SiameseFWI_overethrust.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1e57b362",
   "metadata": {},
   "source": [
    "## Setting Up the Libs and Overthrust Configurations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c9c04f73",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sadalyom/.conda/envs/FWIGAN/lib/python3.8/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import time\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import scipy.ndimage\n",
    "import matplotlib\n",
    "#matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "import deepwave\n",
    "import os\n",
    "from timeit import default_timer as timer\n",
    "import scipy.io as spio\n",
    "import math\n",
    "import numpy as np\n",
    "from utils.utils import *\n",
    "from torch import optim\n",
    "from IPython.core.debugger import set_trace \n",
    "import random\n",
    "torch.manual_seed(0)\n",
    "random.seed(0)\n",
    "\n",
    "####################################################\n",
    "####   MAIN PARAMETERS FOR FORWARD MODELING         ####\n",
    "####################################################\n",
    "\n",
    "peak_freq = 5.                    # central frequency\n",
    "peak_source_time = 1 / peak_freq  # the time (in secs) of the peak amplitude\n",
    "dx        = 30.0                  # step interval along x/z direction\n",
    "dx1        = 30.0                  # step interval along y/z direction\n",
    "\n",
    "dt        = 0.003                 # time interval (e.g., 3ms)\n",
    "num_dims  = 2                     # dimension of velocity model\n",
    "nz        = 94                   # model shape of z dimension (depth) Overthrust\n",
    "ny        = 400                   # model shape of y dimension Overthrust\n",
    "vmodel_dim = np.array([nz, ny])\n",
    "\n",
    "total_t   = 6.                      # totle sampling time (unit: s)\n",
    "nt        = int(total_t / dt)       # number of time sampling points\n",
    "num_shots = 30                      # nunmber of shots\n",
    "num_sources_per_shot = 1            # number of sources per shot\n",
    "num_receivers_per_shot = 567        # number of receivers per shot\n",
    "source_spacing = np.floor(dx * ny / (num_shots+1))                         # space intervel between neigbouring sources\n",
    "receiver_spacing = np.floor(dx1 * ny / (num_receivers_per_shot +1))         # space intervel between neigbouring receivers\n",
    "source_depth = 0                # the index depth of sources, default is 0\n",
    "receiver_depth = 0              # the index depths of receivers, default is 0\n",
    "order          = 8              # precision order of finite difference in space domain\n",
    "pml_width  = [0,10,10,10,0,0]   # pml padding width for boundary (0 for free surface and 10 for absorbing boundary)\n",
    "\n",
    "fwi_lr            = 80        # learning rate for updating the model.\n",
    "fwi_batch         =   30      # number of batches for FWI.\n",
    "fwi_num_epochs    = 600       # number of FWI iteration.\n",
    "sim_lr            = 1*1e-4    # learning rate for Soamese network.\n",
    "survey_pad = None\n",
    "savepoch = 50                 # Saving models each 50 FWI iterations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "546faf4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.21, 0.387)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "receiver_spacing/100, source_spacing/1000"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda360bc",
   "metadata": {},
   "source": [
    "##   Check Cuda Device \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "aaccc70f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.backends.cudnn.enabled = True\n",
    "torch.backends.cudnn.benchmark = True\n",
    "\n",
    "cuda_available = torch.cuda.is_available()\n",
    "device = torch.device(\"cuda\" if cuda_available else \"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6665e0c",
   "metadata": {},
   "source": [
    "## Creating Data Pathes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "744069ea",
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "data_path = 'data/over_94_400.bin'\n",
    "initfile = 'data/over_initmodel.mat'\n",
    "initsafile = 'data/over_initsource.mat'\n",
    "fwi_result = 'results/overthrust/'\n",
    "# Loading the true model\n",
    "model_true = loadtruemodel(data_path, num_dims, vmodel_dim).to(device)\n",
    "model, model_init = loadinitmodel(initfile,device)\n",
    "# The model will be updated during FWI\n",
    "model = torch.nn.Parameter(model)\n",
    "# Creating source and reciver locations\n",
    "x_s, x_r = createSR(num_shots, num_sources_per_shot, num_receivers_per_shot, num_dims, source_spacing, receiver_spacing,source_depth,receiver_depth)\n",
    "x_s, x_r = x_s.to(device), x_r.to(device)\n",
    "# Source Amplitude\n",
    "source_amplitudes_init, source_amplitudes_true = loadinitsource(initsafile,device)\n",
    "source = source_amplitudes_init.repeat(1,num_shots, num_sources_per_shot)\n",
    "# receiver Amplitude\n",
    "receiver_amplitudes = createdata(model_true,dx,source, \\\n",
    "                                x_s,x_r,dt,pml_width,order, \\\n",
    "                                 survey_pad,device).to(device)\n",
    "receiver_amplitudes_true = receiver_amplitudes\n",
    "rcv_amps_true = receiver_amplitudes_true.clone()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f7c4c84",
   "metadata": {},
   "source": [
    "## Plotting the inital and true model\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "10af97f6",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2160x936 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MaxNLocator\n",
    "import matplotlib.pyplot as plt\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 18}\n",
    "\n",
    "y = np.array([0, 1, 2, 3])\n",
    "box_min = 2360\n",
    "box_max = 6000\n",
    "\n",
    "f, ax = plt.subplots(1,2,figsize=(30, 13))\n",
    "plt.rc('font', **font)\n",
    "\n",
    "\n",
    "im = ax[0].imshow(model_init, cmap='jet', vmin=box_min, vmax=box_max, \n",
    "                extent=[0, model_init.shape[1] * dx / 1000, model_init.shape[0] * dx1 / 1000, 0])\n",
    "ax[0].set_xlabel('Position (km)',fontsize='large', fontweight='bold')\n",
    "ax[0].set_ylabel('Depth (km)',fontsize='large', fontweight='bold')\n",
    "ax[0].set_title('Initial_Model',fontsize='large', fontweight='bold')\n",
    "ax[0].text(0, -0.15, 'b)', fontsize = 22) \n",
    "ax[0].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax[0].set_yticks(np.arange(y.min(), y.max(), 1)) \n",
    "\n",
    "model1 = model_true.detach().cpu().numpy()\n",
    "im = ax[1].imshow(model1, cmap='jet', vmin=box_min, vmax=box_max, \n",
    "                extent=[0, model1.shape[1] * dx / 1000, model1.shape[0] * dx1 / 1000, 0])\n",
    "ax[1].set_xlabel('Position (km)',fontsize='large', fontweight='bold')\n",
    "ax[1].set_ylabel('Depth (km)',fontsize='large', fontweight='bold')\n",
    "ax[1].set_title('True_Model',fontsize='large', fontweight='bold')\n",
    "ax[1].text(0, -0.15, 'b)', fontsize = 22) \n",
    "ax[1].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax[1].set_yticks(np.arange(y.min(), y.max(), 1)) \n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f248883",
   "metadata": {},
   "source": [
    "## Setting Up the Siamese and Optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dfc2a6df",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of netD params: 565\n",
      "----------------------------------------------------------------\n",
      "        Layer (type)               Output Shape         Param #\n",
      "================================================================\n",
      "            Conv2d-1         [-1, 1, 2000, 567]              10\n",
      "         LeakyReLU-2         [-1, 1, 2000, 567]               0\n",
      "            Conv2d-3         [-1, 2, 2000, 567]              20\n",
      "         LeakyReLU-4         [-1, 2, 2000, 567]               0\n",
      "            Conv2d-5         [-1, 2, 2000, 567]              20\n",
      "         LeakyReLU-6         [-1, 2, 2000, 567]               0\n",
      "            Conv2d-7         [-1, 4, 2000, 567]              40\n",
      "         LeakyReLU-8         [-1, 4, 2000, 567]               0\n",
      "            Conv2d-9         [-1, 4, 2000, 567]              40\n",
      "        LeakyReLU-10         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-11         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-12         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-13         [-1, 1, 2000, 567]              10\n",
      "        LeakyReLU-14         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-15         [-1, 1, 2000, 567]              10\n",
      "           Conv2d-16         [-1, 1, 2000, 567]              10\n",
      "        LeakyReLU-17         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-18         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-19         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-20         [-1, 2, 2000, 567]              38\n",
      "        LeakyReLU-21         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-22         [-1, 4, 2000, 567]              76\n",
      "        LeakyReLU-23         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-24         [-1, 4, 2000, 567]             148\n",
      "        LeakyReLU-25         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-26         [-1, 2, 2000, 567]              74\n",
      "        LeakyReLU-27         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-28         [-1, 1, 2000, 567]              19\n",
      "        LeakyReLU-29         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-30         [-1, 1, 2000, 567]              10\n",
      "           Conv2d-31         [-1, 1, 2000, 567]              10\n",
      "        LeakyReLU-32         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-33         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-34         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-35         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-36         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-37         [-1, 4, 2000, 567]              40\n",
      "        LeakyReLU-38         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-39         [-1, 4, 2000, 567]              40\n",
      "        LeakyReLU-40         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-41         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-42         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-43         [-1, 1, 2000, 567]              10\n",
      "        LeakyReLU-44         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-45         [-1, 1, 2000, 567]              10\n",
      "           Conv2d-46         [-1, 1, 2000, 567]              10\n",
      "        LeakyReLU-47         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-48         [-1, 2, 2000, 567]              20\n",
      "        LeakyReLU-49         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-50         [-1, 2, 2000, 567]              38\n",
      "        LeakyReLU-51         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-52         [-1, 4, 2000, 567]              76\n",
      "        LeakyReLU-53         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-54         [-1, 4, 2000, 567]             148\n",
      "        LeakyReLU-55         [-1, 4, 2000, 567]               0\n",
      "           Conv2d-56         [-1, 2, 2000, 567]              74\n",
      "        LeakyReLU-57         [-1, 2, 2000, 567]               0\n",
      "           Conv2d-58         [-1, 1, 2000, 567]              19\n",
      "        LeakyReLU-59         [-1, 1, 2000, 567]               0\n",
      "           Conv2d-60         [-1, 1, 2000, 567]              10\n",
      "================================================================\n",
      "Total params: 1,130\n",
      "Trainable params: 1,130\n",
      "Non-trainable params: 0\n",
      "----------------------------------------------------------------\n",
      "Input size (MB): 4905532.84\n",
      "Forward/backward pass size (MB): 1142.03\n",
      "Params size (MB): 0.00\n",
      "Estimated Total Size (MB): 4906674.87\n",
      "----------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "from torchsummary import summary\n",
    "from Model.Module22 import *\n",
    "import  torch\n",
    "\n",
    "\n",
    "#Learning Schedule\n",
    "def lr_lambda(epoch):\n",
    "    return 1/2\n",
    "\n",
    "\n",
    "### optimizer for the velocity model\n",
    "optimizer = optim.Adam([{'params': model, 'lr':fwi_lr, 'betas':(0.5, 0.99), 'eps':1e-8,\n",
    "                          'weight_decay':0}])\n",
    "\n",
    "#Best\n",
    "#leak_value = 0.0001\n",
    "leak_value = 0.00001\n",
    "D1 = 1\n",
    "netD = SiameseNetwork(D1)\n",
    "### init the hyper-parameters of netD ###\n",
    "netD = netD.apply(lambda m: weights_init(m, leak_value))\n",
    "netD = netD.to(device)\n",
    "\n",
    "\n",
    "# Optimizer for Siamese network.\n",
    "optim_s = optim.Adam(netD.parameters(),lr=sim_lr,betas=(0.5, 0.99), \\\n",
    "                    eps=1e-6, weight_decay=0)\n",
    "\n",
    "scheduler = torch.optim.lr_scheduler.MultiplicativeLR(optimizer, lr_lambda=lr_lambda)\n",
    "\n",
    "\"\"\"Compute number of parameters\"\"\"\n",
    "s  = sum(np.prod(list(p.size())) for p in netD.parameters())\n",
    "print ('Number of netD params: %d' % s)\n",
    "\n",
    "summary(netD,[(1,nt,num_receivers_per_shot),(1,nt,num_receivers_per_shot)])\n",
    "\n",
    "\n",
    "############## Init  ################# \n",
    "SNR = 0.0\n",
    "SSIM = 0.0\n",
    "Loss = 0.0\n",
    "ERROR = 0.0\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b616a63",
   "metadata": {},
   "source": [
    "## Main SiameseFWI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "24d27e4c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 Loss:  0.2036917621890704 lr_DL: 0.0001 lr_vel: 80 SNR: 18.10200829611988 SSIM: 0.028737321\n",
      "Epoch: 2 Loss:  0.18014706720908483 lr_DL: 0.0001 lr_vel: 80 SNR: 17.76579342912775 SSIM: 0.032513935\n",
      "Epoch: 3 Loss:  0.16953086679180462 lr_DL: 0.0001 lr_vel: 80 SNR: 17.65139744558985 SSIM: 0.052314367\n",
      "Epoch: 4 Loss:  0.1560403933127721 lr_DL: 0.0001 lr_vel: 80 SNR: 17.48098996568501 SSIM: 0.06267461\n",
      "Epoch: 5 Loss:  0.14644793992241223 lr_DL: 0.0001 lr_vel: 80 SNR: 17.470339802693285 SSIM: 0.07582798\n",
      "Epoch: 6 Loss:  0.1310779981315136 lr_DL: 0.0001 lr_vel: 80 SNR: 17.432258186965033 SSIM: 0.0824697\n",
      "Epoch: 7 Loss:  0.11185746118426323 lr_DL: 0.0001 lr_vel: 80 SNR: 17.476110839712515 SSIM: 0.099169\n",
      "Epoch: 8 Loss:  0.09099734698732694 lr_DL: 0.0001 lr_vel: 80 SNR: 17.555430124404985 SSIM: 0.106691256\n",
      "Epoch: 9 Loss:  0.060707089677453044 lr_DL: 0.0001 lr_vel: 80 SNR: 17.52051869131465 SSIM: 0.11265321\n",
      "Epoch: 10 Loss:  0.03326339609920979 lr_DL: 0.0001 lr_vel: 80 SNR: 17.535128468165762 SSIM: 0.12178779\n",
      "Epoch: 11 Loss:  0.02878266219049692 lr_DL: 0.0001 lr_vel: 80 SNR: 17.640064592920886 SSIM: 0.13230468\n",
      "Epoch: 12 Loss:  0.027034993221362432 lr_DL: 0.0001 lr_vel: 80 SNR: 17.68599106050687 SSIM: 0.13834512\n",
      "Epoch: 13 Loss:  0.02628642451018095 lr_DL: 0.0001 lr_vel: 80 SNR: 17.770763972893878 SSIM: 0.14554274\n",
      "Epoch: 14 Loss:  0.025263176299631596 lr_DL: 0.0001 lr_vel: 80 SNR: 17.834231105060688 SSIM: 0.15298574\n",
      "Epoch: 15 Loss:  0.024654006337126097 lr_DL: 0.0001 lr_vel: 80 SNR: 17.875501192580195 SSIM: 0.15899622\n",
      "Epoch: 16 Loss:  0.023470313722888627 lr_DL: 0.0001 lr_vel: 80 SNR: 17.932620185923188 SSIM: 0.1671531\n",
      "Epoch: 17 Loss:  0.022742516795794167 lr_DL: 0.0001 lr_vel: 80 SNR: 18.003443839001022 SSIM: 0.17211302\n",
      "Epoch: 18 Loss:  0.021294535789638757 lr_DL: 0.0001 lr_vel: 80 SNR: 18.04139493427294 SSIM: 0.18014418\n",
      "Epoch: 19 Loss:  0.02008835437397162 lr_DL: 0.0001 lr_vel: 80 SNR: 18.13472561308028 SSIM: 0.18816216\n",
      "Epoch: 20 Loss:  0.01942090786372622 lr_DL: 0.0001 lr_vel: 80 SNR: 18.188227159489998 SSIM: 0.19433482\n",
      "Epoch: 21 Loss:  0.017621306329965593 lr_DL: 0.0001 lr_vel: 80 SNR: 18.215521657300588 SSIM: 0.19906154\n",
      "Epoch: 22 Loss:  0.016450659651309252 lr_DL: 0.0001 lr_vel: 80 SNR: 18.299734110766572 SSIM: 0.2079932\n",
      "Epoch: 23 Loss:  0.015723201259970666 lr_DL: 0.0001 lr_vel: 80 SNR: 18.349169396698755 SSIM: 0.20876683\n",
      "Epoch: 24 Loss:  0.014834180536369482 lr_DL: 0.0001 lr_vel: 80 SNR: 18.422744650191586 SSIM: 0.21485613\n",
      "Epoch: 25 Loss:  0.013787574134767056 lr_DL: 0.0001 lr_vel: 80 SNR: 18.4547841288466 SSIM: 0.2204061\n",
      "Epoch: 26 Loss:  0.012592505291104316 lr_DL: 0.0001 lr_vel: 80 SNR: 18.495066271638745 SSIM: 0.22529453\n",
      "Epoch: 27 Loss:  0.012318756524473428 lr_DL: 0.0001 lr_vel: 80 SNR: 18.56011826567434 SSIM: 0.22996698\n",
      "Epoch: 28 Loss:  0.011782085988670588 lr_DL: 0.0001 lr_vel: 80 SNR: 18.601786221461808 SSIM: 0.2355495\n",
      "Epoch: 29 Loss:  0.011070037245129545 lr_DL: 0.0001 lr_vel: 80 SNR: 18.66644867648806 SSIM: 0.24247062\n",
      "Epoch: 30 Loss:  0.010616122993330162 lr_DL: 0.0001 lr_vel: 80 SNR: 18.71045916704853 SSIM: 0.24616423\n",
      "Epoch: 31 Loss:  0.0103563383842508 lr_DL: 0.0001 lr_vel: 80 SNR: 18.756775311710328 SSIM: 0.24891241\n",
      "Epoch: 32 Loss:  0.00977400663929681 lr_DL: 0.0001 lr_vel: 80 SNR: 18.82120640366807 SSIM: 0.25631422\n",
      "Epoch: 33 Loss:  0.00959957226489981 lr_DL: 0.0001 lr_vel: 80 SNR: 18.890308384342433 SSIM: 0.25923878\n",
      "Epoch: 34 Loss:  0.009748023655265569 lr_DL: 0.0001 lr_vel: 80 SNR: 18.926787758328928 SSIM: 0.2656825\n",
      "Epoch: 35 Loss:  0.00926486444659531 lr_DL: 0.0001 lr_vel: 80 SNR: 18.988867761374536 SSIM: 0.2684973\n",
      "Epoch: 36 Loss:  0.009136083846290907 lr_DL: 0.0001 lr_vel: 80 SNR: 19.047894988441453 SSIM: 0.27394104\n",
      "Epoch: 37 Loss:  0.009213833852360647 lr_DL: 0.0001 lr_vel: 80 SNR: 19.117671215782938 SSIM: 0.27632216\n",
      "Epoch: 38 Loss:  0.009130508545786143 lr_DL: 0.0001 lr_vel: 80 SNR: 19.169592293536525 SSIM: 0.28348643\n",
      "Epoch: 39 Loss:  0.008597091445699334 lr_DL: 0.0001 lr_vel: 80 SNR: 19.216735530114804 SSIM: 0.28917512\n",
      "Epoch: 40 Loss:  0.008511158792922894 lr_DL: 0.0001 lr_vel: 80 SNR: 19.282074130080197 SSIM: 0.295187\n",
      "Epoch: 41 Loss:  0.008213432629903157 lr_DL: 0.0001 lr_vel: 80 SNR: 19.298156358293284 SSIM: 0.2999877\n",
      "Epoch: 42 Loss:  0.008398157187427084 lr_DL: 0.0001 lr_vel: 80 SNR: 19.373885182480045 SSIM: 0.30234334\n",
      "Epoch: 43 Loss:  0.007952384852493803 lr_DL: 0.0001 lr_vel: 80 SNR: 19.42761174360983 SSIM: 0.3091645\n",
      "Epoch: 44 Loss:  0.007851401343941689 lr_DL: 0.0001 lr_vel: 80 SNR: 19.46231300261615 SSIM: 0.31107286\n",
      "Epoch: 45 Loss:  0.00817806536021332 lr_DL: 0.0001 lr_vel: 80 SNR: 19.538406388613033 SSIM: 0.31367654\n",
      "Epoch: 46 Loss:  0.007898912221814195 lr_DL: 0.0001 lr_vel: 80 SNR: 19.600778730620696 SSIM: 0.3205242\n",
      "Epoch: 47 Loss:  0.007688178572182854 lr_DL: 0.0001 lr_vel: 80 SNR: 19.648280075599914 SSIM: 0.32198185\n",
      "Epoch: 48 Loss:  0.007617302176853021 lr_DL: 0.0001 lr_vel: 80 SNR: 19.736266998283995 SSIM: 0.33034408\n",
      "Epoch: 49 Loss:  0.007228762501229843 lr_DL: 0.0001 lr_vel: 80 SNR: 19.793469732238805 SSIM: 0.33315954\n",
      "Epoch: 50 Loss:  0.007008589385077357 lr_DL: 0.0001 lr_vel: 80 SNR: 19.841626382858774 SSIM: 0.33826062\n",
      "Epoch: 51 Loss:  0.0067343511308232944 lr_DL: 0.0001 lr_vel: 80 SNR: 19.897377059616964 SSIM: 0.34400496\n",
      "Epoch: 52 Loss:  0.006557667876283328 lr_DL: 0.0001 lr_vel: 80 SNR: 19.94916344040493 SSIM: 0.3500508\n",
      "Epoch: 53 Loss:  0.0067189043698211515 lr_DL: 0.0001 lr_vel: 80 SNR: 20.02369646495051 SSIM: 0.3525335\n",
      "Epoch: 54 Loss:  0.0065616815661390625 lr_DL: 0.0001 lr_vel: 80 SNR: 20.072206667077204 SSIM: 0.35631582\n",
      "Epoch: 55 Loss:  0.006470307474955916 lr_DL: 0.0001 lr_vel: 80 SNR: 20.13367673654541 SSIM: 0.35938394\n",
      "Epoch: 56 Loss:  0.006383231235668063 lr_DL: 0.0001 lr_vel: 80 SNR: 20.156157356652496 SSIM: 0.36587352\n",
      "Epoch: 57 Loss:  0.005990652499410013 lr_DL: 0.0001 lr_vel: 80 SNR: 20.219540272248896 SSIM: 0.3679455\n",
      "Epoch: 58 Loss:  0.005921809227826694 lr_DL: 0.0001 lr_vel: 80 SNR: 20.27156657538857 SSIM: 0.3723056\n",
      "Epoch: 59 Loss:  0.005738282413221896 lr_DL: 0.0001 lr_vel: 80 SNR: 20.32275266723024 SSIM: 0.37707886\n",
      "Epoch: 60 Loss:  0.005806690951188406 lr_DL: 0.0001 lr_vel: 80 SNR: 20.356579594434955 SSIM: 0.37925345\n",
      "Epoch: 61 Loss:  0.005504876608029008 lr_DL: 0.0001 lr_vel: 80 SNR: 20.39590872577658 SSIM: 0.3846682\n",
      "Epoch: 62 Loss:  0.00529547855257988 lr_DL: 0.0001 lr_vel: 80 SNR: 20.467542305674545 SSIM: 0.3861097\n",
      "Epoch: 63 Loss:  0.0052046938488880794 lr_DL: 0.0001 lr_vel: 80 SNR: 20.50295428180268 SSIM: 0.3901579\n",
      "Epoch: 64 Loss:  0.0050013270772372685 lr_DL: 0.0001 lr_vel: 80 SNR: 20.565930142276656 SSIM: 0.3942494\n",
      "Epoch: 65 Loss:  0.005002216847303013 lr_DL: 0.0001 lr_vel: 80 SNR: 20.603075681870124 SSIM: 0.39588985\n",
      "Epoch: 66 Loss:  0.004885145463049412 lr_DL: 0.0001 lr_vel: 80 SNR: 20.6472342716162 SSIM: 0.4006003\n",
      "Epoch: 67 Loss:  0.004593005085674425 lr_DL: 0.0001 lr_vel: 80 SNR: 20.708913045072972 SSIM: 0.40326366\n",
      "Epoch: 68 Loss:  0.004356582524875799 lr_DL: 0.0001 lr_vel: 80 SNR: 20.775596024055737 SSIM: 0.40881747\n",
      "Epoch: 69 Loss:  0.00411882009357214 lr_DL: 0.0001 lr_vel: 80 SNR: 20.824171470130445 SSIM: 0.41349667\n",
      "Epoch: 70 Loss:  0.003888727903055648 lr_DL: 0.0001 lr_vel: 80 SNR: 20.877270081576178 SSIM: 0.4181647\n",
      "Epoch: 71 Loss:  0.00394765364471823 lr_DL: 0.0001 lr_vel: 80 SNR: 20.93883590002001 SSIM: 0.4205271\n",
      "Epoch: 72 Loss:  0.004041132004931569 lr_DL: 0.0001 lr_vel: 80 SNR: 20.986578832770107 SSIM: 0.42445672\n",
      "Epoch: 73 Loss:  0.003907737112604082 lr_DL: 0.0001 lr_vel: 80 SNR: 21.051819952745866 SSIM: 0.42858744\n",
