{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation with trained STIMP in the Chesapeake Bay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/home/mafzhang/code/STIMP/..', '/home/mafzhang/miniconda3/envs/torch/lib/python39.zip', '/home/mafzhang/miniconda3/envs/torch/lib/python3.9', '/home/mafzhang/miniconda3/envs/torch/lib/python3.9/lib-dynload', '', '/home/mafzhang/.local/lib/python3.9/site-packages', '/home/mafzhang/miniconda3/envs/torch/lib/python3.9/site-packages']\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import numpy as np \n",
    "import sys\n",
    "import os\n",
    "sys.path.insert(0, os.path.join(os.getcwd(), \"..\"))\n",
    "print(sys.path)\n",
    "from dataset.dataset_imputation import PRE8dDataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import argparse\n",
    "parser = argparse.ArgumentParser(description='Imputation')\n",
    "\n",
    "# args for area and methods\n",
    "parser.add_argument('--area', type=str, default='Chesapeake', help='which bay area we focus')\n",
    "\n",
    "# basic args\n",
    "parser.add_argument('--epochs', type=int, default=500, help='epochs')\n",
    "parser.add_argument('--batch_size', type=int, default=16, help='batch size')\n",
    "parser.add_argument('--lr', type=float, default=1e-3, help='learning rate')\n",
    "parser.add_argument('--wd', type=float, default=1e-4, help='weight decay')\n",
    "parser.add_argument('--test_freq', type=int, default=500, help='test per n epochs')\n",
    "parser.add_argument('--embedding_size', type=int, default=32)\n",
    "parser.add_argument('--hidden_channels', type=int, default=32)\n",
    "parser.add_argument('--diffusion_embedding_size', type=int, default=64)\n",
    "parser.add_argument('--side_channels', type=int, default=1)\n",
    "\n",
    "# args for tasks\n",
    "parser.add_argument('--in_len', type=int, default=46)\n",
    "parser.add_argument('--out_len', type=int, default=46)\n",
    "parser.add_argument('--missing_ratio', type=float, default=0.1)\n",
    "\n",
    "# args for diffusion\n",
    "parser.add_argument('--beta_start', type=float, default=0.0001, help='beta start from this')\n",
    "parser.add_argument('--beta_end', type=float, default=0.2, help='beta end to this')\n",
    "parser.add_argument('--num_steps', type=float, default=50, help='denoising steps')\n",
    "parser.add_argument('--num_samples', type=int, default=10, help='n datasets')\n",
    "parser.add_argument('--schedule', type=str, default='quad', help='noise schedule type')\n",
    "parser.add_argument('--target_strategy', type=str, default='random', help='mask')\n",
    "\n",
    "# args for mae\n",
    "parser.add_argument('--num_heads', type=int, default=8, help='n heads for self attention')\n",
    "config = parser.parse_args([])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 5d: Missing rate is equal to 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "test_dloader = DataLoader(PRE8dDataset(config, mode='test'), 1, shuffle=False)\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = next(iter(test_dloader))\n",
    "device = \"cuda\"\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = datas.float().to(device), data_ob_masks.to(device), data_gt_masks.to(device), labels.to(device), label_masks.to(device)\n",
    "\n",
    "# load model\n",
    "model = torch.load(\"./log_bak/imputation/Chesapeake/STIMP/best_0.1.pt\")\n",
    "model = model.to(device)\n",
    "cond_mask = data_gt_masks\n",
    "adj = np.load(\"./data/{}/adj.npy\".format(config.area))\n",
    "adj = torch.from_numpy(adj).float().to(device)\n",
    "\n",
    "imputed_data_our = model.impute(datas, cond_mask, adj, 10)\n",
    "imputed_data_our = imputed_data_our.median(1).values\n",
    "mask = data_ob_masks - cond_mask\n",
    "imputed_our = imputed_data_our[0][mask.bool().cpu()[0]]\n",
    "truth = datas[0][mask.bool()[0]].cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-05-08 16:22:39.386\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 181: 0.002368027111515403, 9.766314178705215e-06\u001b[0m\n",
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      "\u001b[32m2025-05-08 16:22:44.725\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 200: 0.00248316815122962, 9.713927283883095e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:22:44.879\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 178: 0.001902697840705514, 9.977840818464756e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:22:44.984\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 120: 0.0015478215645998716, 9.942799806594849e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:22:45.165\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 207: 0.001961691305041313, 9.938608855009079e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:22:45.303\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 154: 0.001942677074111998, 9.635929018259048e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:22:45.435\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 150: 0.00280376011505723, 9.921612218022346e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:22:45.580\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 164: 0.0019110547145828605, 9.842566214501858e-06\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# DINEOF\n",
    "from einops import rearrange\n",
    "from model.dineof_per_step import DINEOF\n",
    "is_sea = np.load(\"./data/Chesapeake/is_sea.npy\")\n",
    "H, W = is_sea.shape\n",
    "model = DINEOF(10, [H, W], keep_non_negative_only=False)\n",
    "datas_image = torch.zeros(1,46,1,H,W)\n",
    "datas_image = datas_image.to(device)\n",
    "datas_image[:,:,:,is_sea.astype(bool)]=datas\n",
    "cond_mask_image = torch.zeros(1,46,1,H,W)\n",
    "cond_mask_image = cond_mask_image.to(device)\n",
    "cond_mask_image[:,:,:,is_sea.astype(bool)]=cond_mask\n",
    "ob_mask_image = torch.zeros(1,46,1,H,W)\n",
    "ob_mask_image = ob_mask_image.to(device)\n",
    "ob_mask_image[:,:,:,is_sea.astype(bool)]=data_ob_masks\n",
    "\n",
    "impute_data_list = []\n",
    "for t in range(datas_image.shape[1]):\n",
    "    data = datas_image[:, t, :, :, :].squeeze()\n",
    "\n",
    "    tmp_data = torch.where(cond_mask_image[:,t].cpu().squeeze()==0, float(\"nan\"), data.cpu())\n",
    "    model.fit(tmp_data.numpy())\n",
    "\n",
    "    imputed_data = model.predict()\n",
    "    imputed_data = rearrange(imputed_data, \"(b t c h) w->b t c h w\", b=1, t=1, c=1, h=data.shape[-2], w=data.shape[-1])\n",
    "    impute_data_list.append(torch.from_numpy(imputed_data))\n",
    "\n",
    "imputed_data = torch.cat(impute_data_list, dim=1).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ,  2.5]),\n",
       " [Text(0, -1.5, '−1.5'),\n",
       "  Text(0, -1.0, '−1.0'),\n",
       "  Text(0, -0.5, '−0.5'),\n",
       "  Text(0, 0.0, '0.0'),\n",
       "  Text(0, 0.5, '0.5'),\n",
       "  Text(0, 1.0, '1.0'),\n",
       "  Text(0, 1.5, '1.5'),\n",
       "  Text(0, 2.0, '2.0'),\n",
       "  Text(0, 2.5, '2.5')])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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koU+IfISfPs+Wt97D8tAOst198Xr1TbzrBJu7LCGeahF79pO66GdURiPeI8fQdcxwafUToohI6BPiX6Kv3mDT+yMxbVqJ0coGq869COr5jPTdE+IJyUxM4vJPP2Jz8QTZlWvTfsHvBIXUNXdZQpR4EvqEuONW6CW2jfwC4+aVKCo1NG1PpRdflJU1hDCT8N17SV06D01mOpa9+tPzp+9w8nAzd1lClFgS+kSpF7p1DwfGfY3Fwa2YNFbQqDXl+zyPnZuruUsTotTTZWRyadFiLA5sxWBth8dbH9Dtq0+xsrE2d2lClDgS+kSppM3MYtfMX7k+dzY210LR2TujadaeCj27Y+3kaO7yhBD/knLrNjcWLsD6/HF0zu54/d97dBkzHGs7W3OXJkSJIaFPlCqXDx7n4Hczyd64CqvMVLJ9AnFs25nAdq1RW1qauzwhxAPEXwzj9vKlWF8+i97BBecXX6HDuFEysbMQhSChTzz1UuMS2T1tDreX/IHN9fMYrGwx1qiPb9dncK8qU0IIURLFX7rC7VUrsDp/HEWtRmnSjtrvDqH+811ltK8Q+ZDQJ55KBr2ew3+s4Pwvv6E+shu1QU+2fxAOTVsR0K41lrZyS0iIp0F6bCzh6zZgOroXq/RktC6e2HfuSd1B/anWrrkEQCH+QUKfeKqE7TnM4Rk/kb3lb6zSktA6uWNRrzF+nTrh7Odr7vKEEI+JyWgk8uhxEnftxOLyGTS6bLQuHliFtCSwezfq9emOs6e7ucsUwqwk9IkSL+7GLfZMm03simXY3rqSc/u2Wl082rbHq04t+UtfiFLGqNNy+9Axko8cQnX1PFbpyZhUanR+FbGp0wDfFs2p0rE1/rWqyu8HUapI6BMlUnZGJvvmLOTqggVYnj0ECugCq+LYvBXlWrVAYy3TOQghwGQykXwjnPjjJ8i6eB515A2s0pMB0Ns6YCxXCduq1XGrVQv/RvWo0Li+zAUonloS+kSJYTQYObpsDaG/L0DZtxWNNpNsj7JY12+Cf6eO2LuXMXeJQogSIC06hoTzF8i4fBlD5E3UsZFYpSflvq5zcMHkUw6rgAo4BAXhXq0KvrVrElCvFrYyWbsowST0iWLNaDBycvUmzs3/A93uzVilJaFzcEEV3BDvdu1xrxxk7hKFEE8BXUYGiVeukR4RQfatWxjjoiExDsuUBCwMOgAUVOic3FC8/bEKKI9jUBDuNarh36AOAXVqYGltZearEKJgEvpEsZOemMzRxSu5vmYthoM7scpIQW/riKlqbdxbtsK7Xh3phyOEeCJMJhMZsXGkhkeQces22uhITHExkBSfEwiN+pzt1Bbo3LxR+ZfHrlJl3KpXo2z9OlRsUh/HMrK6jygeJPQJs8tITuXs+m1c37qDlAN7sbp6HrXJgNbRDVXVYNxCmuBTvw5qjcbcpQohRC6T0UhaVAypN26QefMW2qjbKHHRqBPjsMpMyd1O6+SG4huITYUgnKtWwTO4JuXq1ca3WhAWGgszXoEobST0iSfGaDASFXaV8GOniD5+iuRzZ9FfPIdVTARqkwmDpTV6vwrYVKmBZ5PGuFUob+6ShRDikWhT00m6do208HCyb93EFBuNKiEWy9QE1IoJAKPGEr2bNyofP6z9A3AICMClYnncK1XEu0pF3AP90MhKQaIISegrYRRFIS0t7Yme02QyYdQbMBoMGLQ69Fo9huwsdNk6tBkZ6NKzyE5LIzslhazkVLITk8hKTEQbF4cuPgFDfCwkxGKZEo+FyZBzTJUanYsHePthUy4Q1xrVcQuqIK15QoinmlGnI/V2FKk3b5IdFYU+NholKQFVahKW6SmoTcbcbU2oMNg5YXJwBmcXLJxc0Lg4Y+nsgqWzM1ZOTlg7OmLl6IC1kwNWdvZY2dtiZWuLpa01lja2WNpaYWlljYW1JRpLSzRWllhYalBbWDzxbjKOjo6oVKonek6Rl4S+EiY1NRVnZ2dzlyGEEEI8lJSUFJycnMxdRqkmoa+EeVItfampqfj7+3Pz5s1S+Y+0NF9/ab52KN3XX5qvHeT6H/f1S0uf+cm9tBJGpVI90V9GTk5OpfKX312l+fpL87VD6b7+0nztINdf2q//aSbzXgghhBBClAIS+oQQQgghSgEJfeK+rK2tGTNmDNaldA3b0nz9pfnaoXRff2m+dpDrL+3XXxrIQA4hhBBCiFJAWvqEEEIIIUoBCX1CCCGEEKWAhD4hhBBCiFJAQp8QQgghRCkgoU8IIYQQohSQ0CeEEEIIUQpI6BNCCCGEKAUk9JUwiqKQmpqKTK8ohBDiaSefeUVLQl8Jk5aWhrOzM2lpaeYuRQghhHis5DOvaEnoE0IIIYQoBST0CSGEEEKUAhL6hBBCCCFKAQl9QgghhBClgIQ+IYQQQohSQGPuAsTjYTQa0ev15i5DAJaWllhYWJi7DCGEEKWchL6njKIoREdHk5ycbO5SxD+4uLjg7e2NSqUydylCCCFKKQl9T5m7gc/T0xM7OzsJGWamKAqZmZnExsYC4OPjY+aKhBBClFYS+p4iRqMxN/CVKVPG3OWIO2xtbQGIjY3F09NTbvUKIYQwCxnI8RS524fPzs7OzJWIf7v7PZF+lkIIIcxFQt9TSG7pFj/yPRFCCGFuEvqEEEIIIUoBCX2iWNu1axcqleqxjEZWqVSsXr26yI8rhBBCFEcS+kSx0bp1a4YOHWruMoQQQoinkoQ+IYQQQohSQEKfeCStW7fmvffeY+jQobi6uuLl5cXPP/9MRkYGAwcOxNHRkaCgIDZu3Ji7z7lz5+jSpQsODg54eXkxYMAA4uPjAXjttdfYvXs306dPR6VSoVKpuHHjRu6+x48fp0GDBtjZ2dG0aVPCwsLy1PPTTz9RsWJFrKysqFKlCgsXLszz+uXLl2nZsiU2NjZUr16drVu3Pr43RwghhCiGJPSJRzZ//nzc3d05cuQI7733Hm+//TZ9+vShadOmnDhxgo4dOzJgwAAyMzNJTk6mbdu21K1bl2PHjrFp0yZiYmJ44YUXAJg+fTpNmjThzTffJCoqiqioKPz9/XPP9emnn/Ldd99x7NgxNBoNgwYNyn1t1apVfPDBB3z00UecO3eO//u//2PgwIHs3LkTAJPJRO/evbGysuLw4cPMnj2bkSNHPtk3SwghhDA3RZQoKSkpCqCkpKTc81pWVpZy/vx5JSsr67HX0apVK6V58+a5jw0Gg2Jvb68MGDAg97moqCgFUA4ePKhMmDBB6dixY55j3Lx5UwGUsLCw3GN+8MEHebbZuXOnAijbtm3LfW79+vUKkHudTZs2Vd588808+/Xp00fp2rWroiiKsnnzZkWj0Si3b9/OfX3jxo0KoKxaterR34SH8CS/N0II8bQo6DNPPDxp6ROPLDg4OPf/LSwsKFOmDLVq1cp9zsvLC8hZieL06dPs3LkTBweH3K+qVasCcPXq1Yc6192lzO4ubXbhwgWaNWuWZ/tmzZpx4cKF3Nf9/f3x9fXNfb1JkyYPda1CCCFESSfLsIlHZmlpmeexSqXK89zdCYlNJhPp6el0796db7/99p7jFGY92vyOK4QQQojCkZY+8UTUq1eP0NBQAgMDCQoKyvNlb28PgJWVFUaj8aGPXa1aNfbv35/nuf3791O9evXc12/evElUVFTu64cOHfoPVyOEEEKUPBL6xBMxZMgQEhMT6du3L0ePHuXq1ats3ryZgQMH5ga9wMBADh8+zI0bN4iPjy90S97HH3/MvHnz+Omnn7h8+TLff/89K1euZPjw4QC0b9+eypUr8+qrr3L69Gn27t3Lp59++tiuVQghhCiOJPSJJ8LX15f9+/djNBrp2LEjtWrVYujQobi4uKBW5/wYDh8+HAsLC6pXr46HhwcRERGFOvazzz7L9OnTmTJlCjVq1GDOnDn8/vvvtG7dGgC1Ws2qVavIysqiUaNGvPHGG3z11VeP61KFEEKIYkmlKIpi7iJE4aWmpuLs7ExKSgpOTk55XsvOzub69euUL18eGxsbM1Uo7ke+N0II8fAK+swTD09a+oQQQgghSgEJfUIIIYQQpYCEPiGEEEKIUkBCnxBCCCFEKSChTwghhBCiFJDQJ4QQQghRCkjoE0IIIYQoBST0CSGEEEKUAhL6hBBCCPHYnD592twliDsk9AkhhBDisVi/fj3Dhg0zdxniDgl9oliIi4vj7bffply5clhbW+Pt7U2nTp346quvUKlUBX7t2rWLefPm4eLiknu8efPmoVKpqFat2j3n+vPPP1GpVAQGBt6zvUqlQq1W4+fnx8CBA4mNjX0CVy+EEE8XRVH4/fffmTJlCiaTydzliDsk9Ili4bnnnuPkyZPMnz+fS5cu8ffff9O6dWtq1apFVFRU7tcLL7xA586d8zzXtGnT+x7T3t6e2NhYDh48mOf5X3/9lXLlyt2zvZOTE1FRUdy6dYuff/6ZjRs3MmDAgMdyvaXN5XnzmKdS5X4tsLFhZeXKHHr3XbJiYvJsmxUTw9Hhw1lZtSoL7ez4w96etfXrc/rLL9EmJ99z7PBVq9japQtL3N1ZYGXFMl9fdr3wAlE7dhRJ7UatlmMjR7LM15eFtrasCwkhcuvWQu9/belS/q5XjwU2Nizx8GD/66+THR9/z3ZZMTHsGziQpZ6eLLS15e969bjx55/3bBe+ahVbOnVima8vC6ytWe7nx87nnyfp3LkC60i9epUFNjbMU6mIP3as0PUL8bAMBgOTJ09mwYIFAPJ7tBjRmLsAIZKTk9m7dy+7du2iVatWAAQEBNCoUaN7trW1tUWr1eLt7f3A42o0Gl5++WV+++03mjRpAsCtW7fYtWsXH374IUuWLMmzvUqlyj2ur68v77//Pp9//jlZWVnY2tr+18sUQN3x43EoXx5jdjYx+/YR9tNP3NqwgWfPnUNjZ0f80aNs7doVQ3o6Ffr3p0z9+gAkHDvG2YkTidmzh45btgA5LQn7Bw3iyrx5uNWtS41hw7D19iYzKoqIVavY3K4dXffvxzOfPwoKa99rr3Hjr7+oPnQoTpUqcWXePLZ27UrnnTvxat68wH0v/vQTh955B5927Wj0/fdk3LrFhenTiT92jG6HD6OxsQFAl5rKhubNyY6JodoHH2Dr7c2N5cvZ9cILtFy0iAovv5x7zKSzZ7FydaX6Bx9g7e5OVnQ0V377jXWNGtHt4EHcate+by1HP/wQtUaDSav9T++HEAXJyspi7NixHDlyBJVKxYcffkj37t3NXZa4Q0LfU0xRFAyZmWY5t8bODpVKVahtHRwccHBwYPXq1TRu3Bhra+siq2PQoEG0bt2a6dOnY2dnx7x58+jcuTNeXl4P3NfW1haTyYTBYCiyekq7sl264N6gAQCV33gDmzJlCP3+eyLWrKFsly7s6NULlYUF3U+exKVq1Tz71vvqKy79/HPu49DvvuPKvHlUHzqUht9/n+fnrfann3J14UJUmv/2Ky7uyBGuL11Kg8mTqTl8OAAVX3mFNTVrcmzECLodOJDvvkadjhOffIJXy5Z03Lo1tz7Ppk3Z3r07l3/+mWrvvQfApTlzSLtyhU7bt+PTti0AVd9+m/WNG3P0o48IeP55LKysAKjzxRf3nKvyG2+w3M+Piz/9RNPZs+95/fbmzdzevJmaI0Zw5ssv/9N7IkR+kpKSGDVqFJcuXcLa2povvvgi3zsxwjwk9D3FDJmZLHJwMMu5+6WnY2lvX6htNRoN8+bN480332T27NnUq1ePVq1a8dJLLxEcHPyf6qhbty4VKlTgr7/+YsCAAcybN4/vv/+ea9euFbjf5cuXmT17Ng0aNMDR0fE/1SDy5922LaHff0/69etcmjOHzNu3ablo0T2BD8DWy4van30GgCEri7PffINz1ao0mDLlvn9gVCyCW0rhf/2FysKCyoMH5z6nsbGh0uuvc+KTT8i4eRN7f//77pt87hy65GTKv/hinvr8n3kGjYMD15cuzQ19MXv3YuPhkRv4AFRqNYEvvMCxjz8mZvdufDt0yLdOG09PNHZ26O5z+9uk13P4gw+o/sEHOFas+LBvgRCFcvPmTUaOHElUVBTOzs5888039+1TLcxL+vSJYuG5554jMjKSv//+m86dO7Nr1y7q1avHvHnz/vOxBw0axO+//87u3bvJyMiga9eu990uJSUFBwcH7OzsqFKlCl5eXixatOg/n1/kL+3qVQCsy5Th5t9/Y2FrS8Dzzz9wv9h9+9AmJlLh5ZdRW1g8cHvFZCI7Pr5QXya9Pne/hJMncapcGSsnpzzHc7/T9SDx1Kl8z2m8cxvV4j5dAzS2tiScPIlyp4O7Uau9/3Z2dgDEHz9+z2va5GSy4+JIOnuWA2+8gT41Fd927e7Z7vy0aeiSkgi+E5iFKGqhoaG8++67REVF4evry6xZsyTwFVPS0vcU09jZ0S893Wznflg2NjZ06NCBDh068Pnnn/PGG28wZswYXnvttf9US79+/RgxYgRjx45lwIABaPK55efo6MiJEydQq9X4+PhIP77HQJeSQnZ8PMbsbGL37+f0+PFY2Nri98wzHB89GqfKlXNvYxYk+cIFAFxq1SrUedMjIlhRvnyhtu20cyc+rVsDkBUVhZ2Pzz3b3H0uMzIy3+M4VaoEKhWx+/dTaeDA3OdTwsLIjosDQJuUhE2ZMjhXqULUtm2kh4fjEBCQu23M3r0557l9+57jr2/cmNSwMAA0Dg4Ef/YZlV5/Pc82mdHRnJ4wgQZTptwTXIUoCvv372f8+PHodDqqVq3KN998k2cmBVG8SOh7iqlUqkLfYi2OqlevzurVq//zcdzc3OjRowfLly9n9n36O92lVqsJCgr6z+cT+dvSvn2ex/YBAbRctAj7smXRp6ZiWchb6frUVIBCb2/r7U3HQo64/edACGNWFur79DG1uDMAw5CVle9xbNzdCXzhBa7Mn49ztWoE9OpF5u3bHHrvPdSWlpj0eox39q/0xhuEzZ7NrhdeoNHUqdh4eXFj+XLCV63KrePfmv/+O/rUVNKuXePK779jzMpCMRpRqf93A+f4yJE4VKhA5TfeKNS1C/Ew1qxZw/Tp01EUhcaNGzNmzBhs7vzbEMWThD5hdgkJCfTp04dBgwYRHByMo6Mjx44dY9KkSfTs2bNIzjFv3jx+/PFHypQpUyTHE4+m8Q8/4FS5MiqNBlsvL5yrVMkNKZZOTujT0gp1HMs7rVaF3V5jY4PvvwJnYVjY2t53tKsxOzvnuA9oDW46Zw7GrCyODR/OsTsDQSr0749TxYqEr1yJ5k6fW7fgYFouXszBt95iQ7NmQE5QbTRtGofefjt3u3/yvDMiHaD8Sy+x6s7ttIZTpgAQe+gQVxcupNP27XmCoBD/laIo/PLLLyxevBiAZ555hqFDh2JRiK4Wwrwk9Amzc3BwICQkhKlTp3L16lX0ej3+/v68+eabfPLJJ0VyDltbW7ldWwy4N2qUO3r335yrViXx1CmMOt0Db/E63xnokXz2LAHPPvvA85qMxtxbqg9i7eaWe35bH5/73lrNjIoCwM7Xt8BjWTk7027NGtIjIki/cQOHgAAcAgJY37QpNh4eWP/jNljg88/j36MHSadPYzIaKVOvHtG7dgHgXLlywTW7uuLTti3XFi3KDX3HR4zAq0ULHMqXJ+3GDQC0d+YHzIqKIj0iAof7zFcpREHuzsG35c7USQMHDmTAgAGFnq1BmJdKURTF3EWIwktNTcXZ2ZmUlBSc/tVHJzs7m+vXr1O+fHlpYi9mSvv35vK8eewfOJBnjh7NN/Sd+eYbTnzyCS0XL6ZC374FHs+QmclyPz9svb3pefbsAwdzpN248Uh9+o5+/DHnp06lb2Jinj5xZ77+mhOffkqfiIh8R+/mR5uczDIvLwKee45Wd1pK8nNsxAjOTZ5Mr7CwBwa/Hb16cXvzZgbcmabpz8BAMsLD893e0tmZfvcZ7StEfjIzM/niiy84fvw4arWa4cOH06VLl8d6zoI+88TDk5a+f8nMzGTLli2sXbuWffv2ER4ejoWFBUFBQTz33HMMGzYMh4ecBiUpKYmxY8eyevVqoqOj8fb2plevXowdO1Y6vApxR5W33uLCzJkc/egjytSvf0/IyYqN5dLcudT+7DM0dnbUGjmS46NGcXzkSBpMnnxPS8PVP/7AqXJlPBo1euQ+fYHPP0/olClcmjs3d54+o1bL5d9/xz0kJE/gS4+IwJCZed/pZv7pxOjRKAYDNT78sMDtUi9fJmz2bPyeeSbPe5EVG4utp2eebdNu3CBq+/Y8gbrp3Ln3zNMZvWMHF2bOpMGUKbmtpUIURkJCAiNHjuTq1avY2Ngwbty4+06gL4o3CX3/snjxYt58800AqlWrRo8ePUhNTeXAgQOMGTOGJUuWsHv3bjz/9Us3P/Hx8TRp0oQrV65QoUIFnn32WUJDQ5k+fTobN27k4MGDuLm5Pc5LEqJEsHZ1pe2qVWzr2pW/69Sh4j9X5DhxgutLluDxj35sNT/+mOTQUEK/+47onTsJeP55bL29yYqOJmL1auKPHKHrncmTH7VPn0dICIF9+nB89GiyY2NxDAri6vz5pN+4QbNff82z7d5XXiFm925e+8fNkzMTJ5J87hweISGoNBoiVq8mcssW6n75Je4NG+bZf1X16gT26YN9uXKkX79O2E8/YeXmRpN/DT5aU6sWPu3a4VanDtaurqRevszlX3/FpNdTf+LE3O3Kdux4z/XcncfPu1WrfFtchfi38PBwRowYQWxsLK6urkycOJHKD2h5FsWThL5/sbS0ZPDgwQwdOjTPPENRUVF069aNkydPMnTo0NwOrA8ydOhQrly5Qu/evVm2bFnudCHvv/8+M2fOZNiwYUUyF50QTwOPkBB6njvHucmTubV+fc6qGmo1ztWqUWvUKKq++27utiq1mhYLFuDfsyeX5s4ldMoUdKmp2Hh44NWyJQ0mTcoz2OFRNV+wAPvPP+fqwoVok5JwCw6m/bp1eLds+cB9XWvVImLVKm7+/TeK0YhrcDCtly8nsE+fe7Z1q12bK7//TlZMTO7I3zrjxt3Tqlfl7be5tX49tzdtQp+Whq2nJ74dOxL8ySe4FnIKGyEK68yZM3z66aekp6fj5+fHpEmT8LnPNEaiZJA+fQ/h4MGDNG3aFGtra1JTU7F6QGfzqKgo/Pz80Gg0RERE5Fn6S6vV4u/vT2JiIpGRkYVuOZQ+fSWTfG+EECXN7t27+eqrr9Dr9dSoUYOvvvoKZ2fnJ1qD9OkrWjKO/yHUvtPXR6vVkpCQ8MDtN23ahMlkokWLFves9WptbU337t0xGo1s2LDhsdQrhBBCPIoVK1Ywbtw49Ho9zZs357vvvnvigU8UPQl9D+Hueq2WlpaF6