model_evaluation_shot_noise_sim.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prerequisite Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Must-have modules\n",
    "import os, sys\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.core.debugger import set_trace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pytorch modules\n",
    "import torch\n",
    "import torchvision\n",
    "import torch.nn.functional as F\n",
    "import torch.nn as nn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Transform and Dataloaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Initiate input image transformation for data augmentation \"\"\"\n",
    "from torchvision import transforms\n",
    "\n",
    "transforms_noise = transforms.Compose([transforms.RandomAffine(5, translate=(0.04, 0.04), scale=(0.96, 1.04)),\n",
    "                                       transforms.ToTensor()])\n",
    "\n",
    "transforms_ref = transforms.Compose([transforms.ToTensor(),])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Initiate dataloaders \"\"\"\n",
    "batch_size_train = 64\n",
    "batch_size_test = 1000\n",
    "train_loader = torch.utils.data.DataLoader(\n",
    "               torchvision.datasets.MNIST('./ML_data', train=True, download=True, transform=transforms_noise), \n",
    "               batch_size=batch_size_train, shuffle=True, num_workers=4, pin_memory=True)\n",
    "\n",
    "train_loader_ref = torch.utils.data.DataLoader(\n",
    "               torchvision.datasets.MNIST('./ML_data', train=True, download=True, transform=transforms_ref), \n",
    "               batch_size=batch_size_train, shuffle=True, num_workers=4, pin_memory=True)\n",
    "\n",
    "test_loader_ref = torch.utils.data.DataLoader(\n",
    "              torchvision.datasets.MNIST('./ML_data', train=False, download=True, transform=transforms_ref),\n",
    "              batch_size=batch_size_test, shuffle=False,  num_workers=4, pin_memory=True)\n",
    "\n",
    "test_loader = torch.utils.data.DataLoader(\n",
    "              torchvision.datasets.MNIST('./ML_data', train=False, download=True, transform=transforms_noise),\n",
    "              batch_size=batch_size_test, shuffle=False,  num_workers=4, pin_memory=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Definitions\n",
    "\n",
    "The same as the definition code blocks in the training scripts, plus the helper functions to model shot noise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Helper classes and functions for modeling photon noise \"\"\"\n",
    "\n",
    "def shot_noise(W, X, n_photons_target):\n",
    "    # W_til is obtained through normalizing W with its max and min (all entries of W_til are non-negative)\n",
    "    W_til = W - torch.min(W)\n",
    "    K_W = torch.max(W_til)\n",
    "    W_til = W_til / K_W\n",
    "    \n",
    "    # X_til is obtained through normalizing X with each sample max\n",
    "    K_X = torch.max(X, dim=1, keepdim=True)[0]\n",
    "    X_til = X / K_X\n",
    "\n",
    "    # The product of the normalized W and X is proportional to the intensity of each pixel on the camera\n",
    "    W_til_X_til = torch.mm(X_til, W_til)\n",
    "    \n",
    "    # Calculate the photon budget for each multiplication at the input\n",
    "   # avg_photons_per_MAC = X_til.sum(dim=1) * n_photons_max / X_til.size(1)\n",
    "    n_photons_maxs = torch.div(n_photons_target * X_til.size(1), X_til.sum(dim=1)) \n",
    "    \n",
    "    #set_trace()\n",
    "    # Calculate the photon shot noise based on each pixel intensity.\n",
    "    if n_photons_target != 0:\n",
    "        with torch.no_grad():\n",
    "            fluence_Wx = W_til_X_til * n_photons_maxs.view(-1,1)\n",
    "            fluence_Wx[torch.isnan(fluence_Wx)] = 0.0\n",
    "            noise_Wx = torch.poisson(fluence_Wx) - fluence_Wx\n",
    "            noise = torch.div(K_X * noise_Wx * K_W, n_photons_maxs.view(-1,1))\n",
    "    else:\n",
    "        noise = torch.zeros_like(W_til_X_til)\n",
    "    return noise, W_til, X_til, n_photons_maxs\n",
    "\n",
    "\n",
    "class noise_scheduler(object):\n",
    "    def __init__(self, pCounts=[0,], times=[0,]):\n",
    "        self.max_photon_counts = pCounts\n",
    "        self.switching_times = times # switching_times[i] is the epoch index before within multiplicative_coeffs[i] is armed\n",
    "        self.current_photon_counts = 0.0\n",
    "        \n",
    "    def update_current_photon_counts(self, epoch):\n",
    "        earlier_times = (self.switching_times <= epoch)\n",
    "        if earlier_times.nonzero().numel() != 0: \n",
    "            self.current_photon_counts = self.max_photon_counts[earlier_times][-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Definition of digitized fully connected layers \"\"\"\n",
    "\n",
    "def Digitize(tensor, quant_mode='det', levels=16, min_val=None, max_val=None):\n",
    "    if not min_val and not max_val:\n",
    "        min_val, max_val = tensor.min(), tensor.max()\n",
    "    tensor.clamp_(min_val, max_val).add_(-1*min_val).mul_(levels-1).div_(max_val-min_val)\n",
    "    if quant_mode == \"det\": \n",
    "        tensor.round_()\n",
    "    elif quant_mode == \"rand\":\n",
    "        tensor.add_(torch.rand(tensor.size(), device=tensor.device).add_(-0.5)).round_()\n",
    "    tensor.mul_(max_val-min_val).div_(levels-1).add_(min_val)\n",
    "    return tensor\n",
    "\n",
    "class DigitizeLinear(nn.Linear):\n",
    "\n",
    "    def __init__(self,  *kargs, a_quant_mode=\"det\", w_quant_mode=\"det\", a_quant_levels=16, w_quant_levels=32, running_weight=0.001, **kwargs):\n",
    "        super(DigitizeLinear, self).__init__(*kargs, **kwargs)\n",
    "        self.act_quant_mode = a_quant_mode\n",
    "        self.weight_quant_mode = w_quant_mode\n",
    "        self.register_buffer(\"act_quant_levels\", torch.tensor(a_quant_levels))\n",
    "        self.register_buffer(\"weight_quant_levels\", torch.tensor(w_quant_levels))\n",
    "        self.register_buffer(\"running_weight\", torch.tensor(running_weight)) \n",
    "        self.register_buffer(\"running_min\", None)\n",
    "        self.register_buffer(\"running_max\", None)\n",
    "\n",
    "    def forward(self, input):\n",
    "\n",
    "        if not self.weight_quant_mode is None: # Set a flag to control weight digitization.\n",
    "            if not hasattr(self.weight,'org'):\n",
    "                self.weight.org=self.weight.data.clone()\n",
    "            self.weight.data=Digitize(self.weight.data, quant_mode=self.weight_quant_mode, levels=self.weight_quant_levels)\n",
    "\n",
    "        if not self.bias is None:\n",
    "            self.bias.org=self.bias.data.clone()    \n",
    "        out = nn.functional.linear(input, self.weight, bias=self.bias)\n",
    "\n",
    "        if not self.act_quant_mode is None: # A flag to control output digitization. \n",
    "            if self.training: # Update the running average of min and max only during training\n",
    "                with torch.no_grad():\n",
    "                    if not self.running_min and not self.running_max:\n",
    "                        self.running_min, self.running_max = out.min(), out.max()\n",
    "                    self.running_min = (1-self.running_weight) * self.running_min + self.running_weight * out.min()\n",
    "                    self.running_max = (1-self.running_weight) * self.running_max + self.running_weight * out.max()\n",
    "            out.data=Digitize(out.data, quant_mode=self.act_quant_mode, levels=self.act_quant_levels, min_val=self.running_min, max_val=self.running_max)\n",
    "    \n",
    "        return out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" Definition of QAT NN structure \"\"\"\n",
    "\n",
    "class QNN(nn.Module):\n",
    "    def __init__(self, Nunits, ns, **kwargs):\n",
    "        super().__init__()\n",
    "        self.fcs = nn.ModuleList([DigitizeLinear(i,j,**kwargs) for i, j in zip(Nunits[:-1], Nunits[1:])])\n",
    "        self.noise_scheduler = ns\n",
    "        self.photons_per_multiplication = torch.zeros(len(Nunits)-1)\n",
    "\n",
    "    def forward(self, X):\n",
    "        X = X.view(X.size(0), -1)\n",
    "        for i, fc in enumerate(self.fcs):\n",
