From d9552d2826326a3a79a87c3ba9ec80c889c8c7a1 Mon Sep 17 00:00:00 2001
From: Timothy Cera <timothy.cera@noaa.gov>
Date: Wed, 24 Jul 2024 10:19:45 -0400
Subject: [PATCH] WIP

---
 demos/plotting_examples.ipynb | 431 ++++++++++++++++++++++++++++++++++
 src/grib2io/_grib2io.py       | 101 +++++++-
 tests/test_subset.py          |  65 +++++
 3 files changed, 592 insertions(+), 5 deletions(-)
 create mode 100644 demos/plotting_examples.ipynb
 create mode 100755 tests/test_subset.py

diff --git a/demos/plotting_examples.ipynb b/demos/plotting_examples.ipynb
new file mode 100644
index 0000000..6e3d573
--- /dev/null
+++ b/demos/plotting_examples.ipynb
@@ -0,0 +1,431 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "fb56f795-20b8-496f-adea-50b9e6aaa66e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:53.550756Z",
+     "iopub.status.busy": "2024-07-24T03:52:53.550622Z",
+     "iopub.status.idle": "2024-07-24T03:52:54.085554Z",
+     "shell.execute_reply": "2024-07-24T03:52:54.084990Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:53.550746Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "ERROR 1: PROJ: proj_create_from_database: Open of /home/tim/anaconda3/envs/default311/share/proj failed\n"
+     ]
+    }
+   ],
+   "source": [
+    "import grib2io\n",
+    "import numpy as np\n",
+    "import pyproj\n",
+    "import matplotlib.pyplot as plt\n",
+    "import cartopy.crs as ccrs\n",
+    "import cartopy.feature as cfeature "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "53b5d3d7-bfd9-4687-bfc1-f92cfa804338",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:54.086439Z",
+     "iopub.status.busy": "2024-07-24T03:52:54.086070Z",
+     "iopub.status.idle": "2024-07-24T03:52:54.090243Z",
+     "shell.execute_reply": "2024-07-24T03:52:54.089795Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:54.086425Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "msgs = grib2io.open(\"../tests/data/gfs.jpeg.grib2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8cfef760-d3cc-4d8c-a23a-c3c76d4c0863",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:54.091691Z",
+     "iopub.status.busy": "2024-07-24T03:52:54.091466Z",
+     "iopub.status.idle": "2024-07-24T03:52:54.102368Z",
+     "shell.execute_reply": "2024-07-24T03:52:54.102056Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:54.091675Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90.0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "msgs[0].latitudeFirstGridpoint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5dd9519b-b2a3-4aaa-b7ca-d167be32e9bf",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:54.102977Z",
+     "iopub.status.busy": "2024-07-24T03:52:54.102823Z",
+     "iopub.status.idle": "2024-07-24T03:52:54.105035Z",
+     "shell.execute_reply": "2024-07-24T03:52:54.104702Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:54.102965Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "proj_pars = msgs[0].projParameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "50aa1e62-6dbb-4ff6-8073-8928109a0eab",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:54.105616Z",
+     "iopub.status.busy": "2024-07-24T03:52:54.105492Z",
+     "iopub.status.idle": "2024-07-24T03:52:54.108101Z",
+     "shell.execute_reply": "2024-07-24T03:52:54.107730Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:54.105604Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "gfs_proj = ccrs.PlateCarree(globe=ccrs.Globe(semimajor_axis=proj_pars[\"a\"], semiminor_axis=proj_pars[\"b\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "aa58c61d-f0d5-4117-98e3-421ed1d5752a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:54.108752Z",
+     "iopub.status.busy": "2024-07-24T03:52:54.108576Z",
+     "iopub.status.idle": "2024-07-24T03:52:54.115671Z",
+     "shell.execute_reply": "2024-07-24T03:52:54.115252Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:54.108740Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(721, 1440)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lats, lons = msgs[0].latlons()\n",
+    "lats.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "321416c8-19c6-4da2-9c77-95a3d654d7a0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:54.116579Z",
+     "iopub.status.busy": "2024-07-24T03:52:54.116345Z",
+     "iopub.status.idle": "2024-07-24T03:52:56.253010Z",
+     "shell.execute_reply": "2024-07-24T03:52:56.252716Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:54.116566Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<cartopy.mpl.contour.GeoContourSet at 0x7ff5d13c6ed0>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAEMCAYAAABZZbUfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOyddZhV1feH33O7p7uLGGDoBikFMVAEW1TsVuzu7u7AQBQMUGmQ7u6a7o7bec7vjwsDwwzDAEP4+877PDzDPff0PWfvz15r7bUESZIk2mijjTbaaKON/1lkZ/oE2mijjTbaaKONM0ubGGijjTbaaKON/3HaxEAbbbTRRhtt/I/TJgbaaKONNtpo43+cNjHQRhtttNFGG//jtImBNtpoo4022vgfp00MtNFGG2200cb/OG1ioI022mijjTb+x1G0dEWn04nb7T6V59JGG2200UYbbbQyKpUKjUbT7DotEgNOp5OAiCTc5tJWObE22mijjTbaaOP0EBkZSU5OTrOCoEViwO124zaXMuClAhQaU6udYBtttNFGG220cerwOs2seiYOt9t98mKgfmWNCYW2TQy00UYbbfwvsWv08jN9Cm2cIKLFBs8ce73jEgN7zluFzKhvdp30OYOPZ5dttNFGG22cYto68zaOxXGJgZawa/TyNkHQRhtttHGGaRMAbRwPbVML22ijjTbaaON/nFazDLRZA9poo4022mjjv0mriYHDTVJtwqCNNtpo48xysB1ucxe00RJaPWYAWvbwtQmG/z9Iog+3tQKvvRavoxbR50byeUH0IUk+lIYwBEGG11GH11mHz2kBmRyZQo1MqfH/VRz8e9gypRaFNgCZQnXarsVjrcJavB1XXREIMgSZHEGmQJDJsZftoXr3fNyWMkSvq/6f5HX715UrDqyrQJArkB32/0PLlagCotAGJ6IJSUITkog2JAlNcAJyle60XWcb/zu0iYI2WsIpEQMt4cgHs00cnL2IPi/28r3U7luCozITmVKHLrwdkiSSN/9VnFW5SKL3lB1fptSgNIShDUlGHRRL8oUvog1NPu79eKxVWEt3ojZFoQlJQiZXIIkiVTtnU7jsY6zF23HXFR/9PFQ6gtuNIKj9uYeEi0KNoFCBJCGJXv8/n/ew/3v8osHjxOdxILpsOMr3UbVzNqLb3uRxovrfTMdrvz7u62ujjeZInzO4TRC0cVTOmBg4kjZxcOZxVGaT+ecjeGxVeJ11iG47bmslXkcNSBKCXIk2NBmf24GrJh+AsK6XET/8IdSBMSh0QSg0JmQKDYJcATI5giD3d7CCDIU2AIU2ALnaCJKI6HEeNsJ2+j97HJjz1mMt3oazOg9HRSbO6jxcNQW4agoAcNeVotAG4LFV4XNZ8Dot+FwWRI8DSZIACQCZQkNg6jkIMgXm/PU4K7Prr1VQqNCFpeGsysXnttUvNyX2peO136IJTkBC8ls3DvxTaEzIlOpj3sf8Re+S+edDJ/w7uC1luMyleCwVeGyVuK0VeKyVeKwV+Nz2A/cxEIUuCE1QPAHJAxAE4YSP10YbbbRx1oiBIzmagm0TCa2DrWwvWz4eiS48DXVgLJLow1VTQG3mUgAU2gAi+1yPyhSFyhiOJiSRgMR+yNX+PBNuayWu2iKMsV2PeSxNUGyTyw/u6yCiz8OS+xu7BNSBsciUWhwV+wFwmUsQZHLUQbEo1EbkGiNytRG5UguCAAggCHjtNdTsWwQIhHUZgzG+N4boLrgtZdjL9mAv24sjOIeqnbPqj2XOXYvXUdfo3I6HkE6jG4gBQa70n59Ki8dWjehxNLt91c7ZrHwyqsEyQaZAaQhFrtLjqMoBSaz/LnbofbQb/8EJn28bDZEkyS88BRnqgGgE2f/vSVeSJOHdn4t3Xza+ghLEskoQBFR9u6Lq1wNZYFuiuf8FBMk/lGoWs9lMQEAA4VvmHDPp0JmiTSS0DHtFJlkzH6diy+/1ywKSByL63Fjy1iMo1MjkKnwuCwhy1AFR6CI6YCvZeZgrQEKpD0WQK7EVb6fd5R8RO+SeEzofr6OOwuWfUb5xKtbS3eDzNFpHrtKji0pHdNkI73U1Sec/fULHOhY+twNHZRb6qE6nZaQtSRKCIFC8+lv2TLkZlTGCyL43oA1LxVGZhc9pQa42oNSHIFcbqNo5G0vh5kaujOSLXyFx1JOn/HxPFFvZXkrXTEZQqHCby3BbykD0oYvsCEjUZi5DrtKjCohCHRCNJjgRQ0wGhuguKLQBp+y8yjdNZ8e3VxxzvdSx7xA/4sFTdh6niyMHWPaf/sT6wXeI1bX1y4QAI/KIUCSXG19ekV8U9OuObsJlqEcMQFCctePHNo6CaLFR3m00dXV1mExHF3b/b8TA0WgTCQ1Z93oPrIWbCe9xJbFD7kUXlopMrUehNmAv20f5lt9wVGZjK9mJozILj62qwSi0KQJTz6HHA0ub/E4SRUSvE5/bjuhxYC/fh7VwC25rBW5zKZXbZuDzOAjvfgUKtYGiFZ832D5t3HvEDrkXQSZvtXtwtmMp3ML617s3Wh6QMojAlHMwRHdBH9MFXVjaaQ2uPBLR66Zg8fs4KjJxVOfirMpBqQ8lrOtYarOWY85dg89pOeBmOXpMSWDqOYgeJ/aKTLz26gbfDf/4mM3TCVO991+2fDTimOv1fHAVAcn9T9l5nCqaiw+QJImy1CH1n1WD+xD40QsN2ndfcRmuFetxTJ+NZ9MOZFHhmJ68G80Fw+qFbBtnP6dEDJzzVl2LahOc7UEq/xWB4KwpxFKwCXVAFNqwVJS6oAbfS5JE5h8PUbD4PZSGUPSRndCEJKENS0EbkkxAUn+0oUm4zKXU7P0XmVKD21xG/qK3cFblNNhXzwdXEpA8oNE52CuyWPNCaoNlKZe+iTGuB4Igx5TQ+6gm9bwFb5I187FGy+VqA0pjOEptILrITgSlDcXrrMNetofilV82WPdsH/WeCiRJIm/+a2T//RQAYd3H0+nGqcjkZ9eozF6+nzUvtgMguMNI9NGdqctZjb10NwpdEGEZl6INTcbjqKV80zRsJbtA8rV4/0Htz6X7vQtO1enXI/o81GWvwla8HZUxAlNSPzRBcaf8uK3BybS1kteLr6gMx9S/sH01FSHQhKp7J+QpCcgjQ5FHhCELD0HyeHGv24Lty6kou3dCLK/El1uELMCIPCkOZfdOqHp08rsUAoyteHVtnCzpcwbjdZhZ9kjAmREDR3I2i4OzURhIksSuH66nbP1PDZanjf+AuKH3NVhv8b3N+zMTz38a0ecjf8FrDZYHp19ASPoo9v92PwCRfa5HaQhDoQsEQHTZsJXuonL73xwMyANQ6kPp89R21KbIZo8ret1U7ZzF9q8uq1/W9c7ZuMxlZP/9JKLHjtdRV/+dTKlBG5LsFzOhyWhDk9FFphPc4bz/yRHIhrf74XXUknHHP+jCUo+9wRmiZO0P7JlyE3K1kegBtxDV/2Z04e2a9LM3FVhpiOkKgozw7pdjjOuByhSJXK1HG5L0P2UNgjPbTrrXbcG9ZjPuTTvw5RXjK6sAl7vJdWURoSg7puLNKfC7Eg6g6tud4J8/4PKkySd0DtNzbjyh7dpozOH9WkvFwNk11DgDnO5kSS5zKVU7/sFlLkUQZPicFpy1hUT3v5mgdkMRPS6WPRKA6HU12lYdGNPgsyAIdLt7Pls+GQlAYLthuM2l2Mv2wAGNlzv3ZTpc8zUypbZB4Fr1rtlU75oNQGiXS7CX7cGbtxaPvQYBAZlaj1xt4nAhAPij2+uKmxQDkihiK9lJbdZysv95pt7kqzSE0uGabwjpNJq9v96F21xSv402NAVdRAdkSg2Sz4NMqcHrNBPR82pUpogTuMP/P/C57egiOqAJij/hfYi//diKZ9QQ2fgJAET1vZ7A1HMoWvYJxau+Jn/R2wDEDL6T9ld+CoClYDOZMx6hdv8SovrfRHj3y9n66WgArEVb/X8LNwPQ//lstKFJp+y8zwRn82DoIKo+3VD16Vb/WZIkJLMVsbwKlApsX/+CY+pfAIhllbjKKgEQFAIyuQxJkgjTljDc9yFwYgGHR4qINnFw4pxIjaBTbhn4L7wIx+JkRYLPZaNk3Q+Urf+JupzVgIDKEIbHVlXvS00Y+ThJF76IIMjY+f11lG/8BfDPbVcawtCGJhPc8XwM0Z0JSR/d7GjZ57JhLdmFrXgbjoosDHHd8blt+Bxm9v9+f6P1I/tNrB99mhL7YUrow64frqN2/1K8jlpkCjWJ5z9D4dKPcFvK6HjdZCL7Xo8k+hqYrvP/fY/MPw4FWrW/6gsiel2NQnPIdOhz26nLXoW1aCuZfz581GuIG/YAaePea9kN/n+C12WlaPlnuOtKKFh86NoHvVaOyhjWasc5VSJBuvgy1rzcsX4KaFS/iWhCkvwWLkEgZtAdxJxzDyCx6b1zMOeu8QcICnIEmQyZQkOfJ7c1cof9V2jWR+/z4c3Kx7N1F97MPKQ6M6LZijKjA/qJlyOojz1l9XRzeOdcl2OhcmsVzmoXW97bSfXOWgCMCQbsZQ58zkPun6Qx8XS4LgVnlQsEQBAQBNBFaInoG4YuXHvc59ImDE6cdn90bX03QfiWOXRecX6LTuD/gwhoDexT/8L89Nv1n+WpCcjDQ/Fm5yOWVvgXqlWgkIPdiSwiFHlUOPKYSJQZHdBcOJzaB1/Cs3ZL/T6iB9yKKbEvIGGM64kxzh9sJkkSWz85H2d1Lh57NR5rZYNzMcR0rR+JNYVCG4jXUYsqIAZ3XVGD71QB0Sg0Jr+ocJrxOi0giSh0wagDolEHxWLJW+8PODzA0eIQwG9F2P7lpVTu+Lt+WXCHkSiqzXi9DjSaECRJRJJ8B/6KCIIcuVyFXK5Gr4/DZEpBq41sUhgdHLn+lzhSTKkDY9FHptP55ukn5J47UU5GLIiiF3PXeKp3zaVq11w8tkqU+hDSb/gJY2y31jvJM8yR7Ztod+DdnYln5z68O/fjzSlAslgRLTYkqx3JYj3qvkwvPYTumktO9Sk3ieT2IFZW037dW7iqXXgdPrwOL5pQDYZYPaWryyleXkpdlqV+m6COgQx8szcxgyNQmVQ4a1wsvmMljgon+mgd+34+kM/j4Gt5RA9jSjQQOyKaHg93Iaj98c0WaS1RIPl8uBYsx7s/F9FiBbcHFAqQy5EFB6JIikORFIc8PuqsFGrHQ9tsgrME+7R/MD/xZv1nwaBH0T4ZVc/OoFJi//FPJJcb7cUjkIUE4c0rwpdTgDczF7yNg63UQ/vhWrLGP59ekpCFBjPk2Yp6H+2SB/X1me30UZ3JuPMfJK+b/IVvUbzqK7rfv5S8+a9hL9uNz2VFE5KEtXALge2G0e3ueax+PhlnVS7gN+8LchUymQJkCkSPoz4Z0ZEIMgXq4ASiB9xCSMeR6KMzjhnwJv72I6LooapqK1VVm6mt3QuIyGRKBEHpT+cryBAEOSAgST5E0Y3X68DpLAdAodATEtKNjh1vr9/vf1EIgD+tc/WeBeQteIPa/UtIuvAFkkY/e6ZPq40DHC4AfKUVuFdvwr1hG56N2/Fm5vldcyol8tgoxPIqJKut6R3JZMhjIpDHx6Dq3RXdzVcg0x3/aLmliHYHvqx8JIcD9+ZduNdsRr57K646N6LbP1NIkAvoo3UotHIUWgXWAhvOGhehXYOJHhhBaLcQZHIBSZJIujgebajmqMer2FKFUq8gINVUL9QlScJaYKN0TTl7p2aTMzOfwDQTE/aOP6FrOllRYHnjM2xfTkUWHIgQaEJQq8DrRfJ4ESuqkGwNc4EEvPUkzvnLQfQhj4lCFhaMLDwEeUQosogwlO3OXtdWS8XA/3zMwKlA8vmQai2IdjvKrh0JmvYJUp0FeUwEipSE+rm61dfej1TnV9yO6bNbtG/XkjUHDuLXcPKUeHaNXl4vBtJWvIe9fB8F/76DrWQHG97o6U+F67YR1vUyNn8wpMH+PLYqEs57nLwFr7P1swsI6Xg+cm0A9rI9OKvzkSlUiB4nHns1cpUOTUgiCo0JSRKRyRQojeH4nBYqts3AWZmFq7YQY1yPo57/kaNOmUxJWFgvwsJ6tej668/bY8NiySY7ezplZSuxWvOJvfhpYgbddlz7OZsQZHJMCX2o3b8EAGvhljN6Pm34aWQFsNqpGDgOAEVaIsqeXdBcfC6+skp8eYW4120DtxtZSBDy1AQUyfEoUuJRJMYhT4xFHhOJoFKelnNPX/QEq5/aiKPC6T9fnYLoQRFE3tURTYgalVGJ0qgksk8YhthDAz3RJ+Jz+lDqj/88w7qF1P9fEiWKV5ZRvKyUsvWVlK2twF7m72g73dL+JK/uxHGt3ACAEGhCPag3YnUtvoJicHuQJ8QiVtcestwCotmCa8HRrd1BUz5A3a/xdOD/Em2WgVNA7b3P4Zy9uOkvBQFUSgSlEhRy5OEhKDq1Q9W/B/LoCBxT/8I5698Gmyg6pqJIS0QeEYag1yLotKDVIMgElL26Io8MQ2ZoWOQm8Ydo8he9jc9pRh0Qg0ypRh/TFbe5jPLN07GX7MTnsiJJPrShyXidVpB8SD4vPo/9qHnzj7gYDtoA5WojmtBkYgbcijGhF0pdMNqwVARBOKWBbIy9isptMynfNI3yzdOJG/YAqWPf+U9mjavaPZ+tn4xqsOxUzrNvo3mOFAHuDduoe/Q1fEWl4PWhu+kKjA/fhvXj77F9+TOIEsreGWhGDEQ9YiCKxKYzb7YWlydNxm3xULGlCmu+DUelE0EuIFPIEOQC1kIb61/aAsCYOSMxxhsISDEiV52+WRrfp07HnH3IxaAKUKGP1jH8y4FowzTYS+y4LR6UBiUqoxKVSYk6WI02pLHloTbTjL3UjjZMizZMg0wp48/caxF0muN+3yWXG9eqjbjmLMG9fhuyyFBkgSYkmx3J6UYwGZCHBoNMhvay81H16oJnbzaeLbvwbN2NWFGFWFWD5PWiuXA4+luvPmvbnDY3wRnE8c8i6u5/AYDg3z9H0GkRZIJ/5FBQguRyg8eDd38ujt8OWQSUXTui7Ncd9fCBSBYLMpMRZdeOIJfjyy1EHheFa/l63Ks34pg+G8l8yA9pfOoe9DddgSSKuBatxPLA6/7qgEcQ1OE8vLYq7GV7MSUPQB/ZEcnnwVGRRc2+RUiiD0NMBnHDJpH/73vYirc12D4gZRBeew2Oisz6GQ+a4ESc1bmNjiWXawgP709S0jhUqpP3eR/L/F+49BP2/XYfYRmXkn7Dj/+JKoCix8Xmj8/DkrcOfUwGlrz1ABjje9Hu8o8ISOp3hs/wf4+m4p28uYVUjrim/nPYsmn4isuoe/ptfHlF6O+8Dv3145AFHX/GRG9hCd692ciCA5GHhyALDfabrfFbGcdGf4dS17QR9+8xC8j9xx+wqdApkEQJ0SMi+SSUegUemz9AecLecQSmnbpsjkdj9w/72fzODgyxemp212LJt6HQK/BYGmcarUeA9IntiOwXhkKnqHddLL5jJZb8xq4XXaQWacQodJdfiLLLiVkbLG9+ju2LnxstD/rhXdQDj89qebbRJgbOEN6CYqqvuhdBpSTw4xdRdmrX4HuxqhbbV1Nx/DkPsfKwbGtqVeN5vYKAqm83xDoL3t2ZyEKCEKtq/F/ptQ38WkHfvY36nD5Ujb8Tz+ad9csHvlKMsyqX3VMmYi/b699WriKi55WkX/8DpRumsv+3+/FYKxDkSuJHPIwmOJG9v9xOcMdRRPS6Bk1IIju+Ho/H6jebRfS8GlNSf/SRHZGr9GTOfIy6rKOb0EJCutOhw20olS1/dk7U71+x7S+2f3kJcrWBfs/tP2Y+hDNJXfYqtn5+cYOsezHn3I0+Mp2YQbf/z82zPxtoSgg4/phL3SOvgkaNenAfjE/fg+3DyTh+n4OyWzqmVx9F2f74q2gC2H+eifmZdxot7/ZAJwa/25d/xi4kZ2Y+w74YSPrENKp21GAvdeBz+RA9Iute2oJMIeOyJRegMh4y6Z+tGQIlUcJaaGPzOztIvDAOQ7welUmF1+bBbfHgsXjY8sFO8uYU4XM1jpnq92IPogdH4Kh0IXpFECXK1leS9UcujgongTMno0hJOK5zcsycj/XDyfhyC/0LZDIQ/bEUis7tUaTEIws0IQs0IQSYkAWZkB34K09JQKY/uwcdp0QMBH71Bqru6Sekfk+Ws/XhPhzJ5aIs/TwAAt58AvXIwciMhvrvnf+uonbSS+B0gdeLonN7dDeOQ92vB/KocFwrN+L45S9UA3oij4/GV1KB8+8FCCoV2rGjcPy9ENd8f2MV/MfnCGo1dY+9jjeviLCFU5CHBlF73/MN3AypY98hZvCdSKKXwiUfkjvvZXRh7bAWbwNBDpKPwLShxI14GFN8T9SmSFY+E48xrgddbv2z/p57HXVk/vkIAckDiOp3Y8PrliSWPqhD9Pj9kjKZGqMxCUmScDiK8XgsCIKc4OAMkpMvR5IkfD4nJlMKguA3rbVW0J+1ZCfrXukMQOdbfick/fyz0kIgelxs+2IM1XvmN1je5hY4Mxxt9lPdM+/g+HkmABE7FyBo1LiWraNm4sMYHroV/R3XnpB5WHK6sP/yN/Y3P6HDhBT6PNsdZ42Lmt21zL1qCQD9Xu7J5ne246ppOvnP4UT0CeWKNWOO+zyOxo3V2465zuTgjFY7XlNIooTX6cNr9+J1eBE9IqZEI4KscT+w9oXNrHthM4Pf7Uu3BzoxPedG3Ou34py1GGXvDFTdO+ErLsebW4DMaECRmoCg1+Fet5W6SS8CYHz2fgS1CrHOjFRjRqwzIx74K9WaEQ/8w33IqiGLCMX07H2oh/U/6VkHLUnWdCKBk97iMioHX966YuAgyp5dCJn2yXGfVEsRa824N+/EV1CMd0823t378ezJAkFAkRSH/tar8R2I1tVcNOKsieQU7Q7qJr2Ia+FK/wKZDO2VF4Ek4d2ThWfLLoSIUKSyykbbGu6fiOG+ic3uX3K5qbnjKb/V4f1nEbQaxKpaKoZfjfrcQRgfvBl5TCS+yhpcS9ag/nkvldv9iUJkKu0Bt4GAIFeg0JhQmSLx2Kpxm0sITBtK55umoTKGse2LS+q3Sxz9HMkXPt/8df/2I7W1e/B67Wi1kWi1Ychkh0YpTmc1lZUbyMz8GThU5yAgeQBxwx5EH9UJbVgKMvnJB1UVLv2YfdPvbbAsMHUIiaOfIajd8LNGUK57rSvWom3EDr0PXVg7cuY8j8daSff7FhPUbuiZPr3Tjs9lo2jll7hqi5Ap1EiSD1dtEa66IjSBsQQkDyIwZRC6iA6t6ps91hTousder3flKTq1w3Dfjcgjwqi69FZQKdFdPQbdTVegiI1qdj+Hk7H6GVY8tA5HpZMOE1IZ+ml/FBq/G2DpfWvY9vGu+nWjB0dQvLys/vNlSy8gIMmITCVDEKB4RRkLblxO3IgoLvzj3GMeuyWd/IlyqsVBcxQuLuHPEXNQGpWkXZFEaJcgVj25EYVW7s930AICv3gVzbmDml1HkiQkh9MvDiqrsbzzFe4VG/xWowE9UQ/thxBoQrI7kGwO/1+7A8nj9VsUggNRdm6PMj3thDM1QstEgXd/LtXXT/InjoJTIwZ014/D9Fzj5DUnimi2YJ/6tz8l5qqNDZQXgGrEIFR9MnD+tdAf5VlS3uB7eXwMYlUNQkgQ8rBgBLUSwWREPbgPgl6Hr6wCZfdOYHMg2h3IY6NQdW5ovm8pkseLL7cQb3Y+gl6LPCaSuqfeQh4dgf6Wq1B2SEF0unDOXow3rwjH5N/8swg6pqLskIL63EEIBh0V/S+rnxFwNEyvPYruiouOeU62r6ZieftL8PpQpCWhGtQL1YCedLNNwllTQNWuudhKdmBK6IMlfyOV22fS75k9yNV6JEmietdcdv10I3KVjq53zUX0OBoUyokbNgl1YCzasFS0YSmoA2L8Mwr+mNLoXJzOahyOUgIDO9SP+g9iNmfjcJSh0YQi9h9A5p8PYyvxuzQEuRJjXA/ihj9IePfLT6rTbqqeAoAuoj39ntlzwvttLbL/eZbcuS81+V1k3xtJn/DdaT6j08fB8sCWgo3YSvcgyBWIXhdFyz7FY6tEG5KE6POPhNUBMSj1ITirC7AWba7fR/LFr5I46omTPpeW5EIRrXY8G7bhzSnA9vUvDSLMDyKYDERsbn420OEN/x/DZlO0tJQJ+8YTmNqwcTbnWZk1diGVW6oxJRqwlTgY/uVAChYVE9I5iB4Pd2n2OKeys28pZ0oUuGpdbHhtG7mzCqjeVUvUoAgumTsKV7WLis1VGBMMBLYz4a7z8Me//RArq1H16wGiD6nOijw5DkF+fK65g+WfXYtX41q6Bs+G7eA74N7QqJHptAh6LchkKMwV9Vae9JvbMeKr5oVHU5jzrNhL7GjDNegitMwov/Xo92P1Jmque6D+c6uKgbC1M/wRlq2IY84Sv5lGkKHs0Rmxshrt2FFoLhwGcjkoFSjCDpuq4nLhzcxDFh2OL6cI95pNePfl4N6yC9XAnmB34Fq9Cam67tCP0hQBRhQRocjCQlB2aY8iOR7VkL711yfaHbhXbMC9bgt4fQhBJrx7snCv3NBoDiqAPC4KX2Ep6pGD8WzYjlhVg27CWH/WsT2ZyAx6lBkdUQ3ug7JLe2ShgZifeBPXolVHPUXDI7dhuOO6o35/8KcTBAHRYsX17yrqHny5/vukC19EZQynaMUX9ele5doAfI469NGdSTjvCSJ6XY0gCKx5qSP2sj3IlBo6TfwVTVAchcs+wVGZhdtcgrOmoIkZBjJkMiUqVQAymRKv147bXQeIKBQGNJpQ4uLOJzy8f4PO/aBLQJIkPNYKbCU7sZXsonL7X1TvmU/CeY+TPObVExYEbnMZK56MJKLn1YR0uZiaPQsoWePvYI1P3o3+5itPaL9NcSLZKVc8GYXbXNrkdwNezEMTfOIpiM9WXLXFFCz5gJK1k/FY/GJerjYiyOSIPjdhGZeSfNHLaEOTyJ71HOHdxmOI6cK+3x6gaucs0q74jG2fnFe/P4UumPBu44jodQ26iPYoDaHHZVk6kaRootWOd38Okt2J5HT6R4hmK6q+3er91Lbvf8fy4gco0pKQRYSQnFKLMVZPeK9QogaEow3Tsv2z3Sy5ezXdJnWi7/M9Gvj6AdY+v4n1r2xFHaQmtGsQl8wdhUzeUFifDZ1+c5xJKwGAs9qFOlDVpEvhIKciq6Fod4BPRNBpuCK18Swqn9vHnCsXU/hvCX2e7YbX5qUu24LX6UPyiii0CvRROnRRWkSPiMfqRWVUkn5zOyx5Vn7pObPB/hQ6BaIhAMntAbcHQaP2T2dNS0KRmoA8NhJZVDjVF93cumJAf/cN6G+7utE0tuPBOXsx1i+mgFKJTK/FvXIjyARCl05DERV+wvttqlGWRBGfx445bz0eawWmhD54HLVUbPwVQamheMUXeAMF8PrqrQ2Kzu2RR4fjXrsFqdaMPC4KQadFrK5FHheNelh/VD27IE+O82cay8pDM3oosrAQHL/NxvbdNJTtU/xTC5UKFMnxqM8dhOR04Vm/Fc/2vX6LgEyGLCbSb0I6EBQI/nrimlHnoL/pSkS7vT57mWixIZmteLbvxbNzH2JpBZLDCRoVitQkFMlxIEo4/14IgCwyjPSLv2THV2ObvF9KQxgeawURva+l0w0/sf/3SRQsfh+lIQyfy4IqIBqvvYbEkU8SO+x+BJmC3a8Mo7JyA0FBnfF4rHi9FhyOCnw+B3p9PGFhPVGpAtHpoqmp2UlR0Xy8XjsaTRgdO96BwZCA8sqbmzwfj62afb/dR/mmaUg+D13vmkNIesuyXTb1u2/9/EKq9y5oJAhNLz6I7tpLT2i/J4vkcuFetxXXkjVIs7biqNiPXKUnOP18QjpdQHi3cf70vP+PsJbsJH/h25RtmIJMqSWy9wQk0UNd7lo81goGvlyIx1pJ9j/PEHvOXeijOrHpg2HEnnM3ET2vpC5nLdn/PI0gV5