      "Epoch: 74 Loss:  0.004005767156680425 lr_DL: 0.0001 lr_vel: 80 SNR: 21.095235028073297 SSIM: 0.42877686\n",
      "Epoch: 75 Loss:  0.0037332839022080106 lr_DL: 0.0001 lr_vel: 80 SNR: 21.161664329053455 SSIM: 0.4335759\n",
      "Epoch: 76 Loss:  0.003854621814874311 lr_DL: 0.0001 lr_vel: 80 SNR: 21.21596240333561 SSIM: 0.43615547\n",
      "Epoch: 77 Loss:  0.0036076810676604508 lr_DL: 0.0001 lr_vel: 80 SNR: 21.287384171378022 SSIM: 0.43954703\n",
      "Epoch: 78 Loss:  0.00361972003399084 lr_DL: 0.0001 lr_vel: 80 SNR: 21.31411423750862 SSIM: 0.44124845\n",
      "Epoch: 79 Loss:  0.0036643504553164046 lr_DL: 0.0001 lr_vel: 80 SNR: 21.39146867931007 SSIM: 0.44608742\n",
      "Epoch: 80 Loss:  0.003603753400966525 lr_DL: 0.0001 lr_vel: 80 SNR: 21.453027141894466 SSIM: 0.44807398\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 81 Loss:  0.0036703380290418865 lr_DL: 0.0001 lr_vel: 80 SNR: 21.503660952935096 SSIM: 0.45175686\n",
      "Epoch: 82 Loss:  0.003252446752352019 lr_DL: 0.0001 lr_vel: 80 SNR: 21.586117952614675 SSIM: 0.45489553\n",
      "Epoch: 83 Loss:  0.003149925312027335 lr_DL: 0.0001 lr_vel: 80 SNR: 21.65269171699935 SSIM: 0.45973617\n",
      "Epoch: 84 Loss:  0.003145658290789773 lr_DL: 0.0001 lr_vel: 80 SNR: 21.723391717055254 SSIM: 0.46442372\n",
      "Epoch: 85 Loss:  0.0033165232744067907 lr_DL: 0.0001 lr_vel: 80 SNR: 21.791122194622517 SSIM: 0.46777812\n",
      "Epoch: 86 Loss:  0.0031706429552286865 lr_DL: 0.0001 lr_vel: 80 SNR: 21.85739584172975 SSIM: 0.47144702\n",
      "Epoch: 87 Loss:  0.002834254417878886 lr_DL: 0.0001 lr_vel: 80 SNR: 21.92320484409658 SSIM: 0.47661132\n",
      "Epoch: 88 Loss:  0.002953031515547385 lr_DL: 0.0001 lr_vel: 80 SNR: 21.984494175732912 SSIM: 0.48010364\n",
      "Epoch: 89 Loss:  0.0029883032858682174 lr_DL: 0.0001 lr_vel: 80 SNR: 22.068803028467183 SSIM: 0.48420462\n",
      "Epoch: 90 Loss:  0.002821777353528887 lr_DL: 0.0001 lr_vel: 80 SNR: 22.12535131626995 SSIM: 0.48869935\n",
      "Epoch: 91 Loss:  0.0029029699120049677 lr_DL: 0.0001 lr_vel: 80 SNR: 22.19185549927858 SSIM: 0.49079892\n",
      "Epoch: 92 Loss:  0.0027804547959628203 lr_DL: 0.0001 lr_vel: 80 SNR: 22.255332312325322 SSIM: 0.49318463\n",
      "Epoch: 93 Loss:  0.002857497148215771 lr_DL: 0.0001 lr_vel: 80 SNR: 22.32072874066016 SSIM: 0.4976338\n",
      "Epoch: 94 Loss:  0.0028596192326707146 lr_DL: 0.0001 lr_vel: 80 SNR: 22.3859995532248 SSIM: 0.5018913\n",
      "Epoch: 95 Loss:  0.002514733672918131 lr_DL: 0.0001 lr_vel: 80 SNR: 22.464045327667815 SSIM: 0.50512385\n",
      "Epoch: 96 Loss:  0.0024246650476319096 lr_DL: 0.0001 lr_vel: 80 SNR: 22.545659612931097 SSIM: 0.51043093\n",
      "Epoch: 97 Loss:  0.0025612688506953417 lr_DL: 0.0001 lr_vel: 80 SNR: 22.60321224156649 SSIM: 0.5133797\n",
      "Epoch: 98 Loss:  0.0024943022057414056 lr_DL: 0.0001 lr_vel: 80 SNR: 22.687075453012323 SSIM: 0.5185385\n",
      "Epoch: 99 Loss:  0.002510203355147193 lr_DL: 0.0001 lr_vel: 80 SNR: 22.742165050615576 SSIM: 0.5209934\n",
      "Epoch: 100 Loss:  0.002406642632558942 lr_DL: 0.0001 lr_vel: 80 SNR: 22.816483182062434 SSIM: 0.5249375\n",
      "Epoch: 101 Loss:  0.0023047165789951882 lr_DL: 0.0001 lr_vel: 80 SNR: 22.878218851370768 SSIM: 0.5289535\n",
      "Epoch: 102 Loss:  0.002193568736159553 lr_DL: 0.0001 lr_vel: 80 SNR: 22.954531877361696 SSIM: 0.5332684\n",
      "Epoch: 103 Loss:  0.0023350565849492946 lr_DL: 0.0001 lr_vel: 80 SNR: 22.998699795866607 SSIM: 0.53517866\n",
      "Epoch: 104 Loss:  0.002300598449073732 lr_DL: 0.0001 lr_vel: 80 SNR: 23.06455473107981 SSIM: 0.5390588\n",
      "Epoch: 105 Loss:  0.002225055634820213 lr_DL: 0.0001 lr_vel: 80 SNR: 23.117457705675875 SSIM: 0.5424869\n",
      "Epoch: 106 Loss:  0.0022759456071071325 lr_DL: 0.0001 lr_vel: 80 SNR: 23.17308176821266 SSIM: 0.54415196\n",
      "Epoch: 107 Loss:  0.002137301959252606 lr_DL: 0.0001 lr_vel: 80 SNR: 23.220917409627386 SSIM: 0.54865146\n",
      "Epoch: 108 Loss:  0.002134115796070546 lr_DL: 0.0001 lr_vel: 80 SNR: 23.27071776202886 SSIM: 0.5502623\n",
      "Epoch: 109 Loss:  0.002108306542504579 lr_DL: 0.0001 lr_vel: 80 SNR: 23.324502633782352 SSIM: 0.5527125\n",
      "Epoch: 110 Loss:  0.00202883694631358 lr_DL: 0.0001 lr_vel: 80 SNR: 23.3755017961739 SSIM: 0.55614704\n",
      "Epoch: 111 Loss:  0.00189508586578692 lr_DL: 0.0001 lr_vel: 80 SNR: 23.426824041011926 SSIM: 0.5589399\n",
      "Epoch: 112 Loss:  0.001843484095297754 lr_DL: 0.0001 lr_vel: 80 SNR: 23.474514552767264 SSIM: 0.5631103\n",
      "Epoch: 113 Loss:  0.0017683633176299432 lr_DL: 0.0001 lr_vel: 80 SNR: 23.522416081844348 SSIM: 0.56553483\n",
      "Epoch: 114 Loss:  0.0019904074685958523 lr_DL: 0.0001 lr_vel: 80 SNR: 23.557157323092326 SSIM: 0.566082\n",
      "Epoch: 115 Loss:  0.0017828701723677416 lr_DL: 0.0001 lr_vel: 80 SNR: 23.614139467985986 SSIM: 0.5705713\n",
      "Epoch: 116 Loss:  0.0017933339074564477 lr_DL: 0.0001 lr_vel: 80 SNR: 23.646308577192734 SSIM: 0.5719712\n",
      "Epoch: 117 Loss:  0.0017114340444095433 lr_DL: 0.0001 lr_vel: 80 SNR: 23.702906093826932 SSIM: 0.5757996\n",
      "Epoch: 118 Loss:  0.0015969274255136648 lr_DL: 0.0001 lr_vel: 80 SNR: 23.731702086574316 SSIM: 0.5778235\n",
      "Epoch: 119 Loss:  0.0016025426991594335 lr_DL: 0.0001 lr_vel: 80 SNR: 23.770877649815287 SSIM: 0.57967263\n",
      "Epoch: 120 Loss:  0.0017189150773144017 lr_DL: 0.0001 lr_vel: 80 SNR: 23.792280853472676 SSIM: 0.58087665\n",
      "Epoch: 121 Loss:  0.0016036212580123296 lr_DL: 0.0001 lr_vel: 80 SNR: 23.832790424009684 SSIM: 0.5832813\n",
      "Epoch: 122 Loss:  0.0016368268542767813 lr_DL: 0.0001 lr_vel: 80 SNR: 23.84964077953926 SSIM: 0.5839967\n",
      "Epoch: 123 Loss:  0.0014814514686198284 lr_DL: 0.0001 lr_vel: 80 SNR: 23.889951240984328 SSIM: 0.587774\n",
      "Epoch: 124 Loss:  0.0014597806963138281 lr_DL: 0.0001 lr_vel: 80 SNR: 23.92232631475548 SSIM: 0.58876836\n",
      "Epoch: 125 Loss:  0.001423193539570396 lr_DL: 0.0001 lr_vel: 80 SNR: 23.957890398193747 SSIM: 0.59134275\n",
      "Epoch: 126 Loss:  0.0014285536638150612 lr_DL: 0.0001 lr_vel: 80 SNR: 23.979482809626646 SSIM: 0.5925292\n",
      "Epoch: 127 Loss:  0.001533544094612201 lr_DL: 0.0001 lr_vel: 80 SNR: 23.99656349444883 SSIM: 0.59296244\n",
      "Epoch: 128 Loss:  0.001393550547072664 lr_DL: 0.0001 lr_vel: 80 SNR: 24.037470304815017 SSIM: 0.5961856\n",
      "Epoch: 129 Loss:  0.0013382228440605103 lr_DL: 0.0001 lr_vel: 80 SNR: 24.056638422847605 SSIM: 0.5976389\n",
      "Epoch: 130 Loss:  0.0012924519270503274 lr_DL: 0.0001 lr_vel: 80 SNR: 24.08927831896104 SSIM: 0.59909236\n",
      "Epoch: 131 Loss:  0.0013001253906016548 lr_DL: 0.0001 lr_vel: 80 SNR: 24.10200557670155 SSIM: 0.600422\n",
      "Epoch: 132 Loss:  0.0013097680367839834 lr_DL: 0.0001 lr_vel: 80 SNR: 24.136897630742986 SSIM: 0.60194135\n",
      "Epoch: 133 Loss:  0.0013155487986902395 lr_DL: 0.0001 lr_vel: 80 SNR: 24.142595756438645 SSIM: 0.6028205\n",
      "Epoch: 134 Loss:  0.001320487002764518 lr_DL: 0.0001 lr_vel: 80 SNR: 24.16551879497032 SSIM: 0.60344225\n",
      "Epoch: 135 Loss:  0.0012493637953108797 lr_DL: 0.0001 lr_vel: 80 SNR: 24.17870355558303 SSIM: 0.6045393\n",
      "Epoch: 136 Loss:  0.001218681006381909 lr_DL: 0.0001 lr_vel: 80 SNR: 24.203843006963602 SSIM: 0.6058012\n",
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      "Epoch: 146 Loss:  0.0009227698533019672 lr_DL: 0.0001 lr_vel: 80 SNR: 24.386406652639163 SSIM: 0.61735195\n",
      "Epoch: 147 Loss:  0.0009185193805024028 lr_DL: 0.0001 lr_vel: 80 SNR: 24.411681405825316 SSIM: 0.61907655\n",
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      "Epoch: 149 Loss:  0.0008182236585222805 lr_DL: 0.0001 lr_vel: 80 SNR: 24.45018218087961 SSIM: 0.6213995\n",
      "Epoch: 150 Loss:  0.0008398498942066605 lr_DL: 0.0001 lr_vel: 80 SNR: 24.460943546157594 SSIM: 0.6220566\n",
      "Epoch: 151 Loss:  0.0008052231608113895 lr_DL: 0.0001 lr_vel: 80 SNR: 24.481493344268564 SSIM: 0.623658\n",
      "Epoch: 152 Loss:  0.0008004529003907616 lr_DL: 0.0001 lr_vel: 80 SNR: 24.49246806473919 SSIM: 0.62434196\n",
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      "Epoch: 155 Loss:  0.0006914598333726947 lr_DL: 0.0001 lr_vel: 80 SNR: 24.55198585921152 SSIM: 0.62827027\n",
      "Epoch: 156 Loss:  0.0006056037799377615 lr_DL: 0.0001 lr_vel: 80 SNR: 24.566922101125698 SSIM: 0.6295462\n",
      "Epoch: 157 Loss:  0.0006510776564634095 lr_DL: 0.0001 lr_vel: 80 SNR: 24.58467129176574 SSIM: 0.6305719\n",
      "Epoch: 158 Loss:  0.0006498355787092198 lr_DL: 0.0001 lr_vel: 80 SNR: 24.597882048196556 SSIM: 0.63145393\n",
      "Epoch: 159 Loss:  0.0006446239402672897 lr_DL: 0.0001 lr_vel: 80 SNR: 24.61533836278968 SSIM: 0.63226604\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 160 Loss:  0.0006025369317891697 lr_DL: 0.0001 lr_vel: 80 SNR: 24.62185688806081 SSIM: 0.6332443\n",
      "Epoch: 161 Loss:  0.0006285248760832474 lr_DL: 0.0001 lr_vel: 80 SNR: 24.639122209897653 SSIM: 0.6342701\n",
      "Epoch: 162 Loss:  0.0006045391724910587 lr_DL: 0.0001 lr_vel: 80 SNR: 24.658223928882293 SSIM: 0.6346875\n",
      "Epoch: 163 Loss:  0.0006084333018710216 lr_DL: 0.0001 lr_vel: 80 SNR: 24.667888541887844 SSIM: 0.6359163\n",
      "Epoch: 164 Loss:  0.0005345797806512564 lr_DL: 0.0001 lr_vel: 80 SNR: 24.6861369723681 SSIM: 0.6366932\n",
      "Epoch: 165 Loss:  0.0005491528863785788 lr_DL: 0.0001 lr_vel: 80 SNR: 24.695387778779534 SSIM: 0.6375654\n",
      "Epoch: 166 Loss:  0.0005096873200576131 lr_DL: 0.0001 lr_vel: 80 SNR: 24.71176585151643 SSIM: 0.63901585\n",
      "Epoch: 167 Loss:  0.000493935847771354 lr_DL: 0.0001 lr_vel: 80 SNR: 24.72216897152244 SSIM: 0.6396185\n",
      "Epoch: 168 Loss:  0.000499206757134137 lr_DL: 0.0001 lr_vel: 80 SNR: 24.73837826350071 SSIM: 0.6401021\n",
      "Epoch: 169 Loss:  0.0005181139926814164 lr_DL: 0.0001 lr_vel: 80 SNR: 24.749457804803452 SSIM: 0.6411724\n",
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      "Epoch: 171 Loss:  0.0004694177526592587 lr_DL: 0.0001 lr_vel: 80 SNR: 24.774531264552962 SSIM: 0.6426926\n",
      "Epoch: 172 Loss:  0.00042488731754322846 lr_DL: 0.0001 lr_vel: 80 SNR: 24.789233954708052 SSIM: 0.643865\n",
      "Epoch: 173 Loss:  0.00039851120818639173 lr_DL: 0.0001 lr_vel: 80 SNR: 24.803801952700915 SSIM: 0.64455205\n",
      "Epoch: 174 Loss:  0.0004202529079824065 lr_DL: 0.0001 lr_vel: 80 SNR: 24.813561557115953 SSIM: 0.6448917\n",
      "Epoch: 175 Loss:  0.0003806649435621997 lr_DL: 0.0001 lr_vel: 80 SNR: 24.831196846901726 SSIM: 0.64591146\n",
      "Epoch: 176 Loss:  0.00040340698081611965 lr_DL: 0.0001 lr_vel: 80 SNR: 24.840509826818433 SSIM: 0.6466379\n",
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      "Epoch: 184 Loss:  0.0003024600305555699 lr_DL: 0.0001 lr_vel: 80 SNR: 24.94959875812066 SSIM: 0.65319127\n",
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      "Epoch: 186 Loss:  0.0002964787020270402 lr_DL: 0.0001 lr_vel: 80 SNR: 24.972414181487245 SSIM: 0.6544315\n",
      "Epoch: 187 Loss:  0.00031078050912280254 lr_DL: 0.0001 lr_vel: 80 SNR: 24.981012609320366 SSIM: 0.65525264\n",
      "Epoch: 188 Loss:  0.0002837463005562313 lr_DL: 0.0001 lr_vel: 80 SNR: 24.993928706092877 SSIM: 0.65567523\n",
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      "Epoch: 190 Loss:  0.00026140152864779034 lr_DL: 0.0001 lr_vel: 80 SNR: 25.016794799160547 SSIM: 0.6570803\n",
      "Epoch: 191 Loss:  0.00026811914382657656 lr_DL: 0.0001 lr_vel: 80 SNR: 25.026003429720873 SSIM: 0.65769595\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 238 Loss:  0.00027434446528786796 lr_DL: 0.0001 lr_vel: 80 SNR: 25.48887914449676 SSIM: 0.6782504\n",
      "Epoch: 239 Loss:  0.00017731824167033968 lr_DL: 0.0001 lr_vel: 80 SNR: 25.503600538237492 SSIM: 0.6789685\n",
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      "Epoch: 277 Loss:  0.0001443770801415667 lr_DL: 0.0001 lr_vel: 80 SNR: 25.770294567620073 SSIM: 0.68819284\n",
      "Epoch: 278 Loss:  0.00015773148285613085 lr_DL: 0.0001 lr_vel: 80 SNR: 25.774892245235623 SSIM: 0.6882323\n",
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      "Epoch: 280 Loss:  0.00015396509018804257 lr_DL: 0.0001 lr_vel: 80 SNR: 25.78964928193573 SSIM: 0.68860686\n",
      "Epoch: 281 Loss:  0.00014979789605907475 lr_DL: 0.0001 lr_vel: 80 SNR: 25.795997263169156 SSIM: 0.68900007\n",
      "Epoch: 282 Loss:  0.00014430934340149785 lr_DL: 0.0001 lr_vel: 80 SNR: 25.803646933823007 SSIM: 0.6890509\n",
      "Epoch: 283 Loss:  0.0001375861196720507 lr_DL: 0.0001 lr_vel: 80 SNR: 25.80868387885943 SSIM: 0.6892337\n",
      "Epoch: 284 Loss:  0.0001769848160620313 lr_DL: 0.0001 lr_vel: 80 SNR: 25.808867257348055 SSIM: 0.688812\n",
      "Epoch: 285 Loss:  0.0001613697689511658 lr_DL: 0.0001 lr_vel: 80 SNR: 25.817254496150234 SSIM: 0.68957174\n",
      "Epoch: 286 Loss:  0.00014425662787592348 lr_DL: 0.0001 lr_vel: 80 SNR: 25.826539590533905 SSIM: 0.6898558\n",
      "Epoch: 287 Loss:  0.00013784121474600398 lr_DL: 0.0001 lr_vel: 80 SNR: 25.833813293392176 SSIM: 0.6899675\n",
      "Epoch: 288 Loss:  0.00014124068547971547 lr_DL: 0.0001 lr_vel: 80 SNR: 25.83912374176745 SSIM: 0.6903427\n",
      "Epoch: 289 Loss:  0.0001477907038254974 lr_DL: 0.0001 lr_vel: 80 SNR: 25.844546487338302 SSIM: 0.6904946\n",
      "Epoch: 290 Loss:  0.00013855037929412598 lr_DL: 0.0001 lr_vel: 80 SNR: 25.852034240172994 SSIM: 0.69074166\n",
      "Epoch: 291 Loss:  0.00014902020217656778 lr_DL: 0.0001 lr_vel: 80 SNR: 25.852633850168594 SSIM: 0.6905673\n",
      "Epoch: 292 Loss:  0.00016671115226927214 lr_DL: 0.0001 lr_vel: 80 SNR: 25.859181389166267 SSIM: 0.6907691\n",
      "Epoch: 293 Loss:  0.00014286171620672878 lr_DL: 0.0001 lr_vel: 80 SNR: 25.866215009454336 SSIM: 0.6912286\n",
      "Epoch: 294 Loss:  0.0001401044069401299 lr_DL: 0.0001 lr_vel: 80 SNR: 25.873490070662257 SSIM: 0.6914911\n",
      "Epoch: 295 Loss:  0.0001475823080302992 lr_DL: 0.0001 lr_vel: 80 SNR: 25.877975535226916 SSIM: 0.6915483\n",
      "Epoch: 296 Loss:  0.00014672749093733728 lr_DL: 0.0001 lr_vel: 80 SNR: 25.884365218382037 SSIM: 0.69170594\n",
      "Epoch: 297 Loss:  0.00013719292158687798 lr_DL: 0.0001 lr_vel: 80 SNR: 25.890206032928695 SSIM: 0.69193375\n",
      "Epoch: 298 Loss:  0.00017352202121401206 lr_DL: 0.0001 lr_vel: 80 SNR: 25.888106844777905 SSIM: 0.6912345\n",
      "Epoch: 299 Loss:  0.00020901935301177824 lr_DL: 0.0001 lr_vel: 80 SNR: 25.89571251710323 SSIM: 0.6917488\n",
      "Epoch: 300 Loss:  0.0001841139671644972 lr_DL: 0.0001 lr_vel: 80 SNR: 25.89954453047923 SSIM: 0.6916611\n",
      "Epoch: 301 Loss:  0.0001448376434079061 lr_DL: 0.0001 lr_vel: 80 SNR: 25.91114993278984 SSIM: 0.6922016\n",
      "Epoch: 302 Loss:  0.000132424195423179 lr_DL: 0.0001 lr_vel: 80 SNR: 25.917530037035206 SSIM: 0.6924465\n",
      "Epoch: 303 Loss:  0.00013854979115421884 lr_DL: 0.0001 lr_vel: 80 SNR: 25.92259859644571 SSIM: 0.6926819\n",
      "Epoch: 304 Loss:  0.00013837181298489062 lr_DL: 0.0001 lr_vel: 80 SNR: 25.9294046071885 SSIM: 0.69284916\n",
      "Epoch: 305 Loss:  0.0001303108780120965 lr_DL: 0.0001 lr_vel: 80 SNR: 25.93489709948692 SSIM: 0.69305736\n",
      "Epoch: 306 Loss:  0.0001396604088464907 lr_DL: 0.0001 lr_vel: 80 SNR: 25.939793518020483 SSIM: 0.69309133\n",
      "Epoch: 307 Loss:  0.00013319807088313002 lr_DL: 0.0001 lr_vel: 80 SNR: 25.944006568106047 SSIM: 0.6932926\n",
      "Epoch: 308 Loss:  0.00016565301339142025 lr_DL: 0.0001 lr_vel: 80 SNR: 25.944357749893676 SSIM: 0.692814\n",
      "Epoch: 309 Loss:  0.00016900605308668067 lr_DL: 0.0001 lr_vel: 80 SNR: 25.951051640443605 SSIM: 0.69332844\n",
      "Epoch: 310 Loss:  0.00013307628347926462 lr_DL: 0.0001 lr_vel: 80 SNR: 25.959753128010824 SSIM: 0.69385034\n",
      "Epoch: 311 Loss:  0.00014074968106190985 lr_DL: 0.0001 lr_vel: 80 SNR: 25.96542339653462 SSIM: 0.69364744\n",
      "Epoch: 312 Loss:  0.00014186948271041426 lr_DL: 0.0001 lr_vel: 80 SNR: 25.971563285515433 SSIM: 0.6939865\n",
      "Epoch: 313 Loss:  0.0001308697474693569 lr_DL: 0.0001 lr_vel: 80 SNR: 25.97744589521278 SSIM: 0.69419986\n",
      "Epoch: 314 Loss:  0.00013226153484235207 lr_DL: 0.0001 lr_vel: 80 SNR: 25.981383890651657 SSIM: 0.6945667\n",
      "Epoch: 315 Loss:  0.00015199279441731052 lr_DL: 0.0001 lr_vel: 80 SNR: 25.981026072326344 SSIM: 0.69412434\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 316 Loss:  0.00015286199438075226 lr_DL: 0.0001 lr_vel: 80 SNR: 25.99204037875033 SSIM: 0.6946988\n",
      "Epoch: 317 Loss:  0.00014293437998276205 lr_DL: 0.0001 lr_vel: 80 SNR: 25.995930545905487 SSIM: 0.69472665\n",
      "Epoch: 318 Loss:  0.0001382066824589856 lr_DL: 0.0001 lr_vel: 80 SNR: 26.001943872839114 SSIM: 0.695061\n",
      "Epoch: 319 Loss:  0.00013475326219728837 lr_DL: 0.0001 lr_vel: 80 SNR: 26.005077081770555 SSIM: 0.69513553\n",
      "Epoch: 320 Loss:  0.0001401121238207755 lr_DL: 0.0001 lr_vel: 80 SNR: 26.009730604891462 SSIM: 0.69532955\n",
      "Epoch: 321 Loss:  0.0001379470406391192 lr_DL: 0.0001 lr_vel: 80 SNR: 26.01715196186579 SSIM: 0.69538885\n",
      "Epoch: 322 Loss:  0.00012994125669744486 lr_DL: 0.0001 lr_vel: 80 SNR: 26.02384020758061 SSIM: 0.6955893\n",
      "Epoch: 323 Loss:  0.00012542918581554357 lr_DL: 0.0001 lr_vel: 80 SNR: 26.02819483882761 SSIM: 0.6957054\n",
      "Epoch: 324 Loss:  0.00015017100959084927 lr_DL: 0.0001 lr_vel: 80 SNR: 26.029435291542985 SSIM: 0.695316\n",
      "Epoch: 325 Loss:  0.00013673645905024992 lr_DL: 0.0001 lr_vel: 80 SNR: 26.039341075397118 SSIM: 0.69590354\n",
      "Epoch: 326 Loss:  0.00013131129502047162 lr_DL: 0.0001 lr_vel: 80 SNR: 26.042761123929612 SSIM: 0.69591504\n",
      "Epoch: 327 Loss:  0.00013820789972669446 lr_DL: 0.0001 lr_vel: 80 SNR: 26.045322704078284 SSIM: 0.69623566\n",
      "Epoch: 328 Loss:  0.00013543642562581227 lr_DL: 0.0001 lr_vel: 80 SNR: 26.049966621929315 SSIM: 0.6964562\n",
      "Epoch: 329 Loss:  0.00013493513470166362 lr_DL: 0.0001 lr_vel: 80 SNR: 26.05597454319011 SSIM: 0.69652146\n",
      "Epoch: 330 Loss:  0.00013963208281590292 lr_DL: 0.0001 lr_vel: 80 SNR: 26.061223247390643 SSIM: 0.69643146\n",
      "Epoch: 331 Loss:  0.00013828022905120935 lr_DL: 0.0001 lr_vel: 80 SNR: 26.067321658760882 SSIM: 0.69675666\n",
      "Epoch: 332 Loss:  0.00012665055716449086 lr_DL: 0.0001 lr_vel: 80 SNR: 26.072288154587902 SSIM: 0.69694793\n",
      "Epoch: 333 Loss:  0.00012721800837122525 lr_DL: 0.0001 lr_vel: 80 SNR: 26.07881959382013 SSIM: 0.6968931\n",
      "Epoch: 334 Loss:  0.00014516536175506188 lr_DL: 0.0001 lr_vel: 80 SNR: 26.080673553208904 SSIM: 0.6968008\n",
      "Epoch: 335 Loss:  0.00014187582055456006 lr_DL: 0.0001 lr_vel: 80 SNR: 26.084483563751228 SSIM: 0.69704324\n",
      "Epoch: 336 Loss:  0.00014374083281533482 lr_DL: 0.0001 lr_vel: 80 SNR: 26.088606778730913 SSIM: 0.6972217\n",
      "Epoch: 337 Loss:  0.0001370983828868096 lr_DL: 0.0001 lr_vel: 80 SNR: 26.09233472013519 SSIM: 0.6972965\n",
      "Epoch: 338 Loss:  0.00013766919558596177 lr_DL: 0.0001 lr_vel: 80 SNR: 26.09822939159159 SSIM: 0.69732\n",
      "Epoch: 339 Loss:  0.00013366437754787816 lr_DL: 0.0001 lr_vel: 80 SNR: 26.10388910140501 SSIM: 0.69764555\n",
      "Epoch: 340 Loss:  0.00012857436546861816 lr_DL: 0.0001 lr_vel: 80 SNR: 26.107217838789985 SSIM: 0.69784135\n",
      "Epoch: 341 Loss:  0.0001458985129526506 lr_DL: 0.0001 lr_vel: 80 SNR: 26.107277325184533 SSIM: 0.6976631\n",
      "Epoch: 342 Loss:  0.0001407382274919655 lr_DL: 0.0001 lr_vel: 80 SNR: 26.115524979889116 SSIM: 0.69802266\n",
      "Epoch: 343 Loss:  0.00012929867225466296 lr_DL: 0.0001 lr_vel: 80 SNR: 26.119975084349637 SSIM: 0.6978628\n",
      "Epoch: 344 Loss:  0.00014085541479289532 lr_DL: 0.0001 lr_vel: 80 SNR: 26.123895035175508 SSIM: 0.69805056\n",
      "Epoch: 345 Loss:  0.00012866511727528026 lr_DL: 0.0001 lr_vel: 80 SNR: 26.13072832222658 SSIM: 0.698409\n",
      "Epoch: 346 Loss:  0.00012345185289935518 lr_DL: 0.0001 lr_vel: 80 SNR: 26.1360715136272 SSIM: 0.69851905\n",
      "Epoch: 347 Loss:  0.00012054606680370247 lr_DL: 0.0001 lr_vel: 80 SNR: 26.13925313132274 SSIM: 0.6984618\n",
      "Epoch: 348 Loss:  0.00015254367172019557 lr_DL: 0.0001 lr_vel: 80 SNR: 26.138977697433134 SSIM: 0.69802105\n",
      "Epoch: 349 Loss:  0.00016615273392138382 lr_DL: 0.0001 lr_vel: 80 SNR: 26.144423892723093 SSIM: 0.69843596\n",
      "Epoch: 350 Loss:  0.00012444306533628455 lr_DL: 0.0001 lr_vel: 80 SNR: 26.154190277317483 SSIM: 0.6988353\n",
      "Epoch: 351 Loss:  0.00012195506884988087 lr_DL: 0.0001 lr_vel: 80 SNR: 26.157690039820736 SSIM: 0.69889516\n",
      "Epoch: 352 Loss:  0.00012745912972604855 lr_DL: 0.0001 lr_vel: 80 SNR: 26.161153295586352 SSIM: 0.6991213\n",
      "Epoch: 353 Loss:  0.00012937371551136796 lr_DL: 0.0001 lr_vel: 80 SNR: 26.164022344880372 SSIM: 0.69910675\n",
      "Epoch: 354 Loss:  0.00013155046738878203 lr_DL: 0.0001 lr_vel: 80 SNR: 26.169102673606716 SSIM: 0.69906723\n",
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      "Epoch: 356 Loss:  0.00012211292560095898 lr_DL: 0.0001 lr_vel: 80 SNR: 26.178671096004802 SSIM: 0.6994273\n",
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      "Epoch: 360 Loss:  0.0001343140621126319 lr_DL: 0.0001 lr_vel: 80 SNR: 26.19296087917812 SSIM: 0.69949967\n",
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      "Epoch: 362 Loss:  0.00012176605838855418 lr_DL: 0.0001 lr_vel: 80 SNR: 26.202531794200524 SSIM: 0.69990754\n",
      "Epoch: 363 Loss:  0.00012237151313456706 lr_DL: 0.0001 lr_vel: 80 SNR: 26.20780413529325 SSIM: 0.70009565\n",
      "Epoch: 364 Loss:  0.00012275828048586846 lr_DL: 0.0001 lr_vel: 80 SNR: 26.211349368471467 SSIM: 0.7001436\n",
      "Epoch: 365 Loss:  0.00013539056186952317 lr_DL: 0.0001 lr_vel: 80 SNR: 26.21256948270059 SSIM: 0.7000132\n",
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      "Epoch: 369 Loss:  0.00012570793793808359 lr_DL: 0.0001 lr_vel: 80 SNR: 26.233096120389785 SSIM: 0.7006319\n",
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      "Epoch: 382 Loss:  0.00011285766061822264 lr_DL: 0.0001 lr_vel: 80 SNR: 26.287300097280347 SSIM: 0.70202893\n",
      "Epoch: 383 Loss:  0.00013116225648748998 lr_DL: 0.0001 lr_vel: 80 SNR: 26.287050860542323 SSIM: 0.7017281\n",
      "Epoch: 384 Loss:  0.00021001818749937228 lr_DL: 0.0001 lr_vel: 80 SNR: 26.28133902196067 SSIM: 0.7013349\n",
      "Epoch: 385 Loss:  0.00014456720358187644 lr_DL: 0.0001 lr_vel: 80 SNR: 26.294544548198814 SSIM: 0.70168793\n",
