW3HXHZKY9OHWVVh7XXFNJSQ+vEj2TJ7FwmcnP8yWffGvTFhHstq0n1bxUdYyePKGS0aNHr2YrHYy8ctRaj/H+e6o55uSy1VqH3n6x9jerr77+nHp/h4PXLjsp+nmHINocRA8ooXPmeau698JxydreI559/q+7CSuGX8l5F1ZwXqrx7ZWjruLyK0YgpXINTZw4kURSYtmSK8pHUj1lEr8sVG7Vn+dHiMYFzW2CpY1uTEuwpN5NJGaxtFHQ2BLHtiUNzXEaWlWj0boGi+IeMG9xlFjcJhqTLF5m0tDcEeS6vCmO26X2VNPAZWjK+mLJTLNTgO9+MTj/4gruqFXtOR7/Zw0Br87D91Vz/z3V1DVK/u8M9Xt75IGpzK7XMr+h93/RiSQFcRO+XG7w8X9q2e4v5exwcDnT58DPiyymVk/ivruncNKZ5URjFm4Ddh8coDBbEI1pFOa6ueqqqzLjuaRsPKWFkpIeLhbWWzS22sycF2XBkijtEUsVCowKLBt+mie5cOgEdE1VVk6aguwsVeTxzjvU/pTkqObFuVmQF9TYqo9JwGvR0GygaaooYn6Om8ZWFXuU7hUW8EnyspNs1S9OfrZNwNtxiTUtG9O0mTE7wnZbeOlbYrDbYBcS+NtFFRz5f91bwDTNj00GCQtmzY+QNOUGc6k5bHo4wmczJm2xKRumxEU8YdEeMcnPVreEvyyMsWBpLFVWXlV5vXJMFbfeWoUQ8PP8OE2t6uLRVc+h0aNHc9PYSm6ftPpGpuUVqw+Cfump2jWyDv0WK1pz1oYVg6z/zFx+xQiATvWZ0mgCmsMyE3+1JjzywFQuHarWuXIV4vMurEBKaA3btLRblJWPJBa3MAxBfVM8I4rCERUg7HapKUrTBLYNeSEXsYSqPeMy0hdvi4G9NMIRi2DARUu7idetsbwxRiRqMXggzF1sEfDpNLZa5IUEAb+Bx21k9r213eKXxSaxuI2UsHh5klPPLmfhcosBveLEkyqd/NZbqzB0wUWXduxXNK4u8kP+WsZ7L9fy78dq+OtpZfh2G86ciE7/fSvov28Fy/83hRyfzfeNBjFbIHYcyvetBi4NAm6bgSUaJ51ZzrkXDGOPwQEiMYu5SxIEfJKj9vORNGHr/oK5i2OcmxKNAE2tgquurmJQLzc+jyASh09+stEEfDcrRkNLWqCoL3j4VVX4var2UCQGS+o7TE8+j7LqZGfpBAMGSUsQT2okTdXeoyWsAp237Z8kL2QR8NkE/RY5WRYel008ob4vj1sdk9rJlWzZV6coT2OXbfw0tariiwuXaXw0U2JZkt6FHY6H1VWtXhe8Grg0mLfMpqGt21a78egON5fj6gIc4bPZc8HFFURjNhdfNpy6+jgBv8HSRpOTzyzn2cdr+WmRWm55k0U4KjIF4rweA1uqOIULLq5gWYO6aIFyA4y8chQ+r4Zlq8lvdazY0DJtGUrHXVywmpL+oFwza8veR6jP7Hl4WaZWzcqsGBeUNJXrIk1Xvaj+DKSbh1pd2MFbI5DlhaC/C1W0EunU7JPOLCeRtDnzvGHcc6eynqW/01CWnioWqJHl14gnbSIxi5Y2VWslGrcIBnRsKYknbKIxi8uGjiAYMAjHoCDPwOdWxfbawhqWLWhsMfhhThKPWxXGy89x0xo2CWW5sKXk9U/iLFwao6EliRCCWAKaWhIsqIvyj78rS8dD91Vj6BCOSRYtN2lokySSkoAHXLqNpsHCZWo/5y81qW9W58gW+w/j0YemcuT/lfHas7X03beCg44t49nHa/FoKlB+bkQQsQRi8Pl89GotcSnYtzhJqd+F/cVkvnijhs9eq2V5sxJd0bjko2/ayQ258Lp15i62aWzRKC1I0hbW2LJvALchOPO8YYwcOYqAV7melzUlKcl3sXUfN9v0hJ/mR5m12OT7X8LMWhAjL9tmzJjRXHtNJe0RjboGdREcN07dAJ1/cQXZWerYGxq4XRqtYZ2lDTqRuCAnaJLlk5QWKCtQdlYSn8cm4LNojei0RQxC/iQBn80Pc13cNLaSZY2qtlFLWNUH61tiEUto2Bbk+22WNdsU9/By9dVjuPyKEZnfS3ew/H9T6BGSfPjq2pXV+KPixPh0H47w2UxJ99NKmpJIzCZpqiBA05I8/0QtTz2q7lq/eKOG2qm309CSYOToqkwRtOwAZGe5WNZo0hZRBdlMS3Lp0BFYNrSFTcbeWEU8YdMeETzwj992iaUtQ+m4i1+L8XlzDVNS/3JsWSbWJ10c7X//reW7t7u2AqUrSYNylaXjNc762zAsi4wb48/EfXdP4eLLhndy6YASfUFfqrml69enihVLIxg6+DwaSxptTjunnEuHjiBhKlWVSEqSpk0sYeP3atxz5xQsS9LUZhOOWbgNjdqptzNl8iT+XnM7d94xGb9PZ+yNVbhdqlJyOCaob06wvFl1H69vgWRSsqwxRn1TgrawEjiagCyfgd+rUVrgwevW8LoFy5sSCA0K81Tc0HkXVnDmecPQNGhsU66f9phgeSuYNnw320d9k86PcyLsc2QZc+o1WqPw1KM1zHp/KlsdMIxX/52qQA4EU5Wk9+6dKp4jLVpNQFPB9EkbPlxi0CtkIXZUliGAWcs1/v1YDU9OqyEvpDNx4kQCfh2fVyecqhBdmGdSkAs7b+0hJ6hz++238eHXYX6cG2a7gQH6lrqprbmdpx+twbJhfpNGwoQFy0x+ng89CzTC0SSL6yUlPWSndiCGLrAs1aLm/nuq8Xp0GluUtdfnkSxtdBGJiUyVarfLJuA1MTSJrkk0IXG5bLKzTLbqkyQ3pCxITW0qe3ThMpO2iE5ru2BJfQxDh1BAEI6YFOUJ+pV6VvldrUtZjBUpznUucQ6r4vwqNlPSdTNiCWW1sSyJpgk0ITLmZokK7hU7XMaPCyzKho3MpK6blmD2whj52Qaz66DvvhW8822S5jaTBUuTzFygJv1w1GR5U5JlTVqXae9dMaziyowL4rKUy2RdefvF2kyszxEnKgH0l2PLOO6UMo44sYwj/081Vl2ZFa07Q/5ahselgm1jiT9ftte5F1Zwz51T+OcDnVuFSFvVYQl4Be0R81cvQm5DYEslEB//Zw2xuE3vAo3WiMpisiyV5dTarppqelwarWGL8y+uwGVo9MjRCUdtljQkOfnMcsrKRzK0bARVt1yN12Nwy01Xs7QhjmXD0oYELkNj8bIoTa0QiVpE4zYBn7L4SAk5QRVAK1EX9HjCIhKzyQ3ptIYtmlpNpkxWGVjxpKoqnDRVF/PGNrBSxd58bkFzu2RxvcXyVonHgPakYGGTlrl5aE0IdjqknL77VqBrkhefVJaf5rDKjNsmRxCXAlxBiCwGKwnS4tNFEdBdxEyLrQ8cRiqumwOPKSMWl4wYqSygPg8M6GmxvNnFD3MMvvkpyi+LbGbMTXLBxRUM6hOgZ4GL+uYEvyxUwc9jxoxhXr3AZ0jiSfhhic5H30WY8UucLfsaVFWN57JhytIzaXwlV466CtOSLGm0sGwYPuJKxo0br4oooio7fz0zRnObid9r4XFbmKbA67FW/BmgCUlOVpx5dS40IQllSdXYVCorcTQGvyyMommColxB/1Ift99+Gy5DzUM3pVzmY8aouk/BrPWrsvz+zN9eZlMhXdByvR4beyf+IDjCZzNndp2y8vzzgam0hm3q6mP0yPUwYuQoehe56RESyG/uZE6Tzo/zEmgCxt9ayZLlcebXSz6eadEY1Qi4JDNaDL6ap8wGS9s0zvrbsJS1KMm8xXGyA7992l14yXCEIOOCSAddrg0nn6lEy74rdIk/7IQyXKlQgrdfrOWFJ2v5zzO1vPrvWnqEOkwdh6fEUdrCcerZ5ZTkqcqzut7R8+zPxMOpqtsrM2+5xOMSZGepK3JLu7nKMmkB2dSuejv5vRpl5SPRdVXpO+RXgmh5s0nSkvi9Gn6vhtslEAjiCWVxTCQl4ZgknpQ89WgNEsjP8bBwaYy2cIK6+ji2DdGYidejY5qSHjlu1YNK13jovmrCUZUpZBhCpZULFVjrcWskkmrbSVO5cR5JibyjTypjUb2krkk1713WIgj5oThbEk0KGtokvyyMACrFf0GzhikFP7VpNEc19j+6jLqoxdf1kvntSWa3C8SOZbQk1X4vaNFJWiBtm2xDgqZDvF5lDVpRDihNIr+qIcstKc222fuIMmJJSJiSgb10SvIl2VkqwLyxReO7We0sWG7x49wo77xYS1Obzc23VLGk3uSrnxP0yPVw2dARzF4QIeiRBNySt16oZc6H1WzXz0UoYGQET5offonyw5wouSEXW/XxsMd2AbKDSnBEojZLG0xmzkmwZV8vhXk685d4aIu4UsHNgqQliEQ1ljW5iCUMkqbO/js1E/DZhKOqOalhgGnazK9L0K+nj7xsN1rq+wHV9DXd6BQ64g+N9ewf0zf3z5PH5Li6ug9H+GzmfPFGDU+k3DmaUIGai5fFSCRtaqfejtct2H1IGYdvByG/IByTzFkUIxq3+bHJ4Psmi7lRjR9aVTsMy1biwKNLFtXbKpjVlvh9OouW25m72BUZucJr9909pVMtlBVZ095RT6VSrz98tbPbasWaISvG7jz9aE3muaGp7aTFwBOP1PDw/VN57OGpPP7PzSsr5NPXajN3601t9ioWIYCiPAOXoeKD+pT4Kcr3kjBtfB6dvGw3SRNCAQ23IYgnlIsrYUraIypuxpbKtSSEyiLKDqgpybYl4ahJIimpqb6dUJaLpGkz+fbbMqnXbRGTeEJlLqUtg16PBlK5SIIBF7atOq67DIGhC9ojNvffU82BxygXaCwJi1s13lvsoi2mLgqLmwTLWgVL2jW+WmIwu07yxY9RLBs8umRZAgo9EE4KPpjTAvF6sCLKRJZoBSFoSVh8ujjB/HYLTUAvHwzON8EIgOZCbHMWmDHem92A2LGMLxp0FjZrtMQ0PAbst1OAcFSlqRu6ZOEygy9+aMeW8NqztcxYrLPTIeUYhnLVBQMa771cSygAg7fwsGW/ACG/+g7TfDU7SV19nBNPV7/1YRUqJm/+MovtBvoZ2EtnUB+DUEDidcNtVZUkTZvFy+P8tDDBjF+itIahoRVmL3QjJcQTGrMWeMgOWhTlJYklNOqb3XzxQw7RuKYaHAckhi7Iy9bYsq+bxpYk0ZjJfjsHcKUi6tujOll+ybXXj+tkWdR/O7TsV3m7G5vtbmyclhXdhyN8HADY9qBhPPVoDY8+NJVw1MZOdX189KGpfPZaLYvqVfG2xuY4vYp8zF9mMjBkgVDuhVKPTS+fxOdSn/O7JR4XWLYyYS9tSLC8MZ6paLsit3fxWlcU57sprxjZSShBRybI8BFXdnr919Kq3UbnO590LE+vQoOAV+PUs/98sTxrwxnnqmP35LQa7rt7Cs/8q0P0nXP+ME47R9X3aWqzyAvpFOXqeN2CXkUqKLil3aQtnMTtAgH0yDEI+nVygwZet8DnEUx7cCo+j4auC7xuNRW5DMGwiisRAgxdQ0q4+uoxhCNmZtL2eXWVcq4L2sImSdPOuGstC2IJ5boCCPgM2sLKFZYwZSYDy9CVBacprCwzW2ZZNMcEn79ewzcNkgVtOnXhKACtMVUdeVGbTiSpsWWWzbKEYN6H1R0HTFoQWw5WFOKNEG9Efns3CMFPbQLThpaYQH57r1re8IG3AMwYJBrAtkhYEDPBbUB2ls2CpQnmLUkwc54kFpdk+TT6l3rY7i/lBNw2g4qhqU312vtslmTIX8toC8PnM6LcemsVbVFVlyfNNn10svw6vYvcXHL5cN79NsGA/YZRkK1RkKv2UQjweWzyQspNtaQ+zhdzBF8tNpgxz6axJUE8YTN3cZw5S/zEkzrb9IvRtzhCaUEYv8ciElNFIuMJQZYPGltUixuXDr0KVVd6j9vgvelhWtsTjBp1FZeWj+fn+SZjb6ikqEdHrE9+dnf+qh0cFI7w2YxZMXX0+3emcsDRZRx/ahk5QZ36FuXWSN9Jv/5cLVl+DQnkhWx+WKqT7ZMgNAKaxJSCgEtSEJTsPqSMj/9TS1sUljXbWDZ43Roet8YN11Vy+4TVZ3mtTPoCDKqkf0OzSVvEZOTIURnrUfqCmEjaner+PLKShSKdAnzuBcNW24Dz7n9M4f57qjNWsM2Vxx5e1bqTJjvLIBTQ8XkEPQvc9Cvx0bvYRzhqMWeRSjnPDRn4PDpBv05JgZfGVpO2iEVDi4nPo+P36lx4yXA0IcjyKRdUwpS4DIFtS3Rd0NKeRNMgEjUxUhabsmEjuX3SbZimVNlHhsA0bXRdkB10YRiCgE/HtFTzy0jMJJaUytKUtImkSvc0hVU9HdNWls6WuGBOu8y0NGi3AGnSZgssCR8vNlgcsYhZ8FObgHgjYttzwE4qtWDFAaGeWzHkD9PUhmLLIdlGXcxmdpuO2GUELn8PsC2w4+DOgWQEokuZ09TGLx9M5Y3na2lo0QhHLdwujWjMZMHSKMuaLT6fGSdmCmbUR3nq0RremB0D1Pn7+o9NfPNzOz8vsjKFE+vDOnseXsYpZ5fT1GphWpKSAheD+njJ9km2LLTJy1ZWHtNUneajcY2ljRrvfRElFpdIYOknU1ge0Xj/uyRf/BjhpwVJfppr8eNcD60Rg1AgRn52mG9n+2lq09B11cRY15X7qvLacZQWJAkFkiSTIKVkQE8/fUs9+L3KrNO3xIWuCbbfIpb5rQknKiWD4+rqPhzhsxlzz51TOnWHfu/lWp5/ohbbluSk4joaW81MzEw0bhP0G3z+fYyAS9IeE2yRJRmUbRF02WS5JU1hQTQp2OqAYYQTguawyuTJSqVDS2DxMpObxqoeXzdcX8ldNWNWO8aVL8ClBR6CfoMt+uj0L1VBOz5v2j2iis91xf+dUU5hrirL//D9U/n3Y0rYpIOoHdYcl0uJ2CyfTk7IRWNLgnDUZFljnG9mhckOGiRNiddjkEhKliyPoWsCn0fF9rSFTRKmEiJ52S5CWW4K89wM7B2gMM+L32cQCrgxTRUY29Ju0tpuKnGUqvZbW3M7d9ROpi1s0Ro2SSSVGyyRtAlHLdWxPSmZvSjB8mbJ4gbJwnpJcxgG7DeMpWGNL5cbfNdiMOv9qSxNaspaY1uQbCEWbQLNTTLazOywTswGki3KCpRoVULHUoHESsSYoBmgu5Ura9CJyp0lBCTbwQzjSdWVScZakDMeUKIHW31OWmBFETtchthlBF/8EOaBe6rpVWgwa1GS936QzKsXfL3cYHZzDPntvardirQRW52K2P4ivIECnvsuwtJ2jUgcbCkwJbTHBbOXQGsUmtssWtolsxfEqW8XzG3Q6F3sVi0u2gXtUZg132JZYxxdg/d+MQgnU4UdgaBXkrTUurKDBldfNw5dk4Syoixalk00LkgkBfGEqgzdGiaTBZafnaChxUNO0GbbAZKBvSRBv+TGm1VmWUML9C9N8svCDotPIikygc6bO7KbHg6O8Nns6SpuQ9cFpiW55PLh+DwafUrURBQK6PwwN4qhCw4crFEf1WiMayxq15kf1flimaQ5JmiIakRMgceQxE1Be0yycFkCn0fnppur6JHrwudRcRwel8iUtl+RW29WGWC3VSnr0Ngb1N9gQKXwXjZsfKaB5c5bGYy9oZIBvTwU5Hoyn12RrfsH2Lr/qnc76SBqhzXj3AuGsaxRmU10XWSqH7e2m2zR209Bjk5Ta5JY3KalLYFlS3qkBGc4auN2CQJ+ndyQhz2299MjR8dlCPr31NhxUJKt+lp43MrKoSooC7J8SjTbtoo3WjEGJJ60sSxJOGpz9vnDcLs04gkbKSW2LckOKJuBJiASF/zvv7VETaEacmpgfzEZsd0FyGg9aN6UBSc1LcYb1d9YPVimej3eAHZMxfNIWy1vJ8FKqL9ooHtAcykxI6V6LiUtsRg9PapXheoRJ0DooHs7DrAVxatJtuyjusQuXGaSFxRsW2JTF9FVdliiGQD55RTkd/cjZz6B/PZeYrb6fc9uFyolPyFY8FE1WR51uVMVmwUL6qL8vDBJY0xjUJGNzw0BnwpeV3FVNg3NJsuabQbmmOR5bQYfVE621+aDV2p558VaGiIai5fHue7aSmIJDb83xnezg1i2pKHFxLJVHay8EPQtTvLIPVdR3KON/Ow4vYuibN2vBctWIgngneevIBq3MHRJLNFxng6/qmqdW+M4OKwOR/g4rEJjq4XHpVHXYJKf48q0tsjL9jCgpxtDFyyuN5kfgSZLsCzcTjIRpdBn0JbUqIurINC4KTA0aIuqmIpYwuKaayqJJSTtEdX6wrI7xM2KpLtkj6pU4mbsjerv9WOruPpqdQd4/XWVjBw5CstW74+qrOK6G8Z16iSfxtDhgssnZJ6v2EZj7A2VnHRmOddcs2YuuLPPH9apcu7mwDnnD+PkM8vJDbmIxJTlxdAFf6+5ndqa24nEbBYti5Hl0zB0jfwcN26XhstQFp7tBwUoyndhWZJI1CaRtGhuE7hd0LNQEo4Klja4yM9OICV4PHomwyeeVPE5lq3cJk1tJhdcXMEFF1fwr4dV8LkmlEhKJm28Ho1YQgXUmib4PRCOq0B+AF/KAtHw6RTE9pcoC46dhGQLRJdCMqxEje5RViArrgKY7YQSKvaKKdwC+fMzyrKDAFKCSHOp13Sf+l/TwU4wt90CTzYkWiCZ2q6mhCG2CbZJLBHj5leUNem7X2L86+EaivMNGj6dQtKmQ5gBYuDxmf/l9MmQaMWla7z/ci1ZbslOh5TjNqA4R2LbyvW8YLnNwmaN0iwbv0ewcJlqdTF3cYxFy+LMXJCgLWrT0A6RpEj1FhO0xNR2tz5wGLaE3sVebr6liqLcOJGYl7mLTZbWq3G3R2CLXgkO2X0J2w5oJMtvsv2BD9KnuImkqfHaJ/mYlqBilDqv//OxEnqLlrsQwrFLdIXj6uo+nCalDquQ7t2zz5FllEYtXKkeRs2tCbL8BjXVEznzvGEMzJLMbmwGYMhWXr5aDDEbXLqGLS3cuiToU8UGDz+xjMI8wdxFEfKyDWqn3k5l5Rik7BA3j9xzFWdfrDp7jxkzmvHjJ3Q5viyfmoATSTsTGP3vf47k/87pqAJ949hKFiyJcd/dqhKsx9X5hE8XS6yZVJkRVWvKyrFDf1aOO6UMy04FvLqV5cTrMdiit0Z7xMS0ZKoruc0Wvf20R0z8PoMeOfDTvDh31E6mYviV2FLS2GKia4KckIuWNpMl9fGUq8tNyBI0tcHcRRFm/KKz1/YWy5t0wE19UxzLUuncmqasEomksh5JVPkBQ1P1htJ1TkgqF1k6WywSV9lNA/YbRmNCY4XYWWXVsZPIn57qtO9i69OVyLHiHX+1VE0ZaSnxo7uQPz2tXtMMJUikBKmyzrDN1POUiBKp6TbWAGZUfQbAjoLhV6+lxiTDKs7lv8/UIrY8CXxF/O/gcvr7BdJXhBh4PHL288jZz3cad3o/CvYczs6lksZ2QWtUIKWkRwia2sGW6sakOEcCqgWGpilX8ZIWSSQBHgMaIho+Q+J1gaHZ/NRo0HffCmwp6J1tcc114/jnPVdx5D6/8Phr27FoeRjbhoYWVYV72/5uwjEP0ZiLfXb8hegvBzFn0Q7MnOfLtEe5q2YMfYpiJJMuivINBvaKc8KZjhW2K7ojK8vJ6lI4Fh+HDOmU7tIe6g70o1druetjyY/zlDvD59WZtUBNzklLsjSmzPp5gWxem+vCEJL2eIzE55OZ02ayqE2nNaoKsv33mVpqqm/H0MFtaJnAak0TGdfU2RdP5ONXLwVYregBuPq6cUy7dxShgE7VLVdz/z9GE451znvVNcF9d0/J9ALzeyW3VVVyyeWdewGFsiyeerhzNpiD4oUna4kmIDugglVbI5K2cCLVsVwJT7/PoC1q06vIxuPRsSUMu7Iq0wajesokaqpv57bbJhJP2Jim5L67p6AJwdKGJIuXxfhxToyFS6MIAe0RC9MShGOq/ovHrXH/PdXEEjb1zRYNrSoI2tBVsPN/n6nl5adriSVUM1E7FaycLpIY8Gm8k0ppbktqtMQTzG6OMGC/YYitTgUrgZz13Ko7n4xAok1Zc6xEh+ixEmBbSnSkRQ+oooRCS4mdlNVHaB1/IVPDB2mn1mN2iK54i8r0Sj9c/syq5U9PI7++g2+WNDKnfhkDelhk5Q3IWHtWtPqkn+d7bWbU6cxr1SkMKfHSHFZNdy1bWXIWNgpe+VFnQb1k3jJJe0wSjgsSpiCaEOR4JRL1/w+NBo0mzI9qeHTJVr107v/HaApz4nz7cz8+/S6Cneo4H0tI6ppsflnsokdOG1v2XYKUgnuf2ZuHXhQ0t0FbxMY0ISfLpDA3QiJpU1pgOqLH4XfBsfg4ZEindMcSNgceU6a6Sifb+aVeTcKxuEVhnsFFlw6nd6GH3ZpjBH0Gixotik34vkVXgZ3bX6TiJYC2uIah2Rx1UhnZgVSdlPo4hbmqDogQdHJN6fqaFRwL+CwSpsHYG6t48M7RnJOyFAHcPqGSa6+vonZyJVv1VRcs24aSHia2VOO6p3YMu21TT0FOFj/O8zH+1krGXLN2lp//Z++846Sosr/93KrqPDkPGUGQqAjmhAEUI+uaE5jWtAMD5vVd85oFBMMaV13XsOvPHMGcAwgGogQJw8Dk1Lmq7vvH7elhJE3DEEbq4VMfpquqb92u6ak6dc73nLMr8NEb0zjm5BJW1Qp65sOaapOCbINQxEJljMfJTtcJJVK+//EPdQ4rak1OOXsctq0Mpp7FBuGoze7dPVx0yQS6Fvm46667GPXnEtL9qvFlRkAnPeBiVYVGXpZKg9aE4LIrJlKY46a8OoZpt7RW0dbpsWHZUNNot6rV1MzYi0opr7Hw6jpYEbDjLKsOkpnZibrvJm/wc6/rSfm9V2WDaOsY3kJTXiIA7ESl5oQeKB5KjtkqTLXkdUSfU+hXkI0tQRcS37AJFHttltWHwIomM8XEHmfiTSsCd/pG5y76nq7moXtYUpVJllcyqLtg+AklLKk18OuSn2oFQhMsqlG3gZiENF3iM9Qyr0FXalgrgsuduFXEGxH4icYkAa9FwB/ns9lZRKJNNEWUeNylq2rTArBtjVDEy7/f6UZFdZSGkE19U5yuRS6O2GctoYibFWvT8Xl1+vWo3fx53oVpj1CVE+pSOIaPQ5Kbb7qeppCFAD4rS1y4NTf1MY0xF5WSl+UiErWwBYQiFvmZqqBh72JYUSlJC0JTzFKaCcOiwcwk328TigkybUl9UOJzC8IxSX62yjGwrNa+131HPrbevOZ/cR79Dn621bo/nTOJP6GMnKzE9f/Zx67hvL/cQ06GzevPT6Rk4iRmTv8LLz51FXnZGudcrMJbTz18LcGwxk+Lc/C6bYrzTMoqDJ56+FouuHzjnqZdlfdemcZBo0oQQCgqCUUsonFJJKbqy+ze3c/4q1qMxqNPLiEWh16ddFZXm/TqbJAecNGrq45lCwpyPcnGmE0R1fdN0wTdij0YOlTUWFTaOn6f8jJ6PTrLysKYlvJaNLdbGfknVaNmxOiSZFuSZtYt1fD0E1M48qQSMtwJ74utDJH62uUU7j+BihjIHzZsALUV+esriN1PTuiCEtle7iylEdKMFsOHFkNq/TCV8iCJAReAGQRfIVFLa6n907zfghcQ/c5S+/Y5pdU4yRDYwpcS209lWb2LAjONtDUmdWFByIKKcBzsGFLa1MSbwJUGup8mAJnwWhl+MJvAjhMPR8GdCcLAkpCVrhONa8xdmkFlTYyGsCQchUU1Sot04mklzFnYxMj9/GRnNlFRHSU/x0NjyCIn08URwyqxE2Js24Y+3QVDR7T+nA6tcUJd7YcT6nJIEjchGFJpw73TJLsdPB7540Mc3NMkFLapbzRxGRqhiEV5dZyCHBehKKyplfxWp9MUjYBt4k3vBJ5MslySmJXI7oorMXNjWNIQgroGM5EGrTw8U+5VwuJv3ruUuZ+N/d28DL5579INzvnKa+9MipbP+8s9fPv+JWSlx/G6lADV74uSkxFLeoT+79krCUU1JlxzJ7YtaAzpaEJy9fV3IpyHoY2S7lNelew0jVBEKp2ISxX/K85VJ27wEeM49exxvP/KNAqzBXnZHnbr5Mbr1jF0danxe1StnmuuuYaScVeyW5FG3FRhs927xujTLYItISdTZRoV5miEo2Yy1PbVOtW4p7+qfv690dOMlBCKqO/Xh69PY35jwgAxw8nsrZH97a02epLH+/WVhCZIS2SA1bSkvdtxQGzSeyT6nKrGmfsUeAsgVkfZ11M2fKz5z6tl0cvre47Wfb3ofxCtpSIU4fNyFz+vqaOpZqkScUdrld7IjiN/eRLC5dC0AvnTI6r4YuMypWXy5CiDzgyB0PC7JLom+GG+zYJlJr+ujFJWq74DGS7JwceW4PWo1x5PnNkLimkMWRg6HH+IC5chWPBbdtL78MUcQcD7x2ktsa1wxM3th+PxcUgSjVmkBwzKKqIsbjAp8LnJ328Cp+3rYm2NicctiMVtfF6djIDBwpUxMvwQjEDXDIs600vEgl7pFsGYRswGty5J84LHpW5E770yjWEjxlFeY5Phlxh6nH/c9jdicbjmmmvIzshhSJ8gA4A5H13IXkc8ScAXoaHJu9n5//jxhex39JOt1vU/5Bn6J35+/fmJ/Pm8SUlNz7renXdeKuX8y6a005n84/HeK9M46qQSghGbfj39rK6IkJlusHhVLFlWoGuOZNiANABefHYqJeOv5MGpLYLzN56fwC9LfPg8EtBZWxMjGleVvYNhSSSmUdto4HVrLF4Ro3c3N7lZFrMXKEFzJN72+eqawOUSWLbk/ItLqWmwkKaN/OVJ5S2Jh8Dfud2F6knPDyAXv4boPRqEpoyiBGLPK5A/PtTyuvdo8ObizejC2WPH85+nH2B4N/hkeaBtx1zyOmL3k5PH2JgnabPj/H4/megtZptgeFVKvxWiMZbONwti9OmkYRiC8jqB11B1wA44poTcdFUZXUqYOa+Avfeo4k+HZ7FgueCnxQbxuEnXQnUIt2HSFLI488L2MT4dHNqC4/FxSLKmKko0ZrOwTAIa3TNM+uaaNIVtenbyJlOJTVNS22iypl6QGdCImWDZgogFg7MtKsM6IUtgSkFtRGNJtU5TRNURAchJU72AstN1DEPgc6sMHMPQWF0RZXGZnxefuoqmkIev372UUMTDwcc/vNn573n4k3z7/iUAfPzaXwF4dFpL8bOTzprEp29cwalj7uOT1//a6r3Hnj4l+fOj067bZFHFXRWJyghyu1SbicraOB4XXHvttRw4qoQuBQZrq02OO6WEI04sYXl5rNX7TzxrMl0KTXp2ihPwQn62G69bIydDJz9Lx+u2aQxqmJZkz74G6X6br340CUcl//f8VL54Z8OenZNOL1lv3SMPTUp0fXfjMgQuQ9AvuzmLygKXH/lj+/VxEnu1tDiRv77SYoQsfi35s+g9OrHH79LRExlhB3cxeW+exm4Hj1c9pkLlbT7+uoZVeyGXvN4StrNiKjT2i3qwKMyEgE9DAJUhTVXAHjqR4myBaSmB+X+efoCGoEZmWpAVa9zomvIU3XvvPfTs1EQ0bvDDwlwO2XvrOrDvKjgFDNsPx/BxSOL3agT8BuE4dPLrZKdB3y46S8stlper6ruWDWtqTL5drN7z7FMP8Nnb0/C5JQRXkemT5HptKqKSrukWXTIs6uOC6DpP7NG40mrYUpKd7iYSE2SlqQupYQj+OuFOqusNfisPcMCofzLosH+tN9fXn99wxeX9jn6UHz64iMNHPwjAJSV38eGrLTdGt8ti1oyLGX7Sg7zx/IQNjnFJyV0bLKq4K3P8qSU0hqE412Dhb8GkMD3Dr1HbEMPnhrU1JtX1cU44NECPIp3s9PUvL5GoRjiqEfBJenbSKMrzEImqxqEFOWHWVNuEIybf/hzjk5kR6hpVIcMRo9c3bpp5/aUNGzBCKA+lx62an/YpsincfwIYAeT