    "            noise, W_til, X_til, avg_ppm = shot_noise(fc.weight.t(), X, self.noise_scheduler.current_photon_counts) \n",
    "            self.photons_per_multiplication[i] = avg_ppm.mean().detach()\n",
    "            X = fc(X) + noise\n",
    "            if fc is not self.fcs[-1]:\n",
    "                X = F.relu(X)\n",
    "        return X\n",
    "    \n",
    "    def set_digitize_config(self, a_quant_mode, w_quant_mode, a_quant_levels, w_quant_levels):\n",
    "        for fc in self.fcs:\n",
    "            fc.act_quant_mode = a_quant_mode\n",
    "            fc.weight_quant_mode = w_quant_mode\n",
    "            fc.act_quant_levels = torch.tensor(a_quant_levels)\n",
    "            fc.weight_quant_levels = torch.tensor(w_quant_levels)\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scan Accuracy for Noise Levels and Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Path management for MacOS and Windows\n",
    "from pathlib import Path\n",
    "import os, sys\n",
    "\n",
    "scriptPath = Path(sys.path[0])\n",
    "parentPath = Path(sys.path[0]).parent\n",
    "accuracy_ref = {}\n",
    "accuracy_aug = {}\n",
    "photon_budgets = {}\n",
    "models = {}\n",
    "\n",
    "class args():\n",
    "    pass\n",
    "args.cuda = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_validation(model, device, data_loader):\n",
    "    test_loss = 0\n",
    "    correct = 0\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    for data, target in data_loader:\n",
    "        if args.cuda:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "        output = model(data)\n",
    "        test_loss += criterion(output, target).item() # sum up batch loss\n",
    "        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability\n",
    "        correct += pred.eq(target.data.view_as(pred)).cpu().sum()\n",
    "\n",
    "    test_loss /= len(data_loader.dataset)\n",
    "    accuracy = 100. * correct / len(data_loader.dataset)\n",
    "    return {\"accuracy\": accuracy, \"test_loss\": test_loss}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tw329/anaconda3/envs/pytorch1.7/lib/python3.8/site-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 9010). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)\n",
      "  return torch._C._cuda_getDeviceCount() > 0\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-bfc2b4221b9f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnoise_scheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_photon_counts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0mresults_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_validation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_loader_ref\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m         \u001b[0mresults_aug\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_validation\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_loader\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfcs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m             \u001b[0mdf_state\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34mf\"fc{l} fullres\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mphotons_per_multiplication\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0ml\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-9-088aecc5e195>\u001b[0m in \u001b[0;36mmodel_validation\u001b[0;34m(model, device, data_loader)\u001b[0m\n\u001b[1;32m      6\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m             \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m         \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m         \u001b[0mtest_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# sum up batch loss\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;31m# get the index of the max log-probability\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/pytorch1.7/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m    725\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_slow_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    726\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 727\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    728\u001b[0m         for hook in itertools.chain(\n\u001b[1;32m    729\u001b[0m                 \u001b[0m_global_forward_hooks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-7-41106eece186>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, X)\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mview\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfcs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m             \u001b[0mnoise\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW_til\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_til\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mavg_ppm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mshot_noise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnoise_scheduler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_photon_counts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mphotons_per_multiplication\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mavg_ppm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m             \u001b[0mX\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnoise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-5-6019df3dd740>\u001b[0m in \u001b[0;36mshot_noise\u001b[0;34m(W, X, n_photons_target)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;31m# The product of the normalized W and X is proportional to the intensity of each pixel on the camera\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0mW_til_X_til\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_til\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mW_til\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;31m# Calculate the photon budget for each multiplication at the input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import pandas\n",
    "\n",
    "# Specify photon budget levels (photons per multiplication)\n",
    "max_photon_mac = torch.cat((10**torch.arange(-2, 4.2, 0.2), torch.tensor([1.8, 2.0, 2.3])), 0).sort()[0]\n",
    "\n",
    "# Construct MLP model\n",
    "device=torch.device(\"cpu\")\n",
    "Nunits = [784, 100, 100, 10]\n",
    "ns1 = noise_scheduler(torch.tensor([0.0,]), torch.tensor([0.0,]))\n",
    "model = QNN(Nunits, ns1).to(device)\n",
    "\n",
    "# Load the trained parameters\n",
    "model_ckpt = torch.load(\"./RA_4bit_H2_100_100_lr_0.043_0.50_m_0.87_wep_6_randActDigi_v80_ep97.pt\", map_location=torch.device('cpu')) \n",
    "model_state_dict = model_ckpt[\"model_state_dict\"]\n",
    "for l in model.fcs:\n",
    "    l.running_min = torch.tensor(0.0) #running_min is not initiated in model as registered_buffer\n",
    "    l.running_max = torch.tensor(0.0)\n",
    "    l.training = False\n",
    "model.load_state_dict(model_state_dict)\n",
    "\n",
    "# Construct dataframe to save the results\n",
    "df_state = pandas.DataFrame(index=range(len(max_photon_mac)), \\\n",
    "                           columns=[\"max p/mul\", \"fc0 p/mul\", \"fc1 p/mul\", \"fc2 p/mul\", \"accu_ref\", \"accu_aug\", \"fc0 fullres\", \"fc1 fullres\", \"fc2 fullres\", \"accu_ref_fullres\", \"accu_aug_fullres\"])\n",
    "\n",
    "# Test MLP model under photon shot noise\n",
    "model.eval()\n",
    "for i, pc in enumerate(max_photon_mac):\n",
    "    model.noise_scheduler.current_photon_counts = pc\n",
    "    results_ref = model_validation(model, device, test_loader_ref)    \n",
    "    results_aug = model_validation(model, device, test_loader)\n",
    "    df_state.loc[i, \"max p/mul\"] = pc.item()\n",
    "    for l in range(len(model.fcs)):\n",
    "        df_state.loc[i, f\"fc{l} p/mul\"] = model.photons_per_multiplication[l].item()\n",
    "    df_state.loc[i, \"accu_ref\"] = results_ref[\"accuracy\"].numpy()\n",
    "    df_state.loc[i, \"accu_aug\"] = results_aug[\"accuracy\"].numpy()\n",
    "\n",
    "    # evaluate for (almost) full float resolution models\n",
    "    model.set_digitize_config(None, None, 2048, 2048)\n",
    "    for i, pc in enumerate(max_photon_mac):  \n",
    "        model.noise_scheduler.current_photon_counts = pc\n",
    "        results_ref = model_validation(model, device, test_loader_ref)    \n",