od3+vRpAOrVq3ff1+8+f+bMmSKqUAghhHh0iqLw008/MWvWLBRFoWfPnowbNw7r+0xSLkoe6dP3EKZPnw5A586dC/UPICIiAgA/P7/7vn73+fACplUQQgghngS9Xs/EiRPZsWMHAIMHD+all16SOfieIhL6CmnDhg38+uuvWFpaMmHChELtk35n3Vu7fNahtb+zRFpaAasKaLVatP9YESD1zvJTQgghRFFJT0/ns88+4/Tp01hYWDBy5Eg6dOjwxOuQz7zHS0JfIVy8eJH+/fujKAqTJ0/O7dv3JHzzzTeMGzfuiZ1PiMdh1+ErzF6yP/expUZNGVcHgqv40LtTbVwc/7daSnJaFmt3nONk6C3ikzJQqVT4ejrRMLgcnZpXw94u7wCqI2fC2X7gMtduxpOZrcfJ3poqFTxp37QKNSv/91GGeoOR5RtOse/YVdKzdJTzceXFbnUJrlLwahx3HThxnb93nON2dDI21pbUr+nPy93r4+SQ/4Cei9diGDtjEwBzv3wxz7Z/bjzFis2n79nHUqNm4ZQBuY91OgO/rTjMlfB4EpIzMJkUvNwdaR0SRMfmVdFYSO8ekSM2NpaRI0dy48YN7OzsGD9+PPXvTJf0pMln3uMloe8Bbt++TefOnUlKSmLYsGF88MEHhd737iTOmf+aIPWujIwMABwLWDR+9OjRDBs2LPdxamoq/g+5AoAQxUWfLnXwLOOAXm/k4rVYtu6/xKkLt5k8sifWVhquRsQzcc42snUGWtSvQHn/nLWSr91MYM22c1y4GsOnb+fMP6coCrOX7Gf3kasE+rnRtXV1XBxtSU7N4ujZCL78cQvjPuhClfKFGxmfn58W7ePw6XC6tKqOt4cTu49c4ds52/j83U5UreBV4L5b9l3kt78OU7OyDwOebUhCciab9lzg2s0EvvywG1aW964kYjIpzFtxBGsrDVqdId9jv96nMTbW//sVrv7XLTid3sit6GTqVi+Lh5sDKpWKS9djWbj6KFfC43n/lQdPOSOefteuXWPkyJHEx8dTpkwZJk6cSFBQkNnqkc+8x0tCXwESExPp2LEj4eHhDBw4kCl31rQsrHJ31rW8devWfV+/+3xAQEC+x7C2ti4VHWhfe+015s+fD4BGo8HNzY3g4GD69u3La6+9hvrOgvGBgYEMHTqUoUOH5j4ODw/n4MGDNG7cOPd4Q4cO5dSpU+y6s3bp2LFj7/vXY5UqVbh48WLu49DQUMaNG8fOnTtJTU0lICCAl156iVGjRuW5TX/3vP9UtmzZfL/XIkedamWpWM4dgLZNKuNob836Xec5djaCOtX8+O7XnajVKiYO705Zr7wjBV/sVpcdBy/lPl63M5TdR67SpVU1Xnm2YZ5+R706BrPn6FUs1P+tL9KV8DgOnLxBvx716d62JgAtG1bk42/XsOjv40wY2jXffQ0GI8vWn6RaRS8+fbtDbn2Vy3sw+ecd7Dh4ic4tq92z3/aDl0hIzqBt40ps3HMh3+OH1A4osLXQwd6aLz/slue5Ds2qYGdrxea9F3nl2Ya4OMl61KXZyZMn+eyzz8jMzCQgIIBJkyYVevqwx6W0fOaZi7Tv5yM9PZ0uXbpw/vx5evfuzc8///zQnVnv3gY+ceLEfV+/+3xwcPB/K/Yp0blzZ6Kiorhx4wYbN26kTZs2fPDBBzzzzDMYDPm3eNjY2DBy5MgHHr9GjRpERUXl+dq3b1/u64cOHSIkJASdTsf69eu5dOkSX331FfPmzaNDhw7odLo8xxs/fnyeY508efLRL76UqlEp5/ZrbGI62w6EkZiSyYBnG94T+ABcHG3p3THn35ROZ2DNtnP4ejozoGeD+/7bbNmwIkEBHv+pvsOnw1GrVbRr+r/VB6wsLWgTUonLN+KIT8rId9+bUclkZOloUjcwT331a/hjY63hwMnr9+yTnqFl+YaT9OlSBzvbgucBBcjM1vGwU616uOXcgcjI0j1gS/E02759OyNGjCAzM5Pg4GBmzpxp9sAnHj9p6bsPrVZLz549OXLkCJ06dWLJkiVYPGBB9/vp3LkzarWavXv3Ehsbm+cflFarZe3atVhYWNC1a/6tBaWJtbU13t7eQE6rWb169WjcuDHt2rVj3rx5vPHGG/fdb/DgwcyePZsNGzYU+F5qNJrc4/+boii8/vrrVKtWjZUrV+a2LAYEBFC5cmXq1q3L1KlT84RLR0fHfI8nCicmPmcQk6OdNXuOXcXK0oLGtfNv+b7r4vVY0jO1dGlVLfd7VRCTSSE9U/vA7QDsbK1y+7vduJWIj4cTdjZ5A1jFgJzWyvDbibi72t/3OHqDEeC+t3CtLDXcuJWIyaSg/kdr5PKNJ3F2tKV908qs2FzwVE7vT1hBttaAtZWGhrXK0f/ZBnn6Rt5lMBjJzNaj0xu5djOedTtDcXe1x9s9/24l4umlKArLli1jzpw5ALRq1YpPPvnkgYsNiKeDhL5/MRqN9O3blx07dtCiRQtWrlz5wH8Ms2bNYtasWfTq1Ytvvvkm93kfHx/69u3LokWLeOedd1i6dGnuMmwjRowgLi6OV1999bH9daUoSoF9gh4naytNkQzzb9u2LbVr12blypX5hr7y5cvz1ltvMXr06Nyg/bBOnTrF+fPnWbx48T37165dm/bt27NkyZJCtSiK/GVm60lNz0ZvMBJ2LZaVW05jZWlBvRp+LFl/Ah8PJzSaB/+BdTs6BYByPq6FOm98UgbvT1hRqG0/H9KJGpVywnxSatZ9b4G63nkuKeX+/XUBvD2cUKkg7HocrUMq5T4fGZNCano2ABlZWhztc27Rhkcmsu3AJUYOblfgz7C9nRWdWlSlUqAHlhoLLl6NYcu+i1yJiOfrj7rdE1CPnIlgxoI9uY8r+Jfhrb7NsJCBHKWOyWTihx9+YOXKlQA899xzDBkyRKZkKUUk9P3LrFmzWLVqFQDu7u688847991uypQpuLvn/LUfHx9PWFgYUVFR92w3bdo0Dh06xIoVK6hatSoNGjQgNDSUc+fOUalSJb7//vvHdi1anYHXRhZujeCiNu/bl7GxtiySY1WtWvWBE1h/9tln/P777yxatIgBAwbcd5uzZ8/mDq65q3///syePZtLl3L6iv1zveV/qlatWp5bwQAjR47ks88+y3389ddf8/777z/wekqzr37ckuexu6s97/ZvgZuLPVnZemxsCvczk6XNuTVZ2O1dnGz59O3CTT8RUPZ/QVKnN2B5nxB69zmd3pjvcZwcbGhcJ5A9R65Q1suZhrXKkZiSybwVh7GwUGM0mvLsP2/FEepUK0vtqmULrK9rq+p5HofUDqBigDuzFu5l674werbPu/5u9UrefPp2BzKydJy7FEV4ZJLZ/hgU5qPVavn666/ZsyfnD4B33nmHPvdZA1o83ST0/UtSUlLu/98Nf/czduzY3NBXEHd3d44cOcLYsWNZvXo1q1atwsvLi/fff59x48bh4uJSFGU/1RRFeeBfoh4eHgwfPpwvvviCF1988b7bVKlShb///jvPc/9ey/Fh+kd9/PHHvPbaa7mPC/PzUNoNej4EHw8n1Go1Lo42+Hg6597etLWxJDtbX6jj2FrntGYVdnsrSwtqFXKKlbz7aXJv0/5TQbdu/+nNF5qg0xv5Y80x/lhzDIDmDSrg5e7IkTMR2FjlhNYDJ65z6UYck0f2eOgaAZrXr8Afa45x9lLUPaHPxdEWlyo5LZON6wSyausZvvppC9M+7S0DOUqJtLQ0PvnkE86dO4dGo+GTTz6hTZs25i5LmIGEvn8ZO3YsY8eOLdJ93NzcmDFjBjNmzPhvxT0kaysN8759+Yme85/nLioXLlygfPnyD9xu2LBh/Pjjj/z444/3fd3KyirfqQgqV66ce666devet4a729zl7u5u1qkNSqKK5dxzR+/+m6+nM+G3EzEYjA+8xevrlRPWI6KSaBhc7oHnNZlMpKYXrk+fg51V7vldnWxJvM8t3KTUrJzXne8/8fpddrZWfPxGW+KT0olLTMfd1QEPNwc+n7YBJweb3DkHF/19nMZ1AtBYWBCbkDOpe+adgRYJyRkYjCbcHnCuMi52heq3GFI7gGXrT3LsbATtm1V54PaiZIuOjmbkyJFERERgb2/Pl19+SZ06dcxdljATCX1PMZVKVWS3WM1lx44dnD17lg8//PCB2zo4OPD5558zduxYevR4uBaTOnXqULVqVaZOncpLL72Up0/V6dOn2bZtW57+mqLo1a/hz+UbcRw+HU6z+hUK3LZqBS/s7aw4cOI6vTrUemA/zvikzEfq0xdQ1o3QK9FkZuvy9JW7Eh6X+3phuLs64O56Z9Rspo7rNxNo9I8BKwnJGew/fp39x+8d0Tt6yjoCfF35dkT+P9OKohCXmEFgIerR37mlnFnIVlJRcl2+fJlRo0aRmJiIh4cH3377baH+gBZPLwl9otjQarVER0djNBqJiYlh06ZNfPPNNzzzzDO88sorhTrG4MGDmTp1KosXLyYkJCTPawaDgejo6DzPqVQqvLy8UKlU/Prrr3To0IHnnnuO0aNH4+3tzeHDh/noo49o0qRJ7tyA4vFo36wym/de4I81xyjvXwZfz7zTtqSkZbH94CV6d6yNtZWGHm1rsmTdCRatPU7/HvdO27L32FV8PJwICvB45D59IbUDWLczlO0HLuXO06c3GNl9+ApBAe55Ru7GJ6Wj1RnvO93MPy1ZdxyjSaFb6//1zfto0L232g6cvM7Bkzd4p19zyrj8r5UvNT37nvn5tu4PIzU9m9rVfPNs52hvfc/7suPQZSBnQId4eh07dowvvviCrKwsKlSowMSJE/Hw+G9TGImST0KfKDY2bdqEj48PGo0GV1dXateuzYwZM3j11VcLPSL37trIL798723t0NBQfHzyLstlbW1NdnbOSMqmTZty6NAhxo0bR5cuXUhLS6NcuXK8+uqrjB49WiYMfcwc7KwZNqgN387dzqjJa2neoAIV/HKCyfVbiRw4cZ1Kgf/70Oretia3opNZv/M85y9HE1I7EBcnG5JTszl6NoKrEfGM/6AL8Oh9+ioFetC4TgBL150gNT0bL3cn9hy9QlxiOv/Xt2mebX/4Yx8XrsawdNqruc+t2XaWm1HJBAW4o1arOHb2JmfCInmxa908t7nvd4v6xu1EIGdC63+GvHfH/UWTuoGU83HF0tKCi9diOXjyOoFl3Wjf9H+3a/cdu8a2A2E0qFUOzzKOZGv1nL54m7NhUdSv4VckS9SJ4mnLli1MmjQJo9FI3bp1mTBhQu5a76J0UykPO7OnMKvU1FScnZ1JSUm5ZxBCdnY2169fp3z58tjY5D9Tv3jySvv35u7au18N65Zvn767klIyWbsjlJPnbxGfnIFapcoZ/Rpcjk4tqt4zJcnh0+FsP3CJazcTyMrW4eRgQ9WKXnRoVoXqQf99HkWd3sjyDSfZd/waGZlayvm68kKXutSulneU7biZm+4JfSdCb7Fi82kiY1IwKQrlfFzp1qY6jesEPvC8d9fY/ffau3OXHuDSjVgSkjLRGYx4uNrTqHYAvToEY/uP0cxXI+JZu+McV8LjSUnLQq1W4+vpRPMGFejcoppM2fIUUhSFxYsX88svvwDQrl07Ro4ciaVlye3mU9Bnnnh4EvpKGAl9JZN8b4QQj5PRaGTGjBm5MxS89NJLDB48uMTPwSehr2jJ7V0hhBCiBNNqtYwfP54DBw6gUql499136d27t7nLEsWQhD4hhBCihEpJSeGTTz7h/PnzWFpa8tlnn9GyZUtzlyWKKQl9QgghRAkUGRnJyJEjuXXrFo6Ojnz99dfUrFnT3GWJYkxC31NIumkWP/I9EUIUpYsXLzJ69GiSk5Px8vJi0qRJlCv34InKRekmw7eeIndHaGVm5r8IvDCPu9+TkjyKTghRPBw+fJihQ4eSnJxMUFAQP/zww1Mf+EwGWS+6KEhL31PEwsICFxcXYmNjAbCzsyvxI7dKOkVRyMzMJDY2FhcXFywsCl5eTAghCrJhwwa+++47TCYTDRo0YNy4cdjZFbxE39NAn54OboVbAUfkT0LfU8bbO2desrvBTxQPLi4uud8bIYR4WIqiMH/+fObPnw9Ax44d+fjjj9FoSsfHuD411dwlPBVKx09LKaJSqfDx8cHT0xO9XtbWLA4sLS2lhU8I8cgMBgNTp05lw4YNAPTv359BgwaVqjs5Ogl9RUJC31PKwsJCgoYQQpRwWVlZjBs3jsOHD6NSqfjwww/p3r27uct64nSJieYu4akgoU8IIYQohpKSkhg1ahSXLl3C2tqaL774gqZNmz54x6dQtoS+IiGhTwghhChmbt26xYgRI4iKisLZ2Zmvv/6a6tWrm7sss9HGJ5i7hKeChD4hhBCiGDl//jyjR48mNTUVHx8fJk2ahJ+fn7nLMqvMuDhzl/BUkHn6hBBCiGLiwIEDDBs2jNTUVKpUqcIPP/xQ6gMfQLaEviIhLX1CCCFEMbBmzRqmT5+OoiiEhIQwduxYbGxszF1WsZAVE2PuEp4KEvqEEEIIM1IUhV9//ZVFixYB0K1bNz788EOZgeEfsqKizV3CU0FCnxBCCGEmBoOByZMns2XLFgBee+01XnnllVI1B19hZMdKS19RkNAnhBBCmEFmZiZjxozh2LFjqNVqhg8fTpcuXcxdVrGki41BURQJw/+RhD4hhBDiCUtISGDUqFFcuXIFGxsbxo4dS0hIiLnLKrZMWi265GSsXV3NXUqJJqFPCCGEeMyy4+OJ3rWLhOPHSUlO5ue4OKITEnBxcWHixIlUqVLF3CUWe5m3bkno+48k9AkhhBCPiWIyETZnDsdHjkSflpb7vFdgIJrmzZk0aRI+Pj5mrLDkyLh1C9datcxdRokmoU8IIYR4DJLOnuXgW28Re+AAAJb+/qRFR2Oj11MlOZmXZ83C2dnZzFWWFCoybt40dxElnkzOLIQQQhQhfUYGx0aN4u969Yg9cACNgwPWgwbxW8WKrK1fHwC7tDQcZA6+QlM5u0joKwLS0ieEEEIUAUVRuL5sGceGDyfz9m0AyvXuzaX69Vm2dSsAz7i4AGDp6IhK5uErNMXRhYzwcHOXUeJJS58QQgjxH6WEhbGlY0f29O1L5u3bOJQvT6uVK9lfp05u4Hu9Sxfstm0DIGTWLNQaaXcpLIO9E+k3bpi7jBJPQp8QQgjxiIxaLSfHjGFNrVpEbduG2tqaOuPG0eHwYWbs2cOOHTuwsLBg9OjReO/ejWIw4N+9OxVeftncpZcoWltH0iT0/WfyZ4YQQgjxCCK3bePQkCGkXroEgF/XrjSaMQOtkxMfjhzJ9evXsbOzY9y4cVSwtOTvDRsAqDlihEwy/JCybJzIun0bo06HhZWVucspsaSlTwghhHgI2sRE9r/+Ols6dCD10iVsvLxovXw57datI0GtZsiQIVy/fp0yZcowffp0GjRogK2PDzZeXgDs6d+fCzNnYtTpzHwlJUe6pQOKyURGRIS5SynRJPQJIYQQhaCYTFz+/XdWVqnC5d9+A5WKqu++S++wMAL79OHUqVO89957xMXFERAQwA8//EBQUBAAtp6edN2/H8cKFcgID+fw++8T+t13Zr6ikiPV2iHnv5cvm7mSkk1CnxBCCPEAKWFhbGzViv2DBqGNj8elenW67N1L45kzsXJ2ZseOHYwYMYKMjAxq1arFzJkz8brTsneXU8WKdD9xArc6dQDQ2NmZ4UpKJr2tAypLKwl9/5H06RNCCCHyYcjO5vT48YROmYJJr0djb0+dsWOp/sEHqC0tURSFP//8k59++gmAli1b8umnn2KVT78zY3Y2aVevAuDesOETu44ST6XGytub1LAwc1dSoknoE0IIIe4j/tgx9r32GsmhoUDOQI3GP/6IQ0AAACaTiR9//JEVK1YA0Lt3b4YMGYJanf9NtOOjRqFPS6NM/fq4h4Q8/ot4iqg9fUg+f97cZZRoEvqEEEKIfzDqdJwaM4az334LioKNpydN5syhXM+euaNudTodX331FXv27AHg7bffpk+fPgWOyr2+bBlX5s0D7szTJ5MzF5pGY4GxjBdJJw6Yu5QSTUKfEEIIcUf88eMcHDyYhBMnAKjQrx8Nv/8eW0/P3G3S0tL49NNPOXv2LBqNhtGjR9O2bdsCj5t45gz7Bw0CcqZs8Wzc+PFdxFPIztoSnaUXxMeTFROD7b/6S4rCkYEcQgghSr3MqCj2DRzIuoYNSThxAms3N9qsWEHLP/7IE/hiYmJ47733OHv2LPb29kyePPmBgS8zKopt3bphyMzEp3176n311eO+nKeOja0l6Y4eACSePm3makouCX1CCCFKrYzbtzn68cesrFw559arolChXz96nj1LQO/eeba9cuUKQ4YMITw8HHd3d2bMmEGdOyNx82PIymJHr15k3rqFU5UqtF6+XJZfewR21lakWNpjYWdH4smT5i6nxJKfvH85fvw4W7du5ciRIxw5coTbdxbNVhTloY8VGBhIeAELRF+4cIGqVas+cq1CCCEejVGrJfS77zjz1VcYMjMBcA8JodG0afe99Xrs2DHGjBlDZmYm5cuX59tvv8XDw6PAcygmE3v79yf+8GGsXF1pv3Yt1q6uj+V6nna2tlbExmfiFBSUe+tdPDwJff8yYcIE1qxZU6THfPXVV+/7vLOzc5GeRwghRMEURSH8r784PmoUadeuAeDRpAnBn3yCX9euqO4z8nbLli1MmjQJo9FInTp1mDBhAg4ODg881/FPPiF85UrUVla0Xb0ap0qVivx6Sgs7aw0ZmVqcKlUm/sgRc5dTYkno+5cmTZoQHBxMw4YNadiwIYGBgWi12v90zHl3RmsJIYQwn/ijRzn8wQfEHTwIgK2PDw0mT6bCyy/fd9StoigsXryYX375BYC2bdsyatQoLC0tH3iuS7/8wrlvvwWg2a+/4t2yZRFeSelja2uFSVGwrlCR9BV/kR0Xh80DWlrFvST0/cvIkSPNXYIQQogilHb9Oic++YTrS5cCOSth1BwxghoffYRlPi12JpOJGTNm5N75eemllxg8eHCBU7LcFbVzJwfffhuAOmPHUrF//yK6ktLLzjonaKv8KwAQd/gw/s88Y86SSiQJfUIIIUq0a0uWYNRqCXr11TyhLDMykguzZhH6/feY7tyxqThgAPUnTsTO1zff42m1WiZMmMD+/ftRqVS8++679P7XoI78pIeHs+uFF1AMBir060ftL774bxcnALC1zQl92bZOWJcpQ+yBAxL6HoGEvidg8uTJXL16FWtra2rUqEGvXr0e2AFYCCFEwRJOnCDl0iWODR+OysICO19ffNq25erChYTNnp2n75dP27Y0+O47yjxgtG1KSgqffPIJ58+fx9LSks8++4yWhbw1m52QwLZu3dDGx1OmXj2a/vxzoVoGxYPZWFuiVqtJzdDiUqMGsfv3m7ukEklC3xMwYsSIPI8//PBDZs6cyaA7E3UKIYQovIzbtzn60UfcWLYs9zk7X18Sjh/n8HvvkXrpUu7z7iEh1Bo1Ks9qGvmJiopixIgR3Lp1C0dHR7766itq1apVqJqMOh27nn+e5NBQ7Hx9abNyJRpb20e7QHEPlUqFna0VqenZBNWsyZXffsOo1WJhbW3u0koUCX2PUY8ePWjTpg3169fHw8ODa9eu8dtvvzF9+nTeeOMNypQpQ8+ePQs8hlarzTOQJDU19XGXLYQQxZKiKFz84QeOjRiBMSsrz2uZkZGc+OQTAKzd3ak1YgQV+vfHzsenUMcOCwtj1KhRJCcn4+npyaRJkwi4s8bug5gMBvb270/0rl1oHBzosHlz7vq84uEU9JnnYGtFSloWrsHBGLOzSTh+HM+mTc1RZokloe8xmjFjRp7HNWrU4LvvvqNq1aoMHjyYkSNHPjD0ffPNN4wbN+5xlimEEMVeZlQU+wcN4vamTflu41AzGE3vAWTVac7GhEyyV57BYDxFWU9nKviXoUZlH3w8nO7Z7/Dhw4wdO5bs7GwqVqzIt99+S5kyZQpVl6IoHHzrLW78+SdqS0va/PUXrjVrPvJ1lnYFfebZ2VmRkpaNY1AtNPb2RO/eLaHvIamUR5l1uBSxsbFBq9U+0uTM+TGZTPj4+BAbG8v169cJDAzMd9v7/dXj7+9PSkoKTk73/vISQoinTfzRo+x49lkyIyOxsLGhwaRJxB8/ztX581FQ4TFxFldcKnDqWgLZWkOBx/L1dKZlw4p0bFEFOxsrNm7cyJQpUzCZTDRo0IBx48ZhZ2dXqLoUReHosGGcnzYNlVpN67/+IqBXr6K45FIrv8+8rXMXcuVGEtci4vnw9TYcHzUKSycnOm3ZYsZqSx5p6TMDtVpNxYoViY2NJSoqqsDQZ21tjbX0WRBClFLXli5l/8CBGLOzca5WjeDRo4k7fJh0rRHV1EUcva0lIUoNUTEAuDnbUb2SN0EB7jja5fzujIhK5kp4HBevxhAZm8LS9SdYt/McQZ46Nq+ZB0DHjh35+OOP0TzEEmmnJ0zg/LRpADSZO1cCXxEo6DPPwd6K9CwtBr0Rtzp1uLpwIUadDgsrqydcZckloc9MkpKSALC3tzdzJUIIUfwoisKZL7/k5J0pT/y6daPl4sXsnb2AfbbVCPOwxXRDD6ixtbGkRYMKNK9fgUqBHvcM2Gh257+ZWTqOno1g9dazRMWlcvKaEbXGjr4v9uL1119/qJG2+owMzk6cCEDIjBlUfv31orhsUQBHOxsAklIzcatbl0tz5hB/5AhezZububKSQ0KfGYSGhhIWFoadnZ2svSuEEP9i1Go58OabXF24EIBqwz7C4rX3mLzoEGcjHXM2UkPFcmVo07gyzeqVx9bmwatk2Nla0ahWWTas+AVdRhms7D1p3nkAb7zx0kPXeGP5coxZWThWqEDVd9996P3Fw3NwyGkBTEzJpHJQEJaOjkTt2CGh7yFI6PuPZs2axaxZs+jVqxfffPNN7vMbNmzAxsaGtm3b5tn+zJkzvPTSSyiKwhtvvIGVNEsLIUQubWIiO3r1ImbPHkxW1mjGzmKRwZXYX3cBoFaraFq3PF1bV6eCf+EGW9yVnJzMqFGjCAsLw96tPFb2nsSmqlEU5aFa+WIPHuTQkCEAVPzXhNDi8bG1tkRjYUFCUiaqCha41alD5JYt1JEJsAtNQt+/rF+/ngkTJuQ+1ul0ADRu3Dj3uc8//5xu3boBEB8fT1hYGFFRUXmOc+TIEcaNG0dAQAC1a9fGzs6Oa9euceLECQwGA61bt2binVsDQgghIPXKFbZ160bS1etEhTzDzdYvkBJjAtKxt7OidUgQnZpXxbOM40Mf+9atW4wYMYKoqCicnJwYP2YYk+afJCkli9iEdLzcC3fM1CtX2NGzJ8asLMp26ULw6NEPXYt4NCqVCmdHWxKS0gFwq1ePiz/8gD4tDUvHh/+ZKI0k9P1LXFwchw8fvuf5fz4XFxf3wON06tSJmzdvcvToUfbv35872rZ58+b069ePgQMHYmFhUaS1CyFESRWzfz/bevXmsm8wN954j2wbR8g24eZix7Pta9GqURDWVo/2kXXhwgVGjx5NSkoKPj4+TJo0CT8/Pyr4R3D5RhwXrkYXKvQlnTvHlg4dyI6Lw6VGDVrfmaZFPDnOTjbEJuaEvjING6IYDETv2oV/9+5mrqxkkClbSpjU1FScnZ1lyhYhxFPj2tKl/DluOmGNniXT1RsAd1f7nLAXEoSl5tH/QD5w4ADjx49Hq9VSpUoVvvnmG1xdXQFYsu4Ea7adpWXDirzTr+B+YXGHD7Ota1e0iYm41qpFx61bsfXyeuS6ROHc/czbOnch9rZ2nL8czemLtxn1f+1ApWJv//4E9OpF41mzzF1qiSAtfUIIIcxCURS2T5jCytMJJHZ6CwBnB2v6dK1H60YV0RQy7JkMBuKPHCFi9Wr0aWnUnzgRK2dn/v77b6ZNm4aiKISEhDBmzBhs/7E0WtUKnqwBroQXfPcmcutWdvTqhSEjA/eQEDps2IC1m9sjX7d4dK7OthiMRhKSM3F3c8C9QQNubdxo7rJKDAl9QgghnrjM9ExmjZjKSY0Pir8nFpjo0aE2PdrVKtRIXMgJjeErV3J02DAyIiJyn7d0dOR0lSr88ccfAHTp0oVhw4bdMwdfgG9Oi190fBoGg/G+IfP6smXsHTAAk16Pb4cOtFm5EksHh0e9bPEflXHNmeYsMiYlJ/Q1asTNv/8m9fJlnCpVMnN1xZ+EPiGEEE/UwcMX+XnBTjKt/ACoZq/nnY9ewsOt8GEq49Yt9r/xBpGbNwNg5eqK7s78pwcuXmTV0aMAvPbaa7zyyiv3HWHr6myHpUaN3mAiMSXzngEiF2fP5tA774CiEPjCC7RYsAALmSzfrKwsNTg72HIrJoXgamVxq1cPtaUltzZsoPoHH5i7vGJPbe4ChBBClA7JqVlM+XEj05ccJtPSDrvUOAbVdmDMV28UOvApisLl339ndc2aRG7ejNramtqff06XvXtzXlep2JCQgFqt5uOPP+bVAqZUUalUcOc1tVqV5xynJ0zg0Ntvg6JQ5a23aLl4sQS+YsLDzYGIyEQANLa2uNWtS8SaNWauqmSQlj4hhBCPlaIo7D5ylQUrDpGpM6IyGal0cQ9vTRiCb0jDQh8nOyGBg2+9RfhffwHg3qgRLRYswLlKFY5+/TUA0c7O4OjI1+PGERISUuDxdDoDBoMRIHewiGIycXT4cM5PnQpA7c8/p864cTIXXzHi6e7AlYg4MrN02Nla4dG0KRdnzUKbmCh9LR9AWvqEEEI8NvFJGUycs53ZS/aTqTPiFHODjkf/YMQv4x8q8N3atInVNWoQ/tdfqC0tqT9xIl3378e5ShXCw8PZ+NtvAOgdHZk+ffoDAx9ARFQSigJODjY4OdhgMhjY+8oruYGv0bRp1B0/XgJfMePj6QzAtYh4ALyaN0cxGLi5dq05yyoRpKVPCCFEkVMUhe0HLvHH38fI1hpQG/QEHVpFExcd7TatwsrZuVDHMWRnc/Kzzwj97jsAnKtVo8WCBbg3aADAuXPn+OSTT7B3cKAcEBAdjXchQ1ro5WgAyvuVwZidzZ5+/YhYtQqVRkPz336j4oABD3/h4rGzs7HC1dmOK+Hx1Kzii3WZMrgGB3N9yRKCXn3V3OUVa9LSJ4QQokjFJ6Xz9eyt/PLnIbK1BlwiL9N08Rd0rOVJxw3rCx34UsLCWN+4cW7gqzpkCN1PnMgNfHv37mXYsGGkpaXh2rQpTjVrouj13CxE/66cW85XAGhQ1YutnToRsWoVaisr2q5cKYGvmPPzduHSjViMRhMA3m3bErltG1kxMWaurHiT0CeEEKJIKIrCjoOXGD5xDWfDorBQjFTdvZiQv76m+dC3aPbbb1gUYr1xRVG4PG8ea+vXJ+n0aWw8PGi7Zg2NZ81CY2MDwMqVKxkzZgx6vZ6mTZsydepUHHx9AbC4s01BTl+MJDI2FSuNmvTPhhCzdy+Wzs503LJFVncoAfx9XNHqDNy4lQCAd5s2qCwsuLpggZkrK97k9q4QQoj/LDElk7lLD3Dqwm0APNKiqbJyGk4ZCTRbsICK/fsX6ji61FQOvf021xYvBnI+zFv+8Qd2dwKdoijMnTuXpUuXAtCjRw/ef/99Eo4eJWrbNgBcatQo8ByKorB8w0kAAi8fJGX/XqxcXem4ZUtuK6Io3tyc7XBysOXspSgqBnhg5eSEV8uWXJw9mxoffYRKLW1a9yOhTwghxCO7e5t0waqjZGbr0VioqH52Cz7bFmPt7EybLVvwad26UMeK2b+ffa++StrVq6gsLKg7fjw1R45EfWedcr1ez7fffsv27dsBeOONN3j55ZfJjotjb79+KCYTFV5+GZ+2bQs8z6kLt7l2MwELg46yW//A3t+fDps341Kt2n96L8STo1KpKO/nxvkr0ehaGbCy0lCuVy8ODxnCzbVrKdezp7lLLJYk9AkhhHgkKWlZzF12kOPnbgJQztWKivPGY33jIg7ly9N+/fpCBSmjTsfJL77g3KRJoCjY+/vTcskSvJo1y90mIyODzz//nJMnT2JhYcGIESPo2LEjhuxstnXrRtq1azgEBhLyww8FnktRFJasPJhT76ltuHm60XnnThzLl/8P74Qwh4rl3Dl98Tahl6OoW8Mfl+rVcQ0O5vSXX+Lfo4eMur4Paf8UQgjx0I6cCWf4xDUcP3cTjYWaTn5qqn8zCOsbF3EPCaHboUOFCnzJ58+zoUkTzn37LSgKFV99lR5nzuQJfHFxcbz//vucPHkSW1tbJk6cSMeOHVFMJg68/joJx45hXaYMHTZvxtrFpcDzbVu9i4j4TCx0WdTNvkG3Awck8JVQ9nbWlPVy5ujZm6AoAFR85RUSjh3j1rp1Zq6ueJLQJ4QQotCytXrmLj3A97/tIi1DS4CvK6843Eb18SugzSbguefovHMntp6eBR5HURQu/vQTf9erR8KJE1i7udFmxQpazJuXJ7hdv36dIUOGcO3aNdzc3JgxYwYNGjRAURQOvfsu1xYvRqXR0GrpUpwrVy7wnFH7D7D072MAVIs8RY9N63P7CoqSqWoFb2LiU4mIzFmCz61ePcrUr8/Rjz7CqNOZubriR0KfEEKIQrkaEc+oyWvZcegyKhV0b12NLudXETl+NAA1P/6Y1suXo7G1LfA42XFx7OjZk0PvvINJq6Vsly70PHcO77ZtCV+1isg7ffZOnTrFe++9R1xcHOXKleOHH34gKCgo57Vx4wj76SdQqWixcCG+7dsXeM7I7dtZ+PYoMpw9sdFn8d7cL7Fxdy+Cd0WYk4+nE86Othw4eR3I6etX5Z13SL16NXeqH/E/0qdPCCFEgRRFYf2u8yxZexyjScHNxY43e9QmZvhb3Ni5E5WFBSGzZlH1rbceeKyoXbvY8/LLZEVFobayov7Eifh168b+118ncssWFGPOsmie337L1K1bMRgM1KxZk6+//hpHR0cAQqdN4/S4cQCEzJxJhZdeKvCcN9etY+dzz3G7w2AAOrSvi7OnBL6ngUqlokYlbw6cuE5MfCpe7k44VqhAYJ8+nBo3jnLPPisDdP5BWvqEEELkKzU9m0k/7+CPNccwmhQaBZfjixeCudGvJ9E7d6JxcKDdunUPDHwmo5ETn3/O5rZtyYqKwrlaNbrs3UtWTAxratbk9saNuYEP4NTEiRgMBlq0aMGUKVNyA9+ln3/m6IcfAlB3wgSqDRlS4HmvL1vGzt69Mer0xFesC0DzRpX+y1siipnyfmVwsLNm9+Gruc8FDRyIrZcXe15+GaNWa8bqihcJfUIIIe7r8o04Rk1ey8nzt7DUqBn0fAj9qtmwq3ULUi5exM7Pj6779uHXuXOBx8mMjGRL+/ac+fJLUBQqDRpEk9mz2ffaa5z79ltMej1lO3emZ2go2hdfBMCoVtO7d2/Gjh2LtbU1ANeXL+fA//0fADVHjCD4008Lrv/339ndty8mvR6/l/piVOVM/eLt4fRf3xpRjKjVaoKr+hJ2PYbb0ckAWFhbE/zZZySHhnJ0+HDzFliMSOgTQgiRh6IobNh1nrEzN5GYkomvpzNfDutG5aizbGnbluy4ONzq1uWZw4dxq127wGNFbtvG33XqEL1rFxoHB5ovWIDGw4uFr77PUZdqnHxhNDcn/MGN1z7n60V/E7F7NwCBISG8++67qO9MsntjxQr2vPwyKAqV/+//qD9xYoFTclz88Uf2DxqUu32jOT/nvmY0mYrgXRLFSQU/d1ycbNm6Lyx3JK9TpUpUeecdLs6axeXffzdzhcWDhD4hhBC5tDoDs/7Yy4LVRzEaTYTUDuDLD7uSsmAuu154AWN2Nn7PPEOXPXsKHPlqMho5NX48Wzp2JDsuDtfatan622Lmrz/DzGQ/jvf8kGuNehDjU4XQeAPbD14mIt0bG+cAADqMHJkb6m5t2sSevn1RjEYqvvIKjX/4ocDAFzp1Kofu3Pat9v77NPnpJ5wcbfH1zGnhO3QqvKjeLlFMqNQqGtQsx83oJM5f+d/6u/49e+LXrRsH33qLmL17zVhh8SChTwghBACxCWl8MW0D+49fx0Kt4rXejXjv5SacfPdtTozOGaFb7YMPaLt6NZYODvkeJysmhq2dOnFqzBhQFGze/JAj3T5gyv5ELnvXxGBjj5OVilaNKtKnU3U0mWFkJ4ejUqm42PY1rMr64dW8OQC3Nm5kR8+emPR6Avv0odlvv+Wu0HE/15cv5+iwYQAEf/YZjaZNQ6VSoVKpaNUoZ+TvoZM3iugdE8WJj6cz/t4ubN57Aa1OD+QM9Kj2wQe41KjBtu7dSQoNNXOV5iWhTwghBBeuRvPp9+sJj0zC2cGGT97uSJta3mzv2pXLv/2GSq0mZOZMQqZNKzB0xezdy9916xK1fTuZfpW4/MmvrLarzdUMNZhMBKSE81Gfusye2J+OIT4s+fVbbl8+iF3CUdRGPUYrWyqM+BSVWk3Ujh3s7N0bk05HuV69aPHHHwWfe98+9r7yCpDTwldvwoQ8LYJe7jmDQXR6QxG9a6K4aRgcQLbWwI6Dl3OfU1taUmf8eGzc3dnasSNpN26Yr0Azk9AnhBCl3Nb9YXz5wxbSMrRU8C/DN8OfwV+dwYamTYnasSNnhO7atVR79918j6EoCme//ZZNbdqQmKblYp+P2Pvcp1zNsEBlNFDuzA4GO0Yw9JUW1KlTkZMnT/L++++TkJBAYLlyvJSQgEabBYBnz97c3rKFbd265d5Obr1sGRZWVvmePzshgZ3PPYdJq8W/e3cafv/9PdukpmcDYGmZf3AUJZu9nTV1q5fl2NkIbtxKyH3e0sGB+t9+CyoVm9u1IzMy0oxVmo+EPiGEKKWMRhPzVh7h1z8PYTQpNK1XnjHvdUZ35jjrGzfOO0K3a9d8j6NLSWHX889zbPRortdqy/5Bk7nhWwsF8L50mPabp9HVU0vUD1PZ3LYtSwID+XzYMDIzM6lTpw5vlytH5Onz6OycUAHKhdPs6NkzN/C1+esv1JaW+Z7fZDBw4M03yY6NxaV6dVotW3ZPi6CiKOw8lNP6U72id1G8faKYqlLBC293J9ZsO0t2tj73eesyZWgwZQqG9HQ2tW1LVkxMAUd5OknoE0KIUigzW8ekn3ewac8FAF7qVo/3BrQgfP7vbG7XDm18PGXq1+eZI0cKHKGbFBrK2gYNOLPvBAdeHs/FVv0wqCxwibxE46XjaXltB1a3rnJj2TL0KSkAmOLjscrKok2bNrxTvz7nv/yShHI1APBx0nCgV4+cwNetG21WrMDizpQt96MoCvtee42IVatQW1rS9Jdf7rsiyKkLt7l+KxErSwvaNS14uTZRsqlUKprULU+2Vs/aHedyR/MC2Hp70+C779AmJOQEv9hYM1b65EnoE0KIUiYuMZ0x0zdy+uJtrCwtGPpaK7q3qsKhd97hwODBKAYDgS++mDNC18cn3+OEr1zJ382ac8yrLgdf+oI0d38s9dlU3z6PkJWT8DKkkBoWhjErC8/mzcnq0SN3304vv8zrTZpw8PXXAUjo1A8A+x2rMGZn49+9e07gK+CWrqIonBozhmuLFqHSaGj95594Nmlyz3bZWj2//nkIgI7Nq+LkYPNI75soORzsrWlctzwXr8Vw5ExEntfs/fxo+N13ZMfE5EwWXoqCn4Q+IYQoRS7fiOOzqeu5GZWMq5MtY97rTLCnFZvbtiVs9mxQqaj75Ze0WrIEjZ3dfY+hmEyc+OwzVr47kj3dP+Z6g26gUuN75Sgtfv+YCuHHUSkmtAkJ2JUtS9OFCznYsiVpu3YBYFGzJs81asTOZ5/FpNNh/+JrRGKPymik7Mlt+PfoQeu//iqwhQ/g1JgxnJ4wAYCQGTMo17Pnfbf7c9Mp4pMycHe15/nOBc8rKJ4eAb5uVKvozZZ9Fwm/nZjnNfty5Wjw/fdkRUWx+c7ck6WBhD4hhCglDp26wfgfNpOSlk05X1e+HNYNuytn+LtuXWL378fS2Zl2a9dS+9NP850HT5eSwtaez/Lnrksc7vMJGWXKYqvoqLt2BsHrf8De2gJDWhqYTFR85RXaHjjAjEOHSF28GM/UVFS2trT68ku2dumCITMT306duNbgGQC8rhyjUruWtP7zzwJb+ABOT5iQG/gafv89Vd9++77bnQ2LZP3O8wAMfD4EG+v8+waKp0+9Gn54uTvy58ZTJKdk5nnNoVw5GkydSlZUFJvatCkVwU9CnxBCPOUURWHtjnNMn78bvd5I3ep+jBnSkVszv2dLhw5kx8biGhxM96NH8e/WLd/jpF65wqJ23fjLoX5u616FuDCazPkQr2snUFtbo09OxtLJiVZLlxI0cSIffvopKbt3U/fONBnBH3/MvtdeQ5+ailerVpT94htO3kwFoLFV4gMDn6IonBo/npNffAFAg8mTqXFnLd5/S07N4sdF+wBo37Qy9Wv4P8rbJ0owtVpNywYV0VioWbz2BFnZujyvO/yzxa9dO7ITEvI50tNBQp8QQjzFjEYTvyw/xKK/j6Mo0KlFVd7pUoU93Tpz8osvUEwmgl57jW6HDuFUqVK+x7m1eTM/9H2XrSGvkOoViK2FQsN9C6m8+BtslJwRkiatljINGtBoxgzSypThnXfeIT0sjJZhYQAEvvACF2bORJecjGfz5jSfP5+5P64BlRr/2Ev0Xjj3gYM2Tn7+ec6kz0D9iROpmc+6qiaTiZkL95CUmoWftzP9ezZ41LdQlHDW1pa0aVyJtMxs/txwCoPBmOd1h4AAGnz3HRk3b7KlQwe0ycnmKfQJkNAnhBBPqaxsPZN/2cH2g5dQqeDVXo1owy3W1alNzO7daBwcaLFgAc1+++2+I17hTtCaNpPvpq/hdLO+mCytqWCVTZNfPqLM8e1Y2Npi0uW0nlg6O5Nw7Bj7X3uNA926oY2OptOFC1jq9bjVq8ftLVvQJSXh0bgxrVesYMmQ0cS4l0dtNDBk9KtYOTnley13A9+Zr74CoMGUKdQaOTLf7VdsPkPo5WisrTQMG9hGbuuWcs6OtrRpVIlbMcms2nIG5V/rLzsEBlJ/8mTSrl5lW7duGDIz8zlSySahTwghnkKJyRmMnbGRUxdyRuh+0LcxjvMns+v559EmJlKmXj16nDhBxQED8u2/Z9TpWPPWMGacyiaqcghqxUTjzCtUmvw2VqmJqK2sMGZl5c6hd3dKFgAbnY4ely5hk56OnZ8faVevok9OxqNJE9qvX8++N97kUJlgANrV9SOwVv7TqCgmE4ffey838DWcOpWaH32U7/YnQm+xYvNpAF7v0xhfL+eHe/PEU8nT3ZEWDSoSdi2WdbtC80zlAuAUFES9b74h8eTJnIm+9fp8jlRySegTQoinTERkEp9P25izpJqjDe+18uV2365c+f13UKmoNXo0XQ8eLPB2bnZCAtP7DWWZZQ2yXDxxUunpGLoal5+/RKUCVCpMOh2KtQ3xnhWIqtqEiGdeI9UtZ4oXo6MjNomJWLu5oUtORp+Skhv4jnz4Iftv68l09cHBWk3fl9vkW4fJYODg229z8YcfQKWi8Y8/UmPo0Hy3j0tM54dFewHo2LwKLRtWfKT3UDyd/H1caVIvkFPnb7N578V7gp9L9erUGT+eyG3bOPjWWyj/er2k05i7ACGEEEXnbFgk3/++i6xsPT4eTvTMDuV8z5dRTCbsAwJosWAB3i1bFniMyNOhTJ64mKiyIQBUttVRadE3GG9eR6XRoBgMZDp7cKNOJ6IqN0Jv97/bslbedWn253isUxKwsLHBmJ2NITMTz+bNab9hAydGjeLM35u53C9n5G3/3o2xs7n/wA2jVsvuvn2JWLUKVCqa//47Qa++mm/dBoOR6fN3k5Gpo2K5MrzybMOHfftEKVCxnAcGg4kjZ8Kx1Khp26Qy/KO1271BA2oMH865iRNxDAoiePRoM1ZbtCT0CSHEU2LPkavMWbofo0mhip8LddbO4MaeHQBUHDCAkJkzsXIu+FbnoRUbmbvhAplelVEbDbR3TsdiykiMWi1qa2syrOy52qgHt6s1R7HI+QixQI9t1HUMDmXIdizD9eAO1Di6GkVRMGZm4t26NW3XrOHMhAlc+PFHQnuPwKSxolYVH1o1CrpvHfq0NHa98AK3N21CbWVFy8WLCXzuuQJrX7LuBFfC47G3teKDV1uj0cgau+L+qlTwwmhS2H/iOmq1mtaN87Z6l+3UiazISE58+imuNWvi3727mSotWhL6hBCihFMUhZVbzvDnxlMA1PW1peykwaTExWLp6EiT2bOp8PLLDzzGwm9/ZeNtFYpjGRyyU+mhiSDxqykogNHWnkt1OnGjXmdMmpyWuSAXC6JjzlJ2xTzKx8VyqenzXGv4DHo7RxSjEZNej2/HjrRdvZpzkydzbvJkbtZsTaJ/dawsLXi9T+P79ifMjo9na+fOJBw/joWtLe3WrMG3Q4cC6z8bFsn6XTnz8b3drxmeZRwe/o0UpUr1IG8Uk8LeY1dRqVS0alQxT4tfxVdfJe3aNXb360eP48cL7A5RUkjoE0KIEsxgNPHbn4fYcegyAE0dMnAcNQiDYsKtbl3a/PUXjhUqFHiMzMxspnw6m/OKK1hAhawomkYeIn7dGgBiKtTjfOv+aB3dAPBIuMGzL7bkt8U/4797N+XjYkGlIsk350PRJeoqJr0e/549abV0KeenTePUmDFkOntwpf2roMBLz9TD2/3e0bppN26wrWtXUi5cwNrdnfbr1uERElJg/ekZWn5avB+ADs2q0KBmuYd7E0WpVaOyDwoKe45eQVEUWocE5QY/lVpNrdGjOfjWW+zs04duhw6hsSnZS/g9dOizsHj8zeUqlQqDwfDYzyOEECVZtlbPtHm7OXXhNiqVipYZF7Gd/g0AlV5/nZBZsx74IXX7ZixffbOURCtXMJlooY7Cd/9i4kND0dk7E9p6ADFBOXPc2SbHUnXvUjyvnWDDqZp4paZSNTISgCTviiSVrYLKZKTMjTOUf+klWixYwIWZMzkxejQmlZorb3yJzqCiWkUvOreodk8tiWfOsKVjR7JjYrArW5aO27bhUrVqgfUrisIvfx4kMSUTHw8nmY9PPLSalX1RoWLvsasoikKbxpVyg5/Gzo7aX3zB4SFDODF6NI2mTjVztf/NQ4c+RVFQqVRP3YgWIYQoSZJTs/h27jau30rESqOmedgmrNYtRqVW02j6dKoOGZLvVCx3Hdl/hlmLD6CzcsYqM4XnPTLImDuV5Ph4oqs15Xzzl9DbOaEyGih/YiMVD/+NhTFnGovyly/joNUCoABXmub0tyt7fh+1X3iWJrNnc27yZE7c6QSfOfJ7IrOssbOxZEj/FqjVeWuL2rGDHb17o09JwTU4mPbr12Pv5/fA9+HgyRscOhWOhVrFuwNaYG0lN7DEw6tR2QeVSsW+49dQgLb/CH5OQUFUeuMNzk+bhn+PHvi0yX+0eXH3SP86ChP47v6yKeptH7fjx4+zdetWjhw5wpEjR7h9+zbw6LUlJSUxduxYVq9eTXR0NN7e3vTq1YuxY8fi4uJShJULIUqL2zEpTJyzjbjEdBxsNIRsm4Pl8b1YOjnR+s8/KduxY4H7K4rC8sXbWHXkFlja4hIfQd9yRsInf022hTWh3d7Nbd1zjIug1pafcYq/icrBASU9J/TdDXwAMdWakOBXDbVBT6daHjT9fiKnxo3j9LhxOduO+prN2S4AvPZcCO6u9nnqubZ4MXtffRXFYMCzeXParV2LdSF+P6ZnaJm/8ggAvToGU7Gce6HePyHup3olb1DB/uPXUBSFdv8Y1Rvw3HPE7t/P/tdf59lz59DY2Zm52kfz0PP0mUymB37NmDEDS0tLFEWhadOmzJkzhzNnzpCcnIxeryc5OZmzZ88yd+5cmjdvjqIoWFlZMXPmTEwmE0aj8cGFPCYTJkxg9OjRrFq1KjfwPar4+HgaNWrEjBkz0Gg0PPvsszg6OjJ9+nRCQkJITEwsoqqFEKVF2PVYxkzfSFxiOu72GhotHovl8b04lC9Pt4MHHxj4srV6Jn27jFVHI0GlpvzNU/Rziub6N+OI8a3K/v5fEhPUAJXRQNDBlTRZOg7n5CiUZs3Iys6+53gGJzcuNHsRgGYO6bSb8jVHhg7NDXyVvp7CZnV5FAXaNamcZ948RVE4M3Eie/r1QzEYCHzxRTpu3VqowAewdP0JUtKzKevlTM/2tQr5DgqRv+pB3jSoVY4DJ66z4+Cl3Hn8VGo1NT76iMzbtzk1fryZq3x0Rd4O/uWXXzJmzBisra35448/ePk+I8acnJyoUaMGNWrU4I033mDp0qUMGjSI999/n8TERD7//POiLqvQmjRpQnBwMA0bNqRhw4YEBgai/cdftA9j6NChXLlyhd69e7Ns2TI0mpy3+/3332fmzJkMGzaMefPmFWH1Qoin2ZEz4cxcuBe93oi/PVSe+R6WaUl4NGlC29WrsfX0LHD/6LhUvp6yglitGpXJSMPbhwk2RHNp7kbCWvUnok57AOwTbhO8eS7OceHY+voS1aIFlqtWYf/vvtZWVpxt8TJaexfKaAy8MfZtDrzxBlfu/F6r/d1UlhkrkpaRSHk/N157rlHuriaDgUPvvsulOXMAqD50KA2/+w6VunBtEZExKWw/mDN45fU+jbGU6VlEEalW0RtF4Z7pXOz9/Sn/8suc//57Kg0ahHPl/FeRKa5UShHeUz1x4gSNGjVCURTmz59P//79C73vokWLGDBgABYWFhw8eJAGDYpHZ1wbGxu0Wu1D396NiorCz88PjUZDREQEXl5eua9ptVr8/f1JTEwkMjISzwf8ov6n1NRUnJ2dSUlJwamAdSqFEE+XLfsu8vuKwygKBFllUX7qB1gYdAT26UPz+fPzXTv3rpOhN5n+8xay0WCVkUKXrPM4hB0n/Fokpzu/RZpHzojXgJNbqLz/TyyMerw7dGCfry+ey5bh9K9WPgtbW25UaEho+0GoURj3TnvCh73FrfXrUVlY0Gz+fP7WleXI6XCcHWz46qNuuLvmTKOiS01l94svcnvTJlCpCJkxg2rvvvtQ78eshXvZd/wa9Wr4MeLNdg+1ryg57n7mbZ27EHvbJ3tLNfRSFCfO36RNSCWa32mhNmq17B84kDJ169J+/fonWk9RKNJl2GbPno3JZKJixYoPFfgA+vXrR1BQECaTiblz5xZlWWaxadMmTCYTLVq0yBP4AKytrenevTtGo5ENGzaYqUIhREmgKApL1p3gt79yAl9NQzQVpgzBwqCj5scf02rp0gIDn8mk8Oe6Y3w7dzvZaHCOusor1tex2LuRU1n2HOg7ljSPclhlplJ/9XdU27MYC6Mer65d2Z+Vhfuff94T+NS2tiQ6enKh9QAAnm9XncuDXuLW+vVY2NjQ+q+/OO5anSOnw9FYqBk2qE1u4Mu4dYuNLVpwe9MmLGxtabNixUMHvmytnsNnwgF4rlPth9pXiMKqUdmH2lXLsvPwZY6czvl5s7C2pvLgwdzasIGonTvNXOHDK9LQt2fPHlQqFY0bN36k/Rs3boyiKOzevbsoyzKL06dzFvuuV6/efV+/+/yZM2eeWE1CiJLFYDQxe8l+1mw7C0D9xFDK/jAKCxWEzJpFg0mTCrwdmpWt5/s5m1mxLRRUKsqd28XAAB23F//BoaqdCW0/EJPGijLhZ2m26HM8ws9iYWuL5zPP8JfRSOD+/bhkZuY5poWdHVqTilNdh2DSWFK7QhkY8Sqx+/dj5eJCp+3bCfetxaotOb/bBr/YlCoVcu5mxB87xrpGjUg6cwZbb2+67NlDQK9eD/2+nLkYiV5vxLOMAxX8yzz0/kIUVq0qvlQP8mbz3gucDcuZnsirVSucq1fn+MiRxWIA6sMo0j59dwc+WFndfx3FB7m7X+SdeZ9KsoiICAD88ply4O7z4eHhT6wmIUTJka3VM33+bk6ev41apaLR1Z24rJ2HhY0NLZcsIeDZZwvcPyoulUk/biIqKQuV0UDtw3/StkUNjsxfwonuw8l09QaTicoHV1D+2AZUKKS7eBMfUJOIC7eoe/MCFv/6QNPY2aHL1nKq13CynD0p42CF33fvkxpxHTs/P9qvX89NyzLM/nk7AN3b1qRlo5zbYuGrVrGnXz+MWVm41KxJ+3XrcAgIeKT35u6MD2npWhJTMinjYv+APYR4NCqVino1/NHqDKzZdhYHO2vK+5eh0qBBHBs+nFvr1+P/zDPmLrPQijT0WVpaAnD27NlH2v/cuXN5jlOSpaenA2CXz7Bue/ucX1JpaWkFHker1eYZSJKamlpEFQohiqvU9Gwm/bydK+HxWGnUND6+Artda7BydaXd2rV4NWtW4P5nwyKZ+ut2MnUmrNOTaHpyJTVqB7F142FCn/8Uk6U1NmkJ1N40G9fIy8QFBnOlUQ9SfP63Dq5dUjSNVkzEJiMZyGnhM2Rmcqn9ayT4VcPKQkWNBWMx3ryOS82adNi0iWijNVN/2IzJpNC8fgX6PlMPRVE4++23ufP1le3cmVbLlmH1H/ok16/pR1CAO1fC45m38ggfDSq586aJvIrjZ55KpaJxnUAys/Qs33iS159vTJl69XANDub0+PH4dev2wDkxi4sivb0bFBSEoigcO3aMI0eOPNS+d+fFU6lUVKxY8cE7lBLffPMNzs7OuV/+/v7mLkkI8RjFJaYzdsZGroTHY2+jofmuX7DbtQZbHx+67N1bYOBTFIUNu8/z9U9bydSZcI6+SrfzqwjwdWF1uMLZjm9isrSmTPhZmi4eg1VmKsd6fsjxnsNI8QlCZTLiHHUFgExXb7KcPYCcwGfMzOR2kx7cqNEagJprZ2J38xLebdrQZc8eElR2fD17K9laAzUqefNW36Yoeh37Bg7MDXxVhwyh3dq1/ynwAajVavr1qA/AqQv/bWotUbwU1888tVpNy0YVsbOxZNn6E2i1Bir060f80aNE7dhh7vIKrUhDX687fTMUReGFF17g8uXLhdrvypUrvPDCC7mPe/fuXZRlmYWDQ06n5cx/9Ye5KyMjAwBHR8cCjzN69GhSUlJyv27evFm0hQohio2bUUl8MX0DkbGpuDpY0WTNFKyO78GhfHm67tuHa40a+e6rNxj5ZflBFqw6igKUPb+XZ1OPY1RbsNKhHrdqtQbFRNDBldRbN4Obtdqwv9+XxAfWRmU0EHh8I61/HYZ/6B4AbFLjcYm6gtrGBmNmJsn12nAuJOd3c+V9y/G8fIzyffvSYdMmkgwWfDN7KxmZOioFeDD89bboExPY3K4dV+fPR2VhQcisWTSeNQu1pmhuMPl7u+Zct96IVifLdj4tivNnnpWlhtYhlUjP1LF62xnc6jfAsVIlzk2ebO7SCq1Ib+8OGTKEWbNmER0dTUREBHXq1OH9999nwIABVK9e/Z7tL1y4wMKFC5kxYwZZWVkAeHt7M2TIkKIsyyzKlcuZ/uDWrVv3ff3u8wEP6NNibW2NtbV10RYnhCh2wq7FMunn7WRk6fBxsSF43ueowi/jUr06Hbduxc7XN999U9Oz+f63nVy8FguKiSp7l9G2hgenrxg4Wvs59DYOWGalUXvTbCz0Wg68NJaMMmUBcIu+RI3Nv2KfHIPWzpmwZn0ACDi9HQtLS0zZ2egbtuJ4y4EoBhN+53ZT/vgGao0eTb0vvyQmMZ3xszaTlJqFv48LI/+vHVlh59nesycZ4eFYOjvTevnyB04a/bBWbcsZKOJgZ42FumTcWhMPVtw/8xztbWhWvwI7D13iyNlwAp9/nrPffEPyhQu4VLt3PenipkhDn5OTE8uWLaNr165kZGSQlZXFpEmTmDRpEo6Ojvj7+2NnZ0dmZia3bt3KvVd/d/SLvb09y5Yteyrmn6tdO2cagRMnTtz39bvPBwcHP7GahBDF04nQW0ybtwud3kh5d1uqzPwQYiNxq1uXjps3Y+Phke++t6KTmTR3G7GJGWi0mdTeNIemXZux+XwSl0JyJsd3jr5Krc1ziajdIXcCZuusNHxv7KPSlmWoyVk/N7TNK+htHXGMDSfw3A5MOh2apm3Y1+J19NkGPK6fpsbuP2g6Zw5VBg8mNiGNL3/YQmJKJn7eznz6TkcStmxkb//+GDIycAwKot3atbhUrVqk79e+49dYv/M8AG++2ASNTMwsniA/bxeqV/Rm+4HLDOxVHytXV8J++omQGTPMXdoDFenkzHcdP36cAQMGcPHixbwn+0dHx3+ftkqVKixcuLDYTMp8V1FMznzz5s08EzDL5MxCiLv2HLnK7KX7MZkUqnvbEfDtWyjJiXg2bUr7DRuwcnbOd99TF24z7fddZOsM2KbE0mDTj9R4oRd/3dQQXy7nVnC5U9vwCTvAuQ5vkuHmA0D56FC4fZRK+3bl9vEJD27HhTYDUBkNNF02Hse4CBy79GBr3RdJztDjFHOdpptm0n7xH/h16UJ0XCrjf9hMYnImPh5OfPFeJyJmTuXkZ58B4NO+Pa2XLcPaza1I36+T528x+ZcdmEwK3dvWzO3bJ55O5pycuSBGk4lNu89jqbGgVWootzdu4MWoqAdOkm5uRdqn76769etz+vRp5syZQ6NGjVDfmUdKUZTcL8jpGNmoUSPmzp3L6dOni13gK4xZs2ZRtWpVRt/pqHyXj48Pffv2RafT8c4772D4x/JFI0aMIC4ujv79+z9U4BNCPF3W7wrlx8X7MJkUGvjZEfjlIJTkRLxbt6bD5s0FBr7Ney/y7dxtZOsMuN66SIuN0/Hr+SzzU32JL1cDtV5LrU1zsE2L58jzn5Dh5oN1ehLtInaSdusAQft3534AJHtX5GLLvgBU2bccx7gIvPoOYE/9nMBnnxhJi8OL6bFrB35duhAZm8L4WTmBz9fTmU9eb8npwYNyA1/Vd9+lw8aNRR74rt9MYPr83TmjgxvkjA4Wwhws1Goa1wkkJiGN2IBa6FNSiFizxtxlPVCRr717l6WlJW+++SZvvvkm6enpnDlzhri4ONLT03FwcMDDw4Pg4ODcAQ/Fxfr165kwYULuY51OB5BnwunPP/+cbt26ARAfH09YWBhRUVH3HGvatGkcOnSIFStWULVqVRo0aEBoaCjnzp2jUqVKfP/994/5aoQQxZGiKCxdd4I123OmqWpZ3h6HUa9gys7Ct1Mn2q5ciSaf6Z6MRhMLVh9l896cOyllz++lYdh2Ujr1YZmmOiYbK2yTY6m57TeuNXyGhICaAHhdOUZz6xjOHD9M3YgI7t4QzXTy4ET3D1AsNHhdOUbAqS1U+PBjVtrUJCZNj01aAu0vbaDH3p3Yly1LRGQSX/20hZS0bPy8nRn2bC0OdetIwokTqDQaGv/wA1UGDy7y9yz8diJf/rTlf6ODX2qKWvryCTMq4+pA9Yre7LseR6tq1bm6YAEVXnrJ3GUV6LGFvn9ycHCgadOmT+JU/1lcXByHDx++5/l/PhcXF1eoY7m7u3PkyBHGjh3L6tWrWbVqFV5eXrz//vuMGzcOFxeXoipbCFFCGI0mfv3zEDsO5cxu0LWyA8qwfpi0WvyeeYbWf/6JxsbmvvtmZumYuXAPJ8/nTFNSed9yautucr55H0Jdc27nelw/Tdlzuznd5W109s6o9Vqq7VtGZWc9h27epEZERG4Ln87GnuM9h6Gzc8Ip5ga1tvxMrW8msTjehehMBev0JLonHaXHto1YOjhw7WYCX/+0lfRMLYFl3XizgQt7WjcjOyYGa3d32qxYgXfLlkX+nkXHpfLNnG25o4M/er2N9OMTxUKtqr5cu5VAgl9VMratRZuUhLWrq7nLytdj6dMnHh/p0ydEyaXTG5m5YDdHz95EpVLxfE0nMt55EZNOh3+PHrRevhyLfEYuRsakMOXXHUTGpqLWawnePJfKfi7sCmhFrGvObAEVDq9FUau43qArqNQ4xN+kwZ6F2OpTUaKjMUJuC59BY8XR50aS4l0Rm7QEmq6cSLMZU5l7OIEYjTOWWWn0tblJ5++/Rm1hwfkr0Uz+eQdZWj1BAe684BjHqSH/h0mnwzU4mLZr1uAYGFjk71lUXCoTZm0mMSWTcr6ujH2vM3a2j7bqkyh5imufvn+6fCOW4wfPUnfDj7T44w8q9utn7pLy9URa+oQQorTLyNQx5dcdXLgag6VGTb/aLsQNfgGTTke5Z5+l1bJlWOSzhOWZsEim/b6LzOyc2611183Eu2kT1rk3ItPeFQtdFlX2LiOyWlOSfSsD4H92J1X3LMHCoEMhZ3Tu3cBnUltwqtsQUrwrYpmVTuNtc2g9dzazNl8m1sEby+wMBlXR0GbYt0DO4Invf9+FXm+kekUv2t7YwYnvc+YmK9erFy0WLMDyMXTViU1IY8IPm3NHB3/ydgcJfKLYqVjOndDLHhi8/Lm5bl3pDX1Hjx5l0aJF7Nu3j5s3b5KUlITJZMozqAEgOTmZAwcOADlr0so0JkKIp0lSSibfzNlGRGQSttaWvB7iwfX+PTFmZ+PXrVu+ge/uCht/rDmOoii4RF6m7vpZWD7/Kn871MBoaY1tSiyBxzZwuenz6G0d0Ggzqbntd7yvHP3fcYC7vd8UlYoznQYTH1gbtV5Ls0OLaDd9MtNWniHBzR9LbSZD2vjT+MXuAOw7do2fFu/DaFKoU9mL2utncWXDegBqf/EFdcaMQaUu+jGB0fGpTJi1JXewyOdDOuHsWLxHRorSSa1WE1zVlxtH/LDZvAXFZHos/yaKwmMJfXFxcQwcOJCNGzfmPnf3LvL91qezt7fnzTffJDo6mvLly3PlypXHUZYQQjxxUXGpfDN7K7EJ6Tg72vBO2wDO9eqMMSuLsp0702bFivsGPoPByK9/HWbnnb5/ZUP3UH3nQpIGjeaYXQUA3MLP4Zh4mwvtXgPAKeY6dTb+iF1K3n7HuYGPnLn4oiuHoDIaaHF+PS0/fo/vlx8nybMClrosPvp/9u47uopq7eP4d05P770n9AAJvdfQiyCggl3Ehr2Xq9dree1dsYCioCKoCNJ7L6GHkhBKeu89p8+8f5wQqleRQMC7P2uxkFNm9pm1PPllz97Pc1MX4gd2Bxw7hL9d6FjP3KOVH+EznqIoJRm1kxN9v/uOqDM6KTWl4rIaXvtsDWWVdQT7u/Pig8NE4BOualEh3pwIa4EtdScVR47gfZVOXjV5FM3Pz6dbt26sXLnyvBItf0Sr1XL//fejKAoZGRkkJiY29bAEQRCuuLTsUv790QqKy2oJ8HXj1UdHUfL+61irqvDv3Zs+33yDYref977KGiOvzVjDxsQTSIpCm80/0nbbAk7c83pj4As7tAFFpSar03DAUY+v5y//d17gO0UBUvvfTG6HQaDIDCzYSY9xQ/h4eSoV/tHobCZeuG8w8QO7oygKv6xMagx8/aJc8X/xNmpSknEOCWHk1q2XLfAVllTzyqerGwKfB/9+aATeHlfnWi5BOEVSqYjs3gVZUpG2+urtxdvkoW/SpElkZ2ejKArt2rXjp59+oqioiOnTp//X900+Y5vz6tWrm3pYgiAIV9TBo3m8+tlqaurMRIf58MqjI3GpryB3xQoATGVl/BwSwrJu3VBkufF9OQUVvPjBco5lFKO1men8+wcE5Key7643ydQHoLJZid71O8XRnagIa4vaYiRuxee02/wDKvsf96A90WsCWZ0crdAGG4/RKsiVL3eVUxUQjUG28NLjY2kb1wpZlvl24S4Wrj4IwCA/Gy5PTcZaUY5v9+6M2bMH3y6XpyByYWk1r352OvC99OAwPN3FDJ9wbYhpEYzRO5Dk5Wuaeyh/qElv7y5evJjExEQkSaJv376sXLkS54ZaUxe6rXumli1bEhISQn5+/gVLpgiCIFwrNu06yawFO7DLCh1aBfHE1EE4GbSUZ1RCw52P6mPHAKg8ehTZZkOt07H3cDaf/bAVk9mGW3058b+8jSU4kp1jH8Ki1qOrrSDoWCIZXUejqDW4lOXRaflnuFYUgFoNF5g1BDjRYzzp3a8DYIi2CL/CE/zo3onagDCcJTsvPzuRiBBvrDY7M37YSmJSFpIEg8lF21BwOWryZPrMnn3ZOg6cuUs3JMCxhk8EPuFaotGo0YVHUXfkIEaTFSeDtrmHdJ4mnen7+eefAdBoNMyZM6cx8P1VHTt2RFEUjjV8GQqCIFxLFEVh0ZpDfPnTduyyQt8u0Tx7b0Ljl793x470//HHxtf3+vJLbsjKQqXVsmxjMu/P3ojJbMOvJJ2u379Eecf+7BpyPxa1HvfCNDwL08jqMhJFrSHw+C56LXgNu1ZHSZcEqv+g1EtatzGk9RwPQIJ7Dc4bfuN3r27U+obhppV49dkJRIR4U2+y8NZX60hMykKjVjGoYCfajxyBL/6VV+g/b95lDXyvfraqcZfuSw+JwCdcm/xi2+JcXsCmrUeaeygX1KQzfadm+Xr16kXk36jXdKol2V8tfiwIgnC1sNtlvvk1kQ07HRsvxiW056bRnc/qGmGurCSloSm7ztOTqMmTkVxc+WLedrbsSQMg6uROotfM4fjY6eSEORaDBx7bRa1vCMUtuiLJdlpvXUBQ6g5S+08ht/0AALqUlUDmobPGlNZtDCd6TwIgwase1dwZrO1/N0YPP7yctbz8xBgCfd2prDby1sx1ZOaWo9eq6b37J3RbVqA2GOg7Z85lW78HkF9cxWufraai2khooCcviU0bwjXMPSaGSkVhy+L1DE+Iv+q6xjRp6CsuLgagVatWf+v9hoYq9GazucnGJAiCcLmZzFY+nrOZAyl5SJLEnRO6M7xfm7NeYywuZu3w4ZQnJaH39mbomjWY1Ho+/GItR9OKUEnQLnEhPke2sf+Wl6nwCEKy2whJ3kJh657Y9M7o6yrpuOJzjF6BbLv9LaxOp2vjFbboit8ZoS+9y6jGwNffqRLr5++za+wTmF298Pd04qVHRuHn7UphaTVvfOHYXexmUNPl9w/Rp+7HKTCQwb//jl/37pftuuUXVfHaDEfgCwvy5MXpIvAJ1zZ9SBgA5pPHOXQsn/i2Ic08orM1aehTNdSlkc9YlHwxysvLAUR7MkEQrhmV1UbembWe9JwydFo1j9zen64dws96TV1eHmuGDKEqNRWDvz/D1q6l3j+cVz9cTnFZLQaNRNyST6CumsRbX8esd0FTX41/5iFyOw4GwDP/OC13LORkrwlUhDoCpaE8D0XnjNnVi2q/iMbznTnD18ueg3X21+wd/wxWJzdCA9x58cEReLo7kZFTxltfraOq1oS3QaLjty9gKM7Bq2NHhixbhktY2GW7bvnFZwe+lx4cjrvrhdvPCcK1QuXkhMbXj0BjKSs3p/yzQ5+fnx91dXVkZmb+rffv378fgODg4CYclSAIwuWRX1TFG1+upbSiDjcXPU9PG0yrKP+zXlOdlsaaoUOpzcjAOTSU4evXk6248uFHK6g3WfE2SMR++28qfUJJnjgdWaXGtTQHramW/HZ9AQg9uAG13cLe659GUWuQZBshe5biV5LDgTGPABCzZxkAad3GcqL3RAC6lCdjX/87+yY8h13vRHSYD8/dNwR3VwOHjuXzQcMawgCNlXafPoW+vspRLPqnn9C6uV2261ZQUi0Cn/CPpQ8JJcBYwsrUfPKKqggJ8GjuITVq0tDXtWvXxjp71dXVF9Ubdvfu3aSlpSFJEn369GnKYQmCIDS51PQi3vt6I7X1ZgJ93XjuviEE+p39nVd59CirExIwFhTgFhPDsHXr2JVnZvbCdciyQoSLQvTHj5HeZVRjORXvrCMYPQOo9Q1DZbPQInEx+W16U+sbCoC2No/2Sz7Hxa6QeKNjo0XEgdUEnNzDyR7jONnzegDiMrajHNrN3uufQtboaBsTwNP3DMbZoGP7vnQ+n7cdu10m1FZBmy+eR2Mx0faRR+j2wQeo1GouF0enjdVUVDnW8L04fZgIfMI/ii4kHMv+Pbg661m5OYVpN/Zq7iE1atLdu2PHOtr2GI1G3njjjb/8PqvVyqOPPtr47/HjxzflsARBEJpUYlIm//f5GmrrzcSE+/LqY6POC3yl+/axsn9/jAUFeMbGMnzzFhYnlfD1L4nIskIHJyPRHz7C4SFTGwOf/8l9VAa3xOjhh6GqmMj9qzjZczy1vqEYbCY0WRvp+cOreFdVsG/sY9j0znjlHaPVtp852XNCY+Brd2gVpB7iwNhHkDU6OrUL5fn7huBs0LF8UzKffr8Vu10mqjKDdl8+jdZmocenn9Lj448va+A71WmjcZeu2LQh/AMZIqOwFubTJcKdLXvSqK41NfeQGjVp6Js8eTLR0Y5q8e+99x6fNOxS+29KSkoYO3Ysu3btQpIkunTpwpAhQ5pyWIIgCE1mxaYUPp6zGatNpluHMP790Pm3Jou2bWP1oEGYS0vx6dKFgWvW8cXKVJZvSgGgj6oIjy//w+5JL1Aa2RHJasY3I4niFl2QtXq8s5NxK8sjvft1yBodwXIlxpSF9F0+DyerjaTR0zF6+uNUVUL8sk852WsCaT0cdfjaJS6E0iIOjrgfRaWmT5conrx7EBqNmh+X7OX7xXsBaJm1l1ZzXkVn0DN4yRLaPvTQZb1upRV1vDZjTWPh5RdFL13hH8oQFQNAa7kURVFYvTW1mUd0WpPe3tVoNHz99dcMHz4cm83G448/zvfff8/kyZNJT09vfN2SJUsoKChg+/bt/PbbbxiNRgCcnZ355ptvmnJIgiAITUKWFeYt3ceyjckADO3Tmrsmdm/cwHZK7qpVbJwwAbvRSED//nT68WfemrebzNxytBoVgyoPUbF6OUk3vYTV4Iq+thxdfTWlUfEABB3dTnlYW8yu3kh2G3HOlezbuICh+/ejtdtJHnwnZeHtUVtMxC/7lPTu1zXOFLbZPA+rVseJwXecMcYeyLLM5z9tZ9tex/dwu6QVhG3+GZfQUIYsW4Z3XNxlvXYVVfW8PmM1JeW1BPq68eKDw/AUgU/4h9IGBKF298B65ADxna9j1dajjBkUe1UUa5aUP2uM+zf88ssv3HnnnRiNxj/txHHq9K6urvz000+MHj26qYfzj1JdXY2HhwdVVVUXtWZSEIS/z2az88VP29m+LwOAKWM6c11C+/O+3zJ/+YUtt9yCbLUSMnIkYR98xcfzdlJRbcTNRc+Ao8tJP5nH0YG3oqjUuJTmYDO4Ynb1QmU24pd9hKIWXUBS4VJdQlyoje1LFzD00CE0sszJ7tdxstcEkGU6LfuE0siO5DTs7m23fg51XgFkdR4BwPXDOnLjyHjMFhsffruJg6n5qID2G78j+NAmvDt1YsiyZThf5o1z1bUmXv1sNbmFlfh5u/LywyPw9XK5rOcU/jlO/cxbO/N7XJyunR7MeZ++h2IyEfLtQmb8sJWbRju+M5pbk/feBbjhhhvYvXs3gwYNQlGUs/4A5/174MCB7Ny5UwQ+QRCuOkaTlbdnrmf7vgzUKonpN/dl3JAO5wW+E99+y+bJk5GtViJvvBHtSx/y+qxNVFQbCfFzZdCWrzlUDimD70BRqfHMP0m9VyBmVy+cKotxrimjqGU3kFREFSTTJrSW3YvnMezgQTSyTHaHQY7AB7TZMo/iFl0cgU9RiF03m8qgmMbAd9v4rtw0qlNj4DqYmo9Wkum0+H2CD20i7LrrGLlly2UPfHX1Ft74ci25hZV4eTjx4vRhIvAJ/xNc4zpTdzgJZ3MtcW1DWLrhCEaTtbmH1bS3d88UGxvL+vXrOXToECtWrGDnzp3k5+dTVVWFi4sLAQEB9OjRgzFjxtCtW7fLNQxBEIS/rarGyNszHTX49DoNT9w1kLgL1N1K/ugj9jz+OAAx06aRPeZeli5IBCA+xofgr19hW4vBlIe1A8CjII3K4BYAuBemU+cTjF1rQGuqpX91MpnhLiQv/IXBKSmogPzWvUgZdBsA0buXUBUUQ0HrXkiyTOzabyhu3Z3iyDhUKon7J/ehf/cYikprePPLtRSW1mBQrMTPfxPPovQrskMXHAWr3/l6PZm55Xi4Gnhx+nACfC9fGRhBuJq4duoGKhXlK5bQb8ItHErNZ/mmZCaNiG/WcV220HdKx44d6dix4+U+jSAIQpMqLqvljS/XUlhSjZuLnufuG0JMuO9Zr1EUhaRXXuHgK68A0OqJp9jWZgT7Nzo2bAzv4I/1nefZ2OsWjJ7+qCwmnGrKqAqKAUXGrSSb6kDH5jfvnKOMi1KxGBnb0qUMOHYMCSiOiufwsGkgqQg/uI4a7xBKWnRBku3Erv2GgvgEygJi0GrVPHpHf7q2Dyc9p4y3G4ouu1pq6fTTa7hWl9D9008v+4YNAIvVzvvfbORYejHOBi0vPDD0qqpVJgiXm9rNDde4zpT8+hOxt02la4dwlm5IZkjv1s3aV/qy3N4VBEG4luUWVvLyJysoLKnG18uFVx4deX7gk2V2P/54Y+CLfuVNfg/ox/7kXLRaNbd18aH8w9fZnHA/Rk9/9LXlaGwW6nxCUFuMGGrKqfGPBEWmxe7fuXNgKHMLsmDdOvo0BL7S8FiSRj2IolITlLqDOg8/Slp0QWW30n7tN+T0vI6ygBicDFpeuH8IXduHc/hYPq9+toqqWhOe1UV0nfMvPKy1DF68+IoEPrtd5tO5mzl8vACDXsPz9w8lIsT7sp9XEK42nkNGYExNpmbPTvp2iUatkpi/fH+zjqlJZ/peffVVALp3786IESMu+v3r1q1jx44dAPz73/9uyqEJgiD8JWnZpbz11Tpq6syEBnrywv1D8PY8ex2abLOx4557OPnddwCEvTuDH6t8qCqvwMPVwORwGxs//5rjQ+4FwKUsn3pPPxS1Fn1NOVaDCyZ3X3R1VXTZ+j29//04b/zyC+FJSXRq6GhUERjDgTGPIGu0+KUfwOTiSUVYO1RWC7EbviNjwE3UGjzwcDPw/H1DiQz1ZtvedL6Ytw27rOBbdJL4397D3debhGXL8OnU6bJfO1mW+WLedvYczkGrUfH0tARaRvpd9vMKwtXIuX0c+vAI8j/7kDZzf2FAjxas2nKUwT1bnte550pp0t27KpUKSZJ48MEH/1KNvnM9/fTTvP/++0iShN1ub6ph/aOI3buCcPkcTSvk7ZnrMZltxIT78tx9Cbi5nF2Dz242s3nKFLIXLUJSq/H6YDYLc9WYLTbCg70YrWSwaFMqha17AuBakk2tn6MXr1NVCUYPRwjyzkmh99GVhL32Mm/OnEn7lBTa5+YCUOUfyZ4Jz2DTO+OdnYxdo6MquCVqi5G2m+eRNugWjBoD/j6uvPDAUAJ83Fi2MZkfl+wDIPjkHtqv+gqf9rEMWbr0svbQPUVRFGb/uou124+hVkk8PnUgXduH//kbBeG/uFZ3755Ss283+R+9TatvF+DWqx/f/rYLlSTx1tNj0aiv/M1WcXtXEAQBOHg0jze/XIfJbCO2ZSAvPTjsvMBnratj/dixZC9ahEqnQ/3hj8xLVzBbbHRsHURCwU5+TKpwBD7ZjnNFQWPg09VVOgKfIhO1eykdC5JIuf5B3vt1B/HJpwNfrXcwe8c/iU3vjEfBSax6V6qCW6Ix1dF6288cH3IHRo2B8GAvXn10FP7ebsxdvKcx8EUcWE2H5Z8TPmI4o7Ztu2KB78cl+1i7/RiSBA/d1l8EPkEAXDt3w6l1W7Jf+xfYrIwe0I78oioWrTnULOO5qkLfqUnHP6vtJwiC0JT2JefwztcbsFjtdGoXwrP3JGDQn11I1VxezpohQ8hfuxa1qyvV78xjWZqjvdKQXi1psWsx86r9qQ6IQmMxojPWUO8VhMpqRm01Y3HxRGOsIWbX75REd2JT95tJrzPgEtARg59jV2+tZyB7rn8aq5MbbsWZ2HRO1AREoKuvJmbX7xwbfBsWSUPbmAD+8/AIXJx1fPr9FlZuPgpA6y3zabPlJ9pOf4DBixejdXW9Itdv4eqDjUWr757Uk16dIq/IeQXhaidJEgF33IMpM53Cr2cQ6OdOny7RLFp7iLTs0is+nqsq9FVWVgKOzhyCIAhXwt7D2XwwexN2u0yPuAienDoIne7s5c71BQWsHDCAksRENH4B5Lz8HdsyagG4aWgstgVfs9i1E2Y3bwz1VcgqtSPkmeqQtXrsWj2uJVm4VBaR1vN6an1Dzzq+c2UR9W6+7JnwDGZXL1zK87EZXKjzCUFfW0H4wXUcHzAFOyq6tg/j+fuGICsKb3yxlp0HMlEpdjqu/IKopNV0//BDenz2GSrNZS/OAMCyjcn8uuogAHdO6M6QPq2vyHkF4VqhD4vAe8x48j79gLqUw/TrGk2ArxufzN1yxWv3XTWhz2q1smnTJgBCQs6vgyUIgtDU9h7O5sNvHYGvZ3wkD9/eH43m7Pp11SdOsKJPHyqPHEEV2YKUxz7jUG4tWo2KB8Z15OisWWwKH4Cs0eFcU4rJ2QNZo0NjqsNmcGwA8cw7Rr1XMFVBLVDZLETvXkrE/lUAuJQX4FqW6wh8bt44VxRi1zlhdPfDUF1K0Mk9nOw1AQWJQT1b8vhdA6muM/PKp6s4mlaE1mamy6L3CM89zKDffiP2sceu2N2SVVuO8sPvjl6+N4yMZ0T/tlfkvIJwrfG9/kb0oWGkP/4AmIxMGNqRymojs37ewWVojPaH/vavgnPnzv3D51JTU//r82eyWq3k5eXx+++/k56ejiRJ9OjR4+8OSxAE4S85kJLLR99txi4r9O4cxYO39EV9zsLq0r17WTdqFKaSElQdurB3wjMUF9fh4qTjnqExLPpsLlmRvQFwqimj3s1R1kVtNWMzuKC2GNHXV1MZ4pj98s08SOyGOZjcfNg18XkAwpPWsnfCMxg9/TFUl2E3uGB2csO5ohCfkgwy4x19dccN6cDk0Z3ILqjg7a/WU15Vj8FYTeff3sVfbWHo1q34dO58pS4f63ce57vfdgMwYVhHJg6/vP17BeFaJmm0BE1/jOyXnyXrP88R9c4njBrYjkVrDtE6KoDh/dpckXH87dB35513XvC3SUVRWL9+PevXr/9bx5Ukifvuu+/vDksQBOFPHT6Wz/vfbMRml+kZH3HBwJe7ahWbJk3CVleH1H8E2/veQVWlEV8vF27v7s+cWcsoC4sH2Y7eWIvRzQfJbkORJOxaPbq6SqwGV+o9A9CY6mi7ZR7BR7djcvUmaeR0UKkIOLGb7LgE6j0D0NeWY9c7YdU741qag7upipxWvQC4/fpujBrQjsPHC/jgm40YzVZcK/Lpsug9gqNCSVi6FNfwK7dxYv3O48xasBOAUQPaccPI+Ct2bkG4VumDQ/G/814Kv/wE105daX/zHeQVVjJn0W4iQ7xpHX35y7hc0qKPP5qS/LtTlU5OTnzwwQf06tXrUoYlCILwh45nFPNeQ+Dr3jGch27rf17gOzl3LtvvvhvFZoPrbmFj65GY6syEB3lxXYTEVwsSqQuIQWWzICkyZhcPVFYLslYHgNZYg8XFEwC/jCSit/2EV3kRNo2O/WMfbVy3V+MdSr1PMLr6aux6F2xaPW7FWTipFfJD26NWSTxwc1/6do1my540vvppO3ZZwSs3lc7LPiFiYD8G/fILWrcr195sY+IJvv75dOC7bXxXsflOEP4ijz4DMKWdIPv1F3Fq3ZYhvbtSWFrD+7M38saTYy57b+q/HfruuOOO8x6bM2cOkiTRunXrv3SLVpIkDAYDPj4+tG/fnpEjR4rac4IgXDZZeeW89dU6zBYbcW2CHWv4zgh8iqJw+K232P/CCwDY73qCjV7x2CyOMi7dpBK+2liJ1TMArcWIVaMDlQ611YRdawDZjkqWsTq5oTHX03bnL6hyDuJVXo6sUnNw5HRq/CPQ1lejAPU+wWjrq7HpDMgaHR4FJ1G7eVDs6tfY67djm2AWrz3cWMk/8FgiHdd+Tbv77qX7xx9fsQ0bAGu2pTL7110ADO/XRgQ+Qfgb/G++E3NOFicfnEq731YxaXgc3/yayPvfbOCVR0aet5GsKV1VxZmFPyeKMwvC31NUWsPLn6ykstpIm2h/nr9/KPozvlxlu53djzxC6uefA2B9/A02KMEoCnSPiyAk7wi/FxqQNVr0lnrMOkeVAcluRVFrUdksyBrHTJ9XbirxBxaRU19Fi8JCFCA54S5y2w9AZbOgr63A2HDb167Vo6g1eOUew+4fTLXODTcXPc/ck0BMuA/f/babNduOARC1dwWtdvxK9/fepd3jj1/RwLVq61G+W+hYwzdyQFtuG9cNlUoEPuHyutaLM/8RW3UV2f95Do2PH23nL6G4zsac33bRrWM4D9/W/7L9v92ku3fDw8MJDw/Hx8enKQ8rCIJwSSprjLzx5Voqq42EB3vx9LSEswKfzWhk06RJpH7+OYokUf/yl6yXHYFvSO9W6I/sZlGJy9mBT5FBkR2Bz2p2BD5FJiZxMV2PLOaEtZ4WhYUApHcbS277ASDL6GsrGwOfTWdAUWvwzk7GHBJFtc4NXy8X/vPISCKCvfjwu82OwKcotN30A7FJy0lYvIjYJ564YoFPURQWrjrYGPjGDo7l9vEi8AnCpdC4exD82LOYM9PIeOZhAn1cuS6hAzv2Z/L7uiOX77xNebDMhp6RgiAIV4t6k4W3vlpHUWkNft6uPHffEFycdY3Pm8rKWD92LCU7dyLpDVS+8jWJBTYAxifEkrZyDYd1ISCBzmrCrHNGku0oKkdpF8luQ9bq0dVXE7fqCwyBrhwxGYnLyQEgu8MgTvSeCICuvgqjpz9qsxGb3hkkCZ+sw9RGxGJGRWigB8/fPxSdVs3rX6zheEYJKruNjqu+Iro+j8GbN+PbpcsVu3ayrDBv6b7GwssTh8cxaUScuKUrCE3AEB5J4H2PkP/xO+TP+JB2Dz9JcVkNC1bsJyLEi07tQv/8IBfpqqnTJwiC0NSsNjsfzN5EZm457q4GXnhgKN4ep28R1aSns6J3b0p27kTt7UPh69+TWGBDkuDW0fEcWLLWEfgUGY3NgkVrcNzOVakdM32AotbgmZtKu41zqIptw8niYuKysgAojOlCysDbANDVVWFx9UJjrseudwJJwjfrMJUR7TGjolWUH/95eCR2u8zLH6/keEYJGnMdXRe9S6y7ndG7d1/RwGe12fn0+y2Nge/28d24YWS8CHyC0ITcuvbAd+IU8j99j/LVyxnQvQUtI/34ZO4WCkqqm/x8IvQJgvCPJMsKn/+4jSPHC9DrNDx33xCC/E6vgy3dt4/lvXpRffw4+qgYMp6fxYHcOtRqFbePjmPVgtVkuQQj2W1IioJNo3OUZFFrQbaD5Pj69M5OxuLmTdLohzkRM5I2JZUAlIS35+DIB0ClQmOqw+LigdpicszwAb45KZRGdMCOROfYUP71wDBKymt56aMV5BdXY6gpo8fP/0dc1zaM2LwZlytYtL6mzsRbX61j54FM1GoV02/uy6iB7a7Y+QXhf4n3uIm4du9FxrOPYEo7wbiEDjgbdHw4eyMWi61Jz9WkGzm2bNnSVIeif//+TXasfxKxkUMQ/pq5i/awYnMKarWKZ+9NoGPr4MbncpYvZ/ONN2Krr8elaw+SbniOtPwq9DoNtw5tw8+/bKHG2fuszRnIdlCpG2/tqi1GdPU1GD3Prq3VcvsveOcdY+/1T2PX6tGY67HpnVFbTNh1BgD8Co5REuQo2Dygewz33tSbQ8fy+ei7TZgtdtxKsuny+wd0feg+urzxBpLqyv1+nlNQwTuzNlBSXotBr+HJqYPocMa1E4Qr6Z+6keNcsslI1ivPI2m1xC5cTalJYfbCRPp1jea+yX2a7DxNuqZv4MCBTTL1L0kSNlvTpltBEP53LN+YzIrNKQBMv7nPWYHv+KxZ7HzgARS7Hc9R49ja53by8qtwcdYxqXckP/yWiNnZG7XVjF2rP31QlRoUBUWlRldXhdXJFaOnP4rNjGvOYeqiugIgq7XsHfckdq0eldUxs6e2mhsDn295dmPguy6hPVPGdGbjrpN8vWAHsgI+2UfovG4WA7/4lJjbbmvS65JfXMWJzBKKy2pRqSQGdG/RWBdMURS278/gm58TMZqtBPi68cRdA4kI8W7SMQiCcD6VwYmQR54h6+Vnyfz3M0S/P4MR/dqybGMyndqG0j0uoknO0+TFYK5kDzlBEIRz7difwfcN/WCnjOlMny7RgOO76cBLL3Ho//4PAN+pD7A6YjAlxTV4eTgxPNaXH1Ydxq53RWM1YdMaQFHgzF9kJQmNqRaLiwcAlqosIhMXUdljCgA+WUfIihuCXe/kmCXUGk6HR1nGp76EUm9H54zbx3dj5IC2/LziAIvWHgYgOGUb3ZJXMmTtavyasB1lWnYpi9cdZs+h7LMeX7zuMC8/NJzQQE+++SWRrXvTAWgbE8ATUwfi5mJosjEIgvDf6YKCCbjrPgo+/xD3Pv2Jn3ATJ7JKmLlgJ62i/fF0c7rkczRp6Ovf/6/VlpFlmaqqKo4fP47JZEKSJPR6vei5KwjCJTmaVsgX87YBjlpy1yW0B8BusbBj2jTSvv8egKB/vc5vUguqyusI8HWjW5CO+duyQKNHbTM7Ap8sg0p1OvjJdiTAZnBFK1soy9lJ3O41VPW8paGNWgVV/hHYnFyRbFZkjQ6VzYJdq0ey2/CS6ylzDXB02bilLz3iIvj8x22NQStm1xJ6WLNI2JWIS2jT7NpLyy7l11VJHEjJa3ysTbQ/wQEeZOaWk55Txlfzd1BntFBeWY9KJTFhWEeuH9rxvC4lgiBcfu69+lJ35CDZr/0L9+69GT0wli9/2s6c33bz6B0DLvn4TRr6Nm3adFGvt1qt/P777zz//POkp6cTHR3Nl19+iVarbcphCYLwPyA737EWzWqT6dYhjNvGObpFWKqq2DhxIgXr1yOp1QR/8BXzC5ypM5oID/IkQmdh2eEyUKlQ2yzYNfozAp8MkqphA4cGBfCxlHDs2Dp6JidhbZ1AeVg7VFYzdrXWEfjsNhTN6WLNKpsFd41CudodvU7Dk3cPIjrMhzc+X8PR9GIk2U7s+u/o1zGEPt9sQeN86euWaupM/LhkH5t2nQRApZLo0zmKcUM6EBroCUBpRR2Pvf4bOQWVAPj7uPLAzX1oGxN4yecXBOHv87/1LoxHj5DxwuO0nvsrQ/u05vd1hxnQvQXxbS9tQ1ez/iqn1WqZNGkS+/fvp1OnTnz33Xc89NBDzTkkAIxGI//+979p1aoVBoOB4OBgpk6dSl5e3p+/+QyRkZFIkvSHf1JTUy/TJxCE/y2lFXW89dU6jCYrbaL9efi2/qhUKury8ljZvz8F69ejcXUl+NuFzM3RU2e00CrSDy9rDVuz6gFQyTbsDQWWT8/wnQ58KpuFsPpUUpOX0vfQPjTBcWR1GtbwXjs2J1eQ7Q2vdcz0qS1GXPQaKtHj5qLn3w8NJ9DXjZfeW8LR9GLUZiOdl3zEuNtH03/evEsOfIqisH1fOk+8sbgx8PXrGs0Hz4/nwVv7NQY+AF8vF24YGY+TQcv4oR1499lxIvAJwlVA7eRMwJ33UrNrB2VLFtKhVRCRId7MXbQbm12+pGM36e7dS5Gamkr79u1RFIU1a9aQkJDQLOMwmUwMGjSIxMREgoKC6NevH5mZmezevRs/Pz8SExOJjo7+S8eKjIwkKyvrgn2KAd58802CgoIuanxi964gnK261sR/PllFfnEVIQEevPLoSFyd9VQcPszaUaOoz83FKTAQ909/4IcdedhlhY6tg6nNzSW9TuWY1ZNwlGA5dw1fw4yfS20pbrYTHElPYVBKCirvGA6OvB8k1elduQ27ex2BT4vWVIfG1QWjzTGL9vz9Q6mtM/P256uotcjoa8rpufFrrvviA0KGD7/k61BZbeSbX3ay57CjKHRYkCfTbuhF62j///o+RVFE7T3hqvW/snv3QvI/ex9j2gk6rkukuM7GNz/vZOoNPRnap/XfPuaV69T9J9q0aUPPnj3ZuXMnM2fObLbQ9/rrr5OYmEivXr1Ys2YNrq6uAHzwwQc8+eSTTJ069aJvY3/33XdNP1BBEDCarLw9cx35xVV4ezrz/P1DcHXWU7BxIxvGj8daXY1H27ZI/zeTOZvTAOjaIYzso+kU27SNs3iNzg0/KhXBpWnU2NNJyc1ixJEj2L2jSBp+L0gqx5o93an1f2okuyPw6U21yC5uGG0KkSHePHffEFLTCvlsziZsqHArzmLwidWMWbcUt7/4S+R/k5iUyTe/JFJTZ0atkpgwLI5xQzug+Qvr8kTgE4Srk99Nt5HxzCMUffcVwdMfJ7ZVEIvWHmJQjxZoNOq/dcyraqVu69atURSFPXv2NMv5LRYLn332GQAzZsxoDHwATzzxBB07dmTz5s3s27evWcYnCMJpFouN977ZQFp2GW4uev71wDB8vVxJ/+kn1g4fjrW6Gr++fTG9/g3zGwJfny5RHD/iCHwqm+XswHfqpofccPtEkelYlERe7SFyszMYffgwikcYSSOno6g1DQHv9O3gU4WbnUw1WJ1csdoV2rcK4qWHhrF29W4+mrMFGyp8Mw9yq0cRN2xdf8mBz2iy8vmP2/jou83U1JmJDPHmjafGMHFE3F8KfIIgXL20fv54DBpK4bczsRvr6dslmorKerY0bP76O66qb4VTd5oLCgqa5fzbt2+nqqqKmJgYOnXqdN7zkyZNAmDp0qVXemiCIJzBarPz/uxNJJ8oxKDX8Oy9CQT7u3P4nXfYcvPNyFYrYZMmkT/9TX7fchyAgd1j2Lv3BNWKFpW1oejyqaDXuEPXEeA05noGVB5ge9Ux6goLue7QISye4ewf8wiKRtuwdk/buO7v1Fo+Z2sdRoMbsgK9O0XyxF0D+OK9n1i4PROAyJTNPHx9HAO+mIHGcGnlUNKyS3n+vaVs2ZOGJEmMH9qB1x8fRUSwqKsnCP8U3iPHYq+ppmzRL/h5u9Iyyo9Vm1P+dnm8q+b2LkBiYiLAWTNsV9LBgwcB6Ny58wWfP/X4oUOHLuq47777Lmlpaej1emJjY7n++uvx8/O7tMEKwv8oq83OR99t5mBqHnqdhmfvHUJ0qDe7H32Uo59+CkCrRx9nd/x1JO5KQ5JgUPcYNicexy5pHIFPq2vcmdsY+Bpm7FzKCxjgWsT3eSm4VlUxKiWFCv+WHBj9UEPgkxsLNZ8Oimpc7SZqtY5Cx6MHtmNoWy/+9fCHFLgGgSLTLSeR+795BZewsEv6/IqisGpLKj8s2YvdLuPr5cJDt/WjTXTAJV9bQRCuLlo/f1ziOlP62wL8b76Dbh3C+XHJPo5lFP+t/+evmtD3+eefk5qaiiRJxMbGNssYsrMdhUtD/6BG1qnHsxqaqf9VzzzzzFn/fvzxx/n000+ZOnXq3xilIPzvsljtfPjtRg6k5KHVqHjq7kG0DPFg8003kbVwIQAd332f5U6xJCdloVar6BMXxobEkyCpHfXztLrTM3Rweh2fpMInJ5lOYTLfHkzCr6qKEcnJlIS1b7yl27iz99T7FAVUKlwlG7Vqx8zdLWM64bF3Hf9abKXOMwi1xcTEQCPj3/sM1SWWo6qrt/Dl/O2NRZa7dQznvsm9cXXW/8k7BUG4Vrn37k/BjA8w5+YQFRqKp5sTW/ekX1uhT5ZlysrKSEpK4rvvvmP+/PmNz02ZMqVZxlRbWwuA8x+UTXBxcfwWX1NT85eOd9111zFo0CC6dOmCn58f6enpzJ49m48//php06bh4+PDuHHj/usxzGYzZrO58d/V1dV/6dyC8E9jMlt5f/ZGDh8rQKdV8/S0wbTyd2LtsGEUbd2KSqcj/uvvmF/sRvqJQvQ6NfEt/NmyPwskCclud8zUnbtDt0HIyT3ktejGWtlOkCaboYe3URjViUPD72sIfGcExVMkCVeNQq1Ng0at4u6R7Uh78yV+bjEMq6cPzuZanry9D7G94i/582fklvHht5soLqtFrVZx27iuDO/XRmzEEP5RxM+887l0iANJonrXdvwmTia2VRCJSZncNanHRa/dbdLQp1b/vd0kcHo9X9euXZk2bVpTDalZffLJJ2f9OzY2lvfff582bdpw77338uyzz/5p6HvzzTd55ZVXLucwBeGqV1tn5u2Z6zmRVYJep+GZexKINFhZ2a8flcnJaN3d6fjjr3yTVENhaRmuzjoi/VzYldKwPliRUU59P50KSQ3hT7JZCclLpiK6peNplZpws5r8lj04POTus2f2zuGslai1grNBy929Atn68isc7DwORa0h1FnhX/+5Ey9Pl0v67IqisH7HceYs2o3VJuPn7cpjdw4gJtz3ko4rCFcj8TPvfGoXV/RhEdQl7cNv4mTaRPuzfV86JzJLaBtzcbN9TbqR41RwUxTlov8AjB49mlWrVl1SeLwUp9YS1tfXX/D5uro6ANzc3C7pPHfffTf+/v4cO3aMzMzM//ra559/nqqqqsY/OTk5l3RuQbjWlFfW8cpnqziRVYKLs44Xpw8j2FLG8t69qUxOxjk4mPaLVzNjdwWFpTX4eLkQ4KrlSFbF6Z24587QNQQ+bX01BpuR3Ih46lSejpfKdvR1lRweds/pwHfm+xr+NmhV1FsVvD2dmdpCYenH35LUbSKKWkO3Vn688Z9bLznwmcxWZvywja9/ScRqk+kcG8qbT40RgU/4xxI/8y5MFxiEKdOxazfIzx0XZx0HUy+uYQRchtu7f3VHiUajwd3dncjISHr06MHNN99Mnz59mno4FyU83NEIPTc394LPn3o8IiLiks6jUqmIiYmhuLiYgoICIiMj//C1er0evV6s1xH+N+UUVPDWV+spq6zDy92JFx4Yii49hZVjxmCprMSjTRtCZ83no6XJGM1Wgv09sBvrSKuwnV+Dr3HDhuNvQ3UpFlcvrKqzf8kMPrqdY/0mnz+Yhp29kiyj1qoxWWUigr0YWnWQ+csqKe04BICJg9syaWy3S77tmlNQwUffbSavqAqVSmLy6M6MGRSLSiVu5wr/XOJn3oVpvH2oT00BHLU1wwI9OZ5RfPHHacpByfKltQdpbnFxcQDs37//gs+ferxjx46XfK6Kigrg9DpBQRDOlnKykPe/2Uid0UKwvzvP3TcU07b1rJk8GbvJhF+vXri9PZOPFx3AZpeJDvOhrKiMKgtIdusZJVWks9fxSRL6mnJM7o7ZMkWRkRpmAj3yT5IX2//0IBp36Dq6bahlO7Jajc2uENsigNhdC/mJKOoiO6JB5sHbB9Cr86XV3lMUhY27TvLdwl1YrHa8PJx45PYBF30bRxCEfw5Jp0OxWBr/HeTvwc4DGRfdUeeq2b17NejTpw8eHh6kpaWRlJREfHz8Wc//+uuvAIwdO/aSzpOcnMyxY8dwdnamTZs2l3QsQfgn2rInja/m78Bul2kV5cfT0wZT8NMP7LzvPhRZJmTMGOofepXPFzoKpbeNCSAjowCTrEKyWc/esHHOxg2tsQazmzeKYsdWXYDONRBFrcKpsoiq4BanB3FO4NMoNmwqDSjQq30wbj9/zuLIwVic3XHXKjz/yHVEhflc0ueuN1qY9fNOdh7IBKBj62AevLUvHm5Ol3RcQRCubYrVhnTG7n8fTxdMZhs1dWbcXf96zc+rqjhzc9PpdDz00EMAPPjgg41r+MDRhu3QoUMMGDCALl26ND7+2Wef0aZNG55//vmzjrVixQo2bNhw3jkOHTrEDTfcgKIoTJs2DZ1Od5k+jSBcexRFYeHqg3z+4zbsdpme8RH86/6hpH/0PjvuuQdFlom+6y5ybn+Bn1Y66mV2ahvC8bQ/CHxw+m/ZjmS3YXVyw26tx5J3AJ1rAIpag662EqNnwAWKNTsCnw47NsnxO/LwTsHIcz5hXatRWJzdCfHQ8taLN15y4Dt8vICn317CzgOZqFQSU8Z05rn7hojAJwgC9ppqNF6nC697uju+F0rKay/qOGKm7xwvvvgi69atY8eOHbRs2ZJ+/fqRlZXFrl278PPzY/bs2We9vrS0lGPHjp3XRWT37t288sorREREEBcXh7OzM+np6ezfvx+bzcbAgQN56623ruRHE4SrmsVqZ+b8HWzb51isPHZweyaPimfvk09wtGEnfOvnXmBr9GD2b3d02ejSLoR9ybkgNbRB+4OSLJLNgqLRoQDm6hxUpRkYIvugqNRojTVYXD3PnxlUHEWX9SoFs6xGkmBCRx9SZs/iaBdHd56OEZ48MX0UBv3fr79nNFlZsOIAq7YcBcDfx5WHb+tPy0hRwF0QBAdrcSHObU7XMNZrHWuRTWbbRR3nioW+oqIiSkpKqKmpwc3NDV9fXwIDA6/U6f8yg8HAxo0befPNN5k3bx6LFy/G29ubO++8k9dee+0PCzefa/jw4eTk5LBnz57G9m7u7u707duXW265hbvuuqvZdikLwtWmps7EB7M3cTStCJVKYurEHgzqFsW2228j46efAGj33scsJpr05Fw0GhUdY/zYl5LXOCPXuGnjnJIsKqsZWetYGF5bmIRrTSW0HIgCaEx1WJ3cLhD4HDX5DBoJkw20WjWTolRsXbCE3E6jARjaLYK7pvRHde4O379IURT2Hs7h24W7KK9yVAwY0rsVt47rekkhUhCEfxZFtmPOycZn7ITGx9QN9fmsNvtFHUtS/m4Dt79g06ZNzJo1i40bN1JUVHTe8wEBAQwaNIhp06YxaNCgyzWMf5Tq6mo8PDwaQ6QgXOvyi6t4Z+Z6CktrcDJoeeKugbQJ9WDjxInkr16NpNHQ6ovv+DFPT2lFHa7OOsJ9XUjJdmyGumCx5YbH1BYzdp0eSZGpzN6GD07YIroBoLYYseuc/rBYs14jYbYpuLroGetcwur9eZSHtUVSFG67rjOjEv7+hq68oirmLtrNwdR8AAJ83Zg6sQdxbUP+9jEF4Z/o1M+8tTO/x8Xpwo0T/ulMmelkvfQ0bX5chFu3ngCUVtTyxbzt/OeRERfVmeOyzPTl5eVxzz33sHr1auCPy7gUFhYyf/585s+fz7Bhw5g5cyZhl9iXUhCEa8exjGLe+3oDNXVmfL1ceOaeBPz1MmuGDKEkMRGNiwthM+cx61At9aY6/H3ccFXZHIGvoYTKuTN7Z87w2XV6JNlCefoGglwiMAW1BUBltfzXwKdVqzDbZPx9XOlTlsTvua7Uh7VFq9h5/J4hdG4f/rc+b35RFb+uSmJnUhaKoqBRqxg9KJaJwzqi04nVNoIgnK/uyCFUBidcOsY3PlZvtALg4nRx+wKa/FvmxIkTJCQkkJeXd17YMxgMuLi4UFdXh8lkAk4HwtWrV9O7d282bNhAy5Ytm3pYgiBcZbbtS+fLedux2WViwn15+p7BaKvKWNlvGFVHj6L39sZtxk/M3FOE3S4TE+ZDbXkl6XX2P67BBw2Bz4Ks1SPZ6inP2Eygb0dMXiFIsh1FURz9dxtee+b7JUVGUqmwNpwvJmkVy1zaYvVyxV1t54UnxhMZ4s3FysgpY8mGIyQ2hD2ALu3DuG1cVwL9xIy9IAh/rO7AHtz7DkClP71Lt7yyDkmCAJ+LaxbRpKHPYrEwfvz4s4obT5gwgTvuuINevXrh63u6inxZWRk7d+5kzpw5/Pbbb4BjhnD8+PEkJSWhvcTG5IIgXJ0URWHphmTmLXWUW+nWMZwHb+mLOSuDFUOHUpedjVNoKPY3v+WHREfF+bg2wWSczKXapjod+P6gJItksyJrdSjmSqqzE/EP6Y3Z2QO11Yxde4Gir4oMkgqVbEdWqVEU6NouBM3Gpaz17YSi1hDmKvGvZ6fgeRE7aRVF4fDxAhavPUzKycLGx7vEhnLDyE5Ehl58eBQE4X+LtawU4/FUAqc+cNbjRWU1+Hq5XvQdgiYNfbNmzeLo0aNIkoS7uzu//vorCQkJF3ytj48PY8aMYcyYMWzcuJEJEyZQVVVFamoqs2bNYvr06U05NEEQrgJ2u8zsX3exfqdj9+2oAe24dVxXyg/sZ+2IEZhLS3Ft3YbCJz9m215H4OvTKYq9B05gRvMnJVlkJBQUjRZbXRHW4mN4Rg3GqtGhMdVhM5xRCL2xJIvjFnFjDT5gWM8Ysn9fTGpAZwDigww88fjEv/zlqigKB1LyWLT2ECcySwBQqSR6d45i7KBYIv7GTKEgCP+bahK3Iel0eA0bedbj2fkVtGtx8QXbmzT0LViwoPG/f/jhhz8MfOcaNGgQP/zwQ2PR4/nz54vQJwj/MGaLjU/mbmHfkRwkCW4b341RA9pRuHkz68eOxVpTg3v3nqTc8iJHkguQJImB3aLYsusEdukPAl8DyW5DUalQJDXmqizU1SU4RfVHBrTGWqxOrn9QkkWFDhmLpEGSJG4c0pYtv6yiIKADAKNivblt2pi/XPH+0LF8flq6j4zccgC0GhVDerdm9KBYfL1E9x1BEP46RVGo2rYJz4ThqF1P38atqjFSWFrDpBHxF33MJg19qampSJJEu3btGD169EW9d/To0cTGxpKcnMzRo0ebcliCIDSz6loTb89cT1p2KVqNiodv70/3jhFkL1nCphtvRDabcUsYQeKQ+8hOL0Wv09CnfTAbd51EkdQXrsF3gZIspooMDHYZwroCp0qyXCjwOUqy6CQFi6JCr9Nw46CW/P7bVqo9w1HbLNyZ0IKh1/+1qgIZOWX8vPIAB1Ics5N6nYahfVozemA7vDz+N3ccCoJwaUzpJ7Hk5hD5n7Nr+iafKESrUdO5/V8rIXemJg19tbWOytBndqy4GF26dCE5OfmsThiCIFzbSsprefPLteQXV+PqrOepaYNoEx1A2vffs+2uu1Dsdlwn3cqG2OsoK6zC3UVPhzB3NuzPdhRd/i81+DTmemx6R6gyVWbgrHFB9vJHsttQyTbHLd0L3QqWJDQSWBQJLw8nRnePYMHS/VhcfDDUV/HYTd2IT+j1p5+tuKyG73/fy55D2QCoVRLD+rbh+mEdL6o1kiAIwrmqNq1DFxSMe58BjY8pikLS0Ty6tA/F2XDxHb2aNPQFBweTkZFxUc1/z3TqfUFBQU05LEEQmklWfjlvfbmOimojPp4uvPDAUEICPEj5+GN2P/YYAE5TH2GFf0/qK+sJ9HUjUG9ne6pjLRyKgqI6p4h5Q4jT1tdgdXbc8rDUFuLkHICsc3YEQY0Ou7YhdJ1X0kVGpVJhUyAi2IuOIS78uDoZReeEd3kOzz01kfCObf/r57LZ7CzflMLC1QexWO1IEvTpEs3E4XEEid24giBcItlopCZxO4H3PIh0RiOHk1mllFXW8ciA/n/ruE0a+rp37056ejpJSUl/6/1JSUlIkkS3bt2acliCIDSDY+nFvDNrPXVGC2FBnjx33xC8PZw58J//cPCVVwDQPPoyyzQxWI0WYsJ9kSpLSSrl/Bp8pzQEN11tpaN1GmA3VaNz9kNRqdHVVWJx8Tx/MKdKsjTs/JUViG8bgpPNyNI9uaBSE553hOfefADvFtH/9XPtO5LD3MV7KCqtASC2ZSB3TexBaOAFzisIgvA3VO/cimwx4ztpcuNjiqKwdW8aLSP8aPU32zQ2aeibNm0a8+fP59ChQ6xfv/4vb+QAWL9+PQcPHkSSJO6+++6mHJYgCFfYviM5fDRnM1arndZR/jxzTwLOBg27Hn6Y1BkzALC/8CGr671QbDLxbYMpOp5BgV0Psh3OnN07Zz2eoboUk/vp8k9qg2NmTV9TjtnN+wLvc5RkObNkS0LvVqQfzSSpwgKKTHzGdh795k2c/P74i7S4rIY5i/aw70gO4Gh4fvOYLvTrFv23724IgiBcSOWmdXj0H4w+6HSXnmMZxeQVVfGv6cP+9ndOk4a+wYMHc8899zBr1iymTJnC0qVL6dGjx5++b8+ePUyZMgWAqVOnMnTo0KYcliAIV9C2vel8Pm8bsqzQOTaUR+8YgAaZLbfeSsZPP6FIErUvf8X2Csd6lL6dI0jek0KF2uWPa/A1/O1cXkC99znLP2Q7uvoaR+D7gw0bpwKfSiUxLqE9m7YmU2GSUZuNDMzdyu1zP0Pv5XXBz2OzyyzbkMxvaxy3ctUqiVEDY5kwrCNOBlFPVBCEpmXKSMOckUb4cy83Pmazy6zfcZz2rQLp0OrvL4Fr8o4cM2bMwNXVlQ8//JC+ffty6623ctttt9GjRw9cXE6XLKivr2fXrl18//33/PDDD9jtdh5//HHeeeedph6SIAhXyKotR/nut90A9Osazf1T+iDX17FuwgQK1q1D0ekpfmkWB0plAIb1iGLb1iPU61xQ2azIZ+7QPWcDRnDKNvLb9T3rfGpzPYDjVu8fbNg4FfhcnHSMHtSOxasOYJElnCsLGVV3hOt/nY1af4GizUBmbjlfzt9OZkMJltiWgUyd2IMQcStXEITLpHLjWrQBgXj0O109IPFAJpU1Rl544NImxZo09EVHn14Lo9VqsVqtzJ07l7lz5yJJEl5eXjg7O1NfX09FRUVjOyJFUdDpdCxatIhFixb913NIkkRaWlpTDlsQhEukKAqL1x5mwYoDAIzo35bbx3fDXFLM2pEjKT9wADy8yH76c46WmFGpJIZ3DmH9thQsfxb4AP+T+/CuLST/jHM6VxRS7+4LZ84MnkOSbdi1egJ83ejbJYpfVhxAQcI7J4UJHhUMnjcb1QW6/5gtNn5dlcTyTSnIsoKrs547JnSjbxdxK1cQhMtHNhqp2bmNwGnTkTSOiFZeWcfWfWmMGtDuktcON2noy8zMPOsL8dR/K4qCoiiUlZVRXl5+Vk9eSZKQJAmr1UpWVtZ/Pb6iKOILVxCuMoqi8MOSvSzfmALApBFxTBweR/WJE6wdMYLajAyk0AiO3vcOmSVGdFo1g1t5siYxHbvWCZX9nMAHZwW4Fjt+5WSvCWTKJXiUZuFss6Grr6IkuvPpQZzbQ1e2o0gqFJWGdi0CCPZ3Z+HqQ4BESMpWburiT7c3vrrg98nRtCK+mr+DwpJqALrHRTB1Yg883f96CzZBEIS/o3EDxw2OJW+KorB8cwpe7s5MGhF3ycdv8tu7Zwa6v/L8n71eEISrlywrzP41kXU7HG3VbhvfldEDYynZtYt1o0djLitDHduJfTc8R2GpEVdnPd19FVYfLkbR6FDZbcjqC3fZQJGJXT+HgOp0DmX60aIgn9Ybl3F4+H1nB75zNmyobBZkjWO94IDuMdTUGFm34wQALXf8yk1TEmj/+GPnfRaT2cpPy/azemsqAF4eTky7sRddYsOa/sIJgiBcQNWmdXj0G9S4gWN/ci6ZueW88MBQDPpLX0PcpKEvIyOjKQ8nCMJVzG6X+WLedrbtS0eS4N6bejOoZ0uylyxh8+TJ2I1GNP2Gsn3A3VRUGPH2dKaVvYwN2U6gUiPJduRzb82eUVqlw9pvcDfm8XtICP22LMINPbtveIE67+ALt1STVI3dOSQJJg6PZ3dSOtmF1ahsVjqsn83k/zxK1E03nfdZMnLL+HTuFvKLHbN7g3u25JbruuLifPHFTwVBEP4OU0Yapow0wp79N+Bot7Z+53EG92xJx9bBTXKOJg19ERERTXk4QRCuUlabnU/mbmHPoWzUKokHb+1H785RHJs5k8QHHkCRZXTX38KG1qOprTER5OeGb1EaiSp/ACRFPl10uTHwOYKbZLPSacVnqOwVrAsIYMShQ1h8Ikgc9SBWg2vj687doXsq8Bl0GiaP6czvaw9SUWNGV19Ntw1fM/GrDwg+p4yULCss25jMzysOYLPLeHk48cDNfZvsC1YQBOGvqtywxrGBo/9gFMXx3eTipOPWcV2b7BxNfntXEIR/NrPFxgezN3EwNQ+tRsVjdw6kc7sQ9j3/PIffcvSI1N/7JKs8OmE2WogK8URKPcxht4bbpIqCIqnOPmhDMWaV1UznZZ9gUtVxxNubEQcPUthuAMf6TXaExIaAB5y1/k9lsyBr9Xh7ODM2IZafluzFbJNxKS+g9/a5jF84D5/4+LNOWVFVz4wft3HkeAEA3TqEce/k3ri5iPZpgiBcWXZjPTWJ2wi85yEkjYaklFzSc8p47r4hODs13R0HEfoEQfjLTGYr78zaQMrJQvQ6DU/dPYh2Ub5sveMO0n/4AQD9C++wzBSI3WKjbbg3lQcPUOAV8cddNhqKMavNRrou+YAynZV8JycSklM4OvD2s8u0nNtSDZDsNmSNjsgQb7q2D2Pub3tQAJ/sI/Q7vobR65bjFhV11in3Hsnmq592UFNnRq/TcMf13RjUs6XYKCYIQrOo3r4F2WrF74Yp1NaZWbvjOP27xRDfNuTP33wRROgTBOEvqTdaeGvmOo5nlOCk1/LsfQlEeelYO3w4hZs2IanVaN76miV5EooiEx/pSdb+Q1R4Rzh2016gy8apxzWmOroufp9cZwULEt1yi9gz8XmqA6L+cP2eZLejqNUoag2d2oXg6e7Mr6sPAhB6ZDP961IZtmk9Bt/T3TusNjvzluxj5ZajAESGePPwbf1E3T1BEJqNoihUbVyL58Ah6AKDWbbmIFqNitvGN91t3VNE6BME4U/V1pl548u1pOeU4eKs44X7hxKg1LGyXwKVyclo3Nywv/ktS0/UAtA1zIXUpFRqvUNPd9k45Zw+uLr6aroteoeTblrc6usJ0nixY/J/sDq7n54dPBX4zrgNLDe0VBvSuxVFpTVsTDwBikzrrQvoF6Zn4JJ1aJydG09bUFLNx3M2NxZaHjWgHVPGdkarOSOMCoIgXGGmtBOYszOJ+Pf/kZlbRvKJQqbf3PeyLDW5bKFv27ZtzJ8/n927d5ORkUF1dTU2m+0vvVeSpL/8WkEQLq/qWhP/9/kasvIrcHPR868HhuGSf5Jl112HqagIp+AQqv4zkw3JJQB091dxKCUHk2dgQ0mW89uqnQp8+rpKui56lxPuOgIrKrBEdGVv/ymOWcFzA5/i+LfaYsKuMzTs0I1j54FM8oqqUFvNdFg9k/5DutLzs89QaU5/ve1PzuXT77dgNFlxc9HzwM196Rwb2lyXVBAEoVHlhjXogkNx7T2A+b8m0jLSj37dov/8jX9Dk4e+srIybrvtNlavXt34mKjFJwjXpspqI69/vprcwio83Ay8OH0Yyp6trL75Zmx1dXjEx5M7/W22Jzs2Q3R3M3Egy4bVzRu13YZdrTlvt+2p7huGmjK6/vYOJzydiCopJbfXjeS16+c4cUPAA87YsHE68Om1KiaO7MTS9Ucc6/Jqyumy5CMGPHoPHf/1r8a1ebKs8Pu6w/y88gCKAq2j/Xn09v54e7qc+1EFQRCuOHtdHTW7dhD84OMcOVFIcVktj94x4LKtL27S0GcymRg4cCApKSki6AnCNa68so7XZqyhoKQabw9nXpw+jIofv2HPU0+BouA/fARHxj/O/uQCVCqJTnIh+8o9sTu5opb/JPBVl9J10btkuWqJrqwjZfRjVAXGnH79ubt7AZXNgl1nwMtNz7D+bfl5uaPMintRJl1WfkbCJ+/T4vbbG19vMlv5fN52dh90dPoZ1rc1t4/vhkbczhUE4SpRvWMLit2G9/gb+XXtcXrERRAT7vvnb/ybmjT0ffjhhyQnJyNJEmq1mrvuuospU6bQvn17vL29UavFl60gXAtKymt5bcZqistq8fVy4V/3JZD12r9InTEDgMj7H2RLu9GkpBag1ahoW3mS/U7hKFqNI/CpNOfdnj3VKcO5opAuv79HvpOGIJue/ROfxOzq9Ydr/1BkJFlx7NAN9qRVdAALlicBEHBiD523z2PIgp8IGT688a3FZbW8/80GsvIrUKtV3D2pB4N7tbqSl1AQBOG/UhSFqs3r8Rw0lKMVdqprTdwwMv6ynrNJQ9/PP//c+N/z5s3jhhtuaMrDC4JwBRSWVvP6jDWUVtTh7+PK83f25fAdk8lfswYkibZvvstidWsy0oox6DRE5h7kkH8sACrZ7gh8yh8FvgI6L36PapUdZ89W7E24C1mjbZwBPHfDhmSzomi0KGro1DYYlVrFmm3HAIjas4y4jB0M27wR77jTPSmPphXywexN1NSZ8XA18MTUQbSO9m+WaykIgvBHzFkZmLMyCH3uP6zZn0G3DuGEXuZKAk0a+tLS0pAkie7du4vAJwjXoPziKl6fsYbyqnqC/Nx5fFw7do8eSsXhw2icnenwzffMSZfILyjDxaAhIH0/qcEdARxt1S5QQPnUTlvX0lw6LXkfi2yhusMoMrqOdrykMfA13NptCHxqqwm71rF7bUjvVpzILCErvwKV3Ur7tbNpp65iyM4duJ7RCWj9zuPM/nUXdrtMdJgPT0wdhK+XWL8nCMLVp2rLBrR+/hRHtKc89TDXJbS/7Ods0tCnadgt16FDh6Y8rCAIV0BuYSWvf76GymojIQEeTO/jz47B/TCVlOAUGEjsD7/yxdYCyirr8HTR4pq6n/SwhsB3qq3amX104YzAl0P3395BkW2kDruPkuh4xwtkO0rjDF/DzOAZO3RVKIxJ6MDm3SepqjGhq6+i07JPadsmjMGLVqLz8HAcRpb5fvHexvp7PeMjeeDmPuh1oiqVIAhXH9lqpWbnNvym3M7m5HxaRPjSIsLvsp/3/NXSlyAyMhJwbOgQBOHakZVfzqufraay2kh4sBd3RVrZMWIIppISvOPjab90HR9vyKGssg4/Vy2644fIDevgCGlntlW7QOBzL86k+8K3sWn17Jn0L0qi45Fke8OLzum/K0mNgc9JK3HdkI6s3JxCVY0J19Ices1/lS4JPRm68nTgq6u38PbM9Y2B78ZR8Tx6R38R+ARBuGrVHdyPvbYGTcIYMnLLGN6vzRU5b5N+K44YMYKkpCR2797dlIcVBOEySssu5c0v11FbbyYq1IfrbansvflpAEJGjsT/zU95b94ujGYrwW5qjMdTqAhpe36XjTM0Br6iTLouepc6n2D2j34Yq7M7KqsFWXvhXpKSzYpdZ8DXTUeX+GgWrzsMgF/6AeJWfUWX558h/j//aSxnUFRaw9sz15NfXIVOq2b6LX3pGR/Z9BdJEAShCVVv34xzbAeSTU64OOnoERd5Rc7bpDN9Dz74IHq9nhMnTrB48eKmPLQgCJfBsYxiXv98DbX1ZlqE+zDsxApSnncEvraPPILnm5/z3vc7MJqthLtATXoGFUEt/zjwKTJqiwlZq8ejMI1ui96hOLoTuyc8i9XZveE5nWOGsOH1gKP/riyjaLS0DPGkVYtgVm9NBSBy30q6rv6CAV/OoNMrrzQGvpSThbz44XLyi6vw9nTmlUdHisAnCMJVz15XS13SfrzHTuTwsQL6dY1Gp70y1U2adKYvJCSEGTNmMG3aNO666y68vLwYMGBAU55CEIQmknyikHdmrcdssdE60pce62eRtex3kCS6vf8+Jb3HMHPOFmRZIdrJSmFeOfV+4ahObdg45dQ6PrvNUa5FZ8Cj4CSdl33C8d4TyemYAIDK6rhte+6GjVM7ewF6tA+h2mhjx4FMJFmm7ca5xGTvZ/DKlQQNHtx4ys27TzJzwc7GDRtPTRuMt4czgiAIV7ua3TtRZDtV8f2o3ZnDgB4trti5m3zRy9SpUzEYDNx///0kJCQwfvx4Jk6cSGxsLB4eHn+5ynR4eHhTD00QhAZJR/N4f/ZGrFY77SJ96PDT6xTvTkRtMDBgwQIOu8fw/U87AGiprSe7xILZM+B0Db7GosuOvyWb1VGuRWfAM/8EbbfMY8/1T1PrGwY07NDVnh/41FYz9oYeumMGtmV/Sh75xdWorSbil88gWl3H4B078Ip1lISRZZmflh1g6YYjgNiwIQjCtad651bce/blcLGF8CBPokJ9rti5L8s35ZQpU8jOzuaFF15g0aJFLFq06KLeL3rvCsLls/tQFh/P2YLdLhMX7UOLL56hKvUoeh8fBi9ZwsYKA4sX7wWgNRWk1eiwuXqeX3T51Eyd1YSk0Bj4QlK2sfv6Z7DrnVCbjdg1unN26J56nyPwaZGZMLoLKzalOFqq1ZbT5fcPiY4JJmHJOpz8HTX2TGYrM37cxp5D2QBcP6wjN4yIR6W6PO2KBEEQmpq1tATj0WS8X3uf45kl3Dym8xU9f5OHvpycHEaOHMnRo0cbZ/VESzZBuDps2ZPGlz9tR5YVOkd5Ef7ug9TlZuMSHs7QNWv5JamM9TsdmyfaWIs4LnkhG3RnBD67Y8dtw99qUz2oVI5buoVpOFcVkzzkLgD0NeWY3bxPn/yMHroqmwVZq8dNC2NHdOWXlQex2uy4F2XSeelHtBs/mt6zZqFxcgKgrLKOd7/eQGZuORq1ivun9KFv18vTkFwQBOFyqd6xBZXBifzozsh7sundOeqKnr9JQ19FRQX9+/cnKyvrrMddXFzw9PRsrOMnCMKVt2ZbKt8u3IWiQK+WPvi/ejem4mI827Vj4PKVfLcpncSkLCQJWtfnckwfhKJSnxf4Tm3i0BhrUFQa7DoDhupS7Bod+W37AOBcUUi9V+CFB2K3IWt0BLlr6dy5JfOW7gcaduiu/IIu/3qe+JdfbvylMT2njHdnraei2oi7q4En7x5E6yjRYUMQhGuLoihUb9uM59CRbM2pokOrILyu8FrkJk1h77zzDllZWUiShLOzM88//zxTpkwhKurKJllBEM62eO1h5i93hKsBrb3xePFOzOVl+HTuTP/lK/lsURKHjxWgVquIrsok1dWxpvZU4DsV9E79raspR9FosekdM3EWZw9M7r6oLUa0xlpH4Dujd27jOj4JUGtoG+KOV4APyzelABCxfxWx+5bQf94PRJ7RzWffkRw+mbsFs8VGaKAnz9yTgL+P65W9eIIgCE3AeDwVS0Ee+pHXk3OsknFDrnwjiyYNfafKtEiSxIoVK+jXr19THl4QhIukKArzlu5j6YZkAEZ2CkL75M1Yysvw7dGDngsX8868PaRll6LTqgktz+CEmyPwqc6Y4VNUaiS7HUWtxqmyEKPn2bN4skaLc3kBVicXTB5+Z6z7a5gdtFtR1FoA+nQIpqTGyo79GSDLtNv0Pa1LU0nYtAnfbt0aj7ly81HmLt6Doih0aB3E43cOxNnpwvX9BEEQrnZVG9eiCw0nzTUcvS6Hbh2u/IbVJg19mZmZSJJEv379ROAThGYmyzKzfk5kY+IJACb0DEd+dDKmsjJ8u3en26+/8+bcneQVVeFi0OJTkkF6Q+Bz9NE9tWmjYYZPrca1NAe/jINkdBtz1rk8845RGRANGu15ge9UoWaAkX1asi+1gOKyWjTmeuJXzKC1vxMJe/fiHBzcOO4zW6oN7tmSqTf0RKNu0rKigiAIV4ytuoqaXTsIefxZtp0somd8RLNUHWjSb1FnZ8e96VatWjXlYa84o9HIv//9b1q1aoXBYCA4OJipU6eSl5d30ceqqKjg0UcfJSIiAr1eT0REBI899hiVlZVNP3BBaGCx2vnou81sTDyBJEncNaId0rN3YiouxrtTJ9p//wuvfbOVvKIqvNwNeJRkke0W6ghsp/roNoS3U7d03Ysy6Pbb22THJTSeJzZzB4HHdlIZ0toR+Bp6554Ki6cCn1qRGZfQnk17Myguq8WpqoSeC16ja89YRm7e3Bj4jCYr7369sTHw3Ty2C/fc1EsEPkEQrmmVG9aAWo2l70jKq+rp3y2mWcbRpN+kERERANTU1DTlYa8ok8nE4MGDee2116itrWXcuHGEhYXx7bff0qlTJ9LT0//ysUpLS+nevTuffPIJGo2G8ePH4+bmxscff0yPHj0oLy+/jJ9E+F9Vb7Lw1lfr2H0oG41axUM3dMX49FTqsrNxb9mSVnN+4f/m7KC0og5/bxc0+VnkuwU7+uFKUkNZFeWswOeVd4xuv71DYUQoJrsRAL2xhmK7gcLWvU6f/NQO3YaSLLJWj7NKZtiAWJZuTMZotuGVm0qvBa/S74npDJg/H03DL4tllXW88ukqDqTkotWqefSOAVyX0P4v1/YUBEG4GskWM5XrVuE74UYOF9bj6+VC25g/2Oh2mTVp6Bs3bhyKorB9+/amPOwV9frrr5OYmEivXr04fvw4CxYsYNeuXbz//vuUlJQwderUv3ysxx57jJMnTzJhwgSOHTvGggULOHLkCA8//DDHjx/niSeeuIyfRPhfVFlt5NXPVpNyshAnvZZn7h5IzYsPUXnkCE5BQYR9vYC3f9hFTZ2ZsAA3rDmZlLgGINmsjtk9STq9AaMh8PlkJ9Nl8fukRYWzKSAAj5y19F3zGdqackpiOp9upXYGyW5F1urxc5KIi4tm5ZajyLJCcMo2eq76jGHff0vciy82BroTmSW88N4yMvPK8XA18O8Hh9OrU+QVvnqCIAhNr2rTeuw11fjcNo3kE4UM6N6i2eqLSkoTFtErLCykffv2VFRU8Pnnn3Pfffc11aGvCIvFgr+/P1VVVezfv59OnTqd9XxcXByHDh1i7969dOnS5b8eq6CggNDQUDQaDdnZ2QQEBDQ+ZzabCQsLo7y8nPz8fPz9/3r5ierqajw8PKiqqsLd3f3iPqDwj1ZYUs0bX66luKwWd1cDz04bRO7zj5Ixfz4aV1eCf1zG7M3Z2OwyMSEeFJ/IoMbZ+6w2aI0abu36ZSQRv3wGh2KiOBQcTBdXV8IPp7N36H1YXDxO9+A9tY5PkUEBVCqifQ1oXN04nlkCskzrbT/TrvgwQ5Yswafz6YKk2/el8+VP27HaZMKDvXjq7sFih64gCMDpn3lrZ36Pi9O112pRtpjJePph3PsOpObu51m28Qgfvzix2b7jmnSmLzAwkHnz5mEwGHj00Uf57rvvmvLwl9327dupqqoiJibmvMAHMGnSJACWLl36p8datWoVsizTr1+/swIfgF6vZ+zYsdjtdlasWNE0gxf+p6Vll/Lvj1dSXFaLv48rL08fQvZT08mYPx9Jo8Hlw7nM2piFzS7TNsKL/LQcapy9UVvNfxj4Ak7sodOyT9ndIppDwcEkODnhnVZB4pjHzwl8dsetYLvNcWtYpSI+yosqm4rjmSVoLEY6L/uYrlIxY3fvbgx8sizz45K9fPr9Vqw2mc6xobz66EgR+ARB+MeoXLsKW1UVIQ8/yf7kHNq3CmrW77gm3TqyZcsWDAYDb7zxBs899xx33303n3zyCZMmTaJ9+/YX1Xu3f//+TTm0v+TgwYMAdO584bYopx4/dOhQkxxr9uzZf+lYgvDfHEjJ5aPvNmO22IgM9eaZO/tzYNod5Pz+OyqdDtX737PgSDUA8S18SUnJxmJwR201YT+zHy40tkgLSt1Jh7Vfs611KzL8/ZmARH61KxnDbnK8TpbPCHxn7NBVFHp1CGHfsSIsVjvOlUV0XvoxsQN70nfuXLQuLgDU1VuY8eNW9ifnAnBdQnsmj+6ESiU2bAiC8M9gr6mhbOlv+N1wMxVO3uQVHWfKFW67dq4mDX0DBw48K9QpisLBgwcbA9Bf1Vy9d7OzHT09Q0NDL/j8qcfP7ThyuY8lCH9k064TzFywE1lW6Ng6mIdv7smuW6aQu3w5kl6P+Z0fWH+yDoCurfxISs7FpnNGYzFh0xlO35aFxt64IclbiN04h03t2pLv5cXN9SYOB/SgOOaMLyvVqc0eZ+zQle10i4tk5+EcAHyyk4lb+Tmdpt9H13ffRWo4T25hJe9+vYGi0hq0GkdLtT5dREs1QRD+WUoXLQAUQh59muX7s/DxdKZTuwtngiulyYvEnLtE8Frqu1tbWwucLj1zLpeGWYq/sju5qY5lNpsxm82N/66urv7Tcwv/fIqisHD1QX5d5fiFqm/XaO65vgtbb7qR3OXLUTk7U/7adySmOwJfzzb+7E7OR9bq0ViM2HROjcWWG4OfJBF2aANttsxjXWwsFW5u3FBey674idT6hp4dEAEkqbGHrgtWYtpGkNgQ+CIOrKHNjl/o/dmntD5jbe+O/RnMXLADk9mGn7crT9w1kKgwnyt23QRBuLr9U37mmbIyqVy/mtCnX8SodyX5ZCG3XtcVdTOXn2rS0Ne/f39RXqGJvfnmm7zyyivNPQzhKmKzy8z+JZENDUWXxw/twMTBbdk0cSJ5K1eCixt5//6ag9n1SJJE7zb+7EjOR1Frzgh8NhT16X664AhqLXf8zJoO7cHZmeEVVrb1nYrVye3068+8FWy3I2t0+GntuAYGcOhYPpIi027DHKLS9zDgt4WEjR3bOOYffz9dcDm2ZSCP3jEAd1fDlb+AgiBctf4JP/MUWaZ47iwMUTEE3D6NNTtP4qTXMqhny+YeWtOGvk2bNjXl4a44V1fH4sr6+voLPl9X55g1cXNzu2LHev75588q7VJdXU1YWNifnl/4ZzKZrXz47WYOpuY5ii5P7E5C92g2NgQ+xc2TjGe+4Hi+EbVaRc/WfmxPKQKVGo3V1BD4HC3RGoMcELlvJdG7fmNlXBw+Oh2hRhd2Db4JRa05LyA2buBQq2nhqaZS5UJGTjlaq5G4pZ8SailjyJYt+DbscC+tqOPjOZs5kVkCOELqDSPim/03XkEQrj7/hJ95VVs2YDyeSuvvF1JrtrM/JZdJw+NwMmibe2hNf3v3WhYe7mhBlZube8HnTz1+qgj1lTiWXq9Hr9f/6fmEf76KqnrembWejNxy9DoNj9zen/hWAWyaNIncZcuwe/hw/PFPySo1o9Oq6Rzj4wh8gNpqxqY1oLJZkTVaJJsFpWHXbtSeZYTvXcLK+HhayiA7tSal+0DHSWX7WYHvzPIuncPdSC02UW+qw7mmjM6L3iPY15UhG7fjHuOoNn/oWD6fzt1CTZ0ZZ4OW6bf0pWsz9JsUBOHacK3/zLNVVVKy4Ht8rr8R9x69WbL+CM4GLcP7t2nuoQEi9J0lLi4OgP3791/w+VOPd+zY8YoeSxByCyt566t1lFbU4e5q4Jl7BhMd4sXmyZPJWboUq3cgRx54j6IKCy5OOtqFeZCYWgw4Ap9dq28MbGf2wo3evZTgA8tZFR9PZ7uGvFbDqPaPPF2gWaVu2NGrRmW1IGt1qOw2enUIITG1GLus4FWYRqclHxESF0vC0qUYfH1RFIUVm1L4Yck+FEUhOsyHx+4cgL/Pn8+SC4IgXKuKf/wWSaMl7Nl/U1hazaHUPO6Y2B1ng+7P33wFiNB3hj59+uDh4UFaWhpJSUnEx8ef9fyvv/4KwNiGdUr/zYgRI1CpVGzdupXi4uKzCjCbzWaWLl2KWq1m1KhRTfoZhH+elJOFvP/NRuqMFoL83Hn2vgT8PZ3ZcuutZC1cSL1fKIfvfpOKWhte7k5E+rmw57jjVqrKZjkr8KktJuw6xzq6mF2L8Tu4mvXt29MVD451ux6b3vms274ASCokmxVZq8NgqSeuUwzbkwsACE7dSft13xA+fBgDFixA6+qKyWxl5vwd7DiQCcCA7jHcfUMvdFr1Fb1ugiAIV1LdoQPU7NxG1Nsfo/HyZvXiPQT6uTOkd+vmHlqjiw59p0qRnHLqNuaFnrsUZx73StHpdDz00EP83//9Hw8++CBr1qxp3GX7wQcfcOjQIQYMGHBWN47PPvuMzz77jOuvv54333yz8fGgoCCmTJnCjz/+yPTp05k/fz4ajeNyP/PMM5SUlHDHHXdcVDcO4X/Pjv0ZfP7jNmx2mVZRfjw9bTCuTjq2TZ1K5oIF1ATHkHTzv6kz2gnwdcPPVceBtFKQ7agUxTGzdyrwmeux6x27yWN2Lcb78Fp2tGlLK89YkmMHAZyeBTxzp65sR9Fo8bVUEdiuFbsaAl+LxEXE7PqdlnfdRe+ZM1FpNBSV1vD+7I1k51egVkncOq4bI/q3ERu8BEH4R5PNZormzMKtZx98xt/A4eMFZOdX8MIDQ9FcReuXLzr0RUZGNn6Bn1tP78znLkVz1ekDePHFF1m3bh07duygZcuW9OvXj6ysLHbt2oWfnx+zZ88+6/WlpaUcO3aMgoKC84710UcfkZiYyMKFC2nTpg1du3YlOTmZI0eO0LJlSz744IMr9bGEa4yiKCzbmMyPS/YB0D0ugodu6YtWo2LnAw+QNmcO5eHtODjpGcxWhcgQb3QqhSOZZWC3IUkSslrTGPg0plpsBsfmopjExbgf3UBSq1h8YxLICnH8FuqYzdM3lnJpnPFTqYmSqrGEhXPkZBFqZNqv+IKgE3to/+yzdHnzTSRJYl9yDjO+30q9yYqHm4HH7xpIm+iAP/qIgiAI/xjly37DVlFO5CtvYzJbWbfjGD3iIujYOri5h3aWvx0/FUX5wxp8p567lD/NxWAwsHHjRl566SWcnZ1ZvHgxWVlZ3Hnnnezfv5/o6L9eRNbX15fdu3fz8MMPY7FYWLRoEVVVVTzyyCPs3r0bb2/vy/hJhGuVLMt8u3BXY+AbOaAtj90xAK1Wza6HH+b4zJkUtejKvgnPYLZDm2h/sFk5nlOBZLOApEI5Y9OF1lh9OvDtWozTiS2ktemCqvPNlIe0RmWzOjpsaLSOnblqx3tP3eKNd7NQ5uxHXlEVznYT3Rb8H0En9tD9o4/o+tZbKIrCr6uSeHfWBupNVlpG+vHGk2NE4BME4X+CpbiQ8uW/E3j3AxiiYli/8ziyrHDnhO7NPbTzSMpFJqxzZ/MyMjL+8LlLceZxhdNONZ+uqqrC3d29uYcjNDGLxcanP2xlz6FsJAluHdeV0QNjUWSZXY8+Supnn5Eb25/kIVNRgPi2IZQUVZBXXu9oq6bRny6arNGhq6vE4uIJOAKfOm075e0GUNZhhKNun6kOm8GxhOHULd1Tt3hVNivdwp3ZW2THbpfxNlXQ8cdXcTLV0Pe774i55Raqa018OncLh487ZrqH9W3N7eO7odGI9XuCIFy6Uz/z1s78HhenCzc7aG75n72PKT2NDmt3kFNh4vvFe7j7hp4M7XP1rOU75aJv72ZmZv6t5wRB+O9q68y88/V6jmeUoNWoePDWfvSMj0S22dh+992kzZ1LRucRHOs3GYBenSI5eTKfkhrLWev1TgU+Q1UJJndHt4vo3UuQsvZQ0nsKFZGOdmpaYw1WJ7fTBZdVqsYOGzpTLR1aBbEr29ExJqwsjdYL3kGvUTF42TJChg/nZFYJH323mdKKOvQ6DXdP6kn/7jHNcOUEQRCahzH9JDW7dhD55ocoOj0rNu2jdZQ/Cb1aNffQLkjs3hWEq0BpRS1vfrmOvKIqXJx0PDVtEG1jArFbLGy55RYyf/2VE31vIL3LaAAG9mhJ0qEMKo22s2brHHX4dLiU5VHv6Q+SivCD67EXH6Uo4QHqfBzt1NRW09mdNgDJbkPW6HCvKcG/dQz7sh3tj9ql7SBs2SwM3l4kLF+OX48erNmWypxFe7DbZQJ93Xjy7kGEBXk1z8UTBEFoJmWLFmCIisF3/A1s3ptBZY2JF+4fikp1dW5eE6FPEJpZVl45b81cR0WVEW9PZ56/bwhhQV7Y6uvZOHEiuatXkzJ0Kjnt+gMwon9btu5Mpc6qnJ6tAyS7o/CyW1EGJjcfFLUW/5P7sNYWUjzsEew6JzTmeuxqHXa9M5LN6ljH1xD8FLWGgOp8iGjJyfxqdBoV8dvn4Z24CufQUIatWYNTTEtm/LiNbXvTAccGk/sn98bZ6eqoQSUIgnClmLIyqUvaT/T7MyivMbFjfzrjhnQgJNCzuYf2h0ToE4RmlHyigPe+2YjRZCU00JPn7huCr5cL1tpaNowbR97mLRwe8zAF0Z2RJImxg2NZvekIZjto66uxOjvWdTqCmxbP3FQsLp5Ynd0x1JRh1qip6nMLAPqacsxu3qdfr9GeVag5qr6AAt9wTBV1eDppiPvlbZzSDuPRti3DVq+mztmTNz5aQXZ+BSqVxM1juzB6YDtRjkUQhP9JFWuWow0MwmvEWBasOoSXhzPjh3Ro7mH9VyL0CUIz2Xkgkxk/bMVml2kbE8BTdw/GxVmHuaKCdaNHU7B3P0nXP0lpSFs0ahXXJbRn6dqDWBUJXX0VFmcPUGQkWUZRa/DJPIzabqEy1NHux+Tmg8nNBxQZp6pijJ6BjhMrjtefKtQs2W3EqGtJcwlCsdiI8tLS4tMnUJcV4du9O0NWrCCzysb7Hyynps6Mh6uBR+8cQLsWgc149QRBEJqP3VhPTeJ2gh98nPT8Sk5ml/Lk1EHodFd3rLq6RycI/1Crt6by3W+7UBToERfBg7f2Q6dVY66sZO2IEeQfTObApOep8I9Er9MwZmA7Fq05iIx0ekeu3Y4kgaLW4J+2H4+iDE70nnjWeVzK87FrdBg9Ax3lWFRqkFSorBbsOgNaUy2hXgZOGj1Bga5+Ej6vTkWymAkePpxBCxey/UgBX/+8E5tdJjrMh6fuHoS3p0uzXDdBEISrQe2eRBSrBZ/xk1i25QRtov3p2iGsuYf1p66eMtGC8D9AURQWrjrItwsdgW9Y39Y8ekd/dFo1ppISVg8aRO6RY+y56UUq/CNxcdYxemBbfjsn8El2G0igqNQEnNhD/IoZFEfFNZ5HstsIObIFk4snJndfx/q9hj66kt2GrNXhUlmIr687GUYNapXESO86fF68A8liJmrKFAYtXsyCdUf58qft2Owy3eMiePnhESLwCYLwP69m7y5cO3fjRK2KotIabh7b5ZpY6iJm+gThCpFlhe8X72HllqMATBwex6QRcUiShKmsjNVDh5KXkce+yS9R5+aLl7sT/bvF8Nuaw3Bm4LNZUdSOGbvgoztos+kHClt2xy/rMEgSkt2GZ2EamZ1GgEp1xoYNK4pai6JW4VOcjhzZioJaGy7OOsZpcih/+VkkoO2jj9L+jbf48Pvt7E/ObRzrxOFxV+2ONEEQhCtFtlioTz5EyKPPsGRfOnFtgmkVdW20VBUzfYJwBciyzKyfdzYGvjsndOeGkfFIkkR9YSGr+vcnJ6eE3Tc5Ap+ftwu9O0fy+/ojAI2BT2U1O0qsSCpCkrfQYe0s6nxD0JpqkVUaui98G6eaMjK7jHIEvoYNG2qLCUWtBSC0IIXakBZU1Nvw93HlJttRyl97FoBOr71G1Euv8fInq9mfnItWq+bh2/pxw8h4EfgEQRAAU/oJFIuFquiOFJfVMn7o1b1540xipk8QLjObXebzH7exY38GkiRx/5TeDOjeAgBTWRlrhw0jq7SOfTf+C4vehZAAD9pF+7N8kyMg6uqqHIHPYkLWGQAIO7SBdhu/R0LBM/8EebH98Sg8ya4bXqDaP7Kx4PK5GzaiKtLIConFbpVpHeXPoLQ1ZH76EQDdPvwQRk/mXw0bNrzcnXhq2mBiwn2b47IJgiBclYzHU1G5uJJUpycy1HBNtZwUoU8QLiObzc4n329l98Es1GoVD9/m6LIBYCopYdXgwaSXW9g/6XlsWgPRYd6EBniwducJAHT11VhcPFCbjdj1TgBEHFhDmy3zODXvJgFOlcUkjZqO1dm9oUCzY1ZPZTVj1xnQ1VcRRi1pvq1BVujTOZK4bfNInzUTJIleX35JXsdBfPPFGuyyQlSoN09NG4yPWL8nCIJwFlNGGvo27TmZU8G9k3tfE2v5ThGhTxAuE6vNzkffbWbfkRw0ahVPTB1E59hQACxVVawZPpwT1ZB0/VPYNTraRPvj5aZny94MUGS0ploszu5nddyI2ruCVtt/bgx8CpDdMYHUATejqNSNs3rIMqAga/W4lubg7eNOmhQCwA3DO+Iz9x3S589HUqno+c1sdri1YfX8HQD07hTJ/VP6XPWlBwRBEJqDOTuL+q4D0ek09OoU2dzDuSjiW10QLgOrzc6H325qXBf31NRBxLV1hC5LdTVrhg0jpVrFweseQ1FriGsbjAbYeTAHZDtasxGrkzsaYy02J1fA0T+35c7fGgOfrNaQPOh28mIdnTpOzeqdOdPnl5GE1LI92TYNWo2Keyd1x/LG02QuXYqk0dBt7o/8WunJ4aRUAG4YEc+E4R2vqd9cBUEQrhTZZMRaXEi21pue8REY9NrmHtJFEaFPEJrYuYHv6WmD6dg62PFcXR3rRo/mUJWaw6PuQ1Gp6d4xDGO9mX0ni8FmRWOzYHVyPavjRkziYlrsWtwY+EwunhwY/RBVQS0ayrDYkbX60zN9QMjRbVR16EutDTzcDDw2uQeZ0++gaMsW1AYDbecs4KvjNopKC9DrNDx0az+6dQxvjksmCIJwTTDn5gBQ5OTP5G4xzTyaiydCnyA0IavNzgezN3EgxRH4npk2mA4Ngc9WX8+GcePYV6EmefhUkFT07RJFaWk1qVllSFYLatmGzeCCvrYCs6sXAC12/kaL3Usaz1EZGMOB0Q9hdvVytFHTaB07dBvW/Uk2KxHHd5DbfgA2G4QFefLI9XEkTZlI2f79aN3dCZy5gBn7yzGarPh6ufD0tMFEhHg3yzUTBEG4Vphzs1EkCU1UDG1jrr2uRCL0CUITOTPw6bRqnr4ngQ6tghzP1dWxfuxYEsvVpA65C4BBPVuQk1vGydwKVBYTEgo2vTOG6lJM7o4ds+cGvtx2/UgedDuKRnvWWj+1xYRd74S2voag4hNkthsAMnSJDeXO/hFsGT2UmpMn0fv5Ib87l68Ti1AUaBsTwON3DcTd1XBlL5YgCMI1yJSZTr1XEH17tb0my1iJ0CcITeDULd0LBT5bfT0brruOHdUGjg+8CYDh/VqTeqKArMJq1GYjSBJ2nRNOFUUYvRzb/2N2LSGmIfDJKjWp/aaQHT8EAI2pFpvBFWQZSbFj1xlwrijAUzaSHdkZgNED2zEqSs+6Qf0xFhRgiGlJ7mMfsPdAEQBD+7Tmjuu7odGor+i1EgRBuFZVHz9OlV84467BW7sgQp8gXDJbwy7dxjV8587wjRnDtnp3TvadAMCoAe04lJJNbkktGlMdilqNXWvApSyPOi/H+yL3ryIm0bFpw+zkRtKoB6kIbQOAxlyPzeCKympB1mhQVFq88o6Btx/5TkGoVRL33NSbNuZ8Vg0YirWqCm2XXuyf8BTZJ8tQqyTumtiDIX1aN8v1EgRBuBbJJiNyXhbaCSMJDfRs7uH8LSL0CcIlsNtlPvthK/uO5KDVqHh62uCzAt+6MWPYZvImrdd1AFyX0J49+9MoqDCiNdZg1+iQtXrcijOp9QkBlYrQI5uJ2f4zKqAiMIaDo6ZjcvNBZbWgSBI2vbMj+OmdAQg4uZea6A7Uq/S4ueh57M6BuCYnsmbyZGSzGWnYeLZ0vYmq4hrcXQ08ftdA2sZcO8VEBUEQrgbF+w8gyTKdbxrX3EP520ToE4S/SZZlPp+3jcSkrMY6fI27dGtrWTt6NFvtAWT0GA3A9cM6sj0xleJqC7r6KmxaA7JWj0fBSWp9QlDUWgJO7KHV5u8pd3WlPqYnx/vciKLWoDXWYHVyA86+tRucuoPCdn2QkQgN9OSZexKo+PVHNk2fjiLLWKY8wNbg3ljrzEQEe/H0PYPx9XJttmsmCIJwrSrcshXJO5D+4xOaeyh/mwh9gvA3KIrC178ksn1fBmqVxGN3DaBTO0fhZWtdHWvHjGGzEkxW1xEATBoex8atyZTV29DVVWLTOSFr9XjlpVLnGYhd54R3TgotN8wmPSSSmk7jKItw9HPU15RjdnPsrFWbHLd21RYjfjkp5LfrC0CX9mFMv6UPJ959m6SXX0YBKu//D7v0kWCz06ldKI/c3h8nw7VVU0oQBOFqUFFaifb4QTwm34H2Gl4HLUKfIFwkRVGYu2gPG3aeQJIkHrqtP13bO+rbnZrh20woWZ2HAXDjyHjWbTxIuUlBV1uOXe+MrNXjnZ2C1eCCxcUT17I8otd/zZFOA6nrMBqrkysqmxWNuQ6zmzeS3YYk27EbHLt7nY1VFMZ0AWDckA7cMKwDux6czomvv8au1pL3+AekWB0zg6MGtOPWcV1QqVTNcr0EQRCudcd+X4aX1czQl55u7qFcEhH6BOEi/brqICu3HAXg/im9G9vw2OrrWTtmDBvVEeR0TEACbhrTmVWr91FpldDXlGHTu2DXGvDOTkZjMVIe3g5dfTWRW77n2MBbqWnYeetcWYTZyQ2Li6dj/Z7OgKLW4F6YjuzqTnlADFqtmvsn96ZHuyC23HQj2YsXY3H15NiD75NnUqNSSUyd1JMhvVs105USBEG49mXmlOC0dxOug4bh2bJFcw/nkojQJwgXYdnGZBauPgjA1Ek9GNDd8QVgratj7ahRbNDGkNt+ABIwZWxnVqzYQ6VdjaG6FJveGbvOgHfOUXyzkzne90Yku43gw+s5PvReLK7eIMt45Z+gIqQlSCq09TVYnR0zdv5p+6gKb4dZ64SXhxNPT0sg2BlWJyRQsnMnNSEtOHLLS1SZFFyd9Tx+1wBiWwY116USBEG45pktNtIWLyW0toKB773Z3MO5ZCL0CcJftGnXCX74fS8AN46KZ1hfRwkVxwzfWNbrWpAX2x9JglvHdWXJ7zuoUnQYqkuwaw3Y9M545p8gbsUMNk37CABFrSGzx/UAOFUWo6+rpCLUUUpFV1+FxdkDSbYTdLRhw4akIjLEm6fvGYyhppxV/UdQmZJCaVx/DibcjdWsEOTnzjP3JhDk537lL5IgCMI/hKIo7NqZTGDSBkKn3Ixv587NPaRLJkKfIPwFuw9lMXPBTgDGDo7l+qEdgYbAN/Y61ulakN+2DyoJbhvfjUULt1KtMmCoKkZRa7E6ueFelEmX3z9AYzHiXFVCnXfDLJwiE3BiL9X+EVSGtEKy21DZLFicPdAaa/FP209e+/4AdI4N5eHb+mM8fpRlI0ZQX1BI9pDbOBqbAHaFuDbBPHL7AFycdc1ynQRBEP4pTmaVoF79Kzqdhj4fftDcw2kSIvQJwp9IPlHIJ3O2IMsKA7rHcPPYLkiShK2+njVjxrJW14KCNr1RSXDn9V359ZfNVKudcaoqBkXB5OqFS3k+XX9/H63FCEDbTd+TFT8M79xUQOFE70nIGq2jNIvOCbveGdfSHFxqyhsD38j+bbltfFeKt21jw7hxGGvrSZ38HDkBjpnBEf3acNv4bqjVYsOGIAjCpSitqOXEslXE5KTQ98cfcQr4Z9Q2FaFPEP6LrLxy3v9mAza7TLeO4dx7U28kScJaU8PaceNZq29JQeteqCS4e2I3fp63nmqdO4aqEjQWEzV+4Rhqyui26F10xprG4/rmpOBZcJKjg24jr10/AAzVZZjcfQDH+j2bhy9FUXGoGjpoDO3Tmoyff2brrbdSa/Dg8J1vUOHii1qtYtoNPRnUs2WzXCNBEIR/EqPJws61ibTYv4qIG28iasqU5h5SkxGhTxD+QHFZDW98uZZ6k5XW0f48fGs/1GoV5vJyVo8azUavuMbAN+36zvz8w1qqDJ7oq0txri6lPKwtWmMNXRe9h6G2ApNGg8FmA6DWK4ik0Q85unDIMvq6CkfgU2QiktZQ3K4PRr0bzgYtj981kPatgjg6Ywa7Hn6YiqAWHJzwFCa1vqHDxgDaxgQ278USBEH4B7DZ7Wzemkz41oW4RYbT9+tZSJLU3MNqMiL0CcIFVNeaeOPLdVTVmAgP9uKZaQnodBqMxcWsGj6czYG9KGzdA7UK7rm+Cz/PXUWlix+62gq8809Q0KYXKpuFLr9/iGtFAQpQ6u5OqM1GTlg8RwfcgqzVozXWgAJmNx80pjrCD64jp/torJKGID93np42mCB/d/Y+/TRH3n+fnI6DSR14q2NDR6g3T909GF8vl+a+XIIgCNc8RVHYuTcN73XzcbHVM/T3TWjd3Jp7WE1KhD5BOIfJbOWtr9ZRWFKNr5cLz903BBdnHXU5OawcOpRtUYMaAp/EvePi+eXb5ZS7B6KrryY4dQeZXUeDItNx9Uw8i9Kp0evRKgqBFpmkvrdQ2LoHAC5l+dR5+oNag0tZHj7ZyaT3cPR0jG0ZyON3DcSgUth2xx2cmPcTKYPvJLfDQAB6dYrkvsm9MehFhw1BEISmcDA1D/vq3/AoTGfQsmV4tm3b3ENqciL0CcIZbDY7H8zeRHpOGW4uep6/fyjeHs7UZGSwauhQtrccSmHrnqhUEveP78Qv3yym1CscramOFrsWkTLgNgDabPmJwJN7MWm1HG/XjmijhoO9J2Ny80GS7biVZFMdEAU41u+h1pDdydHBY9SAdtxyXRfstTWsHTeOrL0HSZr4HJVBLZAkmDKmC2MHx/6jbjkIgiA0p7TsEopWLCfyxB66f/wxoSNHNveQLgsR+gShgd0u89kP2zh0LB+9TsOz9yYQEuBB5dGjrB4ylMQOYyhs3RO1SuK+se1Z9NUvFPvFoLEYiV3/LYeH3A0qFSHJW4hIWotVpaIouiV6n07sinXswDVUlaCy2xyBT5GJ3L+K0pZdqXX3R6NWMXVSDwb3akX1yZNsnDiRzMIqDkz5DyYXT1ycdDx0W7/GHr+CIAjCpSsureHo8nW0OLiWtg8/TLtHHmnuIV02IvQJAiDLMl/8tJ3EpEzUahWP3zWQFhF+VBw+zKphw9gddx0FbXqhVkncP64jSz7/ifygdqisFuKWzyC1/xTseie8clOJ3TAHCShq24v0npMwu3oB4Jt5iIrAGOwGF3T1VYQnrSOr+xisGj1eHk48OXUQLSL8KNm1i3VjxpDtEc6hG/6FXaMjNNCDp+4eTKAouCwIgtBkaupM7Fq1hRZ7lhA2dizdPvywuYd0WYnQJ/zPk2WFWT8nsm1vOmqVxGN3DCC+bQjFiYmsHTmKfd0mUtCmt2MN35j2rPrkO3LCOiHZbXRa/imlkR2p8wlBX1tB/MrPMbt4ktp/CkUtugLgXFGIe3Emha17AuBRmIZnQRone08EoFWUH4/dOfD/27vz+Jiv/Y/jr0kmmewiiCAREkvUrkhpraVUaailSkstXSmlRbdr66LaapW2t7facmnr1xa3ttba2ilBYo+lEvuSIJN9m+/vD5XLtZNkksz7+Xjk8TDf78yZz8lh5u27nENJH3f2fvYZW0aM4EDtthz4e3/dsAoM7dscD3dNuCwikleys3JYv3Irldf+TKnatWk5ezZOzs72LitfKfSJQzMMg+lz/+SPTQcwmUwMfqo5jepU5MyGDSx7uAPbG3flxD0P4GQy8XxEXVZ+9DmHQu8Hw0bdJV9S6thedrR/DoCQP38hrm5bYhu0x2Z2wZSTTeCuVSSWDckNfEHRK0nxLUvc39fvPdy8Br0jGmKkpbLhmWfYN2Mmu9r042RYUwDaN6/BUxENNeGyiEgeMgyDDX/uo+zy7/Hy9aHt4kWYPTzsXVa+U+gTh2UYBjN/2cLy9TGYTPBi7/tpUr8SJ5YvZ0VEBDua9sxdS/eZTrXZMGES+8IeBOCe32cScDCS+Io1yXLzAmDvg/1y2/Y7uocyh6M5FB5BtsUDc0YKoZsWcKTBQ6R5l8LVxZlnH2/KAw1DODJ/Phuff57zSels6/4m1rKVcHIy0b9rOG3ur26X342ISHG29+BJXBZ+h2dGEu3X/olHgGPMdarQJw7JMAx+WLiN31bvBeDZx5vSrGEox5ctY2VEBLvu686xWi0uBr5HahI9fjzb6z4KQNX1P1Nx1yoA3BPPYk5PJvvv4Od78iDB25eREBhGTPMncreV27uB/c17kmN2JaC0N8P7t8Itbh8rOg7h2OLFnA8IJfrJN0l388bb08LLT7egZtVyBf+LEREp5s6eS+bknJ8IPHmAVr/8QslatexdUoFR6BOH9PNvUSz8fRcA/buF0+q+qsTNm8eqnj3Z06Q7R+o+iMkET7cNI+aNUfwZ3gtMTlSMXklI5OLcdtytZzFH/R/hVepiXvB/ZLu6E/XwC6SUqgCGjcqRv2JzMrO3dR8A6oaV59kONdnx0kDi5s0D4GjtVuxt3QcbJgIDfBn5TGv8SxWvCUFFRAqDzKxsts9fRsU966gzejQVH33U3iUVKF0odA3r16+nQ4cO+Pn54eXlRePGjZk5c+ZttzNjxgxMJtN1f3r27JkP1cvNzFsWzbxlOwDo26UxDz0QRty8efzx+OPsaxSRe71dn1ahHBvxIusbdMVwNhOw/09qrPqOS7PjGcCemjXplJOO1/dfcKZyPTb2HJ17U0fdX7/A6l+JuHvbA9CpVU26+yawvFF94ubNw2Zx49gL77G7dV9smLivXjBvv9xBgU9EJJ9EbthJ+fX/wf/BNtQfM9re5RQ4Hen7H3PnzuXxxx/HZrPRvHlzSpcuzcqVK+nbty87duzgo48+uu0269atS7169a7aHh4engcVy+1YsHIXP/0aBUDvR+/l4RY1OPT996zr04cDjR7lcKOOAPRqXpkzLw9gbavnyHbzxPfEAWov+xoTRm5bp4ODaXD6NPEpGeyMGE58pdrAxalZgqOWs6tNfzK8SmJxNdOnWUUyJr7MuvXrAXBt2JSoR4dyJCENkwm6P1yPLm3raMJlEZF8cvhIPOYF3+Hm5cmDs7/H5OR4x70U+i5z7tw5+vfvT05ODnPnzuWxxx4D4PTp0zzwwANMmjSJjh070rJly9tqt3PnzowdOzbvC5bb8uuqPfywcCsAjz9Sn06ta/HX7Nms69OHv+o9xKH7OgPQ/b5Akl7qxYZmA0j3LoXnuRM0WDgZ55wsDMAE5JjNlDt+nCPBddnduS9ZHj44ZWdSbf3PGIbB9k5DsTmbCfB1o/n+pRyNmAGGgdnTE7eXx7DEFkhyQhrenhYGP9WMumEV7PVrEREp9tLSM/lr3jwCz8TSeulS3MqUsXdJduF4MfcGvv76a6xWKxEREbmBD6Bs2bJ88MEHAEyaNMle5cldWLZuHzN/2QLAYw/VoUvbOhz67jvW9O5NbO3WxDS7eKo9onEQmUOeYHO9ziQGhOCSlsy9Cz7BNT0lN/AB5DhbiG7dj6iOL5Hl4YP32SM0njuR8xWqE9PySWzOZqp7ZFJ74gCss6eDYRDUtRu2rxYxJ9mf5NRMQoJK8e7wjgp8IiL5bOu6KMpF/0HlAQOp8NBD9i7HbnSk7zKLF1+8QL9bt25X7XvkkUdwc3NjxYoVpKen4+bmVtDlyR36Y9MBvp3zJwARD9ai+8P1Lh7h69uX42FN2dvySQA61AvANLwXeyo25HTVRhcnX140BY/Es2Q5O+OSkwPA6dAG7GnVhwxPX7DZCNm6mNKHdxDd7jnSfP0xO5tokrgXj0/fxwQERUQQ+vpoZm45y57NcQC0bxZG74iGuJiL90SgIiL2Fns8AfPSOVj8/Gj6ycf2LseuFPouEx0dDUCDBg2u2ufq6kqtWrWIjIxk//791KlT55bb3bp1KyNGjMBqtRIQEEDr1q1p0aJFntUt17c28hBf/bgBuDgRcs+ODdg/bRqbXniBkyH3sqvtQAAerFsOl1FPcdJcgv1NuwNQY9V3+J3YT6azM6d8fSmbls2eVk/lrrThmXCcWsu/4UL5amzpOgrD2UwpbwsNVk3Hed0yTM7ONJo0CedOPZk4YzXnrWm4Wcw8+3hTmjaobJ9fiIiIA8nMyibm1xVUOnWIZvPm4eLt2DfKKfT9zWq1kpiYCEBg4LUXtA8MDCQyMpK4uLjbCn2LFi1i0aJFuY/Hjx9PixYt+PHHHylbtuzdFS7X9Wd0HP/8YT2GAW3vr85T2GE83wAAMX1JREFUnRuyc8IEtr35Jmcr1mJHhxcwTCYeqOGPz+iBJJ+7wK4+I8HJifJ71hG0axXZTk78Xq8ejTs8xbqzrmRZPDDlZFN5629U3LmSPS37cCb04n8S6gVYKPfJcIwzJ3Hz96fFjz+y27U80z9fRk6OjcCAEgzr14oKZUvY+TcjIuIYonbEErBtOWVaPUjFzp3tXY7dKfT9LTk5OffPHtdZisXT0xOApKSkW2qzXLlyjB07loiICEJCQkhLS2Pz5s2MHDmS1atX07FjRzZt2oTzDdb6y8jIICMjI/ex1Wq9pfd2dFt3H2XKv1djsxk0bxTK010asXXkSHZPmsT58lWJ7jIcG040CiuL/3vPk3rsKIfb9SfFuzSW5PPUWP0DNpOJdQ88RPkWvdhstYEFfE7HUmvFNxgurmzq/hbpPqVxdoI2bgnwxqsYhkHp8HCa/t9P/LAujnWRGwEIrxvMC73ux83iYuffjIhI4ZWX33kJ55O58PsyglMTaTr1U82OQDELfV26dGHv3r239ZqZM2fSuHHjfKmnXbt2tGvXLvexj48PnTp1olWrVtx7771ERkby008/8cQTT1y3jQkTJjBu3Lh8qa+42n3gJJOnryLHZtC0QWWe7RHOxmcGcnDGDJJKBxLV43WyDSdqh5QmeOqrJB+JI7tKLQ5Vvbjebc2VM0jz8iWyw3AyS1XhhNWGc2Y6VTfNIyhqBcdrt2RvsycwzC6U9nLl/t2LyPjlBwDCXnyRCq+PZeIPG4k7cR4nJxM9H2lAp9Y19YEjInITefWdZxgGWyIPUHH/JkL7Pk3JmjXzoLqir1iFvsOHDxMTE3Nbr0lNTQXAy8vrim0+Pj5XPTclJQUA77u8JsDLy4shQ4YwePBgli5desPQ9/rrrzN8+PDcx1arlaCgoLt6/+Is5q8zfDDtd7KybTSsFcTz3Rqxtlcv4ubMIa1kWaL7vkNGNlSt6EeNmeOw7tmNa8mSRFVvgeF88Z/D0TqtOFu53sUGDRvl922i6sa5mDPSiO4wiDNV7gWgTnlPKk97i4yDMTi7uXHfF19wunYL3pqyjIzMbEp4ufFyvxbUCHWMNR1FRO5WXn3nHToSj3nrasw5mTQYNzYPKyzailXoi4qKuuPX+vj4UKJECRITEzl27Bj33HPPVc85duwYAMHBwXf8PpdUrVoVgJMnT97weRaLBYvFctfv5wjijp9j4lcryMjMpm5YeV7s0ZhVXTpzYtkyMn3LsPPZD0hON6gYUIJGv33Oua1bcPHxISs5mQy3/wb5S4Gv7MFIQv+cj0/8US6UDWHzY6NI8/XHGRsPls7C+R/PkpGZiXdoKE1+/JmFB9L447u1ANSsGsCg3g/g5+tpj1+FiEiRlBffeVlZOeyIPsQ9h7ZS/bnn8NSBklzFKvTdrbp167JmzRq2bdt2VejLyspi165duLm5Ua1atbt+r/PnzwP/vU5Q7s7x04m8+8/lpKZnUT3En5e6N2RNRCdOrVqF4VuKvYM/4VxSNv5+ntz3+1ecW7EUJzc3ctLTMbKyqLX8a86ENiSjREnMJhNld6zG+9wJDAP+atSRA/d1wXByxtfFoG3CVpI+/RwDCHr0UYLe/5QP5m7jVHwSJhN0bluH7u3r4uSAs72LiNjb7oMnKbFvC0452dQeNcre5RQqCn2XeeSRR1izZg1z5szhySefvGLfokWLSE9Pp2PHjnkyR9/cuXOBa08PI7cn/nwK7/1zOdbkdCoH+vFyj4as6tCe+M2bcfItyf5XPudkfDq+3m48sG4G1uW/4ezujpGTgy0zk3SzGU9rPJW3L7mi3XSvkux86FkSgmoAUKeUEyGzPyBp7y5MTk40mPA+x8IfYfzXa8nOsVHK15MXez9Azao6nSsiYg9p6Zns3XeMBoe3UfXpvnheZzYOR6VDEZcZOHAgPj4+zJ8/n3nz5uVuP3PmDCNHjgTglVdeuep1YWFhhIWFcfz48Su2T5gwgfj4+Cu2ZWVlMW7cOH7++Wfc3d3p169fPvTEcSQmpfHeP5eTcCGF8v4+DO/ZiPWdOxG/eTOupUpz/K1vOBifjrvFzAOb/4/U5Ytx/vvubFtmJhc8PHDLzv5vg3/fbHE6pAHre79NQlANnHOyeKRkEhXff570vbvwCAyk+bKVLPW7l3//ZwvZOTburRnIxBGdFPhEROxoZ8xJyhzZCanJ1Pr7e1v+S0f6LuPn58e3335Ljx496NatGy1btqRUqVKsWLGCCxcuMHz48Guuu3vp5pGsrKwrtr/xxhuMGzeOhg0bEhQUhNVqJSoqihMnTuDm5sZ3331HhQpagutOpaRmMuHLFZw4k4ifrwev9gpn46MdOLd9OxY/P5Lem0H0nnjMzk402zGf7GW/4OzpCTYbOWlpJHh5UeqyqXrMnp5kpqUT0+xx4upfXKanTFYibS5Ecf6z6QCUa9OG0E+/4tP/RHHqrBWzsxNPRjSkXbMw3Z0rImJHqemZHIg9TaPYbQR364ZPlSr2LqnQUej7H127dmXNmjW88847bNq0iczMTO655x4GDx5M3759b6ut0aNHs3HjRmJiYti2bRuGYRAYGMhzzz3HsGHDqF69ej71ovhLz8hi4rQVxB4/RwlvN0Y92YTtT3Tl3PbtuPn7Y578A7//efHGm6Z//YHTkp8xe3mByUR2SgonfH2JCQuj1aZNALj4+HDeyYPoiFEklakIwL2cIXjpV5w/dBCT2UyDd9/lbLPOjJ++nsysHEqX9GR4/1aEBJWy2+9BREQu2nvgFKVOHcSIP0OtESPsXU6hZDIMw7B3EXLrrFZr7l3G15pWxhFkZGYz8auV7Dl4Ck8PV94Y0Jy9vbsS/+efuJYsSdCs+Xyx4jCGYdDw5FZK/zQVs4cHhrMzOUlJnPHxYVvr1rz70Uck/fADUePGcbxqOHta9yHHxQ3X9GQeMh0l5+tJGNnZeFasyH2zvmPJSTMrN+4HoE718gx+qhk+XlqDWUQkv1z6zlv+1Sw83a+9cAJcXG5t7pJo7t30I/6VKvDw6tUFWGXRoSN9UqRkZ+fw8ber2HPwFO4WF0Y83ZxDgwYQ/+efWPz8qDlnMR8vOYRhGFS3HqbUT1Nx9vDAsFjIOX+eeG9vDnTsyOQJEzg4dix7f/iRXe2e53TVRgCUSYij1ZnNWJctBqBS9+4EvzuJT+du5dipREwm6PFwfSLa1MbJSadzRUQKg4Ox8bifPYpx5BC1Pp9s73IKLYU+KTJycmxMmbWW6H3HsbiaGTGwFSdGDeLE0qU4u7tz37wFfLz6OOkZ2ZTPiCf43+/gbLFguLtjS0jggocHp3r14p3Bg9n82GPsP53Czl7jSfcpjSknm3pndxDy50KssYdxslho9PEnnG3YlnFfryE9IxtfH3de7P0AdaqXt/evQkRE/mYYBjGxZ6h2PBqfqlUJ7NDB3iUVWgp9UiTYbAZfzl7P5ug4zM5ODO/XkgsfjeXIf/6Dk8VCqwULmbE7hbPnkvGxpVNj+micnZ2wlSwJp06R5OZG6osvMuj++1nWohW7arYhrmt7ANwvnKGD7RDpi74jJSUFr0qVaPLTHObtS2H9D+uBi5MtD+nTnBLe7nb8LYiIyP86ecZK1umTmA/spOaXX2LSHKnXpdAnhZ5hGMz8ZQtrI//C2cnEkD7NSJ40hgPffAMmEy1++IGNWaXYtT8aF2zU/uEdXDJSyQoKwnz0KOkuLriOHMlDGRksePZldnYYRnKpi3dNV9y3jralMzj+/SwAAlq2JPSzb/jol2hOnrXi5GSie/t6RLSppcmWRUQKoYNHzlLpaDQWPz9Cn3rK3uUUagp9Uuj99Ot2lqzZC8CzPe4j9b1RxP70EyYnJ5p+8w0pdZoy7/OlANRY8hXeCcdIrlIFr4MHyTGZ8B00CM8/VjEvuzSHH/8HhrMZ15REGu/5jWo+cPz7i5Myh734Iim9XmL8jA1kZeXgV8KDoX1bUD3E3259FxGR68vIzOJE7AnqH4wi7PXXMLvrbMyNKPRJobZ07T7+s3wnAE9HNCDrneEcW7wYJxcXmv/wA+Uf7czIDxdiGFBh9xrKx2wivkoVSh88CIB7tWrEz1vCygd65U7FUvbAFlobRzCST3Biww5MZjMNP/ucjSVrs+LnPwGof08FXuj1gO7OFREpxOKOn6fMwW04maDGoEH2LqfQU+iTQmvNlkNMn3sxhHV98B4YP4Rjq1bh7OZGq3nzCHz4YWb9soVTZ61Yks8TtmY2J0JCKPPXXwDkmF2J9q9PXL2HwMkJl1QrNf+YyQPN63B04SYyzp7FrUwZ6s36ke/2pHJw735MJuj+cD06t6mju3NFRAq52MOnCIqNomq/friVKWPvcgo9hT4plHbuP8m/Zl+8iaJteAjuE4ZyauNGXLy9eXDRIgKaN+dMQhK/rdoDQM2VM4gLKs9Ri4VyNhsnq4UT80AP0r0vTpwcELOJOtGLqfl4V2I+/xxbVhYl69alwmcz+HhJDNbkdDzdXRn0ZDMa1NRajSIihV1Kaga26E04pSVTc/hwe5dTJCj0SaFz6Eg8k775nRybQXjN8pT+YAjxUVG4lixJ2yVLKNO4MQAz//ULNqBU3E6sOeeI9SqBV62mRD4wgAT3i2HPzRpPzT9mUt3XCd82Ldk7eTIAwd27E//UCCbN3YFhGFSq4Mfw/i3xL+Vtp16LiMjtiD0aT8CBSCp0ehSfqlXtXU6RoNAnhcqZhGQ++Gol6RnZ1Aj2I/izkZzfEYWbvz8PLV+OX506ABz79Vf27T0CpQKx+voTG94Jf79QLvwd9sy2bCpvmk+lbUuo0qMbaSdO8NesWWAyUX3M26wtfx/bVl48Sti8USgDut+HxVX/HEREioozmzZSPimBuq+NsncpRYa+5aTQSE7J4INpK0hMTifI35uwaW9g3bsL93LlaLdiBb733APAqdWr+b1LF2r6BRH56FCySpTFu0RZ0gCzk4mQQ5sIXP4dbtnp1HrjNQ7NnElKXBxmLy+CPp/BrEM2zu0+hovZiQHdm9AyXItyi4gUJYlJaXhFrcO9Tn38mzSxdzlFhkKfFArZ2Tl8PH0Vx04l4uvlSt0f3yNt7y48K1ak3e+/4xMaCsD5Xbv4vXNnbJmZJGUncvrgYqo3eRxfP3/CTOfJfPdVnBNO4xEYSPUXXmPne++RnZKCV2gVMsd+wb+2ncRmMyjv78NLfZpTObCUnXsuIiK3K3bzdnzij9Lw84/tXUqRotAndmcYBl//vIk9B0/h5upMo4Wfkr1jK54VK9L+jz/wDgkBIPnIEZa2bUvmhQuc8fFh7T338NKQF3ikXTs2Dx3K/q++whkIaN0av7p12f7mmwCUaNeRHe2fY2/kCQDuv7cyz/RogpvFxV5dFhGRO2QYBhmrl+JaphyVu3axdzlFikKf2N0vy3ey6s+DmEzQaP33OO/4E+/QUNr9/jteFS/OrZeVlMSyDh1IP3WKc56erG3YkLHjx1OzZEl+u/9+ErZtA5OJmq+8QuK+fez55BMASgx7i6WetbgQm4DF1Uy/ruG0aByKyaTpWEREiqKzccfwid1D4GtjcHJ2tnc5RYpCn9jVmi2H+PHX7QDU2/kbnhuWXDylu3JlbuAzbDaWde+Odfdu0lxc2NykCRM++QRLZCQLX3yRnLQ0LH5+NPzwQ3Z+8AHWmBhMrhZsb/+LOadM5CSlE1TOl2H9WlLev4QdeysiInfr9K+LcTa78MCIIfYupchR6BO7OXQknmk/bgCgRtwWyq78Ea9KlWi/enVu4ANYOXw4Z5cuJcdkYkfLlrz7/vvEvvkmRxcsAKD8Qw9RuWdPNg8bRlZiIi4hVTn8/PvsOp4EGDStX4lnezbV6VwRkSLOlpWJKWojptYdcS/pa+9yihyFPrGLs+eS+WDaSrKybVQ4d5iKv3yBe0AADy1ffmXgmzaNI59+ihMQ26wZg3v3ZkuHDqSfPo2Tiwt1R4/GsNlY378/ADntu7O5UVfOHU/CxexE38fCebBJVZ3OFREpBk6uWYdLWjI1X9KSa3dCoU8KXHaOjU//vZrEpHT8Mi5Q48eJuPmVvHiXbpX/Tp8y/8cfOTJkCN5AepkyNPP2ZsvTTwPgW6sWTb78kp0TJnBs8WJsTs5YB7/Nn07lMazplC3tzdC+LQgJ0t25IiLFxYU1v5NWvgr3PtzC3qUUSQp9UuB+WLCVg3HxWEw2an//NhYneHDhQnxr1AAu3pn17bffsmLqVNqlpwPgdvYsJxYvxmQ2U3vUKCp27syaJ5/EGhNDcrkQDvb5B6fSTWBAy/AqPP1YY53OFREpRrLOJeB8eB8eA0fg5ORk73KKJIU+KVCRu47w6+qLK2Hc8+uXuCclED5tGv5NmwKQnZ3NRx99xNKlSzGVKAEhIfDXXzi5uBAUEUG9MWM4Fx3Nb82bk52WzolWPdhb7xGy0w28PS306xpO0waV7dlFERHJB6fXrMYwOVFvQB97l1JkKfRJgbEmpzPt/zYCEHYkkrL7NxPctStVBwwAIDU1lTFjxhAZGYmTkxPDX32VNk2bcnLFCgJatcLs5UXkiBHs++wzUkuU4UDvMZz0DACbQf17Annuiab4ervbs4siIpJPkjZv5FzlOtS9N8zepRRZCn1SYGbM/ZPE5HTKmLMIWvAvLH5+3PfFF5hMJhISEnj99dc5cOAAFouFcePGER4eDkCl7t1Jio1lebt2xG/bxvF7mhHTth9ZOGFxNfNU50a6WUNEpBjLTrRiOnoY934jMJs1N9+dUuiTArEpKpYN22NxMpmo9vMknHOyaDhpEu7+/hw5coSRI0dy+vRpfH19mTBhAmFh//2fXOzcuawfMIDzTh7se/xN4gOqAlAjtCzPPdGUgNI+9uqWiIgUAOuO7ZgwqN5NK3DcDYU+yXfpGVnMmLcZgPrJB/E+so+yzZpRpW9fdu3axRtvvEFSUhIVKlTggw8+oHz58gDkZGSw5dVX2f3PfxHboD2H7ovA5mTGxcWZbu3q0ql1TV3MKyLiAM5HR5FRpiL1mtS2dylFmkKf5Lula/dxwZpG6RJulP5iIgCNP/2UdevW8fbbb5OVlUWNGjV477338PX1BSApNpZVPXqwNz6LfU++Q6pvWQDqhlVgQPf78C/lZa/uiIhIAcs+fJD02s0oXdLT3qUUaQp9kq/SM7KYv2InAHWObsGUlUlAq1asiY1l6tSpGIZB06ZNGT16NBaLBYAj8+ezdNAwdjSI4GzTegCULOHOUxGNaFK/kq7dExFxME5JF/BtfJ+9yyjyFPokX63bepjU9CxKOmXiMv1jTM7OHKlfn9lTpgDQqVMnhg4dirOzM9lpaax5cxy/Rp/ieJc3MZzNODuZ6NiqJp3b1sHdTfPuiYg4qkqtmtm7hCJPoU/y1drIQwD4r5qLCYOEjh1ZuG0bAAMHDqRXr16YTCaOR+9m2rgv2V++DrbaFydprlU1gKe7hhMY4Guv8kVEpBDItHhSrcE99i6jyFPok3x1/MQ5AEod3cPZBg34NTERZ2dnRowYQbt27Yg9Fs+P/5xDdKIztooNAajsa6bPU22oEVrWnqWLiEghkVKqPIHlStq7jCJPoU/yTUZyCslpWWAykVgukFVeLri7u/PqqH9guJbizffncehUEuAOZiiVGk+fPm1pfH9tXbcnIiL/Va4iZmfN1nC3FPok3+wYM5qyhzI5XaUhu1r0o7QtHd+SpfnqP/v/+ySbjXKxUXRoW5cHBw3TFCwiInIVt4rB9i6hWFDok3wRHxnJ7k8+obbZwmn/IEw+ZcHZlcTkTEy2HHzOxFH24BbuDbDQ6pMP8Kla1d4li4hIIeVdMcjeJRQLCn2S5wzDYNmTT4JhcKSkD6Xdj9Ah2Je4n+aQdiQOn7NHKBFQhsZTphDcubO9yxURkUKuZMVAe5dQLCj0SZ77efZsUg8cwAyUc3en5i//4cyJz3EHfHx8qDXmLWoOG4bZw8PepYqISBFQMlihLy8o9EmeMQyDL774gjlz5lClalXuj4nBPS6ONMDV15dao0ZRY9AgXLy97V2qiIgUIX4VAuxdQrGg0Cd5IjMzkwkTJrBq1SoAHhw7lrrx8ZzfuZOgRx+lfJs2OP+94oaIiMjtKBlQxt4lFAsKfXLXkpKSeOutt9ixYwdms5nXXnuNBx980N5liYhIMeHto8uB8oJCn9yVM2fOMHLkSOLi4vDw8OCdd96hfv369i5LRESKERezs71LKBYU+uSOHTp0iFGjRpGQkEDp0qWZOHEiISEh9i5LRERErkEz4cod2bZtG0OGDCEhIYFKlSrx+eefK/CJiIgUYgp9l0lJSWHWrFm89NJLhIeHY7FYMJlMjB079q7aXbhwIS1atMDHxwcfHx9atmzJ4sWL86ZoO1ixYgWjRo0iNTWVunXrMnXqVPz9/e1dloiIiNyATu9e5sCBA/Tp0ydP25w8eTLDhg3DbDbTpk0bLBYLy5Yto2PHjkydOpXBgwfn6fvlJ8MwmD17NtOmTQOgVatWvP7667i4uNi5MhEREbkZhb7LeHt7M2DAABo1akSjRo1YvHgxo0ePvuP2YmJiePXVV7FYLPzxxx80adIEgP3799O0aVOGDRtG+/btqVKlSl51Id/YbDamTJnC/PnzAejRowfPP/88JpPJzpWJiIjIrdDp3cuEhoby9ddf89xzz9GgQYO7PoL16aefkpOTw/PPP58b+ACqVavGm2++SXZ2Np9++undlp3vMjIyGDNmDPPnz8dkMjF48GBeeOEFBT4REZEiRKEvH126bq9bt25X7bu0beHChQVa0+2yWq288sorrFu3DhcXF0aPHk3Xrl3tXZaIiIjcJp3ezScXLlzgyJEjANecty4oKIjSpUsTFxeH1WrFx8enoEu8qZMnTzJy5EiOHTuGl5cX7777LnXq1LF3WSIiInIHdKQvn1wKfCVLlsTT0/OazwkMvLiAdFxcXIHVdav279/PoEGDOHbsGP7+/nz22WcKfCIiIkWYjvTlk+TkZAA8PK6/dMylMJiUlHTd52RkZJCRkZH72Gq15lGF17d582bGjBlDeno6ISEhfPDBB5QqVSrf31dERBybPb7zHEmxCn1dunRh7969t/WamTNn0rhx43yq6O5NmDCBcePGFdj7/fbbb3z00UfYbDYaNGjA22+/fcPgKiIiklcK+jvP0RSr0Hf48GFiYmJu6zWpqan5UouXl9dN209JSQEuThVzPa+//jrDhw/PfWy1WgkKCsqjKv/LMAxmzZrF9OnTAWjbti0jR47EbC5Wf0VERKQQK6jvPEdVrL7Ro6Ki7F1CrooVKwJw/vx5UlJSrnld37FjxwAIDg6+bjsWiwWLxZI/Rf4tJyeHyZMns2jRIgB69erFwIEDNSWLiIgUqIL4znNkupEjn/j6+uYGv+3bt1+1/+jRo8THxxMcHGzXO3fT09N56623WLRoESaTiZdffplnnnlGgU9ERKSYUejLR4888ggAc+bMuWrfpW2dOnUq0Joud+HCBYYNG8amTZtwdXVl/PjxRERE2K0eERERyT8KfXkgLCyMsLAwjh8/fsX2oUOH4uzszJdffsmmTZtytx84cIB3330Xs9nM0KFDC7pcAI4fP86gQYPYt28f3t7efPzxxzzwwAN2qUVERETyX7G6pi8vdOnShZMnTwJw4sQJAL7++muWLFkCQLly5fjPf/5zxWsu3TySlZV1xfbq1avz4YcfMnz4cJo1a0bbtm1xdXVl2bJlpKWlMWXKFLusu7tv3z5ee+01EhMTKVeuHBMnTtSFsiIiIsWcQt//2L59+1WTJR8/fjz3KN6Nbrq4lmHDhlGlShU+/PBD1q5dC0DDhg0ZOXIkHTt2zJuib8PGjRsZN24cGRkZVKtWjQkTJuDn51fgdYiIiEjBMhmGYdi7CLl1VquVEiVKkJiYeNs3gCxatIiPP/4YwzBo3LgxY8eOxd3dPZ8qFRERuTt3850nV9ORPgdgGAbTp09n1qxZADz88MMMHz5cc/CJiIg4EH3rF3PZ2dlMmjQp95rEPn368PTTT2tKFhEREQej0FeMpaamMnbsWLZs2YKTkxPDhg2zy3WEIiIiYn8KfcXUuXPneO211zhw4AAWi4WxY8dy33332bssERERsROFvmLo6NGjjBw5klOnTuHr68uECRMICwuzd1kiIiJiR5qcuZjZtWsXgwYN4tSpU1SoUIHPP/9cgU9EREQU+oqTtWvX8sorr5CUlERYWBifffYZ5cuXt3dZIiIiUgjo9G4x8csvvzBlyhQMw6BJkyaMHj0aNzc3e5clIiIihYRCXxFnGAbTpk1j9uzZAHTq1Cl3zV8RERGRSxT6irDs7GwmTpzIihUrABgwYAC9e/fWHHwiIiJyFYW+IiolJYWxY8eyfft2nJ2defXVV2nfvr29yxIREZFCSqGviBo5ciTHjh3D3d2dcePG0ahRI3uXJCIiIoWYQl8RFRsbi7+/P++//z5Vq1a1dzkiIiJSyCn0FVEVKlRg8uTJBAQE2LsUERERKQI0T18R9dFHHynwiYiIyC1T6CuifHx87F2CiIiIFCEKfSIiIiIOQKFPRERExAEo9ImIiIg4AIU+EREREQeg0CciIiLiABT6RERERByAQp+IiIiIA1DoExEREXEACn0iIiIiDkChT0RERMQBKPSJiIiIOACFPhEREREHoNAnIiIi4gAU+kREREQcgEKfiIiIiANQ6BMRERFxAAp9IiIiIg5AoU9ERETEAZjtXYDcHsMwALBarXauRERE5NZ5e3tjMpnsXYZDU+grYpKSkgAICgqycyUiIiK3LjExER8fH3uX4dBMxqVDR1Ik2Gw2Tpw4ke//Y7JarQQFBXH06FGH/EfqyP135L6DY/ffkfsO6n9+9/9OvrcMwyApKUlHCfOIjvQVMU5OTgQGBhbY+/n4+Djkh98ljtx/R+47OHb/HbnvoP4Xpv6bTKZCU0txoBs5RERERByAQp+IiIiIA1Dok2uyWCyMGTMGi8Vi71LswpH778h9B8fuvyP3HdR/R++/I9CNHCIiIiIOQEf6RERERByAQp+IiIiIA1Doc3ApKSnMmjWLl156ifDwcCwWCyaTibFjx95VuwsXLqRFixa5t/63bNmSxYsX503ReWz9+vV06NABPz8/vLy8aNy4MTNnzrztdmbMmIHJZLruT8+ePfOh+ptLS0tj9OjRVKtWDTc3N8qXL0///v05fvz4bbd1/vx5hg4dSnBwMBaLheDgYF5++WUuXLiQ94Xnkbzqf6VKlW44vvv27cunHtyZrVu38v777/PYY48RGBiYW+edKmpjn5f9L2pjn5qayi+//MKAAQOoXr06bm5ueHp6UrduXcaPH09ycvJtt1nUxl+uTdf0ObioqCjq169/1fYxY8bccfCbPHkyw4YNw2w206ZNGywWC8uWLSMtLY2pU6cyePDgu6w678ydO5fHH38cm81G8+bNKV26NCtXruTChQu88sorfPTRR7fc1owZM+jXrx9169alXr16V+0PDw/nhRdeyMPqby49PZ1WrVqxadMmypUrR7NmzYiNjWXz5s2UKVOGTZs2ERISckttxcfH06RJEw4ePEhISAgNGzZk9+7d7N69m2rVqrFx40b8/PzyuUe3Jy/7X6lSJeLi4ujbt+8190+YMIFy5crlZfl3pXPnzsyfP/+q7XfykV8Uxz4v+1/Uxv7rr7/mmWeeAaBGjRrUqlULq9XKhg0bSEpKIiwsjNWrV+Pv739L7RXF8ZfrMMShHTx40BgwYIDx5ZdfGlu3bjXGjx9vAMaYMWPuqL19+/YZzs7OhsViMTZs2JC7PSYmxihVqpRhNpuNAwcO5FH1dychIcHw8fExAGPu3Lm520+dOmVUqVLFAIw//vjjltubPn36Xf3u8sObb75pAEaTJk2MpKSk3O2TJk0yAKNFixa33Fbv3r0NwHjssceMrKys3O0vvfSSARh9+/bNw8rzRl72Pzg42ChKH5nvv/++8Y9//MNYsGCBcfLkScNisdxx/UVx7POy/0Vt7GfMmGE8++yzxp49e67YfuLECaN+/foGYDzxxBO33F5RHH+5tqLzt1gKxIQJE+4quLzwwgsGYAwdOvSqfR9//LEBGIMHD767IvPIxIkTDcCIiIi4at+8efMMwOjYseMtt1fYQl9GRoZRokQJAzC2bdt21f46deoYgBEZGXnTtk6cOGE4OTkZrq6uxqlTp67Yl56ebpQpU8ZwdnY2Tp8+nWf136287L9hFL0v/v91p6GnKI79tThS6LuRDRs2GIBhsViMjIyMmz6/uIy/XKRr+iRPXbpur1u3blftu7Rt4cKFBVrT9dyo1kceeQQ3NzdWrFhBenp6QZeWJ9avX09iYiKhoaHXPIV/O+OxZMkSbDYbzZo1o2zZslfss1gsdOrUiZycHH799de8KT4P5GX/HVlRHHu5vrp16wKQkZFBQkLCTZ+v8S9etPau5JkLFy5w5MgRgGt+yQYFBVG6dGni4uKwWq12X08xOjoagAYNGly1z9XVlVq1ahEZGcn+/fupU6fOLbe7detWRowYgdVqJSAggNatW9OiRYs8q/tW3ah/l2/fsWNHnrT17bff3lJbBSUv+3+5Dz/8kEOHDmGxWKhZsyZdunShTJkyd1dsIVYUxz6/FIex/+uvvwBwcXG5pevwNP7Fi0Kf5JlLga9kyZJ4enpe8zmBgYHEx8cTFxdH7dq1C7K8K1itVhITE3NrupbAwEAiIyOJi4u7rdC3aNEiFi1alPt4/PjxtGjRgh9//PGq/ynnp0vjcaP+AcTFxRVoWwUlv2oeOXLkFY+HDRvG1KlT6d+//x1UWfgVxbHPL8Vh7D/99FMA2rdvf0srb2j8ixed3pU8c2kaAA8Pj+s+51IYTEpKKpCarufyKQuuV+/t1lquXDnGjh3L9u3bSUxM5NSpUyxYsCD3TrmOHTuSk5Nz98XfopuNx+30Ly/bKih5XfOjjz7KvHnziIuLIzU1lV27djF8+HAyMjIYOHDgNe8ULQ6K4tjnteIy9r/++ivffPMNLi4uvP3227f0Go1/8aIjfUVcly5d2Lt37229ZubMmTRu3DifKio4ha3v7dq1o127drmPfXx86NSpE61ateLee+8lMjKSn376iSeeeCJf3l/y15QpU654XLNmTSZNmkRYWBjPPvsso0aNIiIiwk7VSX4qDmO/b98+nnzySQzD4MMPP8y9tk8ci0JfEXf48GFiYmJu6zWpqan5UouXl9dN209JSQHA29v7rt/vbvp+qdZL2651fWFe1erl5cWQIUMYPHgwS5cuLbDQd7PxuJ3+5WVbBaWgah4wYABvvfUWMTExxMbGUqlSpbtqr7ApimNfUIrK2B8/fpz27dtz/vx5hg8fztChQ2/5tRr/4kWnd4u4qKgojItT79zyT8uWLfOllooVKwIXZ26/9EHwv44dOwZAcHDwXb/f3fTdx8eHEiVKXFFTftZatWpVAE6ePHnXbd2qS+ORF/3Ly7YKSkHV7OTkRGhoKFCw41tQiuLYF5SiMPbnzp3joYceIi4ujn79+t3WhPOg8S9uFPokz/j6+uZ+QGzfvv2q/UePHiU+Pp7g4GC737kL/526YNu2bVfty8rKYteuXbi5uVGtWrW7fq/z588DXPcGl/xwo/5dvv1WblLJy7YKSkHWbI/xLShFcewLUmEe++TkZB5++GH27NnDY489xrRp0257KTqNf/Gi0Cd56pFHHgFgzpw5V+27tK1Tp04FWtP13KjWRYsWkZ6eTps2bXBzc7vr95o7dy5w/WkP8sP9999PiRIlOHToEFFRUVftv53xaN++PU5OTqxdu5YzZ85csS8jI4OFCxfi7OxMhw4d8qT2vJCX/b+R3bt3ExMTg4eHB2FhYXfVVmFUFMe+oBTmsc/IyCAiIoLNmzfTrl07Zs+ejbOz8223o/EvZgpsGmgpEm51RY7q1asb1atXN44dO3bF9suXYdu4cWPu9v379xeZZdhOnz59w2XYrtf39957zzh79uwV2zIzM42xY8cagOHu7n7Va/LbpWXImjZtaiQnJ+duv94yZFOnTjWqV69uvPbaa1e1dWkppq5du16xFNOQIUMK7VJMedX/xYsXGytXrryq/ejoaKNGjRoGYAwZMiRf+pBXbrYiRXEb+/91p/0vimOfnZ1tdOnSxQCMZs2aGSkpKTd9TXEff7lIoU+Mzp07G+Hh4UZ4eLgRFBRkAEaFChVyt3Xu3Pmq1wAGYBw+fPiqfZeWWzObzcbDDz9sREREGO7u7gZgTJkypQB6dOvmzJljODk5GSaTyWjVqpXRrVs3w9fX1wCM4cOHX/M11+s7fy9tdP/99xs9e/Y0OnToYJQvX94ADDc3tyuCZUFJS0szwsPDDcAoV66c0aNHj9zHZcqUMQ4dOnTF88eMGXPdD/GzZ88aoaGhBmCEhoYajz/+uFGrVi0DMKpWrWokJCQUUK9uXV71/9L24OBg49FHHzV69uxpNG7c2DCbzQZgtGzZ0khNTS3Ant3cokWLcv8Nh4eHGyaTyQCu2LZo0aLc5xe3sc+r/hfFsZ88eXLu51SXLl2Mvn37XvPn8v+kFrfxl2tT6JPcdSWv9xMcHHzVa24U+gzDMBYsWGA0a9bM8PLyMry8vIxmzZoZCxcuzN+O3KF169YZ7du3N3x9fQ0PDw+jYcOGxowZM677/Ov1ffTo0Ubbtm2NihUrGu7u7oabm5tRpUoV47nnnjP27duXz724vtTUVOMf//iHERoaari6uhoBAQHG008/bRw9evSq597og98wLh4dfemll4ygoCDD1dXVCAoKMoYMGWKcP38+fztxF/Ki/xs2bDD69+9v1K5dO/eItZ+fn9GyZUtj2rRpRnZ2dgH15tZdWgv6Rj/Tp0/PfX5xG/u86n9RHPtLfbnZz+WfYcVt/OXaTIZhGLd0HlhEREREiizdyCEiIiLiABT6RERERByAQp+IiIiIA1DoExEREXEACn0iIiIiDkChT0RERMQBKPSJiIiIOACFPhEREREHoNAnIiIi4gAU+kRE8llsbCwmkwmTyUSlSpXsXY6IOCiFPhEREREHoNAnInZRqVKl3KNfsbGx9i7ntlyq22Qy2bsUEZFbptAnIiIi4gAU+kREREQcgEKfiIiIiANQ6BMRERFxAAp9IlJgLp+6JC4uLnd75cqVr7g54tLPqlWrcp9zrZsnoqOjGTp0KLVq1cLPzw+TyUTnzp1z98+YMSP3NU8//fRt1fe/U6usWrXqmjdvXKvuW705JTIykoEDB1KtWjU8PDwoWbIkjRs35r333iMlJeWmrxcRuR1mexcgInInxo4dyzvvvENOTo69S7lthmHk1m+z2XK3p6WlsWXLFrZs2cLXX3/NihUrCAkJsWOlIlKcKPSJSIHx8fFh0KBBAMycOZOkpCQA+vTpg7e391XPr1ChwjXb+fDDDxk3bhwAoaGhNG7cGA8PD2JjY3FxccmX2itUqJBb++eff567/dK2/+Xj43PdtsaNG8f48eMBqFevHrVr18bFxYWoqCi2bdsGwOHDh+ncuTPbtm3DbNZHtYjcPZNhGIa9ixARx1OpUqXcU7yHDx++6UoVl59WNZvNeHp6MmPGjCtO5wJkZGRgsViAi6d3+/XrB0Dfvn2ZMWPGDd8jNjaWypUrAxAcHHzdU7SX13IrH6GXt+vq6kpWVhYhISH88MMPNG7c+Irn/vzzz/Tu3ZusrCwA/v3vf9OnT5+bvoeIyM3omj4RKXJsNhsLFiy4KvABuYGvsMrMzMTPz481a9ZcFfgAunfvztChQ3Mfz549uyDLE5FiTKFPRIqcbt260bx5c3uXccfeeOMNypcvf939/fv3z/3zli1bCqIkEXEACn0iUuT07NnT3iXcle7du99wf1hYGO7u7gAkJCTkXvsoInI3FPpEpMi599577V3CHStRogRBQUE3fI7JZKJkyZK5j61Wa36XJSIOQKFPRIqcMmXK2LuEO1aiRIlbet7ldyFfuqlDRORuKPSJSJFz6dRnUfS/kzuLiBQUhT4Rkb9dPlGyiEhxo9AnIsXW5adIs7Ozb/r8xMTE/CxHRMSuFPpExC4K4jTn5atiJCQk3PT5O3fuzM9yRETsSqFPROzCzc0t98/5daPC5at8REdH33T1jJ9++umW2i2I2kVE8ppCn4jYRalSpXL/fPz48Xx5jxo1auSu6Xvy5EmWLVt23ecuXryYxYsX31K7BVG7iEheU+gTEbuoVatW7p9//vnnfHkPs9lMjx49ch8/88wz7Nmz54rnGIbBrFmz6NGjxy0v4VYQtYuI5DWTcSurhYuI5LHly5fz0EMP5T4ODw+nQYMGeHh45G574YUXCA0NBa68BvB2PrZiY2OpVasWKSkpwMWbO1q0aEFISAhWq5UNGzZw5MgRzGYzX375JQMHDgQgODiY2NjYa7Y5bdo0nn322dy6WrZsSc2aNa8IjW+++WbuBMuxsbFUrlz5pu1erlKlSsTFxQFw+PDhK05Vi4jcCYU+EbGbXr16MXv27Ovu/+OPP2jZsiVw56EPYMmSJXTt2pXU1NRr7vfx8WH69Ok0aNDglsJZVlYWbdq0Yc2aNdd9z8uDmkKfiBQGOr0rInbz/fff8/3339OxY0cCAwOvuEEiL7Vv3559+/YxZMgQqlevjoeHB97e3tSsWZPXXnuNnTt38thjj91yey4uLqxYsYJ//vOftGnThoCAAFxdXfOldhGRvKIjfSIiIiIOQEf6RERERByAQp+IiIiIA1DoExEREXEACn0iIiIiDkChT0RERMQBKPSJiIiIOACFPhEREREHoNAnIiIi4gAU+kREREQcgEKfiIiIiANQ6BMRERFxAAp9IiIiIg5AoU9ERETEASj0iYiIiDgAhT4RERERB6DQJyIiIuIA/h/pKNAitqhlbgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imputed_data_dineof = imputed_data[:,:,:,is_sea.astype(bool)]\n",
    "imputed_dineof = imputed_data_dineof[0][mask.bool().cpu()[0]]\n",
    "imputed_our = imputed_our[:]\n",
    "imputed_dineof = imputed_dineof[:]\n",
    "truth = truth[:]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stat\n",
    "import pandas as pd\n",
    "method = []\n",
    "method.extend(['STIMP' for i in range(imputed_our.shape[0])])\n",
    "method.extend(['DINEOF' for i in range(imputed_our.shape[0])])\n",
    "data = {'truth': np.concatenate([truth.numpy() for i in range(2)]),\n",
    "        'imputed':np.concatenate([imputed_our.numpy(), imputed_dineof]),\n",
    "        'method':method}\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "color =[\"#9F0000\",\"#576fa0\"]\n",
    "g=sns.jointplot(data=data, x=\"truth\", y=\"imputed\", hue=\"method\", kind='kde', palette=color, marginal_kws={'common_norm':False,'shade':True})\n",
    "xpoints = ypoints = plt.xlim()\n",
    "plt.plot(xpoints, ypoints, 'k-', alpha=0.75, zorder=0)\n",
    "plt.ylabel(\"imputed\", size=24)\n",
    "plt.xlabel(\"truth\", size=24)\n",
    "pcc1=stat.pearsonr(truth[:], imputed_our).statistic.item()\n",
    "pcc2=stat.pearsonr(truth[:], imputed_dineof).statistic.item()\n",
    "plt.text(0.40, 0.92, 'PCC={:.4f}'.format(pcc1), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#9F0000\")\n",
    "plt.text(0.40, 0.88, 'PCC={:.4f}'.format(pcc2), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#576fa0\")\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 5d: Missing rate is equal to 0.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "config.missing_ratio=0.5\n",
    "from torch.utils.data import DataLoader\n",
    "test_dloader = DataLoader(PRE8dDataset(config, mode='test'), 1, shuffle=False)\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = next(iter(test_dloader))\n",
    "device = \"cuda\"\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = datas.float().to(device), data_ob_masks.to(device), data_gt_masks.to(device), labels.to(device), label_masks.to(device)\n",
    "\n",
    "model = torch.load(\"./log_bak/imputation/Chesapeake/STIMP/best_0.5.pt\")\n",
    "model = model.to(device)\n",
    "cond_mask = data_gt_masks\n",
    "imputed_data_our = model.impute(datas, cond_mask, adj, 10)\n",
    "imputed_data_our = imputed_data_our.median(1).values\n",
    "mask = data_ob_masks - cond_mask\n",
    "imputed_our = imputed_data_our[0][mask.bool().cpu()[0]]\n",
    "truth = datas[0][mask.bool()[0]].cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-05-08 16:24:09.692\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 159: 0.0017314578872174025, 9.730109013617039e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:09.819\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 140: 0.0013141802046447992, 9.925337508320808e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:09.946\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 139: 0.0011402089148759842, 9.956886060535908e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:09.998\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 58: 0.0002077865501632914, 9.97736060526222e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:10.126\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 140: 0.0019777193665504456, 9.919516742229462e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:10.254\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 141: 0.0016729923663660884, 9.933137334883213e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:10.360\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 116: 0.0013833654811605811, 9.932555258274078e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:10.491\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 144: 0.0015701536322012544, 9.930459782481194e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:10.642\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 167: 0.0014423009706661105, 9.912881068885326e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:10.762\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 132: 0.0015777467051520944, 9.909039363265038e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:10.869\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 117: 0.0018124505877494812, 9.889365173876286e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:10.979\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 121: 0.0015570628456771374, 9.691575542092323e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:11.106\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 139: 0.001378058921545744, 9.941053576767445e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:11.216\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 119: 0.0011673335684463382, 9.831972420215607e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:11.339\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 134: 0.0014079093234613538, 9.920448064804077e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:11.457\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 130: 0.0012561194598674774, 9.982846677303314e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:11.575\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 129: 0.001343117794021964, 9.854324162006378e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:11.682\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 117: 0.0012106687063351274, 9.76806040853262e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:11.797\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 125: 0.0013469601981341839, 9.960262104868889e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:11.902\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 113: 0.0010996171040460467, 9.777722880244255e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:12.003\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 109: 0.0007880739867687225, 9.949260856956244e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:12.123\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 130: 0.0011422740062698722, 9.816372767090797e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:12.232\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 117: 0.0012993597192689776, 9.9564203992486e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:12.344\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 123: 0.001426519243977964, 9.90019179880619e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:12.448\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 114: 0.0014031181344762444, 9.758980013430119e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:12.573\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 137: 0.001387285185046494, 9.922194294631481e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:12.676\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 113: 0.0018661736976355314, 9.951181709766388e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:12.819\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 159: 0.0016807927750051022, 9.940355084836483e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:12.955\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 150: 0.0015708176651969552, 9.777606464922428e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:13.033\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 86: 0.0006294612539932132, 9.950948879122734e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:13.180\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 163: 0.0015713460743427277, 9.724986739456654e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:13.322\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 156: 0.0016466426895931363, 9.843846783041954e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:13.441\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 130: 0.0014522622805088758, 9.742914699018002e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:13.613\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 191: 0.0015897249104455113, 9.904615581035614e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:13.743\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 143: 0.0013609019806608558, 9.95676964521408e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:13.860\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 129: 0.0019183253170922399, 9.921728633344173e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:13.974\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 125: 0.0013899278128519654, 9.924056939780712e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:14.109\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 149: 0.0014694456476718187, 9.860144928097725e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:14.242\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 147: 0.001763472449965775, 9.818701073527336e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:14.367\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 138: 0.0018544064369052649, 9.933952242136002e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:14.479\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 122: 0.0014743668725714087, 9.922659955918789e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:14.583\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 113: 0.0006675503682345152, 9.782204870134592e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:14.745\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 162: 0.0015250594587996602, 9.976094588637352e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:14.860\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 122: 0.0016461563063785434, 9.995768778026104e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:15.057\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 219: 0.001722136395983398, 9.750830940902233e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:24:15.173\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 127: 0.0015540922759100795, 9.79006290435791e-06\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from einops import rearrange\n",