859vt3ABgx1t0N7/LsmqmOXus2eASvU4C3UWvws70zvLzwTKb6u8ms6y2vtX+m2Njx2svXIYBsVrAJn2fCfQtsBjUO0B1vcWyNRaNpqAypNHZY9MQkqT7BW6Xxj9u+xuHD1vL8vJconHYa/cQA3upi0DAF8M0NQJemzGX3LPpCTgATqirPXEMH4ckmqYKwBkaDOtmkRkQxEzJFytNXp5v0BSykDb8ViWojGqUB3UuvjSRli4AafH5ojLCcQHSpqxRIzMAB3YziZiCcMIB8Olb08hLV5oRUDdQTUC6T5LmV09/uk5SjLwuP3xwEc8+dg1uY+OagL2PeiL587fvX0Lzn/qnb1yBrtsMHfE4M/5vHCee5bjX20p+lk5OGvy6yqS6QbKmxqJnZz9Lym3qmyw8Lujd1cczT0zh0nF38dRjU+jV1b/eOG9/EWTJKh2PW+L1SEYPr6coz8Plf53IVz9lUl4ZYXWl0n5FYioMpmngc6c+5zNG1NG3h6oZlZPppjDHoH++CZ6srT8hv0P+9Ahy/vOIwZeBy7/BFHPR9/R1vD4Jj4q0WVJVzeI1K8GOcczJJZwxLIOsfScg+pyS1P00/7/BY7cl62xLiTUQj9STmV5IUSDAsCKTnsUGPy8O8uNKwbxKA5eA1VENQ8AHr0/DZQjSAzpD+wYRQvLYKwbnHbeIorwm5i118f7/xuP3xkjzRzl4yG+bPPxbL2z44cTBYWtwDB+HJHlZHoJhkyy/5I1fXTSGJIvKbIjVMTDTZFWlyfzV4Nah3hR0TrOoabA45/zxRE1UFojQVXVaO86apkbe/dUmGIGf63Tqw4LDTyjhyJNKknVgmkJmslKrz2vjcQtu/Pv1G/W4rKlOIxzV+HWFL7muWRj9e979bylSCrp3qgYg3R9NZo2N+POu3YQ0VcqqLKqblNdO12FgLx933HEXX747jRWVkndensZ997Z+cp+/LMilV7T2zL3+0jRuu/1OKmp0ojHBsrJ0hvSVeNwalbUWXo+G0MDrUeGpdL9OVppBuj/1S9UH3+fidkluuvF6qutj6JqgW4FGD5/NyWeO2/wAW0J4rRICbyjV3IqC7kkaP826HDn/OfX/z4/z/mKTF2fHGZxvcv7wLqC72OOw8QzoXIzoc6rq5bXXODofUNpqaNH39G3i+ZGLX4NYPfUxC58h2buvl0/n2ixco7EkqBOXkOW26RWw6JplccnlE9A1QU6mm7W1HpavzuLOknIsW6N7cTlFeRKf12R1ZSZ9eyyh697/2+TxF67w8tp/nH560JLVtbWLg2P4OKxDWUWYhqCF1w0Ey3hvXi3fvq/c8j9XS95fYhOMqd5c8odJZPmV9qKizmZxrQHxJuSi/ylXfqwu2YNoQZVBT79N2FQtCtK86gaY7tfxeHQlXI1BNK7hNiT+hI65uZXFuobN3KU+GpoElXUkmxc2BOHqq69Z7/OMOm0K4YhBr/2e57M3r8Dvi/LpG1dss/PXkdnziHH0Hz6eAcM3bBBk+gVZfpXdlRkQrYycr9/beFbVPx+axAmnrR+mmrOoiflL43w/D5aUudizj0bAp9O9k48uBR4CPp2sdF21uIhY/Ofp1EXI191wJ9/PjXPLrXfy3a82hXkeivK8/Gl/F173tnH5y19fQc59GuJNqlFpn1OSYmekVGnmvnyy9p3AgC5dOPrkEjofUErWvhMYfUYJE0emcUBngzSfqk59YI9cFpavZm55Jf07FSvP0pyp62d7GYFt5/mx4hBajZSCecsiNMYEi+tCyiCKS2qiGpYtyM8UPPrwZPxejR7FJgXZMRpDBratkRFowOsJcdn4u+haWEPXohp8u32yycM+Ou060vyS3MzIJvfbVXAMn/bDMXwckrgMwXP/eoAMn0jUAbHpeVAp2CYFPgPMMAVpFoaQiD1L0DUlLF1apVETl6Rl9wSUiNOVVgy6mwKfG7cmiduCsClYWaNRUa9qsDz2yGTicRuXITB0iMfBtASWLXj8wWvxuuHJh69N9vMBlUEWMwUet6ChKcYNN1zPjTffyb3r3Ijvu0sZSu/+t5S4qfH68xMJeFWcbd6yDRd929UxbRV6NDTYd2QJuftOYJ+RJYw+o4QBw8cRiipB+sDdvOzWef0Q1oZ48dmpHHHihrU5//33VKZNvZ943Ka2Ps6qCoN+PeJYlqR3V52endwcMkQjM33rhK//fGgSd9z2N845KsCXPzaxrCxMfraLFZU2hxy3cd3QVtOcyeUrVlWfXX7I3I39ji5hcL6P3TJUuYX3X5lG2ddTOG0ofL9cZ96yCF4XvDu3lg8W6tRHhKrkrPtUgdCNIOc+tW0+Rq+TVEaX5qIhLujd2U2+v7kytRJl18ShS5ZFVprGddddi2EIflki6FZUx25dGnj3qx7834d7JQ2dnvu+QK/9Nq+xuqTkLgqyYxzShsQGB4dUcNLZHRwcHBwcdnKUx2ZrKze302Q6OB3W43PnnXcihKC0tDS5TkrJzTffTKdOnfD5fAwfPpy5c+fuuEl2MPREC4Dk34YV47fKMtAMKmpWQryB78pNMj0SNIOGsApZLamqRM6eQlPdcvX+xa8RN02wLUwb6uICLSFgrgxrrGrQSfOqVgSmZROKSEwLYiYEI6rnUnW9pnoKRbT1urdbltrn4QcnJVskNPPl25fRs5Py7rhdFn6vSd/ulaQHIuxx0L+5bLyTrbUhAm5JhtemKqxR3qST47HxueCnVRrzqoIsXKuxaI3GguURQpE4Z48d36ZxP3pjGqEo7H3UhkNo06beT0PQpKo2xpc/Ssoro1xeehefz27ilY9iLC2L8MyTW1drp67RJG4KRuybxt79Avy4qImYuWVjiSGlqoHppvZp7qE1ZAIul0tpfjQXQggW1xr8VBFk9qpKfvm4RWf2+EerKft6Cu/+3zQ+emMacv5zVH83mbmrViHnPQuxOuTs7SfGF71OUp/DX0CX3GIGdconz6vChRFTgCtdVXi24nTzq1pdLpfG4UMj7DtAY+T+IeqbfMTiOoN7129xRfQ/neP07mrGCXW1Hx3S8Pn+++957LHHGDx4cKv199xzD5MmTeLBBx/k+++/p6ioiBEjRtDY2LiDZtqxaAxZHHxsCS//J3FB1lyqxog3H2yT3oWdwYoyvzoETcv5frrSdjSnBifrlfQ5FSKVpLl0LKBnusWKsKryWxUXeHRJfUiSHjAI+FzoOjSFIBhW/X1MC+oabUqvvpNQBOqaWuYYN1U6fDS2/l/wu/8txeWy6FrUyAtPXk04YhCOGpRXZmLZgpnT/7ItT1+HxrIFC2oNVkcFMVtg2oL5VQZxW5AXyKA2qi4Vy6sEU6fcn5Lm5sPXp5Hpkxx50oZDS5V1JlLCkrIYbpdqjzK0fzoxU/JbpWCvI7dOiNyjk4bXLamqs1lRHiU/20WvYtV8M1Xk7Ckc3Ctvg9tEr5OUcFnTwV9MN7+k0G2Tk5ZLn7xs7FmT6JeX6Niu++h6YGlSlNzcYmK94zXX55HbvrJxUnTd5xTQ3aC76Zfrp1O6RYZXcsQgncqaGGcfEaCHn6TxM6DYomuBgdsl6N2tkh7FQQSQEQhT3+jBtDrkbcbhD0yH+0Y2NTVx9tln8/jjj5OdnZ1cL6VkypQp3HDDDZx88skMHDiQZ555hlAoxPPPt3PNjj8oMVOyzx4eTj93HEOPGqcaIy54QWWquNJY3CjVU160NnlBPvjYEsTeLVkXovdo9t+tiJyMIgq9NvXhJn6uDIOUuHUYlGVSlG6T7oNg2KS6Lkpj0FT9n2I2EjBN1YDyxr9fj20LstNhwsSruPHv1yuxaziO0OCWm5Un6Nabr+f2W/+GQNIY9OA2LDRNkh6IUZzXQHW9h34HP8uwDfQBc1CYNgRtgfxhMhUxQVVMUOCzWBvTqIoLaqImi6rrmfvJlmXDffym8vxsCLdLEI5aDNzNxwNT7mNNjUVljfLaSQkZ3vWN3Afuu57LrpjImItKOfG0Es44b+PG0Tc/hVmwLMzdd99NTqaHBz9q4oVnptI1V3LcKanrfL74dcOFBeWS11UWlNCQPz5Ep3RV46YmWM+iJp3dDh5PbnpCjxNvZFXQQi58aYOi5N9naLW1ps+WIPY4E7HHmS2VpjUXg7oU06eoEzFL6b465QoK8zxE4zZCQF7AYs8cm0O7GWQF1G1k+FBoaPJz8PEP4/PGCUXdRGJasieXw9YikFu5gPO7gA5o+FxxxRUcd9xxHHXUUa3WL1u2jDVr1jBy5MjkOo/Hw2GHHcZXX321vafZIQlHJWuqY1TWS35YXdfytGnHVSpuvCGRudVS2r57gcbQXBU3EP3OgrRu9Omis08Xk7ApQHNTEEgjzRC4NInXkBRmCdyGwOc1iMRsDF1D11U14GjMImZK7rjjLiJRi+v/3x3kZNjkZbsRqJouuVlubr7lTly64O47rseWkJdls7LCS32Tm6VlWVTUulhalsYXPxZxypj7N/BpHdZldVDHEOAeNpEcQxK0BXVRjWKPTYFbgu4Co20tFPoeuuEw2NfvTeP4U0uUUb0ONQ2Sxx6ZzILfwoAS/D4w5T5ee3EaP340lc/ebp01dsdtf+PnXyMsK4/z1k+CX1ZrfLdUMGHiVdyaMIavvPJqAPoPH8/MpbB4tcVeR46jrjHGaXt5AHj1xWlkBLbA67OZFhBy0cvscdh4akIa9Q1rOLRngPP2tumWbeN1C5XGvuh/EFy58TG2ZW2e3+PNV41PB4wFVxpFWYVUhzUWfvYAtoTyRg3LgvLKCIYuWL46TKds6Jor+fStaTz/jPL+dcoP0hBU5zbdH2HfkY9RkBNJFhN12DqcUFf70aHEzS+++CI//PAD33///Xrb1qxZA0BhYWGr9YWFhSxfvnyjY0ajUaLRlkfRhoaGdpptxyMah7mrBUu/mIoYUtp6o+4C3QeiJe4kBl2M/PlxxICxKryluxjeVZLm04gEbLpkWMRsNxXhGANydObWWOQHNFZVK02AoUfxezVMy8bj1hNVdnV0Df7+/67nnnvu4fZb/4ZpaUhb4nIJdB1sW3L99ddhmja7ddHYo0eMX1d4CEYEuZkqbFPbANGYxtpqa/uexA5KccDCCGnYiSdCGY2zWhq4NEG+y8arQfiHhzYziiJibdyYeOt/66e+h2Jw4mklbSqnf8AxJWgCfqg0iFgGmCFi0o9bwORJ9zHqzyX0PKgUISSTh06ku1dgA7F6jSVV1XiMbL59fxqXXD6B+iaLz39tv2c/MWAs2DGV/u3Nw+v2Ihe8QNa+E/huDRjCoFujBek9gG3rxUkFOUd58eTcp4GEt8mbTdcDS6mKaeS4JKZlE4mqopLxuMTjEvz3uRbvX5dCLx9976Mo1+IwoLI2jTeen8CJZ7XtO+OweZzu7O1HmwyfvffeO6VBhRC88cYbdO7ceYsmtSFWrlzJ+PHjmT59Ol7vxhtWit+12JZSrrduXe68805uueWWdptnRyYvQ2NFDVzwl1KO7tliMMhfX0EMvBDi9eDNR/Q9XWkSzCCHn1CCy5dDPFJLr8JO5KRLlq+JE7fgu9UxiFWA5mJuXR5oOrOqBH5Nku+xsWybgiyJz6shJRgJj0/Aq2Pakjtv/xsuQ2LokBGQ1DYKMtMgGBZEonEy0lx0yo/SJb+RPt2qWL46G5/X5KOZGTQGTW68ef3Kzw4bZkWTTpeAhS0F8z55QBm+lklcGqy2BMTq2z5WODUvylfvTmPMRaUsWrVhI/XUs8cRjEqCEWiICObVayQl+LqHpqa1ZKYXIgZdAroHryGI2BpIiaFJLAlhUyDnP4cYcAFnnDeOzDSdr5doGO1g9yQbhNoxeuQUsCYqiEQaiISjiH7nqK7mhh+ExrxYJvLHbS/YFb1O2mKvkVzyOmLAWFZFNA4sjNM5V5CZZrCywqQ4V8dlwPPPtA553nhz6ySDskofNfUd6rnaYReiTd/MOXPmcOWVV5KWlrbZfaWU3HXXXa28KO3BrFmzqKioYOjQocl1lmXx2Wef8eCDD7Jw4UJAeX6Ki4uT+1RUVKznBVqX66+/nokTWzQqDQ0NdO3atV3n3lHwuAW98i2WrFZtJZov6GLQxaC5IVoL3kJwZ6g36D5mrjGI1y6hR1FPltQ0MLx/JkvLTRojMLTIw9zaTkSCa5PiTB0djwY+lyQaV/Vczjl/PF63RmaagURpQ11CEImp5qUCqKwTDOkTZs4iLxkBiddj0LOTxaBe5RiGRU19OsMGLGHxyi5EYzaT7neMnrbiHjaRPukWobjA75IU71+KVxdEzAjoaWBGlNC9jcgfNp19dMrZ41oE9AmeeWIKBx+7Yb3NsgqwpUAAVREdrw4RG4g1qG7rAy6gPtwE8SaImkS8eQzOcxExBSFTsCosAEnuvhMgWsNLP2fSwy9Z0Rild+YW9ML4PUbiQcwI8FtjFKywOl/xBtIyOtGUMBybG3xuL0SfU8CKp2wAiV4nQWYvhuaa6Bo0hCS2VG1JGkP2er+7DfHLEp277rpzs/ttjNefn8hJZzkZXevSHr22nF5dijab5FdffTUFBQVt2vf++9tfU3HkkUfy888/t1p3/vnns8cee3Dttdey2267UVRUxIwZMxgyZAgAsViMTz/9lLvv3ngqpcfjwePxtPt8OyL/enxK6xXNBk64EnyFqqu1INmxuV9eGvPLVycvrCNGl/D1QouiTJhZqdM/0yZiAWiqkrM3l4itqjSHLJ1BeSaHHFdCpxxBml+g6wJN05T+xyOZszBM50IfHpc6rK5JcjLB77HRNY2R+y9hWVkBA3svJT+7gtqGXBYuz8DjUo/xF/yllKce+91ncliP2MxJHDSqhOVNOhmWJN9vE2vSiRgBZUygQbSm3Y63sRunayP1+WbOUPtn7TuBqA3hmZNVT6zmzuHRGpUyLq1W+hv3sIkUuu1WaeDa0InIuiXoaT3ACiNpB8OnOX4QrVN9wIQOaORkdqJPjsk3y2sh1rhVXpiUp7TkdeWldWUgBoxNhrHWpTktXy54ofUGTWdkjzjhKBRlC8prJXmZguWVku+nt03c7vUIPnq1hCP+tOGq3psjFN54scZdFVuqZWvHcGijuHnZsmXk5+e3edB58+bRvXv3LZ7UhkhPT2fgwIGtlkAgQG5uLgMHDkzW9Lnjjjt49dVX+eWXXxg7dix+v5+zzjqrXefyR6fvoeM59pSS5MVSLn5NZXb5CyC4Gry5iD6n4NYleFtSez9YVE/XHEkkBn3SJfNqZaKrMyA0iDUkAtVQ6LWJxoWq/2EIXLp6Etm9m8RlSApzTHbr4iMas7jx5jtpDFrUNLgpq5DMmh/n659CZKXX0qf7KjzuCMFwGnHToLZBZ+9+6oboGD1tx21AvsfGo0vKgzo1pmo0i7TBDCox7jbm4zc3fpPc84hx1McsIvFEAR4zqIyJ5uadmkuFYddpAhq3bFaFWtLAxcALkeEqEIIliaagS2o2XepC9DpJ9cjaRB8sueAFRO/R6hxFqtViBqn+bjJVQQ3c2cqN+btxYdPNR7caK0yRRzKkOIPBR4xD9D9vvXnja+0NF7ufDO4sfq3QkMB/n5tKcY6gMXEe19P+bYTa+jhLytpW4fv3vPXCBM680PHYOmw72mT4dO/efZM6md/TtWtXdH37W+zXXHMNpaWlXH755QwbNoyysjKmT59OerrTpiBVfl3b+qshF78GoQp1I4xUI9K7qZ5dPz6E6HUSux08nkFFWTSEITsNFtWGwI4p48kMQbyJvEAGLl3FrupjGrM+mIoQYNmSmGmj6xoLf5P0KI6xR49a+nSL4/Po3HHb3zAtG8uGTnnKM9Sjs4+46UIIiccfoymURnVdOkP7tV2L4tBCY0RQH9NojGvk+2zlTbGiylC149tlDoX7b7wT9+qgTqZbV33gQJVV8GYrY0Mz6JOfS6eAB3zqAU3sPYGeAYnQDcSAC8jad4IK15rhZMo5tgVspj5Oxm6q/9yS1xPd0k9BDLhAHWPwZa12FYMuVlof3QNCo++h41kc1JU2rtl72ow7vSW7a1thRjA01YokZgly0nLX20XOntJ6hVDe2eOHGYSigqMStZdMSyUN7J3btnpCD0y5j6o6nVkzLk552m6Xk5CwIbY2lb0lpd1hi6R9dXV1TJ8+neeee45nn3221bI9+eSTT5gyZUrytRCCm2++mfLyciKRCJ9++ikDBw7crnP6I7DwswfYo9hmn5El5O/XcjNKuumFQa4hqQpHkuuXfvEAP5dX4XdDbqYORoB9it2IPUvURV9zURUKEbckICgOWJx4WgmxOFQ3SKrqLJauClFVF2PBcg/pgTB9utVhWpK4JenV1UXcFKyqsMlOd7P/wGjCy+OmvLwTS1bl89nsfMqr/ERjHa5Kww5n5oypVH83mXpL4DNkooCdB6I1Gy2ulyoHjtp0zZy132xcG1T57WQyXRIxpJSeB5WqopnxoNqY1hWvIcnztdyUO3kkPkOSoUt6ZqcTtWFQnou87C6JPRL96GIbz+IUAy/k6J4tJZ7lopfVebETTTODq1VG44ALINAZ+fPjar+FL0G0nl+DOjStVD2t4k2gu5KenuaO7NsUadM9y+LHj6ay4NMHOGQ3kz2P2Hi9I9F7NNgm6B6+/CVOwCPxe6CsSrKkUiMnIMkOSPYd2fJ73FQdpJp6k58W56Q05Tee37jxu6vjpLO3HynL7t98803OPvtsgsEg6enprTxBQgjOO++8TbzboSNw6HEl1IUFvzbo7J3furb/uk+oWfuqi5ToPRq5+DXk/OcRvU7igD7dwIbasEaPNEFVLJumUC3YURB+vJokagpMWyKB6a+qp/gbbrie/CxI81ssL8/hlY9cWHaMgFcnN9Pgqx+jDN7dx8BeQYryGqltSOeXJXnomiTgswj4bKrqDKJbUJHXQRGbOUnpYH7YcmHpGeeN48Vn19eCfPXuluk9mln+5RSy9p3Asi8TBpIroArvJTQqYs8S8vwqAWN1TTmrNZ2CzEI0oXRBuftOoMYE0fd0EJp676+voA2dyD55cb59v/X8ThsS4KV//+5zxIIq1LvnFeBOB082RW6JV0+M26wz0t3IYDl4cpKtHbZrbR7WT5d/fT4MWt/pkyhF4YH0nhCpBF8BljSJxGHRWo0Mj01+mk0goeFO98Hp547jpX9P5e2XN/47taWkorbtnv9/PXItVXVe5i0NM/KUNr/NwSFlUjZ8rrzySi644ALuuOMO/P4ti+E67Nwsr9OpiAp2z7BY1dBy4RIDLkh2gRaDLqFfbuJKqHvUk6w7HbnkdY48qYShBYK5tRpZhqTIa7PEzkGa8WQW8sLPHki60QGuu+46GoMxBu6mEzMFc5ekETfVk3VT2OSvE+7l7//vevr3DOF2Weiazf8+7MSoA6tpCnp4+wsv0bhJp3wP1XXbJzTzR8WetWmjp3j/UupMQY5LsjqqqsEOyFQGcm1Ew6NvO8Oz7rsWr9DvBbsFPiPpNfq9l0rsfjJ5Od2RP0xGDLoE+fOjLeM0ruC7ujj7jCyhIaJx3D5uPpgd5aePNiDkNbxKwxPohDegwmpDulhUNkCOr5AfyhN1rjw5yojQwsqrpOkqy4rNF0BsL5ofSJr/7+TX+XlV2QZ21Dh+UDY+j8W3S/PolRvn1yqdPQosLFuia2Dbqi/fSaeX4PNoyaKFmyLNb1BWEWvTXD994wqCkQw+m92U1Ps5tEbSDnV82mUmHZ+UYwJlZWWMGzfOMXr+wCz/cgq7Z1isDemY64T0m40eAITG/EolDJULX1IFDuPqor+8RsNtSCwEa2ICCQQ0CULQwy8p8Ej2Pmocq+s1vC44+uQScjMFB+2lc/g+ixlzyT38siSCyxDYNmSlu1kx6zQiUYsFy/1IKXjh/U4cd2A1PTuV8/KHLu6//16K8jysWBNhaVlkO56tXQux9wTW1K0lUruUsq+nQP1SaFrJLx9PZe6K5ayuWsGy6sodMjevLlXphQ0gf32FqprlaEMnKkN94IVJIbRc9DJyyevM/K2cRauXM/n1Xzds9IBK7QcQBgUe9V0uqxHMXFXPrA+mKqMIVDKA4VXeHk8WeTndwYrjzeiqBMTbg8RdsktBDwAKA/Y6ob7Ex0jolCIxWFMjKTnBx5JqnXS3xG1Al1xJMCaS2UAxE0xLcuaYzfdPy8lAafrawHPvuGkKgccleOWFLWuL8kenOZ19axeHLTB8jj76aGbOnLkt5uKwE9EUE4QsqIyv/xURu58MdowumRnJrBS56GXwFSL6ncPizx/g6+X1xKNBiIdY0qThTqTBL49orAgJoibURwWWrVoUXH39nXz4bZyiwa8CUF1vETcloYjNwF6SxqAf07I5ZK9KVlX4OOnQagb0XsSbn/XHZWiUjL8Sy5ZU1ZnozhNjuyMGX4bYeyJ5LpUN1By2kUteRy76H6L/eWqdbSWb1m5vNpuq6y9G1i1RjXet6HqhILnoZRWy3URFZbnkdfU5Q2WsaLLI8tisCenIec8qz0qrcJZGt5wC9umczh55JkN6dicqBRj+bWb8rJt91jyXPvkWo88oQdckexaZrUTk/XLc4Cvkg+U6TVHB1f+pYUVNBYcPUhXUy2oEUVPgSTygVDcqsfQLz2zeOKlpgPs3Uk/r7juu58K/lFIy/koAyqtNPp7V1KYaQQ4OW0ubQl1vvPFG8ufjjjuOq6++mnnz5jFo0CBcrtaFzU488cT2naHDDiHbKwmbkixt/buJ/PUVxODLWBWGopx1qnNHKtcJewVUNpAQKqvLpSrXyngYhEbUMigK2Bg6jL2olKefmMKwASp0dvGlExACHntEhS3uv/t6Bhz6NJMPVX2asjNsMtNDvPnZML7+KUZhrpeVa0LUN5rkZOikkIDosBHEniXIH6cpr4gnS/XpMsNU/vDIBveX81Riw/bWsazLyq+mbDI9XP6o2ieIvSeo72mzx2cLWkfIX19B9DqJn2IBlS22zjii92hwZyhjqP95rGjwgJHG8bubWLZJjSePVWuXtQodby2i10ngywNPZjKUJ/qfB9F6+nXtQXWTpHueJByDwUUm3Q8qZUVlGXt2LebwbpKPVyrD0eUJ4Pf5+XyuiUBQFVYPPlVNqrjld9OncfbYDfdi+z233LrhAoZ3/eN6rruhZdvYi0o32MrEoTVOy4r2Q0i5+VOhaW1zDAkhsKyOnYrY0NBAZmYm9fX1ZGRkbP4NuwDNtTvWTX0V/c4iLa2QDENiaCrMsKjeVBVq5z+nLrpmWD1de3Lo5HexurFRZbhoBjkeN31zTb56dxrnX1zKvx6fwtT7rqcpLCjIsfhxkYnL0Jh0/71cffU19OiksWBZnGlT7+f/3XA9li1pCsbRdcGUyfdx2jnjyM00eOQhp9rr1jBg+DgyvJLFtQaV3266AnNHRfQ7S1Ugj1arprtbkV0ldj85Ue9IqtCWFVU1hey4Sg0XOthxcnJ6Uv3dZMZcVMozT0xReh+ht/I8bU2BQ9HnFHBnghHAZRiYEqQZx+syyHNLPLqkS5aNnkhvz00XFOe5OPe4Jt75Moc5C5tYVik46cAAa6sj2DZ8vdDC75bYtmBNUCPba5OXJvnkzWlJcbPDtr9nNI//zCNX4PdtXbHdUDjKmMse2uXvb22yaGzbbtPS0Y0ehw0jZ0+h2Y3SUiBOo/H7yZR9PYWIJeicZZPndYPhU5ubCxtKG+wYq0MJwbHugXgjhtby9LFsjcVFl0xg3FV3EvApMeW0B+7HnajALAT4vZK0gIs7b/8bXo9g8YoQbpfGlMn3ceJpJfz3ualEYm2rMeKwcfLSJPURjargH7NZr+hzigrFRavVd1Pfyqrt0lbhsSWvJ4we5USXi19TdYJ8RRDoTE1jJaL/efxaZnHocSWQ1g258KVW3qat8pbpbvVQYceJR4NIM0qXgE6WISkM2DTGVT+8vXp7KcoWNIQk0ZjN+1/ncPCeQa4bG+e2iwx0HQb08nLwEBfFmRKXDm5D0mAKdAGBxOmqDzqug+3NrlrHR0rJjBkzuOWWW7jsssu4/PLLueWWW/jggw9og99mg6Ss8Xn22Wc32IcrFott9zo+DtuPLl71BWu+UMv5zyWKuY2lIhThkzIX1aYgz5+G6HMqh3aKK8GzpqsbQLxR9TCSEowAAcPm6/eUe/uTN6fR7FQMRyEYUS9MS3LHbX9D0zTKq3SkhMw0m4Ymk+wMHcuWjD6jBK9H/TGv13LDoc0MPWocvQ8ZT2WTxvx6sd37Sm0tfQ9tW/ilOaNKzns2WZhxU1WZNzveuobL4tcS3p/EA6A7oAouBssg1gh2PJnSf0o/E7HXuK06djOi10mguXF5AqqyumYwKFcn4JKsqVuLJiS9sk0MHTRNsKJS0ilXY8++XhqCFjO+9TJnURbzlnqJxSV1TfDxzBjhmGoKu7ZJo3uaRdQSyayg916ZxhUlEzc5r99z/KmbruPk4PB7ysrK2HvvvRk1ahSvvvoqS5cuZfHixbz66qscc8wxyULFqZKy4XP++edTX79+ddzGxkbOP//8lCfg0DFYFdFaZcIAqtgZmtLuxBqRwXKqIjHkov9RHxaJbQkZmRUFzavcN7oHnyHZ7+iWC2GznqcxBH+doOL/wZBJQ9BC1wU1DSaWZXN56V3cc889ZKa5ElWfoTC77Q00HTbMD1XQGNeIWSgjtYOx8LMH2mRENKeUg8pGlAteAN2tavCwdUYQKO1PS+hMXV7l4tcSDwBxxF7jmFtpUNUg2bdQtosmSi55HYROPFQF6d0p8rn4pd7AtKEgq4iIKTAt1Qvto9kRInFBnx4BlqyM43XrFOXqLCuz+W11GIAlK0I888QUAH75eCqVUQ3TFmR6beqCLcf9cUlqZSMcHc/WsSsWMLz88svJyclh5cqVzJkzh/fff5/p06czZ84cVq5cSVZWFldccUXK46Zcx0dKucH2FatWrSIzMzPlCTh0DOQPk5L1QFpWSpW+68tT2p7E03Ty5uLNT3h6gupJ2Arj8mURt2Fpk6CvbrHPyBJy06A4Vyc9oDN1yv3cfNP1RKJ2Uq9z043Xo3l0qmqjXHPNNdgS1lRFaQrb2DaEo06Ia2sQe16B/PEhho0Yx6y18Q7n7YHEZ2iTEdH62qWanbbUmmlPcbac/9w6B9LoVdiZJUGdTLeV7EsmhkyAhqVbdVwx4AKl72lYivDlATZ7ZZtETEG2ZpPukTRFBZomEQJ8LokmoKouTiRqEzdd5GbpDOtvs+A3wd79/PzpjBL8HjhrzHj2KbZZUavx7fvTyN13AseeUkJVA3w33TFktie7orj5ww8/5Msvv6S4uHi9bcXFxdx3330ccsghKY/bZsNnyJAhCCEQQnDkkUdiGC1vtSyLZcuWccwxx6Q8AYcOhLd12Ve55HXEgLEqq8XwK/3EopdbG0BWPKkPQmjEw3VgBMjx6MQt0ITA74GAT1dp6eOuZNrU+7nl5uu57rprqWuM43FpFOS6ycv2UNsQ48Gp93PiaSXYEnwewROP/jFFuNsL+eNDqrWIHdlkC4edmeaMrc3ut07l8XWrPm9r5KKXGTB8HAcWtvaSHNHN5KPF2VvXud3wkemCenc6MlyF7colZkHEhPKIRkGaTWGmUnc0xQTF2ZI3v2ykPixw6Sb9uphEY26K8zT27hvBtAWrKnws+C1MOGrTo0inVyfBKWePo/o7JWge+ScnbOWw7fH5fNTU1Gx0e21tLT6fL+Vx22z4jB49GoA5c+Zw9NFHk5aWltzmdrvp0aMHf/7zn1OegEMHIpG224zoezp5WZ2oirNhoahUnphk1krf08Hw4XK5MDSbuZ9Mpf/w8UTiNgK4/757k6myXjeUrY0RN9UFu6I6Rjhqsaba5PhTS7Bs1VE8bnawR5idFTPYbqnVHQEx+DIO6Jm3dWOkaDhl+lSrlnSv5LRzx/Hff0/FbQCebOTcp5MPDikbQdKmvnopBDqBNKkIx9gtS0MT0N9rUd6gjB+3AWUhDascFjeJRKNWk7w