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      "Epoch: 387 Loss:  0.00011450636132697885 lr_DL: 0.0001 lr_vel: 80 SNR: 26.305076835708093 SSIM: 0.70234\n",
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      "Epoch: 389 Loss:  0.00011368434328081396 lr_DL: 0.0001 lr_vel: 80 SNR: 26.311794296396304 SSIM: 0.7025735\n",
      "Epoch: 390 Loss:  0.00011184387209747607 lr_DL: 0.0001 lr_vel: 80 SNR: 26.316446276961585 SSIM: 0.70274323\n",
      "Epoch: 391 Loss:  0.00013055951179315647 lr_DL: 0.0001 lr_vel: 80 SNR: 26.31634639748721 SSIM: 0.70252144\n",
      "Epoch: 392 Loss:  0.0001182994747068733 lr_DL: 0.0001 lr_vel: 80 SNR: 26.32674887393324 SSIM: 0.70279217\n",
      "Epoch: 393 Loss:  0.00013283568308300648 lr_DL: 0.0001 lr_vel: 80 SNR: 26.326873524582982 SSIM: 0.70285904\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 394 Loss:  0.00013147129623879058 lr_DL: 0.0001 lr_vel: 80 SNR: 26.32966642788122 SSIM: 0.70307827\n",
      "Epoch: 395 Loss:  0.00011049967724829912 lr_DL: 0.0001 lr_vel: 80 SNR: 26.335344763221478 SSIM: 0.7032532\n",
      "Epoch: 396 Loss:  0.00010976581543218345 lr_DL: 0.0001 lr_vel: 80 SNR: 26.339189814317034 SSIM: 0.7034402\n",
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      "Epoch: 399 Loss:  0.00014302340835759726 lr_DL: 0.0001 lr_vel: 80 SNR: 26.350040694289106 SSIM: 0.7036028\n",
      "Epoch: 400 Loss:  0.00011355293900123797 lr_DL: 0.0001 lr_vel: 80 SNR: 26.356434689634376 SSIM: 0.7037256\n",
      "Epoch: 401 Loss:  0.00011603214855616291 lr_DL: 0.0001 lr_vel: 80 SNR: 26.36019833964733 SSIM: 0.70385885\n",
      "Epoch: 402 Loss:  0.00011562269501155242 lr_DL: 0.0001 lr_vel: 80 SNR: 26.363400626782916 SSIM: 0.70397455\n",
      "Epoch: 403 Loss:  0.00012070833060230749 lr_DL: 0.0001 lr_vel: 80 SNR: 26.36641723666729 SSIM: 0.7040498\n",
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      "Epoch: 405 Loss:  0.00011972012459106433 lr_DL: 0.0001 lr_vel: 80 SNR: 26.374300103841723 SSIM: 0.7042498\n",
      "Epoch: 406 Loss:  0.00010856208488500367 lr_DL: 0.0001 lr_vel: 80 SNR: 26.37729236163136 SSIM: 0.7043796\n",
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      "Epoch: 415 Loss:  0.00011520112893776968 lr_DL: 0.0001 lr_vel: 80 SNR: 26.409819411579743 SSIM: 0.7050408\n",
      "Epoch: 416 Loss:  0.00010895967013008582 lr_DL: 0.0001 lr_vel: 80 SNR: 26.41470348927332 SSIM: 0.70506334\n",
      "Epoch: 417 Loss:  0.00010922432629740797 lr_DL: 0.0001 lr_vel: 80 SNR: 26.41858458631163 SSIM: 0.7052944\n",
      "Epoch: 418 Loss:  0.00011241940834831136 lr_DL: 0.0001 lr_vel: 80 SNR: 26.417448951727927 SSIM: 0.70525\n",
      "Epoch: 419 Loss:  0.00012999357835118038 lr_DL: 0.0001 lr_vel: 80 SNR: 26.421008364899286 SSIM: 0.70510834\n",
      "Epoch: 420 Loss:  0.00012691471395858873 lr_DL: 0.0001 lr_vel: 80 SNR: 26.425336413169312 SSIM: 0.70511305\n",
      "Epoch: 421 Loss:  0.00012607269842798512 lr_DL: 0.0001 lr_vel: 80 SNR: 26.42801659005453 SSIM: 0.7053815\n",
      "Epoch: 422 Loss:  0.00011707985565105143 lr_DL: 0.0001 lr_vel: 80 SNR: 26.432470643745496 SSIM: 0.7053363\n",
      "Epoch: 423 Loss:  0.00010939811666806538 lr_DL: 0.0001 lr_vel: 80 SNR: 26.437137887245846 SSIM: 0.7055896\n",
      "Epoch: 424 Loss:  0.00011083033047422456 lr_DL: 0.0001 lr_vel: 80 SNR: 26.439132052534543 SSIM: 0.7056143\n",
      "Epoch: 425 Loss:  0.00011459786546765826 lr_DL: 0.0001 lr_vel: 80 SNR: 26.442356199289407 SSIM: 0.7057303\n",
      "Epoch: 426 Loss:  0.0001189685467882858 lr_DL: 0.0001 lr_vel: 80 SNR: 26.446887586882713 SSIM: 0.70594615\n",
      "Epoch: 427 Loss:  0.00011604221848150094 lr_DL: 0.0001 lr_vel: 80 SNR: 26.449468853576267 SSIM: 0.7060458\n",
      "Epoch: 428 Loss:  0.00012404419879506653 lr_DL: 0.0001 lr_vel: 80 SNR: 26.452898817583762 SSIM: 0.7059354\n",
      "Epoch: 429 Loss:  0.00011277142960655814 lr_DL: 0.0001 lr_vel: 80 SNR: 26.457400053274476 SSIM: 0.70620817\n",
      "Epoch: 430 Loss:  0.00011603196859747793 lr_DL: 0.0001 lr_vel: 80 SNR: 26.459463556732594 SSIM: 0.7059782\n",
      "Epoch: 431 Loss:  0.00011181647738946291 lr_DL: 0.0001 lr_vel: 80 SNR: 26.465161116365042 SSIM: 0.70636207\n",
      "Epoch: 432 Loss:  0.00010223381662702499 lr_DL: 0.0001 lr_vel: 80 SNR: 26.470148579492676 SSIM: 0.70646\n",
      "Epoch: 433 Loss:  0.00010166803331230767 lr_DL: 0.0001 lr_vel: 80 SNR: 26.474491074888384 SSIM: 0.70657575\n",
      "Epoch: 434 Loss:  0.00011059330475594227 lr_DL: 0.0001 lr_vel: 80 SNR: 26.477593107178524 SSIM: 0.70675874\n",
      "Epoch: 435 Loss:  0.00012776437167000648 lr_DL: 0.0001 lr_vel: 80 SNR: 26.469765501321977 SSIM: 0.7064856\n",
      "Epoch: 436 Loss:  0.0001246227417141199 lr_DL: 0.0001 lr_vel: 80 SNR: 26.483483569247802 SSIM: 0.7069257\n",
      "Epoch: 437 Loss:  0.00011158244475761118 lr_DL: 0.0001 lr_vel: 80 SNR: 26.48442276134042 SSIM: 0.7070087\n",
      "Epoch: 438 Loss:  0.00011272776537225581 lr_DL: 0.0001 lr_vel: 80 SNR: 26.488144969905434 SSIM: 0.7070747\n",
      "Epoch: 439 Loss:  0.00010869860416278243 lr_DL: 0.0001 lr_vel: 80 SNR: 26.491936032628992 SSIM: 0.707196\n",
      "Epoch: 440 Loss:  0.00010624890419421717 lr_DL: 0.0001 lr_vel: 80 SNR: 26.49533189698753 SSIM: 0.70707995\n",
      "Epoch: 441 Loss:  0.00011026556482344556 lr_DL: 0.0001 lr_vel: 80 SNR: 26.498309161548555 SSIM: 0.7073076\n",
      "Epoch: 442 Loss:  0.00010922946118322822 lr_DL: 0.0001 lr_vel: 80 SNR: 26.50237491114578 SSIM: 0.70732224\n",
      "Epoch: 443 Loss:  0.00011398036464621934 lr_DL: 0.0001 lr_vel: 80 SNR: 26.50119171932223 SSIM: 0.7073667\n",
      "Epoch: 444 Loss:  0.0001282168962158418 lr_DL: 0.0001 lr_vel: 80 SNR: 26.50410777700776 SSIM: 0.7071575\n",
      "Epoch: 445 Loss:  0.00013009556860197336 lr_DL: 0.0001 lr_vel: 80 SNR: 26.505987313334952 SSIM: 0.7073533\n",
      "Epoch: 446 Loss:  0.00011158020546038945 lr_DL: 0.0001 lr_vel: 80 SNR: 26.513428691093228 SSIM: 0.70762986\n",
      "Epoch: 447 Loss:  0.00011074278760740223 lr_DL: 0.0001 lr_vel: 80 SNR: 26.514349624981506 SSIM: 0.7076338\n",
      "Epoch: 448 Loss:  0.0001146980343037285 lr_DL: 0.0001 lr_vel: 80 SNR: 26.519798023144403 SSIM: 0.7076909\n",
      "Epoch: 449 Loss:  0.00011694221684592776 lr_DL: 0.0001 lr_vel: 80 SNR: 26.52328545273763 SSIM: 0.7077003\n",
      "Epoch: 450 Loss:  0.00011127650068374351 lr_DL: 0.0001 lr_vel: 80 SNR: 26.527025182222626 SSIM: 0.7078357\n",
      "Epoch: 451 Loss:  0.00010817259875087379 lr_DL: 0.0001 lr_vel: 80 SNR: 26.52825816262022 SSIM: 0.70790434\n",
      "Epoch: 452 Loss:  0.00011721363674344805 lr_DL: 0.0001 lr_vel: 80 SNR: 26.530869012399627 SSIM: 0.70791316\n",
      "Epoch: 453 Loss:  0.00011986257110644753 lr_DL: 0.0001 lr_vel: 80 SNR: 26.533539006022156 SSIM: 0.7077673\n",
      "Epoch: 454 Loss:  0.000112021720027163 lr_DL: 0.0001 lr_vel: 80 SNR: 26.539384671585804 SSIM: 0.7080133\n",
      "Epoch: 455 Loss:  0.00010062970783716688 lr_DL: 0.0001 lr_vel: 80 SNR: 26.542463760623846 SSIM: 0.7081477\n",
      "Epoch: 456 Loss:  0.00010127980640390888 lr_DL: 0.0001 lr_vel: 80 SNR: 26.548042741400234 SSIM: 0.7083129\n",
      "Epoch: 457 Loss:  0.00011049712945047456 lr_DL: 0.0001 lr_vel: 80 SNR: 26.54462926918668 SSIM: 0.70819885\n",
      "Epoch: 458 Loss:  0.00012039924137449512 lr_DL: 0.0001 lr_vel: 80 SNR: 26.551346115209387 SSIM: 0.7082762\n",
      "Epoch: 459 Loss:  0.00012156128674784365 lr_DL: 0.0001 lr_vel: 80 SNR: 26.553995647663413 SSIM: 0.7083175\n",
      "Epoch: 460 Loss:  0.00010266712439867357 lr_DL: 0.0001 lr_vel: 80 SNR: 26.559294453090864 SSIM: 0.7084341\n",
      "Epoch: 461 Loss:  9.906564591801725e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.563129620461126 SSIM: 0.70875406\n",
      "Epoch: 462 Loss:  0.00010676245704720108 lr_DL: 0.0001 lr_vel: 80 SNR: 26.564860529265744 SSIM: 0.708737\n",
      "Epoch: 463 Loss:  0.00011305155906787453 lr_DL: 0.0001 lr_vel: 80 SNR: 26.564731800919645 SSIM: 0.70869017\n",
      "Epoch: 464 Loss:  0.00010083701223872292 lr_DL: 0.0001 lr_vel: 80 SNR: 26.570300643868407 SSIM: 0.7088087\n",
      "Epoch: 465 Loss:  0.00010278820215413968 lr_DL: 0.0001 lr_vel: 80 SNR: 26.57225971764173 SSIM: 0.7088255\n",
      "Epoch: 466 Loss:  0.00013522726755278806 lr_DL: 0.0001 lr_vel: 80 SNR: 26.566133546042593 SSIM: 0.70828885\n",
      "Epoch: 467 Loss:  0.00018792085708507026 lr_DL: 0.0001 lr_vel: 80 SNR: 26.57034935601824 SSIM: 0.70807666\n",
      "Epoch: 468 Loss:  0.00013382814868236891 lr_DL: 0.0001 lr_vel: 80 SNR: 26.57845212531457 SSIM: 0.7087243\n",
      "Epoch: 469 Loss:  0.00010809120212798006 lr_DL: 0.0001 lr_vel: 80 SNR: 26.586355864851868 SSIM: 0.7090323\n",
      "Epoch: 470 Loss:  9.635153716468873e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.58973004480483 SSIM: 0.70928043\n",
      "Epoch: 471 Loss:  9.615150702302344e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.595099286388162 SSIM: 0.709588\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 472 Loss:  9.727199964496928e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.59599558552491 SSIM: 0.7095356\n",
      "Epoch: 473 Loss:  0.00010673023255852362 lr_DL: 0.0001 lr_vel: 80 SNR: 26.597254168451933 SSIM: 0.7096582\n",
      "Epoch: 474 Loss:  0.00010654703558733066 lr_DL: 0.0001 lr_vel: 80 SNR: 26.60284185020487 SSIM: 0.7096947\n",
      "Epoch: 475 Loss:  9.896958727040328e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.607799688363517 SSIM: 0.70979136\n",
      "Epoch: 476 Loss:  0.00010479939010110684 lr_DL: 0.0001 lr_vel: 80 SNR: 26.608856298359523 SSIM: 0.709825\n",
      "Epoch: 477 Loss:  0.00010933563510964936 lr_DL: 0.0001 lr_vel: 80 SNR: 26.612276942844282 SSIM: 0.7099907\n",
      "Epoch: 478 Loss:  0.00010526435895978163 lr_DL: 0.0001 lr_vel: 80 SNR: 26.61533694413512 SSIM: 0.7099441\n",
      "Epoch: 479 Loss:  0.00011222164272718752 lr_DL: 0.0001 lr_vel: 80 SNR: 26.612741706214763 SSIM: 0.7099543\n",
      "Epoch: 480 Loss:  0.00012222026780364103 lr_DL: 0.0001 lr_vel: 80 SNR: 26.61913291525289 SSIM: 0.71008074\n",
      "Epoch: 481 Loss:  0.0001026469915814232 lr_DL: 0.0001 lr_vel: 80 SNR: 26.623777102661144 SSIM: 0.7102789\n",
      "Epoch: 482 Loss:  0.00010274859305354766 lr_DL: 0.0001 lr_vel: 80 SNR: 26.6277994080375 SSIM: 0.71032166\n",
      "Epoch: 483 Loss:  0.00010240941313289416 lr_DL: 0.0001 lr_vel: 80 SNR: 26.633043974294278 SSIM: 0.71052545\n",
      "Epoch: 484 Loss:  9.98350114969071e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.635580265225514 SSIM: 0.7106457\n",
      "Epoch: 485 Loss:  9.858849080046639e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.63694841831652 SSIM: 0.7107082\n",
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      "Epoch: 490 Loss:  0.00011459571251180023 lr_DL: 0.0001 lr_vel: 80 SNR: 26.65030969504409 SSIM: 0.7109109\n",
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      "Epoch: 492 Loss:  0.00013299493972832957 lr_DL: 0.0001 lr_vel: 80 SNR: 26.65880459850313 SSIM: 0.7109669\n",
      "Epoch: 493 Loss:  9.538259121958012e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.66631771648421 SSIM: 0.71149796\n",
      "Epoch: 494 Loss:  8.989025178986291e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.67100440686668 SSIM: 0.7117282\n",
      "Epoch: 495 Loss:  9.10908893274609e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.674383560424907 SSIM: 0.711923\n",
      "Epoch: 496 Loss:  8.917015208377658e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.677235728608473 SSIM: 0.7119151\n",
      "Epoch: 497 Loss:  0.00010072245883444945 lr_DL: 0.0001 lr_vel: 80 SNR: 26.67571145420947 SSIM: 0.7119346\n",
      "Epoch: 498 Loss:  0.00010337281491956674 lr_DL: 0.0001 lr_vel: 80 SNR: 26.68058346273749 SSIM: 0.7118394\n",
      "Epoch: 499 Loss:  9.742072058240107e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.683691321296493 SSIM: 0.71207684\n",
      "Epoch: 500 Loss:  0.0001039713652668676 lr_DL: 0.0001 lr_vel: 80 SNR: 26.685428334350725 SSIM: 0.7118875\n",
      "Epoch: 501 Loss:  0.00010758016724139452 lr_DL: 0.0001 lr_vel: 80 SNR: 26.6870116559357 SSIM: 0.71210134\n",
      "Epoch: 502 Loss:  9.452367521589622e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.690325260201714 SSIM: 0.71218735\n",
      "Epoch: 503 Loss:  8.957505160651635e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.694568035416182 SSIM: 0.7123237\n",
      "Epoch: 504 Loss:  9.165796169933553e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.696362035782975 SSIM: 0.71233547\n",
      "Epoch: 505 Loss:  9.455122853978537e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.700928916165452 SSIM: 0.71245146\n",
      "Epoch: 506 Loss:  0.00010189071496521743 lr_DL: 0.0001 lr_vel: 80 SNR: 26.703279501374908 SSIM: 0.7124224\n",
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      "Epoch: 509 Loss:  0.00011294461849805278 lr_DL: 0.0001 lr_vel: 80 SNR: 26.708907197554893 SSIM: 0.71249825\n",
      "Epoch: 510 Loss:  9.071378638812652e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.71162621767725 SSIM: 0.7126463\n",
      "Epoch: 511 Loss:  8.964489510011238e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.71552604608572 SSIM: 0.7127939\n",
      "Epoch: 512 Loss:  0.00010287372836804328 lr_DL: 0.0001 lr_vel: 80 SNR: 26.716501701828854 SSIM: 0.7126256\n",
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      "Epoch: 531 Loss:  9.093535506205323e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.772526326675887 SSIM: 0.7144558\n",
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      "Epoch: 538 Loss:  8.809860943680785e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.786378014577195 SSIM: 0.71465147\n",
      "Epoch: 539 Loss:  8.130697036297836e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.789995392096028 SSIM: 0.7148003\n",
      "Epoch: 540 Loss:  8.476116527162958e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.792921041873136 SSIM: 0.7149151\n",
      "Epoch: 541 Loss:  9.083969101387386e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.78883723436122 SSIM: 0.71491015\n",
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      "Epoch: 545 Loss:  7.685866209309704e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.806485918956383 SSIM: 0.71553355\n",
      "Epoch: 546 Loss:  7.973400158031533e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.809464368180997 SSIM: 0.7156439\n",
      "Epoch: 547 Loss:  8.295067066986425e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.81089643125906 SSIM: 0.71572495\n",
      "Epoch: 548 Loss:  8.194835208996665e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.815294547453348 SSIM: 0.7157183\n",
      "Epoch: 549 Loss:  0.0001027817010860114 lr_DL: 0.0001 lr_vel: 80 SNR: 26.80449104530657 SSIM: 0.71546125\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 550 Loss:  0.00013997893450626482 lr_DL: 0.0001 lr_vel: 80 SNR: 26.813170988910855 SSIM: 0.71544665\n",
      "Epoch: 551 Loss:  0.00019561708565258112 lr_DL: 0.0001 lr_vel: 80 SNR: 26.80895092879409 SSIM: 0.7149958\n",
      "Epoch: 552 Loss:  9.447390305770872e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.818892522227713 SSIM: 0.71542996\n",
      "Epoch: 553 Loss:  8.260803551820572e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.82318675494846 SSIM: 0.71582896\n",
      "Epoch: 554 Loss:  8.15626809829458e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.827742684055487 SSIM: 0.71593326\n",
      "Epoch: 555 Loss:  8.350493480975274e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.82920882038079 SSIM: 0.7160467\n",
      "Epoch: 556 Loss:  7.87415786665709e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.83284721147134 SSIM: 0.7162022\n",
      "Epoch: 557 Loss:  7.918998768824773e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.83561490066577 SSIM: 0.7162771\n",
      "Epoch: 558 Loss:  7.978440577668758e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.840185929530172 SSIM: 0.71644104\n",
      "Epoch: 559 Loss:  8.308339553574721e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.84124908560841 SSIM: 0.71654487\n",
      "Epoch: 560 Loss:  7.941749548384299e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.84436689980497 SSIM: 0.7165759\n",
      "Epoch: 561 Loss:  9.568022178427782e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.84100228671721 SSIM: 0.7164152\n",
      "Epoch: 562 Loss:  0.0001163045397940247 lr_DL: 0.0001 lr_vel: 80 SNR: 26.844056723527302 SSIM: 0.7165881\n",
      "Epoch: 563 Loss:  0.00016286190584651193 lr_DL: 0.0001 lr_vel: 80 SNR: 26.830167452138987 SSIM: 0.71558607\n",
      "Epoch: 564 Loss:  0.00011485883466472539 lr_DL: 0.0001 lr_vel: 80 SNR: 26.848169420306434 SSIM: 0.71632135\n",
      "Epoch: 565 Loss:  8.142504387554557e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.855842298140672 SSIM: 0.7167668\n",
      "Epoch: 566 Loss:  7.36295693059219e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.86114712470865 SSIM: 0.7170643\n",
      "Epoch: 567 Loss:  7.519649698224384e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.862597433288492 SSIM: 0.7172352\n",
      "Epoch: 568 Loss:  7.529423710366246e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.865210100895514 SSIM: 0.71737117\n",
      "Epoch: 569 Loss:  7.770162847009487e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.86743687702736 SSIM: 0.7174306\n",
      "Epoch: 570 Loss:  7.804823580954689e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.86940880714653 SSIM: 0.71746844\n",
      "Epoch: 571 Loss:  7.974875491830365e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.871768655651245 SSIM: 0.7175594\n",
      "Epoch: 572 Loss:  9.02668783965055e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.866854147402112 SSIM: 0.71752703\n",
      "Epoch: 573 Loss:  0.00010463819926371798 lr_DL: 0.0001 lr_vel: 80 SNR: 26.874882063716935 SSIM: 0.7176565\n",
      "Epoch: 574 Loss:  0.000157416945391257 lr_DL: 0.0001 lr_vel: 80 SNR: 26.85207282698288 SSIM: 0.71667886\n",
      "Epoch: 575 Loss:  0.00011557247077386516 lr_DL: 0.0001 lr_vel: 80 SNR: 26.869408167922792 SSIM: 0.7167542\n",
      "Epoch: 576 Loss:  8.444180105774043e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.877047877602124 SSIM: 0.7172134\n",
      "Epoch: 577 Loss:  7.7346316902549e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.882321650299254 SSIM: 0.717518\n",
      "Epoch: 578 Loss:  7.245064783395113e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.886688657452478 SSIM: 0.71789074\n",
      "Epoch: 579 Loss:  7.126637877566585e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.892579416453962 SSIM: 0.7182026\n",
      "Epoch: 580 Loss:  7.310777449068459e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.895090494146693 SSIM: 0.71838856\n",
      "Epoch: 581 Loss:  6.94081934246545e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.90056868645271 SSIM: 0.71876705\n",
      "Epoch: 582 Loss:  7.533474781666883e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.90127660792826 SSIM: 0.71884006\n",
      "Epoch: 583 Loss:  8.375806016071389e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.901463674839263 SSIM: 0.7187953\n",
      "Epoch: 584 Loss:  0.00010900879642576911 lr_DL: 0.0001 lr_vel: 80 SNR: 26.89747869683847 SSIM: 0.71855617\n",
      "Epoch: 585 Loss:  9.134612240207692e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.9038372503031 SSIM: 0.7188677\n",
      "Epoch: 586 Loss:  7.426527408824768e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.908616716743797 SSIM: 0.71913874\n",
      "Epoch: 587 Loss:  7.187224146036897e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.913151777152255 SSIM: 0.7193056\n",
      "Epoch: 588 Loss:  7.12451544435074e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.91602723428701 SSIM: 0.7194471\n",
      "Epoch: 589 Loss:  6.94103529288744e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.92157635479498 SSIM: 0.719713\n",
      "Epoch: 590 Loss:  0.00013264686543455658 lr_DL: 0.0001 lr_vel: 80 SNR: 26.890785182981688 SSIM: 0.71883065\n",
      "Epoch: 591 Loss:  0.00020847785975396013 lr_DL: 0.0001 lr_vel: 80 SNR: 26.90419482494775 SSIM: 0.71815103\n",
      "Epoch: 592 Loss:  9.789652322069741e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.920564152559493 SSIM: 0.71907234\n",
      "Epoch: 593 Loss:  7.602812862993839e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.92668435206722 SSIM: 0.7195513\n",
      "Epoch: 594 Loss:  6.983561021722077e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.93145667880777 SSIM: 0.7199047\n",
      "Epoch: 595 Loss:  7.481954441270015e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.930527940595862 SSIM: 0.71995455\n",
      "Epoch: 596 Loss:  7.706831784162204e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.934215315526778 SSIM: 0.72011423\n",
      "Epoch: 597 Loss:  6.99630824480361e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.93845842875585 SSIM: 0.720363\n",
      "Epoch: 598 Loss:  7.549683420317402e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.93941614528855 SSIM: 0.7202744\n",
      "Epoch: 599 Loss:  7.198705643531866e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.939975974308137 SSIM: 0.7204042\n",
      "Epoch: 600 Loss:  8.074797757823641e-05 lr_DL: 0.0001 lr_vel: 80 SNR: 26.9419022170627 SSIM: 0.7203856\n",
      "Running complete in 36m  8s\n"
     ]
    }
   ],
   "source": [
    " \n",
    "import torch.nn.functional as F\n",
    "# Starting the FWI loop.\n",
    "global model_true, source_amplitudes_init, SNR, SSIM, Loss, ERROR\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "t_start = time.time()\n",
    "model_true = model_true.view(nz,ny)\n",
    "# number of shots per batch\n",
    "num_shots_per_batch = int(num_shots / fwi_batch) \n",
    "criterion = nn.MSELoss()\n",
    "for i in range(fwi_num_epochs):\n",
    "    # initialization of the loss\n",
    "    epoch_loss = 0.0\n",
    "\n",
    "    for it in range(fwi_batch):\n",
    "        iteration = i*fwi_batch+it+1\n",
    "        # Set both optimizers to zero grad.\n",
    "        optimizer.zero_grad()\n",
    "        netD.train()                \n",
    "        netD.zero_grad() \n",
    "        \n",
    "        # Forward modeling based on AWE (DeepWave).\n",
    "        prop = deepwave.scalar.Propagator({'vp': model},dx,pml_width, \\\n",
    "                                          order,survey_pad)\n",
    "        batch_src_amps = source_amplitudes_init.repeat(1, num_shots_per_batch, 1)\n",