    "        results_aug = model_validation(model, device, test_loader)\n",
    "        for l in range(len(model.fcs)):\n",
    "            df_state.loc[i, f\"fc{l} fullres\"] = model.photons_per_multiplication[l].item()\n",
    "        df_state.loc[i, \"accu_ref_fullres\"] = results_ref[\"accuracy\"].numpy()\n",
    "        df_state.loc[i, \"accu_aug_fullres\"] = results_aug[\"accuracy\"].numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>max p/mul</th>\n",
       "      <th>fc0 p/mul</th>\n",
       "      <th>fc1 p/mul</th>\n",
       "      <th>fc2 p/mul</th>\n",
       "      <th>accu_ref</th>\n",
       "      <th>accu_aug</th>\n",
       "      <th>fc0 fullres</th>\n",
       "      <th>fc1 fullres</th>\n",
       "      <th>fc2 fullres</th>\n",
       "      <th>accu_ref_fullres</th>\n",
       "      <th>accu_aug_fullres</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.0813022</td>\n",
       "      <td>0.0791595</td>\n",
       "      <td>0.0890009</td>\n",
       "      <td>9.95</td>\n",
       "      <td>9.8</td>\n",
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       "      <td>0.0797499</td>\n",
       "      <td>0.0875007</td>\n",
       "      <td>10.02</td>\n",
       "      <td>9.96</td>\n",
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       "      <th>1</th>\n",
       "      <td>0.0158489</td>\n",
       "      <td>0.128902</td>\n",
       "      <td>0.122613</td>\n",
       "      <td>0.148765</td>\n",
       "      <td>9.99</td>\n",
       "      <td>9.87</td>\n",
       "      <td>0.129042</td>\n",
       "      <td>0.122926</td>\n",
       "      <td>0.146791</td>\n",
       "      <td>9.52</td>\n",
       "      <td>10.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0251189</td>\n",
       "      <td>0.204402</td>\n",
       "      <td>0.196175</td>\n",
       "      <td>0.236835</td>\n",
       "      <td>10.46</td>\n",
       "      <td>10.1</td>\n",
       "      <td>0.204502</td>\n",
       "      <td>0.19434</td>\n",
       "      <td>0.235998</td>\n",
       "      <td>10.33</td>\n",
       "      <td>10.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0398107</td>\n",
       "      <td>0.32403</td>\n",
       "      <td>0.302665</td>\n",
       "      <td>0.395087</td>\n",
       "      <td>10.85</td>\n",
       "      <td>10.76</td>\n",
       "      <td>0.323367</td>\n",
       "      <td>0.307319</td>\n",
       "      <td>0.403256</td>\n",
       "      <td>10.23</td>\n",
       "      <td>10.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0630957</td>\n",
       "      <td>0.512608</td>\n",
       "      <td>0.489142</td>\n",
       "      <td>0.615838</td>\n",
       "      <td>11.34</td>\n",
       "      <td>11.18</td>\n",
       "      <td>0.514173</td>\n",
       "      <td>0.480176</td>\n",
       "      <td>0.618586</td>\n",
       "      <td>11.64</td>\n",
       "      <td>11.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.813338</td>\n",
       "      <td>0.77056</td>\n",
       "      <td>1.03292</td>\n",
       "      <td>12.98</td>\n",
       "      <td>12.37</td>\n",
       "      <td>0.812195</td>\n",
       "      <td>0.779174</td>\n",
       "      <td>1.02507</td>\n",
       "      <td>12.82</td>\n",
       "      <td>12.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.158489</td>\n",
       "      <td>1.29223</td>\n",
       "      <td>1.24265</td>\n",
       "      <td>1.69202</td>\n",
       "      <td>15.76</td>\n",
       "      <td>15.81</td>\n",
       "      <td>1.29019</td>\n",
       "      <td>1.23498</td>\n",
       "      <td>1.7153</td>\n",
       "      <td>15.46</td>\n",
       "      <td>15.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.251189</td>\n",
       "      <td>2.04069</td>\n",
       "      <td>1.98988</td>\n",
       "      <td>2.84761</td>\n",
       "      <td>20.93</td>\n",
       "      <td>20.54</td>\n",
       "      <td>2.0489</td>\n",
       "      <td>1.99259</td>\n",
       "      <td>2.79844</td>\n",
       "      <td>21.17</td>\n",
       "      <td>21.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.398107</td>\n",
       "      <td>3.24251</td>\n",
       "      <td>3.12082</td>\n",
       "      <td>4.93404</td>\n",
       "      <td>32.51</td>\n",
       "      <td>30.75</td>\n",
       "      <td>3.24235</td>\n",
       "      <td>3.13967</td>\n",
       "      <td>4.91853</td>\n",
       "      <td>32.38</td>\n",
       "      <td>31.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.630957</td>\n",
       "      <td>5.13183</td>\n",
       "      <td>5.0858</td>\n",
       "      <td>8.36729</td>\n",
       "      <td>48.45</td>\n",
       "      <td>46.29</td>\n",
       "      <td>5.1328</td>\n",
       "      <td>5.02294</td>\n",
       "      <td>8.2348</td>\n",
       "      <td>48.23</td>\n",
       "      <td>45.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>8.1318</td>\n",
       "      <td>8.02315</td>\n",
       "      <td>14.0034</td>\n",
       "      <td>67.86</td>\n",
       "      <td>65.47</td>\n",
       "      <td>8.13757</td>\n",
       "      <td>8.01569</td>\n",
       "      <td>14.1132</td>\n",
       "      <td>68.08</td>\n",
       "      <td>66.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.58489</td>\n",
       "      <td>12.8954</td>\n",
       "      <td>12.787</td>\n",
       "      <td>23.0409</td>\n",
       "      <td>85.11</td>\n",
       "      <td>82.4</td>\n",
       "      <td>12.8883</td>\n",
       "      <td>12.805</td>\n",
       "      <td>23.4207</td>\n",
       "      <td>84.8</td>\n",
       "      <td>83.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.8</td>\n",
       "      <td>14.6515</td>\n",
       "      <td>14.5029</td>\n",
       "      <td>26.7314</td>\n",
       "      <td>87.5</td>\n",
       "      <td>85.48</td>\n",
       "      <td>14.6397</td>\n",
       "      <td>14.3717</td>\n",
       "      <td>26.6523</td>\n",
       "      <td>87.16</td>\n",
       "      <td>85.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2</td>\n",
       "      <td>16.2954</td>\n",
       "      <td>16.2203</td>\n",
       "      <td>30.1299</td>\n",
       "      <td>89.3</td>\n",
       "      <td>88.32</td>\n",
       "      <td>16.2603</td>\n",
       "      <td>16.1222</td>\n",
       "      <td>29.9281</td>\n",
       "      <td>89.96</td>\n",
       "      <td>88.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2.3</td>\n",
       "      <td>18.6845</td>\n",
       "      <td>18.7071</td>\n",
       "      <td>34.406</td>\n",
       "      <td>91.87</td>\n",
       "      <td>90.78</td>\n",
       "      <td>18.6819</td>\n",
       "      <td>18.5141</td>\n",
       "      <td>34.4576</td>\n",
       "      <td>92.32</td>\n",
       "      <td>90.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.51189</td>\n",
       "      <td>20.4543</td>\n",
       "      <td>20.2025</td>\n",
       "      <td>38.0072</td>\n",
       "      <td>93.38</td>\n",
       "      <td>91.91</td>\n",
       "      <td>20.4663</td>\n",
       "      <td>20.3935</td>\n",
       "      <td>37.9326</td>\n",
       "      <td>93.17</td>\n",
       "      <td>91.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3.98107</td>\n",
       "      <td>32.4109</td>\n",
       "      <td>32.329</td>\n",
       "      <td>61.5974</td>\n",
       "      <td>96.43</td>\n",
       "      <td>95.62</td>\n",
       "      <td>32.4108</td>\n",
       "      <td>32.1341</td>\n",
       "      <td>60.9049</td>\n",
       "      <td>96.36</td>\n",
       "      <td>95.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>6.30957</td>\n",
       "      <td>51.4115</td>\n",
       "      <td>51.6079</td>\n",
       "      <td>98.0547</td>\n",
       "      <td>97.8</td>\n",
       "      <td>97.09</td>\n",
       "      <td>51.3509</td>\n",
       "      <td>51.3661</td>\n",
       "      <td>98.7552</td>\n",
       "      <td>97.72</td>\n",
       "      <td>96.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10</td>\n",
       "      <td>81.4207</td>\n",
       "      <td>81.0892</td>\n",
       "      <td>156.202</td>\n",
       "      <td>98.26</td>\n",
       "      <td>97.77</td>\n",
       "      <td>81.3247</td>\n",