0STgaJRZ3EQzbZAQMIrE4MRPiFjRVWORmCI7ct6VobXObGYftx67o8TnjjDMYM2YMkyZNYsSIEcmoUn19PTNmzODKK6/krLPOSnncNhs+N910E5Zl0b17d44++ugNup4c/uDE6lq/NiNUNdVTkJ6J7fJTFbWSNwPR6yQlbg4UKl2Q0BKGkUa6JtGA3Q8ZT7bPbhV8sKXkrDHj8bgFoYiN36vRGLJ47l+TOOLEErLTBOGYpHOeQW2jRSTWwf6Sd0LEHmcqDVYHQPQ9nXMPKuLZpzbfMmFTyJ/Wr0e0Xih3c2OkYPSI/ucxvFcmMRMyA+D3aIy5cDxVjTAsz0QMukR5R9l8uG1dw0h5XBMat+BqCvJ3o6Khmt/qcsj324RNQV7AJhIX+NySTj5b6aH6nQVoIC2WVBXSNctC10y6FbkxLcnKCpNgRD2AfPj6NA49roSZc1XV6kuvmIjLEEx74P42f36Hrac9Ki93tMrN999/P6ZpcvbZZ2OaJm63eviOxWIYhsGFF17IvfduuEjmpkhJ46PrOpdeeinz589P+UAOHZ8NXuilSe9sk0hc4Dc0VsQ9iN1PVmGwPc5UGh9XGljNbSSUF8jvspGIpE6gZNyVnHvBeF54ZiqjzyghO8O1XpbWR2+ofU87dxxPPDqZg0aV0BTtWH/IOyW2ue07hbcTcuFLuIdNZJvkj3pykuHZ9tT6iAFjGVyURXUQfl7bhCYCZKerp+/asPLGHNLNzecrClresykjrLm0xJBScGVAvAG55HX2OGw8CyvWghVjTaNBv4IAVU2qMKHPLVldr9EpM6GHE4YKawoNIVR7iuI8F6Yl8bp1aoOga+BN2FSfvd3i4cnP9my+SraDQzvgdrt55JFHuPvuu5k1axZr1qwBoKioiKFDh25xLaKUxc2DBg1i6dKl9OzZc4sO6PDHQS55HTFkAj63icclAZuQmU1VJKA8PoY3Uccnrrw9VpQ8r5t8n40lBR5dMvDwcURMweLPW57gX3tx0270/yYKp325ld2+HRTr6l46ArGZ26hIZaR621SedmXwU50OTavAjvP92gBFWRZNEcgPQH1YkJcm2beTF7H3RA4ojG/a86QZqnCir5ACv5e1c54mfZ8JdAtI8BZAwzLQY6yuTwfAtAXZPpt5nzzAuReoUHJziw2EhteATrkakahNdb1JQY7Bt+9PY++jxvH2y1M55uQS3nul5aFD11TLipPPHIem4bSZ2E5Itr7JaEe1VzMyMjj88MPbbbyU09n/8Y9/cNVVV/HWW29RXl5OQ0NDq8VhF6NhKcEoeFyQ5pVkum2KfC51AzFbmoW60opVo8i4YHVIIxgXNMYEHh3yA1uelbX3UZtvlujwx2LdlPT2RC55fYM9tLa61k6kEvnDZOSi/6lihz9O480FJitrNaXxAdY2akRNwZ7ZJtG4QOx+8kb7eclFLysPXaSKivpKcvedQJZL4nPB3rm2ahdjRWmMqTYTMUvd8MTeE8hKM7jsikT9nUBn5KKX6ZxlU1GnjB4hBLWNFsNGjOOHD6Zy6HEleNeR9lXVSzQhOO6UEjxulVzgsH3YFdPZAaqrq/n444+TIueqqiruvvtubr311i2OPqXs8WnO3DrxxBNbpbU3p7k71Zt3MYRAE9C9yMVv5XEa4xp+XbbcnBIC53yXjdh7Ilhx6i0bj+ZGEzDrg63TanhdHfAv2WGraM4a3CZjbyDkt7Wp5uuGiJMhrPBaFlsZxOws0lw2UsKcD6dywDElNEQEXfK7saq2atNzTXjqxICx1LgyKPBD2BTkpBfSOc3Csm1cusSjg6HB4CyLtTUWvbv56XpgKaP7aZw1ZjyROGQFIByDgBey0nQKM00AMvzqZjn8hBI+eXMahg61TTZpXoFtQ13YKSXhsO347rvvGDlyJA0NDWRlZTFjxgxOPfVUDMNASsldd93FF198wd57753SuCkbPh9//HGqb3H4AyMXv8ZRJ5XQFLJI8wk0JE2mSJTQ15WoGYOIJVTvLsMLlkmDCWu/mbLVx68Opuy0dHDY5ogBF4AnU9W5+v22QZeAJxusMKYNlhQsC+nsfsh4fC6BoYEhYJ+u2Wr/jeh9mkXOcu7TAOx5xDg0IamJxgA3XdPVQ2iWL1F1HQjHJFW1UYbvIcnJ8LCkLIov4dExLQhGJLpuYeiw15HjyE0k7y6r0QGl+3Hp4PdqrK21ePf/nHDzdqM9PDYd7Dnxhhtu4NRTT2XSpEk8+uijjB49mmOOOYbHH38cgIsuuojbbruNV199NaVxU75rHHbYYZtcHHY9fl5r8Ny/HqB3V1VPod4Sqh+StEAI5IIXqAk3qZYV8SbQdHJckj2P2Pow1cLPts5j5OCwLcgMZEJw9freIk8WuALqgUDzsjoCy4Ma8WiQkCmoDGk0RDX8LhuXDgcfW8KArt0o3r80kYkFot85dD6glH5de+AbNgFQLTt+/GgqxZmSPfN0aqIxDA3yM0DTlDg5wytpDKuCn50LfMxfHsXQYXWdwLLB71FZXE1hSUU9rA1q6BrYNmR7bUaMLiErTeAyBKGonQzTOWwfmrO6tnbpSMyaNYuJEyeSnp7O+PHjWb16NRdffHFy+xVXXMH333+f8rhb9LhcV1fH/fffz0UXXcTFF1/M5MmTN9jGwmHXYO03qoBg3LQpDNgUe2zEoMSXU/Oq/4WhPD7ShmgNDabgx48cUaRD6ojBl+3oKWyW+rhELnxp/Q3uLNSju01BeibYFhFb3YyClvo/YqmQVW1YoykqCLgl3TIt+hXm03/4eFyBAvyGJN1j0y/LYvAR45I1QmuaBA1RQY80F/URFYY2LTjs+BJqQ4I0Lywqs1lRHqY+BGU1EHCrxsBREwJejUgM4pagb76Fz63W56VJcjOUgWRZEtuG119yvD3bE9lOSyp89tlnnHDCCXTq1AkhBK+99lqr7WPHjk0WNm5e9t9//1b7RKNRSkpKyMvLIxAIcOKJJ7Jq1ao2HT8WiyULFLpcLvx+P3l5LfW3cnNzqa5e36u6OVI2fGbOnEmvXr2YPHkyNTU1VFVVMWnSJHr16sUPP/yQ8gQcOi5i0CWtXt9/3710z1NiykxfGiCQc59Sep9IFbiz6J2dBu4sgragcP8JO2biDh0bK7yjZ7B5NtDvTPQ6SWVcmRHQ/VREJZkeA5DgClBvKi0OQNwWCCS1EY2FtQY1IY0cv43XkPTPNOmSZaMJyPJLfvpoKgs+fYBDjithXp1O0NQwJXgMSX1IJR58+tY0DA1CUfC5YWWVjc8NPhd4DBXC6ppv4DIEjRGV/h43QdcEmX7weSAak82SPSyrg8VMHLaIYDDInnvuyYMPPrjRfY455hjKy8uTyzvvvNNqe2lpKa+++iovvvgiX3zxBU1NTRx//PFt0gN37dqVpUuXJl+/+OKLrWoIlpeXtzKE2krKzsoJEyZw4okn8vjjjyfbVpimyUUXXURpaSmfffZZypNw6JjInx9t9frMMePoVujm5zITw4Z6l0qnlYteRvQ7B6wwMdtHnwxY+NkksvZtH8NH7D0R+cM2SnF22OmQc59G9DundS+snQ3dv/46TQdfoTLcpA26j86ZHtJjGmVRQUCDurggTZeETIFpa1SbquBnQ1zDaoCgqVFrCmIftnzf9z5qHOleydIane4BVbTQZ0hCcYEeAr9bMmzEOEwbGiPQJQcicaioV26i7vlgWpLaRovGsKQ4C4JRVcOnpkmS5oU1dSpsFvAJZAxCUcfw2d7siMrNo0aNYtSoUZvcx+PxUFRUtMFt9fX1PPnkk/z73//mqKOOAuC5556ja9eufPDBBxx99NGbHPuMM86goqIi+fq4445rtf2NN95g3333bctHacUWeXyuvfbaVr26DMPgmmuuYebMmSlPwOGPwwvPqNBVmjvx16V7EIMvQ/Q9Xd2kzDA1McGiRg0x4ALaq7eoY/Tsesj5zyH6nLqjp7FxwmsQ/c9rvc5OPOE2u02ERs98iS1B2jZNliAqBU2WClHVmYK4JakxBaYNPkPtm2vYHHtKCXsdOY6hR42jdxHETMj32wTjgpCpSkX4DNWU9Ov3plETVkZOhk8ZMUJAQaakKEtiS0maT8O0JJFEv9a4qfYRKC9RXjpYNsRMSSgqeedlJ8y1vWnPdPbfl6GJRre8cvsnn3xCQUEBffr04eKLL25lqMyaNYt4PM7IkSOT6zp16sTAgQP56quvNjv2TTfdxBlnnLHR7TfccAPPP/98ynNO2fDJyMhgxYoV661fuXIl6enpKU/AoWMjBoxt9bqmwaR/F3BpkjyfN1lOXwwYC74ihhaZ9AzY4EojPNNpLuqw5ezUhRetGFjR1rV4/AV0CniUzseTi3D5aIpA2ddTyHMLcgxJscdGF6racp5bUpBof5fptmmIaaS7bDI9kqoGlf7+W4POTysFNSGN3xp1DAGV305m9zwLn0uFzvYZWULYFLh0lbJeG9bwGCoE5jKUBqghpFLqvW5lRPk8ar2mtb5hRqJK3+PQsenatSuZmZnJ5c4779yicUaNGsV//vMfPvroI+6//36+//57jjjiiKQhtWbNGtxuN9nZ2a3eV1hYmKzCvDX4/X48npYekRkZGa1CYxsjZcPn9NNP58ILL+Sll15i5cqVrFq1ihdffJGLLrqIM888M9XhHDo4zam0zfzn6QfoUujFpakLMJoBhk895cZqmbXGoHu2Ta/0jpVd4OCQCr8v4Cl6jwYjgCagi19Dzp6MR0hiqlwOaYZktyxTlX0AOqdZdEq30JDsmx8naGoEXBIrkZVTH1GX7urvJpPptcnw2nRLs0j32Aw8fBxldRoRU+l8XDrk+pQm6Kt3p2HZoGkCl67G0oTy7oRjyqujaxDwCEyLhM6n5XPFLZJVnB22L+2Z1bVy5Urq6+uTy/XXX79Fczr99NM57rjjGDhwICeccALvvvsuixYt4u23397MZ5Gt6gC2F7KNsbyUNT733XcfQgjOO+88TFP91bpcLi677DLuuuuuVIdz+APidetkem16HzKeArdGRdwDoTXg8tMUqsXtSm/VosLB4Y9Iq1R2KeniA68uyfYpl8nAHJOmqKDvoePplGETs1SG5OAjxvHTR1PZ7+gS+hdafLTSBUjWfvMAux8yHk2AprVc4G2pQlKWDWkelaEViat16T4VqmqMafQtUKG2Tpm28uYkjB2BMmgsW70ORSHgkbhdyvsTM8G0VVq7oW+30+fwO9pT45ORkbHFfa42RXFxMd27d+fXX38FVE+tWCxGbW1tK69PRUUFBx54YLsfv62k7PFxu9088MAD1NbWMmfOHGbPnk1NTQ2TJ09u5XJycHBwcHBw2HWorq5m5cqVycyroUOH4nK5mDFjRnKf8vJyfvnll45l+DTj9/sZNGgQgwcPxu/fQAaDwy7L7f+4kzSvehI1hFThLne6alYq9A7ZL8bBYWuQS16nKiaoiWp8N30a+4wswetSoaiAW9IYVW7/YSPG4dJg1J9LCHigKQJ7ZpsMyLRI32cCPpfEa0h++XgqR55UwrGnlGBoUNaoXDF56ZDmVV4cIaCqUVAXFnj1FuHy2kaNYET9fUqp/g9FoTYo8LkhL0PpfyIxpQH67O1pSKn0Ph+90TrM1fXA0u14FndtdkQdn6amJubMmcOcOXMAWLZsGXPmzGHFihU0NTVx1VVX8fXXX/Pbb7/xySefcMIJJ5CXl8ef/vQnADIzM7nwwgu58sor+fDDD5k9ezbnnHMOgwYNSmZ57QhSNnyCwSB///vfOfDAA+nduze77bZbq8XBAWBATzddMm3yfDbC8CjjBwHCYFmV5jQXdfjD8/usLr+mxMYHjSohagplnAA/fDCVNI8k4IH8DEkwLlhRLZASKpp04hb88vFUvJoKR/kTWZPReCK85YVMj9L/BLyCaFzV4fEYEtMGtw6WVOGqw44voSDNJm4po6o+pETMMVPgd0ssG+qC0BBWIa66oOCgUSVJvc/vKYs6LWO2FzuiSenMmTMZMmQIQ4YMAWDixIkMGTKEG2+8EV3X+fnnnznppJPo06cPY8aMoU+fPnz99detEp0mT57M6NGjOe200zjooIPw+/28+eab6Hr7x03bqhsSsq1qoARnnnkmn376Keeeey7FxcXrHWj8+PGpDLfT0dDQQGZmJvX19dskBrorccSJJcRM9YS5uKYRrDA98wpw67Dg0/bT+DQ3UHRw2BlpbjVRkFmIacMeuSYxC3LSlPHSGBZkBSQfvj6NUX8uQQh45+VpDBg+Dl0DryExbcH8Op2BOSbfT2/9Xd93pHpPhk9pdBoigoJ0SSQOlU0ahek2wahIiJ0hL11S0aAqQgO4DWiMCFy60gcJofQ/MVOlwjtsmm19z2gef+q94/H5tk5OEg5HGXf1A3/Y+1t6ejo//vjjZp0wKYub3333Xd5++20OOuigLZ6cw65BQZYgHJW4dJvFdS6wI3gNWB3S6D98PPM+aR/jp9nocQwgh52NngeVIvxFyEgda7+ZTOH+E4jEBX6PMjr8HjA0SSgGJ55Wour3BGGPw8bj0VVGlWUr71COS65n9IBKT++RaxOMqqytwUeMIz9LEAxLwjFJMCrQNEnY1LAk1DSpis/BqDJwXCgvUtQEK+HVaYhqFKQ5ees7EzuigGFH491336Vz586b3S9lwyc7O5ucnJwtmtTWcOedd/LKK6+wYMECfD4fBx54IHfffTd9+/ZN7iOl5JZbbuGxxx6jtraW/fbbj4ceeogBAwZs9/k6wIvPTuXMMeOwpVQp7WYQnyEp9NoE3O1/UXWMHoedjUyvTS9NUqlnArBHnqluYEA4Cj0KNZZX2Hz17jQOPraElfU6pg15PpsfP5rKYceXsKDKIN9nUfb1lA0eQxNQ0SAIxgVizxL6ZWusrLTJTlPHMW3waKoNhltXRQvf+t80DhxVgmVDU0Qwc0ZL37zBR4xrV4+sQ/vQHk1GO1KT0okTJ7Z530mTVBHbgw8+uE37pxygve2227jxxhsJhUKpvnWr+PTTT7niiiv45ptvmDFjBqZpMnLkSILBYHKfe+65h0mTJvHggw/y/fffU1RUxIgRI2hsXL9vjsO25/yLSynO85DmE+yZYyP8Bcyr00lzS1Y1pBbfPXBUSZv3HTbC0Q857BzM+XAq2T6buu9Usc5P35pGXViQ7lNi4domm0/fmsbQo8bh0pXQ2JSChqjGPiNL+PStafTKNvnl44039DU0SU1EY0lQAzuCQFV4tu2EcDkuWN2oq7FtpQs6+NgSyhp0VjXorYwegJ+c5sE7JTtC3LwjmT17dqvliSee4NFHH+WTTz7hk08+4bHHHuPJJ59MCq9TIWWNz5AhQ1iyZAlSSnr06IHL5Wq1fXs1Kq2srKSgoIBPP/2UQw89FCklnTp1orS0lGuvvRZQXWELCwu5++67ueSSSzYzosLR+LQf519cSsCnU1EbJxaXrKoWzK3ViZgW3QIaps1Gn2KbGX1GCRX1UBfWUgqNHXxsCV+843iAHHYuDhpVQkWTRnGGTX2ijYTXpQyVdcNYex4xjrgt6Jxls6JGw5bw6yZqX/UfPp75DTrYJkPyVOgq4IWGEKxu1IlZELMFGS5VRyjTJ2kIC36o0ZE/OBXUt4btpfGZfHdpu2h8Jlw7pcPd3yZNmsQnn3zCM888k6wHVFtby/nnn88hhxzClVdemdJ4KYe6Ro8enepbtgn19fUAybDbsmXLWLNmTaueIB6Ph8MOO4yvvvpqo4ZPNBpt1aekoaFhG85612JpuUXvTvDff0/l3AvGM/PFBzhoVAm/1enUxwWZrs3b3NnpOquq7ZT1QHVhwYGjSvjqXcf4cdh5qAurlHbZoETLXkMJjoWAP581jqoGJTB2G1AYkETjbNboAWiMCfplWHTNkayqEeRnqHT0hoigKM2iMaoRsyRxW2BLiJoJD4Bj9HQYJO2g8WmXmWx/7r//fqZPn96qCGJ2dja33347I0eO3PaGz0033dSm/V544QVOPPFEAoFAqofYLFJKJk6cyMEHH8zAgQMBkn0/CgsLW+1bWFjI8uXLNzrWnXfeyS233NLuc3SA/ExBdYPFuReMp7ZRaXp8btirs8VHvxl49c3/GZqW3KI/9k2FBhwcdhSWFIRsqA3pBDRJjluyu98ibikhs8+tUsy1hBQjFNu80QOw8qspnHbOOEAQjEjef2UaXQ8spSjNIhJXgwVcEl1Tae5NUeGEtDoYu7K4uaGhgbVr166n162oqNgiKcs2K8JwySWXsHbt2m0y9l//+ld++uknXnjhhfW2/T69fnM9Qa6//vpWPUtWrlzZ7vPdVXn5P1N57cVpCFRq7JljlPamLghDC0x+DeqMGL1x7c5RJ5Uwb4WkItQ+X9M9jxhH4f4TSN9nQruM5+CQKmluScQWeITELaBLhjJ6/B4wdIGhK6OnOvGd31AW16YwLXDpcPypJeR4bSxb1eeJmKqWz9qgRkNEkOFd/w7Yf/h49jzC0cc57Hz86U9/4vzzz+fll19m1apVrFq1ipdffpkLL7yQk08+efMD/I5tZvikKB1qMyUlJbzxxht8/PHHdOnSJbm+qKgIYL2OrxUVFet5gdbF4/Ek+5Zsq/4luzp1TTZd8nVyM13kZggy/MqVL804m6o39WuVzrw6nUazfTIRfvxoKqYNQVtQvH9pu4zp4JAKtgSQFHgk2R5VSPDTt6axtFIjHLWJxlW15CyvJBxLbeyla+GVF6YSikFtE1SENLwuidcFPXNVk9Kyr6dQF9UIxdb/m5r3yQP86HiBdlp2RAHDnYV//vOfHHfccZxzzjl0796d7t27c/bZZzNq1CgefvjhlMfrMGU3pZT89a9/5ZVXXuGjjz6iZ8+erbb37NmToqKiVj1BYrEYn3766Q7tCeIAb/x3GtGYpKbeJDNNp2uBQV6GAGymvzqN/sNbF70Ue09EDLqEmAURWz2pthfV303GnjWJ8m+mtN+gDg5tpDBTeaBrYoKoJagM6uw7soSFnz2ARGl9BJCTJvn87dS8PWWNGsNGjCNqChZUG/TIsvC6oCGsqjk3e3l6ZluUNTndRjsau7Lh4/f7efjhh6murmb27Nn88MMP1NTU8PDDD2+RnKbDGD5XXHEFzz33HM8//zzp6emsWbOGNWvWEA6HARXiKi0t5Y477uDVV1/ll19+YezYsfj9fs4666wdPHuH/zz9AM8/8wA1DRZ1TRZVDZIcj5sBw8cRjisPTOcDSincfwLyh0n0z/WwJirAjNAU20CtfAeHDkhOuoaM1NBkCVZFlNelMqgj9hpHOKbCVM0d0VOl/JspxCzVaT0mVTaXacGsD6byxTvTSPPCfker0HKGy2aPw7a+yn7XA0ud0LHDdqO8vJzy8nL69OlDIBDY4shShzF8HnnkEerr6xk+fDjFxcXJ5aWXXkruc80111BaWsrll1/OsGHDKCsrY/r06a36hjjsWF7+z1RCEUlTBDLdNjFLsCYq8BkSUwrWfjMZsdc45tXrZDZXStCcp1OHPwaWLUH3QTwICKpigt/CAqRFeZ2gNlGWbEuLcQpg7idTafx+MrqQZAWUsTP4iHHUh6AmpPHFO9PI8Ejqo4Leh2yd8bMqIshzd1A3QgdDItpl6YhUV1dz5JFH0qdPH4499ljKy8sBuOiii1LO6IIOZPhIKTe4jB07NrmPEIKbb76Z8vJyIpEIn376aTLry2Hn4a3/TeO76dMoSrepjGrsnW+S45Pkem2OP7WEHgENrAgBXSJ/egSs6OYHdXDoAISjkp4ZbpAmQkBTqBbiIeSPDxGOC756d1qyU3uq7DuypJVGx2so70/UFDRGW9cCEkI1L63a2iajZgSP4Rg+24NdOdQ1YcIEXC4XK1aswO/3J9effvrpvPfeeymPt80Mn+7du69X3NDBYV2+encaAV09lc6cMZV5nzyAocOyL6eQ5/NSHtMQe09Qxo/DViF6j97RU3AAdE2wLKQjf3kSe9Yk0FwId4DC/SegJ67GW5pm/t3vMsDCccHXKw0MDXL9NqYl6HvoePL3m4BpQ7bXJtMlEXtteSaXy+1z2ls4bHOmT5/O3Xff3SqhCWD33XffZLmajZFyHZ+VK1cihEhO4LvvvuP555+nf//+/OUvf0nu98svv6Q8GYddj99Xbn7tRXXxrookUlqk0yhxaxG9R+PN6okYfBlIE/nz44gBF4A7A+KNEK4CQC55fQfP9I/PKy9MZa8jxyGGTIDgSvBkI6P1VMQM1n7zaLseK2KBT5OsbNQpDlj43ZIfagzyXJJgTKBrkOWxWRFO+TYAQOH+E+js6aAuhI5Ie/Sc6KC/rmAw2MrT00xVVRUeT+rVrFP2+Jx11ll8/PHHgEodHzFiBN999x1/+9vfuPXWW1OegIPD78nadwKDcnXQ3WD41AX2gFKK9y9F7D1B3bQd2o6UeDTolu6hS3pAPeF7MvHqAtyZkLEbZO6G6D0aMejiHT3brUL0Ogmxx5mIPqeuv22PM3fAjNanVyFgm8hFL4PmBVcGaFtmfGwKtw6ZHkmR30IXUN6g08ljU20KJFAd0VjaqAMSsWfbe+E1s/abyUSsdp+2w8ZojzBXBzV8Dj30UJ599tnkayEEtm1z7733cvjhh6c8Xsp/bb/88gv77rsvAP/9738ZOHAgX375JdOnT+fSSy/lxhtvTHkSDg7r0tzQ8ZiTSzB0WFkt+PGjqex5xDjqTJ0IIAZf5oTA2oA2dCKu7F74NBuvLilMt9GEjoakZ67FbzUaUUtpSsozd1O1lQZfBrE6iLc0It6ZvUGi/3nIec8i9p7APr26sTaos6IhvIEddw5hZygqQYiEcSaVge/Ja/fj1McFy7+cwu6HjMdjSEyp+nUVu22MxCNvkwXYFsRqt+gYTlmI7ccu7PDh3nvvZfjw4cycOZNYLMY111zD3Llzqamp4csvv0x5vJQ9PvF4POla+uCDDzjxxBMB2GOPPZJKaweH9kCihNAFGZI9DlMX70g0BHYE4vU7enodAnvWJPyapMlSDS8zAzCw2GKvrhYBL+xRZDOsm0W+3yagSdANJSbXPZDWEk8XvU7a6DE2ta2992m1f+/RiP7ngTAQ/c7hoCKT3HTolG4h3O3fKqe9ePf/ptE7XYAnR4VyNS9yTvsXDmx+gIhYAluqDDK/Icn322R6bSI29EmzQVpqLg4OOyn9+/fnp59+Yt9992XEiBEEg0FOPvlkZs+eTa9evVIeL2XDZ8CAAfzzn//k888/Z8aMGRxzzDEArF69mtzc3JQn4OCwMd5/Rel9stMFvfJtctIgz+8HzQ1WfAfPruNQ991k+mWbaAKiceX4sGzwuASaUILbwkyJV4NkFT1XBq0uD2ldVBhpAwusb7SsZ8Rkbvji1Cy6FoNamginZCRpLjACpKXl0y1fI90nCHhUVfD1kDJl42pb8evnD0C8AQwvYNPzoNJtcpyeB5Wy8qspzPvkAfpnWXTJsGiKCVw6NH4/mS7ZNj3TDQ7t0lF9AbsOu3JW14cffkhRURG33HILb731Fu+88w633347xcXFPPjggymPl7Lhc/fdd/Poo48yfPhwzjzzTPbcc08A3njjjWQIzMGhPfnvv6fy9svT8Lig8tvJ4M7aJpqIPzKZfnC7IOCBrDSN7AxVG0nXwE5cDXO9NnluyAtkkOPW1TnO2A3SuiUG6YUru5d6ndYN/AVqOyiN0LpGkL9AeWQS63r4lEhd9B7dasFfrPbRjKRxRUbPDRpSvze2cGeQ48/goM6wf2cTv1fD0IVq9aCvn1EqF7yw04TsBgwfB0JXdyI7xm9N28aQXx5pucTP+mAqkbhg8ecPUFavfv8fvj6NgZ1smiI7RxjQYePsyobPn//8Z77//vv11k+ZMoW//e1vKY+X8t1j+PDhVFVV0dDQ0KpF/F/+8pcNqq4dHNoLy4ZDjiuhb4bGQjNtR0+nQ5EZEJiWxGWIpNTF7xGYhsCWklBU0iXLpjM2MVP1UouZUBMUzG90IaXEpUE3n4U/Q7CsUafJSiNNB3/+blREUQbLoEvAmw2u9GRGXis9VnoPsOPIHx9CGzoRaUvI2I08t+pjVaP1pGdAsqwh4Q1K76HGSe8OVlgV/7PjEFwNZpiacIjibA/52Qa1jSZNYYnbgIOKWgwJ0edU5KL/bc/TvVlMOyEsRwMrtNn9t5Tc39XYCSY6tZcl6vccfkIJmgZupxaPw07M5MmTOfbYY/n000/p378/APfddx+33XYbb7/9dsrjbdFjs5SSWbNmsWTJEs466yzS09Nxu92O4eOwTXnrfyr0tfsh48nxOYZPKmiashdMC2RU4na1aH3txD1Poir/ehJXBdtW1WL7pZuYtsBjSNI9EiFgNywaYwJdQF1MI8cFIKjBByKAS4O4OxNsE+w4Ys8rQHeT4xIYmpvOB5TS1atczhELds+1iMTBaDDI8iZKGAS6gtASs5JgpNEzYOMzXMwLorxEmoHfK4jEbGJxePO/0xj15xIqqls8GHLR/xC9TtppvD0AcRvS3G6iUhDHC+G12+Q4Tb/Lulrw6QN0P6iU2MwpiMGXMaTAxZxaA2k6RUJ3dtrDY9NRPT7nn38+1dXVjBw5ki+++IKXXnqJO+64g3fffXeLenGmbPgsX76cY445hhUrVhCNRhkxYgTp6encc889RCIR/vnPf6Y8CQeHVPj18wfQhk7c0dPoUPz331M55exxxE3V9dtrSbweQdxUBo7PDcGI2jfDL4jElYHjc9vYEj56YyqHHFeCoSkDKTdN4o5IsvxgS5uqRkHEFFhodEszqQ6rSsGm1DGERtgWdE+z8LpUwUqBMsKiZsLYcinjbGgXk49/S4TYBLg0yNShKq6xd46JaaMqBad1Ad1N33Qb0wKXoTxaAGW1Yr0Erp3J6AFY+sUDFO9fyh4ZJisa3FREt02xV88GxAzlUVUYtG+2G4HEnjWJ3H2dfls7O7tyVhfAVVddRXV1NcOGDcOyLKZPn85+++23RWOlbPiMHz+eYcOG8eOPP7YSM//pT3/ioosu2qJJODikiow1pvye7geVsvzLKe0/mQ6CZSmjRwh1AYzGlPc2bqnmmB6XanEQjkkaQpAZgEhMhb32Pmoc3oRGCJTRUpABkbgKQfrdylCq+2wyhxxXQpbPwrIh3af2r2wQ5GdIMgKCUESNn+4DQ1ceJ5ee8EZJ6JNhURHSyPGqXm6WBEMI0n0SXYOGMPTNMtCFRWGGJG4KAj6obYIjTiyhOAtCURB9TlG1cnZiZq4OgREAY9t4yzfU5S42cxJizytY2OBGzlaZX9WJDDAHh52FqVPXz3QsLi7G7/dz6KGH8u233/Ltt98CMG5catXHUzZ8vvjiC7788kvcbner9d27d6esrCzV4RwcUiZ9nwmq8FsK9D10PMu/TPQpGlIKCDJdLSm/uwK2VGLmYESFszS9ZZ2uC6QpMS1oikCaF+IJrY8QkO6VSocrlfETSUhoPnx9GkecWELAA4YuOeLEEjL9ymD64PVpHH1yCQLI9Evee2Uax59aQjgGWQHlpXEZyuPUFJEEvOA2BF1zJTkBFaNJ9ymDKhJXc2kKq8J86R4blw4ZfjVONK6eZQ1dZa4Fo0KJh3diyr+ZogpGxpu2WSPejRo0uoc0XVK8f6lTi6eDsKuFuiZP3vB3V9d1vvzyy2T9HiFEyoZPylldtm1jWeuX61y1apXTBd1hu9D4/WSQZpv3P/rkEmqimjKYAKI1EF5DfWKI/Y5uXbV23dTqPxKvvzSNuKmMhYBP4PMIdE1g6AKZMIA0kcgAMxKvNfWzoavXscQ5M3S1HsDrVkZOM7F1zqudkOvoGhx2fAnRuDKcmo//4rNT0XXICmi4dJVh5jYEaT41btxSHiWXrgyxdB/kpqsxpISsNB0pwdCgZ7FOdpogwwdpHolc+NJ2PLtbRqYvDewY8ufHt+tx5Q+TaDJhTdgpC9FR2NWyupYtW9amZenSpSmPnbLhM2LECKZMmZJ8LYSgqamJm266iWOPPTblCTg4bAltrdqcv98E8jM1cr22MphAhRZcaQzNVQb8t+9P47RzxzHmolIGHj6OHN8fV6T/wevTCHgF0UTIy+sWGAlngyZUuEvXWoTPUqoQVDimjJ1QVP3Na6JFL9DcXNOTyASrDyXqBBkw47VpyfF9bmVIed0Cly7QdcFZY8YnjC8wdIHHJUjzCdJ9Gh6XMniAhI5HzSPgE5gWBGOCuiYLl5HwWNmSl/49lXBMGUxijzMTbTguQQy8cLud41Soj0uwIjvm4KbqCu/g0NGwLIs5c+ZQW1u7Re9P2fCZPHlyMqUsEolw1lln0aNHD8rKyrj77ru3aBIODltCWwSZB/c0ee5fD7Cwfp1HncQTttclOfrkEkb9uYRgWGJZklBcUBM11c1yJ+nt1N5EYip9PW5KXEaLkSOEMmKkVIJjV8LrY+gtRkt+hvLK2Os8PUZiMOrPqr1IwAtNUWXI+D1KcyOEMoDSfEJ5iRIFFMMR5Q6ybEkkEaqKxiVNYYlE4vOonlKWrUJskZga37KU16d3IWhCEI5J6posInHJUSeVsLJWI2YK0D3s26srfXO8oPsRu5+83c/1ZrGtHaZDkj+3b1NUh23LrubxWZfS0lKefPJJQBk9hx56KHvvvTddu3blk08+SXm8lA2fTp06MWfOHK666iouueQShgwZwl133cXs2bMpKChIeQIODlvKuvqFDTUu3f2Q8bya6PZOtCaZCSZ/eZIDjikhbik9yII1OqEolNfYLAuquiry50d3mt5O7U0wosTGa+ski1fbuA3Vqdu21UfWEiGu5vCXTIiPDU1Ve7Zsta9lq35qrnXCYpqA/HSJ1608QoYOLl2dx4BPI92nIYC4pbQ7tpRYljJmmrOyhFDCa8uCdJ9GZkC937SUx8nQBW5D4PcKPG5oCEoiUYmhCQJe8OgSTZPkpWWzulGnLKSBGUzWFdq5aJnTtjC0i/cvbfcxHXYMsp2WjsjLL7+cLJb85ptv8ttvv7FgwQJKS0u54YYbUh5vi+r4+Hw+LrjgAi64wOmS7bDjGTB8HHLuU+ut97kSN9IBF4AVxZ41CTHgAgYVpmFLgd8l+XmtQU04xG+NGplebzLLRew1DqwYou/pHUIrkgo1QYEtYVmNTkVUEPCaZPoFMVMZMKat/l9dK+icrc7he4n2IaPPKEkaPs2tLySqLo3HpW7j6b5mrZAgbkoMHU49exxGQshs6Crd3NBVCnpMStL8Gmk+jcagRTgqcbsFsbjy/GhCoAlJfpZgTY1URlnis2hCUJAl+GGZpGe+TTQhWfl++jTS95lAVQySe2uGqvqcMGjl4te20xnfOK1CTdugUalvBxQmdA+bSGzmpO1+3D86u5q4eV2qqqooKioC4J133uHUU0+lT58+XHjhhRvM/tocKXt8AP79739z8MEH06lTJ5YvXw6oENjrr+9ctTIcdg3mftL6iy/6nwfATx9NpXD/CeBKA8OHGDCWvLRMoqZgbnWciCnokmaR5vUjf3qkVYZXt4DW8r5+Z23Xz7Ot+eGDqbh0SU1M4NFU3ZvyWkk0rkyESAwaw+DSZVJcfOwpSgAeM6EhlKjBk0hll1JlXcXMlvo8zRlgbpcSThuGMlK0ROgs4NPwuAUuQ2BoAkODaEySmWbgMpTBJFGGkmlL8jKVtfPZ29OwbfV+XYNYXFJWbTO3RlLbpI7dENXoflAppoSeAQmxBtVt3jbBHQBPljKCEn3Cdha8LqPdtUhLv3igXcfbHO5hE4nHnWKIDu1LYWEh8+bNw7Is3nvvPY466igAQqEQup56RmTKhs8jjzzCxIkTGTVqFLW1tckMr+zs7FaiZweHHYWc92zy54qoVOnCugfQyHLbrI1oIE1+rrb4qTJK4/eTcQ+biBhwgUovBlaEhLpRgmqT8Afj++nT8GrQYAkaYhqNEUEopgyHT9+apjK5NBVaCkZVRhXAOy9PU/qcGFQ0ChrCgvqQ8vDETOVMCUVV2CuW0Ou4XYJYHLoW+fB5VD+trHQ3aX6DzDQDj1vg9WiqZ5hQho6uqzG9Hg2XLhJ6JCWGbgrbrcJnnXM1Mj0GkbgKwy37cgoasFuaRX1MAzumfv8ZPXEFiiBSC1Z8p/D4rEskXAt6amUadjZyDXunLyPQUZFStMvSETn//PM57bTTGDhwIEIIRowYAcC3337LHnvskfJ4KRs+06ZN4/HHH+eGG27AMFoiZcOGDePnn39OeQIODtsCZcRcAvFG1RNJ8yLnPkVNVKM+3KQ0H9EaiNYgBowlHq5RN8hYgxogUgnSUt2/O6p/eDNUfjuZ3QMWXl0SjAu+fX8a0bhKQ4/EoDasDCLTUuJkgIGHj6M2rLGqQeeXj6dSHxHUhDTqQsqzE4yo09WQaD/VKc/AsiX52QYLfwsqb41fx0zEyHRdkObTEUKQETDwuHUMTZAR0FWhRVtpgaRUhhTAG/+dRjSuKkrbtqSuyWZosYnHkMkMs+amn5luWxm+VhSsGPG4ak+/s1VyTmKGd/QMtoryb6Yg56QeenDYPLuyuPnmm2/miSee4C9/+QtffvklHo+qpKrrOtddd13K46Ws8Vm2bBlDhgxZb73H4yEYDKY8AQeHbYY0wZWO0ATSjCmtjytNNa0SRqKBlamMm2aDR1oqtKW5QWpgW39YkTNAxBKsaLJwuVyq7o6EUExpcGIWVEd0OguL3Q4ez9IvHuCXj1vf1H79/AH6HjqeuC1ojICuSYIxjRy/TfciN9G4zYvPTuXiSycgUadcILAsia41h8EEhq4RiphYtiQ9oBHwGcRNSXaGi3DEoqbBJG5CzGzJ/jI0gdsl8LoFVY2SUEwQTXimhuTrxCwIm0L1+9JcEK4A3bPTeXqakXOf3mZjiwEXbFAHl/o4Y7fpPB0cNsYpp5yy3roxY8Zs0VgpGz49e/Zkzpw5dO/evdX6d999N9k11cFhhyNNwIBYA1JzQbyBbrmFrKhrUE//rgy1n26q1+6MxNO2S70W6/xpWFFlNGn6di80t61Z/uUUxN4TSdckqxp0TCnw6pKYlQg16ZLKkIaxCd/wws8eIH2fCXQLWITiOqaE4kxYsTaGoQsOOa6E7gVKvGzZEDdtDENgxSWmJXG7NGxNEorYmJZkVaXN7l0gM83A0DUMwyY306CmwcSy4bRzx9EQlIRjyhv0a5WO35B4DMmcD6dy+AkluHQIxTV8hqRbQTdW1FSBGQIzlNT27KwG0LZC9DsHOf+5HT0Nhy2kPbKyOqjDB4BgMMinn37KihUriMVirbZt85YVV199NVdccQWRSAQpJd999x0vvPACd955J0888USqwzk4bBuEgZz7FKLfOSqE5Uoj12ezotZUmh1TeSfl/OcQfU9X4RDDqwwe3ZPoCq6BFVZeIjMMerrqMt6MtNtcSHFnplfAYm1EozamYQiIfdMi8h42YhwzZ7QtdFEV0cnzWtRFNVbVanTOsolbkjkVBrZtskdXHZdLI27KRFaXhhmzMXRJms/F0lURFq7R6JSZyPgyJaZlYtsSl6Gxe7cAdY0x1lTFSfMJYqbk69UGkWAluNLpFPCQv98EigOCUFxg2YLlEQ3ZVKY8Pb++guhzqvrdWrtYxWJpqm72W4H6W9rFzttOxK6c1TV79myOPfZYQqEQwWCQnJwcqqqq8Pv9FBQUbHvD5/zzz8c0Ta655hpCoRBnnXUWnTt35oEHHuCMM85IdTgHBwcHBwcHh40yYcIETjjhBB555BGysrL45ptvcLlcnHPOOYwfPz7l8YSUbbcBTdPkP//5D0cffTRFRUVUVVVh2/YfqnBhQ0MDmZmZ1NfXk5GRsaOn49BOaEMnIsNVSuuhudX/0aqElsdW4S1pgyu9pWFkrEGtM3zqSdeVkXhq1pA/TlPiaVcA+UPHr1nS9cBSVn41pV3Gytp3AiFb0D/TpDyooyHpnG7TNVficQssCzICKnbWGLaRNqQHNOoabRpCErcL6oLQOUeQEdDQdZX15XbpCGB5eQQhVKbZqz/WKW8dqA7n7mzQDfqk2cRtqIxqNNUtB82FSOuMbFyp0tlDFWB4/3A1miDxXY/WI395stV60edU5KL/bfG4os+p6vvuaHxasa3vGc3j/+26iXi9nq0aKxKJcsddkzrc/S0rK4tvv/2Wvn37kpWVxddff02/fv349ttvGTNmDAsWLEhpvJSyugzD4LLLLiMaVXUa8vLy/lBGj8MfC9H3dMSeqv7MoEyTtEAuLm+mcvtHq1S4w05ofISmFmkqY8eOqxCXK6BCXwmdkMJWRo/QlP5n8GU77kO2E+1l9IDqeK8jCcYEfl0ScEl0TRKJw4pKyW+Vkoo6iy8XSuqDqn3GnKU2lQ2SikbBu0tdrKzXaYrIZDXnipo4lmVjSxUmS/frxC1Jl6wc1fbh11cg1gi2SitbG9GI24KmSJPaJm1ktB4CnZVgXXerrL0/IPasSRv+bLpnixrwiv7nKY2b0ByjZwcikUi5lUsHVfm4XC5EIsmksLCQFStWAJCZmZn8ORVSDnXtt99+zJ49ez1xs4PDzoZc+FLSKIla6iaItNfxEPgS2gc90aBKUxqf5PaAMozMsDJ8AEgYR0lDye7wtVe2Bd0DNmvC6rmqPg4rwi72iJvELEHMggW1Gk1xC79L4HNJ5tQa5Lskbk1CvJFVpJFRLXAbNjHTJs0rqKyNE44qw6chaGHZkOuzWZXw0DWnqPuGTcCjQVVMgG2pVhC6B9yZeDVJxAiAFUYueGGHnZ9tjjsbMeji1mJ83bNlGh07Di5PohaWg8P2Z8iQIcycOZM+ffpw+OGHc+ONN1JVVcW///1vBg0alPJ4KRs+l19+OVdeeSWrVq1i6NChBAKBVtsHDx6c8iQcHLYZQt18YxZgpEFwVSK9WVdGizCUUWOFlSfAjiReR5XHJ7RahbgSqfHYseSYSf6gnoOtoTaqzpFPk+SlSZYEIWoKIpZql5GmS3ShM78OOvtspBmnwgSwVfjRMplXC6atURfT6JWt6vR43ZDmVWnwWQGNSMwGb26rY0ea1pCTXUgkboEnEyImeHLp5rOpiApVx+ePbPQAcvbkZAXzJFtg9IhBlyiDp4PXF/ojsCtndd1xxx00NjYCcNtttzFmzBguu+wyevfuzb/+9a+Ux0vZ8Dn99NOB1uljQgiklAghkpWcHRx2BuSPD6lsFM0AdxYgEwaPJ/F/wsuj+4Cw8vJYUbV/vEF5c9Y1duyo2lckagBJVQdIDLxwPU3FrkxlXLmlXQLcuoRoHVVGJoYGhlCX32yXZHlEoyqqUeDTqIiBV4OIJZURKk0W1RuAiS3dRGw4sKtJTZOkOEdDSsjws/4NXQhWB6MJD4UAbx5CwIqwUDfw3xuuf1R+9znlvGc32Mx3o28fdIlq9aG5kh5Op47PDqQ9ChB2UMtn2LBhyZ/z8/N55513tmq8LSpg6OCwsyP6n9fSusKKqptj81NrMq030Rlbmi1vjDepgoW2qUrvC63F0yPthNanURlIQgOR0Ipo7u322ToChS6bJktQFRe4NQGaC7cmMWWiX5cGtXFBhi5xCVi7Tgq92Gsczb8bYbiQUlIVMfG6DH4oM6j8djJnjRmvurjHoSg9q/XBfYUQrQXDm6z67BGSLJdkDb4/hBi9LWzIQGlrEUPVpDecEPrbSg/nzmAL2zs6tAO7cjp7e5Oy4dMRtD0PP/ww9957L+Xl5QwYMIApU6ZwyCGH7OhpOWxHmo0esdc48BdDeK0yXEhUYRYa6uufuJDHGpRBJARYieJYmltd/MMV4M1LGD5uvIE01VfJSCdpPO0qXoQ2ErbVeRZCGTlpXj+GJjGQxGxBwLCJ2oLwTGXwZO07gaiNet18Lq0I0kxUXU4Yq00Jh/Lzz7Q03xx61DjlnTAbVVaXGVT9uDQP0vAhNDVewCWR3+waRk8zzb3nQEP+/Gjb36jpiXpXjervwHABmvM9d9ghVFdXc+ONN/Lxxx9TUVGBbdutttfU1KQ0XsqGzxtvvLHB9UIIvF4vvXv3pmfPnqkO22689NJLlJaW8vDDD3PQQQfx6KOPMmrUKObNm0e3bt122Lwctj/5+00gzaXTFKpv0e0ILeHRaTZuEus9OcrzE2sAREIIGmsJh8lE4UNpKaNH2mCFWlLjmwXRDgB4NNUgVmgC04YmUy0qo8qgmy6ImBZiyAS8OkSl0u2IIROQs6ckSgWkJ0Je6iIXiYaRPz/OviNVZeYv31X9w4JxoYTqkBChJ37HmgGxOgL+bAq9Nos/376dyncK7HgixKseAjbXR0sMmQBWJPH9bw7rGokMxjCZgeztMGmHDbILi3zOOecclixZwoUXXkhhYWEyw2tLSdnwGT16dFLTsy7r6nwOPvhgXnvtNbKzt/8fyaRJk7jwwgu56KKLAJgyZQrvv/8+jzzyCHfeeed2n4/DjqOqbrX6ofkp1fAlbqK28vkmu64n1pkhlb5uhlvCYdJSN4HmWj/NmVxCU9ukrbxC7o5TE2N7UBFFpT9LqIkLhCYodktWR3SwYqwIu5FzpuIeNhGLhAveUmUyxKBL1Plv1u5YJl6Pn0g8jm/YBAo8Omkum54HleJ32aR7JD0DGsvq7UTdpQBEqtUNHA2/Drl+eyMz/WMj5z7dcj6TmYkbRgy+LOFdS+xnm4nmaonzikaRb9c8jzsDu7DdwxdffMEXX3zBnnvu2S7jpey3nDFjBvvssw8zZsygvr6e+vp6ZsyYwb777stbb73FZ599RnV1NVdddVW7TDAVYrEYs2bNYuTIka3Wjxw5kq+++mqD74lGozQ0NLRaHDo+ov956gbYnIIrRKL/lt0S6LYjynixLZX2q/sADbnwpYQ+IhFmEYmChtgtqezNfzrSVO0xZk/Zjp9u56fAozK3hIAsQxLQpBIcC02dT1vFrOKWRG8WnEtb1dcx/KjzqyU9DxFLgtAJz5xMU1ww95OpLPtyCnM/mcpv9QaWFGp/KfG6DNV+JPF7Lg5YfPv+tB12LnY4moH88aHNfkeT7VekRInC81XIS3ODKx2XLwvLsXscdgB77LEH4XD7ZRam7PEZP348jz32GAceeGBy3ZFHHonX6+Uvf/kLc+fOZcqUKVxwQduzB9qLqqoqLMuisLCw1frCwkLWrFmzwffceeed3HLLLdtjeg7bkaSwOZX3/E742R7drHdV1hUrbwo5u237rUv1d63fU/lt6mPsSsgfH2r7vpsJhTnsOHZlcfPDDz/Mddddx4033sjAgQNxuVp7L1OtQp2y4bNkyZINHiQjI4OlS5cCsPvuu1NVVZXq0O3G7+N/zSG4DXH99dczceLE5OuGhga6du26Tefn4ODg4OCQCruy4ZOVlUV9fT1HHHFEq/VbWkYnZcNn6NChXH311Tz77LPk5+cDUFlZyTXXXMM+++wDwK+//kqXLl1SHXqrycvLQ9f19bw7FRUV63mBmvF4PHg8TkVSBwcHBweHnZGzzz4bt9vN888/v2PEzU8++SQnnXQSXbp0oWvXrgghWLFiBbvtthuvv65Kxjc1NfH3v/99qya2JbjdboYOHcqMGTP405/+lFw/Y8YMTjrppO0+HwcHBwcHh/ZgVxY3//LLL8yePZu+ffu2y3gpGz59+/Zl/vz5vP/++yxatAgpJXvssQcjRoxAS9TLGD16dLtMbkuYOHEi5557LsOGDeOAAw7gscceY8WKFVx66aU7bE4ODg4ODg5bQ3Oj0a0doyMybNgwVq5cueMMH1AammOOOYbhw4fj8Xi22u3Unpx++ulUV1dz6623Ul5ezsCBA3nnnXc6ROFFBwcHBwcHh9aUlJQwfvx4rr76agYNGrSeuDnVHqEpGz62bfOPf/yDf/7zn6xdu5ZFixax22678fe//50ePXpw4YUXpjpku3P55Zdz+eWX7+hpODg4ODg4tAu7sri5uUfoutniW9MjNGXD5/bbb+eZZ57hnnvu4eKLL06uHzRoEJMnT94pDB8HBwcHB4c/Eruy4dPePUJTNnyeffZZHnvsMY488shWupnBgwezYMGCdp2cg4ODg4ODA+zK8ua2SlWOO+44nnjiCYqLize5X8qVm8vKyujdu/d6623bJh6Ppzqcg4ODg4ODg8NW89lnn7WpwnPKhs+AAQP4/PPP11v/v//9jyFDhqQ6nIODg4ODg8NmaA51be3isAWhrptuuolzzz2XsrIybNvmlVdeYeHChTz77LO89dZb22KODg4ODg4OuzS7cjp7e5Oyx+eEE07gpZde4p133kEIwY033sj8+fN58803GTFixLaYo4ODg4ODg4NDu7BFdXyOPvpojj766Paei4ODg4ODg8MG2JWzutqbLTJ8HBwcHBwcHLYfu25OV/vTJsMnOzu7zdWZa2pqtmpCDg4ODg4ODg6p8re//Y2cnJzN7tcmw2fKlCnJn6urq7n99ts5+uijOeCAAwD4+uuvef/993dIY1IHBwcHB4c/Oru6uHnRokV88sknVFRUYNt2q2033ngjANdff32bxmqT4TNmzJjkz3/+85+59dZb+etf/5pcN27cOB588EE++OADJkyY0KYDOzg4ODg4OLSRXTjW9fjjj3PZZZeRl5dHUVFRqwhUc5JVKqSs8Xn//fe5++6711t/9NFHc91116U6nIODg4ODg4PDRrn99tv5xz/+wbXXXtsu46Wczp6bm8urr7663vrXXnuN3NzcdpmUg4ODg4ODQwuynZaOSG1tLaeeemq7jZeyx+eWW27hwgsv5JNPPklqfL755hvee+89nnjiiXabmIODg4ODg4NiV9b4nHrqqUyfPr1Vf9CtIWXDZ+zYsfTr14+pU6fyyiuvIKWkf//+fPnll+y3337tMikHBwcHBweHFna1Oj5Tp05N/ty7d2/+/ve/88033zBo0CBcLlerfceNG5fS2EJ2VBNwG9HQ0EBmZib19fVkZGTs6Ok4ODg4OOzEbOt7RvP4l172Vzwez1aNFY1G+ecjD7Z5rp999hn33nsvs2bNory8nFdffZXRo0cnt0spueWWW3jssceora1lv/3246GHHmLAgAGtjnnVVVfxwgsvEA6HOfLII3n44Yfp0qXLJo/ds2fPNn0mIQRLly5t077NtMnj09DQkNIvtLGxkfT09JQm4uDg4ODg4LBhdoTHJxgMsueee3L++efz5z//eb3t99xzD5MmTeLpp5+mT58+3H777YwYMYKFCxcmbYDS0lLefPNNXnzxRXJzc7nyyis5/vjjmTVrFrqub/TYy5YtS22yKdAmcXN2djYVFRVtHrRz584pW2AODg4ODg4OG0Nu9b9U5c2jRo3i9ttv5+STT15/NlIyZcoUbrjhBk4++WQGDhzIM888QygU4vnnnwegvr6eJ598kvvvv5+jjjqKIUOG8Nxzz/Hzzz/zwQcftHket956K6FQaL314XCYW2+9NaXPBG30+EgpeeKJJ0hLS2vToPF4POWJODg4ODg4OGx7GhoaWr32eDwph9GWLVvGmjVrGDlyZKtxDjvsML766isuueQSZs2aRTweb7VPp06dGDhwIF999VWbe37ecsstXHrppfj9/lbrQ6EQt9xyy7ap49OtWzcef/zxNg9aVFS0nvjIwcHBwcHBYctoz1BX165dW62/6aabuPnmm1Maa82aNQAUFha2Wl9YWMjy5cuT+7jdbrKzs9fbp/n9bZu33GDbrB9//LFNLSp+T5sMn99++y3lgR0cHBwcHBzakXZKRVq5cmUr3e7WiKZ/b5BszEhJdR9o6RMqhKBPnz6t3mNZFk1NTVuU4u50Z3dwcHBwcNiFyMjI2OoMtKKiIkB5dYqLi5PrKyoqkl6goqIiYrEYtbW1rbw+FRUVHHjggZs9xpQpU5BScsEFF3DLLbeQmZmZ3OZ2u+nRo0eynmAqOIaPg4ODg4PDTs7O1qqrZ8+eFBUVMWPGDIYMGQJALBbj008/Tba1Gjp0KC6XixkzZnDaaacBUF5ezi+//MI999yz2WOMGTMG0zQBOOqoozabAt9WHMPHwcHBwcFhJ2dHVG5uampi8eLFydfLli1jzpw55OTk0K1bN0pLS7njjjvYfffd2X333bnjjjvw+/2cddZZAGRmZnLhhRdy5ZVXkpubS05ODldddRWDBg3iqKOOatMcDMPg8ssvZ/78+SnNfZNjtttIDg4ODg4ODn8YZs6cyeGHH558PXHiREB5Yp5++mmuueYawuEwl19+ebKA4fTp01vV8Zs8eTKGYXDaaaclCxg+/fTTm6zh83v2228/Zs+eTffu3dvlczmVm3+HU7nZwcHBwaGtbK/KzRdefDlu99ZVbo7Fojz5+MMd7v72v//9j+uuu44JEyYwdOhQAoFAq+2DBw9Oabwt8vh8/vnnPProoyxZsoSXX36Zzp078+9//5uePXty8MEHb8mQDg4ODg4ODhthV+vVtS6nn3460LonlxAimR1mWVZK46Vs+Pzf//0f5557LmeffTazZ88mGo0Cqk3FHXfcwTvvvJPqkA4ODg4ODg6bYGcTN29P2rt9RcqGz+23384///lPzjvvPF588cXk+gMPPHCLSkc7ODg4ODg4OGyM9tL2NJOy4bNw4UIOPfTQ9dZnZGRQV1fXHnNycHBwcHBwWIcdkdW1M7FkyRKmTJnC/PnzEULQr18/xo8fT69evVIeq01NSteluLi4VXpbM1988QW77bZbyhNoC7/99hsXXnghPXv2xOfz0atXL2666SZisVir/VasWMEJJ5xAIBAgLy+PcePGrbePg4ODg4NDR6NZ47O1S0fk/fffp3///nz33XcMHjyYgQMH8u233zJgwABmzJiR8ngpe3wuueQSxo8fz1NPPYUQgtWrV/P1119z1VVXpdworK0sWLAA27Z59NFH6d27N7/88gsXX3wxwWCQ++67D1Dlq4877jjy8/P54osvqK6uZsyYMUgpmTZt2jaZl4ODg4ODg8O2pTmj66677lpv/bXXXsuIESNSGm+L0tlvuOEGJk+eTCQSAVSfj6uuuorbbrst1aG2mHvvvZdHHnmEpUuXAvDuu+9y/PHHs3LlSjp16gTAiy++yNixY6moqGhz6p6Tzu7g4ODg0Fa2Vzr7mAsua5d09meeeqTD3d+8Xi8///wzu+++e6v1ixYtYvDgwUlbpK2kHOoC+Mc//kFVVRXfffcd33zzDZWVldvV6AGor69v1ZX166+/ZuDAgUmjB+Doo48mGo0ya9asjY4TjUZpaGhotTg4ODg4OOxM7Mqhrvz8fObMmbPe+jlz5lBQUJDyeFtcudnv9zNs2LAtfftWsWTJEqZNm8b999+fXLdmzZpkY7RmsrOzcbvdrFmzZqNj3Xnnndxyyy3bbK4ODg4ODg4OW87FF1/MX/7yF5YuXcqBBx6IEIIvvviCu+++myuvvDLl8dpk+Jx88sltHvCVV15p874333zzZo2O77//vpWBtXr1ao455hhOPfVULrroolb7bqjNfXOBo41x/fXXJ8twg3Irdu3ata0fwcHBwcHBYZuzKxcw/Pvf/056ejr3338/119/PQCdOnXi5ptvblXUsK20yfBZtxW8lJJXX32VzMzMpEEya9Ys6urqUjKQAP76179yxhlnbHKfHj16JH9evXo1hx9+OAcccACPPfZYq/2Kior49ttvW62rra0lHo+v5wlaF4/Hg8ezdXFTBwcHBweHbcmunM4uhGDChAlMmDCBxsZGgFb9wFKlTYbPv/71r+TP1157Laeddhr//Oc/k03GLMvi8ssvT1kslZeXR15eXpv2LSsr4/DDD2fo0KH861//QtNay5MOOOAA/vGPf1BeXk5xcTEA06dPx+PxMHTo0JTm5eDg4ODg4LDzsTUGTzMpi5ufeuoprrrqqladVXVdZ+LEiTz11FNbPaENsXr1/aSbuAAAKxFJREFUaoYPH07Xrl257777qKysZM2aNa20OyNHjqR///6ce+65zJ49mw8//JCrrrqKiy++uEOp1x0cHBwcHH6PbKelI7J27VrOPfdcOnXqhGEY6LreakmVlMXNpmkyf/58+vbt22r9/PnzsW075Qm0henTp7N48WIWL15Mly5dWm1rdt3pus7bb7/N5ZdfzkEHHYTP5+Oss85K1vlxcHBwcHDoqOzKGp+xY8eyYsUK/v73v1NcXLxJ3W5bSNnwOf/887ngggtYvHgx+++/PwDffPMNd911F+eff/5WTWZjjB07lrFjx252v27duvHWW29tkzk4ODg4ODjsKHZlw+eLL77g888/Z6+99mqX8VI2fO677z6KioqYPHky5eXlgGpjcc0112xRWpmDg4ODg4ODw8bo2rVruwqzU9b4aJrGNddcQ1lZGXV1ddTV1VFWVsY111yzRbE2BwcHBwcHh02zK2t8pkyZwnXXXcdvv/3WLuNtcQFDwBENOzg4ODg4bA924VjX6aefTigUolevXvj9flwuV6vtNTU1KY2XsuHTs2fPTQqLmntnOTg4ODg4ODhsLVOmTGnX8VI2fEpLS1u9jsfjzJ49m/fee4+rr766vebl4ODg4ODgkGAXdvgwZsyYNu131113cemll5KVlbXJ/VI2fMaPH7/B9Q899BAzZ85MdTgHBwcHBweHzdAeGp0Oave0mTvuuIPTTjtts4bPFnVn3xCjRo3i//7v/9prOAcHBwcHBweHNtPWzK//396dxzV55fsD/wQ1CYsgGkxAEdSxLsUVp4rWoo5CKVJse63YjsKtpT/qpUjVafWlM+KCWhdql7Eu07E4ba/2jtrpuCB03EpdqkhvqTouVwTGIS5ICUUNS87vD4dnDARITDAJ+bz7el4v8pyT85xvovLtOec5j1WLmx/05z//GZ07d7ZVc0RERFTPBlNdbX7Ix0wWJz5Dhw41WtwshIBWq8XNmzexYcMGm3aOiIiIXHuNj61ZnPjExsYaJT5ubm7w8/PD2LFj0a9fP5t2joiIiMiWLE580tLSWqEbRERE1BSO+NiOxYub27Vrhxs3bjQ6X1ZWxp2biYiIWsH9u7qs/a9tGzNmDNzd3VusZ/GIT1OrpvV6PeRyuaXNERERUQtcbcRHp9NJT4fQ6XTN1q2vt2/fPrPaNjvxef/99wEAMpkMf/jDH+Dl5SWV1dXV4ejRo1zjQ0RERFbz9fVFaWkpunbtik6dOpl8YoQQAjKZDHV1dRa1bXbi8+6770oX2rhxo9G0llwuR3BwMDZu3GjRxYmIiKhlrjbic/DgQWmLnEOHDtm0bbMTn8LCQgDAuHHjsGvXLvj6+tq0I0RERGSaq+3cHB4ebvJnW7B4jY+tMy8iIiKilty5cwfFxcWorq42Oj9o0CCL2jEr8ZkzZw6WLVsGT09PzJkzp9m6GRkZFnWAiIiIzOBMQzY2dPPmTfznf/4n9u/fb7K8Vdb45Ofno6amBgBw5swZk4uMiIiIqHW42hqfB6WmpqK8vBwnTpzAuHHjsHv3bly/fh3Lly/HunXrLG7PrMTnwemtw4cPW3wRIiIioodx8OBB/OUvf8Evf/lLuLm5ISgoCBMnToS3tzdWrlyJ6Ohoi9qzeAPDV155BZWVlY3OV1VV4ZVXXrG0OSIiImqBsNHhjKqqqtC1a1cAQOfOnXHz5k0AwMCBA3HmzBmL27M48cnMzMTdu3cbnb979y62bdtmcQeIiIioefVTXdYezqhv3764cOECAGDIkCHYtGkTrl27ho0bN8Lf39/i9sy+q0un00EIASEEKisroVQqpbK6ujrs27dPysiIiIiIbCE1NRWlpaUAgMWLFyMyMhKffvop5HI5MjMzLW7P7MSnfudEmUyGxx57rFG5TCbDkiVLLO4AERERNc+VFze//PLL0s9Dhw7F1atX8fe//x09evSASqWyuD2zE59Dhw5BCIHx48dj586d0o6KwP2dm4OCghAQEGBxB4iIiKh59TMu1rbhLFraOudBlm6jY3biU79zYmFhIQIDA+HmZvHyICIiInoIrrZzc35