J80UvkzHoWmUKNNiyN8s3T6hNkgV8gaIITCOk4itAuF2NK7NtkEOaOwfOQrDZkYcEtGglKbg+e7Xv8CYY270J/8xWoendFEkXw+vDuycK1Yj2yACP2X//Gu3M/ACnjErEV2zHnWLCX+gcQkf3CkKnkFC/zi8HANBPn/TiEyD6H0k3PvXox+3/1VxWVKWV0u78TXz3+38uCd6YFwbFoLTFwvOb+vLmFbHh9G2XrKlAZlQSkmlAaFAhyAa/Ni7XIjr3UgVwlQ2lU4qx0IlPKCO8ZSuFif12XS+aPwlHuxFHuxG12I1fL2W7rh2Sx4d2fizczF29OAXi8CKFBSJU1pyhmoHsnDPfeiGpQr+Myq7hWbqTm+kn1n1WDeqE+bzDqIX1RxEW3eD8HOdmaAXkL3yYgeQCByQNwWyup2jmbHMvPiBVVKNIS0U247ITnCkseL8iERvdHrLP4f6zsPLyZef64iKw8RLMNqeJAKl+5vGmrhkrZ0IWiUIDXC1oNsuAAv4+q1oz2qosJeOUR2s/siaVoKwUL36Zi6x/+bQQZSCLB6RdgLdhIcMdRpF//ff0uD/rcgzuMRGkMp2z9T6Sn34XRmMymTS8QGNiRTp3uqV+/tnYPW7a8SmhoLzp3PlRRESA/fxbZ2b/Wf5YpdYR0Gk3skHsJSmuY/Khs46/s/O6q+s/x5z6KMa4HuvB2GGK7HZeV4OCoT7RYqZn4SP3sCmX3TgRNfuekxOyxkFwuXItW4d6yE3wiYmUNvuIyfMVlft+dKCKLCkc9tJ//36DeCJrGDf3ZWE2zpUiSRM3eReT/+w7Vu+aiMkaij+lCx2u/Q3Tb2PB2HyJ6T8Brr6HTjT/hqi1h43sD6XLrnxhju2KvyEQbkowgk1Gw+H0yZz5O+ys/I7p/w5gaSRSxFm3FVVuE21qO21yGvWw3Vbvm4LFWojSEEpJ+AaEZlxCWcQmCTM6u0cupvv5B3AeSzshiIpHHRGC454b6KWSizY5n807c67fhXr8Vz5ZdjWf6KBXg8dZ/FAw6JLsTmQJEj0jf53vQ55lu9ffDWmCj8N8Ssv/KI39eEV7HoffbmGDghuxDrrHM33JYfMcqnNV+X3fPAQa+/KtDq/5Gp4OzXQzAiQmCk/H1H44kSs1aLg5iL3Ow8c1t7Ph8D16Hj97PdKPv892P2iYevCbJ48WzeQeulRuxffx964oB09tP4vx9Lr6iUnz5xcgiw9COHYX2svORR4WDRt18AZ3Kaiwvfohr8WokuwNZdASmZ+9HNWIAshMMCjqZRnPpQ0aSLnyR+OGTGiw/E3UVbN9Nw/Lyxy1eP3TJL8hjo/Duy8b6wXe4l68/EFB4YOqhQYdkbWjWz7hzFnVZK8mb/yoA6qB4ut41G0NUp/p1vC4r9tLdGON7UbVzNts+v4iwsD7U1OxEqTTQo8ezKJWHCg8VFy9h375viY0dRWrqtfXLfT43a9Y8iMdjrl8Wc8491GUtx1q0lR6TVhCYMrDB+TmqcqnePZ/q3fOozVxaX+cgMG0oHa7+El142lHvR1O/mfXj77G+9w2GSTejGTUEeWrCKQ0gdG/YRu39LyCWViCPj0ZQq5AFByGLDkceHYE8OgJl904o2iWdkvM4+C601vN7Iu+Wz21nz5RbKNs4FUNsN+KHP4TSGM7O766m9+ObUOlDqdg2k8jeh+bsl2+azo5vr2DASwVoghqKb3tFJq66Yry2KkIzLm3RfZNEH+bctVRu/5vKHf9gK9mBMb4X2r9eRGY04PhzHnUPv4I8PgZf/qEaHME/f4jlzc/91jufDyE4AFXPDFR9uqLqlYHtxz9w/jEXRVoimjHn+S0LMhny+GiuGruRuiwze3/MJPnSBCJ6H72wlMfmIX9+Edkz86ndV8egd/oS1d9vFb2xehu7t9p47OZs7FYfNZVeVGqBZbndUSrPzqQ2zXE2C4KWCoHW6vxPlpaKh8OZnnPjqc0zIEkS3u17cfw+G/tPMw6tqJAjGPTI46LR33Q5mguG1afePRzR7sD6wXfYv/6lfjv93TdguOu6Jtc/Ho6nAcud9xo11xhR9+l6UsdsDSSfD+/O/fjKK/1BSnK5P7OWVoss0Ihg0GP7fAqSz4d+4uWoejX9kolVtbjXbcGbX0xc6QBU+lAkJFSGMEwJvfE66lj9fArtLv+YiF5XNbkP8M8QkCSRgoI55OT8RkhIDyIjB6FQ6DAak3C76zCb91NWtorq6m3ExJxHWtqh6YGi6GPdusfqawiEhfWlwyPzyJv/KvkL3yTlkjdIOO/Rxsf1unFbylBoA5FEL3VZK9n3+/24agpQB0Qjepx0vuX3eiFxtI5PEkXKe49BqjUTtuZP5IfFnbQ23qw8HH/Ow/blVJQ9OxPw0kMoUhNbvL3k9SJW1SLZ7MiT4prs8CSHE9eazeByIWi1x22Vay2ae78cVbls/2os9vJ9pI59h8ptM2h/5adoQ5ufg+91WXFV56MNS0WmUDX6PnvW85SunUyXW/9okOq6ePV35C94nZBOF6KP6oQ+ujP6yPQGVTIlSSJvwRtk//UExifvRpGehnfXfiyvfoJ6+ADcG7fXpwSXRYWDJGG4+3pUfboiTzm2eGzNjuL6yq388HEpH73oFygGkxyZDB55LZ4LLj91z++p5GwUAy0RAWeLAGgN3GY3XwT+1LpiICDZiOy551EP6l3/Xc2tj+P6dxWqft3RXDAM0WrDvWYL7mVrAb/aVvXthnP+cmyf/YjocOLLLmjSDK7s0ZmQ6Z+eyPWeEGUZozBMuhn9xCtadb8tfZBOdVnOE7WaHJk+2OdzYbeXsHFj46I5AQHtCA8fQGTkQOTyhuZul6sWp7MSpVKPVhvJXudiStdOJnnMaySc+2ijKWIV22ay/ctLAX8FxeQxrxIz8DZEr4viVV+TOeMRJK+bbvcuJLj9iGOOgC1vfYHt8ymELvq5VVLDSpKEWFKOd282nj1Z+ErK8azbind/DkqDgq73ptP3hR7IFC0fwW37dDdLJ62vNzkr2iWhveIivPuycc5bhizAhDwhBs+Gbf4YkQMEff0G6mH9T/qaWoP0OYOp3ruInd9eiVxjIuO2Gcg1Jnb/eCMdJ0xGG5LY5HaSJGHOWYOlcDP20t0oTRFEdL8CXUTDmT7OmkIUGmOjeIo1L3WoT6R1OJrgBPRRnRA9TuqqNtdbyxogk6HomIrxkduwff0LksuNIiEW7ZUXoerRuUXX3VodxsFgwH9+reS5u3Prl2f01nP3UzH0GnT6Kla2NmebGDham/v/qfM/klMiBuo3CjAiM+pRndMX03P3483MQ5EQg2fXfuoefhWxtq4+VW7Au0+jSIqjaqy/qpwQHIDxodtQdExBFhSI9YPv8O7LQqqzorvlSvTXjzvZa28xlve/QzWwB+reJ28ZONVzRk+EExEDTdURqKnZxfbt7yGKfh+mXK4hNLQ30dEjCAhoPOrz+dxkZ/+K01kFSIiiF6/XisWSgy6yI/2e3tVoGzg05U4f1QmVKZKavYvQR3chss8EKrf/TV3WcmKH3k/auPfYfcGKY16La8kaau9/AcGgI/DD51H1bH5a1tGQnC4iP7+fXZP316dRVZmUGBMMhHYNJnVcIvEjY1Boj8+qZS208V38rw2WJYyOJW9uIaZEA2lXJuO1eanZV0fMkEj293kQWXAg5T0vQggwYnphEtqLRpzQNbUWkihi+/RHrB98h6p/D7oO/AJteFqD0XlTeJ0W9k69nbKNUxHkSrRhqTgqs5C8btpd8Qmx59zVYP3i1d9iL99HypjXDlXCE0Uqd/zD7p8mYojtStrYtynf/Dsla77FF6pC0S4J19ylACi6dEA/YSzKnl3w7s3CtXw9hrsmII+OOKHrbm0hAGCz+Jg/o5qaKi9DRweS3P7UVTI8HZxtQuB/FUuBlckJ01pXDAx6pw/WfBvaCA2b14fi/HMeglaDsk9XtONG41q8GudfCzE+fBtCcACa4QOQBQf6M9FNm4Xtm1/wZeWj6t8DRad2ePdkIpZXoxrcG8+GbXi27gYgfOM/yAJPvRquGDUB/c1Xtmgu/9FoTUXZ2qLgZMWAJEmUla0iM/MnlEoDTmc1YWG9cTjKsNkKEUUXBkMCwcEZJCZeWp9oaPfuLygrW4lCocfncyFJhwKtYgbdQeq495ArGxci8bls5C96m/yFb+Fz+3OQq4PicVtKUQfG0vGab+oT8rTUL+4rLqPmlseQhYcSPPntFt+Hw3/XRTcvZ+/UbHo81JmIvmEEdwrClGg4ab+/JElM6fQHNXvq6peNX3EhwZ2CUJmUjfYviRLVu2rZ+uFOdn69j6hBEYxfdiFw6q1MTeHNL8b83Hu4l69Df/f16O+9gapzr0XRLomgL1+vX6+pWAbrh99h/eA7Ol43mdCMMZSs/o7ceS/jtdcQf+4jpF76ZoNjbf9qHIGp5xA3rHF+kz0/30Zt1jJUz15L7d2HrFeyqPD6WULBUz9E1adbq1z3qRAC/x9pEwNnBkelk9+HzEb0iOijdZRvqMBr97WuGLi99jpUJr9Pb3rOjbg378S9ZjOuhSv8EbeAakBP1OcOxFdSjjK9HerBvevTF4tOF7W3PYEkExDzihAtVlAo/T4796Fo3bB1M5GHBLXowg++mE01hpcnTWZ6zo1HfXnnXr2Y9tekkHTx2TWvuzUb9uMVBPaf3iMz82dqanbh8zkBkcDAdGpr/b/v4MFf4/FYycmZRllZwxwJJlMKGRmPkJv7J4WFCzg82+DhyDVGgtoNJ/XSt+qDAp01heTNf5WYQXegCU2mavs/5C14HbeljF6PrEdlikQmPzTyPp4gOcsH32L/+ldCZnx5zLzlRz4rXqeXn7v8SdSgCM777pwWH7Ol2ErtrHxkPXunZNUv6/NsNwxxBoxxemRKGeUbKileUUrJynKc1S7kajk9Hu5Mz8czWlRitrWFgq+0AuunP+L49W9kQQEEvPmkv9qbx4ss0ISqdwaCtul6976KKmxf/YL9m18RjAZ0E8Zi/3kmktWG9tJR6CZeTkb2DY2EkM/jRPK6kWuMCILQ4Pe3/zwT87Pv1ifxkoUGY3r9MdyrN+LZsQ9FaiLGR25rkBr8ZGgTA83TJgLOLM5qF1+FHkoIN+Tjfiy9Z82pFQMHOehLlVxuPLv3U3fv8432EfzbZ6i6H4pa92zbg+X9b/DuyQKlErHQP39SUAhM2DuOgKRTbxmYffm/dLguheRLjq+wxemgtRrwlooB0eOiZv9icqfcj91eRmzseSgUBgyGODIzp2CxZAOgVgfjctUgk2nQ6cKxWvMa7Kd371fR62Nxu814PGbkci0KhRa5XIMgyHAM7kPh0o8oWuZPZ93l1j8Jajccc/4Gtnx0yOSdMOopdBHt2fPTRJIufKlRxrnjEQOi1U71+DvwlVZieukhtBc3Nq0f2cD7PCJ7f8pkw6tbsRbauXzVRYR1P3VBXCsfW8+ub/dhSjZiLbD5a74feDMVOgWR/cOIHhhB9OBIIvuFnVCd+cM53ufLV1yGc/5ynPOX4Vm/DcGoR3/7teivvwxBq6HmlsdQdG6P8YGbmtzeszcL+zfTcPy9EEGlRHvFRbiXr8eblYfu2kvR33Z1syZ7z/a9VF/3APrbrsZw9/WNvhctVjwbtuNcvBpV905ox45qYi8nT5sQaJ42IXB2IHpFFt68nOw/8hj21SDmX7Pk9IiBw5GcLszPv4dod+DLK8a7Yy9oNYQunlqfSTBp+oNsfms72jANcrUcR6WT4V8MRB2sIqxHCArVyc0oaCm/9p5J1/s60WFC49S1ZwOtIQhaIgbMeevZ/dPEA6mBBZKSxpGQcKjoidfrxOezU1Ozi7Ky1YSF9WLfvkMpc7XaCFJTJ2CxZOHxWImJOY+8vJlYLDloNGHodNFoNCF4kuNwVuUS2u0y3OZSild+ia14O4JMgSmpHzKFhpq9CxudX7d7FhDc4VDe9RPKHme2YH7mXZz/LEJ75cWYXnkYQRCO2rgvu38NWz/aRcplCfR+phthXU9vNLfP7cNWbMdr9xLYLuC4ghJPhKM9a2JNHeZn3/Un0VIpUfXvgWbkOWguHNZgpC35fEguNzJdYz+3Z08WVZfcgiw0GP2N49FeeRHWj77H/tMMgn98D1WvlsVyuJauRdDrWrz+qaBNDDRNmwg4Oda9tIXsGXmIPpGLZp6HKeH4rVi2UjsFC4spWFhM1fYalAYFxcvL0EVpsZc4jikGWr3XFTRqAl5/vMEy/wv0NwBFy0v54/EN6GN1jF00Gn1U6ySAMedZMCU0H7R0JDHDojDEnX0V7Q7SnAukpewavbxZQVC5/R+2fzUWQ0wGvR7dgCGmK/w5tcE6CoUGhUJDZOQgIiMH4fFY6sVASEg3kpIux+msJDf3T8BftbCsbCWhob0QRQ+VlRtxuWpQlJhQB8ZS9s1UNCGJJI1+joCUwdTsWUD1nvmY8zc0OG7sOfcQ2nUswe2Hn/D1H0RmMhLw/rOo+vfA/NRb9B1SRZc7jp7IJbSrPy31wDd6E5By+qO55So5psTje55PhqM9a3WPvYZ7/TZMrz2KZvSwo5Ywl2x2PDv2oR7Qs9F31ve+QR4dSejc7xHU/sGEsmMquN0ImsZTCY+G4++FeHdnEvLPt2dNwak22oTAsajcXk3R0lLM2RbMuRZ8LpFhnw3AGG/AbXaz9cNdrH1uE3K1HLlahtLQsm5Z9IpkTs8h8488avbUUr2zFvC3XeE9Q3FUOQnrEUL8qFg2vrb1mPs7YTFw0B/f3PdNcfCEowdFtIoQiP9xKY/c4Pe33vxgFHc9GcPSOTUsm1dLSISKYRcE0iFD12Tj8dWnuxgRXMfVGY2rCB7kbHjQj3WvT5Sa/UvZ/s04QrtcTKebfj2U2338hCZnFRxEqTQyZMhkRNGLXO5vzLXacKKihiGKLuLiLqCgYBYhIV2JivJnGhTG+WssCIKApXAruXNfYvdPE1EawlBoAwjrNo4ut/6Bz23HUZGFoyqHwJRBKPUNa2GcTEIdQRDQXXUx8Xl/seSuVeQvKCIg2e+D1kVp6XRre1QG/z3IX1CEIVaPPvrsFYungkaiQJRQ9co4ZpCt9YPvsE+ZQcTm2Q3iBSSHE9e/q1CfOxAUh/IiKLulA+Arq0TZuX2T+5R8Pnz5xf5qpYmxGG6/Fkn0tQmBs4Qz0TYeTzt4pqcL+lw+1j6/mU1vbUdQCJgSDZiSjVRurmbhxOWEdAli13eHZiiJXpF+L/dAG9I43sZR6UQTrEaQCThrXOyevJ9tH+3CnGslakA40QMj6PVEV+LOjUYX3tA65za7WyQGTthNcLzkLypm5si5fj+oAEgQMzSSwe/2pWRVGZogNe2uTmnRvm6s3obd6uWTV4v45ctDVcRe/SqZzj30jOm5HYCAIDl1NT7kcujax8Dwi4OoKPWQtcvBmGtD2bnJyo5NNm64L4q6Ki8jLwtGoTjU0IiihOwoGZ/O9hfhSI60DkiSxPrXuyFXG+h+3+Imk71A01MNW8LGjc+jUOjo2tWfWOhgcaLDj1+24WccFZlUbP0DQa6i96PrG6zjdVrImvkYcpWelEvfbNF0wuY42DiIPpE9P2Sy9aNdeO3+mQ6WPBvqIBUZd3XE5xFZ/9IWRnw9iPSbTqy65Ylysubj1n4ul92/hh1zbITNa/45kEQRsboOeWjjwF/7tH8wP/kWivQ0At97BkVKAp5tu6m67A5QKTE+eAuKlAS8BcV4d+3Hs20P3rwicB4oPavVEDz5nTPqHjhIa3Yw/2VXwalu/85U3YDWomx9BQsnLqd2v5k+z3WnxyNdkB/IIJk3t5C/LpjfYH11kIr4UTGc834/zLlWChcVE9EnjIJFxeT+U0DVjhqCOgQQkGKicHEJokck7Yokuj/UmbBuzbswW5pn4PQ45wGP2VMfEIUEpiQDVdtr+KXHoSpMliIb3Sd1BolGPtLDX5x//6nhsZuzEA/kLVIoBcbdGMbTt2cjHghgH3VZEC9+msySOTV8/0EpFrOP958rRBQlJBFWLPRP55LJYOPK/SS315Cf7WTYhYE47CLfvleKxyPy6GvxJLVr7Adt6kU+1S9Ia1oIPJZyrEXb6HzTtKMKAfB34scrCCRJRKMJpaJiHR6PDfXVdzRaRxAEInv70xeLPg/FK79AEsX6REQ+t4Mtn4zCWrQV0W2nqlMlRpoOTmsJhzcKMrmM9IntSJ94qKM351lZ88xGNry+DYVGTsIFsXS4/vTEkrRmp3D4vlrjeQxIMULBXi6L/Ra5Utbg+XMuXo1n625kIYHorrioSSEAoLviIqRaC5Y3PsO1bB2KlASUGR0JW/or1i9+xvLaMRKNOZzYJk8/42LgTI80zwZOVRt3qqbGNjeb7GiIPhFHuRN7maP+n6vGjVKvQGVSojKpEOQCmb/lUri4BE2wioAUE8M+H4jKqMRe5mBaX79b/OqtlxLapaF1M+68aDremMbenzJRBarp+1w3XHUe1jy9kf2/5DRYVxOiJvHCOLpN6kT2jDxy/i6gw/WpDHi9F/rI1rVanjbLAPjNICWryhE9ItHnROJzeCnbUIkmSMUvPf8CQKFX4LV5iR0ehT5aR122hbLV5bw3JZXBIwMBuGPsHtYvt3Lj/ZEsm1dL9h4nIeFKkttrGHJ+IINGBhAdr0YuP2J6kk/iYNK70iI3uzbb+fXrMgqyXZSXeFBrBULClNTVeHno5ThqKr18/HIRb01OIaWDlviUpqdLNcepeHlO9MU53DrgqMpl9XNJaMPSMMZ1J238B6hNkcfcR0uEwb5931NcvIjU1OuIf+DY61fv/ZctH43AlNiXuOEPogmKJ2f289RmLiPw1/dwr9qE9e0vMTxwE4Z7bzzm/g7nbG/AT9fo8J2SOAoX+U3ujgonHa5PJTD12LEQFVuq+KXHTM774RzaX5uCIAjU7Ktj+YNryZtdCIAgE7jgzxEkXxzf6Nn07NyH9cPJuFdtRNmjE0HfvNkg5bgkSdQ9+BLOvxYimAyoemUgCw1GrKjCm1+ELysf3Y3jMT5x10mnKj9ZWvtZ+i9ZBv5rIuBIWvrb5fydz7IH1mDOsTZYLtfI8TkbZs01xOpJviSeumwLeXMK0YRq0IVrsBTY6k3/V6y9uEGdCnOelQU3LKN4eSld7uxIv5d6kPN3PotvX0XypfGkXJaI0qAgvEco5jwr4T1DGpSvthbZ0Ec37fY+GqckA+HJioHmWHTrCrJn5KE0KrHkWht9P2N9Z5QqgTm/VfPlW8W4XRIdumi48YEouvU2EhrZOElLS/H5JOb/Wc2bj+djrvWhVEFQiJLaai8yuUBAkJzAECUfT0sjOPT4p3SdLYLgcDEgiSKFyz7GXraHsg1Tiex9He2u+Oi499mUOMjJ+YO8vBmYEvqQccffqIzNl6aWRB+FSz+iatdcqnfPA/AXsXrxQTQHUu5aP/0R6ztftVgQnO0iAE5fZ7Bnm53bL92Lw+arzwKecU86fZ7pijas+Sx3kiTx++BZlKwqJzDNhOgVMedYMSUaGPhWH8zZFlY+tp6L/jqXpIsa5uuYnnMjdY++hmvJGrTXXIL+psuRmRoGRfqKy3Bv3EHdAy8AoLnsfALefAJBEJAkCfeqjfXVBM8k/6tC4L8uAg6nud+wLtvMsgfWkvtPAfGjYuhyZ0d0kVp0EVp04RoUWgWiT8Rj9eKuc+OxewlMMyGTy7CXOVj2wBpkChmaYDWGWD2GeD0ByUbCe4VizrGQ9WcedfvN7PslG3WgivN+GELMOZFkz8xj1thFRPYPZ+yi81FoWlfwnrJCRadSDBxJ6bpy9v+Sw45PdnHN7eH88X0FVovYqLqvVi9jaXb3RlaA5ui2pOnlbo/InE115Fa4qbV5WbnHikYpY9amOs5JN7C70Mn29zpRdMl/VxBA4/iB3HmvkjP7eRJHP4u7rgR1UByBKYMwxvdqMlNgS6jLXsX2ry5DF9Ge7vctblSHoCkkSaJi658UDc5FNaBno20OCgLjc/c3m7b6VAuBEwmcbYrT1SHcdMFunA6RR1+P5+YLD+Xy10dpuano6mNuL/pECheVkPlbDiqTCledG32klo1vbEP0SiiNSgyxeq7aOKZRY/Zjh9+IOzeGioe+aLBccrqovedZXItXIwQFoB7QE+esf1H27ELINH8OitpHXkXZPhn9LUcvqHW6+F+LFfj/JAIOp6mkYpve3M6G17ehC9cw+L2+JF/aetVNfS4fvw+ZRdm6SkK7BhM1MIL+L/dAHeiv45I1I4/Zly0CYPC7fen2QKfmdtcsR50efCqrFp7KxtZj87D+1a1semMb0gH/vyCATA5X3hKO1ezjr5/9pW1NQXIefS2e0eOPHkBxtI7/eJj4UTY/LK0mSC9naCcDz1wRTXqslp3nHv/c79Z+yVojqNDncbL+jR64qvPRhCTirM7D57IiU+kI7345iSOfbFQ8piVUbJ3B9q/G0uepHQ3KJDdHc7MFJEmi9s6ncS1YTsTOBQiahoWRToc1oDXLnp6OTsFS52VoyhbufjqGa++IYMHMalQqGa8/mkdA30gumX18yXkOBj/JlDJEj/8F7f1MNza9uZ30m9sx9OP+/vTj5U5q9tbxx9DZnP/rMNIuT2qwn9VPb2TTO9sZ/E5f9v6USemaCtRBKm6tuLa+TOu865ZgLbZx6bzzkSsbV2g820zMx+JsFwH/laDA1uDypMnkzy9iyT2rseRZ6f5QZ3o/1fWkk3kdyZ8j5lC8soyRP5xD2hVNV+90VDiYnDSdvgcCDY+HltzTloqBE7JJHH4CrfGiuGpdLLl3Dbn/5OOu8zT4zhQo55o7Ijj/smDikjU47D68HomYBDU3PxiFUtW4Q24NAXA439ydSKBewbeLKlixx8bf6+sY/fJ+vrkridE9DhVw2jL02Ps62CCcDVMWD+94+8p3gCAgCAKiz4uteDtVO2dTtPJLyjf9SnDHUQQkDySy1zWoA2OOuW+vo46StZORq/Qo9S1L2HOsaYOCIIDH/3zUTnoRwwM3oWyfctpcAmdTY9ZSDCY5/Yeb+OTlIuKT1Vx0ZSgX99hGXY2Pvho7kiQd1yiocHEJ+mgdN+Zdwe/nzKZ0dTl7f8oEAbZ/upsud3Vg+ye72f7ZHgDCeoSQclnjDJ+VO2oQ3SJKg4Jxyy5k87s7WPX4BnxuHwqNgtJ1FYR1D6Euy8I3EVO5dOH5hPcIpS7bjCnJ2GzCqIOcLb/X/7oIgLPntwD/dNav7svFOetfYoZGctHMcwnuGNjqx6naUUPh4pImxfBB8uYVMuvSRfhcPgyxLQ8IPBX384QsA01xvA2yJElsems7ubMLKF5W1uh7mUpGaNdgXn0uhO79Wp58pbWFwOE8+kMB7/5dhkYpMLyLkQlDQrl8QPBR12+JOGiNF/FUFjjyue0ULH6fmn3/Upe9CtHrQhMcj0ypRaExoQ6IIW38+2iC4pAkCWvhFip3zqJo2Sf4XFbSb5hCWMaYZo52nKmF7Q6cM+Zj+3YavoJiki+OJaRTENGDIog7L/qUz0FvLcvAqe4g8jKdfP9RKbVVXnRGGXOmVwPwz+Yu/PtPDYtn1bJ5jZWn3k3A/EDLKx9ueG0r61/ZStf709n42jYCUozUZVkardf76W4EtjMRf55fONZlWwhqH4A6UEX2zDzWv7KVmt219HyiK32e7oar1oXH5kUfpaNwSQlL7lpF13vTsRY7UKhldJvUGUEGP2f8ybmTzyFm8LGDXZvjdNW0P/J3liQJUQS5XCBrj4Nv3inhpc+TkMsFvnyzmNGXBxOXdGKuuePldA1IzhYhIPl82D6fgu3zKQgaNcYn72bCpOJT1mYcfFduq7oWubqxZQv8bjRzjpXzfx1G4ujYo653kBO5l6fUTXA0juflmXn+PPLnFzVYln5re4LamWh3VTIqo38Kx/E0mqdKCEiSxLJdVkRR4utFFcxcV4skwYJn2zGg47GFSktEAZzcy3k6yiB7HWbKN0/DUZmN6HHgqMymcvtfdLt3EcHth5Mz+wVyZj8PQGSfCSRf/CqaoNhm93+iSYQui/6GLe/vJH9hEdU7a7GXOki/qR0jvh50Qvs7Xk62LvqpEgM2i49v3y9hymdlhIQrCQ5VsGuLHa1ehsMmIggw9IJAXvo0ifeeK2TmlEoGnRdA+18urk+41BzOahffxf+KLkKDOceKKkCJLlJH7d46lAYFEX3CyLgnHV24hpx/CsibU0jl1ur67ZV6BR6bF22EFmOcnvCeIWjDtKSOTySoYyAyuYDH5mXWpQs5d/JgjHGH0rKKXpF1L24hZVxCq6eHPvh7tnZsQOZuB8X5Ls4ZFYjV4uOyvtu5aVIUV90awfrlZr54o5jXv0khJFzB+Z23cevDUVx4ZQgLZtRQfO1ANEF+V5joE4n/cRnDLgw6rviowzlT1sizRQzYf56J+Zl3AJCFhxDyxxcginTc8i4qo5LEi+LQhmio2FrFvinZ9H6660nFyO35MZMFNyzjlvJr0IY2FHi5swvY8Po2SlaUoQ5UcWvVtc2KkpO5h2dEDEDLX6YtH+xky3s7iOwXTnCnQHo+3rU+KcORtKThPJUWgY1ZNvo8trv+8xd3JHD753l0T9Lx86Rk2kW3TMm3VBTAib24p/qlOzLw0FKwifVv9KT7/UsIShuCtWQn617pTEDKIHpOOnYnfyJCoKnnS5IkNr+zg5WPraf9dSn0eaZ7i6bNnSlOVggc+awffK4kSWJMz+1UVXi54Z4Ibrgvikt6b6ey1MOYa0IIj1KxbpmZbett3PxgFLc8FMW0b8r5+OUiv8vt1fMbHUuSJCq3VFO2roKKLdXU7K2laEkp0edE0P7aVJbdtwafy4dMKSM4PbBBx68J1ZAwOoaEkbEEpwey9aNdOCqd1GVb6PVYBh0mpFK2oZKl965m0Fu9+WPYHMYtvYCoAUcvWARgK7Gz6okN9HmuOwFJpy9lc0vJnpnHFfoiOvcw8OlrRfzzSxWzt/rf5zm/VZHcXkv7Lo1Nwg67D61Ozmt7Ivht8Cyu3T6W8k1VZP2Ry6B3+vJDynTO+/6cs7aWSlO0RpvkKy7Duy8H9dB+gL9zEwzHN70OQLTZcS1ahWSzY/34e3+1XJmA5HSDz4c+Rseon4fyx9DZIEG/l3rQ+6luJ3zelnwrkxOnMfq34aRelli/XJIkJidMQxehodOt7Um8KB5DM5lOT/YenvVi4Hg4VuN5KoUAQJXFS/jELfWfPdN6sj7TxuVvZ9G3nYGfH0hC2cJCMscjCOD4RMHpVODpcwYjiSIrn44luMN5pF//PQDFq75hz9TbGPRKMSrT0Rv11hICB/G5fKx5ZhP7p2WjCdFw1cZLjnv/p5rjFQEn8lwPe3YPZXIfU/5NRxAE/vq5khfuy+WpdxN45cFDFSaffDuBcTf65z+/+0wBv31XznV3RVJ1QVfihkcDUJtp5q/R8/xuAMEfs2FM0KMJVjP860GEdQ3BWeOiLtPMfZGFaPVyls+v5YFrMplwdwT3PhvbolGsKEoU5blYvdjMoHP9OUKaY9ncWr59v4TXvk4mKta/7tkQg3MQ9esL+PCFQubt7IpSKSBXCKjULWsfDl6Ho8qffjZvTiG5swoY/H4/zAdcLeC30GiCm79PZ5qTbY8klwvJ5sAxYz7WD74lYutcAKrG34k8IYbAd54+4X37istwzl6MWFOH/rZruDD0B+ZevZjS1RVIor9LDGwfwITdR5+11BK+T51OwvmxDP24P7ZSO0vvXUPVtmpq95u5dOH59e9aU7RWe35KAwib40QyPp1NVP+0r9EyAVhzrp5+C20AXPluNtMfTuH2kWE8+0sxwTfU8fOkZC7uFXjM/R9s4FsqCm6s3tbihq41Chu1lIOducZyHaVPvUVol4sJ7z6egOSBIInUZq0gvHvTL1JrCwEAuVrOwDd7IygEdny+p9l1j6dTbo1O5nS5ujxeEVEES6mXxFk+PnPU8dOnpUTEqDiYr2fU2GAefjWOoNBDr/7tj0ZTmOvim3dLUH5cyprinnwX1IWl961G9EpcMn8UEX3CWPnoeu67QKLXIBNQBNUH3HwpAH5fZ/suOqb8m05krKrF5uy3nypg5pRKlud2r0//banzYgxo3Dz9/n0Fbz2RR0YvAxqtv4MVRemMZARtitRfl7Gt0susLRmYAo+/eT34vh/MT594QRyJF8QB1AuBvVOyWDZpLVdvubTZEWVr4axxUbS0lIRRMSi0py/xk/2Xf7B++B1hC6egGXOoaqnh3huRhfvdRMcbAHsQeXQE+luuOtCu/A7oGbtoNJ8bf0QdrGbwu31YcP0yXHVu1AEn7iqIPy+GvLmFSJLEvp+zyfk7n4w7OxLZP4zYYVFNbnOm3CpnNqVXK3CyVoGmOv+mSDHI2Xiegf6LrCiLrFT/tI87JIlBQ/UMX2JjzcyCFomBgxx+3scSBsc7A+F0igLtlRfhWrqGHd9cjubSkRgy1Si0AQS1G9bk+q0tBCRRonpXLaVrytnx1V7K11ci18ipyzYTkNxQBZ+Ief5EO5nTYQU4kn821rFijxWVQiD6lq24vRL92ul5ZWoyj9+cDcC8P6tJ7qDhlocOjUj0RjkPvRzH0jm1aPUydm22kf/dX+TPreLtH1LonVaGq7iU214xHLPhDY9SERSqoDjfhcEkb1Dr42iMvDQIj1vkoQmZvDclDUmSOK/jVh5+JY7xE8P57btySgrd3PtMLBUlbsIiVRTkuDAGKJj+bTmzplXx9T8dGh3ryN/geMVB6boK/r5gPqN/G07s0CiqdtbgqnERNTCi/j4c3hmJXpG/fq7CXOvl5gejqK32Ehjc+k1swuhY+lQ50UdpKVpagiRB7NCo+nMQ5EKrBsWpA1Rs/2w3RYtLOOeDftjLHRQuKibtquRTGrCr6tuNgFcfRTDqkR2WfVI9pC8A3qw8au9/gYBXH0GZ0bFF+2yuLdny3k5Ej4jo9SF5/dYBn8t31PVbQuKFsez4Yg+rHt/A9s92k3hBLIPf69tovbMhruI/LwZOlJaKgMOJ18soGnOogxEEAZtPIl4n8Ml+F+WPbGVEuILB4Qpibmi6EltTtNRacDxWAjh11Q4PRxAEAj99Gcfvc7C8+gkuuQzTq/exf8zOVtl/cy9v6boK/r1lBVU7auqLXwH4nL4GtS1aO1ivtffXWm6ui3oG8NsjKRRUupHLBC7uFUB8mBrKYXPnQN7YXwrAZ68VExyqxOkUyd3nxGr2YbX4CzbZLD4mnLcbQYBr7wxn4AgTl/XbSWmhm9lbMwiPbnqUJEkSkgQymcCdY/eRs89J+y46Pv29HTVVHrQ6ef1IvtH19zVSnO+irsZXv5/nP0qkU3e/SzImUc0/v/pzi9zxeAx3PH5oamtFmYfR40NQKAQKsp3EJKqPWlysJaK6LtvM/mk59Hq8KxG9Q+nzfHeMB2rL7/xyL3nzCrlu9zisxXZmjV1Ivxd7kDAqln2/ZiP+spVXvkjC65GYdF0m/YaauP6e45/1cKz3XBOsput9nfA6vcy9agl9nu1G7NAoNr+3g42vb2NiwZXIVc1HpYs+EVeNG22oBkmUyJtXSPTAiEYBc5IoUbGliqGfDEB2QGyVra1g4U0rMCYYGsV3eGweFDrFSYkE0WLDvXoTmpGDUXY4evE6eUwkqt5dEXTNZ9FsiaVaEiXWPr+Zjjemsm9qDhVbqpApBPb/kk3X+048EVD8yBhMyUY2vbWdzrd3YMDrDbNpng0i4CCtHjNwkNORqONEGtETEQHN8ex2J59luRssMyrgra4aLo1VEjah5aLgIMcSBWdjcCGA5HL7cxWoTi5xh2vFeszPvINUWoro9ie2kWvk6CK1mBINSCLYS+3UZVoI7RZMxr3pLL5jJaLLv64pycB1u8YhV8vP6jnepzrW5SBfL6zg9s/zGi1XKCExTYPRpKC2xktdtZewSCWhEUr6DjVxze0RbFxp4dNXi9i23sb0VR1JSmv6/X/mzmxSO2q54b4o9u+ys3h2DSX5bp77MInXHsnDUuvj1a+aTrqyc5ONmy/aw0Mvx3H5Tc2nrj4SSZLweiU8LolhaVt45FW/NWH5/FqqK7xccm3oUbc9/D3aPy0bmUpOXZaZnV/v46oNYxoloJFECWuRDWOcAdEnsuiWFfR5uhsBKSbif1jKmsVmnnwnAZvFx5tP5PPAC7EEhZzYu9DSd9yca0Efo0eulFG2vgJzrpXU8YmYsy2sfWEzwz4fiFLXeMz314XzkatlXPjHuRSvLOP3wbO4Yt0YInqFsuOrvcgUAukT27HgxmVkz8zntuqG0e6SKCHIBLJn5rHru/1cNMNvxv+x4+/EnBPJ8C8GNtvmOP6Yi33KDEJ+/xzJ68X6xc+o+3ZD2b0Tns27qJ7wAGFLf0UefvTf73C8+cXIYyIQ5H4RdLz9j7PGxVchUxg9bRhrX9hM3IhonNUuChYWM2HveFTGE2/TqvfU4rF6ieh16FpOpwg4YwGEh3Mq5ukezvE2pq0tBABKHSI3rXewvrqxOSlBJ/BgezVpBhnBKgHNBQmYdHIsDhGfKBFiVFBl8RJqVFBh9qJXC9TaRRLDlORWeCgeJsftFDEGKjDXeBFkYAxQYKn1olTL+FVIwhBvwJJrwVXrRhIhcXQsHruX7L/yCO0STEinIOqyzZSuLifxonh+XzoElHIEuQLHrH8x3H09gvLsMBC5lqzB8ec8nP/403Pqo7QotArqshvOY0+4IJag9gEEpwfR/tpk/rpgPpY8K+46D85qf9lbtVZGTLyKi64M4Yb7mvbNnQlOlwA4nOW7LFz+dhZXDgwmJUJFUqSa3akST9ySjd4ox+sRcTklOvfU88hrcXTucWg6358/VvD6o3l4PXDFzaE89kZik8f45asyinJdPPRKw/oE3ZbA3xtq2Znv4Pc1NfRK0ZORqOXOUeFYHD4empzP81fGsLvQSZ80PUbt0Ue0zYlkr1di82oLiWkawiJVvPtMAbn7nXwwNfWYo9TJwRksuHEZkiQx8vshza57JKdSbJ5MzEP5xkrmXbeU63ZdhiXPyrJJa7AW2hnx5SDCuodQtKwUuVpGZN9wJFHCUmBDH6lFrpaz+I6VyJQyhnzUH5/LR+naCmLOadrCsfObfdTsrmXgW70RBIGsGXmEdA4iMNVE2foKZn0fiPGhW/3ppye9iHb8BWhGDMS1dC3uTTswTroZ1/qt1Nz4MIJaRcgfX6BIjMU5bynqkee0yMIg2h2UZ5zPiK8Gkn5TuxOKIyj4t5gZ587lyg1j+OeShYT3CkWmkJH1ey69nsig/yutVx/jdFsD/l+IgbNdCBxOkV3krb0upuZ5EJv4/up4BRtrRIaEKVhR6cWhUXDN4BA+nFVGcriabfkObhkRyteLKvnxviQmfJjDTcNDWLLTyjlXBbHwrxrkcoHBIwNYPq+WjD4G/p5axUufJfHMnTnIlAKCXMZd9huo2FrFL91nknJZAhf8NoJlD6xh64e76PdyD9a9sAXl0AH4isvx5Reh6tcDzQXDkAUHIIuJQEBAHhWOoFEj1poRa+pQJMWd0nsnSRKW1z7F/s2vDZZrQtS4zG4Uajlh3UIoXuFPTjX0k/50ubMjbouHqd3+xJxj5dKF5xM7LApbiQP3y4spLXazYYWFumov7/6YypDRgaf0Go7kTHT6zSFJEle9m83iHRaqLF4MGhlWp4hKLXDXkzGcOyaIqLjG0eler8Ty+bUs+LOaynIvH/6S1sjcf/Bat+Xa+WxeOcU1HmY85p/+1v+J3UwcFsq4/kGc8/ReHr00gjmb6/j1oVRW77Uy5Jk9TL4nkWvOaTgC3FXgYPEOM1cMCCYs4OijsuYEgiRJ3DZmL3c8HkPPgc1PQfzW1BnJJx0z6cvhnA6r08kIAkeFA1WAih9Sf/NndtQpuGD6cMJ7tmy0fTJIksSim1cgU8qoevwrwJ81VHvpqHqf/0HcW3dh+246+omXo+qaftzHuizmG4qXlxGcHog+SseqJzdgiNGRcXfDfVVsqcLr8BHVv7H1acXD69g7JYtxKy7kx7TfEGQCMqUMn8uHLlLLzcXHruHRUs5WMXBKh4Rny8yCUy0EAGJ0Mt7vruXyWCWv7XGxtqqhpeCh9mrcIhgUAjclKREBk83C606RfSVOUqPUPH5ZJHeNDic2WMXmt9MxaGU8OEYiUC/noqv80bMGg5yxE8JQqgSuuT2C8CglvyxLRxQlpkuJAAS1C+DCmecS3MEffdz9wc5ED4mkeFkZSWPi6PdiGD53CHPXT8Q561+Qy7C+/y2CQY97+ToCP3uZ2rufRdGlPfLIMAI/egEEAclmR2Y00JpIHi+1k17ENWcJAFEDwul0a3tSL0+idHU5M0bOJXxAKGNmj0SukeOxeeuT4+yfllNfalSQCRQsLGZYVRYd743EYfdx/9WZ1FV7+fGT0uMSA2dbR95azN1ch9UpMiLDSEaCjjq7jwcuisAz4eg+V4VCYJIuiPuvDmTqimruGLiLxS92YPrqGs7vZiI+TE1uuYv9JU5W7bGyIctOVJCSrFIXQXo5ZoePd/8uxeUR2fF+J1busfLn2jwW7zAzrLOJ6CAld3yRz6q9Nj6+NYENmTaenFJIUoSa7xdXceXAYCRJYn2mjT5pjZ+9owXi1tV4CQhSMOi8ALautzYrBqwWHwuG/soz7yWybkT/Ft3L0+V+Opn05QcrUg79tD8hnYIwHcjJsOa5TQSmmehw3anLVyAIAud+ezAvyWQApr/3bNMruz3IA4woUhIBsP34B+pBvZsdhDTsV+TEjTgUEOtz+fAdcBmuemIDoV2DSb08kd8HzyJlfGIjMZD5Ry5bP9pFlzs61M+WkESJwe/2od3VybgtDVPk/3/l7LAPN0FrWQVOhxA4nIFhCr40yOg6z0qEWqDM5Te8GBUCwU3MNZ4+UEeSXkacTkahQ6RdlBqtWk6IqeFPM/awZI1HjobkSoGPXiziypsE0qormBycQfLFh8y1xngDxngDccOiyP4rH5lCRnDHICZ03Q+3xDE9Zzjq/j2wfTsNVf8eyBNiUXbtiGpwbzyTp5D+71OYkozMvmwRV2+5lICUxuryRNSur6ySigGX1X82JRtRBahIHZ+IUqdAoZWDBBn3dkT0SigEAZVBiSRK7PslG+GwQdyfw+cAMPOIYwwYYWLVInN953Ak/187/iMRBIEXrozm+yVVLNpmYXuegx/uS6JTnBaWHHv7eVvM3PRJDiM6m8ivcHPPV3n0SNbx0/3JTPouH7cX5jydypPjoupzbpTVuumeqGPJTgvrs2zIZAJ90/QsfakDvVP9FsZNb6fz6I+FjOkdCECgXk58mJrzMowYNXKMWjmfz6/gnq/yyfsig9gQFT6f1OS0xa6LJcpqvbxZWMlnrxcjyODZ9xM5f1zz2QpVKoGufQzYbb4GwXuV26rJn1dESEYQKpMKU6IBfZTujMShtEQUOGtc/HXBfC5feREIsOLBdeiitPR89NA2kihRvLyU0Iyjp1E/VTQ1MJyecyOIEu6NOxC0akSLFcvz7yM9chuGO65r0T6OZPA7hywPNfvqCEj1lxq+atMljaZG7vs1m/nXLSVlXCID3+6D57COf98v2XS4IQ1jExazk+F0BHafCKfUTQAn7ipoDTFwuoXA4XSYbcbmhYc7qCh1wqtd1IemIEkSZg8EqgTq3BIKGcgF6DDbglcmkPVpF6KCmp/berggKCtycVm/ndz5RDRX3hqO1yuhVsv4PiQDr9MfJa7UKnHbPHwZ/BNDP+pP59s6NNjfx8rvUOoVuM0eQrsF0+nmdoR0CaJ4ZTkZd3Xkt8H/kDI2kfSb01h08wpURhUX/tGynPZHe/CjPrmLze/uAGDIx/0pXVNOaEYwaVckYYz3B2l9FfYz7lp/gGZgOxP6aB2mJCO7v9vPsx8kMnhkAE6HiM8rIVcIPHDNfrL2OBscp3s/A1//47/e/5XOvzmKqtzc9Ekuy3ZZmPF4KqO6BRxzmxnrali918odI8OoMHvZlG3ntd+LUShkJIWr+O2RVP7eUMeTPxWy+8POGLRy5OM3EBOs5J8n08hIbDgf/vN55dicIg+OiTimf/fmT3IwaORc1i+ICR9kU1Tt4aYRoYzrF4jbIxETouLtmaX8taEWp7thc/bo6/FceUvLgxJfeiCXVYvq0Bnk5O53olAKeD3+fbbvouXnxSceWd6aNCUKStdVMPfKxVy9+RLUgWr+vngBvZ/uSmTf4wvKPN2IPhFBEBBkApve3s7Kx9Zzj2civ+VNBE7e3XwwQ6ZwxCwTR4WDH9J+I2F0LCN/GoJMLqN2fx0/tv+9fp0hH/cn466WTVs8Xk6XIDgrYgYOcrw/ZmtlHDyTYuD6NTbmlfpYPFzP2kov/5b7uL+diiXlPmaXeNhRJ1J+qYmoGWZ0Clg5wsCaai8/5XoYMjSc5648dmVA8IsCt0vk9UfzGXdDGJ++VsSaxWY+mpZGv6EmeodvRKEUuNM1kX2/ZDPvmiXEDo9i7MLR9fvY8cUe9v6cRWhGMB0mpjFj+Bx6P9WN/dNzEOQC2nANXquXwsUl9Hi0C5ve3I5cJ+cu6w0ndY+8Di+LbltJxcZKrlw/BkmCrR/sJGZoJJF9w/FYPax9cQtb3z/6NMUZ6zs3KOzi9UrYrT6MAXLWLjWTvcfJA+FBRAefeOKQ/4+4PSJj38xkxW4rVd93R3GMBEFv/lnCou1mooOVON0SP09K5q0ZpeRXurn3ggiCDXKe+rmI75dUEh+qYt/HXbjq3SzyKtyserUDZodIj4d38dcTqXSI1tDr0d10TdQy+d6ko4oBSZIY/NQejFo5Do/IvmInSeFqMktdVJr9IlcQ4KqBwUxdUc2A9nraRWtYutNCTrmbWU+lcX73gBYl+PJ6Jb59r4Q5v1VRkONi7HWhDDg3gD6DTaxYUMeyebUs+ruG1UU9GpyvzyexfrmZ2EQNsYlnLiOgyykyNbpbi4Pn5l6zmLCuIfR87Mxnblx083JUASoGv9sXS74VW6mDyD5hJ71ft9mNyqRi+aS15PyTz3W7x9VPOa7NNPP3hfNx1ri5Ztul6CP9YtXr9vGZ5vv6fRyZSvhUcKpFwVkRM3CQ1iwC8l8QAgD9QhQsKfextNzLt9luUo0ysq0iMVqBi6OV3N/O/1COj1OwsMxHuVPk0hgVJQ6J56aXMLKqjo43tOP5X4txuUV2FDj4+q4kbvk0h66JOnwijMgw8sXzFcjkAsWCF0GAq28Np11nLakdtQgC3PtsDAqFwHXV2yhJdzEPCMkIanCuzhoXMpWMQW/3Qa6Wc3vtBADizo3m39tXUrO7lsvXjOHfW5bT87EuOKtdtL82BWuxnazfcuh8Z8ej1pU4yIKbllG0uIRukzpjiNaRO7uAHo9mgCSRcU86Sr0S0Suyb2o2a57bxJFRmH2Hmli7xNxg2ejxwUTGNuzkFQoBU6CCbkugOwHQ4dij3jPBwecz+Lp2Z+T4KqWM87sFsGSnBXkLMuWO6x/Ep/MqaB+j4Z7RoQiCwKNjD83SuPrdLKatqgEgu8xN6t3bWf5yB8IDlJz7/F4Sw9X0TNHR9cFdfH1nAlVWL89fGUNprd8s25QlTBAEzutq4sXpJQCkRKqZ8VgqYQFKku7YRn6lG4NaRrdELVNXwKq9Nlbt9WcJfXxsJOd39//2x8rj4fNJPHd3Dgtm1jB4ZAD3PRfL0NGBlJd4uHzgDsqK/efYIaPpfPiPTszm5kmR9dMqrXU+uvU7doKm1uK378r59LViZm/lqLkcoKE1QR+pI6yn332y5f2d1OytY9hnA07peXodXhRaBaJX5N/bVjL43T6oA9UEdwoi4EAtkYMuzZM9zrzrluKudTN20Wi63NWByAHh9UKgeEUps8YuQhOi5vJVF9ULAYCy1eUAaMM1XL76IkyJp772xelMEtccp8UyAK1Xza0lYuBMCwGAOzfY2VTjY+15x/cw1blFPs9yc22CkmGLbaCUU2v3ccOwEJ4ZH8VLv5WQEKpCJhPo397AtJXVuDwiS3daeHdiPMmPBzW7/xfuyyFrt4PhG6+qX+axeZjS+U9Cuwcz4JVeDWp7fxMzFblaRv+Xe7HolhVMzLsCTaiGyi3VeGxe/jx3DldvvrTJeuAHHy3RI7Ljq71sfmcHQe0CMCUZ/Ula3D4MsXqu3XkZxcvLWPnIOqp21+KzN531S6UW6N7PQN8hJs67NLjJHPanww1wNjxfh3OiguLJKYX8urKarE9bNjq0OX1UWb28NL2EvAoXVw4M5rK+QQQZFFzw8j72FTu5cVgI7/xVhtkhkmIQUAgC96WpCFQJmBRw8QoHv/bX0jtYgVEpMHyxlTybyKaRRgJU/s7TeFVqfdzBzgI7GZN2AbDy1Q70a+fvKPYUOeh0/yGLUWSgkvYxanLL3eRV+N1KCWEqpj+UQs/Uhm3WkaLAYfcxKH4zYyeE8vR7iQAsmV3DQ9dnAXD/87Gkd9MREa0iLtlvhfr6nWIqyzw8/mYCZcVuwqOUCILAm0/ks3aJmekrOx01+VFrkpfppCjfRV21l3NGBaI3tnw2xEEe+UNNXbaFAa/2wm12s/6VrQx4rVcjs/rROFqbfbj4qN5dy++DZ3HeD+cQkGrir/PnMfKnIccsSnUieOxeLLkWavebSb4kocF3+6ZmsWDicqL6h3PB7yMa1XdYPmktWz7YiTFej9vioeMNadhK7Ch1CkZ807BQ2+nk/3XVwqPxX48VOEj7WWZkgsDuC5oXA30XWLg9RcVNyY07tue2Oyh0SPQOlvPk812Ouo8qi5eom7cw7eEULu0T1KxZ9K+pFSydW0tyey11VT4m3B3Bp68VsaVAjugWMedZubXimnoVvejWFeydksWd1uuxlznQR+koWV3OsvtWEz86lvbXpBDUPqB+FLT350xsJQ46XJfK5nd3kPVnLhG9wxg1xX9SG9/YRsWWKgoWFhOQZkIQoHafGWeVC5lSIHpwJB+9FsIjE7OoKPVgqfWS0lHLxPujGDI6sMmRz6kQAGfDM3Q8HBQEkiQxZ3MdL08vIbfCRWqkBo1KQC4TSApXE6CT4/SIKOUCv62uoWOshllPNRYTBZVuVu+zUlDhJj5MRVK4mg9mlTFtVTUmrZxOcVqW77YyuKOBJS914Oe3djJxnYMIjcDP/XR8kulmWoGHNIPApz21dAtSsLzCS5bFR+9gGR5JRkeTjJFLbeRYRSb31TE8QsFXWW5+ynPz7zA9NW6JrgtsuL0SWZ92ITG84TuSXeZiyrIqeqXoGdXNVN/5rtlr5eXfipm3xYwoQddELR/dEs/ADkY8XhGZICCXC/Xvicct8tD1WWxYaeGPNZ2JjFFRUeLm/C7+tmjhnq4EhTac3vjHDxXUVnm5aVLDHBaiKFFZ5iE8SsXul6v4Y00t0x/2Z9G76NX9XDM4mGsGh7TIdeHxiGxYYaFrbwM6Q+NOXhQlxvXbQY8BRp55P/HYO2wBu7fauG3MXn5cmE5iWsuqsbYEh93H1C/KGTshlKBQ5WmpG3GwQmZknzA8Ng+b393J2uc20eH6VIZ/ObBBlka31cP+X7LZ9PZ2wnuGMvjdvmx8fRt7fsysz11yW/W1qAPPfHGo4xUGp8VN0NrTBv+/CIFSh0itB+5JO3bWqhq3xPa6pjITwAtdtGyt9Y+Sq3/ad9QRYIhRQe7nGSTcvo0bhobwNUk8/XMRr/9ZwtSl6aSl63h0Yha11R5AYNMqC8EhSspLPHh9EjWVXnony8jZ56Syzs2eH/YTf34cNbtriBkaSdKYeASZgD7Kb04LzQii74s9yJtTxKyxi7hy3Zj6DF37fskhb3YBSRfFE9o1GHupg/iRh+IfrMV2avbW0em29pSsKKN4eRmh3YO56K/zeDS1pD5KfOL9kSydU0vHbnquvj0cpbLpue2txdnw3JwMB5+Pt2aU8sSUInom67jtvDCyy1y4PBIen8TKPVZsTh9alQyPTyI6SMlntyUwZVkVHWM19EjW4/KIfDynjEd/8E9f0all2A9M09KqZDxySSRj+wZy3zcFXNInkJnrapn1/i7Oj1KyYoSM69c6uH+zg98H6ulgkvHJfjfnLbXTJ0hgu1kiQAkeEexeyL7IyFPpajyiRL8Qf8PcKUDGTUkqfBLsMftweyVuPTe0kRAASI5Q88zljau+9Wtv4J+n2pFT5uSS1/ezNdfB2DcymXxvEuV1Xp7/tZhdH3Si2xI536ktZPQxcNujUawcWUfWbgeRMSrColTM3pbB2D7bGT9wJyMvDeLhV+Prn8/Lrj/k03Y6RMqK3FxSqMGfF1sFeyFPJSNQf6jDSY/V1MdmHO353TIUJn9YQkoHLYPOC+C9ZwoYMjqQu5+KZc2SOp6/J5efFqUTGqEkL8vJMx8cStvcGnTsqmfujq7ojXLm/u6Pn7j1Yf89Xj6/luh4NSkdtPVWv6ZcITaLj7xMJ+kHzuvNx/Pp1EPfQDi1xJpwomx8axsbXt2Gz+3D5/ARd1405Rsqcdd56PVkV/q91KPReS+5cxV7f84CCYI6BKIyKUm+LIE9P2b6r1Mu4Kx2nRVi4Mh+t7XcC8clBsYmTmmUu7q1OJvTxh4vDtHv8v6z0EOqQcbVCSr+KHDzSaabPwfqMKlk3LDGRoRWxryhBl7f5eShzQ5ezdDwd7GHb7LdDAtX8GhHDY9udYAkMW+osVk/s1ErZ2zfoHo/6dBOBnYWBDBkv5LCDhJF+S6MJjkqtUDHDC2PvBZfX1b18z/9KZOL8l08dmMmi25ZyZNvx/Pdw/kAdLqlHTl/5VO8oowRXw1CkAsYEvTs+HIPiBLrX91Cyapyxi+9kIAUI8FdgghqH0BQ+wC2f7obS8Ehv702VE31zhoqt1Sji/HPg67cXM1tQQXI5YfE0/njQpqcEtZaIuC/3vkfjX7t/Wb0GpuX20eGYXeJBOoVhBiP/qr//WMhPyzxMe/Zdvy73cKjPxQRE6xk3RvpRAQqqLb6yCp1EheqIipIxcDbN7OmykeqQaB/iJzxq+w8na7m9hQVH3bXcPkqO8MWWxkfq+TbPlqyrCKv7nLh8IFWBtUe0Moh6i8L7Y0yVow45CMeEKpgQKhfJD+7w4UATFI6G53zlhw7i3eY2V/iwuuTUCkExvcPYkgnY31DnxShYeu7nVm608Krf5Rww4c5zH+2HQ9eHIFOLcfpFrn9yr289X0KfYeYSO+m47l7svnmn44kpGqIiFbx/fyOvPVEAdO+qeCvqVU88FwMl98cgd3qw+ORGLJVwX3fFLJkh5mL3/HPNPD4JNRKGZf0CeKSPofcdm9e7583/9aMEm4cFtogmdKHs8qYvqqafu0M7Ml3EJsj0F0VyNvfp6I+YA2LjFEzdkIoxgA5Pp8/oVJsoppvZjWcGXSyHHQ1lBS6Kch21S9/5s4crr8nkpQOWtYts/DWE/l8MaM9IeFKKkrcaPQyjCYFM6dU8tFLhawu6gmATA511d4WHft4C4NV7axBH6Ujd1YBe37MpO/z3Vn1xAYQIf3mNEI6BZM3r5D0m9qRcXfHJmMARK/I3ilZ9H+1J/t/zSHnr3y+i/8V0SsR2jWYno92Iax7SP1g6GzjWOJgbOIUvmjBfs6KPAP/H2YPHE6SXkaKQSDXKpFt84+qsq0ieywiB2c+ba4ViXRK1Lkl1lX7UMnAJ8GT25zUeCBI5bcI5FhF5AJ4RIlpBW76BMvpC+SUudiYbeOCHgG4vSJdH9zFfRdEMH5AMFaHj3O7BnBuV38nHL4Mdj6TzpahkJfp4Mnbcpg5pbJRHviAIAUqtYzH3ohj1LgQ+pxjQpAJRMQoydrt5Lpv61hw/VLcFg/hvcO4bMkFyJQCvw2YRfyoaHJmFVC5tRpX1aFaDVU7ajAlG9n7cxY5f+ez/9ec+u/c5Y76/69cVMeYq5vOjHa6KlP+16n+aR/JoxMBOL97AFFBKh79oYC9xU5mPp521O1+eTAFm9PL3M11rNxjQasSaBetITLI31mFGBWEGA1U/7SPavzTYMH/nE/uq+PlnS6e3eFiRpGHC6KU/NRPx3t7Xby7z827+9x81VvL1vMNzC7xcut6B32CBRJ0crKtPmJ0Ah1nm/mop5bh4Qp+zfdQ7hR5ebeb8TEKtteJpM+xckHZfi7sGYBRK+enZVXM32JGoxLoGKNFKReoMHv4bF4F7aM1XDkwiBEZJlQKgdJaLwo5OFwi1VYf7/1dxg/3J7M5286yXWYeeT0eu1VEpZZx6YRQ3ng0n9su2cPkuR2JilOTlq5j/MQwNq604LSLvPlEARklCl75vYRau485T7fj5hGh3DEyDEGA4Bu28OzlUUy6uOn0vZIk8fhPRYQFKLlx2KHn3aiVUVTtYfVeK73T9Nx2nt/yMKZAU+9SSEhVk9JRS3GBi6Q0LWOuCSU/y4nXI6FSt358wsT7G7pA/trYBWOAXygEBivoP8xEcJgCp0PkhlG7ueTaUG5/LIZRlwXTY8ChTvfhI1JUHy9X5G9u5CaZHJyBs9rFL91nMPDNPgR3DiSoYwB/X7yAwDQTfZ/rQbur/LUwuj3Q/HRQmUKGNlzD6qc2ggS9n+2Gp85DbaaZkT8NOakSxmeCI8WB29z0ekdyxsVAawmBsw2PCLckK3kq3e93e7ijhrvTVCTPsvJKFzXbzj/0smwedej/3YPkdA6Q80wn/3Zf9PKr0SqXxAObXXQyyVhi3Me7DjWfzqtg3RsdSY1UU1jl4Y811Vw5MIjnfi3mnRviCDQ0/Hm7LQGGahl3Qxh//1JJTaWX2x6NZtm8Gt5+soBxN4ZTU+Ulc7cDg1GOwShn40oz91+9ny9ntuPX5Z2wWXyUFbvZ0qMLYV1DEH0ig97uw65v97HykXVct2tcg2PeXjuBwsUlLBo3H3Ntw8BAr8c/d3vUZcFcfFVDK0CbADgxYoKVJIWr8B3wPD1wUQSTF1c2u40oStz4cR5/rKlBq5JxzeAQ3rnxUPa3w+/l30UeVlb6f8e8A0L3xS4aRkcpeGW3i3f3utDIBSYkKnmgvZob19q5db1f9H3T228J6hmo4KscD4uG6vg624OIyDfZbq5efUgcphkEfis6NJqcvamOeVvq8InQLlrNrw8lM6ZXIKoD7iNJkli608LXCyv5cHZ5/QyEg6REqrlndDijewSweIeZhyYXkFfhpnay/1ocNpEh5wdRUeLhn18rue7c3Tz+ZjznXRLMfbpAnqi/V3DN+zn0TNZh1PiDKg/PjPjM+CgmDg+lyuLlxWnFPHJpJLEhhzoTQRCo+aE7Jp2c2Ztq+W11Db1T9Dw/rZiSr7syf6uZZ6cWoT9gtSut8TDv/kpGdQtAmKjj5Ul5TLw/kqQ0Lfc+E9vs79ramAIPtSftu+ho38XfyavU8OyHibTr5G+rQsKVhISfXLEy8BewOjhV+s4nouvLbufsd3BN/BZUahndfkml5wAPckUl3y2pY1uNm8dfjGbMSCscR5XXwHYmXLVu5Go5ZWsquGTuqJM+//8aZ1QMtKZr4Gxq/J1ekQK7xOLyhqYxCfBKYPMePWbz1wEN/X/DIvw/kVuUuCZewbBw/+dJF0cwoIOBDjEa9Bo50x9KpnOChvf+KWPy4ip25DtY+0Y6f66t4foPs/nyzkSuHhTCKxdlsnCrGUEjkNze3zjbLCK11V6MATIuuS6Ua+84FOGbs99J7n4nllofKR20PH9PDhtXWfh7UzBU+/3K393ThfJvt1FU6sTzxFyqKzwMvTAQnUHO7F+rSExQkdFbz4oFfonarrOWdp213PZINDEJh4KU2gTAyVNj9VFQ5aFjjIYd+Q46xmh4clxjv/rhvPp7CX+ureHtG2K5/8KI+kC8pu5nksHfSV2boGRKnodsq0gHk5z+oQr+Gayg3Cny5h4XH+xzs7bKR+cAOSsOiIeb1zsQgNmlXgKVsLFGJN0k48c8iYVl/nUebKfiuxwP+60N35HfHknh0j6BWBwiBo2sUaS+IAgM7WxiaGcTPp/Ellw7guCfZWBxiKRGquv9/S9PL6brRSbOj1MhCAKjnRq6R+jYH6HgjsdjCNwv8dbMUh6/OZu7NQENjjWmdyD3XxjO0M5NB2I9OMZvETDbfczdXMeFPQMaiAEAk06OJEnsL3bh80l0iNHQN1VPxqSdZJY6+fS2RORyAUmSUMjho9nlDO9iovcSyP8oA6NWxtZmf9HTi0wm0G9o603hFUWJD54vZMpnZfWWiJ8/LyMyVsWWNRZmTavmoitDePKdBPoNDWDat+V89VYx1RVerrk9nBEXHXLNHN7HNFu6er+F7g92JjDNxKJbVmAttGGIbb1YjP8CZ0QMtFQE/FetAhqFjGsSFEzJ81Lk8BFzoBKbRi6waoSe8GZMem/udpKol3FFfMMGRCUT+KDHIZ9V4MJ8rj4sduCy/v70ohkJ/nW6Jvo7+ogAJSkRGiIO+Cc7xmipNHsZnG5gfp6NnZusjB4fwujx/pH5w9dn8sqDeTz/cRIAg84L5LkPZUQcmM/vdIoU57vJz3IQn6Jl2dxafntsCjXVXuKT1Xz+RjEAm9ZYydzlQC4HSfKPqABuuDeS+55rOKJpEwGtx5M/F6KQw4W9Ajnn6T04PSKVk7sfdX1Jknj37zLuGBlWb9pu7n6GHpj+NyXPP/c+XNPwWQ7XyHi7m5aBoQpu23BopH9+pAKjQuDqBCWXrbRzeayC6xKUB4Jn/X7pSe1UPNxBjcMHn2W5eSpdzQPt/AFbwX39DbxJd+ypc3K5QM+UphvyLUNhRO8Ipo7ZS2WpB0EG++L17C1yMs/RjogAJc8XOWkXrWbaQyno1P6OOyFMRV6Fm+uGhBxVCByOSSdn94edkckEPF6RvzbUMbZPYL2wmLO5jgcnF7Dm9Y70TtUz6bsCdhc5mXxvIn3TDLS/Zzvdk3W8fl0sBV9m1MdBHLz+Y+VO+C9SU+XhvWcKydztYN8OO/c9F8uaxXWsXWqhrsbHc3fnkpCqQaEQ+OOHSvoNC2DguQG88ag/tum7OR3I6H30HAXNpXRWB6txlDmIGhAOEtjLHG1i4FRyPJaAs7n+QEt4pL2GX/OtvLzTxUc9NChkMmSCQIha4OIVdq5NUHJ7SuPI1J/zPaQZGosBgPErbVwSo2RC4tF9WBf0CGD202kM7eR3PQzoYGDLu4d8Zi9e7Y/s/211Ne/+VcaOTTY6HShZK0kSe3fYSU0/VLhm00oLKR20aHVynrsnB1OAnF6DjFw7fDcvf57M528UojMIPPthKn2HmHj4hkwWz6olMlZF3n4HnsNqfAQGy7nnGf8otU0AnBo2b6ghMUyNWiEw/9l2dIhpfnqY2e6jzu5j28ZqqrWuZtcFCNMI9AuRs9cscmmsgiClv5PyihK7zCJGpYDLJxGoaigSnu2kJu1AYNpLndU8s8NF/1AP1yUcMidfGK1EKRO4JEbBZ1lu7M1Y0I6XwztNrV7OBz+nYbd7ueXCvWzJtVNr83H9Bzl4RYllu6x8dnsCXQ4Ia0EQmPFYKle/l81DkwsY188vTCrNHirMXlIj1fV5EQ7nYMf/14Y6rno3i30fdSEpQo3d5eOCHoFsf68TieH+d/mbuxORCdA92d8BPXJpJM/9UkRehbvJmRQHOVqhpv8ibz1RwIIZ1Zw7JoibJkVy7phgZvzkd3Gde0kQ9z8XS3S8mg9fLOT7D0sxmPzPU3p3Hbs229m02tKsGDjIkaKg4N9ianbX0v3Bzqx8bANRA8MJ6958PYuWHqM5Tsf0yuPhlImBk3EB/FctAocTo5PxUQ8Nd250sqXWxupz/Z2zTwKvCJ/sd3Fjogr1EWlg70hREadrOovYykofEWqhXgw0NbvA45OoNHvxic03pOP7BzP+gDVhy4FlgiDw+5rOVJa6efupfC68IoRn7vIH/K0s6M6WNRZUahkfTWvHP79UkrPfQWiEijHXhFBV7sbrFbn8pjBsVh8f/JzGtcN3smfbodHhjAdS6bGsBenujkKbADg2t6equHW9g1ff3MP5kQoCg+VoDjxjB5+Tg/ex1CHyym4XGhk81qFlU6bkgsBfg3SYvRLOw0JApuZ7eHBL46h/gIVD9fVCAOCOVDXb60Te2uPiugQlkRqBUqfEvZscLBtuoNTpf3ZrD9QEOJksjUfrIEPCFcgqYcqidPQzXXSI8WfsvPjV/YA/XXNFnac+4j8jUUffdnp25NmxOX1+19yqGh75oZCcz7oQFiBjc7adjARtoyJK4/oFseuDzsSGKHntj2I+nVvBG9fFUmn2sD3fwVd3JfHklCJSItV8eptfDAzvYmJbnp12UWq8PumY6aLh2O3m2S4WLroqhPl/VvPvrFr6DjUx46cKdHoZtz0SRZdeBiJi/O3eljUWAB65IZOXPkvmxvsieeXBPD55uYju/Yx07XNsQeDxiNxQtZXfJ1cw6/liIvuF4apxYc62cOEfl9YnXDqVs9ya2/eZEArHlXTo9trrmpxa2Jo37ESEwNncSYxfaWNphY+15xpIPuBvFSWJEodEkErgnk0OJiapGBx2SJdtqvHx+FYHSXoZX/TW8WWWiz8KPRgVAr2C5VS6JL+PttjDqEgFi1Varh4czNTl1WzLs7O/xMXcp9N4958y5ILAl3cmsGyXla8WVNA1Ucu7E+O5/9s8Vu+x8eb1sQzpZEQUYcMgCaVSwGkXGZy4mYuuDGHDajNuh8i05Z1Zt8xM5556VGoZdpuPd54uYOUCM+deEsiiv2oJjVDy9+bOeA9YAzotFgm6we/dvLhXADOaiWhvjrP59z0bSZ1lps4DSgES9TKe76xmZKSSCpdInk1ELghYvRJ3b3RQ7Za4L03Fox01/B97Zx0eV7W18d8+Z3wmnrSpu7tTNyju7s6HXtwvDhd356IXd9dCodSgQt3d06bx8Zlz9vfHnqRJGplYm7Z5n6cPJBk9Z8u713rXu/xRyVqvSVbQJDskKYpKQoby//9xe5TCiKSVU5BsFUzeEaUoJom5qIOVwak6V85TZMCmQdiEti7B60NcDEjZM7Q/c1eU46f7+VcXG8+tVtUnN3dTn0NKyStrw9y9JMST/R2MOqMDAzqoU/o/6/3YLYLebZ27TYZWeYkaksxkK6u2BRnTM4E1R+qsXuYHCV16uSgqjHL8oMVc8K8WnHdNJg/fvJFfv8nj81m9Gbdo99wzTckRD67it0VFJLl0bj4+ky4t7YQikrs/2sqWnDCT+iXxze2dCYRNVm8P0S/WhKntZQu5aGI691bSV8QwJNe/vYnv56pW0ikenSSXzt+P9mThBj+/LizkpZ928Nwl7RjQwUWva5fQo7WTnKIoNx2fyaWH1d2vP14UE4fy63FDE4qVi/1ceORyQjFC2LGbg3Ur1bh6+PWOTDohlfWrAzx+++YSe/LPZvaiTUcHlxyzgmhE8t5vPat8jx8+zeGB6zbQpZeLpf/4OOm8dK67rw3vv7yDj17fyZRV/Rv0O9YFtSEJ4cIwrya/V7+mQ2flLcUTrbndZbw40IgAwP+GuRj1m5dbFvr5aLirJF3QyiVY5zWZsctgTo6ffw5PwKoJBv9SREe3RkQqsSGoaELEhD+yDYqikk4enYgp8UYlYVOSs66Q8PCUEiIAYNFh+rIiTAmGCaGoiT9k4osZyOQURZi3zs/Ee1ex5b99ef6Hnbx0wU7adLNz1uWZON0a61cHyNoUwZOk89X7u1i5yM+7L+1g+QJ/yfdr1sLKsj+9tE2zsTErjP39EIfctIxhXdy0TrPRMtXCttwoNx1Xc9vRxn5vGytGpVuYm2eQZgVNgyvmBjirncE7G8IESp3m27sEcyd58EUll87x83NWtMzfXboqI/QbMKm5hf7JOr/uiLDJL7m0o5XfdxqsLDL5ZmuUR/up0sCvtkb5JSvClJ0GR2RaKyQCAMPTdMY100uIwFWdFREAFaG6vJONP7MNblwQhAXL6dtOlRDOW6fG3sjuHib2SWDdjjDv/ZlT5rUffbMjh5LKey/tYPVSPx/83gtPgk5SioUVi9Tz+w5xk5Cos/QfH0MNDy67+pyaJvjl7m5szwtz67tbePjL7fiCas60TrNyxshUbjwuEyEELrteQgQA/ntle4Z0chMMm2TlR0rC+4GQyTPf7eCIAUk8eX4bbjmhBcu3BJi6pIhkjwUpJf3au+jU3M5HM3I555l17Hq7P8ue682jX25nzfYgb/++i1ZpVo4amFzLUVEzVLYWV2WWVNfXBuiPiyNf689Xs/PI9xk8+JmqCrFZBIM2WUoqol76rCtH9VvEjq1hcnZG6NDVySkXZnDPVRvwFhl4KrFlLiqM8vTdm2ne0oa30OD5j7swYqISP/76TS59BjVunUBtDt7eIiMun4EaRQamrh9Q6UWuC+qSFtgfNoyftoc59+8gfZI0fhvnLuN+VRA2WeM16Zes8+raMM+tCjEmw8J/h+5pcPHehjD9knX6JO95D1LP6cryLQHenZpD23Qb54xJ49KXNzB5UWGFAjIpJR9Pz6EgaHLemHSWbQnw04ICerdxctJjypP9vV+7M3eGl6MMFz1aO8j3GewsjLBme4iWKRbOfHo9GYkWXHaNG47LxGXXOKxvIj/OL8Bt10hLsJDnMzjhkTV8c3tnjh6UXO212h/u5/6EHUGT3j95S34+q62VBKugV6LGwnyDz7ZEKIhAS6fg0o42DknTaenUaGYXWDSlaPcb4LaoMbuqyGDGLoOz2loJm9Dx+yJObm3hlcFlx+tmv0mGXZSkKCqCISVfb42yMN/gm60R+ibr9ElSRPearnbsGuwY04bswiiPfrWdnMIoF03MoF2Gjdvf28K2vDChsEkgAv8+pQUPfradS29uwWkXNSM1w0pBXhTTlKSkqVB/9vYwKRlWLLHvsmF1kNNGL+Wj6zpy0iEV9/SQUpLrNbBokOSO7+z0xNdZvPLzTq46shnXHNUcKSUdrljMK//XjmMGJ1f53HxflF8XFXLC0JQyqYFRdy5ndI8EvpmTzwfXd6BPW9de6X2wLyCl5F9vbOKln7Lp09bJd3d22aMiA+DWdzfz6i/Z5Lw9gMUTBT9+lsO/L1/PlNX9SUqxYJqSz97OZuHfXjatDZKdFcHp0tiZFeGLv3rTvOXu1wyHTEa1/YdTL27Gzf+pmy9CY4O3yGBsh/mNo2thZdjbIrKKco97Y/M5ooWNM9safLgpwtvrw5za1oonJjhKsmkMStWYlxvlsRUhDs+08MqgikVfnRM0UmwVLwC5761iSloyhim5/HBlJvTgWa04fmgyhX5jDxW2EILBnT0kOHWcdo1BndwlCuxhXdz8vdrHH4/somdrJxd9u55QRLLoqZ40T7Zy4fMb2JITZlyvBO46tQXj71nFxS9uIPLJIF75ZSfnjEnDZdd59EtVsua0aXRtWbmQbV8TgCkziyr924QRVfeWaOyYnKVi+ae0tjJlZ5Qvt0YIGqrM1SLg8k5WhqZZGJ1uwWPdPbZKz5ViKVXue6vomqDTNXYgEELS3iUIV+CmXZnupTR0ITiptZVuCRovrQmzJRDlh+3q8/6ZbfDjWDd9YyfviX13L2IbdoZYtqWsPuGwfok8+Nl2+g/zkJqhNv+kFLW8mabk3ReymHRSagkRAGjfxcHCJ3rStUXlY1MIUaV7Y0VYvT1IxFDmQkcMSKJHayfrXupT4olQGb6Zk0+zJEuJlie3KMrT3+1gQp8Evr6tCw98upU8b5QRt6/g4XNa86+j67/JT2PAp7PyeOmnbC45NJ0XLmlboTgTwGXTKAqYfP9PAc8/sINZq3wM6uhizAKd33tHeOLOzfzyZS7d+riwWCE1w0I4JLntsbaEAiZf/C+bUNAkOyvCL1/moumCY06vm3Bwf8Y+IQONgQSU/1tDb0jPDXSyvNDglkUhblkU4oRWFu7o6aCDWw30QakWjmph4bMtUYamRrikgkqDM2b6uaijjbt7Vbx4Ld3sZ1P2bve/js3tnPTYWl75OZuzxqQxtmcCU5cWMqpHAk6bRv8bl9K/g5PpD6kcWyhiYpqSKfd148RHVzOhdwIWXXDO2FQ8dgu3v7eVL/7O457TWvL17HxMKXn2+528fFlburZyICXc+f5WHv0yi+6tHPy6qAibRfD6le3oUm7B3ZcEoKrN/0DCjOwo9y4NcnobKy8Mcpb0ui+MSD7dHObdDRHOaW+jUzl3t8rmS/Co9lzwwno+vqET8qt1/HtxkA1+yZ296mYw0ytJZ/HhHr7aGmFLQJIbklzQwVrp52iVaqV9hpUN2REO65vIMxe3YfIClT9OTNpzSQv4TN55Pou+Qzy0aK3mVf8/IGpIrvl+J1ccnlFCOmqD8mP5RqvJKb2sdE904Ph9E7miYpJVHjNWFDFjuZfp/+kBQIpHZ1tumJkrvIzvnciuQoPjhyZzaN9EDu/fOFtz1we6ZDro1tLB67/u4rghyZVGFD0ONW5PfHQNw7u6uf+Mlpw9Jo2vZudzyqkqumnVYdVSP2Ys/dWlpxOHU+PM8csI+k3sDoEn0cLYI5I46fwMevRr3GmChsReIQP70k8+XiXy3iAFX45y88fOKE+sCPHV1ihfbfVya3cbN3VXG+V9ve30TtJLjIbKQxMqj1sZLLpGuFw51u0nZfLt3HwMU3LXh1v4fUkRLVOsfH17ZwJhybAubr6bm6/6xn+yjae+3cHal/rw413deH1yNv/36kaWPduLbq2cPP1tFj8tKOSiielcd2wm1725ied/2MnNJ2SWtJad0CeBr2YXEAhLvrqtE8cO3h1+3VcE4GDZ/IsxNzfKfUtD/JVjMCRV54E+u3PxAIlWwcUd7VxcQafMqubL5l1h3HadXG+UTud0pe3qxQC8sDpEJ7dG7yStwsY18SDTqXF55/gqGqwWjbUv9ytR2XsDBne8v1l9xvUheg5w8/fUQr75IJubHmpLSrqVt35U/QZg93pkmJJfFxVyxqjUGn3WqsaxlJKVRSYj0yuewxVVAG3KDvHR9Fwm9EnkmqN2n/aFEDx7UVvcDnVgePfaDiXXd8XWAEUBkyGdD7zNa0BHF4ue7sXYu1bw9Lc7OGpgUoXj6oojMujTzklmspU+7Zxsy41w6csb+H5eQcljIuW6oa9eFuC2i9dx+Imp3PVMO5zuhtPA7W+oVzLQkCWB+zqUXB/wWATHtLRyTEsrO4ImV84L8OiKMMsKTV4Y6ODSOUG6JQhSrBWftP4+zIOrihzsMxe2KfOzEIIzRqVxxigV+rpgXDrbcsPouqBdho3J93Tl6W928Mx3a1j8dC+6tLBjmJK/V3k58ZBUJOCwCtplqEX6umOal/Fcf+aitlw4IZ2uLRx8PD2XOz/YwvqdYa45qhlPX9gGIUQTAdjLWJhvcPIMP509Gu8MdXJkC0ulG3RWwGRebpSJmVYcuqiWOA/r6uHLWzsjpSS7IMK/7+lNi0eXcc0/ASb84cOhQYJVMDhF59hWFo5qYS3RGpTGBp/JfUuDzM4xSLEJeiRq3NXTQVt3zcpOi3PqL/64E38Y7A5BZmsr0ajkoRs3sH1TmF4DPZz1f81p19mxx/pkt2osf643Fl2wsyDC9OXeSrUD8Y7jZYUmJ0z38/4hTiZlVh4xKf16eX6Tz9ea9GrrLJMbn7fWx1lPr+OqIzK4+qjdzpCzVno5+5l1nDI8+YAkA6Du7S0nZHLSY2v5aEYuZ47aM3zvsutMKhUhWb8zxPfzCkj16Lx9TQeOe3hNyd9aplo5bnAyo3p48AZNLjk0HTFHXc/GXnJZjIb2lKgRGeg7DRL3QeOmvbmhpJ7Tda+8X3OHxiuDnPT5ycusXQYasLzIYGYOjEiPckJrG99tjdA9UaNzLEf78ppwyYK7MN9gWrZBC4fg3t4O/s4x+OjqxTRLtDLrkR4VvqfTrtGpVLg+1aOzcVeIq47IoH0zG1tybFw0IZ2JfVR+9tLDMtSkiW0mQgge/2o7TpvG1bETTL/2Libcs5Jpy4rwODSuPqoZd1kD5L2/ugGvXuWoTxKwP+oFXlkTJtOh8e1oN64KNuJizM8zmDTVx+GZFsY3r1mY/6MZuVz9300sfaY3l97eiz9uWEiGTZBkEwQMyZ/ZBlfOCzIiLcJXo1xlyEhW0OSwP7wkWATntLNSEJF8sy3KoMlehqXp9ErUOLWNlWbHtichapbJFxf6DbLyI7RMseKJuXpKKUt6L9gFtGhjx2IR3PhAG967exv9digFelHAwLTvaWNcTCi+mZPPzf/bwtheCWU0AjVdCzp6NP6c4KZHYvUnzp1Bk9m5Bke1sPBDb520cuHwfu1ddGxu45nvd9Kvg5uxMSOxxRv97MwPYxgViDUOIBw/NIXTR6byf69spGsLR6WuksUY1SOByw7L4LXJ2fx3shoTD5/TCtOE205qUenz9qabY30dmGvyOoX+6h8DjaBRUXWoy8ZcF7OSvYEMh8aVXWy8tT5MfhTyYun+q+cF8UbhzsVB3BbBsiMTWJgX5fnVYXolqsVxS8Ak0SLolqiRYBW0cAom9UtgfJ/qrVKL0TLFyurtIY4alIzLrvPYV1mszQpxyaFBtuZGSHZbWLzJT89WTtZnhzhqYBJ3vL+VCX0SSsgAgDegRF8/DHfSzRZE9XTfezhYowDlkReWfLctwvXd7JUSgW+3Rmjt0uji0XhtsJNxzSxVkoaKMKZHAq9d0R4pJdvzwjzez4Fd252GuK0H/JIV4ey/Ahw9zc+YDJ1JmVb6J2sszDPIj8DXo1z0jPnOX9PF5OutEX7dEeXN9RHeXB+BP5fhcWhM7JvIYX0TlTnPr9klttYAD57Ziq25YVZtDzGgg5NBHV1MWmljxtdF9Ivq/JFuL9Gq3PTOZtZmhfj13m4VfqcLx6czqV9ijcWC5eHUBT0SdRblG3y1NVKhvmdn0KSZQ2N7UHLh7ABfjXIxMt2yRwrBogsunJjBko0BRnbbvRH2buekRxsXuqaT542S4mn0y3it8fqV7ehzvZfnf9jJ29d0qPbxZ45O5bXJ2Xw7N5/RPTzcckLlJKA86ttTYX8zz2u0o6g+Tue5762qFSHYW9EBgGu62HlvQ4SHl4Xo4NZItwv8Ucmzq0L8MNrFzpDSAKTZBcNSNa7vamdiqfCjlJJ//RPg370cXH1J+xq9d7NkG7nvDMAZ65D25lXt6XvDUl7/bRfv/J5D23Qr63eGcVg1fCGT649philhR36EQr/B/Z9u5f1fsgkYkhu72ugWx2moPtGQJGB/jArkRyRBE/omVx5uf3ltmEEpSkdwYuvaCf9apdk4Oc1Gt6sXc+boVP5VQerqsOYWnh3g4NcdUd5YF+bJlWG6Jmi0iPUyKK1saeXSuLKLnVS7QAj4dw87zklt+WNpIT/+U8A1b2wiLcHCvae1ZER3D899v5Nv5uTz6Ffb8YdMLjk0jS//yufiQzMIRUzOeWY9Jx2Swpe3di55jwvGp5OVr9ywQhHFKOyl1P26LsjKjzBjRRE7p25neJqFFJsgzV47x8x5eQbzco09fr+6yODQP3z8MMZN7ySN1UclUH4vz31vFclndaEoYHDaiFROG1H278O7erh8Ujpv/JbDKY+tYVTPBO45rSVCUGvNRmPFyz9ls2FnmJOGxbdVDenk5tQRKezIj/DaFe3r9N7xeCrsbxt+VaiRz0Bx682GRH1vwrWNDuzN1MTr68LcsShIhl1g1eC2Hnau+SfIa4Od1S7Ya4sMDvnNx4UT0njlsnb8srCQo2LhRsOQe1ijFkNKyQOfbeO1X7JJdOqcPjKVQNikTzsXXVvYOeT2FQB4HIJAWNIixcL2vChWHRw2Ha/PYHCqRl5E5Z6TbIJ5k/beBtpEBPZEdsik949e7u1t54pSYrwr5wY4vIWF41tZ2RYwSbcLbBXUqNdkrnzxVx63v7eFb27vTMbvm6t8bNSUzMoxeG1tmMUFBrf3sHN6Bb03KvssuUVR3A6tZPMu8kc597n1SrjaxU3/G5cxtIubu09tgRCCYETSNt2Gw1bxRn7zO5uZvtzL9Ie6l8yP5x5cwvXl7JQ7x0L+1hrW84cMyc9ZEfIiqqTTpcPPWVHaujRS7YJfsqKc0MpKorXy131qZYivt0aY92r/SksSt+eGOfe5dSxYH2Db6/149vsdrNga5PUr29eaFBRXnDQWfDozlzOeWsdpI1L48IZO+/rj7Jco9BuknNfIfQaK0ZAbb22jA3sTF7S38unmMP/kqRPLI8tD9EvWuHlhgMMzqw7jdkrQ+W2ci76JEd55YjmXzQ0w+9EebMkJc83rm1jz4p71zXneKP/3ykY+/ysPAXgcGs/9sBPTlBQGTNrG6rTH9vQw5f7ugLqO32yNcN38AEenCVbZNP7KNXmkr4PZOVG+2hplq9+kVRz15XVBEwmoGHNyo1z7T5A0u6CzR+OORUEe6mNHCHXaLkZLZ+X3pyZzpXOmnWMGJ6P9tBGqOT1bNMHoDEsZy+2K4ItKtvhNOni0Mp8ltXzoXgi+nVvAt3MLiH46iGVbgizbEqR1mo0Pp+ey9JleldamA5wzNo1+HVzoumDhBj/n3r+CN4Y4ObmVzudbDf43zMmyQpNHlof434YIF3esmriUx+dbIty0IEhEwog0nfZujbc3RHBo8PYwF+dV0WisGEe1sNDMLvB+vKbSe9Ii1cZDZ7Vm/no/Fl2QmWIjEFabeXZBBE2rmUfC7e9t4Yd/Clj4VC/WbA+yenuIIwfu2xLGU0ek8uH0XFZuq7jvRRPqD/uMDDTm6oC9/dksmuD70W6eXRXmkRUhtgYkWwMqYHPhbD/PDXTS3FH54tY3Wd1Gpw5Ht9DpMGsr05OTKAoYrNwapE+shtowJIfet5KZK73YrRqf3tSRkw7ZXVYVDJtc9OJ6midZsK7J59jmssy1OK6VleNaKaIQNU3G/+7nx+0R3hrqYvouLw8vD/HCICcNhSZtQFlIKfkj22BursHTK0P0StJ4Z5iL6bsMlhca5IQl6XbBiw1wT/q2d/HkBa56nSsL8gxOmOGP9fEQlZKTBKdORqKF7MIo17+1OyoxvncCF09Mr5IIgBLmtZm+hdz3coj6TA5J03FbIJZFIDskubGbnU0+k/8sC3J4poXWNSC5Z7a1MirdgiYoed6Hhzjx7Zk12AN/50Tpk6TTPVH9g4rLEYsxrKuHpZuD3P/ptliqQDG///6azfM/7GT9y31x2DSWbg6waWeIlduCXHVkM6wWjWWbA2QkWnA7NFx2nWMHJ9O3vfKjOPyBVQzv6uHt33dx0iFKyFffiGfsTMuO8t0cP+e3t1b7+MZ+6Gvs2Ktpgn1JAGoyUPbF59wRNBky2UvAgCs62Xh5bZixGTpzcg3CJnwz2sWQ1Jpxt/Ihv4eWBXl2dZjhXd282h4ynXVL+Uz6w0uGXfD+cDeXz/EzZWeUFUd60LT6jQ7sDRKwP0YFblwQ4H8bIjh1OKOtlYf6OLBqglsXBjiupYWRGbXTBMQzV/whg1mvraJbglZjAWJlKIxIVhYZ9E7ScZZKb1X0eTZmh+h4xeIyv3v18nZccmjlzXyqm9dTd0Z5fEWQTX7Jm0OddPHojP3dS0FEcmSmlfv72EkvFQUJGBK7BlolYfVlBQa3Lgry6mBnlREZU8qS1xg62cu4ZjqP9auYwFV0LZ77fgcLN/h546oOFPoNflpQQN+2TjbnhDmsXxJSShxnzEPG+pS8eXV7zh+Xzo1vb+aXBQW0TLXxw51dSlImUkpmr/Zx9jPr2FkQ5YtbO3No3/iFyaVRl7V0RaHBxD98tHGpMtm6apIOVrIQb5qgQclAYzv9xzMY9tVnDpuSib/7WFFk0t4laOHUmJ1rcHN3O6+tDdPSKXhlUM0nxAurQ/ywLcIPYz0c8quXkek6T/avn5PiVXP9fLIlypcjXSwvNLhjcYgXBzr4bEuENUUmPgP+GO+mRRULYXVoIgIV44dtEc6fHeDxfg6GpWrctijEPb0dDEzR6fRdIYVR+O8QJye0qjkhqG6e5L63ivc2hLl+QZBNxyaU2bgbEuU/178/2MLDX2Tx9W2d+Wp2Ph9Oz2HWf3qUcROs6Xze4DM55y8/O4Imrwx20S1B4+PNEV5fG8aqwRWdbTSza7y5PszsXIP2LsF3Y9wVRu7Weg2unhfk69GuCjUaxTj3Lz8tnILH+jlZnG/Q0aNV6M9Q1bUoJv6zV3sZfvsK5j3ek/4dXPyyoIAWKVZ+/KeA29/fSqfmNo4enMzTF7blx3/y+WRmLicOS+HYwckIIdiSE+aej7by0FmtyfFG0QT0aB3/elFf66chJUdO9eEz4Ldx7ip7XNQnDkTC0CBkYN3RCSRUIXrZH1DVzd7X5MUflbyxPsz9S0Mlv7Np8MlwFyfM8GMVsOqohDIe8tXhnsVBPtwUZsmRHnr+4KVLgsaPY6vv9x0PdgZNhv3qZUCyxvuHuGj7nZcL2lt5e0Ok5DGntrby0uCak4+9lRLYH4nAS6tD3LM0xKHNLXxwiJOcsOSlNWG6eDSSbYJgVLK00OTG7nacumCTz6SNS9RIGFbZPCmeIzuDJkVRSUd37V0Hy2Oz3+STzREu6mCrtAdHmc8SNhn4i5cL29u4pYedI6b6cOjw4xh3paf1eLAzaHLyDD/bgybHt7JyQXsb6XbBbYuCTNkRJWgqJ1B/LOx/W3c713a1YSm14f+ZHeXmBUGmTXRXSQQAnlkVorNH45iWdSduUkoK/AZuu4bVojHwpqWM6ObhuYvbsmVXiPf+zCFiwCWHZrB0c4Cnvt3BXae2YGR3NQ++nZvPl3/nccH4dMb0VL+77+OtpHosXFNJL4SGWDffWBfmtkVBfhhT84jo/o7y1Wx1EcGnntOVjW+tpOP3RU1koDKUv8D7mgiURtiU3LckxGvrlPHArd3tpNoEty4Kcm1XG9d3tVd7ciiGaZosKTTpm2yh149FBAzJumNqF/KrCD9vj3DO3wHaOgWbApIPhztx64JlBQb3LQvx5lAnh9bQ1KaJCFQOb1TS/Ycizmpn5T99HBSEJX/uMhjbTOc/y0IszDeYPG432csLSwb9XMSVXWz8q6u92o2pPKoqs90VMgkZ1JtodHZOlAtmB/hxjJt2cboRPrEixJMrQ/w2zk1uWHLiDD/XdbVxZ8/Kmw/FgzsXBUvm36h0nS9HqTr/gCH5emuEa/4pK2jTBXRP0OibrNM1QUMXihCMzrCo8LxUpZ9ZAYmB5J5ejj1SBzuCJh6LiHtul0dFm0YwrNqWSym5/q3NOGwa//sjhztPbsHdp7Xk69l5jOqRUKXQ8JEvtlMUMHjo7NZAw6+Va4oMDpvq46TWVm7uZufzLRHObmcjOQ6C2IQ9URSRTWRgf8asXVGOm+7HpkHYhOkT3Hy0KcIb68NMaGbhv0OcNSp5ygmZdP/Ry329bVzZuW4LZXncuSjAa+siJFlhxZEJPLkyxBMrw9zQ1cbtNViU97ZAcH8kA99ujXDRnADfj3bRP0Vn+K9eNvklP45xMShFR7JnDvu1tSHuXBxiVLrOzd3tjKjEN78mMKVk6GQv57S3cV3X+HoKNATCpmTsFB9uC3w72s09S4J8tCnC2qMTalwSWBrbAib9flatn3snaUyJtR7/fluEy+YGSLQIdoUl13ZVbZ83+yWL8g2WFBis9pr4lA8XNg0cmurQmGSF5k6NDT4Tpwa/jPPgi0oChqSTR2PC7z7+r5ONM9vVrHqhIlREDD6ZmcuWXWGOHpRM15aq0qTFxQsY3SOBT27qxF+rvMxe7eOao5qVcRCVUmKivkNDIztkctJ0P4aEt4c6OesvPxv9ktPaWBtECHswoIkM7OfID0sunePnj+zdEuS/DnWzstDk4jkBBqbovDXUSbMqqgxKY3JWhLP+CjD3ME/cp66aYKPPJMMOLovGg8uCPLtKnaqyT4gvCtFEBKrHtOwop830MzBF59vRLjQheH612uT7JuuVLtbeiGRqdoTpuwzu6OEgwSoIGRJ7HfOwX26JMKGZBY8VpmdHGdusbt0La4tF+Upodl57FdI/9A8f7dwarw9x0je59qKzdzeEuaGU94BNg4gJx7eycH57K48uD/FPvskF7W0MT9c5tLkFhy6QUrIjqK5vsnVPI6CNPpORv3k5MlNnSaFkWJrOMwOcvLY2xKh0S4kzY31jTZHBXUtCfDg8Vl0kJTuDkuYOgSYET64I8dvOKD+MUVGQLX6TC2b7+U8fB0PTGjZUL6Wypb51obreX410cu+yMHNzoxRE4L5edq7ssu9I5/6MeMlAwxaFN6HWSLYJPhnh4pVSbPj/5gY4qqWVb0a72OAzOXOWH2+kWi4HqHAxQHNH/ZC5TT6TdzeEeW5ViFsXBnh7fZhvt0XxRiR39LDTLUFjQrP4FrWmksHqETEl188PMDxN58uRTubnGczcFeGHbVH6V0EEft0R4dCpPg5tbuXhvk4SrIK8sGT4r15+2Bap8Dnx4sTWVhKtcN5fAa6YG+DDjYoASimJmpJX1oQ4Y5YfbzS+Mbogz2DC7162+mvmud83Wae9S7C80KRPss5PY914LHDRbD9hM773rgjntrcxbYKbIzMtXN7Jxv29HTw9wMEt3ewckmbh05Fu/q+TjR+2R7hwdoDTZvqJmErIl+nUSLFVrNNo59a4u5edqdkmWwMm3RPUMnxZJ3uDEQGAp1eF6Zuk3isnpPQWH24MUxSBjzaFOa+DlS9G7hZfeiyCTIeGXResLDRYVVRxbaQpJTcvCPDvxTX3ApBSsqbI4Ky/AlwyJ8DwdJ1pE91YNMFvO9TYFlBS0tyEhkOjVGYsyIuyusjk1Gpcyg50CCE4uY2VoCm5bn6QtrHc7JBUCx+PcHHsNB/Xzg/wxtDqu0ftCkmcerX+MNVCSskDy0I8vzpc9rOiLGb/bQ0yOsPCkZk613ermsnvKxKwP0YFFuQbbPRL/tPXxvYgHPGnn3t7KWFb2AR7JXtIpkNjUnMLkVKPsWtwchsrA1PVL2oTJQgZEmusrK5LgiDVZuHQ5ur1rvonQFZAcmsPO0sLTYq7xC7MN+hbRZvjBCsMTtWx1WI/PKKFlVfWhvnPsiC39bDz4iAnY6f4+HxzpE5h99YujcUFBh08Ghd3tLHZb3LSdB+dPaqk9u5eDu7u5WBGdpRTZvo5aYafL0a6qk1RXNbJzlltbdy6KMhdS0Kk2ES1rox1xfMDHURMFREY8ZuP3LDkp6won2yOUBCRHJLmIcO++3Mn2wTvHaLWlrFTvCRbBV+PVlGD6+b7+XxLlH8meciwK43EEZmWuFIK3ojkzsVBZuZE2R6QhExo7RRlumzqQhHCqdkGYzP0Gvk8NKF2aJRk4PK5QTb6Tdb5DFZ7Jc8NcOCqxkjkQMaZba30StTpnbT7GvRO0nmoj4Nr5wdZUqBqs6uCrkHUhF1hWWbCV4aQIVlSoIxrDAnt3Ro9EnXuWRLi5bWKCKTbVN4UFBEQKOOWb7dFEcAvOwxeG7xnOWRTJKBmCBmSq+YGSLWCkNDKKfhutItMh+DKzlWr+Xsn6fTuU/b6uyyiRGAXMCSH/uHjik42zonDGQ9UCuvQP7zc2VP1N7i39+7olS8qiZiwpMDAkPD8QPW3Yk/+d4Y6OaoS1XyyVTAuw0JuSFIQNkq6dS4pMGhmFzRzaHgjkrU+k24JGg5dsNVv4o1K7uttJ80ueGhZCJsGN3V3kG4XbAvUPjIA6nQ8+zAPodihOGxK2rs1CqPgiyoCAzAyw0LPRI35ecoXpBIH4bKvbRWc2NrCJ5sj/JQVLSEDEVMiocZiz8qwotDgj51RVhSZvL9xdzRoQLLG/HyTngmCH8a4aV9F+nDyODfbA5LnVwVZmG/Q0aPj0Ha3JHsk5otwxiw/ppRc08XOz1lRung0xjWzYBGwqMBg6k7lVpoTVh4NN3W309mjMb6ZpYxwMtWmcVxLC99sizIqYtR4zdgfCf++RqPUDMzOifLC6hBL8g02xyJPRzTXeLCvq0Hy3fsr/FFJz5+KOLaltWTRrQw5IVUGeH57G3dV0EmtGBFTcsvCIJ9viRAoFRV06rDyqARm7Iry2PIQZ7WzcUZbK3YN1vlM1nlNPtsS4bcdKsd3WHMLc3Kj5Eegi0fjvPZWLuxgY8bf3vr6+rXC/rhIrCkyGP6bD4AWDsGiIxL4cosSsq0/JgFPNerztV6DBIuoUF8SMSXPrw5zXEsLnRP0KqME/qgsMRh6ZlWIo1tY6JIQ3zFeSsn0XQaHpOlYNcGLq0O0d2scXYoYzMiOcsIMP8e1tLAtIPlxrDqFdvm+kGu72rm6i73kMcqlUOP2RUFm7ooydYKqoGj5dSFpNlh8ZCITf/eS6dB47xBng/vtv7M+zE0Lg9zTS33OeBE0JBfPDjBlZ5Tbe9jJj0g+2BihX7LGxyOqbtkbL8ZO8bLGa9LJoxE2oUeiRluXxl097XT/oQinRbD4iOrnRdSUDP7FiwEc2lznnHY2BqVa+DM7Sq9EjTS7xsL8KDctCLIg3yTTIcgKqu2luBSzhUNwUmsr57W30cYlsGqi0o0+JCUrI9C3nqoIiud+6ffbH9eDmuKAEBAWhCUvrg7x/JowUQmDkjVeGuyio6eJEIByOBv7u48L2lt5PA4joavmBZiWHWXyuIpNUgB+2h7h3L8D3NjNxhGZVjLsguOn+/BFYdERnrgU2qXLsjp7NNZ4VcjPpsG9iZCiQ6IGPqlCU+56OgHFg/1x8nsjkpG/FRE04J1DnBySphoOLcw3OKy5pUx9e0Xo9F0h/+pq59pqVP9RU3LMND939bIzslzFwUafyZF/+nhxkJPxzeILKM7cFWVbQHJKm7KRACklF80J0DtJ58ZudoKGZHvAxERFB4qikqhJjSIDxdGnI//0kh2UzJ20mzBd1tHG3b3sdRZMFn/2n7OiDE7VSbdrSCm55p8gH2+OcFZbK08PcNTY4yBqSm5eGOTDTRE8Fujk0VhRaLL+mIQ6+SWAqko68y8/F3WwVdhO+ZLZfr7frkSDA1IqJ3b/5Bl8vjkcM0XTGJ5uKbmvnb4r5Iy2Nm7sbuPoP/3khCQP9rFzahsrNy0M8r8NEQQwY6Kbzh4Vydqfo4P72xoSLxlolGmCYiTZBHf0cnBTDxt9fvQyL1+dbiePc9O/DirhAwXO2CktaMbXbeyOHnaOyI5y3fxgiaK4PFYUmTg05W0ghGByVoSNfslHw6vPgxbjob4OftkRYYNPlhABUCWSH/thYSm5gQA6WSTjnTDOAZYGPMHtb5O4GB6r4LmBLk6Z6ccWs3pu6dSqtLgtjU9GuGgTR85VCHWCa+NUm1xeWLKwwGR8MwttXIILO9jomRjfexZGJOf85ee+3ntuQEII3hrqovgc8sa6MA8tC3FEC503h7pJK8dZSqfAPFZBv1Jzv7zHQa9EndlRFdI6sbWV1UUGz64Os95n8u4hzlqVx0kp2eSXtHNrmMC//lF+H1d1sbPaa/Lx5gjHtbRwX++aEwFQvUmeHuDkkb4OhICvtkS46p9gGZ1HTbG0wOCORUFm5hgMSNYqLf98fpCTbTP8TJrq46ZuNm7tUXHUMDto8nNWlKyA5M4xyumyGN+MdtMzUePsvwLkhSU/jXXT0aPx3CrV6AlUGvGpv3yc6t7/q9FqQmT2pzWnUZOBYtg0jW6JGrNy1MayrMBoIgNAB7fGfb3s3LM0xMpCg6cGOKvUDrRyKRXzlfOC3LckyLGtrPRL1tjokywrNPgzO8oHGyMc29KKEKpE6pcsVTDdqQbRGCklD/Z2sC0g6ZYo+GtxAA3YYcDiUgL2B5JhbRTe9sKaIvgqLLjfIwlKCEvI0Pdu1KAxoLKFplVvJx0sEDFqngMfFKeDmy4En490IYTgoWVBJmdFWVposvBwDy2dGjd3jz/8nWgVrD06gao+bTF5nZRp4ZU1IZrVVd0KDE+z8M6GCBt8Ju3dGrf0cNA3WefcvwPM2GUwpprOiQBTdkSJSsmkTCsBQ3L+X37+yjVYfmQCbovghzEqOrnZb3JtzHzowg51N8UpjlwUR3qCRu3IwIpCg5Nm+GlmF7wdE+VVRlKcuuCj4S66fF/EB5siXNd1dwTFlJJX1ypfk8NbWDm0uY5eru9I1JRcNjfAGW2t/Lojyp097XT0aERNyYPLQkxwgF3AjwHoXMPdxpSSLQYkCvUazv1wLaiOODQmsrBfkAGA5wa6eGNdiONbWRl8kNlTVoUru9jpm6xz5+Igx0/zcWJrK4NTdU5sZa0wLHpKayuL8pWQ6IU14RJTI4D2LsHpbaz8Xyelmr5qXoBZOeqUVZW4qDxeXhPmnlKWynclg0PA6wVlH3dXftmfs0OSea2svL5uN2N4KlXSqp4a4ezP2LjYz4YofLPQz7CJNXOQnLozytICI6467eIN+riWVj7bHOH4lnrcEYiKXiueO9clQee+Pk5aO+t+nw9tbiHRAsdP8/LWMDcDU3QOz7TQNUHjiRXKk6G60/uXWyNsD5hY1qmNvp1fMiIBZv5dhB4jyT9G4DNTY2vA5Joutno9nPSJCYXn5xuMizMlU4wFeQZn/+WnhUPw1Sh3XAQl0Sr4bbybSX/4eGF1mBtLkb4314VJtAq6JSoi8PGmMAvyTR7u6+CJFSHGN9O5pKONQSkaf2bovLAiRNudIRIF2ARMiWm+xjlgQBzC5dL4LQivl9pL30yXB9zhoDFFGfabXbW9W+OBPk0OVBVhVIaFr0a5eXpliJ+yoryzIcIb68Lc08vByHS9TPpACMEDfRzc19vOK2vCZIckY5pZ6J6gkWAVdPiuCCHglDZWdoXUue7BPjUz+yhv9LIgDKe5YZITFoQgjOqeVgRYUb+f2M3BFr/JMS0tvLUuggG00KG+5CGNiYHXFeUXkOq+25ICgx+2R2tk2tInWWf+4bW/ZncuChI2ZVxaFoCTWtdPHXmSTfDmUBenzfRz6Ww/syd50IXg0b4OTprh56LZAd4e5ir5jO3cgss62SmMSD6ZWURHq+BYKVEjWM2bI92CTVHJDgNcQvJLAD73g8Dk2kQYXhDBY60/V8/OHo0Uq2BObvxkwJCSV9eEeXh5iJ5JGu8d4qpRpKJ3ks6Jra18vz1SQgY0IfhlnKdMnwibJtgVMskPS95cH6ZnooPz2lv595QijrPA3ya8Ugi3JMEjKbAuqkjB4FpUTc4IQjsLJGsqtbjTgA4HsVwsXuIwYURCyWNrsu7VSED4Vjq4KmBmpVWaB9Kiu79ibm6UWxYGWVygDE3GZFg4ra21TK4VKtYZeKOSbQGTrgk6720Icf2CEIsP95BZw9PhDzMK2RCBFhYV5iv/PlEp+TsEg2zg0MQe46Y+BUb705is7HvPD0keKYAHUmB1BJrrMLiKk1Zdv3PYlIQNatQUqzQ+3RymIAKXdIxvF5i1K0qiVdCrHkx3AoYyaPp8S5Q3hjhLDGuKbZmPzLTweH8HL68J08Gt0WZ7iKkByRteeC4VkstF1AKm5F85YBGQZ6r8dy8r3JIMjti4ru8xdvpMH0KoEH48eHR5kCdWhrmso41/97LXqpPk7YuC/JoVYc6kBBbmG1w1L8ARmToFERjbzFJhI6UpM4v4IyB5uQjOdsMXfghIOMkFp7orb+9cHcJScl42nOCC41xw4S64wANHug6syMDegN+UXLiL+q0mqIwMlMf+tPgeqDClZFq2wUebIszKibI1IBmconNopoUEC/xvQ4R1XpOhaUrRPTqWS/1gY5hr5we5qIOVN9ercP1FHax0cGv8nWtgEUrxfV57G30qCY3WdiNvKEKwP43Hyr7z1z7JJz54LwOeKoTWOpzuqXguSikJo8J+uhAEpfKKOHZU/OmFp1eG+HZbhMnj3DUS3Xkjkl1hWaO0EsDE370MSNF5og7ttXNCJp9viXBaG5W/P/QPL83tu0sLTSk55Fcv+WHJzEM9pNu1kuu9JiJJ0iCjgk305ULJH0HoZYFhDkXEetsqF7vWx3h7bHmQ19dFWHKkp1K/ASklz64O883WCIsLTMY30/mkDuWIY6Z4WV5ocllHG2+sD+OxgD8KR7SwMLG5hbNLmTeVHqcBUxKS4BFQKOH3IHzqgz42uDWpdqLgT32Sz3xwhhs2RmFWCB5NgfZNdvg1RrxkoEHSBE0Rgn0PTQjGNrMwtpmFqCn5dluU77ZFeG5ViKABR7W0cHY7K99ui3LSDD9nt7VyTVcbR7WwkheW+KJmSW3wN1ujFEUlbVwaCRaYE5L8b0OEM9paeHZgfCeX6lDReCn9u/oiGPsj/KbajLrFfO5vTFJkD2BeSNLNCmsjsCKiCEIIOD8b/pUIIx3wewDe8cIr0wrLnHqLr81Hm8IMS7Pw8aYws3MMvhjlZmS6Tnu3VmP1/XOrQ0zZGWXyWHdctf1b/CYpMevtuprsLC4wuX+p0hWB4NKONq7+J8hFcwI82d9Bqk1jvU8yJl1j0TwfE0Yk7B4f5caX15QsCsNfIfg7BMe74KxKyFd51Mf6d0xLK0+vCvPsqnClws2/cgweWhZiZLrO+4c4ObR53Zbz09tYeXVtmHc3hrm1u52jW1oY+ZuP89vbGBtLV1Q0D52awColUQkpGox1QFDC9374wgen1aJjevERtb0FPvKpqEATEWhYNEhkAA6MRfhARMBQp8RioxpTSv69OMR/14Xp4Bb8dagHCZz7l5+NPpM/J7hLFMRRU/Lw8hCXd7Zx0nQfK4ok649OKBNK3hebdvHiu7+biVR27W7PlayLwuOp0LaUmNJrSq7LUYtvS4vKsf47WYWx/wpBFys00wU7DUm2Ab1K5X5zDUmKph57ex4kuTX+09fBj9uj3FNBOWC8WOs1WF1kckSL+DQAZ8/yE5aST+vJYOfdDSE6enSGpeocM81PRsDgb0NgEZK3h7m4bZFKn5VvoFV87U2pIjBf+8FE5awnOGCCE2xxEqP6GnuPLFcNv6ZPdNPJs2cU7r0NYa5fEGTTsQm1SgtUBlNKNCGImpI23xQxJE1nomHQpxINwpKw5I8ATIuRpsVhJUrublPVQ8+l1fyzLQtL7svf/fPRTuhlgwG22qceDlbEGxloEDnG/rgQHyxw6qKMY50mBA/1sXNrdzvrfZIeP3rp85OXaTsN1vskH2yKljSaKYhIXlsbZnJWlEPSdJxa2dKnfd1roPiktz+Ov6qu3aRY5PxrH2yJ3Yv/FUnyTTjSCT8EFCG4O0V1n9OFYKRD0Cy2QTTTRRkikG9IrsmBr/zq/p/nhuwik6zl/j2IwLsbwpw8w0c01vDHlJLKzg8BQ9LRrTEi3YJR/RkDgAf7OritBiWLVeHORUEeWBritx1R1npNeiRqvDkpganjXSAEJ83wMyhFR0O1KC7+Pp9NK2R5WPJgnuTcbHVdTnLDS2nwWKrgCJeImwjUJ67rasemwY/boxX+vXOswdH6Ul4e9YHizdaiCYakaizNNSgwVTpgU1TiNyU7DMmaWJM0n6kEfhe7FXEqNFUapY9NlRPn1qIctqdNkFTqkn8fgMcK4NocWBtnc7Ym1Az7TTVBExoOQghu6m5nQnMLk7Mi/J0TZdoupai+YUGQGxYE6Zuk8c4wF0uPTCDRKvjfhgjHtrKWGBHVlQgczKml6q7deKfgY69keghaWSBTlywMgVPAeCf0s0M8srvfApI+NkUObk2W9Iwd3nva4OFUVcu9MN+gZ6LGtL+UbfTWkCQzCn/+5aXHADeH/OrlkxEuBqboHDfNz5gMnWH5EdJ6ujh9qg+bgAITRjjg0sQ9haGlURiRJFigvat+yvIOy7TQ1i24pKMNXQiODUSxaoIMh8bQVJ1lhQZvx0xw/soxsAiDx1aEWBm7/G1dghs62RjfTC9TvlwTFXd9wqErg6V/8iruFtg6JurdEZL0rNd33o3jW9uYlRskQ4cnCmBJBFrq0NWqCMBLaZJhDsFQ+24x8ululdY6Nja+1kUhtRa32CmgIOZSWkyHdprKs+DqpiaG9Y56JwMH64J+IGBgil7iLJYVMPlhe5QnVobIDkm2Bkwy7LuNUXLDktax02JNiYCUkh8CMCcEZ3tgl6HCfwcj4r12CUItiB0tsCwCW0xlyuQ14dF8SNPhykSJU8CXPjjLA55yKb1VEVgegasTy/q9CyFwCNhpSM7+w0dHi9InHOKAoXbB0NjBfeZcLxPt4F3hZ5oQ9I9KknYaYBNMnu9jkhM2GbAopNT21eGbrRGuXxDkxzGuGnmHmFLyyeYII9IstHUrt0SfAeOaWcqU4hWvRRZN8NZQJxt8ksOmeimIqHbgoLrlvTbYSUePRu+kshqJLX6T3LCs0NO+/Hs0FAan6ry3IUJhRJJYLmc+L0YSMuupLXlFOKedldsWBVkehi0xTrLNgDxDpQSK37mYCARMyRinYIwTsmJRrNp+vBPd8HKRGve3JcIHPjW+pgVhelDyYAp0btIR1BuaIgNNqBCZTo2LOtq4qKONdzeEuWlBkH8vDvFIPwdRE9b7TM5sWzN6vi2qQtvf+GF+zJL433nqvwNt4N4ZLREqHQyoCYka6oTPfEokOMEJQ2xKoHVXCmgCtkaVyt0GrIzApqg68ZfGJQmQU/EhE4CNEehkgVwDtmvwpRduSdn99xRdcE4pMdhhpUyCBtoFA2Ok4focycwwjHBWHfE5vpWVHolaGWvbqlB8vf4MSN4PwmP9nLR1a/ySFeW51WG+HuWqtE+DEIIOHkEnt+CffMmJrSxc2slG3yS9hOD6opI/syNs9pvMzTX4cqs6jy4+wkOmQ9snB51LOtp4fV2Yx1aEeLBP2RTOj9sj9E9W3UQbClEJulAbcL4Jp7uhgwXmheEjP3SxqZQAqPD9ffnwYIqkrUWwMXacj1N3WQYrI5IRDsg2YGYIHilUqbDhdmVpLoGf/HB1Un190ybU28rbFBE4cHFuexumhJsWBtkRlEyM9a2Pt2nVlqjk6YLdJ4u0CpQqqyNwykw/F3awcmxLVdEwMl3HYxH8sD1KXlhFJ7b6TcISJjSzxN1ytzGiptGUk12wI6qI1LywOikFgURN8Gq68m0oLuG6J0WSWGpTzDeUrWtPK2RaBF/7JM10GO4QrI5IHsoHN3CGBzpY4f8S4H8+qII3VIm7kuHWHFgUllw3ropSJg3+vThIW5fGbT0ddIiVIxYL2Eqj9PUa5VBEx1gf4JP1AZK7OBmVrlOdhu77bRGWFarTam7YJNUm+HxLhPU+kwV5Bn9mG5io027vREFHt8Y6n4m7HsV5NUVLp8at3e3cuzTERR1sZZq0hU3qbIFcHdwWQdcEjeWFJjaURsWpCfrbJGsi8J4XznJL5odVyupkFziBj7ySb/xqzLWu4S6zKiK5Ow/OdMOpHsGsmPnZ1GDZx00LwdX18i2bAPVABppIwMGBs9upDmQ/ZkXJdApcOryxLsRJccz0d4rUxnKRR6mz21vhf0XKbnScAy70qHz1W7qFt9ZHeCvmb9DcIWhmFywu2FMglRWU+y0ZqI2+QhOCq5Pg6Njp612fCvfnGJI/g3BiqQYwUsJNOZJMHa5PUuVdf4WUSU4zXbIwDG0tMDx20DzKqSI17xTBfzPUKfpfiZKl4Qo/SqXYHJVsjKi0zxAHnDei6poypy44sbWVx1eEGJ4epUMHdT/H/+7j3t4OxjezlFyrv4MqN11oSv5XpNJLjxaoErZH1wYYJgRCVF0F0c4tiEoYnKIxfZfJIb/6EEArp6CTR+Phvg6aOwRfbInwaD8Hz6wK88HGMK59HKy6sIONJ1eG+HhTmNt7qu84ZUeUb7dFGZnesD1afp1RyPJC9f/D7Lv7AwghuCxBjcWHCpST6GC7esw9+UpEeIJLCTFr4jMQlpK78or/X/23hxW2VsBMT6qfquYmxFCnYd5EBA4eWDTB0S0sfL89ylvrIwyxw1Kf5NzJXp4b6ybJKrj7Ty/f+JTd8JluONoF66OwKKLsiA93Cf4KSh7Lh2KBdK6pHAgB3hjiZJPfVP7vSJ5dFWadz+SZAQ7Oamvlkjl+vtmmVoXEg1RA1MEqOMMteccLZqFKF3zphxNcuwVcQsBmQxEviWoVPd6p8qtrIpKlETg/NnW7WAVdrDDBkISlWuRzDUmyBsNqmOzdFIXXvcqB7t0MSLVVXKy0xW/yx84o57S3cVknOye1tpIea1IkpaRbgoZLh99mFPJtLCT8gQ8e1CWtdSVcOwu4O1k5033hhTPjWIp6J1n4bKSLIak6qwsNtgYlQ1Mte5yuj4457f2SFeWUNtZadTqsT7gsgmNaWvl6W5TbekhM4PRZfmwazNhlcNvCAA/3dcTl61BTlOorRkAq2+Pi69HRKng1XfJ3EF4qUsZA/4QUsX8qjZJqlnjhNyX/t0v9f4KAY2Ob/SluFRUo/iynuOA7v3I7HO6QZcptm1B71Kq0cH8t32pC3fDWUCeP93NwbEsLc0LKYGR2BA751Uffn7x87oMQimF+6IMH8uC+POWWNziWT14ZgUPsMDJWLtc9tqlPGJGAXRd0SdDp6NHo5NF5bqCT70a7ObudDSEEbwx1s/GYBC5ob+W3HQavrw0RMfevMqP6KL88wiW4IUmZ4QC8lq5OTh8USXIMlSJ4PBWuSRJYhOCaJMGZscRtBwv8JwXalVpAcw3JbbmqDAzgxUKlT6gpRjoENyTCm+lwxMiy6QEpJQtigrep2UqY6o8JzNLtGhf+7aPHD4X8khXhtSEufCsDBKQaLxahNv7OFnUyfToNUnWBSxMkCmjZ0hb3ejQy3YJNE0QRXD8/SE54d9TJH5W8tT6MISVLCgzW+0wmNBINy3GtLKz1mny7Lcoxf/oBaBPTbLyxPsL7GyNVPb3WsHTa7Qg5Nwyrm5UtA3UIQdvYJZocUH0E7kquOREwpaqFLw5GPZ+2OwqRoguOiH2MXlb1OYozBl/UYpw2oWLUmAw0kYCDF0IILuhg440hTvolayWhO6eAZg6BiVKhRwAHKiow1K7K1oo3n3M9MMYJp7vgmdSah/pcFsGlHa2kWOH2xSFG/+ZlVVFts9v7L4bYBa11ZeriEAKrgG9jFRo7opKdURVyLQ891knw+QJJfqz+O0mDMQ51sj8/Wwm3Bteg9F9KyZyQJGBK+toFx45KZOrOKM+tCvFLVgRfVPLdtiiTpvpYVWQwsZmFWYd6cJUiJBETcsKQUOp3Lk1wc7LgaJfySSg++XpivSwKTcnliXBqDYWsAElWweltrbRzqSXwgaVBLpnj576lQdZ5TV5bG6aVU9TZ1a++ML6ZhaNbWLh4ToC5eQZvD3WiCbigvZVTWlt5ZEWIUC3q+avD8kJFljq4NY5raeHdjZE9DoMdrIJJTtVD4P4URdRqip2xKdxSV1Gl0u2KC01Z0vp8aQQ2RFUlwwg7bK7YgqEJtUCNHAjXHZ0Qt2isCQc2wqbkw40R1vlMXlqzO7l8TSIsCSv18bkeRQbKLw6feCU/BuDZNHALVe7W21Z1TXppRE3JlJ1RZucY/Lg9ws6QZP7hCWXMlPYH1DVK8LVP8oEPTnWpE/QpbpUauCtPNdS5IUmZt5TH/JDk54DqLFdaqLcpKlkdgYlxthJeH1Gd/BI1uC8fvhrlYmS62jxPnuHDG5H4DZg8zo1Ng5m7DEZlVL65bvKZtHVr+KOSmX8X7SEiLD8+ftoe4cLZAf461EO7GvZCKI+TZ/iYnm0weZybVJtgyGQvd/W016jTY0MjYkoeWhaiS4LG2e1sTPzdS/8U1UJ4zBQfP491x12ZES/WFBkM/00dvzWUK+NPY1wMKlcKWlEnzZqM7/yYS2aXCsbrB17J1/7dP/eyKs3IHXlK7Plhxp6N0JqwG/u0N0ETDnzYNMH5McHXlZ1tXD8/SLcEjZu62wmZcOVcPzMCkiP0PbnmYU5lWpKoCdZHJA/kw33JkvFS8ke2QcEqPydU0VTn6n8CPDvAyaRMK+ObWzhhup/xv3v5YYybDPvB0+P0OJfSA3zmVwv1RBO6aYLBdsn0QOUq7gF2wYBye9yysGRnrOLgE6+yKk7QoLMV0is46RlS8lZUY7BpcseERM4ImSV5f29UMi/X4IL2Vo5rbcMRe35VRACgbWxDv35BgNQEGw/3rVoQeFimhW9Gu+pMBAA+HeFiQb5J32SdG+YHSLQKzuvQuASqVk1wbymHyLZujYX5Blv86vRe7BRan+icoJN9QiKvrwtz+yIVnD/iTz/jMjTeGOou8T6orrcIVE5+cw3JTblqLF+WICslo8e64Fu/ShsViwxl7F8TFVCo6D4URSR8Xz0xa4oMNKFBUOyb/mo6JFfRz0JKyfKIOsH6Tcl9AcGhza2VbgQrCw2umBfgis42Tm1jQ0rJT1lRrvsnQERCmk2QE5ZowKAUnWNaWTm6paVSMVtdURfnxPqyb94WlTxTAFmmEm4W51crOy3lG5Jd5m7Dlo1RyauFqjtc+airR8CdyUosVh65hiRBg8NH7kncdgRN3BZRq2jN55sj2DQ4tlV84f+gIXlnfZjjW1lr3Gr7hdUhhqbqDE1TRGVhvsFhf/h4qI+DSzs1LjJQHlN2RDl9ljoyd/JoTB7rrvP6HDUliwtMPBZo7dJKeh5ETMkJ031s9UuObmnhjfURWjrh+9EeWtTgmpcf8wWm5JF85SfQXIc1UTjHA6Mdu9cNU0p8EhI0wetFkimB3WWvRzjhwoSmPQkqT+EXRSQdvy9qigw0Yd+g+JRSuv14eSe34lBisTmOTcC1dklLb7HqYE+0dml8NsLF5B1RgobEoQuObGFlaSeD/64L0ytJp4VTY4PX4Pds9e/+pfDfIa4y7nT1geLvUZsGSfXZx6GlRXBfikoZvO2FNRE4xgVveiUnu5Rj3MqIysuaqLIvHTgvQTWYmReGBOBQBwywq4iCQ6jSrkcL4NUieCRFlrQBfturTKL62ytehA0pae6oeIP4Yoty0zurnbVMl8KdQZN7loS4qbuNk9tYeWpliAeWBrmrV/VNk7YHJXNylbXwiqMSSiyyK8M/eQapNkErp+CXrCgSGJpmIWRIbloQoFuCxoUdGn+5yoTmFl4b7CTBCqPSLSURmLrg++1RLpkTKPm5e4LGh8NdtHZpdE/U2RGMMinTQm5YMmNXlCvmBfhypKtWYfq5IckLsbLFe5JVuunJAuVdMDUAT6Spv2lCULzfn+uBZprqx3GcC45yHdxEoD41fAdPTLUJexXFodse/cvWmhefpEs3FyqGjtqAcioQQkVNSV5Y4rYIpu8yuGVhkO0BsyREel1XO4/2c/L2MBcP93VwSw8HP4xxs/gIDwNTdE6d6eft9TUsnK8F4tnkG6Khk1MTXJwguDYRZofgnjx12nostrgWmqodbAeLOu0XmGrh3WYonccrGXBhoqC/XZCuCzyaIFUXnOdRgq2nCuH+PMkVOfBzAH4KVP5ZrpkXZOLvXgoraCiTbhe8uDpUYhD0/sYwUkrS7IL1PpPNfvUcjwVsQvLMqhCTs5R6zBsp2yTJlJLlhQYpVsGUHVEGpepM/N3LT9v3VNYXhCX5YcmukMmJ032sLDSwaoLPRrq4urNio3cvCbKs0OT5gc5KnQwbG05sbeXQ5tZ6IQIAvZPUvE2ywlP9HfgNyemz/OwMmhzX0spGvzK3enmwiwf7OJmxy2Cdr+aNkuaGJE8UqMNCf5sSIX7rVy6HAH0rCcrYheA4t+CVdHFQE4GGqOhrigw0oUGwIbZArCwymFSJt3v5nw2UEO1UN6TFSEPYlDyxIsQar0mCRfDsQNW3/eMRLkZP8XFmWyuP9nPw1vpwmZP/gFJCqrt62Zm+y8/NC4MMSdXplVQzkVV1m3d1Yqm92YRphEPQw6oEgpuj0MOmHPvKp2rCUpJnqlNWVae6XjbBRR7JLwHwaDDKrgSf88PwpU/y/GF7hh1v7G5jW0BSOjOzND/KrrBkbDMr0yZ60IWgMCK5bn6QrKDkxm52fhq7u43xZZ3s+KKSfy8O4otKDm1u4dy//eSEJSe3tnJtVzvfboty5bwA9/e28+JgJy0dgrc2RBgS64rzzKoQp7S20tIpOHmGD58hmTbBw4fDXQyOPaY4OvHmujBvro/weD8H/etZhLc/oZNH5/1DnJz/d4DvtkV4ZbCTC2cHOOpPH28OceLS4ePNEUak66TYilMI8b/+hBEJvD61kGcLYKAVLk1UGgBQtsOFptIGnFU/Ha0PSDTUWtJEBppQ79jkU02O7Br0S969sFY3iC1CcEuSpFssQjtlZhFbW9p5fV2YFwY68cTyoS6LYFiqzsyJHiwabPZLfsmK8tGmCL+N91AQC2HOyolyd087yVaNM9ta+W5rhM+3ROImA/Ge4CsjOfuqDDdFF5xRtfkfNiFoHueed7hLcHisBHTCiAR+mVHIm0XwqR/+HTT3SAl08uh08qhc5X/Xhrikk41vt0d5d0OEPyboJSLPRKtgzVEJlTr8uS2CpwfsrnM/rLmF51aHaB8rB+yZqPHxcCejMnaH9J9JUS8mpWRpgcH8PIM3hzqZlGnhq61RwiaMSC/7ht9ujXD7oiCXdbRxQSMTDe4LTMq08sFwwRVzAzy8LMSPY9yc+5efY6f7ubSjjWdXhzm2pYVmsfu+PSjpXnkqGlCpo2dWhvltZ5QlhcoB81/J8IlXkYAXUiV+U/UeOKc2zQyaUGc0CQibUK+ImpKeP3rJi0huSYKbx1e9SkyZWYQpJSGpnMtM4HOfOh0cMyoRb1Ty32lFpOhwwZg9X0tKlct+e32IgoiyTf7fhgjPrAzhsQoGpui8O8yJEIIb5gf4KSvKnMM8uKsRttV3KL+mpVaNFcUE57PNEa6YF2Bshs7gVJ1xzSxcNz+ITZN8cIib1i6NjK9UQnjh4R58UclLa8I82d+xR8lgvFjnNXlrfZgbutlLTqWg+gzMyTE4vEXVef6IKcvoCaKm5KmVIR5fGeaEVhZeGezc526DjQm/7Yhyxiw/Z7W1clcvO+f9HWBtkYEEvFG4o6edN9eFSbAK/hjvrjDCtLzQYJ3XxG9IrpwX5JiWFgal6LTdEcKjCYpMycaoKi1+zyv51g9PpkLrGghPN0QkGTq495PUTm1R28NFvALCJs1AE+oVdy4OkheRPNXfwc3jE+PaALcYcMEupSTeHFX2uisjakO2CiVgm1JBjvqBpUGunBdESkn3RJ0Hl4WYnh3lrLZW/neIi89GuHDou8Pgp7axkh2SnDrTv+eLxTBlZlETEagEpRejKTsidPGoJjZPrgyzIM8gYpjkhcEZizhkOgRDU3VaOjW6JOg8PcBZayIgpSQ/Inmgj6MMEQB4dlWY2xYFK3mmqjbY4DMpfepZUWhw5J8+nlwZ5rbudl5tIgJ7YGJzC88McPDV1gj3LQ3x3iFOBqdaSLcLIhLuWxqiICJZVmjyVwXtMNf7TI6c6uOC2QGunBekV6LgraEuru5ip20vN9OCkgRN0Dt2P4tbZX/hU3qQsJRMCUjWRSRG7MwalZLXiyQ35ki8ptKQ3JoHF+2CVwr3LzfSmmBvRBmb0gRNqBdIKTn7rwCTd0Rx63DHoiDJVsGxIxLK+JmXx9jhHq6eXEQnC7TQISSVL/nyMGw3JPzt5YU0GDt8z8lwXCsrH2wMI4RKGyw9wo3bIvh7tjJJyUJ52BdjSKpOz0RtD2FbQ2/UBwIRKI8Zu6JIBHf2dHBYppUByTrD0y2kWEXJZv3hcBcZdkFOyOTvHIMxzSwVlhrGk1aZmm1w6kw/v45zl0k9Adzby15leH9RvsHR0/xMn+CmW6LOp5vDXD8/SDu3xg8VGOg0YTfObmejMCK5f2mIu3vZeX+4yhe9uS7MrYuCRCRk2EWF3RO/3qqEnF+PcpEVMLnynyCfbg5zahsb/10XZmlAlRAWoziuMyMEGT5YEFbi1d0oO2+jEnJK/er3IFxeTbpif8XeSD02zYIm1Au2BSSTd0Q5uoWFpwY4mLLD4JiWFrYFlHr7iEwrF3a00b6cQUxuWJJtwkSnqiNOAJ5Kk7xfBMUeNYdWUMcOSo/QL1nllIUQNHPoFW68xb9bl2lnWaFJ/2SNW34tpK9NleU1oWYwpSQqBYc1t+C2CMbHhJvJNp2nVoZ4f2OYuYd56B3TZqwsNLh4ToBvRrsYUsuNd0yGzpcjXXsQgV0h5WfQoQrjoe6JOl+MVOVxb6xTUYQz2lp5vJ+j3lT4BzJOa2Pl/qUhnl0V5oHedoQQXNTRxuICg/c2Rriko5UeiXsKUHJCqsS0WKNh0wWZseZXJ4einJxS9tp/5INMHQbYIMsoTwTK4vYkSNYF0VJZ7vJ3UkpJjgmpGrWOSDU2NCQpaCIDTagXPLoiRLIVnhvoJNEqOKWNWpxtmsRjEby5PsyhmZY9yECGXeOzI8qmExI0wSdH1nywV3cC92wN0t0KmwtNFsQU0J0skt421Xq1o6XJ1rQqFF9fs5ODnSHJaRX0BOiVqPHcQGeZ69gtUWfOYR6aVdMFsaqFThOCfskav+2I0i9ZI92usTIW6r+3l53zOlRuG5xoFYzOsJQIBa/oZOO+2KbWhOqRZte4u5edu5eESLYKbuqurvX9vR28vzHCjF0V79rt3Rqb/SbZIZMMu8YxLSvXdJhSOWD2scH5HjgjW/3+JJeKFhb3JBhsg4sTdlucW4RA9ThVVUgAqyKSv4KwIgJroyrS2NsmuShBuZ4eCGiICqUmzUAT6oy1XoMPN0UY18xSYk9ajHS7xiuDnRyeqZf0XveXs01dUWjwvX93XhAqzt1X9nO8ef5muuC+FMFL6YL+sahyqq70CHfkwY258HvgwM071hc+WhjApkH/5D1Pg4e3sJb0JwB4d0OY/LCktUsrYzJUjIo87cujMCI5e5aPYZO9nDHLz6J8lZ/+IztKklXwv43hkpB0ZXhtbYir5gU4uqWFe5uIQI1xRWc713W18fSqEJtiZcOagMGpOv/kmXy5Zc/rf2JrCy4LXDYnoCxxS6H0fTal5NGY50VXC6wrxS2+8MP3gd1Rgo7WPXudPJKiPErax4bdp171nLVRuMCj7M9nhVRXxWL4TcmsoPrnbaSdT/d2NVITGWhCnVGcB56da7CkYLeQSEpJUUTSJUHn9aFuNCH49+Ig185Xs3JRvsFDy4JMy47ydahif/Hijb4qIlAbXJQAD6fATUmCV9NV2LGlBV4pgo+8apEoaKSLxL7GeAeYJjy9SvVQDpuSWbuiJff7+vkBlhYY7Aia3Lc0yLfb9twoKiNwFf1uaYHB1J0GLVwa3ROgS4Jati7qYGPKOBdDUq108qjf5YXLGhMB/Lg9wv1LQ9h1uK9X7asZDnZc29VOklXw4DIl1nRbBN+NdjEkVec/y4J8uzVShtCn2jReG+xixi6DbyoYA8WbXa6p9AGXJcAYp2BFRIWs30iHITHSnqKpOXp8BV1O21sFHzQTDIo5Yl6SqCIIdlQvg59jJGB9jFD87JdcnQPPFKp/l++C/xZKFocl/lrM+XyjYdaK6k7/9S12bkoTNKFe4NSVbuCuxUG+HKXidQ8vD/HllghTxntKSlKPzLSwtFARhqKIam07eaybDlmhShfpYjV+fTLl5rpQxwlUW9/+duhpk5ybraoZitHeIknVlFipi1WVPDoPkFBjbdHCIjjZLXl2VRjLjjDdrXBHkWBwqs6LA538nBXllu52mjs0FhyeUFJdEA8qusdDUjWOaWnh861RTmltoY1LvaBVE6TYdR7uq36WUnLe3346uFWqAlSvgfP+DtAvWeOTEa4G61FxMMBjEdzTy87V/wQ5okWEk1pb0YTgmQEOzpjp56I5AW7sZuO2HrtVgROaW2jmEHy8KcJar8mkTAthU7WQ7pesM2FEAm9MVSWobWK70Xd+GGhXVQVzYqahVlRzM0upNWJFrLlWGwu871WkIk2DDlb4v0S4L09VKu1+PHzslXzhhwkOONmtTsNTg/CDH34Nqp8H2yUTHSplUV2FSVQqV04TOMwpOc+jPDz2FupzTWzyGWhCnWFIyXXzA3y0Kcqrg52c1FrlBpcVGGQFJROq6Am/tMAgLyz36GgXr1CmvpX680OSVF3lGZeEYUkEfKaKWiwIg0uDCz0wvJr894EOU0qeKYQ5IbgiAQbb4aiR6l6FTHBosMEv9xD2VXa/qrrPN/9ayHteeHmQnZm5Jg/0dmCvRPg3PTuKRYNDYo2Hbpgf4N2NEdYfnVBiWtWE2kNKyVXzgny1NcInI1xl5u3/zfWzotBk6oSyjldPrgjxxdYIu0KS3PDu7SZNUyf9d7zKffTdDLWR3pAj2VpqEx/rUBv2eR44OmZBHJWSs2O6Ao8Ab7ldrKWumhi96d39c4GpOiOOdagxWzpVZErJZz74xq+qmjYZ6nUH2lXFQ98KqiWKr8dnfvhMFTAhgLfTwVFPB4Z4Oj9Wt0bG6zPQRAaaUGc8vzrEA0tDZNgFP411c9fiIGe3s3JYZvXNXq6Y6+eLLVGuSIB7J9asLmhvl+ztMiT/88LfITjDDSe4Dm7BYVRKni2AuWG4MgG8Bgzt4WBAss78fINr/gny6zh3SVUB1K6pU07I5PedUU5sbY3bCyArYHL1PwGmZxvc2sPO9d0qFxg2oWaImJLTZ/pZUWTy4iBnSTXJUX/6aOfSeHmws8LnFUYkn2yOMDRV55t/fEwOwLJY9iBJg9fS1b1dG5HckVfxexcTBoCvfZIFYdXHIDnGOU3grSJoYYHHUwXPFEhmqWwWOtDKApOcMNIOrko2bCkl66KK6M4JqejC4U4lbKxs/D2QJ1kS+y7HuurXRbGMv0ccGpvyaOpa2IS9hh+2RemdpHF+exutnIKwqSyDK4KUkqdXhemTpHFYppVjW1ro4NG4eT9YrNN1wfWJ6gTxkQ/+CkE7i2SwHYZW0sHvQIZFCP6VJLk7B14qgnQNfpwf5IIEuH5cAs3sWhkiAHt2rowHhVF4aFmI7on6Hq9XGXaEJP/kGWQ4lCtlE+oPVk3w8mAnV8wNcNpMP+e1t/JYPwfZQZPhaZXfn0Sr4JKOSgSwyyHob5OsjarmWaVnTxuLSgtEgM4WZUZWjL9Du70JjncLjq+gh8HEUlzkkgQY44BuFvjMrzb3N4pUWmGcQ3KMS83r0hBC0MkKnaxwulsyOQBveVVTpWKb7+1R1b77v0WKZCwpJ4n4NSApMOFkd93XheIUaUMffprIQBPqjEGpOq+uDXNCK5VDfO+Qyp3mCiKw1mti1eCwTDiqpY2jWu7lD1wHCCE41QNdrZLfg7A6okKYT6fKg9KzwCoE/0qWXJcLiUKlB1I1mDrLy5Kw5NcdNkYUhjky5hVRekGLVwfSzC44sbV1jyqUqpBkFRRF4bF+zhIP/SbUH5o7ND4f6eLdjRFuWRhkTZHJ9qBk5q4ojy4PMqG5pUJPidL336UJ+lTgFbXLUEQAVP5/rAPeiIX7k2p4Kz2aYKAdPvFKfgjAU6ngFPBLAH4NqNTfE6mqE+O8kOQnP9xZyv9ACMEkF2w1JF/7YUNU0sMKH/gqfr/rE5WT4rU5sNOEVE0y3lk/hKCy31fUGr42aCIDTagzijNHr6wNcXN3e5VqbV3Ai4MqDiPuT+hnF/SzQ0hKLsqGhWFVjXAwooVFkKFJTFReNoRakP78rZBNfpP+Zt1SOm6L4O5ejuofWAprvar8rSbixSbUDEIIzmtvI80meHZViJAJc/NMNvgiPLFSNTM6tLmFJKvg7fVhOng0UoKSPrbKQ/SgjMDuSpZsjSr/j2Rd0NqiiGDPSnL31eHzmCh4pwED7KqR1wiH5JZcmBGEsU61jiVUQjbO8igtwVte1bEzUUBp9+ND7HC0C7rGFsNT3JKXilR1Uk6SziWd7AyMdcMsvWnXx2m/vsTVTZqBJtQZvqjk6VXKoez89lae6F/xZv/z9givrwvz1jBXhba01aGx2vrenyexC7g1+eCdG1MCkleL4L5k6F7Jgj1uuIcTfiqim1WFcj/xwbn9XSU556ogpeTepSGOamFhWFr1j/dFJRP/8LHZb7Lm6AScTU6DDYr3NoS5foEqObyjhx2/IXlmVbjMY9I0yDGhuQa3J0OGXrY6oCHxXIHK6b+Utvs9w1JyfQ70tMFViRV/DlPKkiqFXBOuzFG/fzAFHAK0tg66JmgMTdXL6IeklFw0J8B323bnOG7rbiNswuWdyzbaqmozr82aV/61mjQDTdhrcFsE/+7pIN0muGdJiIs62OhZQW43LyLpm6xTGz7ZWIkAqNNEfg16uh+IGOdQm/vfIeheLvQ7LyTZkWJlPDDBCblpNqIFYTbZNNZ5TcY3q/71Q6ZqU51hIy4y4LYIBqfomFKlLprQsDi7nZX+KTq3LAzyyPIQpaeDS4eTWltpkR+h0FSNx67LVTqBQTbJ8W51os4zJFODSjPQ06o23zwTeliV0DDXgLGlQu4BU7LDAKcWKxWuAv9K2vPvLxfCLlNVDf0WkPS0qjSESxMYUvKNH34KqLndXFciwt5W5Wy4NQr3VyF4FkLwxhAnW/ySTT6DE2cGmJptMCvH4KlVYT4c7iTNptE7SavyVF+T6EFdowNNZKAJ9Ybz2tv4cFOEy+YG+HOCe490wRlt1S5hVB+M2m8gpWRlRImUDjaUX3w6/FDIovCe6uccA/xRtUBeN654AXVwTA3eSxOCdV6Tli6dqCn5emuU41pZyrQkLo+AIUlospjeKxBC0DtJ54cxbrwRyfx8g/ywJCphclaU77dFySsnspOoSpS5YbjII0vKAAG6xzbd8hhtlyyMwLtFsLUU43g3Q9aovl9KycxYlUGuCa8V7RYtvpYm2W4okfBoBwyyKeHh/7xwWxKkheANH4zOijCxuaXStKgmBG3dgrZujewTrERMyS0Lg7y3McKZs5QT0rVdbfy7Z9WLR7yEoK7pgqY0QRPqFXNzoxz5p58bEuH2CXsy5+nZUU6c4WfTsQlYBazxmnSvoMlJyJB71JI3xujA1IDKDd6ZXHkt8oGK8gtPn5+KGJKq8+ZQZRNXWW60+HmFEckWv1lhFKkYv2RF2OiTnNfeSl5EkmwVrPGaTPjdxyFpOjYNXhpUsUjws80RrpgXqLDTYRP2LqSUZAUlO0OSsCmZstDPrCC4NRjlUHX6i0plFRwo7Unx5qSjSvv+Dqk+BaVxkQcOd9V87m2ISIqkSgEkafBIPqyKRfWLicHDKdDRKpBScn8+bIrC9UnwVkiwJSAZnKLz2UgX7hqkPW9fGODtDRGK9bBLj/DUSORa3TpY2zRBUwCtCfWKwakWRqbr/KZpe9jCgnLyO7+9Facu+HxLhBOm+/GVU4nftjDA6bP8ezx3woiEMv/2Nf4Oqjz5eAf0aape45A0naUF5h6Rn8oWry+3RBj7u4/5eUaFfwdItQne2RBmS8Ak06Hh0NUJ9NvRLtZ5Texaxe1zQXnjt3Ko8RQ0Dpxo1P4IIQQtnBr9knWKVgQYYhf8K0lwcYKgm1VwYxI8mwrvZ8BRTgiiiIBdqN990ExgspsIdLYoe+IPM2pHBEDZGPexCVpaBG5NcE8K3J0MNyTCSW54LFURgeLPf2MS9EjReaxQNd8CmJtn0P67Ijb7488TRiSUXvI2+Oo3x1hbm+ImMtCEesf1Xe0szDd54o89B+S4ZpYSgWGXBJ23hjp5bW2YF1aF+HWHmukntrZydjsrX22N8O6G8B6v0Rjwd1A58A2zw6UJB1coujIydkVnO+t8Jm+tr7xpUOnnndveyuqjEtjgM7ngbz9b/AYrCw2ipXzeB6damD7RQydP2ZP9sDQLi4/w8Eg/B48uD+Et1QgnbEr8UYkuBK1cOnPzTL6qppFRE/YeKho7DiHItAgsQnB+guDOZGUM1Me6e5NK1JS50Ei7EiD2t4sqK5eyopJ5IUkozrSkRQh62QSFUpULl5cZeDTBtbrB5Z1teKOSlFJR8rm5lRPa8ni4r4OvR7l4op/yVjl6mr/Szo91QU0JQRMZaEK9Y0yGzqh0nW9MrUp9wMAUnUSr4MmVIX7MijJzl5pQw9IsnNrGxm87oiUd6kqz3dLh530RJSgyVURgiB2uTqzev/xAQ2WLzMAUnV6JGstizaqK70tlDmqaUKf6BCssKzQ49+8Ak6b6mLbLYFvA5OYFAfxRSUEFzYcMKcmLSLKDBm+tD3PCdFX4HTYlQyZ7WRbrf3FdVxsODfLD8UcGwk0NqhoM8Z5a+9pU5ODm5N0b/kiH4NV09XtPNXa/ppRcmwuPFcB52fBnQLI+IgmakqVhyZZKPCtMKXm3CLIMJR4sD6tQYunvRrt5JQW+HuXii5EuTmgVv/zOqglGpFs4v4Odh/rYmdTcQmYN7M1rst7VhBA0CQibUO8QQnBXLweHT/Xx4cYI57SvwFkkhl5JOluOS+TH7RG+3hpBSllyyn4+1mzmo2mFLA9DP3vZboWlJ8XecOgClft816tsTy9OOPiIQFXICphs9JscW6pvfUX3pfy9O7S5lV3dJB1cGukOQSePTlFE8vaGCG1cGs+vDvHakLIliFLCwF+8HJ2pUxTd7TJoETA2w0IzuzrnHJZpxaoFiMS5v+8Imkz8w8cJraw82OcgVIU2IPaW5mdFWM3R0ngx9tY6qg8CwJOpktblcv2Lw0qrIIA/g3CaW+4RfairuU9pXNbJzmWdav68hljvak0GauOR3ISDBwNTdE5tbeW+pUEmZVqqFcgkWwVZwYpX7JUR+NQHvW1qM84zK3Yia2hCIKXkuUKYGVJe/EkHeffC0ggYkrP/8pNgEZzfYTcZqO5+ZAVNxk3xcaVL0swmGBZbRxKsgj8nuOmeoNHWrbGmqGwJokUTTB7r5q+cKDd0d5SkETQhSjoWFqO1S2NrnDndn7Oi7AhKXl0b5qgWFkakN52X9idkG5L78qG1rjoTtrXAeCcUmaqF8YaIEiFuMaCi5WZ+LCt5WxI8UgAzStkfl8b+tN9N/Tu+NbHe0wSNUfHdhH2DB/rYyY/Af9dVnvcvDhsOT7fw1Sh3hbn3eyYk8kwanDo6kb6DPdztFWxrWfGpraJJWl8Td01UEYETXGXrnQ9GlJ/n76wPs7zQ5IPhLtLt1S8rxc+fO9fLYVZJRgVi/x6Jysjlz51R3lxfdgxt8pksLjA4u52NTh6dr7ZGyAlVvOE7ddVJsTq8uDrEjQuCDEnV6ezR+HhTk86gsUJKFfb/yCv52icJx9JIjxeoqN1lifB/iYIjXQKHEGTogqF2QWuLIgJjHNC5gsq4fBO6WJQeIVWD9RHwVZA2OhD3uVrR3oouRH2GTppwYCDNrtErUWPGLgNDygpD6sXjZXWRQbJNkFHJRjIp5m0vJTzV38HYKlzrynt119fE3RmLL57gqpeX2+9ROtz/8eYIR7W0xN1ICODR3wtZGam+w9tTA9RJ/7cdUQ5J03FblM5k2q4oJ7W2MTkrwk0LAtzQ1c6VXfZseOXURZnWuRUhYEjejhGO14c4+c+yEKuKDnInqXpEfW6eYSl5NH93cyCBIuljHJKNURW161JJCXyxMPDSSrYpi9idRmhtge8DKl1wf8re7T0ipeTdjRHGZlho5654TazvSGiNIgNT/65e/NHYGFPxybO25RZNqBzxXNvbe9iZl2twxkw/WyoJ1W4LmIyZ4uODjdWfxJo5NCY2t3DSdD87g3t3sS6uYGs6L5ZFXliyrMBkbEZZglbdAaHAhO3lRNSVjaWVhQZnzPLz/ka1Yd/Ww84f4z2sLjL4v7kBnuzv5IrOFWtTrAKyQ5WTgaygyZFTfWwPSj4d4aKlU2NLwGRunkF2PCGFJuw1GFLyWpEqMexogZfT4JEUyDGUKRBAu0rOCWEpWRiGFB0q44Z2oLjS70w3DLRBkVRCxNJo6EPvjqDkxgVBBk/2Vlii3RBokIRYfTVOqOr1ofobUlUEIx40RTkqRk2u4eEtrHwyQnD1PwFG/ublxm52Lu9sw1Yq397MLvj7UA9t4qwX1gRsD5rMyzM4JE2wsshgcIqOpVwOv6GYcw2a5x3QKJ4f326LIAQc0aLi5aSye3CoU3BonD2rbl0UJNMh+GV7hMs62WnhVHfDbdF4bqCTsFlxeef8PIOp2QZP9i+bVioIS77fHuGXrCg/bo/SzCH4aayb3kk6KwsNZsQqWwojkozG31270aEhDl4RKXm8QDUFuzoRRscU+Kk6PJMm2RnTAbStZFezCcFViZLXi+DyXXCUS3Kau2x/hHVRaBuTvHSwCm5Nhk99ku/9qtKgqlLG+kRSKe+Mhfkm/VMqjrhVt8aFpdzDpKkyNFhpYUMMhvKnhqpOpvXVDaopmlAWVV2Pyv42tpmFmRM9nNfexn+Whxj+q5f3N4aJxHJxxZt4vLX6uhAsODyBI1tY+SfP4Nhpfr7ZVnGdbn2WHm43wIaqdW7C7vu9KN+gi0ewphZh9UJTsrCCU/uUmUX8OqOQjbFj2muDnVzeycZJbdTpf2G+wV2Lg+hC0DNR5+HlQVYV7a71DhmSTzeHOWuWnz5JGme2LesK9eraENfOD7LeZ3JbDzs/jnGXpDiSYiHmzh5tD3+DJlSPhlgzZwUl58S6gyaJ3USgGB5N0NEq6Gmr2ntgtEPwXBoc54Lv/HBXnnptKSU5hmR9FIaUCzD1tEJAlnVIbGg4dcFnIxRTvnD2ngZspVHZ+jYtKDk3G54oqPDPe+CAkso21MbdpIeIH5VFhRKsggf6ODi7nZXHVoS4bn6Q51aF+Wa0i5WFBqfODPDzWHelDLgyDE/TWXyEh8xqqhXqI0qwKaryiHvrdLA/YMrMIjS3he1ByX+Wh/h2tIV1XpMz/vByZSK0tQh+9ktmheDeWJ/4XEOSoKma7Q+98FcQOlkl45zQy6rCsm0tgi/88POvXp5Ng2NGJXJVKT3A9Owoy2NeApkOwfhmFjp5NEKG5NlVId5aH2FXWDKxuYWXBjnKRI1ChsrHntjKwmtD9hSAFBsUvTl0/2+1fSCgwJS8VLj75/+r3FE3LiRpgtM80M8ueaMInilUniEayvFwaLlIUDeral/8sQ/678Uo0dhmVh7tK+nkqf70UVonFZSSD7wwJeaTUL7dcmXYr8jA3qolrwwNnf44GNA9UXnXz8uNcsw0P8f+6cOQku6JAkHN4+8ui8Cpw7sbwnT2aAyvohSs/L2ryVgKSsmSMPSt3DLhoIV/Z4RQBM6UBlNmFpFrSDpZVItXgExdbfIAUSm5IgcuT4BuVsnyCERRC/FLhdBGhwIJr6TDkU5lO+vSxB6E/Kou9hJy4LIIHuvnxB+VXDjbz/RdBue2s3JJRxudE/Ykl0VR5ZNf2g+hNNZ6TbonaPSooGdGE/Y+1kUgjEoNWFF5/PpAN6vgsVT40S/51g9+Ced5wFku3WgRgqNdKr0Qlqoh0t7aCy7qGP+XLZ4j80Lwc4wIZGjKVvnCXdU/v0HJQNPmeeChvgjZoFQLP411c9W8AIURiSEF/+Sb9Eup+WtFJDy+IsStPewMT4//eTX5LsvDqhf7pKbD4h4YYoevfOrk9K9ESaouuLTU6a2fXRBzXkWgarjbWFTotW3MY765RbAoLFkQ2t0O2q2JSk9ipc2pAFYVGVw4O8AWv8mHw12MiYkZK7q/ppQ4BPy0LIB7Y7DM38YP9/DrjiiHZe5X56QDFkWm5OVCaKkr6++adCaMF0e6BIc7JSZl9QOlUVzwsjgMA22y0dmPlx7nXayq7bPPhBPc8b9G04ivIZoIzp6le6V/VxP0S9Y5qbWVV9eGmT7RE/fzHloWZJ3X5I2hLqSUPL8qxJ8TPCTUollQvIRgm6Hcy9o3zZg90MYiuDRR8kIhHBuFjlXcB10IBtjhC5+kqxVuKGUA39cm6GXdHR0qL9gqPcZOnuEnySZ4a6iLv3KiXDQ7QKpN8N1oN32S9SrvqSYEA22SaUE4udxi+cqfRWwJwPGVRA2a0PCISMnWKOww4IeAalr0WHLDEIFiaEJUKaAbYldtlR8rUP99a0zjrS9upgvuLXWo8sdpr93gUqj6FuEd7BtxY0J99AZo41I14AvyokzLjsZVRtM7SS9JB6z3SV5cE+buJYFaWwNX9/l3GpLPfTDQfmDoBRqip8MIO6Ro8GsFfu4V4Z8QbK1A86kLgS4EK8KSm3IrX8ju6e3ghFZqw/734iBuHb4c5SJ7mT+u9cYiqDApNS2oWtoGVsX3Ok2oH+QZkgUhyadeybU5cGsePFUIXhPuSIJkfd/OO4sQ3Jusolr5Joz4zccHG+tXUfjwsiAZXxVy35IgoRp02ayvcbrXdNEHEiFoWiTqD+7YJN/oNzl5hp91VbTzXJhv8OP2CMe3UvlggI4ejZNbW5iVE3/XsIpQ1Zh6uRDcQuW5DzTU11zSheBwpzJoyYljIXswVVTZejZdV6Kt0kaPxfMuakr6JescHyMDNk2ghyUL53oreqk9MD8k+TO4Z8rHkJK/gjDCsZv0NVUUNSzCUvJaoeTKHHi4AL4NqDD3bUnwQho8mSboXkmL6r0NIQQD7EpnYAWunx8krwYNsKpD+5i50Atrwhw7zUfWXvZR2atFUvU5sZoIwYEBS2wEDknRWXh42Va1hpT8GYsWzMuN8uDSIK+uCe3xGg/0cfL9aDe7QmZJKVp9wWtKVkbgGBfVdkrbH9CQ8+ZwJ7g1eK2ISrtVek3J20WSn/1VL6LpuupYJ4Tg6XzJDz5JREremlrIxN+9fB1T/E+ZWcRRpsHaKPy159DYAzJmWuMWypK2NJZHVCXDIRXoFJpIQXyI9xqtjUgu3yW5LkdFY87xwEMp8FIaXJ+kNt2MfRwNqAx2IXg0FZwWuHdJsN66XJ7ZzsbN3dQhZ36+Sd+fvMzNrf/WxpVhn1RM19fE2hfta0ujaYGoOzb7JVYBzZ0aLZwa2wImr6wJYUrJeq+KFvy+0+Ct9RG2ByUP991TwefQBc0cGuN/9/H7zvqZPBGp2pxel6OENb0PkCqChhyvLk1wRQIsCMOsKjbmNhbYVQPOtjaqGsjMC8FtedDWrVG0NlDyXXrE0vs/Vl2ODYBPQq6pOk4mlCN3s0OQrkGnKnQhTXO+ctTkutyRB3mmEpA+nApHuwSdrdW3Jm4saGkRHNPCygebIrT6pqikbXddcUsPBy8PUmucREUf9hb2qRyqvur3D4SSw4o+/76OfuwNbPCZtHVr6EKwK2Ry+FQfEVMSNeGqLja+G+1iWJqF0Rk6c3MNOno0pmUrj3prqYVDSsn9vR2MqajjTRyYMrOIfEPyfQCWhFWHM4lSET+TBqmN9JRSV9T3vOlvF6Rrki2VcDKPJpgYR0WGGYssaEJwpkfpEVyx0/y0HQbnlqoaEULQWpcVdrIsD7dQ/9ZHYWS5vy0IKV1IPErxJiFxWdR2HN2Q1LDCwIbChBEJ9AiYuCzw1voIP2VF6VmDvhxV4ZQ2VsY30/lsS4RD0vbeFn3AaKPrUkNeH6itsj4eR78DedFZ5zVLcmXpdo23hzp5YXWISS0sCCEYFpsMVk0wPN1CVsDkrFl+3hnmYkLz3cNXCMGJra1sC5hM+sPLUwOccTfN+XF7hE8sOn/kRrEKwcRMC1ek6WxZH6SlfuARgeKNrKHmiFUo74CKkBWVzAwpD4Hy9dyl8UsAsg04NwFGlnKbO1eXnOfZswRsi7Hb16AqCCEY7ZDMjIWmi7EtKtlhQr8aRICaCEHtsC6iiN7VifsvEQBo4dR4rJ+TLX7Jy2tCDEvTGVlPLa/T7Br/12nv+mA3CjLQEJOqovK3vYXy71n6u9Xm8xzIpGCN12RSrKY7J2TSPVHnrWGVF8dmOjU+Gu6iR+wY+NjyIJ0TVIkiKDe6Xkk62wNmtWSgMCK5cUGAr7ZGGZiicUUnG5d0spFqix0xO5TdGQ6k8HBDfpcMreJKAVDpgZ/8MN4BVQUI3BpsruA1POWiQQvCuw1WRlTc1XoPtNJhsqn0Cx5NEJWSnwIq8lBTU6kmQlBzrI3d1177YfVmRff65UFOLprj55QZfl4f4uToeihLTT2nK7nvrarz69QEB7zLemldwb6atPXVNfFAy1eGTclGv0mXBDUMB0328u6G6st1RmZYSLVpmFKy0S/ZFtidgNaE4OkBTn7YFuXi2f6S/gflETElF83289uOKK8OdvLTGDe39HDsJgIVoGnRjw9drLA6QoVlohoqclC8pftNyee+3Y9bFJaYUjLaIbikgsudFZX8O1fyuVdycy48UqC6H16dCEfFaQg12K4+w7U58Eqh5IpdilAc5qzdSfVAmpN7A8XRl+/i0Hg0JlQ2/5Nsgo+Guzi6pYVL5wRYlF8/+oHUc7pW+5j6HHuNIjKwN1CeEOzPE/hA0Rds9psYEjrG0gT/Heys0D62MmhC8OIgJ3Nzo3ijEk+s33jUlCwqMFhUYHJ3QNLOvecC/9+1YWbsMvhshIuRGQfNNNgr6G2Dz/2wOgpdyx2SUjQY5YBijpZlKPfCSU5JgiZ4pkDZzV6VuKfLm5SSt7zqdTdHlRnNPclKQFgTR7hUXfB4quRHP/wdgmEOFanoUIdh0BQhiB8/xUjA9wE43SOx74epgvKwaoKXBzkZlOPlw00R+ibXTT+wt6MCcBCRgfI4EEhBacT7PRrTgrUjqHaETIdASsnQVEuZ1p3xIGxKLpod4PhWVh7oo+LEQVNtHB8Nd9LOXfFJf16ewaBUvcZEIJ7r11Dls/vLWO1uVSH3JeE9yUALi2CMQ3JFLjydKuloFTycKkuU/U+lQXIlWgIhBDcnSe7NUwLA4veqjTVsM11wfgKcX4/T4WAmBDXRoFhK3a79JTQdz321aoJ+yXq9lzfvLRy0ZKAY+7oSYW+jrhbC9YliMtDMoXH3khDTsqNMHucuUyVQHWya4OvRbpKscOpMH+e3t3F4poVUu8Zmf+WTMtOhsSg/zkbfNURdBKRVPXd/GauaEPS0SmYE4XiXLOMM6TclIRNOckFxM7bWpXaHyohAMSxCEJQSm4D2+oHhCHmgIN7xeapbpWVa66pzZWNHTeZz0JSE6ydLUC3qey3YX4hZg2Jfb4r7Cvt6Y/luW4T2LkGCBc5vb+WGbvYaEYFidHBrpNo0eibqhE3F0Aek6LR0Vj681/pM2sfRGrShUdoaOJ5xuK+9NeLFiW7Vz+HlIlUmWKwfWBmB2/NVfj4xjnsdkZLcco6GNySBLqF/I/R+2Ndzan+AVQhOd8OGKGyO1p+DX0OgpnPtzLY2ZuYYLK4n3cDeRKOIDDSGxW1/OXXVN/ZVaHN7wOTbbVEe62tHCEHnBL1GeoGKMDxNp3h/ubNn5dLyZ1eFmLIjymP94pSfN0I09vHa2Sq4OlHyXCF0t8BHPrgoQdLfBg+nQGKcPGx2CJ4rhNPckpPdgpCUfO2HIqDv3q28qlfU5N41hvUxXsQ7Lkc74Bs/fOCFW5Mb/nPtLfSIDWx/DXoL1AYNMff3/dGoEWF/mnT7OyKmMvVxWwQTf/eypqhuTLooInlrfZivt1Qd+v8lK8KDy0Jc29XGee33w9qmUmjsUYKRDsEIO3zqU1bF7S3KpbCjVVTaKrY0gqZkmF2Fk7/0QciUPFEAM4NwRYLqR9/YUN39qE1FUGMmfRUhnjGZoAlOdcM/YdjRwBtnbVGbuTUtO4pVQLc6Hmz2BfZ5ZKCxLWaN/cTVENgX0YGSCLGA7ok6oTpobnJDJrcvDrKs0OTHKlqLeqOSWxcGGddM544e9kbXk7y2qKuPRUPiTA/ckAMGysI1XoSl5M48ZQz0ZJpglyH5MwSLwnBnsmp33FgQ79xpbPemIRGPQLtdbPcpNKF5I9s7a7seTt4RZVSGTnIDjs+GGkf7NDLQ2IhAMRrr5zqQMDVbycEPa27lxUFOetXByvP77VG+3BLllUFOWrt0ckImtywMkBvezTBChuRf/wTICUse7+c8YIhAeTREe+K6oJkuOM6lQsLFznPxwCYE452KRADsNOCdIpjoaDxEoKbXuS73ZH8lElV95/YWSNZgcpxtr6uDGesnsjKi2iHvqmXEoS73KTsoaePaPwPu+ywy0FgWq8pwsEUI9nZ0ICUW4p2fHyVkwBEtah+yP7GVlc4ejeExK9AVRSZTdxrc0E39fbPf5Ip5ARbkGbw62Flif3ygo7L7ubfH9UlumBOG971wV0r8zzsm1uJ4TkjybAF0s8IFjXvZqBYH27oClUcJLEJwgkvyjhfO9kiSaigezjYkWQbkmzA/FjUqKrX/68Bgu6SNDoc6ISUOW/G6rIHbAyYrikwu7tgIla1xYJ+QgcZOBIpxME7cvYXWMfb83oYIa71mnciAxypKiADAyHQLMw91Y0rYFTQ4e5afwih8PtJV0uvgYEZF868hx7lFCCJSklPDg5ohJd/6lfhwmF25DO4PpWjVoTYeJ/vLmlkVKlpPu1iVdijbIK5GU0FTMi0E04OwopQ8qINFbfi9bSraYBPwVxDmhhVJ+MwP53gkx7p2j58NEUmyBskxklCXa5wflty0IEiCBU5otX9qkfb6yri/DeqDiRDszejAonwDDbinl73KEsDqEDAk9ywJcnimlYmlGhfpQvDsqhDPrw7hjcIXTUSgSjS0wZFTqNxweUgpMVF/S9IgIGGHAQvDMDWoHAqPc8EZ7sblKVAf8yReUrC/rZlVofR3NqVkWqxDb2olS0AkNj6KXQpf98K0oHKdvD4ROljBLir2pzjOrf75TcmFu+A9L/SyKqOr+SHJIwUw3A7XJdX9Gl86x88/eQbPDXSS0MDC1obak/bq6ri/DuomQlD/+DkrysAUnbZuHaMCD/t4sabI4MONERy6oIVDlGkjGjUl3ihc3dnGqPRGplBq5Khvh85WFlgXhBVhSVcrLInAyjBMD6kNH8AGlO5McXJrC5d3stM/peHuXWNw7txf18W6YMKIBN76s5CfYnqB5FJkoMhU5aN/BncTyCRNUmQqDckFHjjSFf+G69IEb6ZL7s+HxwtgvEPyecwSOc+s+/WfujPKH9lGvTUpihdBU1IkIaOeuqoKWVE3kXIoLCwkKSmJt9LVha0NDoQBf7AQAmjY+7XZbzL4Fy+P9HPQK1Hj3L8DPDvAUatUwdICg3G/+0p+7pOk4bEIrBr8mW1wehsrLwyKs4NNE+JCbebBxqjkllz1/ymaWoQTLUorMiRVJ90u2Ow38VgEpoTT2lpxHmCto5tQFtkhk+Om+VnjNRlmV5oQrwnf+yGEKkdtZ1FNrXbF0gjpuupdURsB8NSA5KUKhu7v491xtzuvCMdN8xGV8N1o116LXk2ZWcTD+apr59lumOgEdyV7c3FkpKCggMTExEpfc6+QgQOBCBSjiRDUHfcvDfK/DWEWHq5e/8NNES7qYK31RPpyS4TL5qojxnEtLRgSdoUlF3ewcVwrSxk73CbsOwQMyfsbI8zPMzirrZUR6foBW9XRhPggpeSLLVH+szxIll+lBA5zqvx/6xqUosYDU0qmBGFuCOaXCkH1SNT4cqSLNHvN05V/Zkc5eYafFwc6OK3t3hUO3vZrIW941f+30uHJ1IpJUqMgAwcSCSiPg4UU1Pc9jJiSfj97ObGVleu72UixiXrZrC+c7WfWLoPvx7jo5GlcKYF4WpFWh33RxawJBydqOl7rc2xKKfl9lrfeXq88itezhfkGvqjkmn8CbA9IWrs0/pzgxlHDaNTJM3z4o/D9mL0XFSiGKSWXzw3w5VZVpn2GW0VTyu/R+5QM7CsSEO8grq/B20QIagYpJTcuCPLuxgh/jHfz0powSVbBf/rW3RZ4RaHB6Ck+7uhh5/pu+9antj42/+rQRA6aUF+o7/HaGNfXqtaw4rXjv0OcNaoECJuS9t8WcW9vO5d12jdrTsiQHDPNx4J8Ja4QwEfNakcG6k1A2NgJQFXPqe3gPdDaIDc0vFF4d2OEVk5BrySdI1tYSvra1xWvrg3j1OHolvu2YmBvEIGK3qeJHDShJmjIcVr82nUdk/XhrBnPvtQ9UadbgsYLq0P0T9bL+JAUhCU/ZUUYnWGhpVPDlJLJWVF+zoqyJWASkexTkyG7LnhlsJNDflW6KQn8t1AiBCwIwRkepbGIBzWKDKw7OqHByybiQUMN5LoM3gOdENQH2TOlpOU3RTzYx8ElHW34oxJXDfKCEVOy3meSbBUURCTbg5JlBQafb4mwIN/k2QEOzmq37ww/9hYRqA5NxKAJlaGmY3TBuLI/9/+j5u/ZEOOxqvW2NmvVwnyD02f6yQlL2jgF/VJ0RqZbeHZViKygpJld8NFwF3cuDjIrx6BbgkaaTTChuYWrOtuw1FJYXx+ImJK23xZxX287uWHJZ5sj5IYlRVG4rquNf3Wx0/H7ovpNE+wrMrC3F9naDt4DmRDUV+TniKk+WjgFj/Z1cMRUH7+Mc5NeSriTek7XSq//UytDPLw8VOZ3VgGHZVo4s62VwzMt+1SQVtdxWn7hhdotvuXRRA6aUFcSUBlqMj4b+zj0RiS/74wyP99g1i6DeXkGvZI0Wjk1Zu2KUhiFDLs6iY/JaByeJb6o5Mg/fSwvNPltnJu+yTpfbonwytoQ/+SZ/DLWTWePFhcZaBzfKIbGcrKqbZirKW1QNQrCko0+k84eCwEDhqdbiJp73vfSP5e+B6kxT/pXBjlp4RS0cGpkOkSjKEGrzdiNZ8Et/ZjaEoOqPltjX6CbUDc0FAko//h4xmZ9pQ8aCh6r4NhWVo6N6Qbyw5IEqzIwKwhLftkRYVwzCxm1qDpoKOwISpbHzBiWFBj0Tda5Z0kQb1TyQG87A1J0iuLsCdIoIgONhQRUhKYogUJ9RAZ+3B7hvL8DrH6xDx2bVy+4MU3JV7PzSXBqDO/q4aTH1zBnWRGrj05oVG50Db3gVoT6iBjEg7ou3I15btcWNbkm+2oDrM8x+XZq3zI/X5C7qNLH1nRcNlZiUIyCsOSJlSFaOgWTMi1kBSRbAyYZDo3xzfbdWTo3bHLnohAOHUal63ywKczsHJP/9HVww4JgGTFkUUQ2/jRBQywUNV1o4x28BzMpqK8UwRMrQvx3S5Sdb/UnKz9CrtegV5vKDYG+nZvPCY+sKfO7sT09TL6nGwUfrq6Xz1QfqMk4rg8iUBp7ixQ0oX7QkJtfbdfTysZkeRJQGlURAjhwSMFvO6KcMctf4d+2HZeAdR9oBaSUXDwnwLfboiW/c+oQMMBjgcMzLbwyeHcr93jJwD6jNvVJBOqywMYb5qpL6uBAIAT1gZWJTnK9+Tz6ZRZbc8LMXetn1iM9Kn188Tzr397J9vwIn9/cmWFd3GiaqDSVsLcR7ziubxJQ/nWbSMH+gao0MbV9vdqiJtGAyv5eGSmo6bhsrCmEYak6bZyCVi6NqzrbaO3SmJ1jcOuiIIURSZp975OB9zZG+HZblPtjgsFnVoVJtQm2BiTjm1l4vF/tHFf3emSgsZCAyhDP4D2YogT1FRVIObsLGRcuIM+rjOgfP7cVqYlWLhifXulzfEGDjlcuZlehYsBLnulFj9YVD/R9tYjEM54bighUhiZisP+gtuO2oUgAVE8EyqO+owTQuEjBm+vC3LE4yKLDPQQNGDTZS2ePxoyJ7j3SlQ1d8vvrjgiXzglwVAsrzwxwcPU/Ab7bFuXLkS56JOoV7s+NMjJQX0SgPgZzXRjtwRIlqC8ikHpOV3xBo4QIAPTv4MJEYBgSvRIB4IwVXgr8BgM6uLjiiAy6tazcnKixnizqgtLjuLoFtzTiIR+NkTDUhTQ1xu8TD+LdPBrT2lnZc+orSgCVi4j3BY5rZeGJlYKTZvjZ7FdivUtOaEH6CS2qfW519y3e77bWa/DQshDfbotyaHMLD/Sxc+gfPpYVmrw+xMnQeujIulciA415INeV1R6oUYL6NJFKPacrOUVRml24AAC7VXDz8Zk8+/0O+jLjYQAAKbNJREFUFjzZi/bNKhYTXvHqRqYuLWLBkz2xWWum4N0bC0hjjAo0FGqykO8P33l/JQ91QTz3pTZraGk0RJSgNPYFMUg9pyszVhTxyBdZWHTB3ae2pFcbR43XpOpQ0XfLCphcOS/AtF0GmQ7Bv3vaaePSuHROgJ0htXVvOCYBdxV+LftcQFjf4sC65LfiQV0VsrUZpI2ZENRnVAB2iwH7t3cyoKObC8an0aGZnVZpe5oEzVrp5bXJ2fzvjxyOPj2N+1/sUObvjaW2+WAiAwczDgTisDeIQDHiiWLV1zVtiPndGCpgct9bxfw8g7P/8mMRcG9vB0dkWnDqcMw0P35D8swAJ+1cGsm2qvfkvUYGGvrCNUQ0oCo0kYL6jwoA3PzOZt6dmsP2N/ohhGBtVpA3ftvFbSe2ING1u7HQN3PyOemxNbTt5CCzlZXr729Dl16uCl+7oStBqsK+Fg42oXGgsROFeMdffa+jsHdJQWnUpvSzsaHQb9D7uiW0TLXy9W1dsH63HtjdofWDQ5wclhlfH4UGIQN5/xtQZuFuaOxtIlAadSEF+3vqoL6jAq/+ks2Vr23kgvFpvH5le4QQfDQ9l7s/2sryZ3uXaAYWjIN/nbGalYv9fPFXb9wJ1Y+1fRUl2JflhAcK9uZJdG+jMZCEfUkEirGvCMH+jtXbg3S/ZgkAgzu5OPmQFP5Z7+fTmXkc1cLC20Odcbut7tdkYF+SgNI4GPUE9U0EgP9v78zD5Kyq/P95l9q6qrq7et87SSfp7CQkBEiAQAhiUECQxQUQdRRFRWfkh4LMDD+dcURRnAUdHVEERIGgIBBQlshAIGxZyNqdpPd9X2qvd5k/Kt3ZeqnqrrX7/TxPnidd9da733O+99xzz+WKfzvM8+8PAvAfn6/gK5sLgPB82ZEXeuSZv/RMH/d8rYHMLImzL8zkm/9ajjNz4uSYZMxpTmUxkApONlFtNBKSKRaS4eiied+ieU5P1t8MwLVzH4rqfCK9/4YoOJlX9w6xr8nHc+8P8N4RLy6HxN3XlPCZC3MRo6hvMORVcd20K/3EQKoIgRNJxtDBCIkUBvEYHgDYUevm/LsPsWaenSdur6I0x8SRjgALj80OOPWZ1x/28b2vN7DnHQ/P7lxOScXk1QoTPX0pGVUHJyOVHHCqkyyBEC+HF+37Fe27MiIETiQaURDN/U5HURCPtRxiRVqKgVQUAieSTFEA8RUG8RICANf86AiHWv3sum8JJlnku0+08YM/teN57Ez2XHSywh0aULjnqw289uIA6zdl8h9/iNzpJlIQxGstgqmQ7HYRS8ZyOiNE2yONhmSIg5V/g67BEE9s72P9IieLSq3YIqx7P513aSrvy0TPBSJ/NjNJFKTLVNi0EgOJzHSdLskaOpiMqQiFWAqAEznVUS77xj42rcjkp5+rAMAbUNl+yE3+17NO++1932nimUd7+NYPK7j06hxMUU7fmWojS1TSUawFwVTCvGMRT0c72bFjSayuIxHiQNd11uS/P/r34jMyePSVJXE9ZiwiAhMRyf2fyr1NFWGQjuXE00YMpJMQOJFkRwlSlbEc5Ya7DzGnwMJvb5tLa2+QkKozp8By0rPv7wnx8x+08fSjPXzp2yV87huTF/QYj3gKglhkH8fKoMTbsI8QjYNNlNOPhukKhHgJA13XWVe2k+Cx+eKXfzKXe/5z7iS/mhrxiAaMRzyiBCeSaGEQ7yG+eF9PyouBeGe6jvcix7oHFIsVvGaKMBjPUd70H3W8tn+YXfct5Uu/aMRmFvntbXNPegdu/XgNhz7w8slbCrn5tiJM5skjAqGghqpCW1OAuQutJ2XXxrq4SSLrZkRKosRAolnywvmnfXZg8+sxPcZU7UCshUFdjY+BXgWbXWThsoxxq3FOh0QKgRHiLQhGSJUcjPGYrDrjCPEUBCkrBuKV6TrCdF7i6QiF2S4KJnKW2/YNsemeWl64ewErKjN4s8bN3G9l4/dq2Ozh9+mGiw9QOsfCvQ9WTXqs/Ts9/OSfmtm9w01Wjsxgn8K6izP5ySPzTxIRqRJanIipGp1kGPhYM5bTj5ZYiIR4tftkk+x3JF4JhpMxlXafiOG7ZE2zTEkxEO8pL/EgltNoUqVyXiyJpMf8+oFhLr6nhjuuKuI7V5dgs4hcuaWeg3u8PLZtCS8+1cs/f6UBgG/+Szmf+lLhhPu747NHeeXZ/pM+Kyw18+f3lyOfUpZzpgqCZBv6aImF458MQxiESbV3I1miIFmMd/+TNcUypcRAOoqAiZjs5Z4Na31HEzbXdZ2fvdjJbQ+2YDEJfOmuUs67NIt973n493uaGeg7voDRxo9m8aOHFky4v51vDvOHX3VRVW0l4NdZvDKDcy7MxJk1dj2CmSgI0mGIIBECYCKmIw5iMZyYSMcW6+HUWBOv2gSpxGTPIFmJkykhBuJl4FIp3DnRSx7PcaJUXrJ3LNr6gpR/8fj9yHCIiKKAe0hl8RkZXPv5fL57WyMOp8h/P13N4jPso9tOdfW+E0l1QRDP6ECi20ssRIC25ZExPxevuXFK+5uqMIhljlGsHdx0E6sT/V5M5V6mgyiI9DnE+1rGs3FJEQOJmPuaSkLgVMZ62dOhPvd4xDpprq0vyEU/PcyRA77RzyxWgce2LSW3UOaDdz388I5GWhqDrDnPiXBhFeWbSii9oOi0faXz9KTxSGfxPBUBMJ7Dj4SpioIRohUH8Z56mWiSaUdTJXlzukQrxhJ5/ifauriIgdfqV+GIoF58NKSSQYsF473oRknOsLNTFJ2tT/byqx+309oQoLTSzJKVdi76iItlq+3c87V6dr7pPul3X1U/O2Yd7pkmCNJRDCRaBJzIdAXBCLNJGKSSHU3VKZ+RMJWoTLLO1z2ssmFuiouBVDBm8WK6omCEVHZe0XCqo1NVnddeHOCtVwap3e9j3/ue0e9EEVxLXVhcFlZ/azlzNpePu9+ZNGyQTmIgmSLgRGIlCGBmi4JUtqGpPBQzFlMdnjHEwBikQ5JgrJhOTsFYpKITm4xInFxbU4DGo36ysmUWr8zgt7lnRHWMmSAK0iFvYKr5APEQAhBbMQCpkVsQS9LNfqa6MEg3IQApKgZmkwg4kenOPoiUVHJskPj66dO5j6lw7+I9xXC6bSrVhADEXgzA9KcoJlsYzCbbGQmxsq/pKAQgBcXATB4SiJREVeWKhlg7wWSV2j2RdBUFiShANJ32lSpDAycSDzEAsa14OFPWfUgFkplnYIgBpicGDBFwMjN1ek2sme60qVQtczoRiapGONW2Fq0YSFchMEKsSyCfyFSd2myxk5MRD4E1mc1Ip6TBE0m6GEi35KYTiacRGCGdVldLNLFcmCpdREEih1QSGR1Il1yBiUiEPTCYGokSBekqBCCJYiCRIiCZFc7SKYx4Kqnyko5FvFaojMU1p+qiKKksBiD2giCRQuBUDGGQesTLft7c90HaDg2cSMLFQLxFQLJLm05GrIxEspOPxiNRL3cilqtOJWGQjqsXxrPC4HgkUwCMRTqLgomeX7peV6rZzVkpBmIpAlLd4UdCshtTshvFVBtBIkTAWCRjtbRErJA2HsmeYjiTSHZbj4Zon1c6XdsIybZ9JzKrxIAhAiYnXRpUKjWiZJFKjTcakr1g0Uxtu9GQqu18Os8mVa9pPFLNhqWSPYlUDIy9zNsExLoXMpONyanXlqoNLBoHkWqNztvpo+7pRoLDIao/XYW9OGNK+xl5r1OpEacDI+/0TG7HkzFy7anSvtMhgdog9YgqMnDLwA2YM82T7jRS5zKbDQjMvEaXCKGg6zpd7/Vw9I+N1D/bRN+BgdHvKj5cypVbL532MdJFECQ7KjAWRptOTpuO1X1PV5uUap2UVLIhcYsMTIYxDzZykmU449XgT3z2sWycIa/CoYeP0PiXFuqfaRpzm6JzCzj3e6tjcryHclakVGM+lWTlV0TCbI8ULHnh/IQ61Fje53QVAgbj81DOCoJyENg16bYxiwxEKwJmq7FIJ2JhHKYrCjrf7eb5j72Mt9OProVfVdkmceHP1+FalI27xUPJeYVkFNimfa6nkoqCYKpCINkifaa09xFzOdYqmqcSL+caj3sZ7bnqmkbwzfcRM53IyxYiiGLMzylaJrM1jS+20LC1hTNvX4azwhHXc0m27TjRTgSHgvwi+9HYJhCOJwYMITD7iMZ4RCsIdF2nYWsL731/Dx1vdZF3Rg4XPnAuW857noI1eVz/zhVRnu3USXajHmE60YBkC4GxSAcboOs6nrZ99Ox7jqH6t/B0HiQw0IqmBDBluLBkl5Gz6BLmfPhuZFtWxPuNpO3E+v74+5sZrH+LglXXniRkohYBuk5o136Gv/8AoV37ARBLCrH/3SfI+MRHESyWMX+nDQzh+9OLKIcb0foGCO2rwVQ9D/stn8K8duXkx9U09CE3giMDQR4/oH3NnN/g6/bjafWi+BR0HRSPgqfNy45/3om72UPh2jyuffNyBHFyQTcdkmE7xrITCRMDUzE06WAIDCInUoMSiSgYrB/m1S++Qcsr7ZScX0jROQW0vNpG1/u9ADjK7Nx09FokU2J7IqnSsKdCKoqB8Ui2bdBUheHGd9CUAO07HqLjnYeRLA6y5q7DXrIMq6sCUbYQ8vTi662j/a1f4yhbyYKr7yd7wYaIIgZxO3cliBbyI1mdhNzd+Pua0BQ/uhqi5vEv4+2sofCsT1O24Ws0XdWK7vWhuz0IWZkIZhNa3wCIIlJlKYLNCv4AuqqhtnWGD+APMPSDnxF6ezfywrk4774NQZbwPvk8/mdeQsxzYfv4ZYhZDhAEtMFh1PYu1OZ2QnsOACAvmo/otCMvqiL49m6U/bVIc8tBFDAtX4xlw1pMSxYg5ueCJKE2teJ74jl8W15A9/nBJCMVFyDYM1CbWtH9QQS7DakoH8HpQKyrIdAfHPP+2PKtLPn8Qt6/9wPKNhZTdmExakBD9atoikZwKIQaVEEHTdFAANkqY3KYsBfbWPipKjIrI48opEptFkMMGCScSETBZILgkUVb8PcGuPjB8+l4q4v37/2A3OUu1tx1BtkLMslfmRt3RR8NyVj4JBrSSQhMRDxshq7r+LoO03vwL4Tc3eiaSsc7DxMYaAFANGdQuekOKj90J6J83O6d+J77t25j+P4HUeuaEJwOMhyV5J9xNYVrPoktrwp/bwOt23/JUOPbOMvPJCN/AdacOVhcZZjsuQiijDmzaMoiQlMV+g68QNO2+xms246uBEEQQddO2s6aU4l0/fl4H9oSdqoTYTYhmM3obs9pX0lzynDeeSuWi85FkI7XnFHqm/H84ncEXn0LPRgCXUdw2pGK8pHKizGtWor1souQ8nJGf6NrGoGX3iD41k4QBIJvvY9yuOG0Y4o52dg+dSWm6nlofQOobZ1owx7k8hIERwbasButvZtybQ+u6ixcS7JxltuRM2QEUUCySjhKMpAs4fOte6aRnT/aS3/NILJNRrJKiLKAyWlCtoWjDqIsgA6KXyU0HKJ3Xz8ANzdcF9UQQ7wEQTS2IyFiYKZWEzSYOtMVBI9UbyFnmYtNvz6fX7oeJf/MXK57+3JEKfljkunITBEDY7F463kM1b+FIFuwFy9FMlkn3N7f34KnbS/erhrcbfvor30Vf289gmzGZM9DC3owXXEBS0rvpHFzHVJp4bhh7xPRdZ3gmzsJ7atBrW/C/8Lf0N3e0e8layY51RfjbtuHv68BXQ2d9PvcZR9lxRefRhAjW/clLFoeoWvnEwwcfR014Car6nwKVl2D2VGA4hugc0MvUkkhgs2CrqjIC+YgSBJ6MIRSU4fa04dgsyI67WiDwxAMIWRngq4T2nMQPRBEKsoDUUIqzg8f1+PDvH71hGH66aL1DxKqqUPvH0QPKYh5LsxrViCYTRH9Pl6zCmp+d5S/3vgaANkLMilaV0DBmXlkVTnJnOPAUe7A7Ayfo67r9NcMkjnHgWyVk76EckqLgREMURA5J5ZvjVdp1rFKxEZzrGjGH6+Z8xsO/fYIR59pRLZKnHffWgbrhnn2sr9SfcN8LvzZubz6xe0ceLAWR7md+dfMYdXfL8NRZo/4GLOdmSwEdH+Aga/9M4FX3wRAKivCfsunEZwOhAwrlgvPQZAklCMNeH//Z4LvfoCyvzb8Y4sZuaoS8+plmM9fi/mcVYj2qdWnGO/cih5QCA51YHIWkD3/AmRLuDepaxrBoQ4CAy2EPL34+xqpeeJW5n30X5lz6Z2T7lsLBdj74DX07nsOV/XFuKo34Zq/gZZbQ0kdokgl4iUIvJ0+ml9po/3NLjp2dNG7rx8teDwCk1Fow16aQcijMFAziMkhM/+auSz8xDy+XtWJM0tCjCKqGavIYdzFgFHWNP6MV789VmIgkvrw8RID3iefZ+jb91J2UTH9hwaw5lsZPDpM/socrth66ajKbn2tnSNPNVL72FEki8h1716JoyR2hnsmE2sxoCsKgdffRSotQhAFBKcDqTBv9HttaJjQ3hoEWUYsLUS02QgdPIzW1YvuD6CrKoLZjJjnQsx1IbqykCtLIzq22t5FYPt7KIeOorZ3EXpvL7rHS9aPv4OYl4P7p78m+PZuUFUApIoSTMuqCWx/D8Fixnz2Kiwb12FevQyxKP+kEHc8iMSuqUEf3buf4sAjn6Fg5cdZ9vknTvq+Z++z4Z6/f5iQtx9/XwPezhoU1Uv2f/1/rBevj9fpzyjiJQ40VcPT6mWo0c1wo5uBw0N4O3yg65RvKqH/0CAHHzrMUIMbCA895OXLVC228ZW7Slm4PANJCouDeA4Zpo0YGMEQBaeTiGhArIg2K3n4R7/A9+wrfKX5Mrp397LlgudR3ArnfO9MzvrOytO293R4+f3Kp8ld5mLzkxuxuiYP385mYtE+tcFhQnsOEjp4BLWuieCOnagtHcc3MJtxfPUmzOtWjyZ5oWlj70ySQBIheHKI3LL5QiwXnhtOBnN70BUVdB1UDd3jRW1pD48Rd/WCICBVliKVFSEvnEfGdR9FXjDn+Pl6vKDpqPVNeB97BuVoE6aVS7Df8mmkPNe070c0TGbP/P3N7PhuNVrIh2S2s/KrL5E171wgHGZu/OsPqHv2Lqy5czBl5CDZsvAvNSPPLceyaT2m6qpEXEbakipFiHRdp+/AAP0HB8KzHDp81PzuKEN1w5gcMotuWkDphiIyCqxoqo5slZBt8uj/LTkWMgqt0xomTTsxMIIhCtKPSISAHggSeP0dgu9+gNbRTWjvIcTCPHJ//59cO/ch6p5p5PmrXkGQBK7Y+iEqLjm9x9j8ahsvXPMq+WfmctXLm+NxKWmLr8fP4SfqCQwEOSBdjO3KSwhse4uhe34KsozlgrOxfuQidI8PtbkNtbMbrW8QwWrBvGYFYm42wXf2oBxuCDvg1g70oXCPRnBkIFWWYV61BOsVl6B7fAgWM4HXduD51ePh3rjFjPMf/g7LxnUgCPieehHBace6+UKkwtzRsXddVdF6+9H6BgntPYT733+D1t6FWJSP6HSAWQYEBEkEmxWptAiptAi5qhLLhrWImc7k3eQIicSGBYY62H5XMQBZ89ax+h+2A+Bu3UvN419msG47lR+6k8DPjfc8ElLF+UeC4lPofKeb5pfb2P9gbTiaMAGiLJC9MIuicwooWJ1LxYdKyaoa36mfSlzFQLzHIg1BMHVC3gFev8OFvXgpFlc5JpsLyZqJxVWGZMpA8Q1QvO7z2HIqp32sSKMBuqrSveF6tPauY5nKJnS3l+yffQ/rpRvQht0ohxvou+5Wsudn4uvx89mm6zHZT08a2vfLQ2z70pvcMnQjZkdkSUUzneZX23jm2rfQ3R5EpwNtYAhkGYJBTGcuQ6osxf/MS6O9dsFmDTtfVxa614dy6CgAYn4O8qIqpPISpNJCpNJiTMsWIs0pG3c8WmlqQx8aRp4/B8EafbRGV1X0QBAxI/ZFoxJNNHZL13Xe/t4ivF21VF7ybQpWfwJ3yy4O//GbWDKLqPrYD+m83WnkAYxBOjn+SPD3BfD1+BFlAcWnovjU0f/7e/0MN7rp3dtP+44u+g8MoCk6F/1iPcu+UB3R/iMVA/FLC50GBza/bgiCKBlu3kXL/z4wWvzE074fT/v+MbdtePF7bPiJB8k89ti7ruu0vPZf+HvrqNh0B5as4tO2iWpYQBQRHRloAMFQeOoRMHDrPyIW5aN1dIe3c9rxliwmeOQdntp71Unj0SMoVU0gvc1j33dg/8InIz+HKEllg+Pr8fPUc6sI7txH4NU3UQ4cxnTWCrL/63tIeS6Uo40E/vcdCClYL78YqbgA7Z9uQxscRrBaEPNyRp2MrusE39qJmJONXD0vaucjV5RM61oESUKYZUIAAF3DWb4ab1ctjS/9gMaXfgBA7tLLkB75Cl1ZTgwZkNrtMFZYcyxYcyYX0mpI48XrX6Xu6SbMmbHvCEUdGXim94sxP4nxSCdB0PrGL6j5w5dwLbqEwtWfIHfJZixZxXg7azn67F3YixZjy6vC4qrA11ULgkjpeaffSzXoQxBERFN0vaz2Hb/l4KM3H/srbEYEUUbXwo5XkC04y1cjyhYki53cJZvx9zfh6z5CoL+ZoKcHe0E1Flc5/r5G+g7+BclsRw16MDnyQNfRQn7MV20MT1OyWRHzXOEwbkVJeMz48ecwn3cWYn4uutuDPKcM87rVSMUF6IEAvq3bUI80odQ1EnxvL4IgoCsKuj8AgXChEMHpwHn7F8i44apxr7X/lrsI7T5A/ptPxT0RbCJibagmi7j5/vgi7gceRm0Iz4MXspxYzl+L9YpNWDauM3qRSWSqtio43E1goBktFKD5kz2IOdmxPbEUZjY4+ligBlXcLR623/Eu9c82c8nDF7Dw+nkR/z4uwwQFu19AdCZualeyxICu6/j7m/C2H0CQTJidhWghH8HhLuSMbKw5cxAkGU0JEBxsR/EN0vTKj+iveeWk/eSfcTXlm25n54/XjXmcwrNuAE0lo3gJuhrC3bqHvoN/RTTZWHD1jyk+52YCQx2Y7LmI0sRKUA352fHdhYiylbxlH8WcVUzp+i8iSCY8bfvoeO8xWl77z9FiJIIoYc4qJaNgAdacSmSbC0/7PkLubiRbFsVrbyJv2eX0HXqJxuyXQZbQfX58f3wRfWAo7MBPeXXk5YvQ2rvQg0GEDFu4xy9LyHMr0AYG0br7ABCL8pHnVSAW5iHmupDyXIhFBchzypAXVSGYxg5Y6YEgSs1RBu/8IVrfAPnbn0qJmuiJILhrP33XfBkAx+1fxPrhDROG7w2SR6R2azYsDGQ4/KnhbvXQvr2T1r91sP/BWrSQhmSV2Pz4Rcy9vCKqfaW9GIilENA1DU0NjhYl0VQFb8cBQp5edFVB8Q8R8vTg7TyEu20vw807Ubz9MTv+WMi2bDIKFyFIMp72/YiyhYzCReQu2Yy7bS+d7z6KObOY4FA7si2bnEWXYHGVY82pJGvuOuSMbNSAG39fE4GBFgRBxNfXQNNL9x4/iCCy4kvPkrf0MiAcdVD9Q+i6htlZMGGRk8kMla6q4Wpgja2jWdum6pPVqjY4jO+PL6A2tiG4MpHnVWI6YzFSeXFUTiy0t4bBb/9gdGxbmldB9k/+EdPyyMbM0hld11Gb2vD94c94fvl7AFwP349lfWxWaDQwiBWG458+mqJx8LdHeP0bOwh5FDKKbCy7ZRHF6wrIXebCXhz9tOq0zhmIlRDw9Taw71cfHx07d1VvIjDYhq+rFjV4SqlNQcCWV4WjZDnlG/8BZ9kq7CXLQFMJDnUgmjMwOwoI+frx9zWGy21KJiyZRUi2LFTfICFPH4qvHzXoRZQtCKKErqmj/0BHlK1YXeU4K1aP64x1XSd/xccYrHuDzMqz8XQcYODI/+Ju+wB/bwOaEjj51EUZ0I8d48QdaQzUbhsVA5LZhmSefHw2kh6LIElI+blI+bmY14w9R1bMcmL/7HWT7uv46YbroKtHm1BbO1AONxB8ZzfKoaPIi+eTee+3kYoLMK9dOW70IN3RegcIHTpCaF8Nod0HCO3aj9YdrhZnvWIT1isuMYSAQUIxnHxiaHq5lVe/sJ3hRjfVN1Zx/n1rseUnLp9mRkYGdF3H21lD3XN30737KYrOvhmrq4yhxnexuiqwFSwgc87ZWLJKEUQJ2eJEzsiOuBxoMtFCAYZbdo3OT7Zkl2LOLEYQRTQ1RGi4C/9AC/7eBoDwKmVRhNLjEbrUdR0CQTS3F1Q17MhlCUIq2sAg3t89TfD9fah1TejeY9NsJAmprAjzWWdgXr8G62UXxrUMajzRdR2tqxe1uR21vQutsxu1swd92I0eDKEPe9B6+lCa29H7BwEQ7DZMyxeFIy6rl2NeuxLRYRRbMog/hvNPPIN1Qzy2/E8UrSvgvB+uJX9Vbsz2/fgH19K1cnN6RgYg7JRst23D3baXwtWfxOTIO9b77j02du+iaO2NiJKZkLcPb2cN7rYPGKrfQX/tKwQGWkf3VXjmdeQunRnzdUWThay554z9nWTCkl2KJbuUrDlnR73vWAoB3R+gc8WHRyvCTYTgysJ68XpsH9mIvHAu0vzK8Cpkaer8IRzlUGrqCL69G9+WrSgHj4x+J2TYwvkS2ZlgkhEdduRF87FsXI88vzI8va+ydNbkQxgkH0MAJJeG55pRfCqXbbkYS5Z58h9ESDgp+fQFp8Yipa1t8+GHUGrr6d615aTPZVs2amCY2ie/Gl7041hwQxBlHGUrKVh1LTlLNmPLm4cpIweTPWes3RscIy6JTBYz5rNXEnzz/ZM+lqoqcN5+S1gkyBKC2Yxp9fKE93p1VUU50oDa2ona3I6Y7USqLMO0eD6CJbrGqHb3otQ2oNQ1juZQhHbvDxftMZuwrF+D46s3Ic2tQCopTGgSroHBeBgCIHXIWeoCAV666TUue+piRHn6HYFo6wElXQwM/L/vE/jLa0ilRYi5LkwrFmG97CJMy6qxXfVhhu/9OdK8Csxnr8R25SWsaP8CosmCt7OW3gNbEc12TBk5ZBQswFawcNKVywyOE69sZl1VGbzj31AaW0/7Tj3ahGl5NVJxQVyOPRm6puF76gU8//270Sl6mGQIKQAImQ6sH9mI885bJ128Rtf18GI5L/xtdD9SeQlyZSn2z12P6awVmFcumVIxHgODWGE4/dSn/OISLtuykReu3cYvXY8imkUks8SVf72UvOXRd2anUhgw6TkDHVUXjP5fXrIAtaUdfciNaeUSXL/7d/zPvYL/j38huHs/KCqmNcsRnXYEhx0xy0lhzwpkWzaibEGUrViySnBWrDaiAWOQqKlMSmMrPRvDBYGcd38NwZGBPuxBzM/Fsn510uZSa70DDN7xfQJ/24H18k3YrrwEuXoeYlE+useHUtdI4OXteH/zJLZrNpN5z99PuD+1q4fuc69GXr4I+41XIZUVo3m86P2DaEPD4eJKIQXTmcuwrDOS/gzii+H00ws1oLLr/n307R9ADaooXpWG55tP2qb6hio+9PCGiPY3ngDQhj0R5QwkXQyoHd0M//h/wuVSTxlfznniAcyrlwOgBwJ4H/szod0H0D1eNLcHfWA4PH992BMuWnPCpVjz5pFZuZY5H7oTR2n8VoRKVZI5h1kbGKLvM99E2VeDWJSP89u3Yl57BqIrK+J1yXWfH61/EEwygiwj2KxgMUMwiOfBJ/D+7mnMa8/A8Y3Pj7vyndrehXLoKEpLO0pNHf7nXwVJIvu+72C5cOy8C7Wnj/7Pfwu1uY3Cnc9PfI7+AD2X3njy4j3HEGxWMJvQB4cRHBnkPP4ApkXG4jIG08Nw+DMDTdXYsv45OncNYFq1FMFsQjCbw0XcKkuRjtVgkZcsmLbPTRsxMII27CH4zm60/kHE7EzE/BxMKxZPOB9dDwTQgyFEpyOcsR5SUBpa8D31At6Hn4JgCHnBHC74en1czjlVSNXiJaF9NQze9UOU/YdHPxMcGYgFechzy8J1B1aEM+bF4oKTnvXA7f+K/09/GXffUmUpamMr9ttuxvn1z532vdLURs9Fnzh+XJsV60c2kvG56xAkEbW7D62zZ3RRHqWhBfVoE1pvuL6EXD2PvK0PTXqN2rCH0J4DqO1d6IPD4Q9FEW3Yjd4/GF78p6YOgJwtP8e8aumk+zSYfRhOfvag+BRe+bs3qP19Ha6H7sNy/tq4Hi9SMZD0nIERRKd93PW5h+65H+8jfxr9W3Blofv84A/Pt5fKi5EXzkVt7USpa4ZgEEwyprNW4PjyjRzYMLGzTPWyx6nq7CfDtKyavD8/iNrWSejgEbT+IfSBwXCPvb4Z39ZteP4nXEhHyHQgL5yHVFaMmOVAH544A1ZtbEXIzsR+w9VjbyBw0roHus+Pb8tWfFu2nrxdlhOppBC5sgzLuWciz5+DvGIRUmkRak8fwR27CO2tQR8aBk1H1zTwB1A7e1A7utE6u0E5ZcaE2YToygr/y3Vhu/5ypOJ8IzIwyzAcvMFY7PtFDbW/D3cQQvtrMa9fkxIzh1ImMjARwbd30/ep2077XHDYcf7jbSgHD6McbUQqK0ZeMBfT0gWYllUbiVtpgNrdS2jPQZTaepTa+nAPe2gYbcgdXn3Pf0KBJasFeU5ZONm0OB/z2pVYN503ZvZ/uHjPUUJ7DoSFSHcfWkc3alfP6DoIp2E2IWY5kYoLEAvzCb75HrrHh1RWhJifC4IAoohgNiEW5iEV5SMV5SMWF4R/48pCyHIi2KxGmeBZhOH0DSDypD09EMD/8naCb7yL78mtSJWl2D52Kebz1oT9VowLqqXdMEGkKEcbCbzxHp7/+T1ae1dCwiwGyUHXdfQhN2p7V/hfXRNKbT2hQ0dRDtdDIIhYmIfjq59BKism+Ob7hA4dQTl0dHQdBEwyUnEBUkkhUkUJclUlUnlJOHdBFEEQwsNN7mN5KP3hyIXa2olcVYn91huQ8mNXAMRgZmAIgNnBVLLyoyG45wDe32whsC28BDlWS3jodMViTMurkedVIFWUTmvqdVLFgK7r6D5/eH317MiTxiJFGxiia/VHAXA9+tNwD62kAMFiRAJmC7qqotTU4XnwCfxPh3MLxKJ8TMuqkavnYVo8P1y8p6IkqSsbGsw8DCEws4i3w48EXVEI7a0htHMfwZ37CH1wCK2tc/R7MS8Hx203Y95wdtin2m0RRx/jIgZM61ZjqqpEnl9JqLae0J6DmBZXYV63BuVwPYFtb6F2dKMPu4+Po1otmM9cFi6pumopUlUFUl7OtEL4yuEGej5808kXkuXEdsUliEV5iNlZiNlOhKzMsBCRJAQxHOINl7ktTnqEwyB2hD44BBYz8sK5RnjeIK4YQiA9SQWHHy1a3wBKYytqUyuBbTvwP/vy8S/NJqSifKwf2Yhgz0Dr7kXr6UcbciMV5yNVlIZz6SrLEPJc9Jx3TWzFgLx6eTgBrL4FqbQQ0+oVKPtrUA43hNdWv3h9eGnaTAeC045gz0Ctbyb49m6CO/ehDwwdP7AjI7x8bWlROHxbWYZYkIvgtId/n+lAdDrCmdk9fWjdfajdvegeH0KGDcFiRg8G0f0BdF+A0K79KDVH0Ybc4azuCS7LvOFscn79owgfiYGBgUEYQwykPuno+CdD13WU2jq0rl60/iG0gUGUQ3X4X9gW7uDmuRDzchAcdtSObtSm1uOzm44Rl2ECPaSES8ke64Wp3b2II73wCS5GbWhBbe0IK5iePtTuPtTWDtSmVtTGVnS3d/K7YjGPnwAmS0glhQhZmahHG48venMKWff/E7YrNk1+LAMDA4NTMARBajETnX8s0AaHw+XR65oY/Oa/pFcCoR4IhHv2Q260YQ/6kBtdVZHychDzcxBzXQgmGV1V0X0BdK8X3esPFyHqHUBtbkNtbg/XKsjPRSo+luldmIdYkIuYk22MHxsYGMQcQyAkDsP5R0fa1RkAECwWpHwLTJK9LUgSgiMDjCVdDQwMUoBoHZQhHibHcPqJJaXEgIGBgcFsINaOLp3EheHkUxNDDBgYGBikOYaDNZguya+BaGBgYGBgYJBUoooM6G4PWrzOxMDAwMDAwCCm6O6J13kZISIxYDabKSoqouO8a6Z1UgYGBgYGBgaJpaioCLP59DVcTiSiqYUAfr+fYHCc+f0GBgYGBgYGKYnZbMZqtU64TcRiwMDAwMDAwGBmYiQQGhgYGBgYzHIMMWBgYGBgYDDLMcSAgYGBgYHBLMcQAwYGBgYGBrMcQwwYGBgYGBjMcgwxYGBgYGBgMMsxxICBgYGBgcEs5/8AAdF6fzJLiGsAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.axes(projection=gfs_proj)\n",
+    "ax.coastlines()\n",
+    "ax.add_feature(cfeature.BORDERS, linestyle=':')\n",
+    "ax.add_feature(cfeature.STATES, linestyle=':')\n",
+    "plt.contourf(lons, lats, msgs[0].data, cmap='turbo')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "0c2242a5-8f4c-4ab3-aa96-2d23ef3bdb37",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:56.253534Z",
+     "iopub.status.busy": "2024-07-24T03:52:56.253415Z",
+     "iopub.status.idle": "2024-07-24T03:52:56.289089Z",
+     "shell.execute_reply": "2024-07-24T03:52:56.288695Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:56.253522Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first_i, first_j [283] [316]\n",
+      "last_i, last_j [188] [935]\n",
+      "latitudeFirstGridpoint 19.25\n",
+      "longitudeFirstGridpoint 79.0\n",
+      "newmsg.nx, newmsg.ny 95 619\n",
+      "(721, 1440)\n",
+      "latitudeLastGridpoint 43.0\n",
+      "longitudeLastGridpoint 233.75\n",
+      "95 619\n",
+      "(array([[19.25      , 19.25      , 19.25      , ..., 19.25      ,\n",
+      "        19.25      , 19.25      ],\n",
+      "       [19.28843042, 19.28843042, 19.28843042, ..., 19.28843042,\n",
+      "        19.28843042, 19.28843042],\n",
+      "       [19.32686084, 19.32686084, 19.32686084, ..., 19.32686084,\n",
+      "        19.32686084, 19.32686084],\n",
+      "       ...,\n",
+      "       [42.92313916, 42.92313916, 42.92313916, ..., 42.92313916,\n",
+      "        42.92313916, 42.92313916],\n",
+      "       [42.96156958, 42.96156958, 42.96156958, ..., 42.96156958,\n",
+      "        42.96156958, 42.96156958],\n",
+      "       [43.        , 43.        , 43.        , ..., 43.        ,\n",
+      "        43.        , 43.        ]]), array([[ 79.        ,  80.6462766 ,  82.29255319, ..., 230.45744681,\n",
+      "        232.1037234 , 233.75      ],\n",
+      "       [ 79.        ,  80.6462766 ,  82.29255319, ..., 230.45744681,\n",
+      "        232.1037234 , 233.75      ],\n",
+      "       [ 79.        ,  80.6462766 ,  82.29255319, ..., 230.45744681,\n",
+      "        232.1037234 , 233.75      ],\n",
+      "       ...,\n",
+      "       [ 79.        ,  80.6462766 ,  82.29255319, ..., 230.45744681,\n",
+      "        232.1037234 , 233.75      ],\n",
+      "       [ 79.        ,  80.6462766 ,  82.29255319, ..., 230.45744681,\n",
+      "        232.1037234 , 233.75      ],\n",
+      "       [ 79.        ,  80.6462766 ,  82.29255319, ..., 230.45744681,\n",
+      "        232.1037234 , 233.75      ]]))\n"
+     ]
+    }
+   ],
+   "source": [
+    "subset = msgs[0].subset(lats=(19.2, 43), lons=(233.7, 79))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "92801898-2a31-4fdb-ae0b-1a8690bee554",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:56.289745Z",
+     "iopub.status.busy": "2024-07-24T03:52:56.289576Z",
+     "iopub.status.idle": "2024-07-24T03:52:56.292025Z",
+     "shell.execute_reply": "2024-07-24T03:52:56.291620Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:56.289731Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "lats, lons = subset.latlons()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "53cba63e-c274-4fdb-93c8-236c6866cafd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:56.292758Z",
+     "iopub.status.busy": "2024-07-24T03:52:56.292589Z",
+     "iopub.status.idle": "2024-07-24T03:52:56.296038Z",
+     "shell.execute_reply": "2024-07-24T03:52:56.295540Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:56.292745Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((619, 95), (95, 619))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lats.shape, subset.data.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "57dd0dcc-33c7-4142-86c2-cd6c81cfa75d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-07-24T03:52:56.296973Z",
+     "iopub.status.busy": "2024-07-24T03:52:56.296650Z",
+     "iopub.status.idle": "2024-07-24T03:52:57.029810Z",
+     "shell.execute_reply": "2024-07-24T03:52:57.029106Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:56.296957Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Shapes of x (619, 95) and z (95, 619) do not match",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 5\u001b[0m\n\u001b[1;32m      3\u001b[0m ax\u001b[38;5;241m.\u001b[39madd_feature(cfeature\u001b[38;5;241m.\u001b[39mBORDERS, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m ax\u001b[38;5;241m.\u001b[39madd_feature(cfeature\u001b[38;5;241m.\u001b[39mSTATES, linestyle\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m:\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m----> 5\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontourf\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlons\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlats\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msubset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcmap\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mturbo\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/pyplot.py:2950\u001b[0m, in \u001b[0;36mcontourf\u001b[0;34m(data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   2948\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mcontourf)\n\u001b[1;32m   2949\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcontourf\u001b[39m(\u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m QuadContourSet:\n\u001b[0;32m-> 2950\u001b[0m     __ret \u001b[38;5;241m=\u001b[39m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontourf\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   2951\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m   2952\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2953\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m __ret\u001b[38;5;241m.\u001b[39m_A \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:  \u001b[38;5;66;03m# type: ignore[attr-defined]\u001b[39;00m\n\u001b[1;32m   2954\u001b[0m         sci(__ret)\n",
+      "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:315\u001b[0m, in \u001b[0;36m_add_transform.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    310\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mInvalid transform: Spherical \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mfunc\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    311\u001b[0m                      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis not supported - consider using \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m    312\u001b[0m                      \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mPlateCarree/RotatedPole.\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    314\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m transform\n\u001b[0;32m--> 315\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:359\u001b[0m, in \u001b[0;36m_add_transform_first.<locals>.wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    357\u001b[0m     \u001b[38;5;66;03m# Use the new points as the input arguments\u001b[39;00m\n\u001b[1;32m    358\u001b[0m     args \u001b[38;5;241m=\u001b[39m (x, y, z) \u001b[38;5;241m+\u001b[39m args[\u001b[38;5;241m3\u001b[39m:]\n\u001b[0;32m--> 359\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/cartopy/mpl/geoaxes.py:1655\u001b[0m, in \u001b[0;36mGeoAxes.contourf\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1652\u001b[0m             \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(sub_trans, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mforce_path_ccw\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m   1653\u001b[0m                 sub_trans\u001b[38;5;241m.\u001b[39mforce_path_ccw \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m-> 1655\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcontourf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1657\u001b[0m \u001b[38;5;66;03m# We need to compute the dataLim correctly for contours.\u001b[39;00m\n\u001b[1;32m   1658\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m _MPL_VERSION\u001b[38;5;241m.\u001b[39mrelease[:\u001b[38;5;241m2\u001b[39m] \u001b[38;5;241m<\u001b[39m (\u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m8\u001b[39m):\n",
+      "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/__init__.py:1465\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1462\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m   1463\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minner\u001b[39m(ax, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m   1464\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m-> 1465\u001b[0m         \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43mmap\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43msanitize_sequence\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1467\u001b[0m     bound \u001b[38;5;241m=\u001b[39m new_sig\u001b[38;5;241m.\u001b[39mbind(ax, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1468\u001b[0m     auto_label \u001b[38;5;241m=\u001b[39m (bound\u001b[38;5;241m.\u001b[39marguments\u001b[38;5;241m.\u001b[39mget(label_namer)\n\u001b[1;32m   1469\u001b[0m                   \u001b[38;5;129;01mor\u001b[39;00m bound\u001b[38;5;241m.\u001b[39mkwargs\u001b[38;5;241m.\u001b[39mget(label_namer))\n",