    "        batch_rcv_amps_true = rcv_amps_true[:,it::fwi_batch].to(device)\n",
    "        batch_x_s = x_s[it::fwi_batch].to(device)\n",
    "        batch_x_r = x_r[it::fwi_batch].to(device)\n",
    "        batch_rcv_amps_pred = prop(batch_src_amps, batch_x_s, batch_x_r, dt)\n",
    "\n",
    "        # Prepare the data for Siamese network.\n",
    "        d_real = batch_rcv_amps_true.permute(1,0,2)\n",
    "        d_real = d_real.unsqueeze(1)\n",
    "        # train with fake data\n",
    "        d_fake = batch_rcv_amps_pred.permute(1,0,2)\n",
    "        d_fake = d_fake.unsqueeze(1)\n",
    "        disc_fake, disc_real, o1, o2 = netD(d_fake,d_real)\n",
    "        # Measure the Eculedian Loss.\n",
    "        loss =   F.pairwise_distance(o1, o2, keepdim = True).mean()\n",
    "\n",
    "        epoch_loss += loss.item()\n",
    "        loss.backward()\n",
    "        # Updating the Siamese network.\n",
    "        optim_s.step()\n",
    "        \n",
    "        # Clips gradient value of model\n",
    "        torch.nn.utils.clip_grad_value_(model,1e3) \n",
    "        # Update the velocity model\n",
    "        optimizer.step()\n",
    "        # clip the model value that keep the minimum value is larger than 0\n",
    "        model.data=torch.clamp(model.data,min=1e-12)\n",
    "\n",
    "\n",
    "    # Update the learning rate each 100 FWI iterations .\n",
    "    #if (i>0) and (i%100==0):\n",
    "    #    scheduler.step()\n",
    "    after_lr = optim_s.param_groups[0][\"lr\"]\n",
    "    after_lr1 = optimizer.param_groups[0][\"lr\"]\n",
    "    \n",
    "    Loss = np.append(Loss, epoch_loss / fwi_batch)\n",
    "\n",
    "    # compute the SNR and SSIM\n",
    "    snr = ComputeSNR(model.detach().cpu().numpy(),  \n",
    "              model_true.detach().cpu().numpy())\n",
    "    SNR = np.append(SNR, snr)\n",
    "\n",
    "    ssim = ComputeSSIM(model.detach().cpu().numpy(), \\\n",
    "              model_true.detach().cpu().numpy())\n",
    "    SSIM = np.append(SSIM, ssim)\n",
    "\n",
    "    rerror = ComputeRE(model.detach().cpu().numpy(), \\\n",
    "              model_true.detach().cpu().numpy())\n",
    "    ERROR = np.append(ERROR, rerror)\n",
    "\n",
    "    print('Epoch:', i+1, 'Loss: ', epoch_loss / fwi_batch, 'lr_DL:' , str(after_lr),  'lr_vel:' , str(after_lr1), 'SNR:', snr, 'SSIM:', ssim.cpu().detach().cpu().numpy())\n",
    "    if (i+1) % savepoch == 0 or (i+1) == fwi_num_epochs:\n",
    "    # Save the model and the metrics.\n",
    "        spio.savemat(fwi_result+'SIAMESERec_.mat', \\\n",
    "                         {'rec':model.cpu().data.numpy()}) \n",
    "        spio.savemat(fwi_result+'SIAMESEMetric_.mat', \\\n",
    "                         {'SNR':SNR,'SSIM':SSIM, \\\n",
    "                          'Loss':Loss,'ERROR':ERROR}) \n",
    "\n",
    "# Estimating the FWI time\n",
    "t_end = time.time()\n",
    "elapsed_time = t_end - t_start\n",
    "print('Running complete in {:.0f}m  {:.0f}s' .format(elapsed_time //60 , elapsed_time % 60))\n",
    "# Save the final metrics.\n",
    "np.savetxt(fwi_result+'run_result.txt', np.hstack((fwi_num_epochs,elapsed_time//60,elapsed_time % 60,snr,ssim,rerror)), fmt='%5.4f') \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a432cad8",
   "metadata": {},
   "source": [
    "## Plotting the Reconstructed Velocity Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "173492a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n",
      "findfont: Font family ['normal'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
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k3erB0v705BG4ukc3mej4cXTks+7x5ujoel35rdMha4dHZ6Ojw3VlwOr2ad3nMstLtkRyCyyFhPdrBnXUlc071b6qKyGCeU0qdyoyhYGMlpMDMPNSRkNBuZ5Mmuuv26eRDUf9ET1nI3Pdu6nchyHNcnJgSx2Ln6vSxzae48tFUhUDmTruozpyR/7N/LPgc66iciiNRNIMC295ukr/kv2nKn2zfdOVW2bbqzTLlvXYwSrdaUer9Fw75MrdaTdX6fW2zuUdsiRp8YStrtKPPfhq32CUhmr4LCcnhGMWyXldQXkZmZqfuPLTrtjntiWptlldR13ef+3/x1V6kSWpucM215V7ra2v0vjMZmZr7KEqvXjPiMt7cNFFVXqP9VfpQ1T/ZfZold5vfS7voM2r0pvg3igpaGb2HbuhSk+QI0MJvC/YW1vWbWb2OXt7ld4KEnpmZnt3LkofhmBSbzQPjudan9X71uSI1namC2+xO1y5ddDf2L+TbU6DjX064Bye2bzxZOu3d77d5e2yJVX6Xru2Sn9liy9nD4O6A/vJnI9rUDn0jXy6H+vEfoukTCNf24Q0+xa81xDl4XUoBsDz8eFMfdyunIysWSwfjNJzWEeDyqF/5bUNy0Z7tZykINePaZbLifYL+zJpM/9sUf2XQLoZ1NED7wBd475cM9gQo81FMmvYLq5uXyavQeUiSZ/c3mcxNWQ33IDryEnkzEQuLAfX0YR0IyhbVwatSZ/rSrDhdXVll7jfov7Itb/ufs/M20dULqr/VOTComep+w4X+fy6coCRbFQzUx9zqjKCUR2RLFVOinUmEltI9L4RtSPXx43gXtFY1H1njtqB5SIJ20iqEutrUDkna+e/2+lopD3NsR3w/sH3wjnXpLxT2bdE34E4WVPfkIXn70nN2O9v4NqP9TXpXoH0Xs/SvVV6dDcYKsuZ4XPymnK8LaVRuj5ao/g7CqyymbmvWX5vwvSAvP5x6vxmTam2JuWdqswd4mRloY3NDl8O7x3tkbCPI7ncBuXlJBGZ6Fly8L1GM2luB9Kkz3W/96kry5p7P/hXg1Y+JUk6cXqQJN3Lj1NRERZCCCGEEEIIIYQQQgghxCuZ0k4t9qN40SJJOiGEEEIIIYQQQgghhBBCiFc4+sFICCGEEEIIIYQQQgghhBDiFc7LK4bRBaCZWFcv1CyvEXo6BPsiLVsmpwPPmqORjn+OmcQ1wHvX1XGNNFORmehYY9+h1ixr+kftqKtjjZ+blIefIz36yF4iDdmcPnXUV2xXPUEe0gjqb0I6sjEsF8WYwHSkDR7FdKobB6lJeQOQ/s+QjuJIoK6ymdkoaCujzXH/ok77YqpjN9SB94piLnHe4kwePcsfXPm+Ks1xhTDWCsZ8udg2uXKXjTxZpXf1LnR5221ZlcZYR//cfidb7nubXm9Zvp7PCrXeMzGMZt085ooN9if93GGIV2PmY9a83/6kSt9kd7lyQ2BIx63N5a0b+X6VvqP3DVWaYwDNg9hPQ84wfbuO2myXh7FyMC7U0LCvY01/itPDdWC8o+3DaVze3P8lV26VbbEcKyE4zGp7okrjc5mZLbFdVfohW+PyPmYfqdJtNlGld9l5rtyT374yfRiihoAv6PqpA1V6Qe9+V+yj9utV+jxok5nZXDtcpTH206N2mSuHsaCupuBBDXA2d9lNVXqlC6Jj9hf2nio923z8K7z3YYjr9SV7iyv3le/+/fShaZ6cn4xihLB+Pj4axrxZQOUwPlpU/0CmTWbxXgJ9KsbGaVA5XCuivRVeF+1N+FnwulwMRa4jiouC/c3Pvy9TzszHY4rimGyENI8Z7pmiuHiRjn8UpwLbj/duUrlTifMYtYnbkdsnRfYXxQJAov1N3fieM4n5gGWjmDrR+0HOvqM4NBHYjkbNe/F1deNe1o0Nw22vu9dEor5ncu8OUdzEujGM6tqRWX5/Hb1TcP11Y39Efi3yw3Xfm6Mxqzsfo/elaP4g0btTLrboTL4byPmMJpWL7Bvbgb42isXGeY3WzQvnAV+TG7OZ+JZc3/EaG40ttiuK2YbXRd8b8PqL4DNzHbhX4fcUJFoDc3bK5fDe3B85/zqTvU/dNSV3DV8X1Z+LXcifI98SPUu016zrW6K4iXW/o6j7fU4zyKu7f6q7puTy7hi08oBiGInTQ3HBYGm/dJZjGP28YhidSXTCSAghhBBCCCGEEEIIIYQQ4hXO6ThDI4QQQgghhBBCCCGEEEKIVxKl1VfAEi8JXl6SdHMGS1t18ghcdCQ1Or6PRLIVp/pTW93r6soq1J2Q0dFhBttYVxajrpQEt7eulMSpHJeO2hER2UeD8tDOmpCOJAVYbiUnA1FX5o+J+qpZ87q60jFRHdE10ZFuLNuEdHTsnOXN8Mj7qkx9Zl7egevHccJ7sexce5LYss30oHWPv2P9LHsAbXz91elBUXrLzGzAtlbpC9d7I7tvXZL6mgNyde0gD8Z8wt7nPv/+8Ieq9Ilmd8po0oW7M+nJGyZw3N9KRrAj9WPHqudc1rxGkkK7pS3J6/2s/ZErt9GuqtKrSXpvAuTlbhz7drqm+0pX7qt2W7ovSbCh9Nxn7R1V+qDNc+UOgeRYc6Th8lBObcf9F7m8nIRSx2LfH51d41V6dMjLCFoP2OqRJI/YtfiA5Whv9zaxrDvJD/6o3VmlV5BmHEoRrrGHXB5KGKI8IsrumZmtt3VV+sH7r/ENw9uhFMiALzbrmiRNeHP/nS7vrfaFKn2ZPValWcoPZejm7/G2+flFf7dKj8N1Vzl9MLNL9myr0v930RUu7w67pUqjfOQ/v+OPXTm39gxZHix3CeWhVMrDlIf+Ked3zbxkS4Py8LqhTN2t6kTQL2BX8d4MfeNaytsI6UYmzXWsoryclFOTyuEzX+Hto3dBKjxyH3R+JF/FfhL74M8o7zMowdgH6R1UcD6kOygP5/8iSO+kcudnruH6D1s9DtLneS1LxbqHnDcH0tiOSL+K25trB7c3agfm5drERHWcSjkzP9a4V4n6jfY02TrYjtAWWdcJ1ym89yEqN9fy4L3R3upqe5v5NuNYPEfl5lie3HjyNbl+i66Lnp/fz9EesX5uB9bJzwn90073Ps5tzsF2cCqAHXRRO5xLPcU21ZXpRiKXgfD7TF3pqbr3rls/f88Rya7n3meie52KlD8TtTG6V9130EiCLZJmxM+RDG4kL4rrNMrIDgR1cBubkF4QlIvI9eNM5CLrLm1R/XW/z2kE98rdl+vM7V2ZSO4ykr+r+x1Z9JxRf+Tk+yIpP+a0fCE/tca8xspSknTi9FAsGyztF8+yJN0/lyTdmUSSdEIIIYQQQgghhBBCCCGEEK9w9IOREEIIIYQQQgghhBBCCCHEK5yXnyTdwMkjcE3KjI581pX6io5/5vL4OHbdI6S5I8Bcx+mQxoukz+oeSedyOXkbVq1A6srmNelz3faeqlRb1Fc5u2pQubrSfqc6trnj5JHt1JUYjMox+JyRJER0/L0BaZRRuN4X67g9SW0c+/Q5PvOtIGMxClIVDWr8EDwcz9Ue8I3Hg5PaLCHn6oA09gGpB826Mclofb7/77k8lJp7yF5VpVlG62KQXVvgpGI8X7I3V+nP2dtd3vfuf3360KQLsc34zGxjA9lbu+s63prG7+Y+Lx3mJOPsLpd3iyUZOpR722P9rhxKfW0m7SmURdtiK7PNRXm57WPLXF5fd+rjbfeDDhj1R8dSsNOmlz5avDKN7dGJTpd3YCjJtfUsTh03utnLzs1anGznxI5ul9e1KslIHcF7N718S9RGX2GSv7vofC/zh5J9r7O7XV4P5B219JxtJIm4CnQ9PmXvcXmP2mVVeu9fXpAyWM4L59liygMJkcXXPVWl32V/5YqhbOMEDSjKGzZgkjTJ6Q+CrN2f2Ptd3h8++Mvpw+OQ4auIZejeBL7si4EfQ7h+nNM5mRczP995PcD+R0mVK6gcjgvtA5Zfnjph26Y0l1g6cUXvUJVeZHtc3h/ZB6r0D2xNlUafaeZ9C9vfbfbVKv1Nu7lKX2aPunLoF4bJ7zwE92YfjfSBj95I+nrfK8APh1Jzw5BmmShcE1kWLSe/xfJP57QsNQmODV7H19TdRLJ0Vt125K7jZ6yrfRPJbWE/zg/ysE3cjrqSZtgmli17LlPOzI8L2ibbAH4OfH7Y3mj8cMzqSvRF9442jZFsHrYLJRYje6grJxfB9oGgtBz3RzRf+oI8BN/rae/qujEas7rwdwj4PIHcnmtHIFOIaxF3Da4pp/JuzZwOiado/T3V92nMqysdzu9puObm5On4uui7gZykFuc1KS/3TrSAykXTnevMlcM6onGJ3lUbkOa9T27/xP2Gn7kduesiOTau43S8/0eyjXWl1etK0p3qPMjVEfmFKIRBZGORFGGdNnE7ou+OTjvsk6ObTfnr11tZPiBJOnFaKJYOlvZzZ1mS7sOSpDuT6ISREEIIIYQQQgghhBBCCCHEKxz9YCSEEEIIIYQQQgghhBBCCPEKRz8YCSGEEEIIIYQQQgghhBBCvMJ5ecUwKgZLm4obwFqiDUhHWqJ1dW6ZnJ4vS4eeSgyZmVBXyzZ3TVRHpA8cxQQ6lRhArdpVh1ON9xTpZCN14yxF415X45rrGM2U43Y1g/pRJzrSMG4G5SIt5ZwNz0TbF8LNzLoT4rN82sdnwXYt/sBTLmv3ty9s3cYG+btRkOzlWEQDZetyTSqH9XMd0Mbea5Jw+Ps6P+GKrbWNVfpm8/F8Fj85UqUPXJQq3EJxebZbirHzNbvN5WEMn29/99aUwfFfcCwalJcZs443eR38tvZkuBPH/cC/ve/2Ko3PifGXzMw67WiVPkQ6+BiD5LP2jmy5gzAwGP+G6x8aG6jSozt8fKD5q3ZWaYwpZGZmu5NN9A6mjhzZTIFzXGwzHxuhoyfp/R87QvFOjrfVKte7oJnuvcPHU3H3aqS4Ccf2UayCHhjQIZrwzUyabf0aSDd81j9Y/V+q9KDdX6X3U0yG2+xrVZrH7HaIt9UGnXrX+E2u3MjXOXARgGOB9nyNLzarkfzOov5hl7f7weRbfvLKP6/SG8xLJz/17cvhImoHxhN4ONM+M+97I3+N9XHIm1z8AzMzCL3l4iU1qBx+vsLb8KyuNJd+p/+fVelNttqVw/hAT1DecZjT54KRrbQtrtwy2245MIYUxpZqC/TbF0z4WG/H21I72idSfKNdbX7ub4a4ZxiTy8zH8kI//BnwVWZmu/8/WKN+i2N4RBuXnZDGecxzH5+NY5VgWYz5csDycBtzMZI4dgu2l+O/YFlsB7f3dAQowTZyrBac49g3HAsQ/RW3MRf7aCZxobBOvC7aHEexq6JYSgjXj2PdkUmb+dhH0RjlnsvMx7nhPLxfFGcJy0WxmqJYR9g/0YtErm6ug+0D7412VDcmkpmfn1Hf1421xbb4XCaPxwX7p27Yi+i7hqiOKF5XECPptNz7dID3xnGKYqVxe2u2MYrFU5colmHu+4uZxH/J7Wm4vVH7G5l7sXng+24zqB/bEb1bM7m+imI/RfF8o3fm6Dlz24Vo2YjGLIr7c6pLcW4fyn1Vtx+RRtCOyP7qxiWv+8xR7Cqmbp+eSnuje+W+7zs0aOXEBsUwEqcFxTB6+XGqP0sIIYQQQgghhBBCCCGEEOKVSmlmE89bSryEkCSdEEIIIYQQQgghhBBCCCHEK5yXlyRd12Bpy04egat7lNUsLysWHSedyXFeBI8jN56nzhyRxFuuTTM5Zp27V932mcVSOqdCVAceYa57tDeSlmtSXl3pvagdkSxQ7uhzJAfA9WNZVGRieaK6Nh3ZWN0j0lGfYt4fUh7KJH09JRf+ytOu2N4HL0gfGiyZgfdO0g9dA15yByXTjj1O8hw5n9GkzyB7MGvpmMv6pf7fqtIfst+v0izXhDJrKGlkZjYb5NNQgm2VbXblftd+sUo/9sirfRtREgulxBq+WCiZCZ9ff10aGGy7mZcVe6f9pcs7DBIieN0DdpUrd79dnc2bC3I56yfWpfqO+3aM7GukD0e8bFTP4tQJh0ZTm07c6WUPe966t0qPUx3HHk72Mv/6JLs0t81LNx2aSPUfJTm5y7ofrdI/GFnj8o7C/Zb1Jymu2Tbuyk2Aoe4f9xJvczpTW0bHkoRNo7vpyh0cT3kj95CkG4z7wuvSHNx7xwW+XLTegH9a+vonq/RP2yddsXfZX1Vptqt9Tl8k8S/tN9znIRuo0jvWX+QL52Q92U/iHLmR8vA6VI9kP4kycZEkXRPS/Igs+5cjkpHB+rkd16fkrEuS7xrs91ICF9sT0ETfqBvtriq9xn5QpZeM+Zu1gW/Z0+tlkhaNJL/c7E0PgDKbZt4m1ow97PK6wLU/tSzZMLfjP3T/cpV+v33c5Y1bmp/Ld6W5v3OJby/KI95ht7i8gyAVdc+mv5MyLjFiK6QjKbhX0WeUaEJ5qUhii9fH40EegvJsLL9VV76qI8jjOnPk5Lxa1TkFb06i/sC8qE2R9FndOk6l3EzEIA5m/h6NcySTh/eO5N7Y/nAdPCeTNvN2FG0oWRIRiWTzcO7is3A/sfwggm3G+nlcIpmxXD/yNc8FeTlZvpkoCeUk0lrdr9U1DNeBdoY2fIbVjiLp7NNRZ125sOi+kRw5En2/gMxE1r6ubH5d+fpIMi56X8+9h0fvtHXf/3m/FL13Iw1IR/LpTK6vIhm0U+3fSG4w994WSaRF3y9ERPXX/d4g2q/W/Y6i7tjO5Hu8HFEbT1UKrq7kXd3v4H7oew1aWUqSTpweivMHS/vAWZak+4gk6c4kOmEkhBBCCCGEEEIIIYQQQgjxCkcxjIQQQgghhBBCCCGEEEIIMXNmokolXvTohJEQQgghhBBCCCGEEEIIIcQrnJdXDKPOwdKWnNRM5F82G5CuG0PmTPw6WjeODhLpJfM1kY4p0oB0M8iLNG9PRSM6iu0TtfdU9F7Npj/bFKwBHMUpirRyGzXbFZGziUhLuW7sICaqPxc/ievblynHdWD9t1K5fwkNuYduAHVc+q7vV+ld40tcsZGNKU7F8nWPu7xt90PACAz1c4W/1awFKW7Hid0+fo1hlXBd11Ifb+La3nur9E/ZZ13eG+2OKr3FVlmOJhjSXNLqH7cUy+aDEPBpx04f38N2Qz/eQzfAmCkNSPPYgo1desv3XdZX7Meq9EaIK8SxZuZA+wftfpeHMZjutWurdJOCKf3AUjyf3ZsudHkdC5LG/zGMCfQ4Pcwq0NLfTNr8eLvFYIsUp2jxyhRnBGMAmZnN7kqxpTBu0SIbduW2jOfHfRza3+hturxzwXlhn3J9fZ0p9sIhFzPAbC7ESuiHdvGYYdyVgxQnYce2gSp90fIUc2lo/wpX7thXIM4Da8ljWCRQGL5w9SOu2C/Zf6rSN0FsHDMfuwljYXGcm0ftsirNscI+O57sb+RxaJQPh+NjGkXrAT7nQFCO8zCszhCk2U+i7+L4Q2gG2A5e265JyfnX7HRZ17Yl3zVoSW/6ONnHW+zLVZr9Ux/E/kC74r7H8Rsc875lY/eVVfohmPvX2r2u3JfsLVV6tW3K1r/eUmyzm+2brtwWW9myvWZmc2C+/LH9bJVeYrtcuc/93LvTB1pT7N9Aejd+eDMVxNhEHDsEfcgiysMxxHghURwaJhdXKIpHwm3MietzrBls1xzKw1guM9nY5erANnK8F4xZw/F8MM7Ngczf+V7MnEya41NFcZByMWpycYnM4mepGz8qeq7IxvAz15GL2RMFTWH7yMXA4ffWqP7cs/G9sO85lAPeD8czmhNMNO5INI9xLKJ4TNGzRLGscmM2lwueRbDvoxAb7HfOZPwktr+g/lxs2+j9mfccUfyaHNE0i967ozg0mBfFY4ramPtOIfqeI/reIAJjQkYxkrC9/MzRu3DdmDo9QZ5l8hrBvaL4VM3M37kdUR/WzWsEedGY1f0uI4iFGvZpFCu8rq5S3XGKYjo3g/vmvm+JykXP6e5d18ebJd+lGEbi9FGcP1ja+89yDKN/rRhGZxKdMBJCCCGEEEIIIYQQQgghhHiFox+MhBBCCCGEEEIIIYQQQgghXuG8vCTpZg+W1n/yCFwzKBgds84d2zaLpb5yeZHsXN1j1dER2roybjMhJ1sWHXE/1XZER6TxcyTlV1cyDuHxiqTacuXM8sei68oNMlFfoawT14dyRU1IN6hcJDGIzz0alMM6+PDn2yG9MSWX/uaTrtiO9RdV6dev+7rL+/Ym0GVCqSV65llLQU6u6eXkegdSh4xsgI4LJNimMZCOdb9heZKWuwVk5szMJqDj3mGfcXmdlmTL5k0k2ZBvtd3kyg2BZtXv2i9S/Uk2acc3U785uSqzWH4B5augDxZf/pQr9r/s71dplo062J0M4X67ukpvICNAqTmWpWrYs1X6ibGUN7phoVHBxDT5BViz2uFBj/uCvUuTrNPBppf6WdP/UJUenib5lJgHUi/PjHlJxIHuoVQ/SMCM22xX7vB4knOZ1+mlYy4GWa39bgDNjkI9s8GOWEZrmW3P3xukZNZYemaW2ML232jfcnlNOxfa2Fel1+IEN7N/Z79apb9x/4+7PMOT6eg/Fvtir33Ht1u2d7Jdd1VptKPV9gS1t1GluT/usjTv2myiSn/C3ufKPbXt4vRhI8kO5dYbehYnNdekPBzqyNdi3q20V3sc1CPgut5rvHbdbZ1fq9LvsU+5PJyfK8GhzCPHuMvOq9IsFXi33VClG/Cg+8BWzMwWgHTdl0ieDSUu0Z73Ux1oV1defZ/Lw3nx8FdfU6Vf/WNen3NoIkkp9rV57cR+21Ol79lyc8r4MCl1oO/d+F99HsjhWRfIzk2TOMHxZGeOclADlLcf0miMF1E5XHPPD+pH6srOcTsiabI5QV4OLocya9yO5zLl+FmwTpZswTZGMm65+rhsRyZt5vu0bhtnUgfKGeK4PEfl+jP3YqLxw7HgOnI2MZOXBWwzS8jl7sXk7hfJuDF1JRzrvjBhXvRcEdiOSOqRieTlcnYQ6SQxbI9TsGQcchok7+rKhUXM5DFz9z4VqXa+d937Ru9mkcRbJCeXe+828/uRut9LnKrUPLaxQXm4xYlk8lgaOVd/1I7ce3Gr+00xk/fMuhJpSPRdSSSh1wzqiNqUk+yv+51NdL+ZSBHm6pvJdza5vprJvM3N1eh7nsi+o+es265ISjIas6l2NAetPCZJOnF6KJYMlva+syxJ928lSXcm0QkjIYQQQgghhBBCCCGEEEKIVzj6wUgIIYQQQgghhBBCCCGEEOIVzstLkq54dWk2JT9yikfcoyOldeXqoqPfkXxa7hg3H2eueyQ4dw3fu0F5TUjXPTb7fPerU46fE/sAJXy4T6P+yI1F1B/RcfJIAgDhI9INSHMbsf7o+DHWwX2VayO3A2WTuB0/BemNkPZKWXbhzz9SpfeMeTmv0S8mabF/+q7fqdKfHPlpV669PclBje5r+Bs0QdIC2rjw6qddsf3DSa7oxEYvSZe1l4d9sY4bk+RJW7vvkHf0frZKv88+UaWPWqdvB8gmXWaPurxhkH3pB6mYD9u/d+W+sj5JwU2zP1QrQokFHr8bU7JjsZefuarvgSr9N+NvqNIT7V7e7JxdSZLk88v+rstDqTmUodtDkm4ob7Zj5zLfxiPQd/vg9Ds9y8Lr0lgfHvNyKz3dSUpm97ZU/6yuo67cQP9QlX6HfdblfdN+tEp/b2eSkLr0fD9+KEm3jyYCyqKhnFwj0EOdRzI4c0Gmpc9JPPl+RHm6JjlsvN9BktVZaVta1jHg9NLMrrV7q/R6lNQyb9PYXr7Xew78zyr9pflvdHkftD+s0js2gXTWXebBLh708jjLl6dn+U37cCpm97ty2K62YNFCqb1H7VKXt90uqNJoK2ZmWy1Jmu39bCo3TZIO5y3n5RvlwfVglc/qGdxbpdd2b6zS77ePu3LodxY5uSovwbZ4+0jKoG67b8WVVZrt+2P2kSr9xbG3Vull3dtdueMgGffkN680ymydjvp0gPIeb523/PWPu2Lb/vyS9MGr95l9GOXlroP09y3PW+gzykGhRNqTVG4ppFkKCsepj/LQ3+6BNC/2uAbwfvhQJo9lyxCW/cL5fwDSkTxWXfmtnKyV2fQ2Yh/XlbyLiLRuMC8vZerLcZuiF4mc/NtMZMsi6T0E+7huv0X9EeXNy/yd782ycLk2Rvc61Weuqw2Vky/kOrk+vA6Vf6L2Rnl133G5Dvwc1YHfDcxEqSjnW06VU21HTU63FFxURyRdVzevrtReXcm4mUhx5WSvmzOoA9d0XH+j7wwiKXhs046gHPdHM5NX9zsgs/z3OVwO2xHJ0zUydfPnuq5qJoqZ0Xc9Obn96Dmj7zmicTnVUAo56tp39H1cXWaitlo3jAVe16A87EfcTtJ3NrXCQnxt0Mr9kqQTpwdJ0r38OBWXKIQQQgghhBBCCCGEEEKIVzKlnf4ff8ULiiTphBBCCCGEEEIIIYQQQgghXuHoByMhhBBCCCGEEEIIIYQQQohXOC+vGEa9g6Vde1IzcSY6tHXj0ESxbHIxh7juBqT5uB7mYR0zeZaeTJrZF+TldIrrxiWK4P5ADeMByrs5U47b0YA0PzNeh/fm58c6WGMYY0c0KQ/ruRnm0m6SgkVtZb73QEou/7EUb2HbX17iy2Eohrf7rCuu/L9VetP+1VX62EaKO4B90KR2XJGS/+jKFHMEY9eYmT323VdX6Z61e13eyu4UZ+TBB6/J3ws/N3xW19oUD2FuT9KEP7DhfFdu1sBYlT7xZxTDCFRMZ12Ryg32e01VjEfyEfuYy9tlS6r02+32Kr1mzAdCGu5OcZu+TPEsvma3VWmM+7P3Ly9w5ZzdbvZZrn/WpuSFVz/iit1mX63SH7F/56sYS/FJur6b/v7gGy9y5f6BfapKbxlb6evoblZpjIXy/fXXu3Idq1KMiWNDHPciMX/tzip9YLeP07H8/KEq3U9xV3BcdmwbqNJ/d/mXXTmMLbV5wgeAOdhMsRKW9O2q0nOd/r7ZHPjcbhMuD2OyYNyc19ndrtxaCAj2ZXuzy8N4MBzDCGMVPWRrqvTmET8unRC7afzIbJc3cTwtEMf2wVh0+bgGvYvTvZd1+tgz2MYJeGaMj2RmdovdUaXfvfFzLm/v2mTgH7f3V+k77BZX7p5H/k76sNE8uC5dkRzZB8//fVcM+//NY19xeeu7X1ul8VkwvpOZ2XmWbGK7+Thcd9vrqjSO+8eH3+/Knbgz+aSOW33clWP3wVi4NcQvkLMg1tv7+31sIoyHdZN9q0rfuvXbrtzfrPiRKr3M/Nj2wc2/CQvup+w9rhw+57d33ujybBQW5Nvh77zOvQnSQz6r6+3J5x/5OsSkWUt14HVX0GbiK5kNCq/nv4QfvG+xW1PMuSocZqs6cG9yX+vbTr+OAyxgvBPyk+3QjuPeL+R1HubT5yiuEMaKwb0Kvw8MQbqf8nKxjzgOTdQOrCOKfYJ9wHFj8H4YI443x5jH8YdOg3R/FIMEOZWYKXXvG937VOMrYLoZ1MF77yhWR47TEXcliomBcR6aQbmo/rqxUKM4ILn4rM9XR64Pon5r0GdsP/to/IzPdklQjsc5F5+kbszeKG8mMXtyfRK91zN148ZEcYtz8XEbQTu4TWgjUX9EsXiRyNajeIvYLrQBfuboe4ic7TSoXN2ACXW/K6kTx6VVHXX9Ara3GeRFbYxsMaojZ6eR/2gE9476Cu0j+i6tbuztKD4V15+rg/sD62hSXiO4Donmfm6s634vyNSNWc7gWDQy9TG57x3/7aCVQ4phJE4PxXmDpb33LMcw+k3FMDqT6ISREEIIIYQQQgghhBBCCCHEK5y6/4ZDCCGEEEIIIYQQQgghhBBiktLMjj1vKfES4uX1g9F8M/upk+km5TWC6+r2QnSEHo95RlJwWI7b1IQ0SidER3b5+GrumHh0NHkp5e3LlON7YbsalIfHaFFiyytDWe8VSTNuZCOdhd+Ykm96//+q0pvsYlfsyZ1Jgq2ncdDlnbduV8tyS8/30jzIPPN17AM9gGX2dPa6R0cuq9KrVz7h8lD6p0mdtXH4qiq9fTjJH73hXV5OaRSkV1CWy8zs4ftfkz4sSF760h/9viuHz7J323kub+nyoSp9J2juzLZxVw6bP3rfQpf10BUgJbMADKSn09fRDiefF3sZnGW9aWy2DIPBkA2fuCtJPr3xV/63y/vGHT9epd/W/4UqfZDkcn7PPlSl5zqJILPVtqlKoyzXo92XunJojyi3ZWb28CYYlyHIYCkQfLY3+ayFq5PN3Wx3VumP2q+7cigTx895e/dPVOlPvvGnq/Tf7LzZlbvw/CQzNrqv4fLw847NIGVHR+Ybfc0qfbTHS7yNDKU53mhL5Vafv8mVe3Q8zSWeL+s611fpg4vnZcttGk/zfVGnl55a1Zec0iLbU6X3kFTRpfZolT5q3oZRvm6NPdQybWZ2GdTB44KfO2meLQFZNJyDQ+0Dvg6Q12tr97J5c6H/x9uTkR1p+naMwNhONNpc3ujmNMd7ViUJyuFuL1GFsm6Da/1R9H0gD/iL479bpd/c+SVX7q8uf1eV/o9dH3V5dg/4jKFkdH84+Muu2PeuXlelD3V7aauVsBitsR9UabYdZIIW+3faX7Ysd1P/t9znO9+V96GHfyy1C+1vFelRohzeOlvv8lA2D/2Tjbhi7t532w0u7x9tgWfpgjbeQ5MaVThZngiXbVzfb/XFZi1N0qCNG5su78CfgdwoCgrw3gcu47V+9BpoM6q/8d4Hl9V9JLOGErbvC+r4IqQ/TXn4GafBp2mjhe1gWSrcazX7LAuqgX6R8n4Knu1OysvJpzVIkeRNSbJ1mlQq3u8KkNTjvfFiaD/vjb+OH2CusizSANTBayeOdQPacQWVWwz1NykPZQWx/ddQORxCliJEyeArMmm+ju0Kxx3Hlvfo2G+sdDgA6RshzeOHc7pBefjcKM3I8xHvxTaMcwnvRX6h4wqQsP0iSTNiX61NyVlvH3PFTjwOksSPmwf7DtsxzU4hTWO28Mq0B9v7r0hOGMHrovcqnAcPW5ZZP+Wfs68/STPu/RfQDu57tJ0G5d0FaZY7At9+4bokefzUP73cl8P2v9tnLb8SZLX/FVTI/bE2U5+Z91dOipbK4Xxh+0ZwTRmiPOx/buM18G4yCr7xK1RuANLcRgTXA5772Afs/3De7Q7KYfv5fX0oUx+9kzt72U152H687vq8lO6JL5JceObeswZpTt8H17F9oM/D+rg/IunH3HcUvL5guUhyLJIO435EsM11JfoiecdI5j+SPkeivsI2DgXt4HtznVOw/2tAmsczJ5tXV8qU68hJ3DHc33hdNC6NIO9UpGmj+qPvHaNQBFN0Zv4uhBAmSTohhBBCCCGEEEIIIYQQQohXPPrBSAghhBBCCCGEEEIIIYQQ4hXOy0uSTgghhBBCCCGEEEIIIYQQZ57SzCaet5R4CVGUZfn8pV4iFBcPlvYHJ0V2WfsUdV1ZaxZ/NmtAmrVsoc5ZDdLbPZLiN8zqOlql51IMjzndKU7K3k2khd0OY3Ek6SV3LH7OFTsG8Sd6FvtG9nQnXf/dD14IGX6ce5emmB6HRn2cB6wfdZuXXvmkKzcHYnhwbI7dj6R7X3h50sJuTjRcuQNDEIun3Qu5Xro8xQJ5bNOrq3TP0r2u3PHjKebGxHH/G+ix3UkTHdvB8ULGQcB1/7iPGTCvM/Vpc8y3H/sO44dMHPdxQE5AuYUrffykw2NzWta3st+LDB8Gjf8dj1zk8noGfJ/kQPs4OuFFaw98HeJIQDf2XO/rdrFthjr8DeC6jkuS3fI8WNmZng1jP5mZHdkxH24GGawDDfrXl17uYzXdYHdX6fWWYpq83/7ElXsdlOuh2FXnTjSr9ANtKc7UvXatK/erj6SYLNM0hlFPH9tPuuHz37SzSl/bdq/L+0VL9Q9OJBHxc/b7iIKfXpTiFP2x/azLe2BkbZVuBzsd3e2FpdEvjOyg+B7Hky+Yvyq1t6/N+6Ant6XxxDnMHAJ7xrSZWT/EYxo23w6MMYbxgTDei5nZDfadKr2FOrwNdjI3WYo9c4vd4crhvTFmkZmPI9ZnKbbAfvP+A2PPbCVh7/tBXJ/jy6BPPW7Jnzxqfr7s/i74edLCnn99GqejsEbxuNuONLazLqG1bQi05GHt7Fjq16WBvq1VGsfFzOxm+2aV/sCB/1alC2rvI4vSs3zS3uvyMK7ag/dDkI27fB1uDb/Zz5E3Lv9alcZ5xevXWgigx3a1zJL/xthHbB+4PjbtXJeH6w/ee7YddeVycazMzDrHU9nuPSeq9PuX/WdXDu30K+v/vstz/hWXTo6Lgp9JK73np9P6MLohxbtiO7qsP9nEw+tf4/JsMYxTE9YU1nbHJbHuHq9J5SKd/Zyu/+30+T/Dfuo+ivtzV0pe9F8frNJ7xr0fw7hqT95xpa8DY9T8tM/CfdiO/+33AQ5ciyjGhO2GDvqzlOz9Lb/IjnwROrlJ9WOcFIw5wjExroCx/TTtF4CLPpL66slN1B+5GENmPs4I7JunxbIZgnQUiwdjkHAdaB9rKQ/jh+B84RgNQawcN7cwdtBQcK+b6R0O99F/ku9vcKdmqyg68SfgOrjXrLfS2rAB1gZ+Tox1thbq/wq1Cfv0Rp/VcT3EN9oA8Y14XNBPcBwaJIppgtsYfhasE+M7dVHfbwb74/0qjm3duCj3UF4D0jD/upYecMWObIA99FBQB8ejwz5AG17riy2+/Kkq7d4zzfxcBV/Qez35FoxZS+/aHM9mChfXhkBbMTNrw5iN2B/U371r8wFmRjZDG6FYx9rgXkPzXR76mo5V6bp5FIOvCe9VJ3bTc+ZinDTI/mCPPu37C7D3xath/Lb5/Y3thvlJfXXF5f+3SuMe+sGvUrA3aO+sa/xYru1/oEo/MZbijI7e6ePhYntnrfJ14Lsxvg+E75LchxBjF+Mhzu7yezDcN48f8e/Mx0bTu3vPgma2HBKNO+chGKuU36fxfpjH30Pg9wv4nZWZ/96KY6Hic9oo2EfDL6QYG/rQhH+nO7AZvl+AsVh+uXfmwyMpjuyR3X4u4fdus7sotjJwBL/TGqUv/LpgLcJnCeMgBWtse/CtOIxLB41tW3vrSX2UbCeyCRxPLMfjjnDe6JRd3XadlQ/eX7S4RIgZU/QPlvauDc9f8HTyu8X9ZVkOPn9BcSpIkk4IIYQQQgghhBBCCCGEEOIVjn4wEkIIIYQQQgghhBBCCCGEeIXz8pKkWz1Y2p+ePALHkhMoP8B5CJ5OH6C8SOYE8/BoK586xXsvDvoej5bzvfgzAkfIF1+Xjp0fHvfHg0fgGHQXHW9GWTc83ozXmJnZPnjoUZ+18LqnU3P3p+v4GC7KYzW6my5vx7aBKo3HvVHWz8xsYgIl6fxxWzxa7SSZ+FlQym7Un8rtGkhyD+3B8WM8yu6k9szL0DXsWZc3AQazfX+SBzj2+DmuXO9gMs7VnZtc3gP7k2TasYfhugEvNdLRk/quv2/Y5e3YCdIEOLZNV8xJDa3p95Jj2ydSHQc2wBH0pTTpHof6WQ4F+QwcGX+3r+Ofnv/HVXojaWassdSu99vHs+X+4ZP/o0r/9UXXu7wB0PLYZBdX6Tf/72+6ck66g4+1o7nDQdmfXPfnrhhK6L1n4lO+irZk01+0t1XpT9j7XDmUO2uONFwezulj+8A+WLZiAPp4nz/Kf8WVSY4CpdqOjntpg77OJM+2gG6A16HsHEtxDcGzoByb2XTZuFTfHvd50NJxaK4f5cNQWmO1+XmFUnlt5MxRZgzl5FhCD+XZWLbsdnt7lUbpMDMvbYeyZfNIOnH7eJpzI7u9HN7C5c+0bP/omJdgQ1iSw0lJoJ9nKR3sHpKqWHh+Gpuv2o9VaZRcMzM7/xvJ1z75Rq8thHPw4/ZPqvRX7ieZNTwFz9JqN6bk/BuTXN/b2r7giuF8HCBNn8HxdINdnShL6G19/p7UB3sX+c5CWUEcF5byQ9m5oc4Bl/cR+42W9T35TZLzYnklBKschDFr+rn/M5f/XpX+1Mj/6/I+3PsfqnQkU4vP9mV7i8vDsf3+TnCU1A73LBt9lvO9KLXEz4+SaWzDfwhpWA46folkgYfAh5Js1KyfSuvjiYcDyS600wGq44NQx9dJngjvdyOkee3Bz+zn4fPij4A80c+RvBTOab/ceOmpt4LtfJE1n4M23gpr/52w1rPk2I2QZimxjZDG52xQOfzMcoaYh+M0ROXQrrgdWPZIUG4A0vycRzJpkq7rGkx+8sjtJIGFdWJ7b6U92AYYJ5ZixHHH+RLIdE97r8I6cdyvp3KjmbSZfx/D57qRymEf87M0II3PxWsD1nE9vZvthncClF9s+GLuM4879sGRzN/N/HOupTycWjgW7Mfw2SL5RQbbvBHS3MYFQV5Oeo+V3zCPx4KfZwqW0IvGE/MGat6L+6oJ6eiZI4lBHCe2bwTrZP8EzFoMa8MOWhui+huQxudkd43t5e8v8DO0d9ZaLxmH791OSpLrB9+Ccn1m/l1+mkQf1oF2Rfax8Or0PcTBET8wRzaC38T+GPB1cPiBHCg7P4u+UznRhPb3eCPrgP02SuhP63u0D5bCxO9V9tWVWfMfZy3IP6eTr4N3SZZZO4bldpNhOTnQ/F7TemBPcIQl7zPys9Qf2P8svYcSch30roN0guTdcfpe6WhGApDL5a4xiyX1cnDIBQS/W4u+p+Lvz6beJ3cM/qSNb3hEknTitCBJupcf0XIihBBCCCGEEEIIIYQQQgjRmlzsPfGSRJJ0QgghhBBCCCGEEEIIIYQQr3D0g5EQQgghhBBCCCGEEEIIIcQrnJdXDKOVg6X9x5OaiawBjDq3DcrDY3OXgH7qEOmn1tX97cn83SzWQcbPqEW8yhfrWpz0y+f2eD1SF28Cn2taTKf0nLNIx3Ue6NLO60zpQxM+DlIT4gDd1v81l7fHFkEzkq4rx+k4F0SiOVbJUUuarxhL5PDYHMvB8TdmtyWd2D3D/VDO68cu6U2xPjjGELYZ6zAzW92fYp5g+zl+w+6dqY5ZpC+Les8cgwlBjWQGtYQbfc0qzX01ujsZIOss9/Wn2DPYB0/uXO1vBtrEPZfsdVk93akdu7esSBl3emncxe+HuAk7vU3Y7an+D/78f6zS+0kUfgL6+832ZcuBMW8WfteLfH//ukur9BZb6fJ+y36pSn/vz1+fMlijHOcWzVXU5f6lvt+q0m+32105jH/yGMQjMTP79/bhKn3v/mutDhz/ynUdamhvpjhFr09ximabnyNo08+MpTHjObemLcWPwtgkZmZHJ9KcXtaW4noN2FZXbq4lv7aSghLgeKL/2Eri46tsS6p/bJvL29id4rxgzCGOqfMtCJZwFQVN2QcxhtA2ee7vsvOq9DftZpeH8ZMWmY8pdu9IGusjm5Pe+muv/rYr9ywsaJ0Uq2nT/jR3UWt7Tqef+xj/qUkLZHM8fcb1YHi/94XHmvDcFAcOYwH0XpMW45/t/GNX7MMT/75Ktx33fnL2kRNV+u7eH6nSd1Kf/sHYz6Vm3LXQt2MI0jgnBv0+aPHKZI8rKJDJz1pqM8boWgn2ZubXDY5/hfF87rBbqvQztD6iLWG8KzOzh7/7mvQBp4gP/RQLDy9trQn/Ly//VVds0O6v0hznC+ONLbM0p/G5mI+M/Ib7jPrubp37OtkRxp/gWCiXwLNsgOt4D3YrlNtH9cPy0LE2+e5jG8mffhHSFF/Gx/KCNMt54zrCz3JfkIcuG+Od8Lh/EdJrKe9m6IM/gT54ky+G68HDH3uNz2xAuieTNvN7b47r0rDW8H51CNK890b7pvXXEcV1QfDdge8VSbKjcjr2AccpwhhJbDs4hrhV4Towb4Dy0F7wOT9D5bCN3oXa4nWwP/s4xLXiuYT93aC89kyaY9k0Ic0SJtCu3oF04cjt9IKH/db0We5+2N/sF7FPNwZ5UMes633cDxcv43Z6f8RnwzHiOYHl7qQ8LBvFjwK77bnG79FHd8CayPaci8WzKvie4D7yodgWrIPjD/VAnTuC0Bn4nGx/zcx9+d5Igz7jdRwDKOcLuBx+5jbi/QZSsmMBxcV7mNYYBMcd/Rj7J3zmxb5D8L3TxQTifmtA9fBdg5n/vuEg7PeO3UNth3ZgvDUzs2W9aY+A7xHT9moIrW0dS1PfrepLm5/HNr3aF8R9UcNnzVqV5u6S/rTv37HpIl+wC74T2s0xdbAcpmm+4H44ik3UCOL3wHsbx50+MgTxmCA+UA/FGDoEe6sGxIU2o7jLR/x8xPhGYaymfRRrCsHYRPgdyCgZcVcQ0zgTf8i9e5i59oexmYLvVFxcKB4LnDM4nkH4xhC0lyb5QpzH/P0QxDTisUBOwHVsE1Pv7yPrbrPjGx5UDCNxWij6B0t7x1mOYfQHimF0JlEMIyGEEEIIIYQQQgghhBBCzIzSFMPoZYYk6YQQQgghhBBCCCGEEEIIIV7hvLwk6VYMlvZvTh6B4yPjuePpZv64d+44vRkdOa7XplkDJFmAR8H5fBf8GotHro/tOydbjp9z/uDOKn0U5BHmdHvpur07k2ScNflIcLKJrgVJmqyTpKcWdSYJpSe3eRktd4QX+nThlU+7Yp0gezUOEnQRnSSVtW8kSUNd1bvR5aHc1FFL/cH32rvpgio9q0FjhseW6ag29hXKwh1DaQrzR8hRfodBqbyJ495AUFJqZIeXg0IJpeZIo0rzmI3sTn1l++iYNcgULl6eZANQrsrM7MFN10AdvoquK5L8wLzeZJx7t53nC6LcI82zn7n696o0ymPhWJqZ/Xd7b5V+wrxs3i0HkmxXAc1/8hKvbfAoyL/9vv2cy/ub/w36PCgRxNIa0B1vXP2/Xdb/a39RpVGGap75Y+F32w1VGqXwzMw270+6L84Ghlh3E2j6j+iH1vQnyTicEwzPkT1jIDMJR9xX9z7hyqFUIMusISj1dTHJXL3L/qpKo/SbmVk7yLhhG+81L9eH0nX8nF+121q2aYHtd59RVoyl8faAPWIbv2frWtZtZraZNJOwXWwTs0Fe7sFtV1fpC5f7/j5kSXbyVfaQy8P588D+q6r0XJIvuKHz7io9TUYQ+hglx+7ZeYMr1wsSF4dI6sFJJOL6RXI8b7w8zZ932Gd9G+1ua8WysR3u8190v7tKsyza57a9I334DPigaL9wI+XBconrdCfJnF7WneY7z6UtY0n+EiUdj9K6MfppkGnhPQfKVKHUEkuTQR9j/5qZ/ShoHl1r91ZpnGNmfty/Y37ct9uyKv0H9qEqvWM9SbsgDfqMCp1oHywugFOQx+waa83D9DmSFr4R0huCciyrBcx6N8i3fAX2eyQ/tnxdGsBtn6NFJZJh+jqkcf6QrFjHFbCHvIv2kGg72Mf8nJ+ANMunoazWYniPuD1QNbmRPuOz4dhG8lK8/h7JpDdSuVWZNLdjY77che96pEo/9eeX+0x0Qzh+/B6BefdQXgPSOI8jGdyNlJeTB+Rnxv5uBu3AOnjODUCa7RQ/41aWnyXK22itYR8XSfShTe/LpLkd3FdYRwPSX6RyOB95vmAe7ic3Uzm87nqaCPiuhnXwv+QdgDS/Z0aSZtivjaCNQ5Cm+dhzfZLAc/J3LK+H10Xv0+ir1gblmEbySSjXdGIHyWZF8pQ522T/30jJjgGSmsM1fSM8KI9Lze8oUMru2CjJs++APU3DZ02z9xZ1m5mXx2LJ1mbrKrqu8bJzjd5UcPemC33hzLrac6uXTuzrTnvxbdu8XLiThgvsA/dnbe1+khx5HGTc0H/wOETjgtc1gnJ46x4vudYBMn/HhmCdpjnRsTg9SyhdD/bWRfJjR/admz6wbDTI4fUu9u9BuJ939kxycixhiOS+95gmY5eTrjPz/q8nkK7D707Yhp3sZmtJ5mmwxKC7F1zXTuXwnYCfv52d9lR95BhwnKIwFhEo88ff+0z1x08OWvnIBknSidNCsWiwtLefZUm6/yJJujPJKZ8wKoqiqyiKxUVRnKpqpxBCCCGEEEIIIYQQQgghhHgRUDuGUVEUV5jZ22zy3wgOGvy7i6IoRs3sfjP7lpl9vizLR1rVIYQQQgghhBBCCCGEEEIIIV58PK8kXVEUP2FmP29m1+GfWxTFiu4xs98ty/KLP2wDZ0KxbLC0Xzh5BI7VmtyRYMrDo9o5eTozf/yYJRGQ4MzVrCtArqRJR3Gb2A7oTj46jJICl9D47cscX6Xj6B2DwVHt3XBUOzhK7Y6/87H2Hug8PD69g47DRuMCEmmzQLbnBMn29IAU0ujjC81npmTvQNIRiCTdjk74o8ONtlT//nEvjzUObTnSnFel8Zi5mZeAYrkmBI+ar+1/wOVtGU86Gdx+lA709fnfhOfBEfWD0F4zs2Ob4Tj84vyx8IXnJ423vesvsBwog7ao30uToWzeW3q/7PIOWeqfVaCFgfJuZmYHLbX/9bu+5/LG+tLhye2dSTLpdnu7K/exkV+t0kfume/ynOwE2vegdww/fv4XqvSP2ddcHso8oWTaZ+wdrhxKh+0h6b0d94O0E/iWWQtIOhFs8crl97u8ofGBKr2uc32VRkk+M7M2kKLaPrzM5a3sT2OBElUT9O8OcMzWkETabJCTvNm+2fLvZmbPgBRcG8lj7SeJuilQPs7M2wdL42GbUVKrjbRdltgzVXoj6V2g3BnK8B03LxdxGOx5Wt54ykOJT743tmsL6eWglB331c2gA/PJiSThOK/NS1XgdShxZ2Z2AYz109BXqy0vRThEukAHx9NYOJ+5O5hzq/w8++Xz/0OVfo99qkr3kJRf/1iSNhnqXu7ycMzQF9yz/u9Ylh30GSXO0PTX5quYJuWE+xOsg2V70ByvpzzwSUuvfrJKo88x81J+N9h3XN6Vu9J1z/WntfmTbe915R6yNVWaJemevP/K9AH2GSxTc+TTMNYbzYN7JjTvASqXk1Iz8/uHL0Ka92pYP+/xUCIM61tL5fA63vugDd8F6UjSbSPlDWXuZWYX/acHq/STW16VMj5D+0SU6GNJKexvbC/LvaGdNikP+/XTmftyHTyX0N7XQpptHedPJI+F82WtL+Zs6T7KwzqwHL9H7M6kzfLyb2x/qNLBe168d3Qv7I+1QTt2ZNJ8HfcpkpubZt4XNoJ25OzNzI8Z/xPGVZlyPH4Dmfua5aX3bqRyeO+vUN7aTP0s84VzdYDycMzwOrYx/LzR8mB/8LgcyZTjdjDtmXQkq8h90My0I5pLPEewzQPwjvs4+bhce83y48TPj/57Fb3jNuF9Fds7QHVEcxXrx+ecJrsZyGOhDbu+oTpwnnF/RDbh2pHPQrl29/1FZAM87iAV6CS7HqcvTrBOto+laZyWLh+q0ruG/TuACwEwZJ4rMnLyJPNX97uHnu5UB77fmnk5tmkSbDAWPavS3nWcJMyO4fs6fyeEY43yaU1fbtZSGD9uRyS7hveLNIXwe5/RoGB7xgbMvHQbPeesxRn747UNbY6fC/sE50Ej11ibPl+6MnN1WrmgTlcO/A5L0iHsF7Ao+rVofrPfmWrjTw9a+Zgk6cTpoVg4WNpbz7Ik3SckSXcmyUrSFUWxriiKe83sf9rkj0VTjiTnUDD/ejP7XFEU9xRF8drT1VghhBBCCCGEEEIIIYQQQghx+okk6f7WJk8N4Q9EJ2zy35Q9YmYHzOw5MzvHzPrM7PKT/7WdvKY0s2vN7N7nuY8QQgghhBBCCCGEEEIIIYR4AXm+H3IKMxuzSYGPvzKzb5dleShbuCjm2uRB/3eZ2VvMrNvyJ5KEEEIIIYQQQgghhBBCCCHEi4BsDKOiKPaY2W+b2R+VZRmpHueun2dmHzKzny/LctEP1cq69xwYLO1fndRMbFDm8Uyay+KTstZnrpyZ1yrF+qM4SKx9n6uf9IEXv/+pKr1704U+M6PVPP/mna7YUdDiHd3McX8CfVksBrq/bRTnBmPxzAYN4Llt2d8bbd+Ij02Cur9Yfye1aXR36sjepRSrBNqBmsCr+za5cuOW+oNjsjQnGlV6TZuPyXIQBJ/3w4AusV2+HRDfYx8NPMYMacdYIhM+1hHHHUG2bVmdPmBMqiGKGbU0je3SlV64fse2gfThPriONaih+dPi6KBmNDSj9wov7P2fOv+/Kr3JVru8G+1bVXqdpdhEc8e97XQ/dqJK71zrY6FsBlH3PZbczz8c+++u3OhXwPZZu3soJV/9gRTcYpVtccVusTuqNI/7A3ZVlb4bYn98a+RGV+4IxrXalxc+XnplijnC8XDQdjDGkJnvj7mW+tHZjZlduPLRKo12b2b2zra/bFl/08515bA/1tgPXB7OLYwnM2he6xZtYped5/I6LfkTnFdbbYUrh8/JMZIw7s8QXDct9g7EQcJYR1wnxp1qULAPbAfH2xmF+gdIZB37cSu066h5bXOMrYTtNTN7h322Sq+3dVCH12LH/t48stLlLetNY71nPD3nkk5v6xh7jMF4WOjL2Q8/Opxiap3Y4DXWO65JcffW9aU4XO+0v3TlFtj+Kv1aW+/yNsJ8xD741/ZrrtwiS3HaNtnFLm/3g7Dm4lLxsHkuCfIQ3C9QnJuuVSkO0JLeZ1zeh+3fV2m0qxVkR+wLkDnWej3+cfuS+4xxw8bJ/p7aBv3zGVg3eO+DcTbYxeFz4x4m2j9x3BW8DutvULlbIR3FssH14FYq14Q0jy22keMWIRiPifek+PlmysM2470HqFwD0hzjpAnpHUG5SAv/M5BGW+e+wjHcSHkDkA5ibro62HbwM9bHtnNXkPemTDs4NgL2PT8ntgPbzzLuaBP8T/Zwm4R21KByazP3anW/KaK4UMwQpAcgzfaQi/9qln+WASqH13F/5+IyNKhcFBsG/QReF8Vj4jzu4ym4D7F+ttNczB4ud0+mnJmbZ12XpLVhWvxNhPvUxYij9398d7jnFH059lUD0txX0T9Xzdlm5I8aQR72wUBQjp8Fr4viMeG9G0FcYayj4Ys5++DnxPth/ceDWEfch+ij8R1/dxAXpWl5sK+mxbiCB21Sp+JzYtZSHz9q4fK039m7zb8DuFjIQWzpnoEUE+i8br9ffXIbxG/dTe/JOYL4TlkfQdfxu/C8zrR32/EIxKvl+QFxf2b1+H0bxlaKYi65+DgUuxpjFc+i73NcvKAoTlF7Pn5Sdr7z36NYShjDp6tmHKRp94Pnxv7g+EDRd4ZoB/jM3KdQ/7Qxwz4NYhhhfGqOSc3xtqbgGNr4ndkoxa6uYoy/+Rorf3C//oG/OC0UCwZLe8tZjmH0ScUwOpNEW7blZVkeDvJDyrI8aGYfK4rid061DiGEEEIIIYQQQgghhBBCCHHmmZXL+GF+LDoT9QghhBBCCCGEEEIIIYQQQogzQ1aS7qVIx+CrynM3fMXMzNroDClKIR0e88c4R4dAliqSaXAyJ/74tI3CkWY8t9Xu+7cDZNyO8dFQOBLbsTjJ7/T3eZk1PLbMkmB9/UmOB/tgz3C/KzfYvwHK+ePHKKk0QbJXSBPO1LM8Vj9I+mD980iSCSWUeMxQYgvriNrEYLsOg2TSLlviyqFszzqSMdoHcjwod8Ttwv5gqR+UkWLZMpTDewba1Uf3wjaz9BRKBqFUFsuFYd+xFFy/JTu7zJI02WbzElXYB4/aZS7vV+1jVXqJJUkBlhW7zB6r0nOpr1aCrsL8DXD83ZuwlXCi+/b5b3J5KL/1JXtzlX5y/ZW+ko2QJrmB17/r61X6XSB7tca8LCHOl0/Y+1zet+zGKo1j8dRO36dOhq7H+4zlK71s1xQ4XmZeko3nUnOkUaXbQW5gWbeXq1oLHXKt3evyOsGucF7d5LR+/DzAuTNZR5JLQF/Acm84rzaYP2GM16Gc4zM0p1FWjCXYsP6HbE22HQhLe3nJu9bP1eo6BMveZl9zeblnY1+L9eOcM/OyfyifyX4M2882jL7mMLTp6aCvWLYR6xgGiUi0SzOzRm8zm3dkB8jupGLWO+glPt7c+eUqjX7MzPs/nMdN0odBWczvgS8x8374dnt7lWbbwXG5546/4/IW3vJ0lb7KHqjSPwBbNPOyhO+yv3J5uG4M2v1VmuUX8dku3O776sllScOlDzRV/tg+4MqhHfH6hWAer1FtgWQm+vx/ax+t0o9989X+BuijSQ2layBJNK3oHUp1bPN9anfCXo0lnxop2bEq7cGO3XOOL3cXpEm+quvdIBX1ONjsPb6ckzv6Ja/51AXyJUf+M8lNgaTZhT/xSJV+6i8v9+UiCSVcHrD9Q1RuI6RJj6Djfal/lvWl8Xzqs9QOfE6WfHoT7KNxD72RyuG9WfoMx/AuSLN8EMoC3ernwchm6CAeJwSXIpYgwjZiHdz3bHNIToZuFb2nbQTlmGZQfyT3htex1kRunvG9Io2KRqY+fsdqZsqZ+fa79y/SSNsMmfycMF/mX5GkuQ98+nxfbgDSkUQkpGcNkiTzw60lmc0sP+4s94Z9wDaWs4+gvb3XkK0/vrhlOTNzY9E1CH5sB/kg7OOGz7LFMKdB6mvWwJjlmE2SUu5+ID3Vs9h3SF932sewvLLtSHNk4evTejsx4fdPB+E9/NgQ+fkF6VlQIu3ouN9PjuyAlxOWicvJTZEEVu+qNE59nX5/NjQ8UKVP7Es2xu//2I8sX4Xy7CgL3EYyWkd2Y9/nZeKQaf3xODs9qOPKp1v+fe9OilzwOMxpWjc6Lklrzzz4TuXARprT0fc50MRZjdSPWJ+Z2SGQC58mAbYP1iywlS6qA8dimo1lpDa7Fh9wxbAOHEszs7k9raWFGRz32Z1Hs+X4OzKcZ7iPw3cUZq75fyuOZTkPwXe1SHY9J6fM5VhCGd+No/rwfT36PjHXdjP/XsXk3qcZfEfkPTX2I+7fO+m+/B1fq/rvGPx1279hSJJ04rQgSbqXH9F2vyVFUXSZ2WvMbImZZYVCy7L81A/RLiGEEEIIIYQQQgghhBBCCHGWmNEPRkVR/KKZfdTMznm+smamH4yEEEIIIYQQQgghhBBCiJcjpU0/wSxe0tT+wagoin9oZr/9PMVKMytO/l8IIYQQQgghhBBCCCGEEEK8BKgdw6goigfNbI2lH4UiyrIs6weaOU0sHVxUfmjDO8zM7AmKz4JxRlgHFGMPYFyAPRQ0BeMfcJwKrD/SC8V7cwyStkzMnv0UBwR1UftI5Ho/iH5j/QMkTn8I4vmsgvgB3MYofs8iqH8/Cctj3ATsU66D+xHJxSrqCa4ZpbHFOCYIx2HA9nKbovHEeBGoG4v9y3Vy/XhvvI5j+0RtRDB+D7cDx3YB2Q7GTMJyjWnC9YlOipfxWoj/dO6BJM5+qHuWK/dQZ4orsWbcxwTqHjlRpb+/6NIqzfZwt91Qpb9gb3N5G0aurtJH7gNNbpZUvTUl33D1V1zWe+wvqjTOpX1k63faj1bpLRTQ4p5tN6YPqClOmu2zQFt6Zb+fj6iljPrrc9u8fezYmfzYRef7uEdom6st5d1gd7tymMfzDOcMPmdkz1tdYACvuYx+jW0MfTLHQcK810H7eZ6i7XOsLdSkxvayf7oaDIbjgeH8wfZfbL7vo/hl529POuWPL1vu8oag77B+7iv0w0MQx8rM64bjmsh63Rjr58q/edLl7XxDmj+/Zb9UpXkdxVhTrPmdG/dnKQCCj33n63ho/6uq9LHdcMi5x+vsz1+a6ljS5vsb4xbhnP5p+6Qrhza2gtZOjGGEaxvHbcK1gfcSOGcwlhz7OLTN88h2MCYfjifHWFu+Z2+VHuv1frh7a/K12y5JsRx5HX3ArqrSHBcKwTVr9YFtLq+ApfjAJT4oAdomzlWOC4VxvjB+lJmPi4R2z3uTLRCT72ftj6c/xEkwbhOvxTh+vMf4mt1WpW+CWFgc8wt18KfZOsSyWm1PuDycd1junRBnz8zbHPcj+rV77TrLMWBbq/R1FNMO68S4ZxdTezFGF2v6Y/uxj3nfgnsTniNoL2+zL1TpYZpzuVhsZj6GAPogHnf0VxwnAPsD28F+Euc7+/Lcnnc9xVGbG8x3XC9xXnHf43zhfRy2GceC30V8rEu/98E5gr6W+yOKhYox+TA+BMaGNPN+4VzqU9yD5OKiTrY/+QWMHWfmfS3GxOBnwfHj+nE9R/+E8edaXYeg72pzcTp87Az0GXvMx4bBdYTj86EvwzFjO8X78Z4DxzOK34jjyXFG8N5ofxyfD8eF+w3tD9vP8Wsn3Nz37+SHMvWzv0ZfWzcmMI8ZxqmM3h9xv8exF5EoTgxex3P6UpjTvPagr835bjPvk/j9AOvAPuByuO/ieYZ+AvfXbB+YF80rtEW2D6yTY4Tic2M/8vghHJs4N4YcDweJYiRHMZ7RFvm9Cv1mFPcH34Ma9mz2Xu3U37j+YBv5WXBd5b7BOrBNR/PRMabZZq4+vle0H8F+jPwYtpH31Ph+nYuJxO1gn4GcG7wj4vxh+5iaqx8Y3GhPbDioGEbitFD0DZb2Y2c5htFfKIbRmWQmknQXWzo59HmbDC+b/9ZaCCGEEEIIIYQQQgghhBBCvCSYyQ9GTTNbZGZ/W5bl25+n7IwoiuIXzOw6Mxs0c/8k/b1lWf7Z6byXEEIIIYQQQgghhBBCCCFOA4ph9LJiJpJ0nzSzf2BmPyjLcu1pbURRNM2st0XWjH4wWj64oPzwhjeZWSupgLz8hz+umS/nZQn80VA8ApqT3zEzWwnybyyVgvJy2A4+Xop18pF/f4Q+P1tRdoOP4uKRaTwmPkTyUrly3EaU6uB7oVQe9ykeTcY8Pr6LsilMNJ5IdJwXpU34eDMe68Y+ZfvD/uC+8lI6+fHDOvm4Nx7r3m4XVGmWnUNJFT7GjUeV8V4so4V5i0jCAecIPueyCW+n+9pSf7CkBfb//Zak5b4KUj9mZneN31SlR+5b7PLcEO6G9Fpf7J+u+50qfYN9x+Wh/MATdnGV/pK9xZXbCJXu2OJlWexIOuHduyo1ZHanl4eJJAZxDLHvhye85E5/WxoLPub/DvtslUZ7Ywk2lJhhGTcc6ycCWR2UJmOpG5TzY1kgf680l9iHorQEPkvkr7l/sR9RooVlhvBe7GewLPqI6XKlSbZhwYTv7x+0JVmPZ0gKA+UNUCpluvRe6kee0+g3uX5kJcip3TTxLZeHcxXbv77Ny9l8y9J8ROkcM2+Pj4FdsUzX0YnU/svavPQZSoPcNZzudWI0L//RtcBLZqzqTc+Jvvsq2+jKoU1fS1JcuE7jWs/zAKVeWWoJQTmbSH6WpVJus69WaZyrbH8so5IDfe3V02SS0tyaO3bE5R3s7mnZjqsmHnDljrelPtgIEndm09fLKW4ev9O343iS0Jton5XNG+pO8o4sU4NjxvfFPsa1fiXJ9uLe7RD5MVzPsI5orWS5MGwX2wTKhuJ6fv2u77tyx7pTutnb4/JQRgXnJvuPxXtGqvSRbpdl+7uTVCXu8VhyZ9nYDsuxofvVLf/Ockro/1hqbvV46o9HO5P9rZ7w0qCH2lJ/byb/hOOJ85YlZng9Q3JyOfNs1JWLpJxwLHDeouytWbxfzclo8b1wP89rCq6raH88X7C9/C7ykL0K0umZUabRzPvGSP4Yx4h9HM5bXqdxvcH6WaYb+4fl9XCO56RAzfw7AO+b0Tdif0eyhNynm2AfGkk34Z6G5VDRH/I7aG6ecX+jL+O5inYVyWPh3OK94By3rqY1nP3TMMjt8TtRTo6cJfrQh/JzoswszqvpUlmpT7kdOBaYx3MJ7SB610bfzXaK/c32jdeh/bHcFvou3g/nJOT4WfC6aN3DPRiPLc5HlsFlidXU9rzsHNeB98Y5wusXzn0eW7xfJEeZK2cW245vR36fm5Mqi6Taprej9fpl5uXUcG7O5Flye2C2sUiWH/NwLea6o37kNT13r4lgXcLxzM0rMz8vInnbSFIQP/Nzct/lwHawrbzqpCTdTw7usEc2jEuSTpwWir7B0m45y5J0/0OSdGeSWc9fpOJfmtl+M1tTFMUfFEWx+PkumAEPmdl/N7OfNcvs8oQQQgghhBBCCCGEEEIIIcQZobYkXVmWO4uieL2Zfc8mf9j52ZMng0ZaFy9Xtvh7ru4qen1RFL9S9zohhBBCCCGEEEIIIYQQQgjxwzMTSbp5ZvY3ZvZqM3u+Y4tlWZb5s+jxfYbMbEpPZEaSdAsGl5dv2vBhM/NHaM38Uc5+OsSEx1xRRoClTBA+ko5HVvHINR9vxuP1DB5LRQmUpp3ryuFRcJZDQYkpPB7LsiYopRAdCcZjv/wseMyV24FSB5EsQZQXyVMg+Jx8lJ+P5U/BY4t9HI0Ryy+gHWA7+Eg3ji3LHmB/R5ICKPPEUhg4FmgDLGOEfXqUjiajdEIk24O2xMebV0CbN4Fs2Uzs4167tko/ANJFe++/wJWzhyHdsCw9N++t0j/b/ccub83J49hm0+c0tvmL9rYq/dDYGldudPPCdK9Ve13eIZDLmtuTxqyv28+lPpIORHJ9xVIPt9g3qjTb2Br7QZV+3cTdVfo7bTe4cjjuKH9ixsfrU7np/ZaXVdwCEhoorcQyQ+h32A/gvMA2YX1m3he81ta7PGz/kvFk93d3+v5AuB0oi4FtYh+E8FzaF8iL5qQ7zqV2RHJnCNo6S9Ggb5kuweZtNUckX4qSRF+AucT9kZMaMfM+OydhZma2fz/Ml+O+b04Mga4WKkUt9XX0DiT5yEWdfp6hL98+nNr7of7fd+VQupL7FPsjkoC5zb5Wpb9sb3Z5aHNtgdQN7mkieVHMY6mbt9iXqjTbA8oCoYTPX9o7XTnM47mK8x/bwVJwESg/u8vOq9KHScoF/RPPObTbtSBTyD4O11Vev1ASC++dk0gym273RzPSP2ZeygRldnhO43W8TqM8Jfo13i+gz2ApJ2wzPhv3KfqWi2ncUdoUZTG5T6P5jmXRR/C+Gfs02lviusp9ivdC6VUzb9NoYwfNywHiHOfnwjHDecb7J5TcYf+P8ko47jj/zPy85X0t3g/fl1gWKZLjQXkztCu2Rdyv8rNgf0RyXngdS6/mJJ/Z1nEu8b4F7QD7jdfiSBILbQ79yT56N8P2r6Qxy9k62zPWwWOGfcrvhTiP8V6RbHkkqYd2NF1aPf8O4J8zP1+w/by25eQYn6VnQduPJCdRyiqajywvinME+22Y/GmuTWa+//Hel9ljrhz2G68bvo78Hh3nO+/jcntNlrbGsdhKdeD+CW2Y142cJL2Z9+34nDx+kfReTgac34sRtmEfzqC1zfJ1bOs5uTr+jqJ9Avxfm58HztYnkq2jDPBk/fl/I452wOVwDUNZam4HwmOGdeJz8ncZmBdJIka+FmFZPnzOqA6eg4j/3ifVx3tNtAO2K/RDkbwo1sHSnTgf8d7TZSbTc+a+u/zo4DftqQ3PSpJOnBYkSffyo/YJIzP7mJldbWblyf9ynFWHUxTFz5jZz5iZdV8w/3lKCyGEEEIIIYQQQgghhBDih6Y0s2MvdCPE6WQmMYz+nqUfiorgv7NKWZZ/WpblYFmWg10Le57/AiGEEEIIIYQQQgghhBBCCOGYyQ9GU8d3DprZrTYpAtVWluWsFv+dkhydEEIIIYQQQgghhBBCCCGEOPvMJIbR/Wa21sy+UZbl3z1jDfohYhgVqwdL+9OTmol82GhBOhvXs6Dpstrak77n+JGkM7qs12vqol41a/mi3ilqz7IGK2op19W5nUsar6cS24d1czFmQD/FQsnFrGDNZdQAZg1jrB91plk3GHXwWU82F5eBNVhRt5jr4NgoU7AW7KWgX86atwhrOmO/Yr+x1jbq17J2OtvBFKwtjfb3NMU88Drz6dmW2DMt6zabbhPYRtTrnq7bnCYXa/bmNIH5Xqwjjux+8ML0YWO2mNliSPtQA/b6q79epa+yB6o062Tj5/W2zuVhjIJv77wxZYx2uXLzV+2s0hwzBZnXmWyf4w9h3IHh/d5m+/pSfCPU2f8p+6wrh+PEz4lzBnXxN9jVrhz6K57vOC9QS5p9nI915H3XPhgo7wu9Xjfq27P9ob9Cf8JxihCOa+Dje6TnYk3/cRdLZNTlYWwOjHdyh92SbQf3RxSrDn0NziuOJYKxx9h34XNimn0ownMYr8M8jru1IhNTh+v4lt1UpT9r73Dl0E+wn0T/NDyR5si8Nr+mbNs5kD7QfMS1f/ThhZZlB6SvoLzRTJr2HLOWjlXpE1/v9pkoBjya+buZOdMnH+e6H0IOzbpmzBU78TDcm+roWPpclZ7XSP04uy0fy+HwuPf5jc7USIx5g3GazPIxPMy8v8LYTByXIorBhzaG6z7Hj8Jy0TqNexNeo7GOqI13W4qJxv4JY/XxvMW5hX1q5vs12qsgHE8A1xvse55zq8A/7SPjQV/m+/u7rtyQrajSHKsJ+zGKV4XPibERJ9v/dJXGOAlsYzjWvA9Ff4X7Sd7n4zrCNpHbo7J9YMwGHj9c2zCOGK77Zn5t4PUR1+0oDgjC7cAYCBjTM4rjx3vN3D6R69gM6+gKituJY52LScN1ch6OIe4reO7zfhvB9uO4sx1hnXXjmHK8CY6Pg+BcjWIAYf1RnFiOq4a2j/bHz4LPyfGCL7YnoI1pP8Xjjr42asdx9y7s44qgv5oeG3ZOyzyOz4Lt4DmNfRf1I14XxXTC9nK8HbwuipWDaS63ANaNyHf1ZPaFZt5O2TbRF6Bv4TmXi0nIdfJ1CMb2Yv+U+x6FxwjL8XxHPxz5pyg+JI5hLgawWT7WsVne/rhv0H+3Ux343QOW43ZE9oz3jt4PsF1cDu2K/UL0fRTCNperA4nq42tyPpptDOvkOHO5WFA8ZlgH2w7aGdeP8HcsCI411s/24WMq+nZMtfG7g79sIxs2K4aROC0U8wdL+9GzHMPodsUwOpPM5ITRH9qk5NyriqLIezAhhBBCCCGEEEIIIYQQQry8Kc1s4iz/J84orX8mb803zexLZvYWM/tOURS/bWaPmLU+7lKW5dOt/t6KoijeaFb9BI4/Rr26KIqp+u8py9L/k2YhhBBCCCGEEEIIIYQQQgjxQzMTSboTNvmb4dSRxejCsizL2j9GkQxdjpvKsrwrrGfRYGnvOHkErosy2zNpM3On7VF1Y5TKtWfKMVhuFeU1gzrgc8eCJBVzbPc5rljH4pTX2ZWXjjkOcjwTx/1DL+tL0gYs7bJvJB2PXdCbjjqzjBYe4eVjrtuHk1TFCZQFOkISKrvhBOwRegDsn0aSFLRmhy+H10VWhzJDbB/4UyTXgWWbQf0RWGcjqH930A60U25/rg8iG2aw/vbM3818+xdTXu4n3QZ9zqvPeGknfM61x1yxLpBQelvvF10eSnesAykgPrr+VbutSm+hyXrvcJIMOjGafsee1eOPnWPe8pWbXB7K7Dxql1XpoxN+HixrS+1lSZJ/Yh+v0niM/Wrzx31RWoKPsW8FCZtIloU/Iyi/gEfcp0tJJoPh+lC6EseIZSA22cXZOlAKA/tgPzlUbC9LjeTklNgXoqzHEySFdKPd1fI6lnVC2QaWyMR2sGRQTgKV7RRtjKX3UN4LpX+4HErqRdJFCEuNXAV1cH+jTeBY87N80t5bpQdsq8vbaFdV6V12XrZdKB3I8n04RzYMp5Prl/V7yaeclKmZl2XZMpzaf6LZ3aq4mZktXv2U+7z7uyC7mfP/Zn69YT+cV1DyoD+N/DXmNakcriO8TtfdP0X1Yx7U37H2OVfsGEgGszQo3q9r1YEqPa/XN+ToeKpjZKPvkIXrnm5ZDuVEzfweiTmyL0lL9ixOHTw6RBKIuMb2+E696Py0jjwz5ufqOOyhjg2lvWHHgO+rtvY0MEeG5ru8nlV7q/TsrjRXD436tefIbriOx73RWub50Khfe6L9H66lJ4Zg/rBtR/vtJqQXwx5hN+0To3cAfDY0l0voocHmZjW89ONs2IsfaaY1a1YX+UKQwD7WpPV2H+yHoz0YzumGb2MH3O/YDnh34PkY9SmC1/G+swlp3vvxu88UbEfBuMxaALKeaB8sOd60PMcz6QaVw2fjNi6GP6Df4T5FIhtDOx0lO3VtotfsJtgHtpfb0ZVJm/k+iN4PoveNnuD1H20Yxylav6I1xfUblYvmdO5dZNqzBO2oe6+oTxdAX41i31AfYl7Dv+vYEbARuBfODzPag7RT/e2tNwwdPV62LPpOAX07StiiTzMzm9uW/PqOnV6GtAeu6+tuLaVm5t+Rjh7xeXg/lMTNSZaZTX+vwvqxvXNIVix6r0L2ORntZ10eflfC76Aoib0C9rzjJJEWyTsimMfPjFJq06UIUx/PISm73LsUv5vh+wxLy+XkAfmdC+vn9mP/H3fyenmZSbaJnLTftP4eTv29pN+/6xwcT+2f0wnShuN5+4j2kHNpDiJo+zzP8HNzXyMoB3vBfV76vPoO7h8NWvn4BknSidNCce5gaTedZUm6L0iS7kwykxNGU0ztQuRYhBBCCCGEEEIIIYQQQgghXgbM9AejM/IjUVmWA2eiXiGEEEIIIYQQQgghhBBCCPH8zOQHo09ZLEMnhBBCCCGEEEIIIYQQQohXAqXVl00XLwlqxzB6KVBcNljaX5zUTORYAEgzyMvpHpv5n9cirePoXnX1wHGisV43EulTO438oFyUtxTS3DfYB9wO/BzpO2M51lSv29+RU8I81FRvBvfiPGxjNBa5vjeLxz2nRR7FKWpQXhPSaPus+d20PLkYE0upHNoBxyLCMYx+jm5k7mvm9N2XLh+q0hjzxszHkFlmT7s8jJWDmsgft/e7chhv5sFN1/h2wHjOv2RnleY4D+2gF7ys27cRdZxRW3q1+VhH2I6b7U6Xd4vdUaVRj3kuaW33wTPfYbe4vH6IP4bxWTCukpmPS4Mxb8x8/2MMnOOkyY361K+yh1wexspxMZ1Ixxp1xOcFgQJQh5ufBbXCMY6VmdlDtqZKvxbyOKYO1sma37NB1xvjKvG44NhybByMsbOG+grjDw1BDCrWBked78vMx+JpWtKrXgAT97t2rSu32p6o0jwWaFfbzevRI9j+BbRYYB+j7fRQn2K8p/vNSxGvt3VVGm2TxyyKjTXhdM9TmscW24Wxmcz8WLzZvlylUWPezOwBiLl0t93g8nJxrQ6O+AUG9cbnD5COOsQ/wVg24brBa2VuDW9SOfTlj1Me+u8oLgWvI7l2ROsXdk+T8hqZ+rgcrllcP9aBbof3k1iubqxB7vtoXzGUuReDdQwFeQOUh/fD66K1/grKw+swPiSPO94riuuC7Y1ibUV7q2j/4eL+UF4zc6/oWaLxbGb+bubbe6r7ybrxX3A8uY4o1iXW0YQ0PwuOE9efu47rYH+CYJ1dmb+3+ozUjVHTzNyL64/eS/BedeOHRjGdolhezaAc3nsm7yJWMy96f4zeC+vUx5+jWE1RHY1MXjQu0dqZu4Y/N4O8XDwtvncURzca28gvIJEfq1t/nb+3qqPuXKo730czaTNvA9F7d93vcyJfG33fcirflTB1/3l33ZjOUZ9GzxnNpZ5MmuuM1rZovueeZyZ+IWffM+mPnB+O6oj8U137YKK9Ve5euT79mUErNymGkTg9FI3B0l53lmMYfVkxjM4ks+oWLIpizfOXqsr+0ak1RwghhBBCCCGEEEIIIYQQQpxtav9gZGbfKIpiVVSgmOTPzOyf/FCtEkIIIYQQQgghhBBCCCGEEGeN2pJ0RVGcMLOnzeyGsiy3t8hvM7NPm9k7zKwsy7KNy5xpegdXldds+G0z89JNZl4uhmVqUOIH5YSagRYISwuhlBPmdYJskZmXWkK5IAYlclDiiUE5IjOz5kijSvf37qnS23YOuHILz095h8d8HYdGU3+c2NydMhZ7W0GJHJbpOrJjfvrQSOdtexpe+mf8SGeV7uzyfTW6Y2H60A73bvdnezt6DmfraHQ3Uxsn0nMta/MmjPJE3N84FixdtBW0XubaYUh7WapxkHlqJ9vcB+ebV4FeDkpZmXmbRokxzkMmLD8Nx0l66jDMg1x9Zl6Ki+XCUM4K5w/KwpmZDdjWlvc1M7sY5NpQ0ozbhJJdPGZ32Y1VGqWhNtnFrtzuBy9MH+jo96zGmLViWb+3HRwLHjN8brQdHr8fBRm6m0Bqz8yPIfoWlujDZ+a+yvmyOWSn2I/9tsflbbS1LetnW8d2sXxaH+j9oIzbIuoPlEi7037U5aEEGUrN8b3wmbmNW21FlWYJNgT7nuXTUNYO28s+Am2C/fUSe6ZKr4A5YebHAqXgnqWxfAvIorH9IW2gibCf9D/X2gPZPJRgw7Vzk6125XCtY/u70b5VpVG6rpP8Ara/Yc+6vG/azVUa5/Tt9nZXDuUeedxxbFBq71q7N1vHugMPuryt85MO04Xbk1bZkfmumHV9N6XHXu//bc6uzvScF+1Jel4PLroo2172kyjR9x2QvEO7NDObAK2KPpIKRLt6csurqvTCld63sE0jOO67xtNzoWSemdk8WPtHdnv5PjvSAensrWI5lLryelfAXuIIKXBgnZHsUiQrFkkAIg1ID1HeAKTvo7y1ratbeLWXZd27M8ld2hAtbjlZliZVGvUjPje6jKXHfLkhGFuWTWlgGjruYWovXseyebn+ZgnlulJckUxNXfmgJqTZPurKdEXtqCsbhe3geRX1x+JMmufcviAvN1e5P9ZCegflDWTuVVcaiu8d9T3WH0l9I5FfOFV5qWjO5Z6zrmwRXxe1A2U4G0EdkZxcM7hXJMeYu1ckacmS5nXJtZfrxDy2dSwXSc1he2eybtSVgqvru+pKeEX1R3+P/GROMi2SxmdyUlyRLGbd+ti3YB2RhC3C9+qBNXF3h887FYm+un1aV1LQLPZdOQnDmdSfs53oXhFO5jT4HvO43+Phe/0J+K6Ly/m5SvWPQtm6IRFqh4+ge3G7kK5kVz0LmlV6dpd/rzp6JL3j4ndiZund+/HB99nYhsclSSdOC0XvYGnXnWVJuv8jSbozyUxOGJmZLTOzbxZF4X4tKYqiw8w+Z2Y/eboaJoQQQgghhBBCCCGEEEIIIc4OM/nBaOqfka4yszuLougzMyuKosvMvmxmb4ayQ6eldUIIIYQQQgghhBBCCCGEEOKMMxNJur9rZp83s9lmVpjZ983srWb2l2Z2g5mVJ//+iJm9sSzLZ1rXdOZYOthffmDDO83MS62Yeck4loMatySLhpI4l9mjrhzK2ww5rQQv7YSyPUehboblcnKwbBRK2UWSO9gHLAmGEn0sTZaDnwUln1gSLCdZxRJp2PfYJjMvU4VyPHyvfXAdSjyZeYm3lbYle69IZu0gnJGeoHPVOake/jv2QSQxOCcoh/JNPO4oVYb35nLHof/5WXCOoO2wnBn2I+fhvVHqax/pRaA8Fkv0YZ2Xwhzke91lN7Vsr5mXaNq9JcmPuaPkZjb/ip1V+gDJJM1fnCSrGm3NlCapLByXzSCXZma2zJJMEMqW3QQSXWZe7oyl5rBPUS5sY06byKbbH86zR+2yKo1jbhbbJs5pnFdcB/qalVDOzOzciWaVPmdDOk7/5+v84dRIJg6l59BHsH+KpODQ5tB/RPKI3Kf32nVVGucZP/P3QDqMeb99vGV7zfxYYH+wJB32P/sulAtD+UyeL2jDLM+G68MTIEPHawq2cSutj+fCmoj2zWvsIpBBZFvcYitb1n+vXevKoZ9gyTu0l3X2vZZtZ1heD9uMUn7c9/PG0udN3V5qjmVxp2Bf2Dd2oErv6vZ6KDgW2B9byAfdDHKXxwOJUrz3HJBXZXhNwTUd+4r9E65Rd4K8oJn3SRMTqb55bXkpPN5LYJ9Gkqpof7yPQ1+wACQLebzQNttIkwRtDucItxc/ozyimfdX7JPmuP1UWsN5T5N7FjM/huhPc3bZCiy7y86r0pEP2kdtRBlfnLc8fnivg6RZg/2D9s1zKdpboX/CMeN1A/ubxxPXB+xTloEcDSWmW69tbAO4VvC6FO1lkWhfjv1fV+aU9y28v5yCfS2OO/sdtCuE9/lYbi7VgfaH/cvtxTzu05ykL9sp9mnkg7C/ec5hHvc9thnHmfc+WMf098dG9rqc7bB9RHt7fL/GPphN8uxo09y/uA84DHOQZYFxLvG+qN3dO81p9teHg3FHP5HzuwzL0CPYH2wfeK9orcA2cn9E6wHeD30SSxB72V7vu7As2gDLQWMdvE/E8cTrIvuI6kfbicrxewrua/fA3iSa0+PBPMM0f6eCthjtJXJ7Kf4ctRH7I1rPub/RdiIJYszj5+x0a6cfC5x36Hei7yh4Dc/1QbQHi2T5o/0Z5vGeI1c/7yvQZ/Aaju3HvprejjRO0XcluHZyf2B/8/o7tab81eAf2fCGHZKkE6cFSdK9/Kh9wqgsy/9jkz8Qjdvkj0NXmdmTZna9pR+L7rPJGEdn/cciIYQQQgghhBBCCCGEEEIIcWpEIS+nUZblHUVR/LiZfdEmQ7jhP7e4w8x+oizLesdmhBBCCCGEEEIIIYQQQgjx0qQ0s2PPW0q8hJhJDCMzMyvL8q9tMl7REZs0idLM/oeZvUk/FgkhhBBCCCGEEEIIIYQQQrz0yJ4wKoriqee5dkqGrjSz68zsyaKo5C/LsixX5i48G7CWKGo/s2Yqxh3x8SC8jivq+bI+K9aJ10UastyOnAY/69V2TiRN0/G2zmze2raN0F7WYk86qZGONWr7Rtq7/Cyoa42azjwuWCdrt6LePeqScx2omc3jEmmF120H6vSyhuyhjA43xyTAOlGfe7KONDZef90rO7IuLZKLOcE2jGPBesyYh/OF7c/rLLNWbutYIFE7WKf9aYivgrEuON4EsmHYy5aeaHanD13wzxy6Ony7IEbGvPM57k96ls5ARxjtlGMToV3dYHdXadbPx7hIrHV8v6Vnw7gG3G+s0Y1g/0ca1DjWrIG+zta3rONSikOz/Mm96cN3qSGXpuQj6y6s0hgLxsz7nYdsjcvDew9Zik+1yS525VbYUJWO4kagHjXHfMCx4Hg4GMcJ/QDHULjNvlalMf6XmR+zAWgvg3OO44Cg32E9etTh/ibMJdaVRzDWDIPznfsUn43XlB/AGN5ht1TpNfaQKxf5WuQBu6pKs295dD/E6Orzc+Jvt6S4Z99cnPrjvG5fLopr8NimV1fp16/+epW+e/gGV26gfyilbavLQ/vGGEMYM8vMbF53ssf77WqXh2N4u729Sl9Fc+mrdluVfhX191rbWKXRV/F6vsnFrvLa9xhTEf0/r3O4JnJsBIxJ0mxrwN/9uOBYRHs8nC8cVw7z+DmxT6PYBQug/RzDA8F4Dbz/QK3+cykP43HwepDTzOc5jeV4zLC/Mf4Lxtkz82su2oqZ97c4V7mvohgkz8Aadl6w90bQ3sz82omxmngviH3F9peLUcOxn9D3sq/F8c3FcuR2cF/lnpvjouC9uO24hs8J4hRh+3keYBvRTrl90Z4a74c2zDaAa0UUaxCZHrsl3YvXaSSKK4RzJIrrEsXEOOTirvhnqRv3Avd4e4K1eI6LW+LnBM45jluH+5MobkfdOrhP0d6x33jfgmPL70u5d0uOvYhrDL8f5OI4zST+ED4bjgvPOfQTPJ45m6gba8ssb3/sP3B/xvMAr4tjpuRjuGGbsb3R2sPvGLn4NbyOYv1sYzhXczHsuL08Zrm4UHV9xGSdad5F8wXfK3g9QNvEOcF2hHOE68jF/YnWF64jF4OP83A82f/jOwF/J5Hrb75X9B1FLvYx2x/bLYL2h98d8b38Nb6vsGzuuczMhm0R5Pl3S9x3RWsg9jGvS7k5zes55u2BNpmlvivshAkhRI5Ikm7A0o9CrZg6XWRmtozKldOLCyGEEEIIIYQQQgghhBBCiBcjdWIY1f3xZ6pc7gcmIYQQQgghhBBCCCGEEEK8XMgf1hQvQaIfjJ62l9hJocM2xx61STkaPnaJskYs/YNSLz3BkV2UaGKZEzwCitIoLO8THQlGUJ6D5X0m2lDebNjlHW5LR0/b3NFkfy88lt9PdeD9IgkYPA6LkjVmZqtBWoelsxA8Js7HivGYP5bj9k6Nudl0CRu8N0oW8DFo7B+WgIlkJnLSDAetx5WLpDuwv/HIO8ptmfnn5rHISThwObRHlknqdFIEeckChPPw2HwkS7ARJKUiGZJd42n8Rh5f7G+OaniBJ1u8ejsU8yuYl37MS/6h9MVhai/K+7BveY99quW9lkx4O320LdkwS7AhaBNs6zgPWD5oA8jaRTKNON/fZl9weSg9t/wTIDs3Qo0ccxc5jl1iLWHpFZQeYRkSlDo4GkiYYT+yBCCOE84Rni+RPAX2MdoOy6wtXw999RhVgqpd7CbxM7i8Y7dtc8Xu7X1tlUYbMDPbAGsbyrjt3enXx64ekGw5np9Mx4bOqdLLr37c5X1rIsm9tbX5fmvub6S89tRXz/Y2XLnd65NMoXXRzY+kZMeq51KbRkl+AtvvVXBs8crk8/bvT/IOB7v9uO9+ENqx2TwDKfntb94KF/liTw1cntI9l/vMZkpuvybZ9+pOL0mH9sdzdbMl1d+ntiV5sKPL8/IZbB+/bh+t0jgnWN4H/fpqks3DNWWN/aBKs3wG+nmWBcK1COcxPqOZ2aDdX6V5HxD5DAT3TCybh/tGlC1jP4B9yusGysvhvoglT3CdZv+H+wCWFsK2oL9+liR3UEJ0mqyxk+FEOR7/LJFMJsrPRHtNlLljmVMv7ZJsgvsjkltpz9yb5YMOgkPlOrA/ovWgPdP3DM4zXntwT8pzegvYO+ZNl9yZ2zJt5uUScQ1kCSxsP/sFzMP1lscFx4z76qgbi9b9a+alytgvYB/kpM7M4vcI3978XjaSLcP6sU3sJ3FPFsk6oS/k97utUCe/i+Tex+aQPaPUI/s4zGPprJxMHM9ptBeWbcTnzo0f18m+KycRybaD5di/Yh/jSpSr22x6P3qpqLlcvALHKXpPnhNIpKEP5bUNy2K5SM6L11/0eTju7BcQ3lPn3sN5HkRzkGX/plhAMqF168M25qTZW90Xv+uJpPdz/WY2XWZsCn5HzEkbzgQcJ15H52Te+dmP8bqXy+O5hDYcvT/mJAvN8u9SXAfuQdj/5fYZkfwi7wM6wV9hv7HM6WjmO5UIXl/QPnJ2b8Z7Om9T2A5e93B/HEl4HwrkEodP7pWPBd9HCiFE9puhsiwHzmI7hBBCCCGEEEIIIYQQQgghxAvErBe6AUIIIYQQQgghhBBCCCGEEOKFRT8YCSGEEEIIIYQQQgghhBBCvMLJStIVRfH2sixv/2FvcLrqqcOEzapix3DsINRx3kf6r1gW9aNZWxo1mCM941i3GTXbWe8UdcOTZirruMY63K01fFkT2Wu8ev1X1GvFWADnUcwUr1/uY/Y8YGur9FGn9+91VvE61rJF7VbUXV0R6OqzFjvq3G6BuFPDFOMK9V/3BdrP3P6cti3bH2rIHh734zm7M/X/xER6To4DcnAk9dXRI942Gwua6V4Q04PLnTie6v+23egbTWWn6FrwrPuMMU6mxQ/Z11ElZy1NwWzwvkxHl7e/Y/vOaV2wx4dU62ik+bKkz9tmTuu9j3SyjwbzcUnGzlgT/jKI7XOt3evyctrb97Zd6z6jX2ANdJx3qPXO5XD+rLd1Lo/nf669bzvwf6p0sYsK/wWksQvGqBzceu/f935hI/gF9MP3Q4wlMz9/WK8bx3albanSPC7Y9zhGZt5P4pxeaw+4chgzhXX8lz8JsYm+BhlfMg+usosoDx+NQ2i9ISX/+g3XV+mP2/tdsR9AnIo9497vcEycilEfIGju4v2ty5nZgcfPTx8Wp0BC2+73AakuuvrBKr1lmGL8QYykcfAzGEfIzKznir2WY3Tjwip9bEfyEbMWewM8sTn5oObShss72JwH5bqr9O4F5HMax1Lam6bZkQ4oBz5paeHLtae83qV+/o18JcVjG7krpTespTkNfn1um19rdmwbqNILl6c4ajvuuMhy3LW2L5u343ha6xee7+fSg1vSpH6ox8dYQ9++5/xk4I9to1hs7WmezWrP6627tWKHt9ML1z1SpVdNCy6VwDnN/gNjc7C+PfrNCyD2DGv6ow/iOD8+DlK6F+8dHuUAbwDGAmDfjb4RY6bwWnOXpZhiHC8jF7+G4+Jh+++0m10exp3CvSDHQcIYfxzrAuO94V6T24Ht53Ua9+LRuGOdHIMU9wV4L2y7mdkmuzibh+3CMeP4LLiv5TUL24H71ShWJI8tvnPg3oFjHGAfcHwFtCWcI6PT4g8l/8114JxpTqQ2zmvzcRiwfo6ngnUeCuLE4N6H4zzkYqg+tcnHlVu8+qlsO3D/jjHKeE7jOHHe7Ex8GW6vj4Wajwt1cDzl4TuEmbc/flfFd5YoD58linXEz4ljsSuIGxbFEsI6sBzHGMKx4DY27dwqjXbEvrxh6f2G3/nx2bA/TjUOUvQuievILjvP5eXik0yPrZeP64K2hP0bxeXl9RHnP/Yjx2lDOJ40xopB2+d4Ozhm/L0G9iPaWBSXl+dSbiymf0eRrov8ZBSnGO/F31Hg/XLzzyyO45eLa8XvuxirqY9ij+H9+HsrnFtoizznEPahuZh2/Jxow2z3uXZM97X1YmjhPOZn8eOen0vR36O1M/ddYBSXjH0Xzi32OwjOrdx+OPpeUYgZU9r07zTES5rohNH/LIri4aIofrooiu6g3DSKouguiuK9RVE8Ymaf/eGaKIQQQgghhBBCCCGEEEIIIc4k2RNGJ7nUzP6bmf1hURRfMbM7zWy9mT1almX1k35RFO1mdplN/tvym83sTWbWZWaFTf7OKIQQQgghhBBCCCGEEEIIIV6kFGXZ+vecoigeNnNnZ7ngQTN7zszOMZt2PhO1WR4uy/JVP2Q7a9E3OFDesuGjZjZdhmC1JWmeTbba5aEsGh5zZakRrJPrxyPCeDSZj3TjkWA+eorHWaOj63i0l/NQigDzoiP0kcwa5vEzY14keYdHffnYeU7SzcwfK8Zn5mfh9iMoe7VnOPX97C4/LkdG01j0ggyQmdkESOSMNr2po7QOSumwzBrWcWLUj7uNwnQBBZ6lq590xfaNpGc5Qu0wkImzI6m+3lW7XbHxI6kfj+ye7+toQhqVgBYc8eVQzoqy3PCOQnrxMV8OZZ26fF5vRh5rXqeX7ohkCg866YT0zM+MeRse6E7XsS2iXAzK2QzaBlfutba+Si+gY/7oa9AXRBJsfDQcpXpQMu0J8mPod1jSB/PWQXsv3/WUK2f/C9Le/PzxYpxyb/TFdv79ZFd32C0u72t2W5XGZ2Y/iVIjLNuDUlQXg19nOREcz0tJku4Z8GVYx/wNZNDYrG/7LHsa0njK/zEq12V5lkH6n/is31zxC1UapZAeAgk6M+9TUTrMzGzpytRX6D9QVtLM7Bj4hYvO9zJ2KO2yfzjV0UayYvNAIvLoES9pMTqU5OR6BvKycz3dUMeEH8/mvkaVXt0P6/mwnwfoXxeu9FIpR8dTu0YeTlJwtsD7IJR4Q8kaM7Mnt6Vt0YXLn5j2DFOgD8L7mpnN6Uy2uXvLipRB65I9DMZzCfnQ0eRDsU/DNWofHRbvgTrRJnaQvB50lfPrZt6+USXuEiq2+ECVPjJEaw9Ou6XpA6+jbe3JCXUGayzKHqIcopnZyA6QbeT+hrVtViOQVMXPIMNqZtYx8FyVPrYbpA7JtfRcksZsTndecmd4xEv6zO1JZRttzSrNUnBop7bbtxHX6Wx7uc1eXdTtT3AtZplJlK39keV3uzxc2w5NpHnL/uM49Pc028H9yT4wRvK7HQvSc07QeLp50YD6WKYX9lazFngpTPSHxx6HfhzwAz8f9jfT/CTIbkZ97y+iz1gW5yP/88BrYO6TDbu+6yG/gzwM162iPNzXwX4P55WZ2Qnsg9G8nTrz5mdp5puIMqejmxfmCzaC5xyCduG9Bqgcbrd5zI5n8ngPXXfcUW1qaVAH1x/JxOC443VNKodTnMcC7RHvxfsgfDauA+8d7Z+wD/g58Tpcv1jJtD2TZrD+AcqDZ2H7dn4BbX0fPVgzaAd+5ufMtINxEuHo7/he0TyDOT0L1t9p77SwTvP7XG6dxrXdzL+TT+sraFfXUthX7DvXl9sN/npV3u90ub2r9/n4XQHvmxt9TWsF7/fwvZv3Lfhei99l8DsRSrzxO2JOOpFl8lB+MJJ7Yzk5rB+/i+F2oNwgS7BhW3KSbmZeWq2uRGQU+oElF/HeKImI7zlmXgqO5eowrACOH+5hzLyt93X6eYDtiiR9fZu87bBMetUmkjnFsWaJ3P6T79dPDL7XDm14jDb+QpwaxbzB0q7a8PwFTyd3F/eXZcki8uI0EUnSvcrMPmhpS1bA/wub/KFo6cn/F5RvZrbXzH7WDIJWCCGEEEIIIYQQQgghhBDipc9UDKOz+Z84o2R/MCrL8kRZln9sZsvN7P1m9oD5k0OtKE6W+8dmNlCW5Z+UZXnidDVWCCGEEEIIIYQQQgghhBBCnH6yknQtCxfFcjO70cyutslD3g2bPEg9bGb3m9m3yrLcdrobWZe+wYHytg0faZmHR1b5uCYeUeUjvAge/+TjtjnZNb7XFltZpWfT8VI8grzEkiTOQTpnfhik7PiobLs70pzq52OuewIJLKwTj+k2Jxqu3EDb1pb1mfnnxuPBfBQZ28VHpLH9eO+5bYey5fg5908k7YS+tqRfwO3Ae/OYRfXn+mdem68jkhjEtmDe/vE+Vy46+ozSJiizxrKHONZ8nBllzI4GY4Z1LnCaEL7948GcQxsep2PnKAGFc2mZeXmpXXYepL3UHB7dxr7nduCzXGv3ujzsjxvtrirN85Hl8JAH7KqW995gV7ty2B/8LJeBnFof9Pd2uyBbjp/zBvtOlZ7/P0DTgmXWUP1tF+X9PUiDDN3frP0RV2wjHCrF5zfzcpIoY3mDeamilaAbwsfw0Q7QnheNHLAcHSyvtxXSqNJFClVO/uM5y4NuoTNbyuwi//HA65PUxh/Yh1zevXZtlcY5+Oj4Za5co7NZpXft97azrK91X7HPxznI0ow4D7AdXAfCaxuOO9Yf+adFJHOKPhqlL56h+YISg0O2wuXh+n4YJCGGaf1CX8DzEUEftMq2uLzNsNbzfGTJjylYWmMfaP/weoASFzhGLMGB7Wepyi3jSUcK1xf2ydj+qB2Yx7Jfi7q9tCQyNDyQ6utvLUnK7Xjqwct9Jkoh4bLU8MW61ual8TqWpkmOEosoh2hmdmIoOY35gztdHpZd1O9t2JUbaWTzUPrmGMmW2ddBngerYKkilGFiqSL4jHKGLI2398G0xvRe4uVtD4FkkJPcZfm+zdDexT7LHoa0k7ClclBFz6CXtBx9GGTGWPoWq4DxnCbpuwMkx0B1aNYAyRg9DovF0uDdaR/827rdlIfqSvyvIi+BOlGquIfuBfJN+FyMe06SEA2lhXFcUOCD2wvXda3y66+TlEKbaNaUnTPzfdXMXGPmbSeS81qQ6V8z18c9i/2+Fm0d7XsW9ylwguctlgXZq1kkmYkSZuxDs/Jmm71kV8cVz7UuR/e29nr/LJdtzPmnfSRjiVKE+Myj1EaQiDzG8xHnD44Ztxclwng8MzKn1iR5M5SgpDZaT8rrgT4YJ9myYzuoD5AmpNEWG1QumgeYB21iydZpY+HqgH5som+hcpFJ5NrIspU4x/1U8s89mkmbedm/LvJ/PNZVO/J+choswzkFyRO7cnX/FTuXawRlm3hvSLNPxv7hMasrq+iuob4C6T32ayjrG8kIotweS1bjdxSjsEfqoRAAeN0hkjrEdmAetyPaP3W4Nh5veY2Zl9Lt6PH7IqwT/cJx8tdze1rv883MZreB1CEM2sQE1QHfd/F7Fb4vRaEZ8HsrDgsx9R3Ow4M/Y2MbNkmSTpwWip7B0l51liXp/laSdGeSukuLmZmd/DHoz0/+J4QQQgghhBBCCCGEEEIIIV4GRDGMhBBCCCGEEEIIIYQQQgghXhIURXFBURT/tSiK7xdFMVwUxbGiKA4VRfFEURSfLIriVS2uWVAUxW8XRfFkURRHiqI4UBTFXxdF8abgPm85WebAyWuePFlHX6b8jO/xQjCjE0ZCCCGEEEIIIYQQQgghhBBWmlleIfqF4kIzex/9rd0mgwVcZGb/T1EUN5Vl+bdmVRie75i5+A+dZnazmd1cFMVHy7L8t1hZURS/ZmYfpXusMrN/ZmZ/ryiK15VluR3Kz/geLxQzimH0Yqd/cGn5zg0fMLPpcVfOA+1+jieAcQ4wxgGX64QAF1udwK6Ps9FnSYOf4zxMuPhADZe3CAKI4L2HbZErh9dxfA+89x64brOtcuXwOTluAmqhYpwRjjeB7WBt1SHoH46bgHCdCMZ7GoA4MRx7B8evn+Je4L2327Iqjf1k5p+5kwKZDAfxnnCcMPbT0SCQyWyqH+NVYUwMjumE9XPeHIgLgnGVGIy/w3E0MLbIPks/hLON4b1YDxfrnweiy/vN/7CO/chjgTE3sE2bR1a6cgt687EucNzPC+xvjT1UpfvNx9hYaw+ke1n+XjiXPmvvcHn4nGh/kRYx+x18FrRTtkWMu3L5157yjfwupJ+GNHcNhmu5lPKuScm/fsP1VRpjtZh5++N4Kutsfcv2zv8GCXZjrJ8Rage2H7W8WTcc6+Dhw5A12FUcrgan6hjlZfTMd17n46I8ZGuq9B12i8u7026GW3m/hnMLY7zMo3gCSzrTIEZzCcfiKtvoyq2fWJfqp/hrGE/qUUvxk3i+oJ0O0fqIPgP9DvuFZ2FN4ZhAOM+wTznuIK5n3B84B3Ge4TMy7E9RD3w/iM6jLzEze8IurtI835G6MdZ4nqFPwr7nvQnGWONYTXg/7JsBisvm4+zlhfwxFhvvwTD2GOehveB48h4J10D2ydvHU/tv7ryzSvMeA21nDsXrwnUV28jxtFzMuXHvNDAWFLLjQQpgtgB8Hsf9OZLiJvQs9TF7lnWnfoz6CseQ59lW2DOh7fBe81yof58LsGB2eCz5k55uiHG135dDvf9Lz3/U5WGsLLQrnnOrYH7ycz6b2Q83x325OZ1prHn/hLaJex+ej+iTeW3bM57m1rLO1rHjGLZNjEOYmxN83dD4gMsb6BxqeR23g/sRQZ+EY8TvCgjbRy7uGcbcY3jMFnWmeXduGF82H4MvF6OWfRD2B48L20Gq71n3GdeDaF1COA5tLu7qZB3pOfE9IorLy7bO8ekQfB6cg/xOhHD768bn4+sQnGfYJn6vwj0T7xfQ3nNxYlt9zoHv03xN9C6M7Uf/FL378lzNtTGaj2zfuNahTfA4RG3Mrf3o+8z8HOf3f/SvaCv8zGjfvB6gPWLfRDExp/vylHfIxdT1fYp+J3pvw+t4DuBawe/TeB3aPc+56B0/FwuabSBaDyI/n9u/c/3Yx5Ft8r4rB/cj1olxTPl7CL9++XZ0upjUaDt+PY9sM/cezv3B3/Xk2oFtZH+NsH1jf0R+LPI1U3VsHXy3Hd7wqGIYidNC0T1Y2mVnOYbRhjiGUVEUgzb5w823zGynTX6Lc72Z/QtLB2j+qizLd50sf6eZ/ejJv683s39vk9+M/YZNKrSVZnZ9WZb3nix/g1kVMPyEmf1LM3vMzH7F0jdo3yjLsvoiaKb3eCHRCSMhhBBCCCGEEEIIIYQQQrzkKctyg5m9k/78jaIorjSzt5z8fI6ZWVEUayz9kFOa2dvLstxhZl8simLqpFJhZr9oVv3r51+Aev97WZa/ebKu+81s28nybyyK4vKyLB85xXu8YCiGkRBCCCGEEEIIIYQQQgghXnYURdFTFMUtZnYd/PmOk/9/A/xt28kfcqZAzZ6bMul7phInJehQG+cN9P+Z3OMF42UlSbd0cFH5oQ2TklB8NPQCkJxgqQCUYkF5C5Y88ceP/dF4PM6Kx3K5DpSEWWBebgCPjeJR1v0k9dAH1z1kPkYXSrJFR4zxXn3UDpRbwePjLEGUk60w88fwsY0sGYdSL/yc2GYcl0higY/MH3V56brp8i1bW7aX28HSNNhmLBcd82cOZWR8oiPpfFQbr0M73TXh5XKWtCV5gOioNl7HshgoP9DW7scCy+KxeR4XnBcsk4TPjXOV5y2OJ9s3jhNK/t1SrQWT4LzluYoyNWgvPLZP2Ooqzc+Csoo4ftgmMz9+K8AWzfxzo7ze4Nj3Xbmub8OHb5gHTwbjVOUQfCsg/RafhVJrKOvE/YHjzv298EtJphDU6cy6qR1otqygggeO0SX1UjlUV9pKeaiA15/5u5nZAUifY1m2DS6s0l+mjvua3ValWRrqgf1XVelGX9PloeTT7C6QUWhjSZU0t66jf4SCdoWypCh9ZOalMCP5D7Th7U5y12wZ7IcOkwTCWpDAwznB6wa2a6OtdXlo+yhBcdDpEvp5zFIMfp1Otsh14JxDGT6uP5IyxXuzX8C+Qj+DexEzLx3IsrLIUSdrkpfPiPY+KB2zCXyamZeS5D6NpLOQSJYFwf0Ty2xEsnnznERJXiYE2x/JhUVr8UqQP0JfaJaXpllFsofYjukySWlOrwjkAdGuuO9xzYpkutAnRftaHrOcTBfL9mA53v/h/bCPeU1B2WEes9waHsm3RG3GctynuX2WmZfIQXms6faX2st7XqwTx5bHD22Mx2EPXBfNpUgiEsE8flfA+ciScTnZnjm0d0VbZ2mhXDleX3gvi7Q72aH8nEP4OZFIAjs3fmb+nS6SesT3wkjyCYlk1iJZKrYrlqScgvsb6+B7Yx/kZL/MvFzsYZKDQiIZZiSSI0N/ws+Cc5/7I1cf9ymuB/yucziz3kT2x3nRu2UOfp/BNnv/MerK4VjwXMV24F6w7vutme9jnBM8Lgj3R10JL5wvvLbhuheFIkB43HP7P24v2hjbcF4y0+9JkUjyDvsjGr/I/iLpfdxXRFKp3I841vjMXK6uFCaOZ93v0ibrbD0WPK8OB2tFTkaQ6cw882S7UBov+YzRoFwkK4v9yGsK1v8M6a5P1f/E4Hvt0IbHJEknTgvF3MHSLjnLknQPFNvMnDP407Is/5SLFUXxn83s5+nP+8zsD8zsN8qyPFEUxe+Z2YdO5t1XluWPwPW3mtn/gWvPtcmTQPiN0a1lWd4B19xnZlPa/79XluUvzPQeZVk2Wz73WUKSdEIIIYQQQgghhBBCCCGEeCmwL4phVINOM2uzyfhD+M+Y+V9E8OdWv+5H10yVn+k9mi3uc9bQD0ZCCCGEEEIIIYQQQgghhHg58XtmdruZNWxSs+afm9kCM/tVm9Sm+cdmNgblWeeGP4/a5AmjqAx+njrWO9N7vKDoByMhhBBCCCGEEEIIIYQQQrxsKMtyq6VABV8pimKXmX385Of3FkXxQTN7Ci5ZTFWcB+n9U1JxRVE8a1ZpakbXTGnYzvgeLySn9INRURTtNhn9gn8BqyjL8ulc3pmiYSP2FvuymZl9wd7m8jCGAmumot4p6qKutk2uHOqC7sloPZt5DVa+l4svQ1qiqKGKGrWsATwMMRWiZ6mrY82gtjmmOa7BoUB3GvWBUcd1M8SvMPPxD1j7HuvA+BXT+yONxVEySXzOXW7+eTCPNWlR95djH/Vl4llwf3NcEAR10FFrNrIPfk7sk+ZEaiPHH8L2c38PQxv721KbWNt3WWeyCdaOR91f1BXme+E8iOJaoQ436/e6eD52v8tD28R4AnNIo/wyCILDY4a2iX6BY1bgHOR4J6jXjXGLMMbXZDtS7Bacc1z2wm/sThnfNc8DkOaYPaisuiSTNjP7e/k8tJ1F0G/sgy76LsTt+7Z5sE48jMsr0XxIr6E8lIjHMDocfwgoL/KfCyyL3e1DyNjutSkw0rzxvLb5Fogvw3aKPojHdqBvKJvX7G5UabRN9n/ou7aCzZqZrbYnqjTGVGA/hlzlDMnHjzvXaazn1xTWo8f52Ofily2qVY7bjHFdov7mdQ5jDdxtN1Tpayn20x12S5Ve54Jt+bHmeAUIjifHvsM4GOgXuN/Qn0QxWTAvisPAawo+G44nxjMy82s/x/fwMU7SmG2htd7Hzshr66OvZR1/fLYhsvVxt07n4+fhvfk5cc+Bz8Xxku61a6v0WpovqBF/Kfj1PRTTBP0mxzvB536C9l3Yjzjud4/f4MpNHE/zoqfb+y6M6Yn9zXMpF6/LzI8FtoPX8wcgFhnHEfoBOHf0VWw7zbFGlb642+/LfSyyvJ+M4gngvgDn4LRxmQCbaPOLLMbzwTE7OO7vhfsnjjGJ+y5sL48L+gyOi4d2lYuPYWZ291iylzndfj5iHeiTec+PcU3Zl+MedYmLP+efBdu/j57l6ESqA/eyHLdkeALiNrV5W0e/0OniRuTjxAyND7i8eZ2tY0GxP83di9scxYzCOrmNeB3aVaOz6cqh7XMcEHw2fC6mEaz1+E7A7za5WHKnGgckes9EojUF28uxtvDZ2D/l4idNj2Wb7DaKpYT+Y26bX6ejmDL7x1L9OFd5rcd7cXw0juUyRRQ7iP0fthHX1brvt2ZmG8fXtizX1+n3e1g/v+/Odr4x2Q7bCtoijzu+k+KcYxvIfS/DbYzmN64vbOvYRrSjyD9xbKJc/KRnaZ+P94piP/E7f1vGRvg5o1hyE9AW3CeynUbfoyDonzj2p4/703B5ubWevxtAm+N33Fxs7CjGEH8HkosrdB7tSU+FNfYD99nHKvXflUy1eTvZlBAvN4qimFuWZasX5BOQbrPJiNV/A3+7oCiKC+B3jddB3rcoPfUt2g1m9mcn77vC/LdLf0P/n8k9XjBm9INRURTXmtmv2WRHdARFy5nWLYQQQgghhBBCCCGEEEKIlxD5f0/5QnFXURQ7zOxOMxuyyd8qBs3s/4MyT5VludfM9hZF8S0zu8km5eb+V1EUv2lml5nZe06WLW1S3m6K37f0g9FPF0WxxcwetUmpuynuLMvyETOzsiwfOoV7vGDU/lGnKIrXmdk3bPKHItbqE0IIIYQQQgghhBBCCCGEeCGZbWZvO/lfK0bN7H3w+R+Z2XfMbKmZvdbMvkDlf70sy3umPpRl+e2iKD5mZh8xs1lm9jEq/zTVP+N7vJAUZVnWK1gUf2NmN9rkr11m8Y9GZVmWbUH+GWFwTVFu+Pxk+q8vut7lXTueJGe6d51wefetuLJKNzPHZic/J0kEliJAiRmEjzfjUXaUtjHzx3lRioaPUqOsDrcjJ1PDckd41DeSasPj0yztgsd5WdoF5fvwmDUf7Y3A9mPfj0477p2O0rL8Ah6pz0klmPljxXx0H4/zomwK53lJJn803tfvjyaj/AzKq3B/Y/tZRsAfwcYj9P70ZdRXKE3zhF1cpVF+jOuMj6DnbYflOhB8TrQrtHszb8M8nighhDIFC2hOYx0s04Vji/Msema2TRwXlH/i+YLH4a98/ElfKZrcdyD9EN0cqxyjPJR/WwtplqRD+bcrLA8O5x7KQ6k8VsE4B9IXQpoVPi+wPFgHPnMXlcM2dlMefkZlBhJZfa4vHaR9tM3LDbJsQw6UL2A5L5SpYh+N8jxoiyxthfbIawX6P/RBLGOE9sg+GmWj1tn3oB1+0HAusTTjxSDviu1faZtduT2Z9pqZrQGDXwXX8bzFuc+SKvickfwnrs0oXWfmZf/Qj7FsWSTHg/f+nq2r0tNlK9J1kXSn71PvJ9GX83PimoX9yD4Ox5Z9N44h1rfRrnLlcG/C/ZHbF7BMCPrXSMIH+54lgvBeXH9OVob7I1qnUV4EfQSvPUgk7RrJDuNa/DTJsqDMKcvP5NZOli1De+R2bLCrW+axPBFKqrL9ob/KSV9yO7i/8VlQIo3liVBOiH0G2hLui9jHYx3sQ9GWUGaHfT7Wwe8H4xmVb96rRbafk4jkZ/YSkX4eoLwc9ilL/3h5xLx0GNpzJGXK+6KcPFskjcf7d7w3zrN+2rgcDHxL3T16TqqI4fHMkbMHs1h6FMeP537O55t534Ll2E5nB9J+6E+cHBuNO9pmJAXnpZD9GnjUyZfm5auwr7i9aEvzKK40tytdk5dxi2Rqce1kn4/jFMn3YRv5vQefhaXPcuMS2WkEzjNeN3DMeFzQt+fG2czbfuT/onmA/pvfmTGvM1jnsO9ZGg/BdvA+LhqXyDYRtMUeamNOYprHBfdPkaxdRFtgpzkbM5veJ6mOvGQwPxe+I7EEag7er9aVKEV4TclJaPKzRHOa+ydXDv0kf6+EY4j35v0TzqUFtB+Z8mW/Pfh5e3rDXh0GEKeFYu5gaas2nN2bPlTcX5blYC67KIr3mtmbzexKM1toZnNt8keizTYpD/eHHE6nKIpFZvYvTl63zMwOmdn3zez3yrL8UuY+bzWznzOzV5+8x3Yz+5KZ/ebJ00tcfsb3eCGYiWzcayz9WLTXzNab2YjZKe42hBBCCCGEEEIIIYQQQgghThNlWX7SzD45w2v2mNkvnvyv7jVfNLMvnsl7vBDM5AejqR+L9pjZq1r9SiaEEEIIIYQQQgghhBBCiFcApb0YYxiJH4KZSNJ9x8yuM7O/Lsvy1jPaqlNkcFVRbvidyfTet/S4vIVfg6PmB/x1e9+dynZOpKOns48cc+W6tqZ0SVJOw/N7qzQeeeVjp9GRdJTywGPR0dHbSFYhJ5fG7YrkF/BI7bN0PLju8Xo8vs9He5fYMy3ba+aPzuYk17h+lhDAfsRjuVwH9g8foceykRwZSs5Esht8zNrLuaS8aMz4SHruqHlOUsFs+rPkjmezfWCdfMwc249H7Vn6x8sDtmfz8Cg/Hy1HW+L+xrHYTNKPCMoxsv3l5hK3A2UyWHYIj9CjZNWr1z/mG4KHYFni7duQBh9E6i12aCSl57IE20WuwQkuh9Pz/6G8I1YPVNTro7xeSOPB4fm+2IFFSV+O7WPhLvDl0N6xJbNcue6RJD2K0nJmZofaWkvksF+oK7mIUjqRv+Y5jX6S5wjKjC2ZoMEGtrcl2+T5iHMEfcSyCS9/13Y8tWOoc8Dl5WTtljmjNbtwz+4q/fii5S4P57ST6Rr382VT5+oqzVIjuT7AsTTzMkYoh2VmtuJAauPm+Utbto9hv/AtuxHamPe12Ff7aCLguv0QSP6hjNhk+5vZNqIUHEql8BqL3GU3uc9o77i+X0DyiBtgskb1o79jKaScJImZl9NAKTuWMEMpQpYMxv4ZAtlAvi+OE0s+oXxYTsrKLN77zCWplylYpjHyGSh9y9JnaFcs6ZiDZSyxT3C/x762rowg+i5+fpSujGQ8WZITwf7muYTjhO14wla7cijVGEkc4X6Sy+Ez854jJ5uH8nRmZgOwiG8n7VWUncR5xpLX6Be4jdiOaP+H7WepL2wztoMlmTCP30U2Qf+vgGfmOY3zIJKPxDyWrovkNNEP4b6ZJap4/UVyfp7bG+3fcR6j35kuF57mI/dpTp6NZSCxDq4/kjvPSWezFFL0LoU2gX0fyetFkne4D4jenbi/0b5xnKZLfM5peQ2De3v2Qe2ZfuM2R++ZuFfh8cR3YVyn+X3DzwPf3/jc6J/4XlF/597DI0lV3tPkZCYjnxytv7n6zOL1C58zZysMy4vic+J10dgyub0Er8Vop7y3wrFF++Bypyonl7NbnnNRf2OdLIeXK8dzNfe9Vd092OR1yfbxWSIZ3Mg/Rc/s+9t/e47ji8/F++tIvh/bgfOHx8Xf14/t1Jr4vsHH7fENY5KkE6eFYs5gaSvOsiTdY7EknfjhmPX8RSp+zybjFl1VFEV+VyWEEEIIIYQQQgghhBBCCCFeUtT+wagsy8+Z2R/ZZKCovy6K4uaiKM59nsuEEEIIIYQQQgghhBBCCCHEi5xsDKOiKCZyeWb2GjO742S5VvllWZYziY8khBBCCCGEEEIIIYQQQgghXiCyMYyKojjRMmMylJXZpDxdjrIsy7xY+Rli8MKi3PDrJz+8njI/D+mtlIexOjDGxn4qh9edQ3koEZ+XajYbgzSHZECZ8i5IP0XlejPlzHyckX7Lg6PDP+1B+4/AvQ52+7hQGO/peJsf7vaJ9HvjcFtqCOuznjvRrNLnDPuYUSU82zi0qY0CqXVAnx5Ywh3Suk0Mtp+1zZ1O7IRvP8fPyIEauxy3o9nZSPXBvaN4KpE2ce4aM6+HO117vLVWLpdDrXfWtM/FauI6sL0cq2QrxJ/AWA57KM5D1FdYR05n2szrX2M8o8l2JR1kjC3AWverIDYCx5RYPZYC+nR9zTXQsxHSo5QHZY+BHP1hiil0DsqqcxgdirlWcRF9xuFkv4B+B2MOcUgTLMf3hfvtXZb8CcYcmawy2S3r+KON4bhHsT7Y76DdRvdCjWvWJc/F6+J4EKgRzzaMOvORHriPaecdO9om56GuNcZvYI1vvBfr82O7opgE2Af8LKi/jvfeZBe7chhvgWN/bLGVVRqfZQXEBjOL4xDg2MwL4lmg/8A4e2Z+DNFnHqI4D/NgIrOGfS5eIev4Izkfb+bjO53ztF9HcZh2LvHBws7fkwI67lyU8tjHbYE4cKzjjz4UdeA5dly/i7nRcHl9mfh/HKsEiWwdY+WsG/m+K/dE74VVmtc8jO+BzxnF9uE1JRfjj/X4cb7wXML4YBznJrIRJPIZ2Ebsb44xhGsbzyuMu4T34rgaaLdRrBycIxxzCf0C+ydsV2cm9omZfzae79hGXFO4P/A6jg2DdoC2w7YexRfIxSLj/Q3OOZ4j2MZFsJ6h/5ysP/UVr525eD7RuhH1RxQfA9sRrdPYRp5LOf/BbUS/zu1FW4xi2qGtcGwLtBeOPZGzK97LIzxvcazRrqL4SxybA/uKx31fpg+4XGTDubg0UWwYBuduFHcFqdvGqB08V9FG6san4rFAG/ax0vx+AWH7wzpyMW+5fp5nOduc/p6Z7sU+NGf7HE8Q28X9ndsn8vhFsY9z7Y9iyEQ+7mjwzAj7llwdvEfC/R6v37kYzLz24H6S4+GMZva8M4nfiHXye72PP5SemX3Xcefn/Z4p954S7YvYdnLvfmzDaI88z3LvMNEenW0iinNYF6yj7poS9TfmcZ8+695V/Ro+NVf/6eAPbNOGUcUwEqeFomuwtGVnOYbRZsUwOpM8nyRdq1+TCsv/WNT61ychhBBCCCGEEEIIIYQQQgjxoiWSjfuO6QcgIYQQQgghhBBCCCGEEEKIlz1ZSbqXIoMXFOWGXzn54Ucp80uQfpryroM0SijtoXJ4HSuf4QleVB16hsqhlB3Xj58vhTRL46GsHcvfYbt6g3J4opmfBfO6M/WZmTvhHdUPHCOprA48zTtChVFpA5/5AiqH7SIhRJTUO9rV0bpR5qXl5k6QRFrbQJVmuRU8oo5HjPkIM0sHILlj1gweW+b6+Hh5usYPBB4TP2g9XLwCjy2zHAAfafb1p/ZjG/lo/GE4Js4yAjnpB5aiQdkariMngTVMkmCRzATKHmDeOlvvyqFMyPnfPuDynOoJStKRnJwl5TorSYWpABsuYY4cppPwc1dYHuy6dZAeo3JYB88zvB/6MVYJuQKqv8AfYt3UubpK15WeWjLu8w51ghwASETiPDXz0jzT7aO1pADbGB7zX0QOGyUMUb6A5zB+ZqkAlBbi67bbsiqN0iiRFBy3HyUdIwkRnC8spYPjhO1nuUts76vsoWwd2EaW20I7YJvA+nH8IskJHvfVtqlKR/IZC0BSJJIhQRtj/4Gfse1mZmugfyLpKbQrfk6UGoqk8VaNJ1mx8U4/ZsgQTH6W50C4P/BzM5C+qCs1grCEY07y1MzLKeE485xD+2MpF5Q6RFkWlq/Ce7OdHs+sgTyvsF3cp2gH+0iaEetE6ZtI2orB++G9BkjeEW2TJSJz0k6894meEz9HcjxoS5GcIRLJ4LKMDPpUbAf7j7oyODkJn8l7JztgH4rPifOYbRj9Jj8LthntA+UW+V4sDTpo90NeWrP7SVIQ52Nd6aloL8xEe14ksh2cx9GeNLJTBKWtWGIR+5TrwDUlklOOnhl9F0qlstwW+qTIhqM+iPbG0Z5m3EmwpfcP9n/RWOSk7PhdBN8BOA/JST2aeb85XUq39frOY4vPyX44J1tW972H87C/IzlU9ovz3NqW93GR9DmSk9Qy8/3D76Ao+4z7okh2LpLGw+tYajQnE8pEPjmyncgmcnCf4v2wHTwncM5F6wbCe0GsP/ILDD5353iqA+X0uc3zxry2OoY0wHs1xvwXP4e60xdSHEbgnF1Jbvm5Jen7HA5n4OB/Fg/vvCW8Wxf8/RN+90Wu5Ri8/+J3WMeCcBRHu/y78ER76oNn2xqpXCAVyFK9OQnKmYzt1Hr5vsHH7fENY5KkE6cFSdK9/IhOGAkhhBBCCCGEEEIIIYQQQkyntOn/mFi8pKn9g1FRFE+dTH6tLMsPZsq828xeZWZWluUv//DNE0IIIYQQQgghhBBCCCGEEGeamZwwGrDJ3wwXBWV+3Mx+4mQ5/WAkhBBCCCGEEEIIIYQQQgjxEuB0S9K9sBJ3E5bi4HRTHkp4ciye7kyaJZHxeB3H/sDPKCc9SuVQVph/epsPadRT5RhD2H5uY2cmzeBz8qhh/RhrJXrm+ZSHUqsgYdxORxRLuPdx6o8JiCfVhf1B7T0Cz7Kre7HlQH3jKA7IrrYlLi8Xq8Qsrw3LmuLIMOnQ5rSluY0+z2sYzwNDq1uO2+4181M5fP7J69IAsLY0gnmsqYta7Kw7jf2x2VZV6aPTnqW13rqZ1+9GfX6OVYK61qwDfZk9WqXX2A+q9IXbd7tyLrbZF3yWC28BcYo4XhfGJtpDYZD6wb6Li1J6WgQJNLnoZ33M4zhFCMdEgvl+BFRiOTbYprYU84A1v1GPecC2VulzJ5qu3DmPgSY1+bHu7mSbGKNsSbcPGIfjHsW/whgNUSyl9S74k9fr3mIrqzTajZm306024PLQHnk+znFxxPLa5qidvsq2uDwf9yfNb+4PjM3GcdpwbqEmPD6zmY/n8ANbk60/F69m8l7Jvz5EdeRiI3B8IIztwD4D/QnGlOihOAwY74PjumAbMaYH+yAcpyEa91ycCo4HgfOnn2JoPWBXVWm0W27H/s4Ue2aX+bUNY0VssDSp2a9jXBCeI0/A/EHb5/UL7Y912tEP43rAcwltgucBjvUddkuV5vhDOBYc5wHtCuN/3UlBMdHGNtpal4ftQlvnWA687vl2JPvg2Ag4No+BnXJ/4L3Z/nAMfTww3ybePyB+P5XKsf/wcSrysUoQbgd+XmWbXR7aBNoAz6UoxuTT0EaMDcZx69AvcNvRznCeRWsKg/2DcR7ZP0WxUHDvifbN8cD8/snva++2G1rWz74WbbpuzDKOJXI4sD8ewxy4drKPy/naKK4D+1CcgxiThm0d28u+C2ORYf08v3mcEPTDGFOM91kPgE/idSN6x8jFMmQbxv3DM9TfbKtTcJ+ugHWV24E2jP3N8V+iuFa4f8KYfPPopXwiWNuwX/E6nge4D2Cfif0YxR9CO2V/jX2A+y6O14X2yPMAxx39DNsftpH9JI4T5kV7H64D7QXbz2Obi8/C5Npk5p+F60AbjvakUUxM7GMsx+sctovbgX3QCePH3yGgLXJ7cQyxHfxedbwtH7cT65875oPsTrSn+DsHOzE+FY/Z8ZbXMH0j6YW32etjKc8dT301+8gJfyF03ewj8I4YSV/lQ5tZgVsy/r4Mrivp+0T+HqtqBn031Ql1eIswG29LN8Q1kH05rs3sJzEOdWSn6Gv4nWjq/aYw6mshhADyHn2GFEVxnhl8gyGEEEIIIYQQQgghhBBCCCFeEoQngoqi4N/RCzP7iRZ/Z+r9szAhhBBCCCGEEEIIIYQQQrz0KM1IVEG8xHk+Cbmi5t+mKE/+t/FUG/RD0WVmJyWbxhb5w1PdfXDckqXVujJpllnDU7rcc7ljryz5FKlTYFk8eco/z2G7uB14HT4LS/QFE7mEk9AFPHNJ0ngF9qM/weyODj+3JElWtR33DzPR3pbNQ/YuSUdqWbIBj+ny8Xo8fhsd+Y+OpGOdkSQd3otlGfDIMR8XxrawTAGCsgf8nHg0HmUJ+Ph77ug650XlsH6WZkC5PZTk4OP13AcIyi/guPC9sK9YdghlavDoPUrLmXkZlZUkdYOSRAsfBxmLO6jBKDX3NOVhs0AZZJj8wDyYn/0sBYdzFeUi2bfgULOM5aWQviKoA+51jPzf0a7kU4c70xycLkuQPq+YGLIc5zwAkgLsx7B/vLKG7b0oHcNHeQ6WezsaSFXgfBkGf8IyaziPR2nOoeQJzjOUuDPzNoySL5zHLANjwjnIsgSRPBvK/6D/QMkuMy/dwW1abZtato/lglAih2WHsL9R4g2vMfO2w/Wj5F0Eysqw74rGE1kJ0n4sJRbJHyHo11eSVCCCvpElcdCPsfQP+miUr4raFNlOJPuCska8BuJ4Ypv4Xvhsw7SG56Rd2J6fDeT7Dmdkr1iCA+ViWFYMJQvbnSSOd1Ao08r9jXZ1Hjwzy/ChrbNfiPYLKMmG/R2NO/dVTi6W+xvrZL+Afccyu4jfPzVcHtZZVyYJpRO5DuzHOeTz8d6RJGdOgtPMPzP3B/Yd1s8SM+jzeF1qc34+2S1LjaLPYPvAvsN783zkzwiOJ+6t2Bfm7tvq8xQ8H1GqjevH8US/syBYX1gSDOduTq7PzK+P7IfR7+CekeURcb6z5CTaS2SLCPsFvDfn5eA9TTRX0ZZwTWHZTdz7HKZ2HHfvRPk9GMoCT5fHbi3RxHMO+5j3B1gnzjm2S7TvzdRXKD26HTbL7E9RqpLJ+flOEqlCf8VrhZMLy+w7zfwcidZzzIskjrkOHEP0H5H8J7/74fzEfcUQrV88x3Nt9OuGlzfD8eQ+xXHHfovkKNk/4bqBzxVJn3MeEkm8ow1wHThO6Nfa2nx7cW8yhyREXX3dftxz9+b9E9rEgs592byhXvjeh9avTtBxG+/0YzavN9kZ7iHP7W66ctH3C75Necldnp916uf5Mrs7PQuvBzk/2UblIhlVfCeIxhPr4DGbWjuPTfvyQgghEnUk6coZ1FeY2Qkz+81Ta44QQgghhBBCCCGEEEIIIYQ42zzfCaPvWPrB6PUn0/vN7BEqV5rZYTN7wsw+VZblA6ezkUIIIYQQQgghhBBCCCGEEOLMEf5gVJbljVPpoiimNN3uKsvyJ89ko06ZDpsmX1SBMk8sH4cyT3iCl+vC61jNAU9dR7qNKAHFJ8bxlCvem+tDKbi8qoSrr+z1WeNwXVvQ3sP9SU5u9pFjLu/Q/KR71T7hj+geakvHavHY7+E2f9wWj3vPbfPHsfHILpbjo8IoAdWwZ10eyh7gMXk++o3H5lmGJDraj/IOKGnB8jD4LHPp6HBOsoWPDqMU37N0bJ4lAXLgvVlaCPvOl/P3QrivrrL0WzG2kY9wR1JcOWkrlk7AMUTpJq6f5UUQlM1jSbr5XwOdxfWQwdU9Buk9PqsE1ZPDMJz9XvnHS0ZeRHl9mTya0052jmXtYL6jtOSu+V53Dm2RJT5wLqG9sQ2g1MO8Ee8zCuw7UEY5QvJ3zbXp4VjuA8ca781SlXhEHyWkzLzEz7mBLa4AuS22WZzjOEdYgghtn+c+SgosIuNB6YecZJeZlzKJ5qqXxajn08y8/97v5IN8O7xcSV4WCNvIciVrAzVbtDm0zTX2kCuHdsrtwPajnfJ6gPWz/aFMDUqUcN/jOLFcopcySZOT74V5LBeG9rIvkLnCfmPbzMnbsK9F2HawfpR0Q9/K1/EavgDmGd6b7QPby9JC2KdYB4+L7w/fjpz8IteB85HXl3nweQgcMcuh4lijnzHztsNyqwedrEwaa/bX2MdsO1uhXWjD7FtwjrOUGMoroTTXA3aVK4fPzf2NfYd9usK2unKR/BHaeyTvhfceoDHDZ4n2JpEsEPbVoN1fpdl2Iok3tG/c13Id+CzTZHCgjWgrLPGE7eVxwb0gPmc/rVE4ZjwuOWkr3i/gdZGEMu6vuRzu3XjvjfXjGsgyf9gfXD+uS9HeB/O4r5CcJOTkvdtbluN2oZ3ymo3t2EjzEfuRZQlzUl88J5rB3h7t+3jg8/FZWE4p95w8LiiHyv4JwWfhOYfPjDLUZt4v4FhwO9D38nsVzjvsD5aoiuTIse9yMuvcrkjW0+8Z8++BvP7WbS/2G++vUX4L9/K8fqFtcjuw/cfhXpHsayQhis/CYxv5a/Q1kZwy7jsjO81Jkpr5tTnan6EUMtsHrg1sf1gn+x3MQwnK49RXaKcshZmTZpxno64cygpGsnDog1heNNrL4hhiH3A5/P4l6u9c3c9HTl50en+0DhVg5vvf+2TfDrQ5Dscw9T3NEdO/8xenGcUwelnxfCeMkE/Z5Emi/3uG2iKEEEIIIYQQQgghhBBCCCFeAGr/YFSW5U/j56Io5lo6B7OrLMt8tD4hhBBCCCGEEEIIIYQQQgjxomXWTC8oiuLNRVHcbWZNM9t08r9mURT3FEXx46e5fUIIIYQQQgghhBBCCCGEEOIMU5RlWb9wUfyemX1w6mOLIqWZ/VFZlh86DW2bMYNrinLD5yfTOy/yQTHO33ggfXiaLsTwJ3jmaoTKQUgTkoZ1MYfGFqXf4br3n/DloBnPXdrhso63JT1S1kdHUJ/03ANHXN7w/BT7I9JxnTeWdFK3dF/o8lDrF3VRWe8a28HawUgUVyOKV5DTYmftXWS6nnuqH+uYrvGa2s/awajBzJq9eF2k04553I+olYv1s/ZzFKcInw3jWXB/1NX2xTggHEMB+4fbhJrDkdY7PjP3B16HNsH6vagffa3d6/JQsxd1zm8Yu8eV68L4Qw+bB2WRMYYR+w8It3CM4qN1YOwgMLniAl/OxR/i2GlXQBrD9PiwTVZCHKR983tc3txxiC/Tmfqmc9zbwL7O1N88tuhrnoX4ZdNjOeT906Hu5BvxXnfb61y5jba2SvO4ow3jfBmlcqhrHcUxQdscoFgiqD8/h3S9c/FUIu141Ko28/6Dr0Mbxnhg7P8wDghrluOz4RzheE/YVxwDIhcDbL2ty5bro3gZufWBnxn7P4qVg/rzHMMo8vOYh/Gj2D8NQ/9wfBn2V1OwjWGsCF43cAwxj/sJ7QVjJ5l5H4rt575HnxxpsUexRNDn83Ni/WhHPLYIxxTDNQbr55g6OA9Y3x5jIGA/cnvx2Xi+4GfsX45/gOW4fmwHxi7g+FFof2xTGBOI4/Kgxj3OA47fyH2HYEwWnMccN5FjJSIYMwqfmeMlHQ3iD+Vi8bCfxLGO4nZEMTwicnsrfn6MS8OxKHoydfC6hO3nNQX9gh/bpiuH8539M/pv9EFRvJ1hWg841t4U0TrKediu4Uz8Q7N47cRYGtEaVbcdCM9HnC9sY1HcMwTXG94XcVyQKfiZcc/LNuX3xsm39FP8F7QdtuFnMnFXzLyf9/GvfIwQJHrnqhtTh8nVwWPJsZty7cJ78Tjk1nOzfBwurgPXcI5dhXMrFzPQzK/bHFcoR/R+F/lJ9DtRLJso3g4+cxRXk/c0uXfQ6F4MP9vz/d1s+prSnvHD/CzoN6fvF9J8X1DTPzE45zozdm9Wv698rCrvx9E3sg1HMdywD7B+bkcUgwltPzcnuE72tTiG0f6mbow/JIrfyO2I4jjl6pz+fUvr/o7WR7Y/7CscI97HoT3m9kWfHvwvtnvDzlbf6woxY4rZg6Ut2HB2b/pMcX9ZloNn96avHGpL0hVF8R4z+7mTH8uT/00rZmYfKIpiQ1mWnzoN7RNCCCGEEEIIIYQQQgghxIuN0syOvdCNEKeTmUjS4Y9FhZkdNLONJ/87ePJvU3k/N/1yIYQQQgghhBBCCCGEEEII8WKktiRdURRjZjalQ/QbZvbvyrIcP5nXaWb/wsw+ejL/cFmiQNLZ4epXF+V3755Ms8za5XueSh/8iXE7BgoaB3uT1NL87V5OCU+eHun1WQe7kwRU50Q6DvpsW8OVO3eimao77o+XDnemY7ooZ8CyOpiXO15r5o8f8zFUPNrP0glePg2laPwxeWwXH2vH67B+Ph6cu+9k/fnjzUgkYZM7Lsz9gTICLKkS9TE+Gx4DZhkIPHbNx4XxODzL7OTazNIMOZkMlv5BeYBIegqPRW+1AVcOn5llcNBG8Lh0H0laeIlBLxWA445SNzy2K20LtMmP82X2aCo3sq1Kd2w0D56YXU95KEkJUpKscHJsDNJ+qtpcVHrB7vYKMGbXQPoSyrsY0vCYz13oJS3P2Z/+OceBRV0ur30C5FbA78w+QpKZAUe70r8vwOsm6JzqRHsq92jnZS4PpQhwnj1kr3LlUC6B5yrOx2jeorwZy9SgBNZq21Sl2U4HQG8Q+9DM9/dYb3rmzZ0rXTn0jZtstctDqSFuf04aNJIs4Dl9rX23Sq+2J6p05IeZ3DrCdWy2VVWaJXLwudGX85zG+utK/7Dk03kwQVlKB2UbUN4iWgNZKtW3Iy/jEbUf5Wf2BHJN6JNZOgdtE22Hpes2gQPh58SxxTFjqSwclz0kA4S+Fvtgq61w5VBmLZKZxDyej5GkCsLyWwj2/fR9Ueq7h0Dzk+0Z1/dXkSQiyiSxlBgSzelIegXrR1/Ito59ijK1k2W9bOEULHmCcnhcf84/cd+jT1qB+q3m+xEleLm9kQQP7hHwXizpG8k24rx41NKahbY9WUcal0jKCW2T/Xok1YugzU1fA9O9IpnTSAoJx4zHPbf+RtJ40T4O05FcKc9ptHW0Z5aOHYI9Kj8Lthn33ryvRenRSKotAucqj9mcTJ/yvIrGBfsA7X76+1fqU7a/aG3DfdGp+uGchBe/x+I7UfScdWXReG3Dscb28zNHMmZexi21N5Lz4nHn970pIql2nmdYNtpXoC/gOtBG6krXsSQiztWo35CDRvLYGSnnaK1kG8A24nXsT3Gcon5DieZIBjIC+4a/M8BnjmQ9o305tr/ufiGSQWPYfyNzMlKKvPbkpEzN/LzD+RhJu0ZyrpiO5AZ5Pi6Cd0Ycd+7TSI43F4qA980If7eTs2G2gZzULbcD+4MlxyPZ66k90+2Dv2t7NmyXJJ04LRQdg6U1zrIk3T5J0p1JZnLCaOrXk0fKsvzXUz8WmZmVZTleluW/sRQFJL9qCCGEEEIIIYQQQgghhBBCiBcVM/nB6G9tUm5ud1Bm2CZl6b4blBFCCCGEEEIIIYQQQgghxEuZ0swmzvJ/4owykx+M/p2ZnTCz1xZFsYwzT/7ttWZ23Mx+7fQ0TwghhBBCCCGEEEIIIYQQQpxpZhLD6HVm9l4z+wdmttPM/siSBN3lZvYBMzvfzP7AzD7P15dl+Z3T0N6QqwZnld9eP6ljPNzmdfZXHdhRpQuKM7Jt0cIqjfqeS8Z9sJJDnajN7Ct5AmI05GKwmHlN1khPNooVgVqrHKMnp43Nmrqo98xxL1DfHnVcWYPVa1X7/kC95ChmBWrsstYs6tKi7i9rfkcxA+rGPMB7RzFCWMsbdeYPZTSi+TO3EevEmBWNsRFX7lB3ikvDet3zxkZblkM9fjP/bKxtjmMTxY/C9j9DtoPa6ahZzPaHcTB4jiDY96+zu7Pl1h140H0u7oMPGJvoMboQh5Nim7kYRtD8/fstyxwfOsjmYhiP6yB9KV2I3XgB5c1PyRLk1w/2UgyjXRBTZ5H/twAYc+hwT7oO4/CY+dhH+ymOBNoO2ibGLJr8nGx9fds6l4dzFeNrsW9Be8HYO2ZmgxNJF/dgW7KP8/ccsCxb6TO6K7SJ7VQOx5rG1k3xKD4VjietPc4OOA+nOLaRbQdiaJE8tdlGSOMUHKNy+JxrfdYRUATe352Mkf0H+tpl1JFfsrdUabQjjieA/g9jIpl5m0DbZH+9xVIMKYxzxhx0a7HvfLQ59te5mAq8jka65LmYBxwrJ1pTML4M+m7ujx+4WDzeyWE7cPyiceE9B44FPnPUb7ymoL1wPA5kDcQL2gzjzNdFWvLYB7wHQ58U7QnwM8d0wv7xtu79KcZy4XHBe/NY5Ornfqu7v8Rx4ZgHuGfiGFoYmyPqq1wcSW5z/VgihyivdRxJ3lcMQUwtnku5mAocEyMX39PMz0GMwcS+EOuvG/eMfQ7OLd43Yz9G4xfFhkGimCYYJ4rrR7uN4jZFsUvwOvSv7K/9nPZ5GFcTYzvwXh7Hk+NfDWfizLHN4jsLx99AovGL1o3cvTkuShSzAv0O52H9aDtP01qP/R35J9zX8XzBe3Ef+LguaX5H84CpG0c3Wn+RaC1m20cwNgyOGbc9F8fPzPsF9HF8X3x3Z7+b85MRdeuIfBCD+7h2957pnyWK55OLHcnvmbi+sG/NxW+su15F7eA1KopvlJsjvI5iX0XrUt2YyFxH3Xh36Bei+FcM9n8U+7Mu0foVfW+FfRztW3Dd4FjN/P3LFPwdUxT7Duvwsba8fcyz9N0Oz1u0VYwfxTGMeA+CTH1H9LuDt9v2DXsUw0icFor2wdLOOcsxjJ5VDKMzSWuv15q7bPKQmdnkD0Mfo/wpR/NzJ/9DyhneSwghhBBCCCGEEEIIIYQQQpwlTuVHnKkfjfiX6NzfhRBCCCGEEEIIIYQQQgghxIuYmUjSnXj+UlnKsixP7dzpDBhcW5QbvpHJRHkp+pls94reKj0MRzn5GCceIWUJgJxUBR5rNTMbAG0krgOPzuJxdS6H8PFVvF8kn7HChqo0HyfPSchFx9O5jgl3VDYvi4HyEdFxb5Q9iI678zF/LItHmAfg+bkd0TFuBo9190/AsfY231ftE6kdLAPmbPNhSLP0GcpIdQeNQuksVkroDfJyPx9zOVDg2busxxedSPa4tW2gSvMRbrRplMiYvN1RyEudsHDjqCtnT0J6o+XzUFnSq/xZCXmHSaWhA5p8+EhKHyPpsD5UovKqVF4+DNNvoHJo0uwpQdLsGIzF9t7FrtiKA7ur9DiNGcoUoi2ydOdFe5J0pwUKb842A/uY9izQp7sXJWNcvJUGBseMZQR3WWueo88wZqSa4uXfcF49TeVwLrFkHI41Np/nLU6RaM5FcxqfuTfI4+iCE5m86KT4hfQZ7bYzkzbz436RzzqwrLWc5kHz/gPzWFoI1xhcK3hdWgUydCybl5Ow4XtFEiI5mZNlYztcuQ3dr67SLHGE0k4o6xnJvUVyF1gHr224RrF80MW2qUpH+5tIhgnHEOViWaIvklDKSSFFMmgsWXiz3Vml77Sbq/QA6VHi2EbSLtiPbEe4N+Q+xfZjH0TSK4tIkg6l8XgfivLHKLnIfYrtZ4mSQ86GU5+irIkZS2f5uYr9mLNFM7PL7NEqzfaNcn5Yjm0nkurJ7ZV5P4lSPSjzYubHdzXMCbaPCJSeOg+c8vS9cV4aKre3ZdvBZ+F3DASldHhs0edFe2oc50i2KPILkQQb7vf4ObH/cZ/Ito7PFvmW2cG9EH4WrBP7bcEEydq1pb6K2hFJIUUS3jh/IulOtA8ed4TXPdwbHmrLrxvor1CGz8yP57xxeObOvETf9Ha1fm/jcYn6KpLVRlA6MJIpxOcaJn+ak5disH7uU/TDkdRXNFebgRQX1oF2xO9mfv2dTXnJlrD9vK/C61j2kPdTOXKyX2Z5yX7uG7QdlonLycVOlxtsbQNmeVl+npu4HnB/YBt7Mm0yi8fF15ffu0bf5+CayHJv6DcjWftIThPHDNe9aI3l9uf8Js99Py552VC8LpIG5fnunzPZ6WHyhbk2meX9xJzAXy+g70rw2dC3cN3Yj/g9gZnZs/Mn383eMDhuGzec0D/4F6eFom2wtO6zLEl3UJJ0Z5KZnDD6tTPWCiGEEEIIIYQQQgghhBBCCPGCUfsHo7Is9YOREEIIIYQQQgghhBBCCCHEy5DaknQvBQZXFeWG/3TyA8vloOzQIsqDk+EHrkjSOXx0ePHjSXfouYs6XB5KETRG0tHeZm9esqvtOB2V7Wx9vLlzPC+Rxsf8G2OpjW0soQTs6Z0P98off8djunyku650Gx7n5aPreISepRP4iPAUkSTJwu0kW/YApPHnUa8Ak5eoMvNSUdspD09dRxJeaEpcDuvAE9jcxkBW0dURyWihVBQrAxzPpLuCciQ9NU0ua4pL6fMwpFlhIdenW6kcSo6xlBj03aGHLctBGGtWNJsP7ehD9Qh+xvmQ5ue8AtIgLTfNB3E/AjuXpBvMmwCJjzY/P3C+rBzZ5vJwvuM8nr/niCvn+vghagjaH45LNF/qyh6yneZk58y83WJ7WQ4Q28uHlPGkNLaf5eSiudSdyWP7wDayZBzC7W/PpNnWEW4jG/UU/JxomzyeCNotK/pg+3ssD7aRZfjQXlhGEJ8bZfOeonI47tyOnK/lduAY0hRxfYVwn2L9/CzYDrwX9z2uASsoD/sKfSjbH/Ybz0ecxzi2I0G5+ZSH98PreH3Be7Mf6M2kub1IpGyDz8LrKM5B7m+8N/oIto+LgrzcesPjgn3FNpab+2a+T7AOtjG8H/tTbDPWz/2BNs32h6D98b2wjTye6J+6M2mug/Ny+9xo38Lk9md8DdY5THnYj9G9sBy3EetEic+oPu4PHDP0Vbxu4FizjeHcQr/Ozxytj71BXg7eG6PNYZvYP6E/5WfJyb6yreMc5HHBPKyPfVD0PpBrRyTWzntjHAtsEz8L+oLITplc3ky0SPB+ufcBrjNalyKp3pwvNMuviZGdRn4Yr6v77mQWzxEE52okR45jy+1For6P9mD4zLymYLsiXxjtaXLzIHqnjfxT7hozbwPnUF5uj8p9j33A9edsk/cc0FdH6B2gC/oHJcePds3yt9qTokIcoT3Ywe70MPPG0ncgE+2+jvHOJDXH73547wnq707o74O96buvOaNeXr8d+nt4vjfAvpE0QQ/3pDr4e7DZR060LNeqbPq7j5iRay+3eX9vauPcCf/91rNtjSrNkncoCYjyg5GEMkvStWXk8Pj7LfwukL8jwzAI80bScxVswzieGynv5LAM/prZhq2lJOnEaUGSdC8/ZrINrCiKYqmZ3WJmA2bWXZblPzudjRJCCCGEEEIIIYQQQgghxIuY0ur/QyHxkmBGPxgVRdFuZr9tZv+Erv1nRVF81cxutUkTuaAsS/43J0IIIYQQQgghhBBCCCGEEOJFyKznL+L4H2b2QTPrMLPi5H9T/LeTn9vN7O2npXVCCCGEEEIIIYQQQgghhBDijFM7hlFRFG8zs8/Z5EEz/KGoLMuyrSiKOWb2rE3+mPSFsizP+o9GgwuKcsObpz5Q5pOQDmIY2XmZv5t5bVuODZHTX49iYkSgvCxrEaNGPLcxF6vpMSoX6cWjXGuk2xzFPEApV6yf+w11hbn+nLY+92kUT2U4U4519vFeFAap9pgh3I7OIA+eu4RxL+j833547o7gbOA5aBOsDY6foxgQmfaZmddx7qc81FaOdLJzsQv4OoTHDNq19Umf1QfPuSN4zv5cnCJuF9o6xxvCcux31kAatKsPLPFGNQEGOHfcayljnDKMUXa0y2szo+bykpG9Lq8DbQ778XFqL86t9ZSXi0HC8xH7I4ojMZ75u5n3J6wHjqCf5HFBG4s08tGeeW7mtOPNvE1gfIjA3p6jOBLt0I9zr6PCOE7YP6y9jv6K2w/9fwjqm0P9UUC5/XQ2eA7kzcX5HcWhicBn4TpyMSvM/LNFMeHmB3lom2g70Z4gihmF9fG4RLEccvHoovhRDPYP9gfHGMIYWnXXeu6PqF14XRQzAOcjxwLoyuTxfgH3Z+xbsB9xPeBnrrtvwWfme0WxUHKxFyJ7Zt+FNsd+B9tcd1yivRv61/OoXC7ehJm3K7Tvmex5c/tQHpe6MXyifQXOd+6PXDy6aJ/PbTqVWEp143bMRNoD24y+gJ85F5fHzPc/xs2J4vixr83FweS4K1hHtCfFZ+FxwXux7eRivrB9RHM6iqOD1PXl+JzcH9F8QftAXxvVcar1RzGuuI+R3J4psp26Mct43OvGuIvmbTTPcu9Lkf9gsE604SjuVNTfdWNe8Z4jN98jnRnu39yeI+rT6P0ximPVOlzNdCIbiOrH/o/217k9I9ePcH9EMUJzPinau0bPEq3Z0doTxdqq+94Wfb+Qu3dd++D6T9XHYV/h2sb7FtyT8Vjk1sS6saWZKMZatD5im8cyaW4Hx4KeimH0cbMNOxXDSJweilmDpXWd5RhGhxXD6EwykxNG/xDSXzGzBzCzLMvDZvaoTf6YhF+TCiGEEEIIIYQQQgghhBBCiBcxM/nB6DUn/7/dzN5mZk+1KLPz5P+XtMgTQgghhBBCCCGEEEIIIcTLgdLMjp3l/8QZJTqMzJxrkybwg7IsJ4qi5cnFKY2k6JDwmWPEzL52Mn0B5aHEx0OUh2VRqiOSR+AjnzmJGT4+jsesWfoMj6WiTAiPEt6bj+Li0dm6zxIds64r/cPHx1GO5lTlKHJWxOXwM8sHZZ77GI1fe3TMH2CZuOwM4voiKQx4ziI4wjwfjiofp/4+BnnHgqPJxwJpk8PQRq7ftQPtlCUiAOzTg4E8xzzqjz1sZzXqP8x5MC6XQv3Txg/vzT9z42d8Zj7wikfXqX9LyCsg7ygZ9wQaDNn9uM1OWWDPXSN+hTxnK8jQ8fH67ZBGqTOWFhnJlDPLy+JEkgKRLAH6p8COps0xHLNIBm0tpPlZsA6Ur+JnxPrZH41kyrGEHsyrOTSncc5NkwrAvEjOAOdqsKKjDF0RyFz18TzIyWSQjzgE/Xic+hH9Tt+Fre9rZt5uI9mXnDwd18F5OanXh6kc9mNNuZ+SfFwRSXzkZHCC8TtEfYXjeRj6Kup7ljJFe0R/WrANB5KqKJV6Dsi5dJBf5/Yjh7mPW9RnZtYBMn/PURufgz4gVUXHRfAsw2Rj2D19mf5l2NXi1IreYTDvnHt8Hq6/LB+5H9qyFGz4APUv1j+fxuI47IGxD/rpXtinc2hPcwDaiNc9R32Fwqm81vOzTRH19wGyv3My4zSf1obnYL/NfjgHzyVsP7fR+ddACgn9BO+zOjL9UVI78DqeZ8+BQeI+iPsa2z+X+gr3kB3g80uap0Wwfz8EdcyFtb4kO3V+kvtqJFMu2MceIvuYA2s/7kPZFqM9bwdLdE7B+9rAl+fkpp9jvw7PyX4x58t5Xx/5j/Zgjcn5YV430Ham9SO0ZU6wl4/eudCX4RrAz5mdc2Z2DNro3hXITucGcnJYR25umlleKsvM73EiiUjch0bvmZE8ZVQuI1vG78KRn0Q7OOX+QOpKbUbfX0QSZtH3Cxlb530c+gyeO87+IinuSKot14+RrH30XpWT4DTzcnts97iRiWwY28Xyx9H3VtB+59dnYus5P8zUlRfFNPt/fA/ivWvuu6+6Usuch88VSShzX0Wyw3XLzeRbYCHEK5aZnDCacrsLW2UWRTHLzC4/+TFSOBZCCCGEEEIIIYQQQgghhBAvImbyg9Emm4xP9JqiKH6kRf4vm9n5NnkK6bHT0DYhhBBCCCGEEEIIIYQQQghxFpjJYcSvm9k6m/zR6C6Dg55FUTxuXpjp/5yOxgkhhBBCCCGEEEIIIYQQ4kVKIPMrXnrM5AejPzKzD9qkemeHmTVO/r0ws4uh3AEz+9PT0biZMn7c7MmTGqoXsfYpfmZ9ftRZRt1S1n/F61gXFXRBUas60igfpnY4nWW4rmZIl2lln3syW8xJsM6hPOyqecG9UJue60C9e9aSRyIN9Kx18tjiw/A1WCf0Peu+O31grgPbz+OOurSRfi+Wo7gMTrM2aEexIqU7OE4AtiuIIdOBYpGsBw51oG4z64ajvnjdOHMcYwjZT23E282HZ2Ed9XNAq5lDskzTC56CbRH1gfspD+0K+t7F5+LPVP/B3jRLztmVeqsx5lU7u3Disqb1VhAVj2IMbYQ0+zjsVGwjx/zCclFcLyy3gvJQC5v7G8cax/M8Kod+k/sDr8N78eYE86LYRJH2+FimnJk3ulwMO6qjgwy1A9cY9n+52G8s+Iox+LivMH4Xrm1BkJdpsXgyPpT7Yy6kn6PALhjvBGNKTIsf0J5JExhbgDX4XZuoflxzF8H85rhpufhAZn496wjKHR/L5/XDWGAMIPZxHA/GETw3shR8HMf3yMW020G+ZW7rYmbm+wr7g+OdRHHs0BxxL3GcbH0YPnPYs9x+hNceXG84D68bDsrhZ27H4Uya5f4d5Luwv3l9PAhpXIt3UJV4P+57nJ7Yb0N0L3y2fmqjmzIY64jagftajgm0E/cZmTbxZ87LwfFYnsP2B3nnQHs5hoeLo0N9ldvLsj9FX8DxZTCeTRR/CMd9Ea3TeB36E/ZBaBNcP/qFdugrjpPVEcxpF28HjIXHpb1u/LUg/hCOCz9LLjbRTvL50V72MMbUgb9zXC+8F8fowXc6jinm2lEzrguOM8dWieIbRXFoophAyDm5dxYm+sII7YDKzcHYQXCvMG4O9amzzcA/Re9cbj3G+vm7gSiOCV6HDjUY52O0qHTwO8cUwf7gGOXl7Cpal3mu4j4u8sO52HTcDpwvdeP9mfmuw66J4mJFMcoOQZq/b3Hdxn2KfRd8UTMnk+b68Tl5z5ErZ0Z7n+35cm5PQHkHMuXMvNm6e9E+cSioA9uf2++ZxXawFPbvWI7rQJvg+vg1v7qGbBbXGJ4j5+S+02K7x3LsT/G7B9yQ5RpoNv2LDrT3KG5YnXi4L0zkeSHES4TaknRlWe4zs5+ytLaW9J/Z5Br3zrIsOQ6wEEIIIYQQQgghhBBCCCGEeJEykxhGVpblN83sajP7jE3+A4Xi5H9jZva/zOw1ZVn+9elupBBCCCGEEEIIIYQQQgghhDhzzESSzszMyrJ8wszeWRRFYZMHJAsz21eWZRlfeebpbDe7aOocbKT/wUcyQfHJSTnxsXM4s7uVjuLi8d6cJImZl3jjI8E74Mg0XscnnSN5LyfnAmk+lovl+ARse6Ycn4rHvPO5IfAseJx3mnQCHnGPjsTi0Vs+Wo/HhXlsl1hrAummafXjMWC2CTxyfGFQDuE2Og02SLPcALaRZ+5IJh3ZOkuqwOdzsE+5P/De/JxY5//f3rvHaZaV9b2/h66a6q6eru7pHrphrj0jcwYQEKSNiAFF8QJRESQK0SAkUSNJjuIJOdEYlXhNctBgEs/RQxRzIYKiCAbwBooSo/aYKFcZcO4zdDPd05fpa1XPyh/vW/P+9q/qeXq/NXXprvf3/Xz603u/a+21nnXda+9d6/cUUgcsV6RSKR1ZDK7fSppM4bpiyS6VT+N42ld2Jsdqh0oMEltIB6FRXlvvkIg8n2i9cXt+hI7/Ks93SbvfnxxX40DhduLt9JW8Y5UG9ytty8qOLC+dyKpt+BzG41ul/Hg8qhQct9MNyKmkTLhfVeOd81YZwUoeJpMY1L5Oc1doGpncXiFnOCfSCXN085h/IEkP6PYrbQu+j1BZVGqFZY1OSJvxdScLiaNZavdtIg/D+R3SPkGw7NBu6X88/7HsjUpgMRrWuV9yP1XpFSqnSvSxbA2nd5P2D86ruh9QeiF57WP7pY/tpkUO26ESenz+JEn/ZNI/FJYfUykTlkDleNdKep2wPKvO2m2JkimVU8cLSzqqjRyV7506/bEUocoT7eP65/ouZHVULnE3rSXmintgR6awkGFKZV6A+n5Ads0Vc/4U3fdUMonlqzptoXMhnc9W8jPUfjqfcnvG3m7YXCZZJfE6Y0n65mxSjyoh1ZGZlLad5XFHHXdP3/vyMmk+aof+vpAcaxrVeoHXjPLsx/LN01RXc7rOqmTFePysZG2i6XMbaV49ZVlLqM2mVTOzWJ8tufcvomsTtr+S0KvCMnliALPZdZXUd7XG4/uSpsn9QMfOliReJV2stm9P4iksFajrxGwcFDJr0ypFyG1L1y2p60omnp8fq/HC9JTO1bz4XlHJyU1Xz+4FnH51H+X56sTD3bAzyTsbhe/9pyWsr8uBSq6u44qgZ3qVsr/CkkF7kt8vljfL0LG9el+qJOn4Oq4DXVvxezZdP2V579W+zu9DJGiKZcZ53Fb3hur9RSZ9CXQrXA3J1io6TxbPFY+O6SqOMWbiWelyFMMPRA+uoi3GGGOMMcYYY4wxxhhjjLlc2PBtJGY16f3BKCKeBuCbAHwRRn+EcQjAHwF4e2vtw6tvnjHGGGOMMcYYY4wxxhhjjFlr4mJKchExC+BnAbwSo93ci/83+v+/APj7rbVqZ+6acmB3tIMvHJ58iwT+Fh1/XMJ0K+oixX7b20WSLtsqq0lUqqN8X78AAGgGSURBVAr3JemtNdV2Xg7Trdq81fdJsiV9D2+VrbbbVtvVtyfHurWXt9JW2+uzY2CpFEGWfl+pCrWjkrTIpL5UluBCEcZ1XEnSVR0wkxUo5ABKeUDu/DrG+Dqtj0wSofq8rZ2YOyvvtVepJU5fy892FH2sPWd0HDrg+ToOU0m6w0k8oDsZVO33V0k8oFsflRxKJTORjREdw3uLsKrdMzuqsbS3iMfn1XZ7rnu1l+d57WPcLnyd1lslcVTNC9lN4Ho557oSyYxURkXt4Hgif9S7nBxP7o/l3MhwXVUSgBXVvMNl4/R1jqvkHSp5NqaauzgNnpO0jFwWlf3iOuW20Lz6yosmkjVLqO5LTDW3KFzOqu4ZkXHrpMF9Xe2t2ozzO5X8rmnoeOH5hMui82chZ9jRdslkddWOStZT+1XWd6pxoPNRImu3REqykjhim/k+XeWl90eu10o2qpIozfKq0LJkNvZdW2oanL7ee84lx0DetpUd2u5979MVmXRxtSbtK2dbUaWRycIBuUythjHaT3kuqNa8laRb33VLT4m+JWO/6gdZO+mY4z5RzRl959qqrjiskIgs02AbtT/37X/V+qNax2X3WCWT/tX0K3ure2w2z1eSiFrm7DmzolpzVHXKdlVjv6+kpZKlqfXBNlbzZF85PK23rH9L3TQ6V4m+o5QmLx20+PxuSqXaWKJUZeHmk3iHinhaHRyX7ahs1NseS8jNF/G4bJo+LzM4vd0ybjtSc7rG4z6SvWsAunOoGsnreU5f13scJpL6je5Tp7ePXNIfm9nVibdABl914Vgn7OSWwRu/Fx84jj8/uLBEsdOYlRBxoAEH1zvX21prB9Y504mh3GEUEVsB/B6AZ6P7kYi/MsXw37cA+D8i4ktaa+dX31RjjDHGGGOMMcYYY4wxxhizFjzuIuFvArD4tW7xI1HIv8WwAPDXAPzUKttojDHGGGOMMcYYY4wxxhhj1pB0h1FE3ADg76D7oeg2AL8J4J7h+Q0AvgrAszD6aPRtEfETrbV71tBuY4wxxhhjjDHGGGOMMcYYs0pUknTfipFS5xkAr2qtvWOZeN8XES8H8B8xUGDdAuDVAH54Fe3sx04ALx4eq8OdW+hYxVVZc501R7V26Pxa0aGdJ31Z1k/d0Y2GT9GxOntS+dNFTuZmLEkjY7oI02JOJ2FqH1fxHtX8fgods3ar6mdXPZA1fCsfRpym2sFas5UPDw6rNNv7alyrDi2HaRrcl1jXtvJTpGGZHnNfvWRNn8O0Tu9P4imss1z5slHYjkqvm9u90g3nOtB8WRO48mvF6YnGdWRa6Zo3H2tZeIyohjb3Jc5L65R921R+Lzjvyi+K9jHuB1X7VVrsmV8o9b1TafdnY6maS3Ty4rg8/2uZ2deUjrHMr0GlP699rPIRwvD9S8d05XuB64rHrdrBYVrOp9Ex14f2AR4v6mcpG0vq64jzrvow+1Soxm3lC6XyAYEiLNPu1/5Q3VMyf0Fap09L4ml+U8mx5qVk9w2dW3h+OiFhPHYr30HV/JHdl9S+as7n/Pg6bS/WhK/8b1S+n6q1BOfHiyT1A1LNCyjCMh9J4/iuqvwKMdzuOrdwGOd9i8SrfFZk40DrlOvxbgnL/MZU955qvZDN65p+tT7j9e8460mu48q/R7Y2Brr3Oo6n7cfjVtcBTDbPALUvHqaaX3m9U/nx47oaxw9SNifpM2I2f2jYziIe17368srmaO1HXAd6/+K8eT5RHxvc7pWPK22XQ0k8nfN57FfpV3NL5WepFMsnuO6072T31b6+W4H8vqTtUs0tmS+8Kl5f36KVzyKl73qHqebCiuo5s/K/1teOvn2sSiOjWmdV807md0vPK59RnIb0MX7O1Pc5+6jv7KvmYe5j4/hUTJ5x91U+eysfpIWNjcKiqm+mepdROTGq1hxcP/r8yP2Pn1PU3mq9mvkX1Hah9I/e1M3gAhXuTuyn37udbBeOPXp8ZMvVnbDzuGL4/4a5nzfGXAZUknRfNPy/Afie5GPRIEJrvwLgezCSqHvu6phnjDHGGGOMMcYYY4wxxhhj1prqg9HNw/+PA3hzj7TejNHfFNxcRTTGGGOMMcYYY4wxxhhjjDGXDtXG5Ksx2F30P1trF9343FpbiIg/A/CC4bW9iYhnAfibAJ4H4EYAjwfwCAYKbr8K4I2tNRUtWsoOAF8+PNbtpZWEzSfomHdy6jZUksKYlW2jTyWpniO0rX+bbFHdT9ubj0qtztMx77y9DzlqIsvhcePOaTwq524pS7B0BdeVZsafBXUb7d7kuJLmqeTqOKySAtEtwWwzbQs/K2WZobBz0ne28FZwkU44tWf0zXX21COPHi/ItvPzW0fxTs/MdsJ2HR917TNXjjab7zg+34kX1VZz3r7OdazyLSwXo3JQO5OwYot0KTdQSapUZJIZVbvr9nce71xmLUshbTBPg+bIztGFsxdOd+Jte3jUTmdu6YoFzN1NbcjjoJIf03Imsi9nRbLryPZM1BJYoIntPDXaLKQsdH5eGvd0Z3YZMYPznfNzwy3uy4VldmwpNAp2iF7dzIVRQz20Zdejxw+LAOieJRpQy9s4tXc0EW+T+ti9jxrjELqoxM8ilXyVhrGJN0kYS+ZQU5zY0+1jV5wd9bEtkv4U3WPirzhA8qoklCrpG4brQ8vC45OlelS6g+dNlcHh7ngDHes8w+3UV7pI66OSHOM0uCyV1E2VfiUFUtV9JbvG8DpDFwJcx5U0D6dftUtVp9yvdEWZDX9tW05Dr+FztlHrje3SKSK7B1byiNV9me29Q8LYLu2nmcxfZYu2H1+n9z2Oy31Yy1JJKLEdupZguK0rGSbtmwz39S+UsEzmVMvC9mq7Z084leROJYlYyX5VcjkM26/tx3b1lXXWOZnXINVYyuREgW4/6is/pnXNaag0Ho+DSgqukujL5IQrSV/tz3xv47mwkkHTcarz5iLVek/Sb/TMFbdTgErAVmtvrg+tx+uTsEp2U/tmJoendbVQhPE44/u59o9KipDzzmQagVxCVMP6yslV0rQrkYVbLs2Mav7rK7dayaJzXM5rnPt01q+qeEomA659oJp3qjbL8lL6PuNWsqSZZKFSPe9maVTtp2OpkqzO1pfj1E3WZmJjZBJ9Sl85xkpqjm2s1omV7CuH6fzBaxqtD503k7zmKa+pC91Cn9yiTi+W58H0YXX03KwydsYYw1S3p8XbSfVqU3lo+H91e1yO7xj+U54x/PeNEfHc1lr1WsQYY4wxxhhjjDHGGGOMMcasgOqD0eKfMW+LiBuKeMzin6CP87czixwF8J8AfACDvyv4Vgx2HQHAUwF8F4B/sYJ0jTHGGGOMMcYYY4wxxhhjTEH1YScwkKR7EZaKaVyMGDP+WwG8vrV28tEEIt4L4FYMdhgBwHPGTNMYY4wxxhhjjDHGGGOMMcb0oM9OoHE//oxNa+2Dy/z2SER8EqMPRhf1YfTINHBq78BXzLmZKzphu28goVTVKs38CWjtsAyoasOSD4g9rAUrGujs+2hPpdlLYc9QrdZKv5y1VW9Ifge6ZdE0Mv9DGo810VUilXRjj+5VEf4RXT8m3YKyb5/DO3P/LMew69HjK8RnynnyVXIaI99B6rtlYftIv3VK7OA0z+/s9qu95ADgwZmRj5dKD1Z9wxzZOao89qHykBR5FmdGx+e69l8xM/Kf9OmdNz56fOv2uzrxHtw9Ei6+eqo7pB7aPWqnqRtygWD22TOtuuzcR7jvVGKS2q8y3elKz1j0+U8d6Odbin28fGrvdZ2wXTj26PEVhaD2yZ2jeps91Z0YuvrDo+OFJ3bTYN9Y8xLGfq1Ys/ik+OxR/zsM9zkuy4L0U05T/Q9xffCYY39Aep3axOlXdcppaF9ndmx59G8MloxbtlH9Ge079dlHj7dS3Tfpi43EVUN9ElDceeqz01qsStSV5uh50drmdNhv0bkt3YhbqGPNnHsEGWfpHjVT6MPrGJnmeYh9HVX3Lx3vrO19QxGv8rdDNLqlhN6Ln5xfl91jVUT3LN2nta46+bEfP53ye2qssz+9rdpXKp8bfFs9kvwOdNcqmn7mF0R13/n+rtrrnH6lP1/5buF+wOnpbZ/7kfoZWUjiaT/iOnhmYQdzi5xzfd9chHGdVv4VNV++B+jaiuPyPTbzkQIsFYlmvyA8pqt2VzKfPVpOTlPnnUzHv/K9oDb19d/Ql2wNo+dalixvrftxRL4X0bml8iXH8071hFf5a818YuocVJUz82ep8yKXRZ/yKr8dGWoH+/Gs/CDdQ8fqr5DhcVb54FN4vqrar5jLI/Mtqn2qGHONxk9UfkYofV0XBdm1+My9yPYjozVIyx/9OusMfY5g/6HTye9Ad92seQX3F6qDJvPHArWF+qg98ZRR7nNHuj5lU3u1Tqm/d9aTcn/kOtY1GMPrwiXtwmmqHZl/Qb1P8/jR+0Hh87VD4nd1yXlfLZpqvdDXP5qS+U/WcnFelR8azrvKV9Pg+29ff3Q6f2T+gXQ+7fs8rXMo51f5qazaNrtPV/1UfQieS46Vyn+o9ulFdC1S+V/juJyetguXTdctnGbhF4/nkwtT3TlodvvondBeuml9Erd24vFzuD7X3zN8TtZ3AcYYw/S5Xbcx01yVD0wRsQfAl9NP71qNdI0xxhhjjDHGGGOMMcYYY0yXi30wWvPdRctmGrETwK8DuGr40/swkK1bLu63A/h2ALj++nUxzxhjjDHGGGOMMcYYY4wxZlMRrY27gWhtiYjrALwXwNOGP70fwNe21nJdoiHPODDd3n1w8I3pxts/2w2stkhnUgTVltpKjoevq7bU6g7QvvILlTTelXTMMhO6VZbllVSGierq5M7l5bAA4EEy5IIkMpPIXqlsFMtc6ZZYlnXjbbQaTyXesvSPkL07cLITj2W1+Bplx/FuhbMcGYexjBgAbFnop0+0ZWEk57C16ANL5BdYdoObQrdS9+UwHWsf4/S16rNt55VciY4Dlt2o5Ku4DlRShe1nO3TbOaepYzqrf50XKhkctpHlNDQNjldJOVUSVYymcSIJq8r8dAnjslXyR1xOjZfJV+k8yXOctkPWJ7QPVBIR3D8qGYVqrt2ahGkf43gqEcFSMjqW2JZK7q3KO/uTEJXXq+Sb2H7uc1qnLAOm/Yrrm+1Xeyu5VW53riutD67TSsiW27aS5yhkdcoxyONA5Si4PXnNUc2TmkbV7hnax3h8ct5SH2dp/bD1cDes07bcJ1QyhO2vJJ8qabwsHtCtj0pOpJD/SKW4tC9yXlX/4Pq4UsKqPpflBeT1U0l9VVJife89VR1U64xs/gAA/iMvlRhkuO9UskDctjo/cdsW8qKdOUPbjMtcyW72TaOS1VkNuA60n/Jcq+2XScjpeOwrN8jpaV7c51Raje/pnEa1jlMbszqu4lWym4z2xUrCkW3ke5nOhdxOVTk5rFqb6JimclcybiynrJJxLKOqkq2nt48k6k7PjGTAH3+4ezNmqfKpC93Ja2ELPfudGz378fMRAFyYelwadn7rNIWN0r/ibDceP6vN3d+VfGrJvKaScRdo/qjkflkCMEsbAM5J39yS3CvOb32cxBuVjesG6EpzV/Az/9whqY9EClj7UUdusFojVVTyadl8Ut0bFLKRJYi3ahrVeoTt4nWQ9oFE6nEJfaXbq3VzlQaXrbr3VM8ilf3V/SC7N+szYiWrmFG9B6vmRkafFTheVR98j9K0OZ5KI3PZeO1Q5LVEkpPq+HaS1L8T+zvx2N3DeZGQv2MY9ycO/AbuOvjghmwSMJuPiAMNOLjeud7WWjuwzplODI+7eJT1IyKeDuCPMPpY9HYAL+7zscgYY4wxxhhjjDHGGGOMMcasjL4uB9eciHgBgF/D6O8X3gjg9e1S2wJljDHGGGOMMcYYY4wxxkw8DcD8RWOZy4dL4oNRRLwUwC8Bj+6V/K8A3gngiyMe3SF5trVW7m9rIBmzP5bAL6Rj3VLLW5p5K6tug76pyDzb3vtkOecar2Tt2MaPS7ztSTygI7WRScspKpfGMgIzF0ZGXUGycADwpHOffvT43Ex3mytLDGx7eDRpLNmezllrWCbZUsn26LZfCns87zlWCSxul0oWQ9p59wLtd6Y0pmdkotQ0GZZi4bKp9A8Ruu28kBNK41USb4zG4/Qrpb1Ksoav0zbjuvpIki/QrR+VXMzs0jQqORTeNl/NlFxvKoOTSfT1lR8DcsmFSnpA+zf3Ma4rlcvh6yp5op3J70BXYkXnp0zWs5L503lhdxJPpQdY6qCSpOP6UDvOJfGWy2+Rao7TNLh+dIxwW9xBxyq9V8HSN2xX1f+0X1VjNUPHXyZBoelV7c71w2NO7a3kiThNlqWq7ilK1oe1bSs5jUxOU2W5sjID3bKxFJLWKfexam7huhd/kFtvT/ICuuXm9HQ+zeQogW5ZKvlg7leVBBbboX2xkpBjuypZJ05f54Fszlcq2TxG5+i+9+lMdhPIZWU0bR63Wo+cZiWZWUnT8jqd61H7WCUHzflVawLmbjnPZEMrOblqvq7kViuZRU5fZfMytI9l6zOdF3jeUVmgTPZVy1ysW07tHQlYbL9/JId16pqusMX2u0kqS8p8lvJjiWaVwDp9C+V1pCu9xXJTLBf24O5u4z7+/lHDH72mm8FVR0cN89Du5WWoga68GT/3AOjMqZ14c914LG92cnvXxplrRgXg9I9d3403e24kysHPcxcLm9k5Sv/0llHYlp3dwc+yRju2duW9+XlyC3XGJv2UnxHPbekOCpavO7Vz1LYqs3ZhajRBqSQdw8+nKvc2e2rUhtxnga58HbfZFWe7bcaScZo+S5yppBQzVTxLsfQU1+OUyMyxTNz89m4Yy8RxXiHz6dyR/CUfx+3I00kanabWcmWyZdXzhpLNO5V8uobRPWZr9gyu6JqO58ZqXVHJ5vG9iOfdqiwqY8l1UNlRrWsrG7M60Xt9JRWd9e9qnaz3Sk6T60PT5nt4JQ9Y1UdfyXS2Ue+jfI9VyTtOo1hPdqRBi7piFw76ru4YdlG2D3bCZnEGAPA49JOsNMZMJpfEByMALwE6wpqvHP5j7gJEmNMYY4wxxhhjjDHGGGOMMcY8Zi4pH0bGGGOMMcYYY4wxxhhjjDFm/bkkdhi11l4N4NUbbIYxxhhjjDHGGGOMMcYYY3rRUPvHMJcbl8QHo9Vi5vQCbv5fnxmcfFgCWSNUtcFZM5R1RlUXlePtk7BML1390GR+QICunirHOyDxMh1XoKMHG5Te3HHRJS58t2w/q2KriwGSBum1bj8rwqtsI9djpfev9ZFpw1Y+bzT9rE4rHfzKl5LqAy8kx0qllZv5otG+U2kMZ3lXPoa0mbPZoLJ9HN3p7DrVKT6RxFMd5VNFGKdJdXBCfBdMkb3bxI6ofFh0Ekny1euqtuDrpG3nqf7nKb2jlfazsI30yw+RHerZbD+NweknSiCNs/miT9xO/XmJuxPSYz5a+GCaI0n+QxIvK4s20Y48+aFq84Dp5HdNQ+uKZcQ5b813Gx3rkOYwHTqcHw+JkxKP06hcpbGc9jYJO5HEU/Q6hsumZcmu0/7BdXqt+J1iV3tsr6Y9R2PphPTT7DayW+aPBWrQM0VfJxcKOCHjew/ZMSf3tkbpH6ZG2yF2zFJjNBnvwXr3nL6uRXj83CBhfL/kNLRhuLIq/1RTyTHQXXfp3MXzH1+nZan84XAa1dyd+e/RNCsfcZkvLE2D8658Bo5zHz2VHGubzSTHel21ruC2ruqDqXzvaDmzuOpzrlpDcntWa4IqLPN/pXlVZavWAQxPlNpmWd+sfAFWfYXKon5/On5N5XmG/ddcsbdaAOacp0o4d9MojZlzXf8KJ24e2cH5AsA5Uiqf2s6+cbqTEPtvOH1NN/3TdIeY2T4KOyKVf+GaLWnYPbtHYbtw7NHjMzu79p4G+/3pdvYL1FBs0+xOXXXwNd2BsG3LyP/QQ9QnzkuHuzAzuk7TODkzWlzNiK+L81tG9a1+MDp24HQaxn6F2MfTgozpju9cCTuxZ9Qn2L/RFplAOH32MaTps18eThvo+rU6LWOJ0+zYIQ6HTm6ndhcbZ86NbDy2c1T3O051H8AqH0nz1Lzs42qrzNfsr0v9Pc2Sv6PT20dh6vuJ/THpmvds4summu40cD65L6kPJ/aLpL6fprMpSd8NcJpqZOaDtPJ5I+Vvup4aErrQ57zUjqck6as/Sx4j+v6JYXv1fl75JGS0DjL/Sfo+5FQRlvms1Xt95c83S1/vgZWvweydULVG0vSzNVNVZoVvMWTvWVlj8Pyk7dkojb3UMPfLQuVW/OWy8YDRvaOa740xxpJ0xhhjjDHGGGOMMcYYY4wxE44/GBljjDHGGGOMMcYYY4wxxkw40VrbaBtWjQPXRzv43cOT90jgX6djlZXgbaNH6LjahqpbT2eSeApvM1Y5igzdKsvyFKofxDZzXrpFupJgy7bA6vbaShKLtwRX8Sq5Ny5bX0k3jXd/Ek/heivk3lSKa0q3Uw9RuS1WXxBhv46MFMsrnZB42Y5uDavg61QIg6Wu5pNjoCtToJIFfWFBC1URYLu4blTqi/OuJLY4fS0zn1dyW5xXVfcq1MGCJVyPS2S0EpsA4M7EJk2Dh4umwXGremMbdWrhNLmvVPJjlURaparAba1TV9a290o8bhctJ5dF+3dfOE1OT+3letS82EZts0xpSMd6Zsdy+WXxVhttW86P21bLwmIJmkamVKmqZVV9ZPKD2k8rWb6MasxVcbmdNV8eZ1pO7vvXFvnyHKSSdyy9N0froib3r5PFPXwuW4/o/ZzRgu5LwlSKixv+KRKWrbvUdk5fB1kmLazxqvUIrwmqNV4lkVvIxM2T/dMUdvSaboWr9BdzcmY0CmcvjO5aC1u6C5rZU6OF3MntV3bCdh0fSSqd3/o4uqYrcaRSVAzLELFE2g6RUGYJpe1HuunPU11N0yRxam8uyfTg7m5Zrj46KgvnpWWJai3L5SykllshIXeO1vpbaQye2tMty/b7yS7p9ywbxXI2VTtw+wFdWTGW9rogC16Wzjq/NV8NcrzT27v9lOXUVGqOpduuunDs0eNDW7p3B5aJm7nQHZwqc/eoTcUgviAD/jxJ47FMnkq6sf1aliy9XeeOdcKysQl0y8Lpq+wcn7O9y9nMLNB1VTxOU+XwWJKNbVwim4dcNo/bc2GJZtXyqL3cd7g9Z2WVfohuPrvwUJp+1U85Ta3vLA3tf2foTl3JQ3X6DtUTAJyk1ZXKBnIdsx1VPB0H2k4ZXDatqyk657adkngLRf/Ixpam0bUpr1NOv5qDqvE+e476wEy3D3A9Lunrp0YTPUsMqnTiyS1522b3+lJuUO9lfF5JwfFaRddnmcSxplnJqLJSY3W/5WZKvCgsyVfTLKTgO+n3fSCo1qsalshIq+zj1jvoROuNuxKld/ve6zrRbrmfno7lAfWvnvkEAMBLDjyIDx+cDxizCkR8fgM+tM65zt7WWlMnLmaV2FQ+jIwxxhhjjDHGGGOMMcYYsx40rPzPcc2liCXpjDHGGGOMMcYYY4wxxhhjJhx/MDLGGGOMMcYYY4wxxhhjjJlwNpcPoz3RDn7V8OR3JfAL6fhKCWMdUNY7PSLxWI9e9UhZd5T1/9X/0D3JNUBX77Svf6NKn7/Sf2Ud07MSll1Xad5KWKOydPz3iFY/+1CYEoHE6UQw8YTo1Z6g9NUVAPuKOJH8rqgSe+UTo288zrvyYVSl3zfvyncG14/acSY5Xg20TquNqn3reyVUvmwqeFhpGivxQ6OSyNwHKn9UlduLqg+w3xj2fVL5KTokYdyG7EFA/b9Uvpoyn1Eaj9NUG9mujn+WIl7f8a52cBrqmygr5zhzS5ZeRTWW1B8Y26XtaS4vtG2ZzCcSUPs2477E/Ujnlmr+4+sqX2+ZzyUN66QhLhP2sP/JSiOffRHpGuwG5HCalX/F65O8gNzXpa6zOEzT4MrTdSKVZz7z24SuP5+zPTX41Q8N+4pZ6i9jtJBmnxga7wgtZtWPBPs7uZ+ci+6T2Zb9dlzZ8ewFPEwzP4cdw65OvKuXLOgvTmWv+mTJUJ8YO06NFrBbZKCxLyH2P6HxLlD/mBEfDcFx+Vjde1TmZw4z+zrLLK47K+N2K93sq36qdZBxQSYevo7DqrpX31Xs1+qKs92wKu9uGqMB3/Gpc67rj+TYzK5RXtL/Mt9Ele8W9alTjVUeM5yX+hFifzsalvkLUt8t5zo+gbqNweVhvzyVvUrls4aZoRvHaXT9XWW+bXRMn1zyUoHTH9nB6WtZeI5T2zMfWtrulV+rzO+P/t4tZ7dduO9w+jtkTma0LNyenP6srE44ffU79RC1e9X3uW9eJfXNabD9J+VJgtuv6sOVf6c9eHDZvIBufR/B1Y8ea5m3FWPpGK4ie3PfbNwW6kOL7Zq60K3TzFcd+7cDur6b+t4flR3nRnZUPvPYx1/RBYC75ZzvP7wk0Gor/Eh24GJqGpUPI4bXf/pAzevGqpy8iNb3h7fT8f3doPmXDv5/zpcCt/3PZh9GZlWIeFYDfn+dc91pH0ZriH0YGWOMMcYYY4wxxhhjjDFmTBrG+wsjc6ljSTpjjDHGGGOMMcYYY4wxxpgJZ3NJ0s1GO3jr4Pje/9UNu+7JdKL7qngbaSbnAKDRttTQLZ+8LZXTV6k22gJ7RravnqGtp3O0w31aZUd4+2q1VZa27DYpy8niuiNkFwuUVFJIJyQs26CuVc/xKim1aiscp6F2MJXkUyXVxk3dV9JM7WW7tG44Dd6N3FeiSukrXbTS9CeRqk65HrU/ZH1alVf6ytBx+joeWUjhuiINLouOl0wyTvOryszxVCaOw3TnfWaHjiW2i9Oo6kNt5Po+nfy+3HVMZn8la7fWaD/NxvtKpRmN6Yv2RR6POs54jPB1On9UawQOm01jdedeTb9zr6c13ZzKifC5Ss2xXme2LgS667hqgaPKLtuTY12Tso1VGpWNjMry8XW8nlTJv5nkGF1JPZbz2irr01M7R3/bxrI3QFf6hmV2Lkhhrj46koI7ubN7t2BpHZYE2yayQCzFpVJOp6kHzhRyWJwmSxABS2WIMnZcGK0iH9zSbXjOm23cdep4Jx5LpM2cE+k9ksM7tP3xZF/3TqGSVcz5juRYP6ksDWN5Im4XLr+ysKWbRiajpW3L/UXLxTJVWf3qdWeKWYhlxXagq7F9mCaGysbTxWzINqokXdU3D9HkdQ1pF2leVV31laJiebZKRos5V6StfYfP99Id5gFc04m3h3SpVI6MpcR4rKqcF9fHeanvKzr9ZVRXlZSawtJqnFdVZu2bfeeWKwoJNk5Ty9kXLstMIZHGaJ/iuFyPlYSj1kcWpn2dbdT+wXXK/bkqi6bBUnA8v/aVi9S4PN5VspHT79tPBzaKH4AkDW7bvlKBWha2UcO6snz5HLTj+GhRNi1N0WhZECzPprcyvu64hGUuKDReJee6tQhjeC1UpVHIE3cUfm+XsFcM/jvwKuDgxyxJZ1aHiGc24P3rnOseS9KtId5hZIwxxhhjjDHGGGOMMcYYM+H4g5ExxhhjjDHGGGOMMcYYY8yEUwlQGGOMMcYYY4wxxhhjjDHGLEODHV9sLjaVD6PP3xLtD4fys4dEA/060nOfVh110iq9kzRIVYOf3A+VviK2Jb8DXZ8hlV+NCk5ffaEw1VBl/xuVP4vKZwpTfXnkNLTeOO/K3srHRuZbBch9E2lZKjsqnyx98tIw9WmS+TCq/DFV/qQqe1n5Xet0M03tVZ/oC/eJyocRo3ll/jgqHxuaRubPpxpLlY+kbHwrff0PVX5Gck8DtY8h5nARdi0d9x37alflY4jrW+dJTp/HqtY915uWk8/VDk6/soPPdV7IfBhpu2ymsW/MasBjSdc3PMZ1TLMLI76uWnPovb663/Cct48k/nernyUiVN+e4/Jaea/EqzTyeYLi69R1wVwRltm8Rc7ZTY9ew+lzWa6ReOyv4OkSxmW5iY7F/8Gpa0aiDNuPPNIJa+rjaTEJed5gn0DqT4b9Z7DfiJO4shOP/WCorwj2RcE+LCofJlfKHeEw9eLqOvYpo/4s2K+G+tJg2C/F0voYrZTY10flU0f94bCNla8c9mPS1z9VVS5Ng+GyqJ+OvmlUflLYD8gRcW7Wt5xs4zFc1YnH9bin82Sc+1ZS/1eM+lmaKmxkeBywvyGg60Nqz6ncY+ZW9TtCnNpD4/2O0Xg/cUt3Np87NFpBnZX5aUvyYD/V34URgvzkcfoz5/J4Oh8F2XGW5vIlaVC8eZm72OYF6rbqJ4Z9zrE/NKDrO2321Mhg9VvXTbB72qgOonhx0vFXo74GKayyt2OX2sg3+MJ1F9ejtnvQ8DlL96wLU10BoO3HyS61g9qT87pQvJg5vzX343fF2Uc0+rJMVy8pFJ7mqA6arCuyfgp0bb7i7GjMad/hOtC+mTqf1Xmg8h3EYfcjh68r2qwTpv2I7dW1T3br0DJz+urD6MWD/w68ATh4h30YmdUh4vMa8FvrnOsT7MNoDbEknTHGGGOMMcYYY4wxxhhjzITjD0bGGGOMMcYYY4wxxhhjjDETzqaSpDtwRbSDTxgc33FPN2yOtm4uyJbgbbQF9E7aylnJJOlOX97cy7t0dSdrJUnXsYmOVZ6kklbjsEzS6GJwfnxdJZmnkkaVTBLD0i5VPE5fd0FXdmV1XNm7vKDC8mGZtJjayPmpxFbWJyrJuEqWqmp3DtOd2X375qUC9x3tA9kYqcat1mk2ZnQ88nkli1b1K5Zn03iZrFslj6jtl/XvShrv3iJNlkXaI1voz9Acuq0Iq/pbJvGpYdWYyK7Rc7ajkmrT+Ymlp/rKkFbSb9qHs/JU85/22UwesFKSqObTShKxkvYzxqwv2TpOw6q1TzVfZWlUEn0alkmK6j1lrquq1YUXAj0lgnBLEcayLzoZcmFU4ojTZHkblcZjtGE4LsvKqBwgl03SZzmeYztHUnaz57rScqdnRnf/mQtdDZtDW0YFZWk5lTdjObkdcvc8gquXDVMJtl3nRukfm9mFjNPFamULNZRKmHF+LPOnEnosi8bxgG65rz83erg8OdNdhe04R7J5M117tf4fzXfmimV/B4Adx7ud7PDOUWfnersw1a3TbQ/nI5dlqjK5NEDkpnQscXfRNGZ6xqsk2Tguj0ddZLBSo45HlmHi8ahjjsPURs6b3ymo7jDnpelnslEqYan2M1xXnJ7mxec6Z2TSoyrFNVWEZf1F7dhehGUvFTRtrh+tG24XVkTU+wSHqZyX1s8iKudVSBZ2bB5DYjC1g8dOpcWd2Q5021nLzHWv/TKTSKvSrx4WtB6z+5nOLWxjIbPbsUPH47kkHtBduPAcV/V1fVmSzRlaH33nP77dVPOC1genz/WoZeF4Kkk3XLcceDdw8EFL0pnVIeIZDXjPOud6vSXp1hDvMDLGGGOMMcYYY4wxxhhjjJlw/MHIGGOMMcYYY4wxxhhjjDFmwtlcknRXRjv4zMHxoQ91w3bQFtgzsr35RLKVuNoRrNIavCN2vojHog0qUJDJMOku60yBQ6nkzTIJM02zr1SbpsH5sWhDJaGnghOZqEIl+VRRSZMxleycKD8skTfMbKpkr45gfLSu+spXZVJcQHfXNcfTNuO8Ksk7DtM243jazn3bidVhKumcikqSbh+Wp++YUzi9vpKWmibXm0rSZRJBSiUFx3ZUkndMJeOmsmX7aNv8IZauk3iqAMBw3Coe21HZWM2TVR1wXWk5GW5rtfdQEZbdDzSvvtKEVd/sK+u50nG2ErRPVGPEGHP5kUkXq8JMdT/j+bWSWGUVJk2P579KJpnXf3t0YZ5J2d0g8SopnU5mdKyydiw/oxI2nPdNSb6api5IuLIqWadKuiiTV9or8VguR+uUpXr6yhMpfF0Vj58LVU4pk9GqpNpUNoplgrjN9Oa7vQjj6yppqKrMW4ow7UsZlY18zvVRLZy1rrKHoiqNSvKpattK8i6TGasemlU+bU8SVo2lSjYvk/zTMG3LTAasklKrZPKYql10jPTtY9WiNHvQ1GsqeTatuwyuA60rbpdKJq6SR83Su1LC+F6hDwvZnFTJm1Uygpp+Zr/OH3yufSeTi9U63Z4cK5X8LNuv91i+7kjyO9Ati85HWZ+rHkwelvNzybHC6Wt9ZJJ0ai/Hu1vChnV14ChwcN6SdGZ1sCTd5sM7jIwxxhhjjDHGGGOMMcYYYyac6u9CjDHGGGOMMcYYY4wxxhhjEqptouZywzuMjDHGGGOMMcYYY4wxxhhjJpzNtcMo8GiJdojW5zSVdFadk6hu8SKFtq/6xGA/D5WkLoepXHfWGKqjXoVlvo/G8fmQ+b2ofGBUNnJdadVvE59ADNcH+5nSeqr07TMq/xhVWSpOJMdAtw76+vNZop+fHOs5X6c+R/hc6yDzJ6BpcF6VBD+j42C+COP6qdJne9WPS+abSKWZq7HK0wLbUfmnUv8ynDeXWduvGltZ/x7HL1TftKsbQuZDRq+5lsb0p2QOPZpoNVdjeiV1czGqtmB43Fa+glgyWu8NVR/jOr22SL+ahzk/bffKx1NfuI55HFR+8VbqZ47RNLJ+UPnF68tqpGGMGY9sXttIH2WVH79tfD8TJ6fTdN7xOXdHnpfO+TzvZD4UAfH3JL452M9SVD4g+AGh8jPCvhyeKPEyHwoKN+g4fn/4PPPjAnTt1Rs6+9LgG7A+gLEd+lyS+T5SHyHF80znebKvTxMl8x1U+SOp/BSpnxG+rlrocxpVW5xNfge67an+ZbKyVfWtiysO4+v0ub7yHcR+ZCr/Q1W7c5qZH6Hl8u6ThrYtj09NL/OxNo5fqKxtKx8slU8nTr/qH9WY5uuOSzwuc+WXrPJTVM1xPC7YV53awXlVPsXYXs3rVBH2xCSeloXrquqz1TzMDzvattW81te32VQST+GwysdQ5WeJ7a38X1VjhO2oNlRUdlS+9ThNjbeQHFcPfpLGHw7XLepiyRhjGO8wMsYYY4wxxhhjjDHGGGOMmXA21w4jY4wxxhhjjDHGGGOMMcasAw3W6thcbK4PRvMA7h8cbpOtsQuFvNyea0bHjbZrzsk1Z2j77XWyffoobUHmIaISVazMoBI7HJelMGYlnpatYyNtj2VpilLaS8rC8n2cxhnZHrywkIcxp8fIuxNG5ZymdjkheVVyTSzdMVXEy65RThT9qLKjr1xd1Xe4T4wjB8XwruVK0qySv2MqOyqJNJXtYjgu76xWeRiOp/0725Fd3b40jWyHt8qgzSXHCqcxjuxhVv9ahzzOlswZSXpVX9Ewrp9K7vIQjZGq3fcmv2veWqdZG4pCUFkfmeSi9gG2Q23MpNo0r3uLNCqJSKZSOqjGWSYzqVRSmNncUkkzriersSS93Je1lYRoNicvF9eYSSdbf6wFlRRmJUnH9/A5lT9iqAB6H526J78su2/o/fFEEk/TOJH8DnTvKZUMc7UmQM8wfp6ZloVV9uwEdJ8fqzT4eWa6khzrKwmmslHZ85JKpLFUVCVXp2xPjm8o4mmnOJXEU9TmLH16Pl8iyVTJ1XFYJb9VyWNljCMXxpMIL3q1LFwfKvXFZctkqIDupKFpZJJ00kaN8oqzRRjLgOlzcSU3OJPEUzJ5RE2D2Zv8vtw1faXEeELRsmRymlXd65jgsrEuWNU/NI1Mvk/jcTtVY6KSiOQwrW+2WR/IsvGudrCN2i5Z36kkC7UeMxk6jVdJEXLebK+mwXZofWSSdFVf1HJmc4HaUdTp0eV/NsaYDpakM8YYY4wxxhhjjDHGGGOMmXD8wcgYY4wxxhhjjDHGGGOMMWbC8QcjY4wxxhhjjDHGGGOMMcaYCWdT+TBqC8D8UCdU9aNZa7qJvmxsX/5Y9anZj47W3D66bp7inZG8drPfH9H9zfxIqM+iOdaC1bC+Otl9NbSJWfEdxPU4JWmwfyPWLJ9fyOOpH6STVI9cN+P4aMhQrXS+Tn1xVL5FGK6CSpu+8huTXaNp9vVHonn19bPEqCZ8Vd+ZP5XKjqr7Vdr3jMoDZ3lpGpy3+pdhOfDK1we3hZYz849T1ammcR8ds2z4HonHaVZ9rKpHDtP0maptDxV5cR1wval/qsqOrJ9qXlxv6osis1/TqGzk6z6cpK3ouK3GY3Zd1Yc178pnWd/0ue7s8+bSpPJB1fd+Y4xZX6r59FARxtx38Shjk60R9Pe+jxgr9dWXzWvqNqfy/cn5beNnLvVRy8cSxmWbLdKYomeYJT6jkryqZwWlr+/FTl0V/ph2iI+T2cxPii6w2a+Q+vfgBVvll+eaJB7Q7Qh307F2sux5V9PkwaS+jrjyCl+8pU+dzCYg95lS+ZYq/BR36lF9Bz1chHHZuE/Igjp4wSc2xh10wu9D1BdRlpdex21R+YXSts188RQ+kUsfRlz3mlfmawbo1h3boXX/AB3rOOA02abKP1Dlb4f7vbYLX1c5e9PxyP2g8kF1OIkHdPsm15vWRzWmuZxcFu1jnL7awedZX9Q0tT35uspf0vGeYez3V+It8YXXg3mZx/i926zM+dcO/79i/GyMKWiwZ6zNhXcYGWOMMcYYY4wxxhhjjDHGTDj+YGSMMcYYY4wxxhhjjDHGGDPhbCpJungcMD3cZntCttBvo+2mU7J9dZ62r3a2f+o21CqMdt5x+irVltm0JIzyUqm207RldVrCOO/o27rVrkEqp0r5dSTjJA2VdFhkiVxOEg/I5ZpUYoeL2Vc+Tqnk5JhZOT/dIz2gu/u7kt2oqGSjkIRpXh15QAljCbZKMo6luSr5Iw6rupi257V0zNdpmfk8kykDlkqJMaxGoW07lcRTqvbr2x9ZBaIqC/cjzbeydy4J2y3b01lCc0HlVnheo9+3Sbx9yOGycZlVfYHtPSJhmQSg1ls15jhNtreSh9GwbJ5QaUO2seor2mbZONA0OH3Nm+G+qLZX/Tsbg5X8otk43A7GmMdCJmO5GvKWK02jutczKj9bycr2yatC76OcvkoKZnJy48BpZBJ3Gq96xjqt8kd0vo9kqaZkEdaRJqskn66kY5WNKqTVU2mxSu5NyWTGNO2zRRinz+kVz/+ltJpKfWV5VfqO53rG00VvJvWlZeZyqowWv1dhiTGV8quk/ZhK9qt6aMzebVTXaFjVnishkzoDahnBrP9VUn4VnL5OQpy+PPt10AeCrL61nav2vJ+O+7pE0DJn/bvqO9WYY3tV+7ySRJzqGY/fkUldHVU5vyH6TmmObFQ3Gdn7RXWFMcfzsLT7U4bnW61dbYwp2FQfjIwxxhhjjDHGGGOMMcYYsx40+M8XNxeWpDPGGGOMMcYYY4wxxhhjjJlwNtcOo8ch3crO2z+3yZbxbFvnlGwvZYkmlbVj2bgFuk6l2k5QGionx3SksmQLaaM0Va6Oy8nfdrfp9mZCt7kybL9KVLEshH5HzuQXKsknJdtdXnVa3VXLslSVHNt0cgx021rrIJPHqiQiVKYsk8Or5C50xzjX1UrlNDIbtb77SuNVdnBZVEKEVQ9Ylkvt4HOVQeO+yXZon+K8VZKOy1ZJdlWSY5k8oMbj+ujbPypJME1jH/XhSqJkD23Lb1JZ2RyndXOU0lfpGLaL26zqY/sL6QSWxdR+xGoM2tfnsDzaLlw2TSOb/yrJSS3n/iIsk3dUO1iGrpJtZMaZQzkNrmP/7ZAxBlgduS1jKjnovv2qUqXie+U4Cjx9H5SrtWbfNDLZOaBeS2R26BqsWkN20qBnyXHWmpmE7W55BmVJ9pMiPbWD1nz8fKrP0/psnMKVpddUknSZdJZIPqUSehq3spfT0MblNKoOzo2t0nhZWVSyK5O/U6jNVK6enxX0/QK3YdW2VRi/w9G+k6HvSqaSZyJ9V8J26LsiDmMZsHHKUr1XytA6PUFl4y6gEtV934HoMx2Pf05D64rLomp4nB8/f+n8VMlqZ+kpHfltcU+RvTfQsvC7uj2F9KX2K4bl5XTY9r7/8DtDeXbvSNQn4woQVxtSltlrBv8/7u6+BhljJhHvMDLGGGOMMcYYY4wxxhhjjJlw/MHIGGOMMcYYY4wxxhhjjDFmwtlcknTGGGOMMcYYY4wxxhhjjFkHGmr9VHO5sbk+GAUe1efcIVrBrO95QjRHt7GfHqoRTaPSqF1I/HZoBfcdPneSLu8e0ehlndttop3MNlfavmyv+jRhbdhKkzu7RuNOFfGyazRulh4gmrpF+qxRO1f0fNWk5brqrZsrYazLW9XVXHKs6Ws52f/OkSKvrG01rPKZUtmR+ew5IvGyMqsdlZ+iqq1Zg7mvrnyVfubzBliqzc6wP58qDUbrm3WQ2YeMlmV/z/R5HBySOWKO5jidW1gX+ShdV42JqsyZbyZNc1YEtedV9HrItXLOfU4vyexSO7iOVVub7ef0lvhAS9JTdDxmGtfVuNW2SKqqRMeV/ZMYYyrWel7wHJRT+f1h1rveKl88feNl/nDG8T/EafTNayVpA901Qt+H68pPkZI9E1XrWk0v80eisA+ScXwYpf5r5TlzN60hl/gtobBq/TRP8ap1F1OtSbWuMn8qp+Wcn7+qZ64qXvX82Bde7/X1LaXx+vqnqtaWlT/fKn2mqqtpanduC/VDW/mQmb+wfNg29fHKPoYqn06FHZyG9p3OdRRvh4R1+oTYyM+Fmf9eoH7+7ztfd8aP2FHNy9zWbKP6nq36ftbnqrGvfYztv4/TkLJ0fLdKu2f+ZauxpGTzcFX+Jc/Jid8ifXbv+E7TsEWfRvcXGRtjJh5L0hljjDHGGGOMMcYYY4wxxkw4/mBkjDHGGGOMMcYYY4wxxhgz4WwuSbrHYel2yyG8XXNBtp7yeRWPt3nOP9wNy2SB+kohAcA+2ja60FMfQWXnts2Mjlm6blpaurN9tUif5dmqLeiVNNm2QrIrk/LTNPpKUOiWXT7X9uzEo/qYKuydL7ZgVzZW8lvZFuRKeq9iT894lfQZo9vrq+3eDNeH2sRyYdrVr0vS1/rIZOf0vNoyXg2zTNZD+zP3K+07fbeaV/XBZakm7HuLvLjfcv0ukU4g7lO5uiRe1e+1LJ1t/nSscoDX7R0dH7qnG5bJGej8wWVTaRGWW2GbqrFTyQ1wHagdlbwjx9X6vS6JpxIOmdSI5rejiMdpHJIwzrvqw5aKMsasBZ5bulRrmr4ypCtJo6/sl8atZKiqNVK29qnk5CqbqvUqp9FXrrmiitdXjreSr2YbdU2gclZZGhqP08zk2IBaJi6TO9OyqM1Mtraq6krJJLZWKq3el77PgVW9aR9eDdnJrN2rMV2NRz6uZA8rucSKal7I6nGcuZCv61tvK+lvmoaG6bNPRtV3+Nm9mj8qSfO+c/lK6kPz7itn2JdxpPezOVT7Do+Ryl5uvyXSiUVYNk9qvfFzbPVuaqp6h1U17uK7gVbEMWZsGrxy31x4h5ExxhhjjDHGGGOMMcYYY8yE4w9GxhhjjDHGGGOMMcYYY4wxE44/GBljjDHGGGOMMcYYY4wxxkw4m8uH0SMAhr6FYoySZfra8yLy2vH7I76SFsjfB6cxjj8S9ke0e+dylg7tIF9HU8cl/bOU94Xlj4GuryP2UwTkOqlqb2+/QpQ35wtIPYrPFNaNrbSwWfNVberoChf6r1oHDF+3xB8TlY3tqvwP9dU2V03d3WyH9D9u9xOFr6bMJs27ildpNWdaxwr7a9H2zNp9t8Sr8uIw9slS9R3VKe5bH3yd+rhiuG9Wvsy0D/f1SXAfHV8rYZnOsua1jexXG49geVTrnuut0qlnG9VvzpnDo+NKBZfz0npaif68+vHiNLU/zCbxKu3/CrU/04jv64ehCjssYX19NKxEX9wYY8zqUfmpYCo/RdU6oLouo1oL9r1nVfeevv4m+/pIqvyuVFT34r6+Zip/SZWPkJWsaap2qfwhVmuCau3N1+1Ifgfydb6mWbVL5SuH6evDqHom6uujpqLvM2JF37JUVL53+z6f9n3F0ndM9C1/lXdf24G8j40zL2RtUfmn0udYfjbrO48pWVn0ma2vD9JxfNVl6Vd+hat3ZKvt9aRv/6vmSU2D25Dc7ZZ+ofqGLZmD6L3PGXm/MEfv0/j95DjvPxffmeKRMa4xxkwcm+uDkTHGGGOMMcYYY4wxxhhj1oGGlf9Jg7kUsSSdMcYYY4wxxhhjjDHGGGPMhLOpdhhdOAecuGNwPLenGzZFJZ0X+bGONAOFLdkm3/NjabW9tKIjh3Q8i9UNU4k3hreyqiQd18cSqQoqQF+5C4Wv43Jp3U/TebUVvNoWvi051rxne0rjsbwbsLTumEx2Q7tK3+3qO5Lfge6W42kZudNXUvrUPyq5gb7SWWpvJSeXbb1Xua1KGiSrR7VXt/Yz2fb3cSS1OC5LdxyV/sA26oTKdZWlB9TyMAxfp/1jXxGW5b1P4vF46SsPU41bTf9oEk/z4v6i0nhZ/czKOcsx7EBONQ64HrUPZ3J72i+5fjSvSmqT06+kKphqnFUSQXuRw3ZU7W6MMebSZJy1D99vKhntSlo3uz9UEkSrIXna929aK7lwpVqvIgmrZJ36SoJVdcV1X8kpjfOQnz1LVXJKunbI1gjaV6rnCLZ/dxGvarO+a5Xq+XGuCOubV9a/KzlD7Tsct5K5qsZSJbuW2dG3PsaR6Mvy1jJX8071zJVRpc82jSORll1X1Zs+i2RjdZ/I389diRR+Z3OIXRvkl5TS51Vf7ytZrfW2O4mnz1FsR985VOPtKMKydxQ6P2XvZQBgD70/4rpf8p6teAfXdxywi4El9ZF1/nEm/cX02xjXGGMmDu8wMsYYY4wxxhhjjDHGGGOMmXA21Q4jY4wxxhhjjDHGGGOMMcasBw3WIdlcbKoPRg834ENDibOvPpvH022d88nxVLGvuu/2UpUIqra/s6RSJTV3hGTcTojEW9agS7ZZ07blbbL1mamkGPpu86+2p/eVoKi2KTNa/qw+zki9gc6nivpQqq3smR3a7hzG26LnpA/w9mO1kfvj7p2j49MibVhtm8/kEsZp96ydqm3+uiWd884k3QDgvuQaTZ+vq+QANQ3ert5XkqSSmuP0Kxm0vpIqh+Rc5d+Ya+mY+xVLMQLAjkTaAKhlBLJ4Ov4yiQTd8s9z4Y7t3TC2q+oD3Hcqe9lGjVdJu3BZuM00jUqmoepLnB/boelXY5rTqCQnDifxjDHGXJ70XaMrmcRWtc6q0qjW+XyP6pteJWOs92lea1Xp95Vozmwah0p+jKke0Pm6SnK3WvNW+anEL1NJzTGVtHD1jJFJn/XtHxq3kjDrK49dxav6ZpZ3JbelddVZhxYyV0y11uzbb9UOzltl0TvxijCWfGf7T0u8vrJz1fipwjS/5fIF6nrL5rVx7OW419GzmbpVQPWehupxP6Wh/ePEw3QsYSwtqWM/k9ocp49xXfG8UD239pWgrOQdq+e2bHwD3efkqj937Niah508lYdx3lm/VJsAcVPAYcX7wyUsNvz9Y1xjjJk4LElnjDHGGGOMMcYYY4wxxhgz4fiDkTHGGGOMMcYYY4wxxhhjzITjD0bGGGOMMcYYY4wxxhhjjDETzqbyYcQutlQvlH3WVFq5nWtE45X1fPtqq2ra7H+i8iEzR/aqbukeOlcfRpnGc6lXLuVknWI+XqJVTddp+fvqD7O9qiGe6eZqepnPG71uW6FJW+nLVprRHf9PFLZS7fhOPNHDrTRqp8h+Lkul4ay655m+tsar6jvzg1TRV8Nd/d9UPmoyPfC+GuV6vm8nejH3cPec55BOelvyeOqbiP307EuOgVoDnfvpHItVyzhgTeSbxP/V3BGKV4z9zLePZsd1MHdlNx7rnKsvJYbrQH3vzCfxNG5f7XiFr+vrv0ypfCtlaFn6+odgKnuvlXOOq33TGGPM5Udf/6HVNZV/o2zN3td30jhhVby+PmR43VKVs6IqZ+VrkKn8tWb38ypeta7t62tlHL8/fX0rcby9RRizu/DdqvRtM46nvltW6i+oT/r6HqLyGVU9uzKVX6HVYJ46Mec1L4NuYaEIo3rs+ETS9y2Fzx5Og+tG82Ibtd62JROK+jfOfFxX6PxRPRd35h3Ke14W21wW9esa9K4guJxSp5zGPn2/0LM9+/rzWZA0OE1+xtBxxeOi7zuhceagbJypX2gu5xnxiV71K0av66RBx1W/4vd9S9qd8+Z3FJUPI0kDtwz//0RxjTFj07ByL4/mUsQ7jIwxxhhjjDHGGGOMMcYYYyYcfzAyxhhjjDHGGGOMMcYYY4yZcDaVJN0WjCSFKqk23STHEgBLZNcI3iGsFcfSQtVWU95iq3Zw2GEOkLJUcgNZvCUSVcV1C4m0mpal2kLfF7ZD6yOTu1DbOzIQsq2Ypa54e7Bu3d9N23lPiKwYS8OpdN1CIjmmNlbtzlumOzIQuiVftxITsYfyZrkw6Tucl26bzyQoqj6m9d2ReOMt7mIH561yFBw2nfwOAFRkXCt2HO3Zh5klY4S2dU9z3RftsEcLw3bRJHHocDdaJe2iUnwZXFdaH3tuoBPeuq6dkdqpFfIOu6l+K1k1lU+bS7bXh8ojUNg2GXNHSSqPVPKW9A+2S+drrtOyfxdpZJI+1b1B66pq92xu1Hg8pisJB+7fKt/HaBpVXGOMMZNBtX6qxEf43qMSQXxdJQW30odVzq+SIGYqSam+ctPVs2RVlkrqtq+0Hz+b9ZWGBurng8wOrY++bVatK7LrThSycNqeWTtVdbokLHm+q2T+qv7BZdZ4C0XYNn5+ouOqD1dhzJLnTLZJ6jt7lqqeJSu5MJVM6+RVyF5zGqepPqr20/comVRlNY9VfZ2vG6dvH0qO56Ru9rGUnybCdVAUQGXuGZaM0zT4OpbG12fEDiKLxn2J38Wo64cqyaxP6+/ZOxWgK3lXyW4eLaT9O/VRSPmp1DrDddB5v1U8C6N6J1Tk1blO5eoW0/D2AWNMwab6YGSMMcYYY4wxxhhjjDHGmPWgob/XN3M54G/KxhhjjDHGGGOMMcYYY4wxE44/GBljjDHGGGOMMcYYY4wxxkw4m0qSbiuApwyPT0tYpcfMsI8J1dqu0uPzzJ8RUOs7Z5rXuqmP81J/E1mDqu+MyidQpvOturxVOfv6wGG0vjmNM8nvmr5qXE+fxbKwHxSg669miabuqeXjAUt9BC0yjn+nOdKXrTSGS1hvl3Rtp8Qf03SiyQ1067jTP0Q3t9Ls5TCum7nCx5DCdrGvGR1zPMZ17GTjoOqnO8Q30WzmPEj7FDtTOtINOkG+ipboThMLybGeV+Oqoz8v9Xvi/tEx+xGC+Ac6TYP6Pgm7k44rv0VcbUuKTH2C+842GUez3IcLjXW2Q/sH+086JGH30XFfvwM6T2Zz4zh+GCr/Atm8WemoV/eKShu8r/66McYYczEyn56VD5m+fgL1vlkJn/S9j1b3ysxXSfUAvcQPTRKvui9r+tk6cZwH+ey5Cug+T1Z+m/rWd+UjhKnqu6J6tqz8Fvcle07WeqvqlKl8UTL6LNz3/UWWl9Lx2XMhD1Myn7LVuK3GOzOO76C+Y6lql2x+qnwuVWv7vmXWdzYMPztV75+OnsrD+HldfTV3fAepb+ye7yFOF3l3nv/lOZnz42f3lY796lm4arOs/+kzc+c9xE4JS/wYZ78DWPLeII2rPob4XNtF42ZwXvruZbFComdaxpiJxDuMjDHGGGOMMcYYY4wxxhhjJpxNtcPIGGOMMcYYY4wxxhhjjDHrxUr39ppLkU31wWh6Gtj3hMHxkfslkLZh6pbgbKt5JfemwyBLQ6XxmGrrLUsj6fZrDquk8aot71VYH5uArnSRyhhl270rKYlq63C1xTiT0AO626BZcqwq/27ZKrzA0lmyJZjT1K39nXiUxoJ0HpYqm+Wtz7plmc91KzJrc9GW8bkru9HOkBRfJedQhbG91dZ1rnutG5YLq+TNmP2yLZy3vKsd26iOT1J9qDQZ9zGVYNtG5/so7xMi83fmnuWsHaZJxye4PiReJQPG9cPjRRXzuE51rN5LeW/7xOh4z0weT9slkyjReZLDqn40V9x9bicpv/skjFX/OG9t20r+I5PB0Tm5r+RMJkmqYdrO1XKK23db8rvaUckZZv2oyhfo2r8SaRRjjDHrTyXpy6xUFqi6piORW8SrZMsyqShNg+3XtU9WFv29krftS/bcA3Tro5L6ZrRdMhmwvrLiGlZJbPWVCNP6ztIf5zm2b//juhtH0oyp2oyv43XnOHll/VvfDfSVbu/70qaSYGP6yr1VdlTS+wrXXRWP7ahk6CvJ5yy9Ko1x6NtmFTwOeL5Wm7i/qOQ9v4eoZOE6MuDy7KeS7Aw/y3P6KhfO8VSunuNymfXdQDXP95X65+uWzI2JxLm+Q+i8i6k07/k6fS/D7xQKafUO+k6F86rS4HpUO84lx8BoYvO7fWNMgSXpjDHGGGOMMcYYY4wxxhhjJhx/MDLGGGOMMcYYY4wxxhhjjJlwNpUkHTMtJbuOpLmOHu+G8VZf3r5ayQFUuzcrOYBqG3fWGFVelYRDlVe13bvvlvFKJi6TMdoh8Tpbkws7Klk7Rrcbcz/gLdihuku8TbeokFY0Bm+51m3WnbyLrd8d2TmNx9uM1UYtz5BpsXeO0tAt47ylmcuiY2mhkNfjbd28xf2QyL0xVf/rSLrJuOW+c0S2WWdSGJrXySKsIzl2PI/Haeg4YMmMT/WMp7D9XGYd0yypcqeE7U/yOlTUm0q8kUpcZxzvRY7OH1xX3GbTYgfbWI33Sv6uksFhuL9pu1TSoJlsjf6u9ZhRpV/JNFR5c50kU8SSNLUeF5Ljlcp4GGPMpDCOXNNqkM3z1fNGJQe90jlfpWoX0fscp7/SB9JqjZAt2fX36nmmL1X9ZPfYau3XN69KplupJACzco8jYc5pzia/a16VjFtVzr5UZZ5OjisqaayV9AGgfk5h+tqo/Tsb75WkoLKS8VmN90ymcaXpV8+Sle1cH9V8XUlbr+S9iaZR9e999OxeScHzc/2UFHpOZN37MsvvIjhv6WSz9Bw3q3nxuxmKV71TqTij0moJ+v6iE9b3fYvWd/YuRiQAO2HiHqATt6oDTZPpe3Mr3r/gjuH/PevTmH40XGpvCSLiWQD+JoDnAbgRwOMBPILB67lfBfDG1trDFP8tAL61SPK21tqBZfL5OgD/CMCzMVgu3APgXQB+rLV2ZJn4VwP4XgBfB+B6DD5L3AbgTa213xi7oGvEpv1gZIwxxhhjjDHGGGOMMcaYieI7hv+UZwz/fWNEPLe1dnyZOL2IiDcA+AH5+UkAvgfAyyLi+a21eyj+jQA+COAGij8D4IUAXhgRP9Ba++GV2rOaWJLOGGOMMcYYY4wxxhhjjDGbhaMA3gTg6wF8DYBfprCnAviu5LrFnUn87+9yhIh4HkYfix4B8H0AXgrgfwx/2w/gzZLuf8DoY9EfD+N/3/B6AHhDRDy3T8HWGu8wMsYYY4wxxhhjjDHGGGPMZuCtAF7fWntUMTki3gvgVgx2GAHAc5JrD7bW7rxI+t9Nxz/fWvvxYR63AbgLQAD4yoj43NbaRyPi6QC+fBi/AXh5a+1eAO+MiJsB/L3hNa8D8N/7FXHt2FQfjC4sACeGjjZU45W1SlkLFgBOJ/qe6p+FtWGPio8a9h9S+cRgVA93dxGWUaW/GrrN7N9J86r0mDNU1zzzU6TpV36QOnZIuzDsu2pa2ly1fhnWwFW/P1mY6uaepPx2iFZuZELISlG2jkZtkQbbqH6WpsT/0yKlvQKPJa6DPTLm2H9NpXFd+V3hPjcn6bO+8b3JNUCtoZ35Itsmec2RwYeKNtpPx/dKWKVjnfnv0nrbR8dab1w/+2huVH9uzC1yznVwKPkd6JZFbeT65rlA7eWy9PU/VLVtRZVG5j9Kw3j+r/T4K59zSqfP0XHl86DSX+/rC0/tz3wNrLdvDmOMudxY73mR76uct96/Mj+jQO4XZJwHxsyPZF+fhJpG9YxR+TGp7r9908junWpvVc7Mx4muffq2S+YLBui2dVWW6pmLqdZ42q+ydYuS9Q+lyiuLp1R9p6+/mb7+hyvfx32fmat14kp9/fTNq+ofzErrdKPSUP/G/Pw7lzz7AsCJ4pkua9sVuuXp+hwVm/id1omHkcLx9Fm18hfUea5Xfz7sj6jyqcPXacc6u3xY9T5B82rJ+wWl48dJ6jH43SDbq32gsovr8Vzyu6L1lj3IHe2e9vXxFNWNmtOQPoHFdwDV+yVjNgGttQ8u89sjEfFJjD4YZbPrByNiHwYj+SMAfhHAm1trj1CcF9DxH1Ie90TE3Rj4TQKALwPw0eH/i9w1/Fi0yIcw+GCk6W4Ym+qDkTHGGGOMMcYYY4wxxhhj1oOGDfgz0qsj4iCd/1xr7eeqCyJiD0a7fADgXUnU64f/XwHgucN/XxURL2+ttYi4CsBVFP8zcv1nMPpg9DnD/2++SPxF9kTErtbasbQg64A/GBljjDHGGGOMMcYYY4wx5nLgwdbagb6RI2IngF/H6EPP+zCQrVvkOID/AuD9AO4BcDWAf4jBxyIAeBkGvo3ejqX7Ms8X51cO/9+ehC93fiWAY8uXZH3YVB+MjjTgLcPtof/nHgnk7aayr32Wm6ynJNhu2V56mLaRHkIO70LVeLy9mU2s5AZmJSz7nrtburJuVWZ4aytvh2WZLwA4StuDdXdtJtGn9vWVV+LrZMdupxNr2G5qs862ft1+S2VbIjdAfUdl3JjqWzqHHZJ65PbkdpmTeJ2pRacmhiq82s6sdb8tKZvKlu2m7emxsxuWjaVp0d3YRg2lMn9naGydIZsqCax5qSuWH7ilkBScu3J0rDbiGjremRwDnblFpx3cTsdcjyKJeJrnDwljiZlKnmMf2TX9RDWEoHj7pB/tu59OpE6fQed3UDztYh0bZZv/nhvopGed4nA36Caqq6dTnd4n9cZzgfb1jvQDHfeVaKnSeJLE43n9PgljGysZkj3J70C3bNUYqeQXs2s0biW9Z9aWSi6xalvuV/skjMfuETrWflpJ3xpTkclojYPnmv5kdVWttZVsvI8j6bsaEtvZs0Mlj6159ZXz4rx0bZU9KFcytdV8XZHJXCnZ+kPR50d+ZqzS5zX0GXk2qOqR+9lqvGDgNeRuWSd25KUKWTF+dj9xpBvEz7W961vs4GfESmKrep7m5x6V28rkzlV6v2OHhHUonhHZLrXjTCJHppLuKuefoc9jnTQ5vZ5yWVX7aR/uQGGnJahyMTDOnNqHzrsMsfdQIR/Oc8sZeg6al2ciLot2U5aNn9Lr7seyLJGr79nnOoghp6mc2t/KNiQ6Lhck/Wm2g8up84e+f8ng9MQ+rn99jzKfHGsfqyRVeS7nd0c6Hjvv+6Qd4pnDg3tgzMQQEdcBeC+Apw1/ej+Ab2CJudbady1z3a8B+DhGXia+FoMPRurcRt+y8/mi7N2pJHy580KIdH143EYbYIwxxhhjjDHGGGOMMcYYs1pExNMB/BFGH4veDuDFrTX9e4EltNbOAriNfto3/P0hAA/R70+QS/nPuD89/P+vesY/stFydIA/GBljjDHGGGOMMcYYY4wxZmwaBtvZ1vPfxYmIFwD4AwDXDX96I4BXtNbOSby5iHjqMtdvA/Bs+ukBOv4AHT+PrrkJIx9IwGA3E/8PADdEBGvwPD9Jd8PYVJJ0xhhjjDHGGGOMMcYYY4yZTCLipQB+CcAVw5/+K4B3AvjiiFiMdra1dhADpd8PR8T7MPBz9GkAj8fAh9F+SvbtdPzTGPg1AoBXR8SnAXwMwPdRnN9prX0UAFprH46IDwB4AYAA8MsR8eMAngrgVcP4DcCbVl7q1SNaaxttw6pxS0T76eHxH0vYC+n4Wgm7i47/go7VH05Fxw8NHVda25Xvj93JMbDUb1GWBqN5VZrf00mY6gOzbxXV8uW4WXpAXm9AtyyV/ncVxnXFutNL/NWwWqRWYqVlq6qVy6WnaWrY9p7xWB9YtaT5OrZJ/L/wdU1s72hvs0Z3VZZCtbOR7vFJicfa1SdE9zfr3+p3i3V6K81vzntKdIpZG1vTn2PHMYUG8BEq551SFq5+9lmmY2lbcgyIHjMdaxeuxsu+xA+Xlpnr7eOqoU3HJ5HDdulcex0d79s7Oq501NWn06fo+ON0XM3XWlf7kmMd+lzfOsedSY7V/0vlY4jbqfJDwOmrT4KqLTo68MnvQLfu7K/GGGPMatHX346ui/ie1ddfSOVniZ839B7I9z1dS/S9J3Leep/OfHlV92Jdx/G9ntPX5zsOW6lPp76+EdVvZ+Zns/IjqazkL1krG6u8qud1tiPzqbtSKl+DVdzqebd6v5Dlpc9b/Iy0xIfRueXjjePjKrWp8kFVUPm5ZSq/zb3zkncBmc1aH1Vf3JbUd+mfqqefLKVznaaxpQjjuqveQ3C5tW44zSoNDtM2y/wFVS/TqoHA7znUIR35Nx7rfQ5TTfRZG1YTgdYpPUN3HmTVJvZB9XEJe83gvwOvBg5+vAWMWQUibm7AD69zrt9yW2vtQBYaEW8B8K0XSeSu1tr+iNgP4I6LxP2Z1to/kDx+BMA/S+LfDeD5rbW7KP5NAD6I7usx5g2ttR+6iB3rgiXpjDHGGGOMMcYYY4wxxhgzadwH4BUA/jMGn1kfwuAT8GcAvBvA1+nHIgBorX0/gJdiIDd3DMB5DHYn/RSAA/yxaBj/Dgwk7v7NMN754XXvB/CSS+VjEWBJOmOMMcYYY4wxxhhjjDHGbAJaa68G8OqececBvG34b9x83omB1F3f+IcBvG7475JlU0nSRcRJAH+50XYYM+FcDeDBjTbCGOOxaMwlgseiMZcGHovGXBp4LBqz8dzaWlMlVWNWRMRNDfjBdc71NaUknXlsbLYdRn/pzmLMxhIRBz0Ojdl4PBaNuTTwWDTm0sBj0ZhLA49FYzaeiDi40TYYYy5d7MPIGGOMMcYYY4wxxhhjjDFmwvEHI2OMMcYYY4wxxhhjjDHGmAlns0nS/dxGG2CM8Tg05hLBY9GYSwOPRWMuDTwWjbk08Fg0ZuPxODSrSAOwsNFGmFUkWmsbbYMxxhhjjDHGGGOMMcYYYy4jIvY34PvXOddvu83+8NYOS9IZY4wxxhhjjDHGGGOMMcZMOP5gZIwxxhhjjDHGGGOMMcYYM+Fc9h+MIuLrIuK3I+JoRJyNiNsj4o0RsWejbTNmEoiIZ0XEj0XEH0TE3RFxJiJORcSfR8QPRsSVG22jMZNIRLwoIhr9u3OjbTJmUoiIrRHx3RHx3yPioeEa9e6IeF9EvHKj7TNmEoiIGyPi30XEJ4Zr04WI+GxEfCAi/k5ExEbbaMxmYXjP++WIuEPWn69O4l89fG9z+/AeeXT4Xudr1tl0YzYVfcdiRFwREd8eEW+NiI9FxJGIOB8RD0TEr0XEl2xQEYwxlwCXtQ+jiHgDgB9Igu8E8PzW2j3rZ5Exk0dE/H8AvqOI8jEAz22tHV8nk4yZeIZ/NPERAE+gn+9qre3fGIuMmRwi4okA3gvg85Io72itvXwdTTJm4oiIGwH8GYDdRbT/t7X22nUyyZhNTUQcA7BzmaDXtNbeInFvBPBBADckyf1Aa+2HV9VAYyaEvmMxIp4A4IGLJPftrbX/f/WsM5uViBsb8E/XOdfX2ofRGnLZ7jCKiOdh9LHoEQDfB+ClAP7H8Lf9AN68/pYZM5EcBfAmAF8P4GsA/DKFPRXAd22ATcZMMj+LwceisxttiDGTxHDHwtsw+lj0YQB/H8BXAHgZBt5g/3RjrDNmovg2jD4WnQDwdwB8FYB3U5xv9054Y1aNDwP4eQCvBXD4InH/A0Yfi/4Yg/c434fBex0AeENEPHctjDRmAhhnLAKDj7evxWCt+g8AfJbCfjIiZlfdQmPMJc/URhvwGPhuOv751tqPA0BE3AbgLgAB4Csj4nNbax/dAPuMmRTeCuD1rbWTiz9ExHsB3ArgGcOfnrMRhhkziUTEqwB8A4DjAH4SwBs21iJjJooXA3je8PjjAJ7TWjtN4b+2/iYZM5HsouPfbq39AgBExFEAXzv8fcvwnzHmMdJaW7z3ISL+7yxeRDwdwJcvXgbg5a21ewG8MyJuBvD3MHiX8zoA/33tLDZmc9J3LAI4BeBLWmsfpN9+JyI+A+Adw/MrATwNwJ+suqHGmEuay3aHEYAX0PEfLh4MJejuprAvWzeLjJlAWmsf5I9Fw98eAfBJ+unh9bXKmMkkIm4A8G+Hp/8Q3fuhMWbteRkd/xmA/zTUgj8dEQeHH3SNMWvPb9HxV0TEayLiK9CVM3+3JZONWXf4/cxdw49Fi3yIjvl9jzFmlWmtnZSPRYv8pZz7XY4xE8hlucMoIq4CcBX99BmJ8hkANw6PP2ddjDLGPMrQf8qX00/v2ihbjJkUIuJxAH4RwByAt7fW/nPmaNgYs2Y8g46/WcKeDeAXI+KprbX1Fvk2ZqJorb0rIl4H4J9jIE338xR8HsD/A+BHNsI2Yyacm+l4ufc4i+yJiF2ttWNrb5IxhvgmOr4dgx3zxlyEBmBho40wq8jlusNou5yfL86tS23MOhIROwH8OkYfdd+HgWydMWZt+R4AXwrgfgDfubGmGDOx7JLznwPwouH/i/yTiHjqullkzORyL4D7lvn9CgDfCMCOko1Zf/hdTvUeB/C7HGPWlYh4BQb+xABgHsC3tdbaBppkjNkgLtcPRqfkfKY49/ZJY9aJiLgOA4nILx7+9H4A3zCUqDPGrBERcS0GfyndALymtXZ0g00yZlI5S8f3A/jO1tr7MPiI+8Dw9wDw1ettmDGTRES8EsAvA3g6Bn8h/XkYvKj+VgzulU8C8N7h/dMYs37wu5zqPQ7gdznGrBvDXblvxcC33zkA39ha+/2NtcoYs1Fclh+MWmsPAXiIfnqCRHkiHX967S0yxgwdmP4RBk4RAeDtAF4szr6NMWvD4zF4yA4AvxkRLSIagF+gODcOf3/nRhhozIRwFx3fvfgHE8P/OWznulplzOTxWjr+mdbaX7TWTrfW/iOAPx/+vh3A16y/acZMNH9Fx9V7nCOWozNm7YkBPwXgJzF4ljwO4EWttXduqGHGmA3lsvxgNOQDdPy8xYOIuAnA9RT2/nWzyJgJJSJeAOAPAFw3/OmNAF7RWju3cVYZY4wx6w7/JeYNQ99iiz7GbqCwu2CMWUseT8dziwcREXwOf7w1Zr3h9zM3RATfG59Px/y+xxizBkTEDIC3Afju4U93A/ji1prHnzETztRGG/AY+GkALxsevzoiPg3gYxjpbQLA77TWPrrulhkzQUTESwH8EgZ68ADwXwG8E8AXD57JAQBnW2sH1986YyaG+wC8bpnf/xqAVw6PHwLwL+Cdt8asJb8I4PsxeCF9DYB/P9zV9/XDc2AgsfPujTDOmAnizwHcOjx+XUQcxmBnwzcAuJni/el6G2bMZiQivhLA7PB0loI+PyKODY//sLX24Yj4AIAXYLCb4Zcj4scBPBXAq4bxGoA3rb3Vxmw++o5FDOQh34fRh9pjAF4P4KqI+Ot03Sdba4fXzGCzSWgYuL0ym4W4nP2XRcSPAPhnSfDdAJ7fWvNfcBqzhkTEWzDQg6+4q7W2f+2tMcYwEfFqjGTpPA6NWQci4hsw+EOK5f4wawHA326t/dL6WmXMZBERTwHwIQBXFdHe0Vp7+TqZZMymJiLuBHDjRaK9oLX2e0NVmA9ipE6hvKG19kOraJ4xE0PfsQjgTgB39EjyNa21tzw2q8xmJ+L6tvzfr64l/9dtrbUD65zpxHA5S9Khtfb9AF6KwbbmYwDOY/CX0z8F4IA/FhljjDHGmPWktfYOAM8B8CsADmPwkejw8PyL/LHImLWntfZxAM8E8G8xUKE4DeACgKMYSEd+B4Bv2ij7jJlkWmt3AHg2gH+Dwfub8xi8z3k/gJf4Y5ExxhizsVzWO4yMMcYYY4wxxhhjjDHGGLP+eIfR5uNy9mFkjDHGGGOMMcYYY4wxxpgNoWEgqmA2C5e1JJ0xxhhjjDHGGGOMMcYYY4x57PiDkTHGGGOMMcYYY4wxxhhjzITjD0bGGGOMMcYYY4wxxhhjjDETjj8YGWOMMcYYY4wxxhhjjDHGTDhTG22AMcYYY4wxxhhjjDHGGGMuNxqA+Y02wqwi3mFkjDHGGGOMMcYYY4wxxhgz4fiDkTHGGGOMSYmIVvw7GREfj4ifi4hnXgK2vkXs27/CdO6kNO5cXSvXnoh4H9n/TyTs97iONspGsici4iNk0ys22iZjjDHGGGOMmVT8wcgYY4wxxqyUKwE8GcC3ATgYEa/fYHtKLvcPQX2IiBcD+Krh6VEAP7OB5lyU1loD8KP0009ExNaNsscYY4wxxhhjJhn7MDLGGGOMMePwXgCnAWwD8AUAHj/8fQuAfxURH26tvW+DbPtTDD5iLXJqhem8B8De4fHhx2TROhIRAeBf0U8/01p7eKPsGYO3AfgxAPsB3AjgHwH41xtpkDHGGGOMMaYPDcDCRhthVhF/MDLGGGOMMePw2tbanQAQETsBfADAsyj8uwFsyAej1tq/B/DvVyGd166CORvBCwF8Lp3/4kYZMg6ttUci4r8A+GfDn/5BRLyxtfbIRtpljDHGGGOMMZOGJemMMcYYY8yKaK0dB/DT8vMXaLyIeFJE/FRE/EVEnIiI8xHxQES8JyL+dkQs+SOmoW+bb42I3xzGPR8RpyLiroj4YES8MSK+TK5Z1ofRot8eDHavLHKjxP09Suei0nUR8cyh76ZPRMTDEXE2Iu6JiF+NiJck1yyxLyJeFBG/HRHHIuJ0RPxJRLx02Qq/ON9Jx3/aWvvUOBdHxLMj4iGy7+GIeOEw7IfE9i+NiK+LiA8N2+VwRPxiRFwzjL8rIt40rJNzEfHJiPinEZE9f7yVjm8E8DXj2G6MMcYYY4wx5rHjHUbGGGOMMeaxoJJtLAmHiPi7GOz6mZF4TwDwouG/74yIl7TWPkvhP4uBbyRmGsANw3/PA3ArgPc/JutXQET8AIAfxNI/vrpu+O+lEfFuAN/UWjtTJPWjAP6W/PYFAH41Il7ZWvulMWzaCuDF9NPv9L12eP2zAPw2gF3Dn44D+ButtQ8ll7wOwNfR+SyAVwH40uFHpvcC+BwKvwXAjwO4FgPJuQ6ttY9FxGcw6BcA8FIA7xqnDMYYY4wxxhhjHhveYWSMMcYYYx4Lz5TzzyweRMRXAvg5dD8WfRSDjxnH6bcvAvArdN216H4senh4zXsA/DmAE2Pa+PsA3oGB76VFTg9/W/z3+30SGn4AewNG6+gG4CCA3wPAH4e+FoOPXhV/C4Oy/C6AuyXsR/vYQzwH3Xr+474XRsTnYVC/Vw1/OgLgy4qPRcDgY9FDGHxk4g99NwD4Xxh8LPoUBpKFjcJfGxE3JGmyzV/a03xjjDHGGGOMMauEdxgZY4wxxpixiYhtGPjMeb0E/RYd/yi6f6D0va21nxhefw2APwBw8zDs+RHx4tbae9CVjgOAr+aPFxGxBYOPTNf0sbW19oPD6+6ktD/bWnt5n+sl3x+Rn//W4k6giHgygD/CaJfOt0TEv2ytfTRJ8g4Az2+t3RsRswD+BCMfRDdHxP5Ff1E9eLacf6zPRRHxDAw+WO0e/vQAgK8obF7kHgBf2Fp7ICJuBfAJCpsF8BsAvr61diEi3gjge4ZhjwPwAizvX+mjABbl/PZHxJ7W2pE+5TDGGGOMMcZsBA/8JvBDV69zpg+uc34ThT8YGWOMMcaYcbgjIrKw4wB+DAAiYh+AAxT2AIB/vXjSWrs/In4SwL+jOH8Dg11Eutvm+yPibQBuB/CXrbUHAfzhYynECnk2RpJpwMBP0KOyca21T0TEmwH84+FPgYFMXPbx5V+21u4dXns6It6P0QcjAHgigDt72vYEOe/7oeV3AewZHt8F4IU9fR/9bGvtAQBorf1lRBzD6EMZAPx4a+3C8Pj3MPpgBOQf+o7K+T70L4cxxhhjjDFmnWmtffVG22BWF0vSGWOMMcaY1eB2DHam3DE8111Cn6APCIt8RM73A8DwI8p/oN+/GsAvYPCR6LMRcUdE/PRQum492S/nav9yv+k1zEE5Py7n6vepYpec95Xt478GfEXPj0XA0o9gJ4twDcvKpTbv6mmLMcYYY4wxxphVwDuMjDHGGGPMOLwXA/8/bfj/vRh8yPmtZT4IPRa+DQO/Ot+MgX8e/rCxH8A/AvCiiPj81pp+kLhc0N0zj6X+jsn5HJbu2LkYPx8RX9Ja++zFoy7J7xE+aa3px68+zF0kD2OMMcYYY4wxa4g/GBljjDHGmHF4bU+/OnfJ+ZMjYot8VPpcifPoNa21BuCXhv8QEbsAPAnA3wXw94fRnoSB5NvbetreesbL0DI9bZk4aZnWmENyvgf9Phj9a4z8UD0FwG9HxAtaaw+tpnE92SPnWiZjjDHGGGOMMWuIJemMMcYYY8yq01o7BOA2+umJID82EdE5H/LfhmGzEfG9EfFkSu9Ya+0ggHfINfvHMOsMHe+JiHEk34CBhBx/xPiCiPibiycRcSsGH7SY94yZx0q5Tc71w9WytNb+CYCfp58+D8D7ImLHahk2BmzzXa01+y8yxhhjjDHGmHXEH4yMMcYYY8xa8f3o7ur5VxHxFxHxWwA+BuBzKOxDGH1cuQLAjwH4eETcHRG/GxG/FhEfAPAbkscnx7CH414J4M8j4lcj4lci4qLOWoe7o35Qfn5bRPxJRLwfwJ8B2E1hb22tLefnaC34IwDn6PwLx7j22wH8Gp3/NQD/LSJmV8OwMWCbP7DOeRtjjDHGGGPMxOMPRsYYY4wxZk1orb0PwHcCOE8/Px3AVwDYRb/9CYCXDWXolOsBfBmArwfwpQB4V9DvAHjXGCb9gpzfCuClAL4BA3m7i9Ja+1kAP4LRh7AA8AUAXgCAP7C8FwM/TOtCa+0suruZXjjGtRcAvBLdjzTPA/DrK9iFtSIi4nMB7KOffnU98jXGGGOMMcYYM8IfjIwxxhhjzJox/MDydAA/DeAjAB4GsADgMID3AXgNgL/eWjtMl50E8M0AfgYDGbj7AJwFMA/gMwB+G4NdMS8Wn0gXs+XXAbwKwJ8COP0YyvTPMfhI9GYMdi2dxuCj2H0A3gngZQD+RmttxXmskJ+h4wMRcUvfC1tr5wC8BF1puxcC+JWImF4l+yq+mY7vwlCe0BhjjDHGGGPM+hHL/yGnMcYYY4wx5nIiIgLAXwB42vCnHxl+3LqkiYgtAD4N4MbhT/+4tfbGDTTJGGOMMcYYYyYSfzAyxhhjjDFmkxARL8JImu4ogBtbaw9voEkXJSK+GcB/Hp7eBeDJQ4k9Y4wxxhhjjDHriCXpjDHGGGOM2SS01t4L4DeHp7sBvHYDzbkow11R30s//VN/LDLGGGOMMcaYjcE7jIwxxhhjjDHGGGOMMcYYYyYc7zAyxhhjjDHGGGOMMcYYY4yZcPzByBhjjDHGGGOMMcYYY4wxZsLxByNjjDHGGGOMMcYYY4wxxpgJxx+MjDHGGGOMMcYYY4wxxhhjJhx/MDLGGGOMMcYYY4wxxhhjjJlw/MHIGGOMMcYYY4wxxhhjjDFmwvEHI2OMMcYYY4wxxhhjjDHGmAnnfwOpnMUMrhkOWQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 2880x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The reconstructed velocity model has SNR: 26.9419022170627 dB and SSIM: 0.7203856\n"
     ]
    }
   ],
   "source": [
    "\n",
    "f, ax = plt.subplots(3,1,figsize=(40, 20))\n",
    "plt.rc('font', **font)\n",
    "\n",
    "# True Model\n",
    "modeltrue = model1\n",
    "im = ax[0].imshow(modeltrue, cmap='jet', vmin=box_min, vmax=box_max, \n",
    "                extent=[0, modeltrue.shape[1] * dx / 1000, modeltrue.shape[0] * dx1 / 1000, 0])\n",
    "\n",
    "#ax[0].set_xlabel('Position (km)',fontsize='large', fontweight='bold')\n",
    "ax[0].set_ylabel('Depth (km)',fontsize='large', fontweight='bold')\n",
    "ax[0].set_title('True_Model',fontsize='large', fontweight='bold')\n",
    "ax[0].text(0, -0.15, 'a)', fontsize = 22) \n",
    "ax[0].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax[0].set_yticks(np.arange(y.min(), y.max(), 1)) \n",
    "\n",
    "# Initial Model\n",
    "init_V = model_init\n",
    "im = ax[1].imshow(init_V, cmap='jet', vmin=box_min, vmax=box_max, \n",
    "                extent=[0, init_V.shape[1] * dx / 1000, init_V.shape[0] * dx1 / 1000, 0])\n",
    "#ax[1].set_xlabel('Position (km)',fontsize='large', fontweight='bold')\n",
    "ax[1].set_ylabel('Depth (km)',fontsize='large', fontweight='bold')\n",
    "ax[1].set_title('Initial_Model',fontsize='large', fontweight='bold')\n",
    "ax[1].text(0, -0.15, 'b)', fontsize = 22) \n",
    "ax[1].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax[1].set_yticks(np.arange(y.min(), y.max(), 1)) \n",
    "\n",
    "# Siamese Model\n",
    "Siamese_Vel = scipy.io.loadmat(fwi_result + 'SIAMESERec_.mat')\n",
    "Siamese_Vel = Siamese_Vel['rec']\n",
    "im = ax[2].imshow(Siamese_Vel, cmap='jet', vmin=box_min, vmax=box_max, \n",
    "                extent=[0, Siamese_Vel.shape[1] * dx / 1000, Siamese_Vel.shape[0] * dx1 / 1000, 0])\n",
    "ax[2].set_xlabel('Position (km)',fontsize='large', fontweight='bold')\n",
    "ax[2].set_ylabel('Depth (km)',fontsize='large', fontweight='bold')\n",
    "ax[2].set_title('SiameseFWI',fontsize='large', fontweight='bold')\n",
    "ax[2].text(0, -0.15, 'f)', fontsize = 22) \n",
    "ax[2].xaxis.set_major_locator(MaxNLocator(integer=True))\n",
    "ax[2].set_yticks(np.arange(y.min(), y.max(), 1)) \n",
    "\n",
    "\n",
    "cbar_ax = f.add_axes([0.8, 0.1, 0.01, 0.8])\n",
    "f.colorbar(im, fraction=0.005, pad=0.04,cax=cbar_ax)\n",
    "cbar_ax.set_ylabel('Velocity (m/s)',fontsize='large', fontweight='bold')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "snr = ComputeSNR(Siamese_Vel,  \n",
    "          model_true.detach().cpu().numpy())\n",
    "\n",
    "ssim = ComputeSSIM(Siamese_Vel, \\\n",
    "          model_true.detach().cpu().numpy())\n",
    "\n",
    "print('The reconstructed velocity model has SNR:', str(snr), 'dB', 'and SSIM:',str(ssim.cpu().detach().cpu().numpy()))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3e22606",
   "metadata": {},
   "source": [
    "## Plotting Velocities Profiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "31c3ecbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#torch.save(netD.state_dict(), fwi_result + 'netD')\n",
    "#netD = torch.load(fwi_result + 'netD')\n",
    "#netD"
   ]
  }
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