+vln1HmZ7HYt3bg4KCgJgux0UiYiIiB7Umk+JsDjxsfUOikRERNQ8V17jY2sWz1c9uIOiu7s7srKykJmZiT59+uCrr75qjT4SERG5NFe+nd3WLB7xsfUOikRERESPisUjPrbeQdFSer0eQ4YMgUwmw/fff29UVlxcjJiYGHh6ekKlUiElJaXRGiQiIiJn48o7N9uaxYmPrXdQtNRbb71l8rb5uro6REdHo6qqCrm5udi+fTt27tyJuXPntnqfiIiIWhOnumzH4qkuUzsofvbZZ5DL5fjkk09s3T8j+/fvR3Z2Nnbu3NlocXV2djbOnTuHkpISKTFat24dEhISkJ6eDm9v71btGxERETk+ixMfW++gaK7r168jMTERX375JTw8PBqVHz9+HCEhIUajQZGRkdDr9cjLy8O4ceNMtqvX66HX66XXOp3O9p0nIiKyAu/qsh2rdyH08PDAsGHDWjXpEUIgISEBSUlJGD58uMk6Wq0WarXa6Jyvry/kcjm0Wm2Tba9cuRI+Pj7SERgYaNO+ExERWYtrfGzHrBGf1to6Oi0trcXne506dQrHjh2DTqfDggULmq1ragdHIUSzOzsuWLDAKD6dTsfkh4iIqI0yK/Fpra2jk5OTERcX12yd4OBgLF++HCdOnIBCoTAqGz58OF5++WVkZmZCo9Hg5MmTRuXl5eWoqalpNBL0IIVC0ahdIiIih2KLxckc8gFgZuLTWltHq1Qqs6bI3n//fSxfvlx6/c9//hORkZHYsWMHRowYAQAICwtDeno6SktLpbvLsrOzoVAoEBoa2ir9JyIiehS4xsd2LF7cbA89evQweu3l5QUA6N27N7p37w4AiIiIwIABAzB9+nSsWbMGt2/fxrx585CYmMg7uoiIyKm52kNKW1ObecR6u3btsHfvXiiVSowePRovvvgiJk+ejLVr19q7a0REROQgnGLEp6Hg4GAIE2N2PXr0wJ49e+zQIyIiotbDqS7bccrEh4iIyJUw8bGdNjPVRURERNQSjvgQERE5OI742A4THyIiIgfHu7psh1NdRERE5DI44kNEROTgONVlO0x8iIiIHBwTH9vhVBcRERG5DI74EBEROTgubrYdJj5EREQOjlNdtsPEh4iIyMEx8bEdrvEhIiIil8HEh4iIyMEJGx2WSEtLg0wmMzo0Gs2/+yQE0tLSEBAQAHd3d4wdOxZnz561Ks5HgYkPERGRg6uf6rL2sNTjjz+O0tJS6SgoKJDKVq9ejYyMDHz44Yc4deoUNBoNJk6ciMrKShtGbntMfIiIiMik9u3bQ6PRSIefnx+A+6M969evx8KFC/H8888jJCQEmZmZuHPnDj7//HM797p5THyIiIgcnIANRnz+1ZZOpzM69Hp9k9e9dOkSAgIC0LNnT8TFxeHKlSsAgMLCQmi1WkREREh1FQoFwsPDcezYsVb8JKzHxIeIiMjB2XKNT2BgIHx8fKRj5cqVJq85YsQIbNu2DQcOHMCWLVug1WoxatQolJWVQavVAgDUarXRe9RqtVTmqHg7OxERkQspKSmBt7e39FqhUJisFxUVJf08cOBAhIWFoXfv3sjMzMTIkSMBADKZzOg9QohG5xwNR3yIiIgcnC0XN3t7exsdTSU+DXl6emLgwIG4dOmSdHdXw9GdGzduNBoFcjRMfIiIiBycEIDBysPaDQz1ej3Onz8Pf39/9OzZExqNBjk5OVJ5dXU1jhw5glGjRlkZbeviVBcRERE1Mm/ePMTExKBHjx64ceMGli9fDp1Oh/j4eMhkMqSmpmLFihXo06cP+vTpgxUrVsDDwwMvvfSSvbveLCY+REREDs4ej6z4xz/+gWnTpuHWrVvw8/PDyJEjceLECQQFBQEA3nrrLdy9exezZs1CeXk5RowYgezsbHTs2NG6jrYymRB8eseDdDodfHx8UFFRYbT4i4iIqKHW/p1R3/7Qca+hXXu5VW3V1VYj/9Bml//9xhEfIiIiByeEDEJYd7eUte9vK7i4mYiIiFwGR3yIiIgcnD3W+LRVTHyIiIgc3MM8Xd1UG8SpLiIiInIhHPEhIiJycAYByKwcsjFwyAcAEx8iIiKHxzU+tuNUU1179+7FiBEj4O7uDpVKheeff96ovLi4GDExMfD09IRKpUJKSgqqq6vt1FsiIiJyNE4z4rNz504kJiZixYoVGD9+PIQQKCgokMrr6uoQHR0NPz8/5ObmoqysDPHx8RBC4IMPPrBjz4mIiKzDxc224xSJT21tLWbPno01a9Zg5syZ0vm+fftKP2dnZ+PcuXMoKSlBQEAAAGDdunVISEhAenq6S+9SSUREzo1rfGzHKaa6zpw5g2vXrsHNzQ1Dhw6Fv78/oqKicPbsWanO8ePHERISIiU9ABAZGQm9Xo+8vLwm29br9dDpdEYHERERtU1OkfhcuXIFAJCWloZFixZhz5498PX1RXh4OG7fvg0A0Gq1UKvVRu/z9fWFXC6HVqttsu2VK1fCx8dHOgIDA1svECIioodQv7jZ2oPsnPikpaVBJpM1e5w+fRoGgwEAsHDhQrzwwgsIDQ3F1q1bIZPJ8D//8z9SezJZ4+eQCCFMnq+3YMECVFRUSEdJSYntAyUiIrLC/TU+MisPAuy8xic5ORlxcXHN1gkODkZlZSUAYMCAAdJ5hUKBXr16obi4GACg0Whw8uRJo/eWl5ejpqam0UjQgxQKBRQKxcOGQERE1Oq4xsd27Jr4qFQqqFSqFuuFhoZCoVDgwoULePLJJwEANTU1uHr1KoKCggAAYWFhSE9PR2lpKfz9/QHcX/CsUCgQGhraekEQERGR03CKu7q8vb2RlJSExYsXIzAwEEFBQVizZg0AYMqUKQCAiIgIDBgwANOnT8eaNWtw+/ZtzJs3D4mJibyji4iInBo3MLQdp0h8AGDNmjVo3749pk+fjrt372LEiBE4ePAgfH19AQDt2rXD3r17MWvWLIwePRru7u546aWXsHbtWjv3nIiIyDpCWD9VxcTnPpkQ/CgepNPp4OPjg4qKCo4UERFRs1r7d0Z9+71G/j+4tbduPaqhVo8rJza5/O83pxnxISIiclXcudl2mPgQERE5OIMNMh/e1XWfU2xgSERERGQLHPEhIiJycELIIETTm/Ga2wYx8SEiInJ4Bgdpoy1g4kNEROTguMbHdrjGh4iIiFwGR3yIiIgcHEd8bIeJDxERkYNj4mM7nOoiIiIil8ERHyIiIgdngAyAdbejG6x8f1vBxIeIiMjBGQDrp7ps0ZE2gFNdRERE5DI44kNEROTghLB+cbLg4mYATHyIiIgcXh2sXeHDp7PX41QXERERuQyO+BARETm4OgHIONVlE0x8iIiIHFwtEx+bYeJDRETk4Oogg8zKVT6C+/gA4BofIiIiciEc8SEiInJwnOqyHSY+REREjk7YIHFh4gOAU11ERETkQjjiQ0RE5PAErB+y4ZAPwMSHiIjI8THvsRlOdREREZHL4IgPERGRw+OQj60w8SEiInJ0QgDCYH0bxKkuIiIich0c8SEiInJ0wgYb+XDEB4ATjfhcvHgRsbGxUKlU8Pb2xujRo3Ho0CGjOsXFxYiJiYGnpydUKhVSUlJQXV1tpx4TERHZisFGBzlN4hMdHY3a2locPHgQeXl5GDJkCCZNmgStVgsAqKurQ3R0NKqqqpCbm4vt27dj586dmDt3rp17TkREZCVhsM1BzpH43Lp1C5cvX8b8+fMxaNAg9OnTB6tWrcKdO3dw9uxZAEB2djbOnTuHTz/9FEOHDsWECROwbt06bNmyBTqdzs4REBERkSNwisSnS5cu6N+/P7Zt24aqqirU1tZi06ZNUKvVCA0NBQAcP34cISEhCAgIkN4XGRkJvV6PvLy8JtvW6/XQ6XRGBxERkUPhiI/NOMXiZplMhpycHMTGxqJjx45wc3ODWq1GVlYWOnXqBADQarVQq9VG7/P19YVcLpemw0xZuXIllixZ0prdJyIispIt1ugw8QHsPOKTlpYGmUzW7HH69GkIITBr1ix07doV33zzDb777jvExsZi0qRJKC0tldqTyWSNriGEMHm+3oIFC1BRUSEdJSUlrRIrERER2Z9dR3ySk5MRFxfXbJ3g4GAcPHgQe/bsQXl5Oby9vQEAGzZsQE5ODjIzMzF//nxoNBqcPHnS6L3l5eWoqalpNBL0IIVCAYVCYX0wRERErcUWU1Wc6gJg58RHpVJBpVK1WO/OnTsAADc34wEqNzc3GAz3v8iwsDCkp6ejtLQU/v7+AO4veFYoFNI6ICIiIqfEfXxsxikWN4eFhcHX1xfx8fH43//9X1y8eBG/+c1vUFhYiOjoaABAREQEBgwYgOnTpyM/Px9/+9vfMG/ePCQmJkqjREREROTanCLxUalUyMrKws8//4zx48dj+PDhyM3NxV/+8hcMHjwYANCuXTvs3bsXSqUSo0ePxosvvojJkydj7dq1du49ERGRtbiBoa04xV1dADB8+HAcOHCg2To9evTAnj17HlGPiIiIHhGu8bEZpxjxISIiIrIFpxnxISIicllC2GDEh4ubASY+REREToAbGNoKEx8iIiJHx9vZbYZrfIiIiMhlcMSHiIjI0fGuLpth4kNEROTomPjYDKe6iIiIyGVwxIeIiMjhiX8d1rZBHPEhIiJyeIZ/T3c97PEQt7Nv2LABPXv2hFKpRGhoKL755hvbh/aIMfEhIiKiRnbs2IHU1FQsXLgQ+fn5GDNmDKKiolBcXGzvrlmFiQ8REZGjs3a05yEWR2dkZGDmzJl49dVX0b9/f6xfvx6BgYH46KOPWinIR4OJDxERkaOr38DQ2sNM1dXVyMvLQ0REhNH5iIgIHDt2zNbRPVJc3NyA+NcfDJ1OZ+eeEBGRo6v/XSFae1dkQ63N2mj4+02hUEChUBidu3XrFurq6qBWq43Oq9VqaLVa6/tiR0x8GqisrAQABAYG2rknRETkLCorK+Hj42PzduVyOTQaDbTFOTZpz8vLq9Hvt8WLFyMtLc1kfZlMZvRaCNHonLNh4tNAQEAASkpK0LFjR7t9uTqdDoGBgSgpKYG3t7dd+tBaGJtzYmzOqS3HBjhGfEIIVFZWIiAgoFXaVyqVKCwsRHV1tU3aM5W4NBztAQCVSoV27do1Gt25ceNGo1EgZ8PEpwE3Nzd0797d3t0AAHh7e7fJf6wAxuasGJtzasuxAfaPrzVGeh6kVCqhVCpb9RoNyeVyhIaGIicnB88995x0PicnB7GxsY+0L7bGxIeIiIgamTNnDqZPn47hw4cjLCwMmzdvRnFxMZKSkuzdNasw8SEiIqJGpk6dirKyMixduhSlpaUICQnBvn37EBQUZO+uWYWJjwNSKBRYvHixyXlXZ8fYnBNjc05tOTag7cfnCGbNmoVZs2bZuxs2JROtfg8eERERkWPgBoZERETkMpj4EBERkctg4kNEREQug4kPERERuQwmPnaUnp6OUaNGwcPDA506dTJZRyaTNTo2btxoVKegoADh4eFwd3dHt27dsHTp0tZ/bkwLzImtuLgYMTEx8PT0hEqlQkpKSqPdSR0xNlOCg4MbfU/z5883qmNOvI5qw4YN6NmzJ5RKJUJDQ/HNN9/Yu0sWSUtLa/T9aDQaqVwIgbS0NAQEBMDd3R1jx47F2bNn7djj5h09ehQxMTEICAiATCbDl19+aVRuTjx6vR5vvPEGVCoVPD098eyzz+If//jHI4zCtJZiS0hIaPRdjhw50qiOo8ZGjoGJjx1VV1djypQpeP3115utt3XrVpSWlkpHfHy8VKbT6TBx4kQEBATg1KlT+OCDD7B27VpkZGS0dveb1VJsdXV1iI6ORlVVFXJzc7F9+3bs3LkTc+fOleo4amxNqd/rov5YtGiRVGZOvI5qx44dSE1NxcKFC5Gfn48xY8YgKioKxcXF9u6aRR5//HGj76egoEAqW716NTIyMvDhhx/i1KlT0Gg0mDhxovTsPkdTVVWFwYMH48MPPzRZbk48qamp2L17N7Zv347c3Fz8/PPPmDRpEurq6h5VGCa1FBsAPP3000bf5b59+4zKHTU2chCC7G7r1q3Cx8fHZBkAsXv37ibfu2HDBuHj4yPu3bsnnVu5cqUICAgQBoPBxj21XFOx7du3T7i5uYlr165J5/77v/9bKBQKUVFRIYRw/NgeFBQUJN59990my82J11E98cQTIikpyehcv379xPz58+3UI8stXrxYDB482GSZwWAQGo1GrFq1Sjp379494ePjIzZu3PiIevjwGv4bYU48P/30k+jQoYPYvn27VOfatWvCzc1NZGVlPbK+t8TUv3/x8fEiNja2yfc4S2xkPxzxcQLJyclQqVT45S9/iY0bN8JgMEhlx48fR3h4uNEGXpGRkfjnP/+Jq1ev2qG35jl+/DhCQkKMHuwXGRkJvV6PvLw8qY4zxfbOO++gS5cuGDJkCNLT042mscyJ1xFVV1cjLy8PERERRucjIiJw7NgxO/Xq4Vy6dAkBAQHo2bMn4uLicOXKFQBAYWEhtFqtUYwKhQLh4eFOFyNgXjx5eXmoqakxqhMQEICQkBCniPnw4cPo2rUrHnvsMSQmJuLGjRtSmbPHRq2POzc7uGXLluFXv/oV3N3d8be//Q1z587FrVu3pGkUrVaL4OBgo/fUPzlXq9WiZ8+ej7rLZtFqtY2e8Ovr6wu5XC49DdiZYps9ezaGDRsGX19ffPfdd1iwYAEKCwvxhz/8AYB58TqiW7duoa6urlHf1Wq1Q/e7oREjRmDbtm147LHHcP36dSxfvhyjRo3C2bNnpThMxVhUVGSP7lrFnHi0Wi3kcjl8fX0b1XH07zUqKgpTpkxBUFAQCgsL8dvf/hbjx49HXl4eFAqFU8dGjwZHfGzM1CLKhsfp06fNbm/RokUICwvDkCFDMHfuXCxduhRr1qwxqiOTyYxei38t/m143lq2js1U/4QQRucfVWymWBLvm2++ifDwcAwaNAivvvoqNm7ciI8//hhlZWVNxlIfz6OIxVqmvgdn6He9qKgovPDCCxg4cCAmTJiAvXv3AgAyMzOlOs4eY0MPE48zxDx16lRER0cjJCQEMTEx2L9/Py5evCh9p01xhtjo0eCIj40lJycjLi6u2ToNRzEsMXLkSOh0Oly/fh1qtRoajabR/8XUD/s2/D8+a9kyNo1Gg5MnTxqdKy8vR01NjdTvRxmbKdbEW3+XyeXLl9GlSxez4nVEKpUK7dq1M/k9OHK/W+Lp6YmBAwfi0qVLmDx5MoD7oyD+/v5SHWeNsf5utebi0Wg0qK6uRnl5udHIyI0bNzBq1KhH22Er+fv7IygoCJcuXQLQtmKj1sERHxtTqVTo169fs4dSqXzo9vPz86FUKqVbxMPCwnD06FGj9STZ2dkICAiwKsEyxZaxhYWF4ccff0RpaalRvxUKBUJDQx95bKZYE29+fj4ASL94zInXEcnlcoSGhiInJ8fofE5OjlP/EtHr9Th//jz8/f3Rs2dPaDQaoxirq6tx5MgRp4zRnHhCQ0PRoUMHozqlpaX48ccfnS7msrIylJSUSH/X2lJs1ErstqyaRFFRkcjPzxdLliwRXl5eIj8/X+Tn54vKykohhBBfffWV2Lx5sygoKBCXL18WW7ZsEd7e3iIlJUVq46effhJqtVpMmzZNFBQUiF27dglvb2+xdu1ae4UlhGg5ttraWhESEiJ+9atfiTNnzoivv/5adO/eXSQnJ0ttOGpsDR07dkxkZGSI/Px8ceXKFbFjxw4REBAgnn32WamOOfE6qu3bt4sOHTqIjz/+WJw7d06kpqYKT09PcfXqVXt3zWxz584Vhw8fFleuXBEnTpwQkyZNEh07dpRiWLVqlfDx8RG7du0SBQUFYtq0acLf31/odDo799y0yspK6e8UAOnPX1FRkRDCvHiSkpJE9+7dxddffy3OnDkjxo8fLwYPHixqa2vtFZYQovnYKisrxdy5c8WxY8dEYWGhOHTokAgLCxPdunVzitjIMTDxsaP4+HgBoNFx6NAhIYQQ+/fvF0OGDBFeXl7Cw8NDhISEiPXr14uamhqjdn744QcxZswYoVAohEajEWlpaXa/3bul2IS4nxxFR0cLd3d30blzZ5GcnGx067oQjhlbQ3l5eWLEiBHCx8dHKJVK0bdvX7F48WJRVVVlVM+ceB3V73//exEUFCTkcrkYNmyYOHLkiL27ZJGpU6cKf39/0aFDBxEQECCef/55cfbsWancYDCIxYsXC41GIxQKhXjqqadEQUGBHXvcvEOHDpn8+xUfHy+EMC+eu3fviuTkZNG5c2fh7u4uJk2aJIqLi+0QjbHmYrtz546IiIgQfn5+okOHDqJHjx4iPj6+Ub8dNTZyDDIhHHAbXCIiIqJWwDU+RERE5DKY+BAREZHLYOJDRERELoOJDxEREbkMJj5ERETkMpj4EBERkctg4kNEREQug4kPtTljx45Fampqm7puQkKC9EyphxUcHCw9XPWnn35qst4nn3wiPRKFbC8hIUH6Hr788kt7d4fI5TDxIbKRXbt2YdmyZdLr4OBgrF+/3n4dMmHp0qUoLS2Fj4+PvbvS5h0+fNhkkvnee+8ZPbONiB4tPp2dyEY6d+5s7y60qGPHjtLTu+2tpqYGHTp0sHc3HjkfHx8mnkR2xBEfavPKy8sxY8YM+Pr6wsPDA1FRUbh06ZJUXj+1c+DAAfTv3x9eXl54+umnjf6vvLa2FikpKejUqRO6dOmCt99+G/Hx8UbTTw9OdY0dOxZFRUV48803pWkNAEhLS8OQIUOM+rd+/Xqjp83X1dVhzpw50rXeeustNHyyjBACq1evRq9eveDu7o7Bgwfjz3/+80N9Pp988gl69OgBDw8PPPfccygrK2tU569//StCQ0OhVCrRq1cvLFmyBLW1tVL53//+dzz55JNQKpUYMGAAvv76a6OpnKtXr0Imk+GLL77A2LFjoVQq8emnnwIAtm7div79+0OpVKJfv37YsGGD0bWvXbuGqVOnwtfXF126dEFsbCyuXr0qlR8+fBhPPPEEPD090alTJ4wePRpFRUVmxd5SXBkZGRg4cCA8PT0RGBiIWbNm4eeff5bKi4qKEBMTA19fX3h6euLxxx/Hvn37cPXqVYwbNw4A4OvrC5lMhoSEBLP6RESti4kPtXkJCQk4ffo0vvrqKxw/fhxCCDzzzDOoqamR6ty5cwdr167Fn/70Jxw9ehTFxcWYN2+eVP7OO+/gs88+w9atW/Htt99Cp9M1uz5j165d6N69uzS1ZMnUxrp16/DHP/4RH3/8MXJzc3H79m3s3r3bqM6iRYuwdetWfPTRRzh79izefPNN/PrXv8aRI0fM/2AAnDx5Eq+88gpmzZqF77//HuPGjcPy5cuN6hw4cAC//vWvkZKSgnPnzmHTpk345JNPkJ6eDgAwGAyYPHkyPDw8cPLkSWzevBkLFy40eb23334bKSkpOH/+PCIjI7FlyxYsXLgQ6enpOH/+PFasWIHf/va3yMzMBHD/exk3bhy8vLxw9OhR5ObmSolpdXU1amtrMXnyZISHh+OHH37A8ePH8dprr0mJZnNaigsA3Nzc8P777+PHH39EZmYmDh48iLfeeksq/6//+i/o9XocPXoUBQUFeOedd+Dl5YXAwEDs3LkTAHDhwgWUlpbivffes+i7IaJWYtdHpBK1gvDwcDF79mwhhBAXL14UAMS3334rld+6dUu4u7uLL774QgghxNatWwUAcfnyZanO73//e6FWq6XXarVarFmzRnpdW1srevToIWJjY01eVwghgoKCxLvvvmvUt8WLF4vBgwcbnXv33XdFUFCQ9Nrf31+sWrVKel1TUyO6d+8uXevnn38WSqVSHDt2zKidmTNnimnTpjX5uZjqz7Rp08TTTz9tdG7q1KnCx8dHej1mzBixYsUKozp/+tOfhL+/vxBCiP3794v27duL0tJSqTwnJ0cAELt37xZCCFFYWCgAiPXr1xu1ExgYKD7//HOjc8uWLRNhYWFCCCE+/vhj0bdvX2EwGKRyvV4v3N3dxYEDB0RZWZkAIA4fPtxk3E1pKS5TvvjiC9GlSxfp9cCBA0VaWprJuvVPGS8vLzdZ/uDnQ0SPDtf4UJt2/vx5tG/fHiNGjJDOdenSBX379sX58+elcx4eHujdu7f02t/fHzdu3AAAVFRU4Pr163jiiSek8nbt2iE0NBQGg8Gm/a2oqEBpaSnCwsKkc+3bt8fw4cOl6a5z587h3r17mDhxotF7q6urMXToUIuud/78eTz33HNG58LCwpCVlSW9zsvLw6lTp4xGQurq6nDv3j3cuXMHFy5cQGBgoNHaoQc/qwcNHz5c+vnmzZsoKSnBzJkzkZiYKJ2vra2V1sDk5eXh8uXL6Nixo1E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"text/plain": [
"<Figure size 640x480 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"lai_array['lai_north_masked_30'].resample(band='1Y').std().sel(band='1982-12-31').plot(cmap='cividis')"
]
},
{
"cell_type": "markdown",
"id": "595814ce-2a3d-4d46-8aaf-f6f998e32721",
"metadata": {},
"source": [
"Persisting values "
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "13247037-dabf-4427-9a7e-ceef2e1ce681",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.10/site-packages/dask/array/core.py:4830: PerformanceWarning: Increasing number of chunks by factor of 14\n",
" result = blockwise(\n"
]
}
],
"source": [
"lai_array['lai_north_masked_30_annual_mean'] = lai_array['lai_north_masked_30'].resample(band='1Y').mean()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "85ac6103-35e3-4e26-b30f-ae7aa8ce2b38",
"metadata": {},
"outputs": [],
"source": [
"# dump_to_disk()\n",
"# refresh()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "bcb792a0-5fa7-4fd9-af56-362004f116b3",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.10/site-packages/dask/array/core.py:4830: PerformanceWarning: Increasing number of chunks by factor of 14\n",
" result = blockwise(\n"
]
}
],
"source": [
"lai_array['lai_north_masked_30_annual_std'] = lai_array['lai_north_masked_30'].resample(band='1Y').std()"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "3ad05f13-04e2-46a3-a87a-1823f294df23",
"metadata": {},
"outputs": [],
"source": [
"# dump_to_disk()\n",
"# refresh()"
]
},
{
"cell_type": "markdown",
"id": "f926981d-b712-4107-af76-f395bbf8a2ff",
"metadata": {},
"source": [
"Show persisted"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "3c700d73-ad93-43e8-8342-3ae6efa78b92",
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;lai_north_masked_30_annual_mean&#x27; (band: 408, y: 360, x: 720)&gt;\n",
"dask.array&lt;where, shape=(408, 360, 720), dtype=float32, chunksize=(23, 198, 54), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 1982-02-28 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 0\n",
"Attributes: (12/16)\n",
" STATISTICS_MAXIMUM: 540\n",
" STATISTICS_MEAN: 87.72932166302\n",
" STATISTICS_MINIMUM: 0\n",
" STATISTICS_STDDEV: 134.37581892842\n",
" bands: 408\n",
" band_names: Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band...\n",
" ... ...\n",
" file_type: ENVI Standard\n",
" header_offset: 0\n",
" interleave: bsq\n",
" lines: 360\n",
" map_info: Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,W...\n",
" samples: 720</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lai_north_masked_30_annual_mean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 408</li><li><span class='xr-has-index'>y</span>: 360</li><li><span class='xr-has-index'>x</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-9f7ca1ef-da82-4b7e-9815-e0f557021bae' class='xr-array-in' type='checkbox' checked><label for='section-9f7ca1ef-da82-4b7e-9815-e0f557021bae' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(23, 54, 54), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
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" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 403.42 MiB </td>\n",
" <td> 0.94 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (408, 360, 720) </td>\n",
" <td> (23, 198, 54) </td>\n",
" </tr>\n",
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" <td colspan=\"2\"> 1904 chunks in 128 graph layers </td>\n",
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" &#x27;1982-03-31T00:00:00.000000000&#x27;, ..., &#x27;2015-10-31T00:00:00.000000000&#x27;,\n",