       "      <td>81.2436</td>\n",
       "      <td>156.036</td>\n",
       "      <td>98.31</td>\n",
       "      <td>97.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>15.8489</td>\n",
       "      <td>129.152</td>\n",
       "      <td>127.694</td>\n",
       "      <td>248.335</td>\n",
       "      <td>98.65</td>\n",
       "      <td>98.06</td>\n",
       "      <td>128.98</td>\n",
       "      <td>128.562</td>\n",
       "      <td>247.989</td>\n",
       "      <td>98.54</td>\n",
       "      <td>98.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>25.1189</td>\n",
       "      <td>204.635</td>\n",
       "      <td>205.428</td>\n",
       "      <td>391.472</td>\n",
       "      <td>98.74</td>\n",
       "      <td>98.41</td>\n",
       "      <td>204.282</td>\n",
       "      <td>204.435</td>\n",
       "      <td>398.321</td>\n",
       "      <td>98.66</td>\n",
       "      <td>98.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>39.8107</td>\n",
       "      <td>323.914</td>\n",
       "      <td>320.868</td>\n",
       "      <td>622.533</td>\n",
       "      <td>98.79</td>\n",
       "      <td>98.36</td>\n",
       "      <td>323.848</td>\n",
       "      <td>324.362</td>\n",
       "      <td>622.284</td>\n",
       "      <td>98.81</td>\n",
       "      <td>98.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>63.0957</td>\n",
       "      <td>514.054</td>\n",
       "      <td>512.755</td>\n",
       "      <td>1000.81</td>\n",
       "      <td>98.8</td>\n",
       "      <td>98.36</td>\n",
       "      <td>514.824</td>\n",
       "      <td>512.087</td>\n",
       "      <td>992.737</td>\n",
       "      <td>98.89</td>\n",
       "      <td>98.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>100</td>\n",
       "      <td>812.684</td>\n",
       "      <td>810.831</td>\n",
       "      <td>1566.31</td>\n",
       "      <td>98.88</td>\n",
       "      <td>98.43</td>\n",
       "      <td>812.285</td>\n",
       "      <td>808.705</td>\n",
       "      <td>1584.85</td>\n",
       "      <td>98.9</td>\n",
       "      <td>98.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>158.489</td>\n",
       "      <td>1289.81</td>\n",
       "      <td>1289.21</td>\n",
       "      <td>2483.96</td>\n",
       "      <td>98.94</td>\n",
       "      <td>98.45</td>\n",
       "      <td>1288.71</td>\n",
       "      <td>1285.77</td>\n",
       "      <td>2487.46</td>\n",
       "      <td>98.82</td>\n",
       "      <td>98.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>251.189</td>\n",
       "      <td>2040.68</td>\n",
       "      <td>2029.01</td>\n",
       "      <td>3951.43</td>\n",
       "      <td>98.87</td>\n",
       "      <td>98.57</td>\n",
       "      <td>2042.92</td>\n",
       "      <td>2055.57</td>\n",
       "      <td>3900.05</td>\n",
       "      <td>98.83</td>\n",
       "      <td>98.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>398.107</td>\n",
       "      <td>3244.28</td>\n",
       "      <td>3240.76</td>\n",
       "      <td>6194.74</td>\n",
       "      <td>98.9</td>\n",
       "      <td>98.41</td>\n",
       "      <td>3241.53</td>\n",
       "      <td>3233.84</td>\n",
       "      <td>6262.11</td>\n",
       "      <td>98.97</td>\n",
       "      <td>98.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>630.957</td>\n",
       "      <td>5134.75</td>\n",
       "      <td>5137.41</td>\n",
       "      <td>9877.67</td>\n",
       "      <td>98.96</td>\n",
       "      <td>98.43</td>\n",
       "      <td>5138.55</td>\n",
       "      <td>5074.94</td>\n",
       "      <td>9862.35</td>\n",
       "      <td>98.95</td>\n",
       "      <td>98.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1000</td>\n",
       "      <td>8153.75</td>\n",
       "      <td>8162.77</td>\n",
       "      <td>15711.8</td>\n",
       "      <td>98.97</td>\n",
       "      <td>98.6</td>\n",
       "      <td>8133.63</td>\n",
       "      <td>8105.58</td>\n",
       "      <td>15684.6</td>\n",
       "      <td>98.96</td>\n",
       "      <td>98.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1584.89</td>\n",
       "      <td>12881.4</td>\n",
       "      <td>12876.9</td>\n",
       "      <td>24651.3</td>\n",
       "      <td>98.92</td>\n",
       "      <td>98.61</td>\n",
       "      <td>12922.5</td>\n",
       "      <td>12886.8</td>\n",
       "      <td>24820.2</td>\n",
       "      <td>98.93</td>\n",
       "      <td>98.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2511.89</td>\n",
       "      <td>20431.1</td>\n",
       "      <td>20372.6</td>\n",
       "      <td>39441</td>\n",
       "      <td>98.97</td>\n",
       "      <td>98.62</td>\n",
       "      <td>20418.3</td>\n",
       "      <td>20500.1</td>\n",
       "      <td>39336.9</td>\n",
       "      <td>98.92</td>\n",
       "      <td>98.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>3981.07</td>\n",
       "      <td>32378.3</td>\n",
       "      <td>32085.7</td>\n",
       "      <td>62141.9</td>\n",
       "      <td>98.93</td>\n",
       "      <td>98.45</td>\n",
       "      <td>32340.7</td>\n",
       "      <td>32361.5</td>\n",
       "      <td>62875.3</td>\n",
       "      <td>98.95</td>\n",
       "      <td>98.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>6309.57</td>\n",
       "      <td>51394.5</td>\n",
       "      <td>51290.4</td>\n",
       "      <td>98531.8</td>\n",
       "      <td>98.92</td>\n",
       "      <td>98.44</td>\n",
       "      <td>51321.8</td>\n",
       "      <td>51493</td>\n",
       "      <td>99367.6</td>\n",
       "      <td>98.93</td>\n",
       "      <td>98.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>10000</td>\n",
       "      <td>81428.7</td>\n",
       "      <td>80855.5</td>\n",
       "      <td>157042</td>\n",
       "      <td>98.92</td>\n",
       "      <td>98.59</td>\n",
       "      <td>81252</td>\n",
       "      <td>81360.7</td>\n",
       "      <td>157332</td>\n",
       "      <td>98.96</td>\n",
       "      <td>98.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    max p/mul  fc0 p/mul  fc1 p/mul  fc2 p/mul accu_ref accu_aug fc0 fullres  \\\n",
       "0        0.01  0.0813022  0.0791595  0.0890009     9.95      9.8   0.0814653   \n",
       "1   0.0158489   0.128902   0.122613   0.148765     9.99     9.87    0.129042   \n",
       "2   0.0251189   0.204402   0.196175   0.236835    10.46     10.1    0.204502   \n",
       "3   0.0398107    0.32403   0.302665   0.395087    10.85    10.76    0.323367   \n",
       "4   0.0630957   0.512608   0.489142   0.615838    11.34    11.18    0.514173   \n",
       "5         0.1   0.813338    0.77056    1.03292    12.98    12.37    0.812195   \n",
       "6    0.158489    1.29223    1.24265    1.69202    15.76    15.81     1.29019   \n",
       "7    0.251189    2.04069    1.98988    2.84761    20.93    20.54      2.0489   \n",
       "8    0.398107    3.24251    3.12082    4.93404    32.51    30.75     3.24235   \n",
       "9    0.630957    5.13183     5.0858    8.36729    48.45    46.29      5.1328   \n",
       "10          1     8.1318    8.02315    14.0034    67.86    65.47     8.13757   \n",
       "11    1.58489    12.8954     12.787    23.0409    85.11     82.4     12.8883   \n",
       "12        1.8    14.6515    14.5029    26.7314     87.5    85.48     14.6397   \n",
       "13          2    16.2954    16.2203    30.1299     89.3    88.32     16.2603   \n",
       "14        2.3    18.6845    18.7071     34.406    91.87    90.78     18.6819   \n",
       "15    2.51189    20.4543    20.2025    38.0072    93.38    91.91     20.4663   \n",
       "16    3.98107    32.4109     32.329    61.5974    96.43    95.62     32.4108   \n",
       "17    6.30957    51.4115    51.6079    98.0547     97.8    97.09     51.3509   \n",
       "18         10    81.4207    81.0892    156.202    98.26    97.77     81.3247   \n",
       "19    15.8489    129.152    127.694    248.335    98.65    98.06      128.98   \n",
       "20    25.1189    204.635    205.428    391.472    98.74    98.41     204.282   \n",
       "21    39.8107    323.914    320.868    622.533    98.79    98.36     323.848   \n",