    "from model.dineof_per_step import DINEOF\n",
    "is_sea = np.load(\"./data/Chesapeake/is_sea.npy\")\n",
    "H, W = is_sea.shape\n",
    "model = DINEOF(10, [H, W], keep_non_negative_only=False)\n",
    "datas_image = torch.zeros(1,46,1,H,W)\n",
    "datas_image = datas_image.to(device)\n",
    "datas_image[:,:,:,is_sea.astype(bool)]=datas\n",
    "cond_mask_image = torch.zeros(1,46,1,H,W)\n",
    "cond_mask_image = cond_mask_image.to(device)\n",
    "cond_mask_image[:,:,:,is_sea.astype(bool)]=cond_mask\n",
    "ob_mask_image = torch.zeros(1,46,1,H,W)\n",
    "ob_mask_image = ob_mask_image.to(device)\n",
    "ob_mask_image[:,:,:,is_sea.astype(bool)]=data_ob_masks\n",
    "\n",
    "impute_data_list = []\n",
    "for t in range(datas_image.shape[1]):\n",
    "    data = datas_image[:, t, :, :, :].squeeze()\n",
    "\n",
    "    tmp_data = torch.where(cond_mask_image[:,t].cpu().squeeze()==0, float(\"nan\"), data.cpu())\n",
    "    model.fit(tmp_data.numpy())\n",
    "\n",
    "    imputed_data = model.predict()\n",
    "    imputed_data = rearrange(imputed_data, \"(b t c h) w->b t c h w\", b=1, t=1, c=1, h=data.shape[-2], w=data.shape[-1])\n",
    "    impute_data_list.append(torch.from_numpy(imputed_data))\n",
    "\n",
    "imputed_data = torch.cat(impute_data_list, dim=1).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ,  2.5]),\n",
       " [Text(0, -1.5, '−1.5'),\n",
       "  Text(0, -1.0, '−1.0'),\n",
       "  Text(0, -0.5, '−0.5'),\n",
       "  Text(0, 0.0, '0.0'),\n",
       "  Text(0, 0.5, '0.5'),\n",
       "  Text(0, 1.0, '1.0'),\n",
       "  Text(0, 1.5, '1.5'),\n",
       "  Text(0, 2.0, '2.0'),\n",
       "  Text(0, 2.5, '2.5')])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imputed_data_dineof = imputed_data[:,:,:,is_sea.astype(bool)]\n",
    "imputed_dineof = imputed_data_dineof[0][mask.bool().cpu()[0]]\n",
    "imputed_our = imputed_our[:]\n",
    "imputed_dineof = imputed_dineof[:]\n",
    "truth = truth[:]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stat\n",
    "import pandas as pd\n",
    "method = []\n",
    "method.extend(['STIMP' for i in range(imputed_our.shape[0])])\n",
    "method.extend(['DINEOF' for i in range(imputed_our.shape[0])])\n",
    "data = {'truth': np.concatenate([truth.numpy() for i in range(2)]),\n",
    "        'imputed':np.concatenate([imputed_our.numpy(), imputed_dineof]),\n",
    "        'method':method}\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "color =[\"#9F0000\",\"#576fa0\"]\n",
    "g=sns.jointplot(data=data, x=\"truth\", y=\"imputed\", hue=\"method\", kind='kde', palette=color, marginal_kws={'common_norm':False,'shade':True})\n",
    "xpoints = ypoints = plt.xlim()\n",
    "plt.plot(xpoints, ypoints, 'k-', alpha=0.75, zorder=0)\n",
    "plt.ylabel(\"imputed\", size=24)\n",
    "plt.xlabel(\"truth\", size=24)\n",
    "pcc1=stat.pearsonr(truth[:], imputed_our).statistic.item()\n",
    "pcc2=stat.pearsonr(truth[:], imputed_dineof).statistic.item()\n",
    "plt.text(0.40, 0.92, 'PCC={:.4f}'.format(pcc1), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#9F0000\")\n",
    "plt.text(0.40, 0.88, 'PCC={:.4f}'.format(pcc2), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#576fa0\")\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fig 5d: Missing rate is equal to 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "config.missing_ratio=0.9\n",
    "from torch.utils.data import DataLoader\n",
    "test_dloader = DataLoader(PRE8dDataset(config, mode='test'), 1, shuffle=False)\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = next(iter(test_dloader))\n",
    "device = \"cuda\"\n",
    "datas, data_ob_masks, data_gt_masks, labels, label_masks = datas.float().to(device), data_ob_masks.to(device), data_gt_masks.to(device), labels.to(device), label_masks.to(device)\n",
    "\n",
    "model = torch.load(\"./log_bak/imputation/Chesapeake/STIMP/best_0.9.pt\")\n",
    "model = model.to(device)\n",
    "cond_mask = data_gt_masks\n",
    "imputed_data_our = model.impute(datas, cond_mask, adj, 10)\n",
    "imputed_data_our = imputed_data_our.median(1).values\n",
    "mask = data_ob_masks - cond_mask\n",
    "imputed_our = imputed_data_our[0][mask.bool().cpu()[0]]\n",
    "truth = datas[0][mask.bool()[0]].cpu()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m2025-05-08 16:25:46.860\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 60: 0.00013774332182947546, 9.50467074289918e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:46.913\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 56: 0.0001352647814201191, 9.51731635723263e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:46.937\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 22: 0.007202259264886379, 5.593523383140564e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:46.961\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 26: 4.797338624484837e-05, 8.481751137878746e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.023\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 67: 0.00022221436665859073, 9.723269613459706e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.087\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 69: 0.00014980521518737078, 9.589522960595787e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.189\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 107: 0.00020429532742127776, 9.966766810975969e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.251\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 67: 0.0001600992400199175, 9.530049283057451e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.309\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 62: 0.0003824131563305855, 9.706564014777541e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.385\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 79: 0.0004236020031385124, 9.884592145681381e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.469\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 91: 0.0003311572945676744, 9.79736796580255e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.538\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 74: 0.0002372031012782827, 9.232040611095726e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.598\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 65: 0.00016072219295892864, 9.979601600207388e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.663\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 70: 0.00023136925301514566, 9.905328624881804e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.725\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 66: 0.0001833214337239042, 9.494164260104299e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.779\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 56: 0.00014705469948239625, 9.364623110741377e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.830\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 55: 0.00015558071027044207, 9.921423043124378e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.882\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 55: 0.0002287646639160812, 9.493494872003794e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:47.947\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 70: 0.00015768100274726748, 9.70863038673997e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.001\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 57: 0.0002158077695639804, 9.82517667580396e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.046\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 47: 0.00011789263953687623, 9.304123523179442e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.096\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 54: 0.00015408174658659846, 9.693350875750184e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.153\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 59: 0.0001644960866542533, 9.591953130438924e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.206\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 56: 0.0001693888334557414, 9.87284875009209e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.258\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 56: 0.0001659023982938379, 9.27771907299757e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.323\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 70: 0.00017116148956120014, 9.291557944379747e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.415\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 101: 0.00021653684962075204, 9.828043403103948e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.467\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 56: 0.00018471057410351932, 9.786526788957417e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.515\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 52: 0.00028928351821377873, 8.90000956133008e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.558\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 43: 0.000747488287743181, 8.304486982524395e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.613\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 59: 0.0001940839138114825, 9.864699677564204e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.675\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 67: 0.00014330112026073039, 9.880095603875816e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.737\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 65: 0.00018797042139340192, 9.63297497946769e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.795\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 63: 0.0002210592938354239, 9.56872827373445e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.859\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 68: 0.00037349379272200167, 9.214330930262804e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.923\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 65: 0.00015307463763747364, 9.705196134746075e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:48.976\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 57: 0.000520966190379113, 9.903335012495518e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:49.033\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 61: 0.00022527057444676757, 9.968134691007435e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:49.093\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 65: 0.00016766160842962563, 9.550771210342646e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:49.161\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 73: 0.0003789346374105662, 9.836279787123203e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:49.225\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 68: 0.00023893857724033296, 9.76253068074584e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:49.288\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 70: 3.1005638447823e-05, 7.696908141952008e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:49.342\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 59: 0.00017916885553859174, 9.662500815466046e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:49.396\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 57: 0.00033074163366109133, 9.978772141039371e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:49.452\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 61: 0.00014282879419624805, 9.454699466004968e-06\u001b[0m\n",
      "\u001b[32m2025-05-08 16:25:49.513\u001b[0m | \u001b[1mINFO    \u001b[0m | \u001b[36mmodel.dineof_per_step\u001b[0m:\u001b[36m_fit\u001b[0m:\u001b[36m102\u001b[0m - \u001b[1mError/Relative Error at iteraion 66: 0.00012892748054582626, 9.723647963255644e-06\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "from einops import rearrange\n",
    "from model.dineof_per_step import DINEOF\n",
    "is_sea = np.load(\"./data/Chesapeake/is_sea.npy\")\n",
    "H, W = is_sea.shape\n",
    "model = DINEOF(10, [H, W], keep_non_negative_only=False)\n",
    "datas_image = torch.zeros(1,46,1,H,W)\n",
    "datas_image = datas_image.to(device)\n",
    "datas_image[:,:,:,is_sea.astype(bool)]=datas\n",
    "cond_mask_image = torch.zeros(1,46,1,H,W)\n",
    "cond_mask_image = cond_mask_image.to(device)\n",
    "cond_mask_image[:,:,:,is_sea.astype(bool)]=cond_mask\n",
    "ob_mask_image = torch.zeros(1,46,1,H,W)\n",
    "ob_mask_image = ob_mask_image.to(device)\n",
    "ob_mask_image[:,:,:,is_sea.astype(bool)]=data_ob_masks\n",
    "\n",
    "impute_data_list = []\n",
    "for t in range(datas_image.shape[1]):\n",
    "    data = datas_image[:, t, :, :, :].squeeze()\n",
    "\n",
    "    tmp_data = torch.where(cond_mask_image[:,t].cpu().squeeze()==0, float(\"nan\"), data.cpu())\n",
    "    model.fit(tmp_data.numpy())\n",
    "\n",
    "    imputed_data = model.predict()\n",
    "    imputed_data = rearrange(imputed_data, \"(b t c h) w->b t c h w\", b=1, t=1, c=1, h=data.shape[-2], w=data.shape[-1])\n",
    "    impute_data_list.append(torch.from_numpy(imputed_data))\n",
    "\n",
    "imputed_data = torch.cat(impute_data_list, dim=1).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-1.5, -1. , -0.5,  0. ,  0.5,  1. ,  1.5,  2. ,  2.5]),\n",
       " [Text(0, -1.5, '−1.5'),\n",
       "  Text(0, -1.0, '−1.0'),\n",
       "  Text(0, -0.5, '−0.5'),\n",
       "  Text(0, 0.0, '0.0'),\n",
       "  Text(0, 0.5, '0.5'),\n",
       "  Text(0, 1.0, '1.0'),\n",
       "  Text(0, 1.5, '1.5'),\n",
       "  Text(0, 2.0, '2.0'),\n",
       "  Text(0, 2.5, '2.5')])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "imputed_data_dineof = imputed_data[:,:,:,is_sea.astype(bool)]\n",
    "imputed_dineof = imputed_data_dineof[0][mask.bool().cpu()[0]]\n",
    "imputed_our = imputed_our[:]\n",
    "imputed_dineof = imputed_dineof[:]\n",
    "truth = truth[:]\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stat\n",
    "import pandas as pd\n",
    "method = []\n",
    "method.extend(['STIMP' for i in range(imputed_our.shape[0])])\n",
    "method.extend(['DINEOF' for i in range(imputed_our.shape[0])])\n",
    "data = {'truth': np.concatenate([truth.numpy() for i in range(2)]),\n",
    "        'imputed':np.concatenate([imputed_our.numpy(), imputed_dineof]),\n",
    "        'method':method}\n",
    "data = pd.DataFrame.from_dict(data)\n",
    "color =[\"#9F0000\",\"#576fa0\"]\n",
    "g=sns.jointplot(data=data, x=\"truth\", y=\"imputed\", hue=\"method\", kind='kde', palette=color, marginal_kws={'common_norm':False,'shade':True})\n",
    "xpoints = ypoints = plt.xlim()\n",
    "plt.plot(xpoints, ypoints, 'k-', alpha=0.75, zorder=0)\n",
    "plt.ylabel(\"imputed\", size=24)\n",
    "plt.xlabel(\"truth\", size=24)\n",
    "pcc1=stat.pearsonr(truth[:], imputed_our).statistic.item()\n",
    "pcc2=stat.pearsonr(truth[:], imputed_dineof).statistic.item()\n",
    "plt.text(0.40, 0.92, 'PCC={:.4f}'.format(pcc1), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#9F0000\")\n",
    "plt.text(0.40, 0.88, 'PCC={:.4f}'.format(pcc2), transform=plt.gca().transAxes, fontsize=12, verticalalignment='top', horizontalalignment='left', color=\"#576fa0\")\n",
    "plt.xticks(fontsize=15)\n",
    "plt.yticks(fontsize=15)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "torch",
   "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": "3.9.19"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}