" &#x27;2015-11-30T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-c17c40a7-464e-43cf-bc5c-0195b80157da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c17c40a7-464e-43cf-bc5c-0195b80157da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3902ee52-6d5b-4ab6-bdcb-b9b02149d871' class='xr-var-data-in' type='checkbox'><label for='data-3902ee52-6d5b-4ab6-bdcb-b9b02149d871' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ..., 178.75, 179.25, 179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.75 89.25 88.75 ... -89.25 -89.75</div><input id='attrs-20de4bb2-5112-4240-85bf-88812a2e722a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20de4bb2-5112-4240-85bf-88812a2e722a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd236392-7e95-4055-b9b7-2301b928d732' class='xr-var-data-in' type='checkbox'><label for='data-cd236392-7e95-4055-b9b7-2301b928d732' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 89.75, 89.25, 88.75, ..., -88.75, -89.25, -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-a0488d08-4f22-4357-b7f0-3e43d085c638' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0488d08-4f22-4357-b7f0-3e43d085c638' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79a43cb7-12ad-4efe-af06-6be64ded4b67' class='xr-var-data-in' type='checkbox'><label for='data-79a43cb7-12ad-4efe-af06-6be64ded4b67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.0 0.5 -0.0 90.0 -0.0 -0.5</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4de1b121-4cd0-437d-ba48-5b1d326fd22e' class='xr-section-summary-in' type='checkbox' ><label for='section-4de1b121-4cd0-437d-ba48-5b1d326fd22e' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-535518cc-e788-45ab-a095-8bd98eadfd3d' class='xr-index-data-in' type='checkbox'/><label for='index-535518cc-e788-45ab-a095-8bd98eadfd3d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1982-01-31&#x27;, &#x27;1982-02-28&#x27;, &#x27;1982-03-31&#x27;, &#x27;1982-04-30&#x27;,\n",
" &#x27;1982-05-31&#x27;, &#x27;1982-06-30&#x27;, &#x27;1982-07-31&#x27;, &#x27;1982-08-31&#x27;,\n",
" &#x27;1982-09-30&#x27;, &#x27;1982-10-31&#x27;,\n",
" ...\n",
" &#x27;2015-03-31&#x27;, &#x27;2015-04-30&#x27;, &#x27;2015-05-31&#x27;, &#x27;2015-06-30&#x27;,\n",
" &#x27;2015-07-31&#x27;, &#x27;2015-08-31&#x27;, &#x27;2015-09-30&#x27;, &#x27;2015-10-31&#x27;,\n",
" &#x27;2015-11-30&#x27;, &#x27;2015-12-31&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;band&#x27;, length=408, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b461d432-1652-463c-98a2-81f127ddc498' class='xr-index-data-in' type='checkbox'/><label for='index-b461d432-1652-463c-98a2-81f127ddc498' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75,\n",
" -176.25, -175.75, -175.25,\n",
" ...\n",
" 175.25, 175.75, 176.25, 176.75, 177.25, 177.75, 178.25,\n",
" 178.75, 179.25, 179.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3fa07d13-543c-46ad-82fe-cc57afcf72b7' class='xr-index-data-in' type='checkbox'/><label for='index-3fa07d13-543c-46ad-82fe-cc57afcf72b7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 89.75, 89.25, 88.75, 88.25, 87.75, 87.25, 86.75, 86.25,\n",
" 85.75, 85.25,\n",
" ...\n",
" -85.25, -85.75, -86.25, -86.75, -87.25, -87.75, -88.25, -88.75,\n",
" -89.25, -89.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-390ebfd6-727e-46ee-9cd5-d2e4f2fbb60a' class='xr-section-summary-in' type='checkbox' ><label for='section-390ebfd6-727e-46ee-9cd5-d2e4f2fbb60a' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>540</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>87.72932166302</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>0</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>134.37581892842</dd><dt><span>bands :</span></dt><dd>408</dd><dt><span>band_names :</span></dt><dd>Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band 7,Band 8,Band 9,Band 10,Band 11,Band 12,Band 13,Band 14,Band 15,Band 16,Band 17,Band 18,Band 19,Band 20,Band 21,Band 22,Band 23,Band 24,Band 25,Band 26,Band 27,Band 28,Band 29,Band 30,Band 31,Band 32,Band 33,Band 34,Band 35,Band 36,Band 37,Band 38,Band 39,Band 40,Band 41,Band 42,Band 43,Band 44,Band 45,Band 46,Band 47,Band 48,Band 49,Band 50,Band 51,Band 52,Band 53,Band 54,Band 55,Band 56,Band 57,Band 58,Band 59,Band 60,Band 61,Band 62,Band 63,Band 64,Band 65,Band 66,Band 67,Band 68,Band 69,Band 70,Band 71,Band 72,Band 73,Band 74,Band 75,Band 76,Band 77,Band 78,Band 79,Band 80,Band 81,Band 82,Band 83,Band 84,Band 85,Band 86,Band 87,Band 88,Band 89,Band 90,Band 91,Band 92,Band 93,Band 94,Band 95,Band 96,Band 97,Band 98,Band 99,Band 100,Band 101,Band 102,Band 103,Band 104,Band 105,Band 106,Band 107,Band 108,Band 109,Band 110,Band 111,Band 112,Band 113,Band 114,Band 115,Band 116,Band 117,Band 118,Band 119,Band 120,Band 121,Band 122,Band 123,Band 124,Band 125,Band 126,Band 127,Band 128,Band 129,Band 130,Band 131,Band 132,Band 133,Band 134,Band 135,Band 136,Band 137,Band 138,Band 139,Band 140,Band 141,Band 142,Band 143,Band 144,Band 145,Band 146,Band 147,Band 148,Band 149,Band 150,Band 151,Band 152,Band 153,Band 154,Band 155,Band 156,Band 157,Band 158,Band 159,Band 160,Band 161,Band 162,Band 163,Band 164,Band 165,Band 166,Band 167,Band 168,Band 169,Band 170,Band 171,Band 172,Band 173,Band 174,Band 175,Band 176,Band 177,Band 178,Band 179,Band 180,Band 181,Band 182,Band 183,Band 184,Band 185,Band 186,Band 187,Band 188,Band 189,Band 190,Band 191,Band 192,Band 193,Band 194,Band 195,Band 196,Band 197,Band 198,Band 199,Band 200,Band 201,Band 202,Band 203,Band 204,Band 205,Band 206,Band 207,Band 208,Band 209,Band 210,Band 211,Band 212,Band 213,Band 214,Band 215,Band 216,Band 217,Band 218,Band 219,Band 220,Band 221,Band 222,Band 223,Band 224,Band 225,Band 226,Band 227,Band 228,Band 229,Band 230,Band 231,Band 232,Band 233,Band 234,Band 235,Band 236,Band 237,Band 238,Band 239,Band 240,Band 241,Band 242,Band 243,Band 244,Band 245,Band 246,Band 247,Band 248,Band 249,Band 250,Band 251,Band 252,Band 253,Band 254,Band 255,Band 256,Band 257,Band 258,Band 259,Band 260,Band 261,Band 262,Band 263,Band 264,Band 265,Band 266,Band 267,Band 268,Band 269,Band 270,Band 271,Band 272,Band 273,Band 274,Band 275,Band 276,Band 277,Band 278,Band 279,Band 280,Band 281,Band 282,Band 283,Band 284,Band 285,Band 286,Band 287,Band 288,Band 289,Band 290,Band 291,Band 292,Band 293,Band 294,Band 295,Band 296,Band 297,Band 298,Band 299,Band 300,Band 301,Band 302,Band 303,Band 304,Band 305,Band 306,Band 307,Band 308,Band 309,Band 310,Band 311,Band 312,Band 313,Band 314,Band 315,Band 316,Band 317,Band 318,Band 319,Band 320,Band 321,Band 322,Band 323,Band 324,Band 325,Band 326,Band 327,Band 328,Band 329,Band 330,Band 331,Band 332,Band 333,Band 334,Band 335,Band 336,Band 337,Band 338,Band 339,Band 340,Band 341,Band 342,Band 343,Band 344,Band 345,Band 346,Band 347,Band 348,Band 349,Band 350,Band 351,Band 352,Band 353,Band 354,Band 355,Band 356,Band 357,Band 358,Band 359,Band 360,Band 361,Band 362,Band 363,Band 364,Band 365,Band 366,Band 367,Band 368,Band 369,Band 370,Band 371,Band 372,Band 373,Band 374,Band 375,Band 376,Band 377,Band 378,Band 379,Band 380,Band 381,Band 382,Band 383,Band 384,Band 385,Band 386,Band 387,Band 388,Band 389,Band 390,Band 391,Band 392,Band 393,Band 394,Band 395,Band 396,Band 397,Band 398,Band 399,Band 400,Band 401,Band 402,Band 403,Band 404,Band 405,Band 406,Band 407,Band 408</dd><dt><span>byte_order :</span></dt><dd>0</dd><dt><span>coordinate_system_string :</span></dt><dd>GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.017453292519943295]]</dd><dt><span>data_type :</span></dt><dd>4</dd><dt><span>description :</span></dt><dd>L:\\NDVIseason\\LAI\\glasss\\GLASS_LAI_82_15_montlhy0_5deg.envi</dd><dt><span>file_type :</span></dt><dd>ENVI Standard</dd><dt><span>header_offset :</span></dt><dd>0</dd><dt><span>interleave :</span></dt><dd>bsq</dd><dt><span>lines :</span></dt><dd>360</dd><dt><span>map_info :</span></dt><dd>Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,WGS-84</dd><dt><span>samples :</span></dt><dd>720</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'lai_north_masked_30_annual_mean' (band: 408, y: 360, x: 720)>\n",
"dask.array<where, shape=(408, 360, 720), dtype=float32, chunksize=(23, 198, 54), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 1982-02-28 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 0\n",
"Attributes: (12/16)\n",
" STATISTICS_MAXIMUM: 540\n",
" STATISTICS_MEAN: 87.72932166302\n",
" STATISTICS_MINIMUM: 0\n",
" STATISTICS_STDDEV: 134.37581892842\n",
" bands: 408\n",
" band_names: Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band...\n",
" ... ...\n",
" file_type: ENVI Standard\n",
" header_offset: 0\n",
" interleave: bsq\n",
" lines: 360\n",
" map_info: Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,W...\n",
" samples: 720"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lai_array['lai_north_masked_30_annual_mean']"
]
},
{
"cell_type": "markdown",
"id": "0b0c3c33-685a-4316-a2f9-b25ac689c77c",
"metadata": {},
"source": [
"### For Temp"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "ff1327b9-8be0-44e7-bfd8-8fddd6bbeb71",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.10/site-packages/dask/array/core.py:4830: PerformanceWarning: Increasing number of chunks by factor of 14\n",
" result = blockwise(\n"
]
}
],
"source": [
"lai_array['temp_north_masked_30_annual_mean'] = lai_array['temp_north_masked_30'].resample(band='1Y').mean()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "23cb2b0e-05a3-4671-8412-de3512153535",
"metadata": {},
"outputs": [],
"source": [
"# dump_to_disk()\n",
"# refresh()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "e586a2b2-31ce-4e1d-9f2a-0379e8f911dc",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.10/site-packages/dask/array/core.py:4830: PerformanceWarning: Increasing number of chunks by factor of 14\n",
" result = blockwise(\n"
]
}
],
"source": [
"lai_array['temp_north_masked_30_annual_std'] = lai_array['temp_north_masked_30'].resample(band='1Y').std()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "0088e343-1691-4851-9329-343ac644705f",
"metadata": {},
"outputs": [],
"source": [
"# dump_to_disk()\n",
"# refresh()"
]
},
{
"cell_type": "markdown",
"id": "264384c3-2564-4a27-b250-680660c40955",
"metadata": {},
"source": [
"Show persisted"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "04383376-a184-4297-b3a8-6f781cb4a117",
"metadata": {},
"outputs": [
{
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"dask.array&lt;where, shape=(408, 360, 720), dtype=float32, chunksize=(23, 198, 54), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 1982-02-28 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 0\n",
"Attributes: (12/16)\n",
" STATISTICS_MAXIMUM: 540\n",
" STATISTICS_MEAN: 87.72932166302\n",
" STATISTICS_MINIMUM: 0\n",
" STATISTICS_STDDEV: 134.37581892842\n",
" bands: 408\n",
" band_names: Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band...\n",
" ... ...\n",
" file_type: ENVI Standard\n",
" header_offset: 0\n",
" interleave: bsq\n",
" lines: 360\n",
" map_info: Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,W...\n",
" samples: 720</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'lai_north_masked_30_annual_mean'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 408</li><li><span class='xr-has-index'>y</span>: 360</li><li><span class='xr-has-index'>x</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5b5fc509-8c61-4f3a-83f0-387cb79d081b' class='xr-array-in' type='checkbox' checked><label for='section-5b5fc509-8c61-4f3a-83f0-387cb79d081b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(23, 54, 54), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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" <td>\n",
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" &#x27;1982-03-31T00:00:00.000000000&#x27;, ..., &#x27;2015-10-31T00:00:00.000000000&#x27;,\n",
" &#x27;2015-11-30T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-69e06b59-6840-4df7-930f-701f46225b72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69e06b59-6840-4df7-930f-701f46225b72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb1fe488-13a6-48a4-b87e-90f1448b136a' class='xr-var-data-in' type='checkbox'><label for='data-fb1fe488-13a6-48a4-b87e-90f1448b136a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ..., 178.75, 179.25, 179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.75 89.25 88.75 ... -89.25 -89.75</div><input id='attrs-73bdb9d4-6e70-40b5-89fb-1b53b6eb3a90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-73bdb9d4-6e70-40b5-89fb-1b53b6eb3a90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76c8208b-22d1-4cff-a8c8-8a04b2261644' class='xr-var-data-in' type='checkbox'><label for='data-76c8208b-22d1-4cff-a8c8-8a04b2261644' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 89.75, 89.25, 88.75, ..., -88.75, -89.25, -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-2fca7223-209c-4b84-aa1e-a711378a97ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fca7223-209c-4b84-aa1e-a711378a97ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01d3f5fa-193d-4d3f-8912-92f8c4ec2109' class='xr-var-data-in' type='checkbox'><label for='data-01d3f5fa-193d-4d3f-8912-92f8c4ec2109' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.0 0.5 -0.0 90.0 -0.0 -0.5</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7f377b36-2d0b-454d-b2d3-655b7f4cf42a' class='xr-section-summary-in' type='checkbox' ><label for='section-7f377b36-2d0b-454d-b2d3-655b7f4cf42a' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b117d64-c7e1-48b6-b595-aa1a9b1d606e' class='xr-index-data-in' type='checkbox'/><label for='index-9b117d64-c7e1-48b6-b595-aa1a9b1d606e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1982-01-31&#x27;, &#x27;1982-02-28&#x27;, &#x27;1982-03-31&#x27;, &#x27;1982-04-30&#x27;,\n",
" &#x27;1982-05-31&#x27;, &#x27;1982-06-30&#x27;, &#x27;1982-07-31&#x27;, &#x27;1982-08-31&#x27;,\n",
" &#x27;1982-09-30&#x27;, &#x27;1982-10-31&#x27;,\n",
" ...\n",
" &#x27;2015-03-31&#x27;, &#x27;2015-04-30&#x27;, &#x27;2015-05-31&#x27;, &#x27;2015-06-30&#x27;,\n",
" &#x27;2015-07-31&#x27;, &#x27;2015-08-31&#x27;, &#x27;2015-09-30&#x27;, &#x27;2015-10-31&#x27;,\n",
" &#x27;2015-11-30&#x27;, &#x27;2015-12-31&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;band&#x27;, length=408, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6295fd82-b3dc-4f5d-bbe6-7c2e110beca0' class='xr-index-data-in' type='checkbox'/><label for='index-6295fd82-b3dc-4f5d-bbe6-7c2e110beca0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75,\n",
" -176.25, -175.75, -175.25,\n",
" ...\n",
" 175.25, 175.75, 176.25, 176.75, 177.25, 177.75, 178.25,\n",
" 178.75, 179.25, 179.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-17b268cd-eace-4324-a31c-6254e3f3bff6' class='xr-index-data-in' type='checkbox'/><label for='index-17b268cd-eace-4324-a31c-6254e3f3bff6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 89.75, 89.25, 88.75, 88.25, 87.75, 87.25, 86.75, 86.25,\n",
" 85.75, 85.25,\n",
" ...\n",
" -85.25, -85.75, -86.25, -86.75, -87.25, -87.75, -88.25, -88.75,\n",
" -89.25, -89.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f3a4ca9e-630c-49ab-b732-1331110c39cf' class='xr-section-summary-in' type='checkbox' ><label for='section-f3a4ca9e-630c-49ab-b732-1331110c39cf' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>540</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>87.72932166302</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>0</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>134.37581892842</dd><dt><span>bands :</span></dt><dd>408</dd><dt><span>band_names :</span></dt><dd>Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band 7,Band 8,Band 9,Band 10,Band 11,Band 12,Band 13,Band 14,Band 15,Band 16,Band 17,Band 18,Band 19,Band 20,Band 21,Band 22,Band 23,Band 24,Band 25,Band 26,Band 27,Band 28,Band 29,Band 30,Band 31,Band 32,Band 33,Band 34,Band 35,Band 36,Band 37,Band 38,Band 39,Band 40,Band 41,Band 42,Band 43,Band 44,Band 45,Band 46,Band 47,Band 48,Band 49,Band 50,Band 51,Band 52,Band 53,Band 54,Band 55,Band 56,Band 57,Band 58,Band 59,Band 60,Band 61,Band 62,Band 63,Band 64,Band 65,Band 66,Band 67,Band 68,Band 69,Band 70,Band 71,Band 72,Band 73,Band 74,Band 75,Band 76,Band 77,Band 78,Band 79,Band 80,Band 81,Band 82,Band 83,Band 84,Band 85,Band 86,Band 87,Band 88,Band 89,Band 90,Band 91,Band 92,Band 93,Band 94,Band 95,Band 96,Band 97,Band 98,Band 99,Band 100,Band 101,Band 102,Band 103,Band 104,Band 105,Band 106,Band 107,Band 108,Band 109,Band 110,Band 111,Band 112,Band 113,Band 114,Band 115,Band 116,Band 117,Band 118,Band 119,Band 120,Band 121,Band 122,Band 123,Band 124,Band 125,Band 126,Band 127,Band 128,Band 129,Band 130,Band 131,Band 132,Band 133,Band 134,Band 135,Band 136,Band 137,Band 138,Band 139,Band 140,Band 141,Band 142,Band 143,Band 144,Band 145,Band 146,Band 147,Band 148,Band 149,Band 150,Band 151,Band 152,Band 153,Band 154,Band 155,Band 156,Band 157,Band 158,Band 159,Band 160,Band 161,Band 162,Band 163,Band 164,Band 165,Band 166,Band 167,Band 168,Band 169,Band 170,Band 171,Band 172,Band 173,Band 174,Band 175,Band 176,Band 177,Band 178,Band 179,Band 180,Band 181,Band 182,Band 183,Band 184,Band 185,Band 186,Band 187,Band 188,Band 189,Band 190,Band 191,Band 192,Band 193,Band 194,Band 195,Band 196,Band 197,Band 198,Band 199,Band 200,Band 201,Band 202,Band 203,Band 204,Band 205,Band 206,Band 207,Band 208,Band 209,Band 210,Band 211,Band 212,Band 213,Band 214,Band 215,Band 216,Band 217,Band 218,Band 219,Band 220,Band 221,Band 222,Band 223,Band 224,Band 225,Band 226,Band 227,Band 228,Band 229,Band 230,Band 231,Band 232,Band 233,Band 234,Band 235,Band 236,Band 237,Band 238,Band 239,Band 240,Band 241,Band 242,Band 243,Band 244,Band 245,Band 246,Band 247,Band 248,Band 249,Band 250,Band 251,Band 252,Band 253,Band 254,Band 255,Band 256,Band 257,Band 258,Band 259,Band 260,Band 261,Band 262,Band 263,Band 264,Band 265,Band 266,Band 267,Band 268,Band 269,Band 270,Band 271,Band 272,Band 273,Band 274,Band 275,Band 276,Band 277,Band 278,Band 279,Band 280,Band 281,Band 282,Band 283,Band 284,Band 285,Band 286,Band 287,Band 288,Band 289,Band 290,Band 291,Band 292,Band 293,Band 294,Band 295,Band 296,Band 297,Band 298,Band 299,Band 300,Band 301,Band 302,Band 303,Band 304,Band 305,Band 306,Band 307,Band 308,Band 309,Band 310,Band 311,Band 312,Band 313,Band 314,Band 315,Band 316,Band 317,Band 318,Band 319,Band 320,Band 321,Band 322,Band 323,Band 324,Band 325,Band 326,Band 327,Band 328,Band 329,Band 330,Band 331,Band 332,Band 333,Band 334,Band 335,Band 336,Band 337,Band 338,Band 339,Band 340,Band 341,Band 342,Band 343,Band 344,Band 345,Band 346,Band 347,Band 348,Band 349,Band 350,Band 351,Band 352,Band 353,Band 354,Band 355,Band 356,Band 357,Band 358,Band 359,Band 360,Band 361,Band 362,Band 363,Band 364,Band 365,Band 366,Band 367,Band 368,Band 369,Band 370,Band 371,Band 372,Band 373,Band 374,Band 375,Band 376,Band 377,Band 378,Band 379,Band 380,Band 381,Band 382,Band 383,Band 384,Band 385,Band 386,Band 387,Band 388,Band 389,Band 390,Band 391,Band 392,Band 393,Band 394,Band 395,Band 396,Band 397,Band 398,Band 399,Band 400,Band 401,Band 402,Band 403,Band 404,Band 405,Band 406,Band 407,Band 408</dd><dt><span>byte_order :</span></dt><dd>0</dd><dt><span>coordinate_system_string :</span></dt><dd>GEOGCS[&quot;GCS_WGS_1984&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.017453292519943295]]</dd><dt><span>data_type :</span></dt><dd>4</dd><dt><span>description :</span></dt><dd>L:\\NDVIseason\\LAI\\glasss\\GLASS_LAI_82_15_montlhy0_5deg.envi</dd><dt><span>file_type :</span></dt><dd>ENVI Standard</dd><dt><span>header_offset :</span></dt><dd>0</dd><dt><span>interleave :</span></dt><dd>bsq</dd><dt><span>lines :</span></dt><dd>360</dd><dt><span>map_info :</span></dt><dd>Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,WGS-84</dd><dt><span>samples :</span></dt><dd>720</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'lai_north_masked_30_annual_mean' (band: 408, y: 360, x: 720)>\n",
"dask.array<where, shape=(408, 360, 720), dtype=float32, chunksize=(23, 198, 54), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 1982-02-28 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 0\n",
"Attributes: (12/16)\n",
" STATISTICS_MAXIMUM: 540\n",
" STATISTICS_MEAN: 87.72932166302\n",
" STATISTICS_MINIMUM: 0\n",
" STATISTICS_STDDEV: 134.37581892842\n",
" bands: 408\n",
" band_names: Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band...\n",
" ... ...\n",
" file_type: ENVI Standard\n",
" header_offset: 0\n",
" interleave: bsq\n",
" lines: 360\n",
" map_info: Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,W...\n",
" samples: 720"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lai_array['lai_north_masked_30_annual_mean']"
]
},
{
"cell_type": "markdown",
"id": "d06dd939-a502-4cd7-ac27-475ef3fdea6d",
"metadata": {},
"source": [
"## Flag \"Spring\" and \"Summer\", and calculate mean values (Item 1.3)"
]
},
{
"cell_type": "markdown",
"id": "92acbe2a-5144-45dc-8a90-8ee0dd758cd1",
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"source": [
"### For temperature"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "a98e97a4-0aa1-42da-9194-d0279d54bba5",
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"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;temp_north_masked_30&#x27; (band: 408, y: 360, x: 720)&gt;\n",
"dask.array&lt;where, shape=(408, 360, 720), dtype=float32, chunksize=(408, 198, 54), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 1982-02-28 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 0\n",
"Attributes: (12/16)\n",
" STATISTICS_MAXIMUM: 33.700000762939\n",
" STATISTICS_MEAN: 1.#SNAN\n",
" STATISTICS_MINIMUM: -53.799999237061\n",
" STATISTICS_STDDEV: 1.#SNAN\n",
" bands: 408\n",
" band_names: Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band...\n",
" ... ...\n",
" file_type: ENVI Standard\n",
" header_offset: 0\n",
" interleave: bsq\n",
" lines: 360\n",
" map_info: Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,W...\n",
" samples: 720</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp_north_masked_30'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>band</span>: 408</li><li><span class='xr-has-index'>y</span>: 360</li><li><span class='xr-has-index'>x</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-eeb22217-230d-4259-8081-03842acf1d60' class='xr-array-in' type='checkbox' checked><label for='section-eeb22217-230d-4259-8081-03842acf1d60' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(408, 54, 54), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 403.42 MiB </td>\n",
" <td> 16.64 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (408, 360, 720) </td>\n",
" <td> (408, 198, 54) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 56 chunks in 29 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"220\" height=\"150\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"50\" y2=\"40\" style=\"stroke-width:2\" />\n",
" <line x1=\"10\" y1=\"9\" x2=\"50\" y2=\"49\" />\n",
" <line x1=\"10\" y1=\"18\" x2=\"50\" y2=\"58\" />\n",
" <line x1=\"10\" y1=\"27\" x2=\"50\" y2=\"67\" />\n",
" <line x1=\"10\" y1=\"60\" x2=\"50\" y2=\"100\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"60\" style=\"stroke-width:2\" />\n",
" <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"100\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"10.0,0.0 50.0,40.0 50.0,100.0 10.0,60.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"50\" y1=\"40\" x2=\"170\" y2=\"40\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"50\" y2=\"40\" style=\"stroke-width:2\" />\n",
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" <line x1=\"130\" y1=\"0\" x2=\"170\" y2=\"40\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"10.0,0.0 130.0,0.0 170.0,40.0 50.0,40.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"50\" y1=\"40\" x2=\"170\" y2=\"40\" style=\"stroke-width:2\" />\n",
" <line x1=\"50\" y1=\"49\" x2=\"170\" y2=\"49\" />\n",
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"\n",
" <!-- Vertical lines -->\n",
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" <line x1=\"167\" y1=\"40\" x2=\"167\" y2=\"100\" />\n",
" <line x1=\"170\" y1=\"40\" x2=\"170\" y2=\"100\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"50.0,40.0 170.0,40.0 170.0,100.0 50.0,100.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"110.000000\" y=\"120.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >720</text>\n",
" <text x=\"190.000000\" y=\"70.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,190.000000,70.000000)\">360</text>\n",
" <text x=\"20.000000\" y=\"100.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,20.000000,100.000000)\">408</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></div></li><li class='xr-section-item'><input id='section-6e7dcd82-c817-4e06-afae-cc4f159a9d87' class='xr-section-summary-in' type='checkbox' checked><label for='section-6e7dcd82-c817-4e06-afae-cc4f159a9d87' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>band</span></div><div class='xr-var-dims'>(band)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1982-01-31 ... 2015-12-31</div><input id='attrs-da89cfa4-6ad4-4b35-918e-2cb5146f40bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-da89cfa4-6ad4-4b35-918e-2cb5146f40bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d9c723e-2f7e-4156-adc6-c28cfbac12de' class='xr-var-data-in' type='checkbox'><label for='data-9d9c723e-2f7e-4156-adc6-c28cfbac12de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;1982-01-31T00:00:00.000000000&#x27;, &#x27;1982-02-28T00:00:00.000000000&#x27;,\n",
" &#x27;1982-03-31T00:00:00.000000000&#x27;, ..., &#x27;2015-10-31T00:00:00.000000000&#x27;,\n",
" &#x27;2015-11-30T00:00:00.000000000&#x27;, &#x27;2015-12-31T00:00:00.000000000&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-179.8 -179.2 ... 179.2 179.8</div><input id='attrs-d09c2225-e4ff-4373-bd7e-8b32dd3630f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d09c2225-e4ff-4373-bd7e-8b32dd3630f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-456ff4c3-5354-4b6f-90b1-615c769d0473' class='xr-var-data-in' type='checkbox'><label for='data-456ff4c3-5354-4b6f-90b1-615c769d0473' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.75, -179.25, -178.75, ..., 178.75, 179.25, 179.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.75 89.25 88.75 ... -89.25 -89.75</div><input id='attrs-c449a86d-b374-4987-9fd1-58ae6e734435' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c449a86d-b374-4987-9fd1-58ae6e734435' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e3c2463-c233-439a-b3f4-1eb861442f90' class='xr-var-data-in' type='checkbox'><label for='data-7e3c2463-c233-439a-b3f4-1eb861442f90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 89.75, 89.25, 88.75, ..., -88.75, -89.25, -89.75])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0</div><input id='attrs-d6a36ce0-42cf-4873-a857-07d8be55fda1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6a36ce0-42cf-4873-a857-07d8be55fda1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-023fbda5-84a6-4ff5-bdb6-32bfc746803a' class='xr-var-data-in' type='checkbox'><label for='data-023fbda5-84a6-4ff5-bdb6-32bfc746803a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>crs_wkt :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>semi_minor_axis :</span></dt><dd>6356752.314245179</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>reference_ellipsoid_name :</span></dt><dd>WGS84</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>0.0</dd><dt><span>prime_meridian_name :</span></dt><dd>Greenwich</dd><dt><span>geographic_crs_name :</span></dt><dd>WGS 84</dd><dt><span>grid_mapping_name :</span></dt><dd>latitude_longitude</dd><dt><span>spatial_ref :</span></dt><dd>GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;D_unknown&quot;,SPHEROID[&quot;WGS84&quot;,6378137,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0],UNIT[&quot;Degree&quot;,0.0174532925199433],AXIS[&quot;Longitude&quot;,EAST],AXIS[&quot;Latitude&quot;,NORTH]]</dd><dt><span>GeoTransform :</span></dt><dd>-180.0 0.5 -0.0 90.0 -0.0 -0.5</dd></dl></div><div class='xr-var-data'><pre>array(0)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e57ee445-2ebf-4494-a555-57e91c28567c' class='xr-section-summary-in' type='checkbox' ><label for='section-e57ee445-2ebf-4494-a555-57e91c28567c' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>band</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fbaa3519-5084-493f-a90f-a683e2c1a277' class='xr-index-data-in' type='checkbox'/><label for='index-fbaa3519-5084-493f-a90f-a683e2c1a277' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;1982-01-31&#x27;, &#x27;1982-02-28&#x27;, &#x27;1982-03-31&#x27;, &#x27;1982-04-30&#x27;,\n",