       "22    63.0957    514.054    512.755    1000.81     98.8    98.36     514.824   \n",
       "23        100    812.684    810.831    1566.31    98.88    98.43     812.285   \n",
       "24    158.489    1289.81    1289.21    2483.96    98.94    98.45     1288.71   \n",
       "25    251.189    2040.68    2029.01    3951.43    98.87    98.57     2042.92   \n",
       "26    398.107    3244.28    3240.76    6194.74     98.9    98.41     3241.53   \n",
       "27    630.957    5134.75    5137.41    9877.67    98.96    98.43     5138.55   \n",
       "28       1000    8153.75    8162.77    15711.8    98.97     98.6     8133.63   \n",
       "29    1584.89    12881.4    12876.9    24651.3    98.92    98.61     12922.5   \n",
       "30    2511.89    20431.1    20372.6      39441    98.97    98.62     20418.3   \n",
       "31    3981.07    32378.3    32085.7    62141.9    98.93    98.45     32340.7   \n",
       "32    6309.57    51394.5    51290.4    98531.8    98.92    98.44     51321.8   \n",
       "33      10000    81428.7    80855.5     157042    98.92    98.59       81252   \n",
       "\n",
       "   fc1 fullres fc2 fullres accu_ref_fullres accu_aug_fullres  \n",
       "0    0.0797499   0.0875007            10.02             9.96  \n",
       "1     0.122926    0.146791             9.52            10.38  \n",
       "2      0.19434    0.235998            10.33            10.33  \n",
       "3     0.307319    0.403256            10.23            10.86  \n",
       "4     0.480176    0.618586            11.64             11.7  \n",
       "5     0.779174     1.02507            12.82            12.76  \n",
       "6      1.23498      1.7153            15.46            15.66  \n",
       "7      1.99259     2.79844            21.17            21.41  \n",
       "8      3.13967     4.91853            32.38             31.1  \n",
       "9      5.02294      8.2348            48.23            45.99  \n",
       "10     8.01569     14.1132            68.08            66.33  \n",
       "11      12.805     23.4207             84.8            83.48  \n",
       "12     14.3717     26.6523            87.16            85.77  \n",
       "13     16.1222     29.9281            89.96            88.24  \n",
       "14     18.5141     34.4576            92.32            90.39  \n",
       "15     20.3935     37.9326            93.17            91.52  \n",
       "16     32.1341     60.9049            96.36            95.39  \n",
       "17     51.3661     98.7552            97.72            96.93  \n",
       "18     81.2436     156.036            98.31            97.65  \n",
       "19     128.562     247.989            98.54            98.01  \n",
       "20     204.435     398.321            98.66            98.19  \n",
       "21     324.362     622.284            98.81            98.15  \n",
       "22     512.087     992.737            98.89            98.48  \n",
       "23     808.705     1584.85             98.9            98.45  \n",
       "24     1285.77     2487.46            98.82            98.38  \n",
       "25     2055.57     3900.05            98.83             98.6  \n",
       "26     3233.84     6262.11            98.97            98.43  \n",
       "27     5074.94     9862.35            98.95            98.56  \n",
       "28     8105.58     15684.6            98.96            98.52  \n",
       "29     12886.8     24820.2            98.93            98.51  \n",
       "30     20500.1     39336.9            98.92            98.31  \n",
       "31     32361.5     62875.3            98.95            98.42  \n",
       "32       51493     99367.6            98.93            98.43  \n",
       "33     81360.7      157332            98.96             98.4  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%matplotlib notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.interpolate import UnivariateSpline\n",
    "\n",
    "SMALL_SIZE = 12\n",
    "MEDIUM_SIZE = 16\n",
    "BIGGER_SIZE = 20\n",
    "\n",
    "plt.rc('font', size=SMALL_SIZE)          # controls default text sizes\n",
    "plt.rc('axes', titlesize=SMALL_SIZE)     # fontsize of the axes title\n",
    "plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels\n",
    "plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels\n",
    "plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize\n",
    "plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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Fv080OtcX2yvIZxes8kEIKX9Sn/ellej2ADEad/i+dzSfmiQrCFw7GZfy3YA4rQe2gYBSGUzjp8jgAACCCCAAAIIIICAiQIEICZWhT4hgEDgBJwEIPbiuz04O4iwnz2v32TXxfGKNr0rQe+GKGuz29O7ErQdXZDXuwRatWpV/O1pPa6iFyPHLmrqI4n0uf/6zojS/dJHDNmPzNJH6ZQXgNj76beMY9/3of045phjrPc/6CKkbrELgrHjc/rC49gARI/XBU5dUNd3j6hBbACid0Koo4YCGi7FbvoYsvPPP996JJPejVPWO0C03xoq6V01GmToY7v0UWF6B4MGEvZLs8urpdZGQ5ryNvucGhr07NnTCgT0/S0aWOmdO3qnhd4RpO/aKO1auk1914OOR98no3ej6Pg0UNI7SfQuAD2HfddGogGIBkLz5s0rdzx654o+Qko3+z0Y+l6I2E3rp9eA9sEelxrrdVbepo+I03GVt1V0fDzXppsBiL6XxcnYK/ocsGta2QdlRY/A0vd06DtaYjcN0TRs0JBM75zSu130OtEaaCARGziUF4Bo6Bd7F1Dp+eN0btifP3q96tyK3TRE0kAlngBEj2/Xrp31OD8du77/Q+9A07tLkglAKvu8La9GpT/vnPQtns8tff+NPn5P33USu2nApaGgHYA4rYe2QQBS2Yzj5wgggAACCCCAAAIIIGCiAAGIiVWhTwggEDgBNwIQ+5vS+i6N0i8it0H0robs7OwyfSoKNmIPcBqApOIOEH2fgv2uBLcDEB2z3pmgoZF+o10fwRR7B4jeYaALveXdAaKPidF3BJQVgMR66t0GuqCqi4P6ngJdYNS7FrRtvZunZcuWZdYrdpG49A7lvQPEXqzUxwrpI57sOyU04Ih94Xlsexr+6GPMYjd9xJfecaQLwPpILw2o9E6eRAMQvZtEv52uYVNZmy6k23etOAlAtm7dagVJld0BoneN6JywH6NW+tz6mKTSL9+296nsDpDYazOVAUiiNS3LvaIApHQgq8fr3TQ6br1DKXaz3/XjRgDidG64fQeIjkcDRA089RrUEFPD22QDEHsuxvuPVunPOyd90znm9HPL6R0gTuuh4yMAibfK7I8AAggggAACCCCAAAImCBCAmFAF+oAAAoEXcCMA0fdD6F0J+sgnfXRPvJvbAYg+g1/fq6DvX9B3MdgvsdZFOH2/hX7rX7+lrZuGNvo4HX1sjr7zw97sx3zpz/SRO/Zmv29E3xuh38TXzYsARN+VoYu3Gn7oi+RjF331xeH6LWh9D0i1atWsPuiY9ZFVa9euLfcdIOXVRV90rXck6OO+tBbqowvz+v6DeLfyAhB9F4g+Okz7p33XkEAfm6bvptDzlheOlXd+rYl+E95+V0aiAYiOU1+krO8z0MClos1JAKLH6ztA9H0lGjCV994Nfc+LPuJLz6vvOYlni+fajCcA0Tso9E4KDTLs68rul9OxJ1PTWIN4AxB9Yb3O99j37egjo/Ta1s8mNwIQp3ND56Le3aQBln5e2J8/ib4DRF30fTj6SD79jLIf/2dKAOK0b04/t5y+A8RpPdSPACSeTxj2RQABBBBAAAEEEEAAAVMECEBMqQT9QACBQAu4EYAogD7uZcCAAdajafRdFY0aNbIWt2fMmGH9rt9aL29zOwDR87z66qvW42f0XRL6/hJ9J4B+41n7M2bMGCtc0M1esNd9tP8aBOjdE7pAr3+nj2HRR8VoEKH91Efq6COcdIHbvjvBiwBE+6aPwdLHYekWG4DoC6B1oVm/9a53U2h4oMGTPmpK30+h72WJHZt+K177qAvC+qJrrZGGHHrc2LFjrQVVfa+KfTeC/nno0KHWXQz6DgRdoNfHZWlQoe9xueOOO8qtZXkBiD2Gfv36FT++S0MnrYP2RR9to3da6Ps49JFU+vx/7Ztu+hJ0fQGy3nmii9y6kKz903ea6GPItGaJBiAaiunj0vQaHThwoHVXhi5g69036qmhgL7/QTenIYBeYzounQPq2qZNG8tPX+ys31rXa0vfF6N3n2jQoCGbXnP6Dg29xjQY0btxKnpEmNNrM54AxDbU963o9a6PkLLv3HI6dqc1rexDM94ARPusj4XT+anvy9CQSB8jpterPmrMjQBE++x0buhj0/SuKn1njYZdeueS1kI/h/QxcvE+Aqssr4oCEH08nIa9pTf9PFy5cqUV9rl1B4jTvjn93NJQUN8Bon3Uea53eOndbnpt6R1f9iOw4qkHAUhlM46fI4AAAggggAACCCCAgIkCBCAmVoU+IYBA4ATcCkB04PpCXH0Rtz4aSheydQFY70rQc2goUt7mRQCi59LwQBcsdUFaF3P18Vy6KKrfRI7d9HFKL774oqxYscJa/LYDA/1vfUeILmbqops+YkcDAW0zdnHaqwBEH6Wj783Qd26UfuyP3sGii776Tg3tp77MXO9K0fcf2FvpR2Dpwqe+JF3fQaALibqorYuk+rz9K6+80jKyN7XTuyz0Rcu6aNukSRPrfQP6eCy9w6G8raIARI/RGuidEban1l4XrvUxato/ffeLBiK6UKvj0U3vSvjvf/9rHbdhwwarLxoA6WK3/ZiuRAMQbV9DkOHDh1vG2i8NJfQOIn3Xi3rpI3l0cxoC6L5aO62Phjg6F7TP+k4UDTY0QNNt9erV1tg17NF3JGgwoou+eo0NGTLEWrwvb3N6bcYTgOTm5lqPI9Paa980YLIXm+MZu5OaVvZBGW8Aon3X60UDQH00mt5tpIvn77zzjhVyuhWA2J8rTuaGfm5oEKGBnYZ7GoZpCFC6P2VZlPeZErtvRQFIeb5aT938CED0vE4+t3S/iRMnWuGjBs0awOqj8vRz4eKLLy4RgDitBwFIZTOOnyOAAAIIIIAAAggggICJAgQgJlaFPiGAAAIIIIAAAggggAACCCCAAAIIIIAAAggggEBSAgQgSfFxMAIIIIAAAggggAACCCCAAAIIIIAAAggggAACCJgoQABiYlUq6JM+AkQf9TF9+nTrkQb6eA19PIg+lqD0pi8t1pfv6mNaMjMzrceG6GNoynqetb7AWJ8NrY910BeO6qN29HE2+kx4NgQQQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEgiZAABKwiunzt/VdAPqc+nbt2smzzz5bZgAya9Ys6+WXuq8+L1tfCqvP8V67dq0VnugLcO1NX9irz4DX/fr06SNTpkyRW265xXqR8dNPPx0wIbqLAAIIIIAAAggggAACCCCAAAIIIIAAAggggIAIAUjArgJ9mapu+iJVvftDg4yy7gDp16+f9QLi+fPnS61ataxj9AWi+vLLgQMHWi8U1U1fcqovIe7fv7889dRTxRr33HOPFYLMnDnTegkqGwIIIIAAAggggAACCCCAAAIIIIAAAggggAACQRIgAAlStUr1tbwAJC8vzwo9NNQYNWpUiaP69u1rPeZqzpw51t+/8sorct5558mkSZPk0EMPLd53+fLl1qOw9O4QfRQWGwIIIIAAAggggAACCCCAAAIIIIAAAggggAACQRIgAAlStRwGILNnz5YOHTpY7/S4/PLLSxx1ww03yMiRI2XLli1StWpVGTJkiAwfPlw2bdok1atXL7Gv3l3Su3dvefXVVwOsRNcRQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEoihAABLgqpd3B8jEiROlW7duMnr0aDnrrLNKjHDYsGHWHR3Lli2Tpk2bysUXXywvvfSS9Y6Q0lv79u1lr732kk8//bRMpe3bt4v+SnQrKCiQNWvWSP369a1HerEhgAACCCCAAAIIIIAAAl4J6OOEN27caN3pnp6e7tVpaBcBBBBAAAEEEEDAIAECEIOKEW9XKgtAXnvtNTnzzDNLNGsHIPqIqyZNmlgByMsvvyxbt27d7fQagLRq1Uo++eSTMrt2++23yx133BFvt9kfAQQQQAABBBBAAAEEEPBNYMmSJdZ7ENkQQAABBBBAAAEEwi9AABLgGpcXgCTyCKzNmzdLTk5OCY3KHoGV7B0g69evlxYtWljvJKlZs2ZSldixY4f10vcePXpIVlZWUm1xMAJBFmAuBLl69N1NAeaCm5q0FXQB5kPQK0j/3RLQu8/btWsn69atk9q1a7vVLO0ggAACCCCAAAIIGCxAAGJwcSrrWmUvQR8wYIA8+eSTJZo59thjZcGCBcUvQdf3e5x77rkyefJkOeSQQ4r3XbFihfWILC9fgr5hwwbr/3hoEKIvbU9m0/9j/9FHH8nxxx9PAJIMJMcGXoC5EPgSMgCXBJgLLkHSTCgEmA+hKCODcEHgzz//lAYNGrjy/z9c6A5NIIAAAggggAACCKRAgAAkBchenaK8AETPp4++Gj9+vMybN6/47orFixdL27ZtZeDAgdaLz3XTb0E1a9ZM/v73v5cIS/Tn+q6QmTNnSseOHT0ZAgGIJ6w0GnEBFrkifgEw/GIB5gIXAwK7BJgPXA0IFAkQgHAlIIAAAggggAAC0RMgAAlgzT/++GPRR1bpC/wuvPBCOeOMM6Rfv37WSPQOCH2U1axZs6RLly7SuXNnGTx4sPWS86FDh1qBx/Tp00Ufb2VvepfHrbfeKkOGDJE+ffrIlClT5JZbbpH+/fvL008/7ZkQAYhntDQcYQEWuSJcfIZeQoC5wAWBQDACEH0pdV5BoeTlF8qOggLJ3/m7/rn47woKZUd+gfXnvAL796LjCvRXYaHk7/y9oFBi/lt/LpJfWCh6nvwCsfa1fhUUSn6h7Px7/W/dJ+ZYq72iY+397battqy/l6J2du5rt61/1rbsrVB2/SH27/XnTvaLacpqsmQbZbe9+zEl/yY9LU3S09MkPU0kIz1N0tLSJEP/Lk12/v3OP6eLWPvqz639Sx0T8/OifaSorZ1tlzjWPt/O9so7/y63GEMHnrEj1BqVtW1Yt07699iXO0D4gEQAAQQQQAABBCIkQAASwGLvtddesmjRojJ7ru/T0J/rNnXqVBk0aJBMmjRJMjMzpWfPnjJixAhp3br1bsc+8sgj8vjjj8tvv/1mvRz9ggsukJtvvtnTx0kRgATw4qPLxguw6Gt8iehgigSYCymC5jSuCuiirS6q6yK/LqLrAr8GArrYHvtn++f69xoKFP9s52J80Z93tbE9d4dMnfaD7NfpAJG0dMnXEGFneGCdwz5Xif8u2kfPr7/vCiB2hRDW3+08Nm9nQKEhRlFQoX0r+nl5x9rndRWRxhCoQKBg+xZZ8lA/AhCuEgQQQAABBBBAIEICBCARKrZpQyUAMa0i9CcMAiz6hqGKjMENAeaCG4rhb0O/vZ+bXyDb8wokN09/z7d+t/5uR9Hv1p93/mzXfjv/fufPi4+z9tu5f4k2itq1f7Zb+/lFYYX+YisSyMpIk8z0dMnMSJOsjHTrjoKs9DTJzCj6u8x0vctA/16sOxfsuw6K/rvoroYSdzkU71PZHQ+77nCw714ocZdEqfPtfsfEzrsodvbDrmdabGG1gzFb7J9if5Qmu35S6pCYn+gdF7saiz2mxE4ixX/Uq8wO24ruVinrLpadd8vE3glj321Twd0yJdqy7rLZdafMrjt1dt2JY98ts+sOnkIpbwzxOpXlsn3LRnnr6t4EIHzQIIAAAggggAACERIgAIlQsU0bKgGIaRWhP2EQYNE3DFVkDG4IMBfcUDS7DV0wXb91h6zdkivrtuyQdVtyZe3O3/XP9t/bv2/anrdbyLFDn4EUkK1owb9o4V8fHWQHAPbf2z/T363/ztgZEOgjjdJE1q9dI40aNpSszHQrMIjdp7gN/fvicGHXubJKBRG6v4YSdhARG1Toz4r+vnR4Yf+5qF+7taHt7WxX+6uBBhsCbgvwDhC3RWkPAQQQQAABBBAwX4AAxPwahbaHBCChLS0D81GARV8f8Tm1UQLMBaPKUWFn9Bvjm3PzZe3mnUHG1l1BxtrNRUGGHXTEBhwbtu0o9S6E5MecnZkuVXb+ys5IlypZGaK/69/bP7P+u9TPijqrfxUAACAASURBVI8p/lnRPtmZGVZ7pY8v2j+j6O8z0q1QIjbgKAoziu56sN+lkEwgwHxI/tqghXAIEICEo46MAgEEEEAAAQQQiEeAACQeLfZ1VYAAxFVOGkPAEmCRiwsBgSIB5oI/V4I+Cmq9dfeFfQdGUahhBxe77tYo+rn+/fqtuZLMnRg1q2RKnepZUqdattTJyZK6OdlSNydLau/8Xf+sf1+zamZx6FAcSuwMNzSM0EcuJRMy+CPu7KzMB2dO7BV+AQKQ8NeYESKAAAIIIIAAAqUFCEC4JnwTIADxjZ4Th1iARa4QF5ehxSXAXIiLK+6dNeSYs3KjzF6xUeb+sVHm/LFJ5q7cKKs35cbdln2A3g2hwYUdWGigUVeDjZ1BRnHAUX1nwLEz8NBHMbFVLMB84ApBoEiAAIQrAQEEEEAAAQQQiJ4AAUj0am7MiAlAjCkFHQmRAItcISomQ0lKgLmQFF/xwfqYqbkabuwMOeZYv2+UlRu3l3sCfX+DhhZ1qml4YQca9t0ZdqBR9Oddd2xkS9Ws9NDegeFONRJvhfmQuB1HhkuAACRc9WQ0CCCAAAIIIICAEwECECdK7OOJAAGIJ6w0GnEBFrkifgEw/GIB5kJ8F8Pm7Xkyd+UmK9yIDTuWr99WbkPN6lSTto1rSLvGNaVtoxrSvklNaVEvR2pVzbJe0s1mjgDzwZxa0BN/BQhA/PXn7AgggAACCCCAgB8CBCB+qHNOS4AAhAsBAfcFWORy35QWgynAXCi7bltz82X+qk3Wo6v0EVZ6d4eGHkvXbi230I1rVbFCjqJfNaTtzsCjZtWsYF4cEew18yGCRWfIZQoQgHBhIIAAAggggAAC0RMgAIlezY0ZMQGIMaWgIyESYJErRMVkKEkJRH0ubNuRLwtWbbbey6EBx+wVRe/oWLxmixQWlk3boIYGHUV3dBSHHY1qSu0cgo6kLkYDDo76fDCgBHTBEAECEEMKQTcQQAABBBBAAIEUChCApBCbU5UUIADhikDAfQEWudw3pcVgCkRlLuTmFcjC1Zt3e3TVb39uloJygo561bOtR1ZZIUeTmtJu53/XrZ4dzGLT60oFojIfKoVgh8gLEIBE/hIAAAEEEEAAAQQiKEAAEsGimzJkAhBTKkE/wiTAIleYqslYkhEI41woKCiUifP/lO8XrSl+dJWGH3nlJB21q2UVP7LKDjk08NA7PdiiJRDG+RCtCjJatwQIQNySpB0EEEAAAQQQQCA4AgQgwalV6HpKABK6kjIgAwRY5DKgCHTBCIEwzYUV67fJm98vkde/X1LmuzpqVMksfnSVvp/DfoxVo5pVJC2Nl5EbcUH63IkwzQefKTl9wAUIQAJeQLqPAAIIIIAAAggkIEAAkgAah7gjQADijiOtIBArwCIX1wMCRQJBnwv5BYUyfvZKGf3dEhk764/ix1nVqpopx3RsLPs0qSVtd76vo2ntqgQdXPgVCgR9PlBeBNwSIABxS5J2EEAAAQQQQACB4AgQgASnVqHrKQFI6ErKgAwQYJHLgCLQBSMEgjoXfl+3VV6fssS642P5+m3Fll33qidndW0ux+/XVKpmZRhhTCeCIxDU+RAcYXoaFAECkKBUin4igAACCCCAAALuCRCAuGdJS3EKEIDECcbuCDgQYJHLARK7REIgSHNhR36BjPl1pbw2ZbF8OWeVFO58eXndnCw5rfOeVvDRplHNSNSNQXojEKT54I0ArSJQJEAAwpWAAAIIIIAAAghET4AAJHo1N2bEBCDGlIKOhEiARa4QFZOhJCUQhLmw+M8tVujx5tSlsmrj9uLxHt66vpzVtYX0/UtjqZLJ3R5JXQgcbAkEYT5QKgRSIUAAkgplzoEAAggggAACCJglQABiVj0i1RsCkEiVm8GmSIBFrhRBcxrjBUydC7l5BfLZLyvkte+WyIR5q4sdG9TIltMPai5ndWkuezWobrwvHQyWgKnzIViK9DYMAgQgYagiY0AAAQQQQAABBOITIACJz4u9XRQgAHERk6YQ2CnAIheXAgJFAqbNhQWrNslrU5bIW1OXyprNuVYf09JEjmjbUM7u0lx67dNYsjPTKR8CngiYNh88GSSNIuBAgADEARK7IIAAAggggAACIRMgAAlZQYM0HAKQIFWLvgZFgEWuoFSKfnotYMJc2LYjXz6ZuUJGf7dYvl24pnjIjWtVkX4HN7d+Na+X4zUF7SNgXCBISRDwS4AAxC95zosAAggggAACCPgnQADin33kz0wAEvlLAAAPBExY9PVgWDSJQNwCfs6FOX9stEKPd374XdZt2WH1PT1NpEf7RnJ21xZydPuGkpnB3R5xF5UDEhbwcz4k3GkORMADAQIQD1BpEgEEEEAAAQQQMFyAAMTwAoW5ewQgYa4uY/NLgEUuv+Q5r2kCqZ4LW3Pz5cMfl1mPuZq6aG0xR7M61Yru9uiypzStXc00JvoTEYFUz4eIsDLMAAoQgASwaHQZAQQQQAABBBBIUoAAJElADk9cgAAkcTuORKA8ARa5uDYQKBJI1Vz4edl664Xm707/XTZuy7POnZGeJsfsU3S3h77jQ//MhoCfAqmaD36OkXMj4ESAAMSJEvsggAACCCCAAALhEiAACVc9AzUaApBAlYvOBkSARa6AFIpuei7g5VzYtD1PPpixTF77brHMWLq+eCwt6uXImV2ayxkH7SmNalX1fIycAAGnAl7OB6d9YD8ETBAgADGhCvQBAQQQQAABBBBIrQABSGq9OVuMAAEIlwMC7guwyOW+KS0GU8DtuVBYWCg/Ll0vr01ZLO9PXyabc/MtmKyMNOnzlyZyTtcWctje9SWduz2CecGEvNduz4eQczG8EAsQgIS4uAwNAQQQQAABBBAoR4AAhEvDNwECEN/oOXGIBVjkCnFxGVpcAm7NhQ3bdsh7P/wuo79bIr8s31Dch70bVLcecXVq52ZSv0aVuPrGzgikWsCt+ZDqfnM+BNwWIABxW5T2EEAAAQQQQAAB8wUIQMyvUWh7SAAS2tIyMB8FWOTyEZ9TGyWQzFzQuz2mLV5rhR76YvNtOwqssWVnpsvx+zaxgo+urepJWhrv9jCq6HSmXIFk5gOsCIRJgAAkTNVkLAgggAACCCCAgDMBAhBnTuzlgQABiAeoNBl5ARa5In8JALBTING58OWcVXLvx7NK3O3RrnENK/T424HNpE5ONsYIBE4g0fkQuIHSYQQqESAA4RJBAAEEEEAAAQSiJ0AAEr2aGzNiAhBjSkFHQiTAIleIislQkhKIdy78smyDDPv4V/l67mrrvFWz0uXE/fewgo/OLepwt0dS1eBgvwXinQ9+95fzI+CVAAGIV7K0iwACCCCAAAIImCtAAGJubULfMwKQ0JeYAfogwCKXD+ic0kgBp3Nh+fqtMuLTOfL2D0ulsFAkOyNd+h/WUq7o2Ya7PYysLJ1KRMDpfEikbY5BIEgCBCBBqhZ9RQABBBBAAAEE3BEgAHHHkVYSECAASQCNQxCoRIBFLi4RBIoEKpsLG7ftkCfHz5fnJiyU7XlF7/g4cf+mcmPfDtKifg6MCIRKoLL5EKrBMhgEKhAgAOHyQAABBBBAAAEEoidAABK9mhszYgIQY0pBR0IkwCJXiIrJUJISqGgujJu9Uq5/Y4b8uTnXOkfXverJTSfsIwc0r5PUOTkYAVMF+LfB1MrQr1QLEICkWpzzIYAAAggggAAC/gsQgPhfg8j2gAAksqVn4B4KsMjlIS5NB0qgvLnwyczlcuXoH2RHfqHs3aC6DD6ug/Tu2Jh3fASqunQ2XgH+bYhXjP3DKkAAEtbKMi4EEEAAAQQQQKB8AQIQrg7fBAhAfKPnxCEWYJErxMVlaHEJlDUX3pv+u1z7xgzJLyiUkzrtIQ/06yRZGelxtcvOCARRgH8bglg1+uyFAAGIF6q0iQACCCCAAAIImC1AAGJ2fULdOwKQUJeXwfkkwCKXT/Cc1jiB0nPhjSlLZNDbP1ovOj/9oD3l3tP2l4z0NOP6TYcQ8EKAfxu8UKXNIAoQgASxavQZAQQQQAABBBBIToAAJDk/jk5CgAAkCTwORaAcARa5uDQQKBKInQuvff+73Prez9bfn3tIC7nz5H0lnfCDSyVCAvzbEKFiM9QKBQhAuEAQQAABBBBAAIHoCRCARK/mxoyYAMSYUtCREAmwyBWiYjKUpATsubCidkcZ9skcq60Lu7WSW0/ch/d9JCXLwUEU4N+GIFaNPnshQADihSptIoAAAggggAACZgsQgJhdn1D3jgAk1OVlcD4JsMjlEzynNU5A58I1T38s/1uSYfXt8qNbyw192xN+GFcpOpQKAf5tSIUy5wiCAAFIEKpEHxFAAAEEEEAAAXcFCEDc9aS1OAQIQOLAYlcEHAqwyOUQit1CLVBYWCj3fzJLnvhygTXOa3u3kyt7tiH8CHXVGVxFAvzbwPWBQJEAAQhXAgIIIIAAAgggED0BApDo1dyYEROAGFMKOhIiARa5QlRMhpKQgIYf93z0qzzz9ULr+EF928llPdom1BYHIRAWAf5tCEslGUeyAgQgyQpyPAIIIIAAAgggEDwBApDg1Sw0PSYACU0pGYhBAixyGVQMupJygYKCQrnt/Z/l5cmLrHOftle+DP/HcZKVlZXyvnBCBEwS4N8Gk6pBX/wUIADxU59zI4AAAggggAAC/ggQgPjjzllFhACEywAB9wVY5HLflBaDIZBfUChD3v5R3vh+qaSlidz1145SY+WPcvzxxxOABKOE9NJDAf5t8BCXpgMlQAASqHLRWQQQQAABBBBAwBUBAhBXGGkkEQECkETUOAaBigVY5OIKiaJAXn6BXPfmDHlv+jJJTxMZ2a+TnLhvY/noo48IQKJ4QTDm3QT4t4GLAoEiAQIQrgQEEEAAAQQQQCB6AgQg0au5MSMmADGmFHQkRAIscoWomAzFscADn82WR8bOk8z0NHn4rAPlhP2bCnPBMR87RkCA+RCBIjNERwIEII6Y2AkBBBBAAAEEEAiVAAFIqMoZrMEQgASrXvQ2GAIscgWjTvTSPYGfl62Xkx/7RvIKCuXBMzvJ3w7c02qcueCeMS0FX4D5EPwaMgJ3BAhA3HGkFQQQQAABBBBAIEgCBCBBqlbI+koAErKCMhwjBFjkMqIMdCJFAjvyC6zw45flG+TYvzSRJ8/rLGn6AhACkBRVgNMERYB/G4JSKfrptQABiNfCtI8AAggggAACCJgnQABiXk0i0yMCkMiUmoGmUIBFrhRicyrfBR4dM1dGfj5H6uRkyecDj5KGNasU94m54Ht56IBBAswHg4pBV3wVIADxlZ+TI4AAAggggAACvggQgPjCzklVgACE6wAB9wVY5HLflBbNFJjzx0Y54ZGvZUd+oTx05gFyyoHNSnSUuWBm3eiVPwLMB3/cOat5AgQg5tWEHiGAAAIIIIAAAl4LEIB4LUz75QoQgHBxIOC+AItc7pvSonkCefkFctqTE2XG0vXSq0MjeXbAwcWPvrJ7y1wwr270yD8B5oN/9pzZLAECELPqQW8QQAABBBBAAIFUCBCApEKZc5QpQADChYGA+wIscrlvSovmCTz15XwZ9vEsqVk103r0VZPaVXfrJHPBvLrRI/8EmA/+2XNmswQIQMyqB71BAAEEEEAAAQRSIUAAkgplzkEAwjWAQIoEWORKETSn8U1g/qpNctzDX0tuXoHcd9r+0q9L8zL7wlzwrUSc2EAB5oOBRaFLvggQgPjCzkkRQAABBBBAAAFfBQhAfOWP9sm5AyTa9Wf03giwyOWNK62aIZBfUChnPjVJvl+0Vo5o20BeurDrbo++snvKXDCjZvTCDAHmgxl1oBf+CxCA+F8DeoAAAggggAACCKRagAAk1eKcr1iAAISLAQH3BVjkct+UFs0ReP6bhXLHB79I9ewM+ezao6RZnWrldo65YE7d6In/AswH/2tAD8wQIAAxow70AgEEEEAAAQQQSKUAAUgqtTlXCQECEC4IBNwXYJHLfVNaNENg0Z+b5diHvpatO/LlrlP2lfMObVlhx5gLZtSNXpghwHwwow70wn8BAhD/a0APEEAAAQQQQACBVAsQgKRanPMVCxCAcDEg4L4Ai1zum9Ki/wIFBYVy7rPfyqQFf8qhe9eTVy86VNLT0whA/C8NPQiIAP82BKRQdNNzAQIQz4