+      "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/axes/_axes.py:6536\u001b[0m, in \u001b[0;36mAxes.contourf\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   6527\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   6528\u001b[0m \u001b[38;5;124;03mPlot filled contours.\u001b[39;00m\n\u001b[1;32m   6529\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   6533\u001b[0m \u001b[38;5;124;03m%(contour_doc)s\u001b[39;00m\n\u001b[1;32m   6534\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   6535\u001b[0m kwargs[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mfilled\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m-> 6536\u001b[0m contours \u001b[38;5;241m=\u001b[39m \u001b[43mmcontour\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mQuadContourSet\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   6537\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_request_autoscale_view()\n\u001b[1;32m   6538\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m contours\n",
+      "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:858\u001b[0m, in \u001b[0;36mContourSet.__init__\u001b[0;34m(self, ax, levels, filled, linewidths, linestyles, hatches, alpha, origin, extent, cmap, colors, norm, vmin, vmax, extend, antialiased, nchunk, locator, transform, negative_linestyles, clip_path, *args, **kwargs)\u001b[0m\n\u001b[1;32m    854\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnegative_linestyles \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    855\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnegative_linestyles \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m    856\u001b[0m         mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontour.negative_linestyle\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m--> 858\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_args\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    859\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_levels()\n\u001b[1;32m    861\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_extend_min \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mextend \u001b[38;5;129;01min\u001b[39;00m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmin\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m]\n",
+      "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:1523\u001b[0m, in \u001b[0;36mQuadContourSet._process_args\u001b[0;34m(self, corner_mask, algorithm, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1520\u001b[0m         corner_mask \u001b[38;5;241m=\u001b[39m mpl\u001b[38;5;241m.\u001b[39mrcParams[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontour.corner_mask\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[1;32m   1521\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_corner_mask \u001b[38;5;241m=\u001b[39m corner_mask\n\u001b[0;32m-> 1523\u001b[0m x, y, z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_contour_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1525\u001b[0m contour_generator \u001b[38;5;241m=\u001b[39m contourpy\u001b[38;5;241m.\u001b[39mcontour_generator(\n\u001b[1;32m   1526\u001b[0m     x, y, z, name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_algorithm, corner_mask\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_corner_mask,\n\u001b[1;32m   1527\u001b[0m     line_type\u001b[38;5;241m=\u001b[39mcontourpy\u001b[38;5;241m.\u001b[39mLineType\u001b[38;5;241m.\u001b[39mSeparateCode,\n\u001b[1;32m   1528\u001b[0m     fill_type\u001b[38;5;241m=\u001b[39mcontourpy\u001b[38;5;241m.\u001b[39mFillType\u001b[38;5;241m.\u001b[39mOuterCode,\n\u001b[1;32m   1529\u001b[0m     chunk_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnchunk)\n\u001b[1;32m   1531\u001b[0m t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_transform()\n",
+      "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:1563\u001b[0m, in \u001b[0;36mQuadContourSet._contour_args\u001b[0;34m(self, args, kwargs)\u001b[0m\n\u001b[1;32m   1561\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;241m2\u001b[39m \u001b[38;5;241m<\u001b[39m nargs \u001b[38;5;241m<\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m4\u001b[39m:\n\u001b[1;32m   1562\u001b[0m     x, y, z_orig, \u001b[38;5;241m*\u001b[39margs \u001b[38;5;241m=\u001b[39m args\n\u001b[0;32m-> 1563\u001b[0m     x, y, z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_xyz\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mz_orig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1565\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1566\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m _api\u001b[38;5;241m.\u001b[39mnargs_error(fn, takes\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfrom 1 to 4\u001b[39m\u001b[38;5;124m\"\u001b[39m, given\u001b[38;5;241m=\u001b[39mnargs)\n",
+      "File \u001b[0;32m~/anaconda3/envs/default311/lib/python3.11/site-packages/matplotlib/contour.py:1610\u001b[0m, in \u001b[0;36mQuadContourSet._check_xyz\u001b[0;34m(self, x, y, z, kwargs)\u001b[0m\n\u001b[1;32m   1608\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mndim \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m2\u001b[39m:\n\u001b[1;32m   1609\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m x\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m z\u001b[38;5;241m.\u001b[39mshape:\n\u001b[0;32m-> 1610\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   1611\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShapes of x \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and z \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mz\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m   1612\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m y\u001b[38;5;241m.\u001b[39mshape \u001b[38;5;241m!=\u001b[39m z\u001b[38;5;241m.\u001b[39mshape:\n\u001b[1;32m   1613\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   1614\u001b[0m             \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mShapes of y \u001b[39m\u001b[38;5;132;01m{\u001b[39;00my\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m and z \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mz\u001b[38;5;241m.\u001b[39mshape\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m do not match\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
+      "\u001b[0;31mTypeError\u001b[0m: Shapes of x (619, 95) and z (95, 619) do not match"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ax = plt.axes(projection=gfs_proj)\n",
+    "ax.coastlines()\n",
+    "ax.add_feature(cfeature.BORDERS, linestyle=':')\n",
+    "ax.add_feature(cfeature.STATES, linestyle=':')\n",
+    "plt.contourf(lons, lats, subset.data, cmap='turbo')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "83924ebe-833e-4804-b6ce-e41742d59705",
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2024-07-24T03:52:57.030227Z",
+     "iopub.status.idle": "2024-07-24T03:52:57.030567Z",
+     "shell.execute_reply": "2024-07-24T03:52:57.030482Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:57.030474Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c40ed50-670e-4857-850e-0c6c3209c929",
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2024-07-24T03:52:57.030933Z",
+     "iopub.status.idle": "2024-07-24T03:52:57.031104Z",
+     "shell.execute_reply": "2024-07-24T03:52:57.031025Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:57.031018Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "msgs[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7ef44bc-9c2a-4d88-91bc-3a7037466bf3",
+   "metadata": {
+    "execution": {
+     "iopub.status.busy": "2024-07-24T03:52:57.031650Z",
+     "iopub.status.idle": "2024-07-24T03:52:57.031881Z",
+     "shell.execute_reply": "2024-07-24T03:52:57.031796Z",
+     "shell.execute_reply.started": "2024-07-24T03:52:57.031787Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "subset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f527023d-eb46-4f12-a54f-3404d40352a3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/src/grib2io/_grib2io.py b/src/grib2io/_grib2io.py
index c15bade..5972a4b 100644
--- a/src/grib2io/_grib2io.py
+++ b/src/grib2io/_grib2io.py
@@ -469,20 +469,20 @@ def write(self, msg):
         msg
             GRIB2 message objects to write to file.
         """
-        if isinstance(msg,list):
+        if isinstance(msg, list):
             for m in msg:
                 self.write(m)
             return
 
-        if issubclass(msg.__class__,_Grib2Message):
-            if hasattr(msg,'_msg'):
+        if issubclass(msg.__class__, _Grib2Message):
+            if hasattr(msg, "_msg"):
                 self._filehandle.write(msg._msg)
             else:
                 if msg._signature != msg._generate_signature():
                     msg.pack()
                     self._filehandle.write(msg._msg)
                 else:
-                    if hasattr(msg._data,'filehandle'):
+                    if hasattr(msg._data, "filehandle"):
                         msg._data.filehandle.seek(msg._data.offset)
                         self._filehandle.write(msg._data.filehandle.read(msg.section0[-1]))
                     else:
@@ -1334,6 +1334,97 @@ def interpolate(self, method, grid_def_out, method_options=None, drtn=None,
         return msg
 
 
+    def subset(self, lats, lons):
+        """
+        Return a spatial subset.
+
+        Uses the minimum and maximum values in `lats` and `lons`.
+
+        Parameters
+        ----------
+        lats
+            List or tuple of latitudes.  The minimum and maximum latitudes will
+            be used to define the southern and northern boundaries.
+
+            The order of the latitudes is not important.  The function will
+            determine which is the minimum and maximum.
+
+            The latitudes should be in decimal degrees with 0.0 at the equator,
+            positive values in the northern hemisphere increasing to 90, and
+            negative values in the southern hemisphere decreasing to -90.
+        lons
+            List or tuple of longitudes.  The minimum and maximum longitudes
+            will be used to define the western and eastern boundaries.
+
+            The order of the longitudes is not important.  The function will
+            determine which is the minimum and maximum.
+
+            The longitudes should be in decimal degrees with 0.0 at the prime
+            meridian, positive values increasing eastward to 360.  There are no
+            negative longitudes.  West longitudes are converted to east
+            longitudes by adding 180 to the absolute value of the west
+            longitude.
+
+        Returns
+        -------
+        subset
+            A spatial subset of a GRIB2 message.
+        """
+        if self.gdtn not in [0, 1, 40, 10, 20, 30, 31, 110, 32769]:
+            raise ValueError('Subset only works for regular lat/lon, Gaussian, mercator, stereographic, lambert conformal, albers equal-area, and azimuthal equidistant grids.')
+
+        newmsg = Grib2Message(
+            np.copy(self.section0),
+            np.copy(self.section1),
+            np.copy(self.section2),
+            np.copy(self.section3),
+            np.copy(self.section4),
+            np.copy(self.section5),
+        )
+
+        msglats, msglons = self.grid()
+
+        la1 = np.min(lats)
+        la2 = np.max(lats)
+        lo1 = np.min(lons)
+        lo2 = np.max(lons)
+
+        first_lat = np.abs(msglats - la1)
+        first_lon = np.abs(msglons - lo1)
+        max_idx = np.maximum(first_lon, first_lat)
+        first_i, first_j = np.where(max_idx == np.min(max_idx))
+
+        print("first_i, first_j", first_i, first_j)
+        last_lat = np.abs(msglats - la2)
+        last_lon = np.abs(msglons - lo2)
+        max_idx = np.maximum(last_lon, last_lat)
+        last_i, last_j = np.where(max_idx == np.min(max_idx))
+        print("last_i, last_j", last_i, last_j)
+
+        setattr(newmsg, "latitudeFirstGridpoint" , msglats[first_i[0], first_j[0]])
+        print("latitudeFirstGridpoint", newmsg.latitudeFirstGridpoint)
+        setattr(newmsg, "longitudeFirstGridpoint" , msglons[first_i[0], first_j[0]])
+        print("longitudeFirstGridpoint", newmsg.longitudeFirstGridpoint)
+        setattr(newmsg, "nx" , np.abs(first_i[0] - last_i[0]))
+        setattr(newmsg, "ny" , np.abs(first_j[0] - last_j[0]))
+        print("newmsg.nx, newmsg.ny", newmsg.nx, newmsg.ny)
+        print(self._data.shape)
+        setattr(newmsg, "data" , np.copy(self._data[
+            min(first_i[0] , last_i[0]) : max(first_i[0] , last_i[0]),
+            min(first_j[0] , last_j[0]) : max(first_j[0] , last_j[0])]))
+        if self.gdtn in [0, 1, 40]:
+            setattr(newmsg, "latitudeLastGridpoint" , msglats[last_i[0], last_j[0]])
+            print("latitudeLastGridpoint", newmsg.latitudeLastGridpoint)
+            setattr(newmsg, "longitudeLastGridpoint" , msglons[last_i[0], last_j[0]])
+            print("longitudeLastGridpoint", newmsg.longitudeLastGridpoint)
+        if self._sha1_section3 in _latlon_datastore.keys():
+            del _latlon_datastore[self._sha1_section3]
+        newmsg.grid()
+        print(newmsg.nx, newmsg.ny)
+        print(newmsg.grid())
+
+        return newmsg
+
     def validate(self):
         """
         Validate a complete GRIB2 message.
@@ -1589,7 +1680,7 @@ def interpolate(a, method: Union[int, str], grid_def_in, grid_def_out,
     a,newshp = _adjust_array_shape_for_interp(a,grid_def_in,grid_def_out)
 
     # Set lats and lons if stations, else create array for grids.
-    if grid_def_out.gdtn == -1:
+    if grid_def_out.dtn == -1:
         rlat = np.array(grid_def_out.lats,dtype=np.float32)
         rlon = np.array(grid_def_out.lons,dtype=np.float32)
     else:
diff --git a/tests/test_subset.py b/tests/test_subset.py
new file mode 100755
index 0000000..1cda305
--- /dev/null
+++ b/tests/test_subset.py
@@ -0,0 +1,65 @@
+import itertools
+from pathlib import Path
+
+import grib2io
+import pytest
+import xarray as xr
+from numpy.testing import assert_allclose, assert_array_equal
+
+
+def _del_list_inplace(input_list, indices):
+    for index in sorted(indices, reverse=True):
+        del input_list[index]
+    return input_list
+
+
+def _test_any_differences(da1, da2, atol=0.005, rtol=0):
+    """Test if two DataArrays are equal, including most attributes."""
+    assert_array_equal(
+        da1.attrs["GRIB2IO_section0"][:-1], da2.attrs["GRIB2IO_section0"][:-1]
+    )
+    assert_array_equal(da1.attrs["GRIB2IO_section1"], da2.attrs["GRIB2IO_section1"])
+    assert_array_equal(da1.attrs["GRIB2IO_section2"], da2.attrs["GRIB2IO_section2"])
+    assert_array_equal(da1.attrs["GRIB2IO_section3"], da2.attrs["GRIB2IO_section3"])
+    assert_array_equal(da1.attrs["GRIB2IO_section4"], da2.attrs["GRIB2IO_section4"])
+    skip = [2, 9, 10, 11, 16, 17]
+    assert_array_equal(
+        _del_list_inplace(list(da1.attrs["GRIB2IO_section5"]), skip),
+        _del_list_inplace(list(da2.attrs["GRIB2IO_section5"]), skip),
+    )
+    assert_allclose(da1.data, da2.data, atol=atol, rtol=rtol)
+
+
+def test_da_write(tmp_path, request):
+    """Test writing a single DataArray to a single grib2 message."""
+    target_dir = tmp_path / "test_to_grib2"
+    target_dir.mkdir()
+    target_file = target_dir / "test_to_grib2_da.grib2"
+
+    datadir = request.config.rootdir / "tests" / "data" / "gfs_20221107"
+
+    with grib2io.open(datadir / "gfs.t00z.pgrb2.1p00.f012_subset") as inp:
+        print(inp[0].section3)
+        newmsg = inp[0].subset(lats=(43, 32.7), lons=(117, 79))
+
+        print(inp[0])
+        print(newmsg)
+        print(newmsg.section0)
+        print(inp[0].section0)
+        print(newmsg.section1)
+        print(inp[0].section1)
+        print(newmsg.section2)
+        print(inp[0].section2)
+        print(newmsg.section3)
+        print(inp[0].section3)
+        print(newmsg.section4)
+        print(inp[0].section4)
+        print(newmsg.section5)
+        print(inp[0].section5)
+
+        print(inp[0].data.shape)
+        print(newmsg.data.shape)
+
+    with grib2io.open(target_file, mode="w") as out:
+        out.write(newmsg)
+    assert False