" &#x27;1982-05-31&#x27;, &#x27;1982-06-30&#x27;, &#x27;1982-07-31&#x27;, &#x27;1982-08-31&#x27;,\n",
" &#x27;1982-09-30&#x27;, &#x27;1982-10-31&#x27;,\n",
" ...\n",
" &#x27;2015-03-31&#x27;, &#x27;2015-04-30&#x27;, &#x27;2015-05-31&#x27;, &#x27;2015-06-30&#x27;,\n",
" &#x27;2015-07-31&#x27;, &#x27;2015-08-31&#x27;, &#x27;2015-09-30&#x27;, &#x27;2015-10-31&#x27;,\n",
" &#x27;2015-11-30&#x27;, &#x27;2015-12-31&#x27;],\n",
" dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;band&#x27;, length=408, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0dec001c-77b4-40a7-b0b1-b5ec4d733606' class='xr-index-data-in' type='checkbox'/><label for='index-0dec001c-77b4-40a7-b0b1-b5ec4d733606' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-179.75, -179.25, -178.75, -178.25, -177.75, -177.25, -176.75,\n",
" -176.25, -175.75, -175.25,\n",
" ...\n",
" 175.25, 175.75, 176.25, 176.75, 177.25, 177.75, 178.25,\n",
" 178.75, 179.25, 179.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=720))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-909de6a7-1cf6-45e6-bad0-dcdbfe91bfae' class='xr-index-data-in' type='checkbox'/><label for='index-909de6a7-1cf6-45e6-bad0-dcdbfe91bfae' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 89.75, 89.25, 88.75, 88.25, 87.75, 87.25, 86.75, 86.25,\n",
" 85.75, 85.25,\n",
" ...\n",
" -85.25, -85.75, -86.25, -86.75, -87.25, -87.75, -88.25, -88.75,\n",
" -89.25, -89.75],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=360))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-90d4c25e-48ca-40f7-8896-8691afe122be' class='xr-section-summary-in' type='checkbox' ><label for='section-90d4c25e-48ca-40f7-8896-8691afe122be' class='xr-section-summary' >Attributes: <span>(16)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>STATISTICS_MAXIMUM :</span></dt><dd>33.700000762939</dd><dt><span>STATISTICS_MEAN :</span></dt><dd>1.#SNAN</dd><dt><span>STATISTICS_MINIMUM :</span></dt><dd>-53.799999237061</dd><dt><span>STATISTICS_STDDEV :</span></dt><dd>1.#SNAN</dd><dt><span>bands :</span></dt><dd>408</dd><dt><span>band_names :</span></dt><dd>Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band 7,Band 8,Band 9,Band 10,Band 11,Band 12,Band 13,Band 14,Band 15,Band 16,Band 17,Band 18,Band 19,Band 20,Band 21,Band 22,Band 23,Band 24,Band 25,Band 26,Band 27,Band 28,Band 29,Band 30,Band 31,Band 32,Band 33,Band 34,Band 35,Band 36,Band 37,Band 38,Band 39,Band 40,Band 41,Band 42,Band 43,Band 44,Band 45,Band 46,Band 47,Band 48,Band 49,Band 50,Band 51,Band 52,Band 53,Band 54,Band 55,Band 56,Band 57,Band 58,Band 59,Band 60,Band 61,Band 62,Band 63,Band 64,Band 65,Band 66,Band 67,Band 68,Band 69,Band 70,Band 71,Band 72,Band 73,Band 74,Band 75,Band 76,Band 77,Band 78,Band 79,Band 80,Band 81,Band 82,Band 83,Band 84,Band 85,Band 86,Band 87,Band 88,Band 89,Band 90,Band 91,Band 92,Band 93,Band 94,Band 95,Band 96,Band 97,Band 98,Band 99,Band 100,Band 101,Band 102,Band 103,Band 104,Band 105,Band 106,Band 107,Band 108,Band 109,Band 110,Band 111,Band 112,Band 113,Band 114,Band 115,Band 116,Band 117,Band 118,Band 119,Band 120,Band 121,Band 122,Band 123,Band 124,Band 125,Band 126,Band 127,Band 128,Band 129,Band 130,Band 131,Band 132,Band 133,Band 134,Band 135,Band 136,Band 137,Band 138,Band 139,Band 140,Band 141,Band 142,Band 143,Band 144,Band 145,Band 146,Band 147,Band 148,Band 149,Band 150,Band 151,Band 152,Band 153,Band 154,Band 155,Band 156,Band 157,Band 158,Band 159,Band 160,Band 161,Band 162,Band 163,Band 164,Band 165,Band 166,Band 167,Band 168,Band 169,Band 170,Band 171,Band 172,Band 173,Band 174,Band 175,Band 176,Band 177,Band 178,Band 179,Band 180,Band 181,Band 182,Band 183,Band 184,Band 185,Band 186,Band 187,Band 188,Band 189,Band 190,Band 191,Band 192,Band 193,Band 194,Band 195,Band 196,Band 197,Band 198,Band 199,Band 200,Band 201,Band 202,Band 203,Band 204,Band 205,Band 206,Band 207,Band 208,Band 209,Band 210,Band 211,Band 212,Band 213,Band 214,Band 215,Band 216,Band 217,Band 218,Band 219,Band 220,Band 221,Band 222,Band 223,Band 224,Band 225,Band 226,Band 227,Band 228,Band 229,Band 230,Band 231,Band 232,Band 233,Band 234,Band 235,Band 236,Band 237,Band 238,Band 239,Band 240,Band 241,Band 242,Band 243,Band 244,Band 245,Band 246,Band 247,Band 248,Band 249,Band 250,Band 251,Band 252,Band 253,Band 254,Band 255,Band 256,Band 257,Band 258,Band 259,Band 260,Band 261,Band 262,Band 263,Band 264,Band 265,Band 266,Band 267,Band 268,Band 269,Band 270,Band 271,Band 272,Band 273,Band 274,Band 275,Band 276,Band 277,Band 278,Band 279,Band 280,Band 281,Band 282,Band 283,Band 284,Band 285,Band 286,Band 287,Band 288,Band 289,Band 290,Band 291,Band 292,Band 293,Band 294,Band 295,Band 296,Band 297,Band 298,Band 299,Band 300,Band 301,Band 302,Band 303,Band 304,Band 305,Band 306,Band 307,Band 308,Band 309,Band 310,Band 311,Band 312,Band 313,Band 314,Band 315,Band 316,Band 317,Band 318,Band 319,Band 320,Band 321,Band 322,Band 323,Band 324,Band 325,Band 326,Band 327,Band 328,Band 329,Band 330,Band 331,Band 332,Band 333,Band 334,Band 335,Band 336,Band 337,Band 338,Band 339,Band 340,Band 341,Band 342,Band 343,Band 344,Band 345,Band 346,Band 347,Band 348,Band 349,Band 350,Band 351,Band 352,Band 353,Band 354,Band 355,Band 356,Band 357,Band 358,Band 359,Band 360,Band 361,Band 362,Band 363,Band 364,Band 365,Band 366,Band 367,Band 368,Band 369,Band 370,Band 371,Band 372,Band 373,Band 374,Band 375,Band 376,Band 377,Band 378,Band 379,Band 380,Band 381,Band 382,Band 383,Band 384,Band 385,Band 386,Band 387,Band 388,Band 389,Band 390,Band 391,Band 392,Band 393,Band 394,Band 395,Band 396,Band 397,Band 398,Band 399,Band 400,Band 401,Band 402,Band 403,Band 404,Band 405,Band 406,Band 407,Band 408</dd><dt><span>byte_order :</span></dt><dd>0</dd><dt><span>coordinate_system_string :</span></dt><dd>GEOGCS[&quot;GCS_unknown&quot;,DATUM[&quot;D_WGS_1984&quot;,SPHEROID[&quot;WGS_1984&quot;,6378137.0,298.257223563]],PRIMEM[&quot;Greenwich&quot;,0.0],UNIT[&quot;Degree&quot;,0.0174532925199433]]</dd><dt><span>data_type :</span></dt><dd>4</dd><dt><span>description :</span></dt><dd>C:\\data\\Course\\Grad_course\\2023\\Greening_climate_project3\\data\\CRU406_temp_1982_2015_month_mean.envi</dd><dt><span>file_type :</span></dt><dd>ENVI Standard</dd><dt><span>header_offset :</span></dt><dd>0</dd><dt><span>interleave :</span></dt><dd>bsq</dd><dt><span>lines :</span></dt><dd>360</dd><dt><span>map_info :</span></dt><dd>Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,WGS-84</dd><dt><span>samples :</span></dt><dd>720</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'temp_north_masked_30' (band: 408, y: 360, x: 720)>\n",
"dask.array<where, shape=(408, 360, 720), dtype=float32, chunksize=(408, 198, 54), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" * band (band) datetime64[ns] 1982-01-31 1982-02-28 ... 2015-12-31\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 0\n",
"Attributes: (12/16)\n",
" STATISTICS_MAXIMUM: 33.700000762939\n",
" STATISTICS_MEAN: 1.#SNAN\n",
" STATISTICS_MINIMUM: -53.799999237061\n",
" STATISTICS_STDDEV: 1.#SNAN\n",
" bands: 408\n",
" band_names: Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band...\n",
" ... ...\n",
" file_type: ENVI Standard\n",
" header_offset: 0\n",
" interleave: bsq\n",
" lines: 360\n",
" map_info: Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,W...\n",
" samples: 720"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"lai_array['temp_north_masked_30']"
]
},
{
"cell_type": "markdown",
"id": "e1a84d80-0201-4517-a516-b9cd1aaa0f2b",
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"source": [
"Using datetime convention for season: \n",
"\n",
"- **DJF**: December, January, Fevruary - **Winter**\n",
"- **MAM**: March, April, May - **Spring**\n",
"- **JJA**: June, July, August - **Summer**\n",
"- **SON**: September, October, November - **Fall**\n",
"\n",
"Implement two-level group-by for averaging (year and season):"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "a0364e64-a223-4cae-b2ca-e0bfa73f4e13",
"metadata": {},
"outputs": [],
"source": [
"grouper = xr.DataArray(\n",
" pd.MultiIndex.from_arrays(\n",
" [lai_array['temp_north_masked_30'].band.dt.year.values, lai_array['temp_north_masked_30'].band.dt.season.values],\n",
" names=['year', 'season'],\n",
" ),\n",
" dims=['band'],\n",
" coords=[lai_array['temp_north_masked_30'].band],\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "ee007ce3-26df-472b-9c54-dc4a2677d485",
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"outputs": [
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".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
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"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
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"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-index-preview {\n",
" grid-column: 2 / 5;\n",
" color: var(--xr-font-color2);\n",
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".xr-var-name,\n",
".xr-var-dims,\n",
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".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
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".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
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"\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
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"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data,\n",
".xr-index-data-in:checked ~ .xr-index-data {\n",
" display: block;\n",
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"\n",
".xr-var-data > table {\n",
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"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-index-name div,\n",
".xr-index-data,\n",
".xr-attrs {\n",
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"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data,\n",
".xr-index-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
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"\n",
".xr-attrs dt,\n",
".xr-attrs dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
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"\n",
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" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
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"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2,\n",
".xr-no-icon {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;temp_north_masked_30&#x27; (group: 136, y: 360, x: 720)&gt;\n",
"dask.array&lt;stack, shape=(136, 360, 720), dtype=float32, chunksize=(1, 198, 54), chunktype=numpy.ndarray&gt;\n",
"Coordinates:\n",
" * x (x) float64 -179.8 -179.2 -178.8 -178.2 ... 178.8 179.2 179.8\n",
" * y (y) float64 89.75 89.25 88.75 88.25 ... -88.75 -89.25 -89.75\n",
" spatial_ref int64 0\n",
" * group (group) object MultiIndex\n",
" * year (group) int64 1982 1982 1982 1982 1983 ... 2015 2015 2015 2015\n",
" * season (group) object &#x27;DJF&#x27; &#x27;MAM&#x27; &#x27;JJA&#x27; &#x27;SON&#x27; ... &#x27;MAM&#x27; &#x27;JJA&#x27; &#x27;SON&#x27;\n",
"Attributes: (12/16)\n",
" STATISTICS_MAXIMUM: 33.700000762939\n",
" STATISTICS_MEAN: 1.#SNAN\n",
" STATISTICS_MINIMUM: -53.799999237061\n",
" STATISTICS_STDDEV: 1.#SNAN\n",
" bands: 408\n",
" band_names: Band 1,Band 2,Band 3,Band 4,Band 5,Band 6,Band...\n",
" ... ...\n",
" file_type: ENVI Standard\n",
" header_offset: 0\n",
" interleave: bsq\n",
" lines: 360\n",
" map_info: Geographic Lat/Lon, 1, 1, -180, 90, 0.5, 0.5,W...\n",
" samples: 720</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp_north_masked_30'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>group</span>: 136</li><li><span class='xr-has-index'>y</span>: 360</li><li><span class='xr-has-index'>x</span>: 720</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e9152bac-cc5d-4194-9ad1-4f4bca93ac2b' class='xr-array-in' type='checkbox' checked><label for='section-e9152bac-cc5d-4194-9ad1-4f4bca93ac2b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 54, 54), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
" <tr>\n",
" <td>\n",
" <table style=\"border-collapse: collapse;\">\n",
" <thead>\n",
" <tr>\n",
" <td> </td>\n",
" <th> Array </th>\n",
" <th> Chunk </th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" \n",
" <tr>\n",
" <th> Bytes </th>\n",
" <td> 134.47 MiB </td>\n",
" <td> 41.77 kiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (136, 360, 720) </td>\n",
" <td> (1, 198, 54) </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 7616 chunks in 438 graph layers </td>\n",
" </tr>\n",
" <tr>\n",
" <th> Data type </th>\n",
" <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
" </tr>\n",
" </tbody>\n",
" </table>\n",
" </td>\n",
" <td>\n",
" <svg width=\"204\" height=\"134\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"10\" y1=\"0\" x2=\"34\" y2=\"24\" style=\"stroke-width:2\" />\n",
" <line x1=\"10\" y1=\"9\" x2=\"34\" y2=\"33\" />\n",
" <line x1=\"10\" y1=\"18\" x2=\"34\" y2=\"42\" />\n",
" <line x1=\"10\" y1=\"27\" x2=\"34\" y2=\"51\" />\n",
" <line x1=\"
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