k5AQIIIIAAAgggYJwAAYhxJYlOhwhAolNrRpo6ARa5UmfNmVIn8J/Ji+SWd2dKtawM+eSaI6Rl/eqVnpy5UCkRO0RIgPkQoWIz1AoFCEC4QBBAAAEEEEAAgegJEIBEr+bGjJgAxJhS0JEQCbDIFaJiMhRL4LfVm+WER76Wzbn5MvTEjnJh91aOZJgLjpjYKSICzIeIFJphVipAAFIpETsggAACCCCAAAKhEyAACV1JgzMgApDg1IqeBkeARa7g1IqeVi6wfP1WOWPUJFm6dqsc3LKuvHHJYZU++spulblQuS97REeA+RCdWjPSigUIQLhCEEAAAQQQQACB6AkQgESv5saMmADEmFLQkRAJsMgVomJGfCh/btou/Z6aJPNXbZa96ufIG5ceJo1qVnWswlxwTMWOERBgPkSgyAzRkQABiCMmdkIAAQQQQAABBEIlQAASqnIGazAEIMGqF70NhgCLXMGoE72sWGD91h1yzjOT5edlG2SP2lWt8GPPujlxsTEX4uJi55ALMB9CXmCG51iAAMQxFTsigAACCCCAAAKhESAACU0pgzcQApDg1Ywemy/AIpf5NaKHFQtsyc2T/s99J98vWisNamRbj73au2GNuNmYC3GTcUCIBZgPIS4uQ4tLgAAkLi52RgABBBBAAAEEQiFAABKKMgZzEAQgwawbvTZbgEUus+tD7yoW2J6XLxe9+L18PXe11KqaKa9dfJh03KNWQmzMhYTYOCikAsyHkBaWYcUtQAASNxkHIIAAAggggAACgRcgAAl8CYM7AAKQ4NaOnpsrwCKXubWhZxUL5OUXyL9enSaf/vyH5GRnyMv/OEQOalk3YTbmQsJ0HBhCAeZDCIvKkBISIABJiI2DEEAAAQQQQACBQAsQgAS6fMHuPAFIsOtH780UYJHLzLrQq4oFCgoK5fo3Z8jbP/wu2Rnp8vwFXaRbmwZJsTEXkuLj4JAJMB9CVlCGk7AAAUjCdByIAAIIIIAAAggEVoAAJLClC37HCUCCX0NGYJ4Ai1zm1YQeVSxQWFgot73/s7w0aZFkpKfJqPMOkt4dGyfNxlxImpAGQiTAfAhRMRlKUgIEIEnxcTACCCCAAAIIIBBIAQKQQJYtHJ0mAAlHHRmFWQIscplVD3pTucB9n8ySJ8bPl7Q0kYfOPEBOPqBZ5Qc52IO54ACJXSIjwHyITKkZaCUCBCBcIggggAACCCCAQPQECECiV3NjRkwAYkwp6EiIBFjkClExIzCUJ8bPk/s+mW2N9O6/7SvnHtLStVEzF1yjpKEQCDAfQlBEhuCKAAGIK4w0ggACCCCAAAIIBEqAACRQ5QpXZwlAwlVPRmOGAItcZtSBXlQu8PLkRXLruzOtHYcc10EuOap15QfFsQdzIQ4sdg29APMh9CVmgA4FCEAcQrEbAggggAACCCAQIgECkBAVM2hDIQAJWsXobxAEWOQKQpXo4+wVG+XYh7+SwkKRK3u2kev6tHcdhbngOikNBliA+RDg4tF1VwUIQFzlpDEEEEAAAQQQQCAQAgQggShTODtJABLOujIqfwVY5PLXn7M7E7hq9A/y/oxl0qdjY3nq/IMkTV8A4vLGXHAZlOYCLcB8CHT56LyLAgQgLmLSFAIIIIAAAgggEBABApCAFCqM3SQACWNVGZPfAixy+V0Bzl+ZwMLVm6XXyPFSUCjyv6u6y1/2qF3ZIQn9nLmQEBsHhVSA+RDSwjKsuAUIQOIm4wAEEEAAAQQQQCDwAgQggS9hcAdAABLc2tFzcwVY5DK3NvSsSODGt2bIG98vlZ4dGsm//97FMxbmgme0NBxAAeZDAItGlz0RIADxhJVGEUAAAQQQQAABowUIQIwuT7g7RwAS7voyOn8EWOTyx52zOhP4fd1WOeq+cZJXUCj/vexwOahlXWcHJrAXcyEBNA4JrQDzIbSlZWBxChCAxAnG7ggggAACCCCAQAgECEBCUMSgDoEAJKiVo98mC7DIZXJ16NvQ92bKS5MWyeGt68ur/zzUUxDmgqe8NB4wAeZDwApGdz0TIADxjJaGEUAAAQQQQAABYwUIQIwtTfg7RgAS/hozwtQLsMiVenPO6Exg5cZt0v3ecZKbVyCvXnSIHN6mgbMDE9yLuZAgHIeFUoD5EMqyMqgEBAhAEkDjEAQQQAABBBBAIOACBCABL2CQu08AEuTq0XdTBVjkMrUy9GvYR7/KU18tkM4t6liPv0pLS/MUhbngKS+NB0yA+RCwgtFdzwQIQDyjpWEEEEAAAQQQQMBYAQIQY0sT/o4RgIS/xoww9QIscqXenDNWLrB2c650u3esbMnNl3///WDp2aFx5QcluQdzIUlADg+VAPMhVOVkMEkIEIAkgcehCCCAAAIIIIBAQAUIQAJauDB0mwAkDFVkDKYJsMhlWkXojwo88PkceWTMXOnYtJb876runt/9oedkLnDtIbBLgPnA1YBAkQABCFcCAggggAACCCAQPQECkOjV3JgRE4AYUwo6EiIBFrlCVMyQDGXjth3SbfhY2bAtT544t7Mcv1/TlIyMuZASZk4SEAHmQ0AKRTc9FyAA8ZyYEyCAAAIIIIAAAsYJEIAYV5LodIgAJDq1ZqSpE2CRK3XWnMmZwBPj58l9n8yW1g2ry+cDj5L0dG/f/WH3irngrD7sFQ0B5kM06swoKxcgAKnciD0QQAABBBBAAIGwCRCAhK2iARoPAUiAikVXAyPAIldgShWJjm7NzZfu946VPzfnysgzOslpB+2ZsnEzF1JGzYkCIMB8CECR6GJKBAhAUsLMSRBAAAEEEEAAAaMECECMKke0OkMAEq16M9rUCLDIlRpnzuJM4PlvFsodH/wizetVk7HXHS1ZGenODnRhL+aCC4g0ERoB5kNoSslAkhQgAEkSkMMRQAABBBBAAIEAChCABLBoYekyAUhYKsk4TBJgkcukakS7L9vz8uWo+8bLig3b5O6/7SvnHtIypSDMhZRyczLDBZgPhheI7qVMgAAkZdScCAEEEEAAAQQQMEaAAMSYUkSvIwQg0as5I/ZegEUu7405gzOB0d8tliFv/ySNa1WRr27sIVUyM5wd6NJezAWXIGkmFALMh1CUkUG4IEAA4gIiTSCAAAIIIIAAAgETIAAJWMHC1F0CkDBVk7GYIsAilymViHY/8vILpOfIL2Xxmi1y64kd5R/dW6UchLmQcnJOaLAA88Hg4tC1lAoQgKSUm5MhgAACCCCAAAJGCBCAGFGGaHaCACSadWfU3gqwyOWtL607E3jnh6Uy8PUZUq96tkwY1ENysjOdHejiXswFFzFpKvACzIfAl5ABuCRAAOISJM0ggAACCCCAAAIBEiAACVCx4u3qDz/8IHfccYd89913sm7dOmnRooWcc845cv3110tOTk5xc9OmTZMbb7xRJk+eLJmZmdKzZ08ZMWKE7L333vGeMq79CUDi4mJnBBwJsMjliImdPBQoKCiUvg99JXNXbpIb+raXf/Vo4+HZym+aueALOyc1VID5YGhh6FbKBQhAUk7OCRFAAAEEEEAAAd8FCEB8L4E3Hfjll1/koIMOkvbt28tNN90kDRo0kK+++kruuusuOeGEE+S9996zTjxr1izp2rWrHHDAATJ48GDZtm2bDB06VNauXSvTp0+Xhg0betNBESEA8YyWhiMswCJXhItvyNA/mblcLv3PNKlZNVO+GdxTalXN8qVnzAVf2DmpoQLMB0MLQ7dSLkAAknJyTogAAggggAACCPguQADiewm86cAtt9wid999t8ybN09at25dfJJLLrlEnn76aVmzZo3UrVtX+vXrJ+PGjZP58+dLrVq1rP0WLVokbdu2lYEDB8q9997rTQcJQDxzpeFoC7DIFe36+z36wsJCOfHRCfLzsg1yZc82cl2f9r51ibngGz0nNlCA+WBgUeiSLwIEIL6wc1IEEEAAAQQQQMBXAQIQX/m9O7k++ur222+XVatWWXd/2NugQYOsx1vp3RdVqlSxQo/+/fvLqFGjSnSmb9++snDhQpkzZ45nneQOEM9oaTjCAixyRbj4Bgx93OyVcsHzU6RaVoZ194e+A8SvjbnglzznNVGA+WBiVeiTHwIEIH6oc04EEEAAAQQQQMBfAQIQf/09O/tvv/0mBx54oBxzzDHWXRz6KKsvv/xSzjvvPCvweOSRR2T27NnSoUMHefzxx+Xyyy8v0ZcbbrhBRo4cKVu2bJGqVat60k8CEE9YaTTiAixyRfwC8HH4evfH6aMmydRFa+Wi7q3klhM7+tgbEeaCr/yc3DAB5oNhBaE7vgkQgPhGz4kRQAABBBBAAAHfBAhAfKP3/sT6fo+//e1v1ns+7O2qq66Shx56SNLS0mTixInSrVs3GT16tJx11lklOjRs2DDr3SHLli2Tpk2bltnZ7du3i/5KdNMApHnz5rJ69erix28l2pb+H/vPP/9cevfuLVlZ/jxvPtG+cxwCbgowF9zUpK14BL5duEbO+/f3kp2ZLmMHdpfGtbwJz532ibngVIr9oiDAfIhClRmjEwENQPT/26xfvz7p///h5HzsgwACCCCAAAIIIOC/AAGI/zXwpAd6B4iGAY0bN7be5aF3gHz77bfWS9BPP/10ee6554oDkNdee03OPPPMMgOQ5cuXS5MmTcrsoz5iSx+1lez26quvSk5OTrLNcDwCCCCAgI8Cj/+SLnPWp0u3xgXSb+8CH3vCqRFAAAEEEChbQO9uP+eccwhAuEAQQAABBBBAAIEICRCAhLTYekeHvtx8wYIFUr169eJRPv/883Lh2Fi1twAAIABJREFUhRfK+PHjrWAjmUdgcQdISC8ehhVoAb7lG+jyBbbz05eskzOe/k4y0tPki2u6y551q/k+FuaC7yWgAwYJMB8MKgZd8VWAO0B85efkCCCAAAIIIICALwIEIL6we39SDTb09m4NQWK3mTNnyn777SePPfaYXHLJJdat3wMGDJAnn3yyxH7HHnusFZ7wEnTva8UZEHBTgOe8u6lJW04FLnpxinzx60o5/aA9ZcQZnZwe5ul+zAVPeWk8YALMh4AVjO56JsA7QDyjpWEEEEAAAQQQQMBYAQIQY0uTXMd69uwpGnZoiFGjRo3ixp555hm5+OKL5d1335WTTz7ZevSV3g0yb948qVmzprXf4sWLpW3bttajs4YPH55cRyo4mpege0ZLwxEWYJErwsX3aei/Lt8gxz38taSliXxx7VHSuuGuf3N86pJ1WuaCn/qc2zQB5oNpFaE/fgkQgPglz3kRQAABBBBAAAH/BAhA/LP39Mzvv/++nHLKKXLIIYdYQUaDBg1k8uTJoi83b9Gihfzwww+SnZ1tvSC9S5cu0rlzZxk8eLBs27ZNhg4dKmvWrJHp06db7w7xaiMA8UqWdqMswCJXlKvvz9iveHWafPjjcjlh/6by+Dmd/elEGWdlLhhTCjpigADzwYAi0AUjBAhAjCgDnUAAAQQQQAABBFIqQACSUu7Unkwff6V3cPz444/Wi/6aN28uJ510kgwZMkTq169f3JmpU6fKoEGDZNKkSZKZmSl698iIESOkdevWnnaYAMRTXhqPqACLXBEtvE/Dnr9qkxzzwJdSWCjy8dVHyD5Na/nUk91Py1wwphR0xAAB5oMBRaALRggQgBhRBjqBAAIIIIAAAgikVIAAJKXcnCxWgACE6wEB9wVY5HLflBbLF7j+zRny1tSlcsw+jeTZAV2MomIuGFUOOuOzAPPB5wJwemMECECMKQUdQQABBBBAAAEEUiZAAJIyak5UWoAAhGsCAfcFWORy35QWyxZYsmaL9BgxXvIKCuWdyw+XA1vUNYqKuWBUOeiMzwLMB58LwOmNESAAMaYUdAQBBBBAAAEEEEiZAAFIyqg5EQEI1wAC3guwyOW9MWcoErjl3Z/kP5MXS/c2DeQ/Fx1iHAtzwbiS0CEfBZgPPuJzaqMECECMKgedQQABBBBAAAEEUiJAAJISZk5SlgB3gHBdIOC+AItc7pvS4u4CKzdsk+73jZPcvAIZ/c9D5bDWu94rZYoXc8GUStAPEwSYDyZUgT6YIEAAYkIV6AMCCCCAAAIIIJBaAQKQ1HpzthgBAhAuBwTcF2CRy31TWtxd4O7//SLPfL1QDmpZV9669DBJS0szjom5YFxJ6JCPAswHH/E5tVECBCBGlYPOIIAAAggggAACKREgAEkJMycpS4AAhOsCAfcFWORy35QWSwqs2Zwr3YaPla078uX5C7pIj/aNjCRiLhhZFjrlkwDzwSd4TmucAAGIcSWhQwgggAACCCCAgOcCBCCeE3OC8gQIQLg2EHBfgEUu901psaTAyM9my6Nj58m+zWrJB1d0N/LuD+0xc4ErF4FdAswHrgYEigQIQLgSEEAAAQQQQACB6AkQgESv5saMmADEmFLQkRAJsMgVomIaOJQN23ZYd39s3JYnT57bWY7br6mBvSzqEnPB2NLQMR8EmA8+oHNKIwUIQIwsC51CAAEEEEAAAQQ8FSAA8ZSXxisSIADh+kDAfQEWudw3pcVdAo+Pmyf3fzpb2jSqIZ9dc6Skp5v37g+7t8wFrlwEdgkwH7gaECgSIADhSkAAAQQQQAABBKInQAASvZobM2ICEGNKQUdCJMAiV4iKadhQtuTmSfd7x4m+A+TBMzvJ3w7c07AeluwOc8Ho8tC5FAswH1IMzumMFSAAMbY0dAwBBBBAAAEEEPBMgADEM1oarkyAAKQyIX6OQPwCLHLFb8YRzgSem7BQ7vzwF2lRL0fGXneUZGakOzvQp72YCz7Bc1ojBZgPRpaFTvkgQADiAzqnRAABBBBAAAEEfBYgAPG5AFE+PQFIlKvP2L0SYJHLK9lot7s9L1+OvG+c/LFhuww7dT85u2sL40GYC8aXiA6mUID5kEJsTmW0AAGI0eWhcwgggAACCCCAgCcCBCCesNKoEwECECdK7INAfAIscsXnxd7OBF75dpHc/M5MaVKrqnx549FSJTPD2YE+7sVc8BGfUxsnwHwwriR0yCcBAhCf4DktAggggAACCCDgowABiI/4UT81AUjUrwDG74UAi1xeqEa7zR35BdJjxHhZunarDD2xo1zYvVUgQJgLgSgTnUyRAPMhRdCcxngBAhDjS0QHEUAAAQQQQAAB1wUIQFwnpUGnAgQgTqXYDwHnAixyObdiT2cC/526VK57c4bUr54tEwb1lGrZ5t/9oSNjLjirL3tFQ4D5EI06M8rKBQhAKjdiDwQQQAABBBBAIGwCBCBhq2iAxkMAEqBi0dXACLDIFZhSBaKjBQWF0vvBL2X+qs1y47Ht5fKj2wSi3wQggSkTHU2RAP82pAia0xgvQABifInoIAIIIIAAAggg4LoAAYjrpDToVIAAxKkU+yHgXIBFLudW7Fm5wEc/LZfLX5kmtapmyjeDe0rNqlmVH2TIHswFQwpBN4wQYD4YUQY6YYAAAYgBRaALCCCAAAIIIIBAigUIQFIMzul2CRCAcDUg4L4Ai1zum0a1xcLCQjnhkQnyy/INclXPNnJtn/aBomAuBKpcdNZjAeaDx8A0HxgBApDAlIqOIoAAAggggAACrgkQgLhGSUPxChCAxCvG/ghULsAiV+VG7OFMYOysP+TCF76XnOwM+WZQT6lbPdvZgYbsxVwwpBB0wwgB5oMRZaATBggQgBhQBLqAAAIIIIAAAgikWIAAJMXgnG6XAAEIVwMC7guwyOW+aRRb1Ls/TntyokxbvE4uPnJvuen4fQLHwFwIXMnosIcCzAcPcWk6UAIEIIEqF51FAAEEEEAAAQRcESAAcYWRRhIRIABJRI1jEKhYgEUurhA3BCbOXy3nPPOtZGemy4Qbe0ijWlXdaDalbTAXUsrNyQwXYD4YXiC6lzIBApCUUXMiBBBAAAEEEEDAGAECEGNKEb2OEIBEr+aM2HsBFrm8N47CGc55ZrJMnP+n9D+spfzfyfsGcsjMhUCWjU57JMB88AiWZgMnQAASuJLRYQQQQAABBBBAIGkBApCkCWkgUQECkETlOA6B8gVY5OLqSFZg2uK1cuoTEyUzPU2+vLGHNKtTLdkmfTmeueALOyc1VID5YGhh6FbKBQhAUk7OCRFAAAEEEEAAAd8FCEB8L0F0O0AAEt3aM3LvBFjk8s42Ki3/44UpMmbWSjnjoD3l/jM6BXbYzIXAlo6OeyDAfPAAlSYDKUAAEsiy0WkEEEAAAQQQQCApAQKQpPg4OBkBApBk9DgWgbIFWOTiykhG4Odl6+WERyZIeprIF9ceJXs3rJFMc74ey1zwlZ+TGybAfDCsIHTHNwECEN/oOTECCCCAAAIIIOCbAAGIb/ScmACEawAB9wVY5HLfNEot/uuVafK/n5bLSZ32kEfPPjDQQ2cuBLp8dN5lAeaDy6A0F1gBApDAlo6OI4AAAggggAACCQsQgCRMx4HJChCAJCvI8QjsLsAiF1dFogLzVm6S3g9+KYWFIp9cc4R0aFIr0aaMOI65YEQZ6IQhAswHQwpBN3wXIADxvQR0AAEEEEAAAQQQSLkAAUjKyTmhLUAAwrWAgPsCLHK5bxqVFq99Y7q8Pe136d2xsTzT/+DAD5u5EPgSMgAXBZgPLmLSVKAFCEACXT46jwACCCCAAAIIJCRAAJIQGwe5IUAA4oYibSBQUoBFLq6IRASWrNkiR48YL/kFhfLev7pJp+Z1EmnGqGOYC0aVg874LMB88LkAnN4YAQIQY0pBRxBAAAEEEEAAgZQJEICkjJoTlRYgAOGaQMB9ARa53DeNQos3v/OTvPLtYjmibQN5+R+HhGLIzIVQlJFBuCTAfHAJkmYCL0AAEvgSMgAEEEAAAQQQQCBuAQKQuMk4wC0BAhC3JGkHgV0CLHJxNcQrsGL9NjnyvnGSm18gr198qByyd/14mzByf+aCkWWhUz4JMB98gue0xgkQgBhXEjqEAAIIIIAAAgh4LkAA4jkxJyhPgACEawMB9wVY5HLfNOwt3vnhL/LchIXSZa+68ualh4dmuMyF0JSSgbggwHxwAZEmQiFAABKKMjIIBBBAAAEEEEAgLgECkLi42NlNAQIQNzVpC4EiARa5uBLiEfhz03bpfu842bojX164oIsc3b5RPIcbvS9zwejy0LkUCzAfUgzO6YwVIAAxtjR0DAEEEEAAAQQQ8EyAAMQzWhquTIAApDIhfo5A/AIscsVvFuUj7v90ljw+br7s16y2vH9FN0lLSwsNB3MhNKVkIC4IMB9cQKSJUAgQgISijAwCAQQQQAABBBCIS4AAJC4udnZTgADETU3aQqBIgEUurgSnAuu37pDuw8fKxu15Muq8g+TYfZs4PTQQ+zEXAlEmOpkiAeZDiqA5jfECBCDGl4gOIoAAAggggAACrgsQgLhOSoNOBQhAnEqxHwLOBVjkcm4V9T0fGztXRnw2R9o1riGfXH2kpKeH5+4PwsCoX92Mv7QA/zZwTSBQJEAAwpWAAAIIIIAAAghET4AAJHo1N2bEBCDGlIKOhEiARa4QFdPDoWzenifd7x0ra7fskIfPOkBOPqCZh2fzp2nmgj/unNVMAeaDmXWhV6kXIABJvTlnRAABBBBAAAEE/BYgAPG7AhE+PwFIhIvP0D0TYJHLM9pQNfzs1wvkrv/9Ki3r58iYa4+SzIz0UI1PB8NcCF1JGVASAsyHJPA4NFQCBCChKieDQQABBBBAAAEEHAkQgDhiYicvBAhAvFClzagLsMgV9Sug8vFv25EvR943TlZu3C7DT91PzuraovKDArgHcyGARaPLngkwHzyjpeGACRCABKxgdBcBBBBAAAEEEHBBgADEBUSaSEyAACQxN45CoCIBFrm4PioTeHnyIrn13ZnStHZV+fKGHpKdGb67P9SAuVDZlcDPoyTAfIhStRlrRQIEIFwfCCCAAAIIIIBA9AQIQKJXc2NGTABiTCnoSIgEWOQKUTE9GMqO/AI5+v7x8vu6rXL7SR3l791aeXAWM5pkLphRB3phhgDzwYw60Av/BQhA/K8BPUAAAQQQQAABBFItQACSanHOVyxAAMLFgID7AixyuW8aphbfmrpUrn9zhjSokS0TBvWUqlkZYRpeibEwF0JbWgaWgADzIQE0DgmlAAFIKMvKoBBAAAEEEEAAgQoFCEC4QHwTIADxjZ4Th1iARa4QFzfJoeUXFErvB76UBas3y6BjO8hlR7dOskWzD2cumF0fepdaAeZDar05m7kCBCDm1oaeIYAAAggggAACXgkQgHglS7uVChCAVErEDgjELcAiV9xkkTngwx+XyRWv/iC1q2XJhEE9pGbVrFCPnbkQ6vIyuDgFmA9xgrF7aAUIQEJbWgaGAAIIIIAAAgiUK0AAwsXhmwABiG/0nDjEAixyhbi4SQytsLBQjnv4a5m1YqNc3autDOzdLonWgnEocyEYdaKXqRFgPqTGmbOYL0AAYn6N6CECCCCAAAIIIOC2AAGI26K051iAAMQxFTsi4FiARS7HVJHa8Ytf/pCLXvpeqmdnyDeDe0qdnOzQj5+5EPoSM8A4BJgPcWCxa6gFCEBCXV4GhwACCCCAAAIIlClAAMKF4ZsAAYhv9Jw4xAIscoW4uAkOTe/++NsTE2X6knVyyVF7y5Dj9kmwpWAdxlwIVr3orbcCzAdvfWk9OAIEIMGpFT1FAAEEEEAAAQTcEiAAcUuSduIWIACJm4wDEKhUgEWuSokit8M381bLuc9+K1Uy02XCoJ7SsGaVSBgwFyJRZgbpUID54BCK3UIvQAAS+hIzQAQQQAABBBBAYDcBAhAuCt8ECEB8o+fEIRZgkSvExU1waGc9PUkmL1gjAw5rKXecvG+CrQTvMOZC8GpGj70TYD54Z0vLwRIgAAlWvegtAggggAACCCDghgABiBuKtJGQAAFIQmwchECFAixycYHECkxdtEZOe3KSZGWkyfgbekizOtUiA8RciEypGagDAeaDAyR2iYQAAUgkyswgEUAAAQQQQACBEgIEIFwQvgkQgPhGz4lDLMAiV4iLm8DQLnj+Oxk3e5WceXBzuff0/RNoIbiHMBeCWzt67r4A88F9U1oMpgABSDDrRq8RQAABBBBAAIFkBAhAktHj2KQECECS4uNgBMoUYJGLC8MWmLVigxz70NeSniYy9rqjZa8G1SOFw1yIVLkZbCUCzAcuEQSKBAhAuBIQQAABBBBAAIHoCRCARK/mxoyYAMSYUtCREAmwyBWiYiY5lFvfnSkvT14kx+3bRJ4876AkWwve4cyF4NWMHnsnwHzwzpaWgyVAABKsetFbBBBAAAEEEEDADQECEDcUaSMhAQKQhNg4CIEKBVjk4gJRgc3b8+SQe8bIpu158spFh0i3Ng0iB8NciFzJGXAFAswHLg8EigQIQLgSEEAAAQQQQACB6AkQgESv5saMmADEmFLQkRAJsMgVomImMZTR3y2WIW//JK0aVJcx1x4l6focrIhtzIWIFZzhVijAfOACQYAAhGsAAQQQQAABBBCIqgABSFQrb8C4CUAMKAJdCJ0Ai1yhK2ncAyosLJQTH50gPy/bIDcfv4/888i9424jDAcwF8JQRcbglgDzwS1J2gm6AHeABL2C9B8BBBBAAAEEEIhfgAAkfjOOcEmAAMQlSJpBIEaARS4uhxlL1snJj38j2RnpMvmmXlKvenYkUZgLkSw7gy5HgPnApYFAkQABCFcCAggggAACCCAQPQECkOjV3JgRE4AYUwo6EiIBFrlCVMwEhzLorR/l9e+XyCkH7CEPnXVggq0E/zDmQvBryAjcE2A+uGdJS8EWIAAJdv3oPQIIIIAAAgggkIgAAUgiahzjigABiCuMNIJACQEWuaJ9QazfukMOvWeMbN2RL29eeph02ateZEGYC5EtPQMvQ4D5wGWBQJEAAQhXAgIIIIAAAgggED0BApDo1dyYEROAGFMKOhIiARa5QlTMBIby4sTf5Lb3f5Z2jWvIp9ccKWlp0Xv5uc3GXEjgAuKQ0AowH0JbWgYWpwABSJxg7I4AAggggAACCIRAgAAkBEUM6hAIQIJaOfptsgCLXCZXx9u+6cvP+z70lcz5Y5Pc8de/yIDD9/L2hIa3zlwwvEB0L6UCzIeUcnMygwUIQAwuDl1DAAEEEEAAAQQ8EiAA8QiWZisXIACp3Ig9EIhXgEWueMXCs/+U39bIGaMmSbWsDPn25l5Sq2pWeAaXwEiYCwmgcUhoBZgPoS0tA4tTgAAkTjB2RwABBBBAAAEEQiBAABKCIgZ1CAQgQa0c/TZZgEUuk6vjbd+uee0HeXf6Mjnz4OZy7+n7e3uyALTOXAhAkehiygSYDymj5kSGCxCAGF4guocAAggggAACCHggQADiASpNOhMgAHHmxF4IxCPAIlc8WuHZd83mXOvl57n5BfL+Fd1k/z3rhGdwCY6EuZAgHIeFUoD5EMqyMqgEBAhAEkDjEAQQQAABBBBAIOACBCABL2CQu08AEuTq0XdTBVjkMrUy3vbr6a/myz0fzZL9mtWWD67s7u3JAtI6cyEghaKbKRFgPqSEmZMEQIAAJABFoosIIIAAAggggIDLAgQgLoPSnHMBAhDnVuyJgFMBFrmcSoVnv4KCQuk5crz89ucWGXbqfnJ21xbhGVwSI2EuJIHHoaETYD6ErqQMKEEBApAE4TgMAQQQQAABBBAIsAABiMvFKywslNWrV8uqVatk69at0qBBA2nYsKHk5OS4fKbgN0cAEvwaMgLzBFjkMq8mXvfom3mr5dxnv5UaVTLl25t6SfUqmV6fMhDtMxcCUSY6mSIB5kOKoDmN8QIEIMaXiA4igAACCCCAAAKuCxCAuEA6d+5cef311+Wrr76SSZMmyZYtW3ZrtW3btnLEEUdInz595JRTTpGsrCwXzhzsJghAgl0/em+mAItcZtbFy15d/spU+einFXL+oS3lzlP29fJUgWqbuRCoctFZjwWYDx4D03xgBAhAAlMqOooAAggggAACCLgmQACSBOWbb74pjz32mEyYMMFqRe/+0C09PV1q164t1apVkzVr1si2bduKz5KWlib16tWT/v37y7XXXivNmjVLogfBPpQAJNj1o/dmCrDIZWZdvOrVyg3b5PDhYyWvoFA+vvoI2adpLa9OFbh2mQuBKxkd9lCA+eAhLk0HSoAAJFDlorMIIIAAAggggIArAgQgCTCOGTNGBg8eLNOmTbNCj06dOsmJJ54oXbt2lS5dukjjxo1Fgw572759u/z888/y3XffWWHJBx98IBs3brQCkquuuspqSwOTqG0EIFGrOONNhQCLXKlQNuccj42dKyM+myMHtawr/73scHM6ZkBPmAsGFIEuGCPAfDCmFHTEZwECEJ8LwOkRQAABBBBAAAEfBAhAEkC37/C47LLLZMCAAdK+ffu4WtFAREOQRx99VL7++mu5/fbbZejQoXG1EYadCUDCUEXGYJoAi1ymVcS7/uQXFMqR942T39dtlQf6dZJTO+/p3ckC2DJzIYBFo8ueCTAfPKOl4YAJEIAErGB0FwEEEEAAAQQQcEGAACQBxDvvvNO6c8ONuzY0AFm3bp2cdNJJCfQk2IcQgAS7fvTeTAEWucysixe9GjvrD7nwhe+lTk6WTB7SS6pmZXhxmsC2yVwIbOnouAcCzAcPUGkykAIEIIEsG51GAAEEEEAAAQSSEiAASYqPg5MRIABJRo9jEShbgEWu6FwZ/3hhioyZtVIu6t5KbjmxY3QG7nCkzAWHUOwWCQHmQyTKzCAdCBCAOEBiFwQQQAABBBBAIGQCBCAhK2iQhkMAEqRq0degCLDIFZRKJddPfezVEfeOlYJCkTHXHSWtG9ZIrsEQHs1cCGFRGVLCAsyHhOk4MGQCBCAhKyjDQQABBBBAAAEEHAgQgDhASmaXgoICmT17tvWYqwYNGkjbtm2TaS5UxxKAhKqcDMYQARa5DCmEx9144LPZ8sjYeXLY3vVl9MWHeny2YDbPXAhm3ei1NwLMB29caTV4AgQgwasZPUYAAQQQQAABBJIVIABJVrCC44cNGyYjR46UtWvXFu/VtGlTue222+Sf//ynh2cORtMEIMGoE70MlgCLXMGqVyK93ZFfIN2Gj5WVG7fLY+ccKCfuv0cizYT+GOZC6EvMAOMQYD7EgcWuoRYgAAl1eRkcAggggAACCCBQpgABiEcXxs033ywagLRp00b69OkjNWvWlHnz5smHH34oubm5MmrUqMiHIAQgHl18NBtpARa5wl/+T2Yul0v/M00a1MiWiYN7SXZmevgHncAImQsJoHFIaAWYD6EtLQOLU4AAJE4wdkcAAQQQQAABBEIgQADiQRH1/2TWq1dPjjnmGHn77bclLS2t+Cw//fSTdO3aVVq2bCmzZs3y4OzBaZIAJDi1oqfBEWCRKzi1SrSn5z/3rXw9d7VcfnRrufHYDok2E/rjmAuhLzEDjEOA+RAHFruGWoAAJNTlZXAIIIAAAggggECZAgQgCV4YgwYNkqFDh0r16tV3a2HFihWyxx57yIsvvijnn3/+bj/v1KmT9V6Qbdu2JXj2cBxGABKOOjIKswRY5DKrHm735rfVm+XoEeNFc/WvbughzevluH2K0LTHXAhNKRmICwLMBxcQaSIUAgQgoSgjg0AAAQQQQAABBOISIACJi2vXzllZWdKkSRO577775Oyzzy7RSl5entStW1d69eol7777bomfzZw5Uw4++GDrDhANQaK8EYBEufqM3SsBFrm8kjWj3WEf/SpPfbVAjm7fUF64oKsZnTK0F8wFQwtDt3wRYD74ws5JDRQgADGwKHQJAQQQQAABBBDwWIAAJEHgGTNmyBVXXCHffPONHHnkkfLoo4/KfvvtV9ya/Q6Qdu3aSe/eva13gMyfP18++OAD686PJ554Qi699NIEzx6OwwhAwlFHRmGWAItcZtXDzd5sz8uXQ+8ZI2u37JBn+h8svTs2drP50LXFXAhdSRlQEgLMhyTwODRUAgQgoSong0EAAQQQQAABBBwJEIA4Yip/p5dfflkGDx4sq1atsgKNO++8U2rXrm0dcNddd8kDDzwg69atK26gefPmcvvtt8sFF1yQ5JmDfzgBSPBryAjME2CRy7yauNWj96b/Lle/Nl2a1q4qX9/YQzIzePl5RbbMBbeuPNoJgwDzIQxVZAxuCBCAuKFIGwgggAACCCCAQLAECEBcqNemTZvktttuk8cee0zq1Kkjw4YNkwsvvNBqWR+HpS871300/GjWrJkLZwxHEwQg4agjozBLgEUus+rhZm/6jZok3/22Rq45pq1cc0w7N5sOZVvMhVCWlUElKMB8SBCOw0InQAASupIyIAQQQAABBBBAoFIBApBKiZzv8Ouvv8qVV14pY8eOla5du1qBiL7vg61sAQIQrgwE3Bdgkct9UxNanPvHRun94FeSkZ4m3wzqKU1qVzWhW0b3gblgdHnoXIoFmA8pBud0xgoQgBhbGjqGAAIIIIAAAgh4JkAA4gHtm2++KTfccIMsXbrUetSV3hHSoEEDD84U7CYJQIJdP3pvpgCLXGbWJdle3f7+z/LCxN+kT8fG8nR/gnUnnswFJ0rsExUB5kNUKs04KxMgAKlMiJ8jgAACCCCAAALhEyAAcaGmmzdvFl3Mr1+/vmRnZ1stbt26tfgdINWqVbPeDXLZZZdJenpqn9k+YcIEueeee2TSpEnWy9f33HNP6d+/v9x6663FI582bZrceOONMnnyZMnMzJSePXvKiBEjZO+993ZBp/wmCEA85aXxiAqwyBW+wm/nWARaAAAgAElEQVTNzZeu93whG7flyYsXdpWj2jUM3yA9GBFzwQNUmgysAPMhsKWj4y4LEIC4DEpzCCCAAAIIIIBAAAQIQJIo0ocffihDhw6VGTNmWK1oeHDUUUfJyJEjZb/99rP+bv78+XL11VfLRx99JPvvv7/1WKzu3bsncVbnh7766qty/vnnS79+/eScc86RGjVqWP1ZtmyZ1W/d9P0k+riuAw44wHqZu4Yk+rO1a9fK9OnTpWFD7xbaCECc15I9EXAqwCKXU6ng7PfG90vkxrd+lOb1qsmX1/eQ9PS04HTex54yF3zE59TGCTAfjCsJHfJJgADEJ3hOiwACCCCAAAII+ChAAJIg/jvvvCOnn366pKWlWYFG06ZNZfbs2VZoUKtWLZkyZYq0bdu2uHUNS6655hpZuHChnH322XL//fdbx3i1/f7779K+fXvrbo8nnnii3NNoODJu3DgrGNF+67Zo0SKr7wMHDpR7773Xqy5ad83Url1b1q9fX3zuRE/G/7FPVI7jwibAXAhbRUVOfvwbmbFknQw6toNcdnTr8A3QoxExFzyCpdlACjAfAlk2Ou2BAAGIB6g0iQACCCCAAAIIGC5AAJJggTp16mTdPfHFF1/IEUccUdzKww8/bAUH+rirxx9/vETrubm5VqCgvzIyMqyFf6+2O+64Q26//Xb57bffpGXLlmWeJi8vzwoeNCQZNWpUiX369u1rhTVz5szxqosEIJ7J0nCUBVjkClf1Z/6+Xk58dIJkZaTJpCG9pEGNKuEaoIejYS54iEvTgRNgPgSuZHTYIwECEI9gaRYBBBBAAAEEEDBYgAAkweJUrVpVOnToYN3xEbtt2rTJChX0PRoajpS1LV68WK677jrRl6V7tfXq1cvqmz4Ga9CgQTJz5kypV6+enHrqqXLfffdZfdQ7VnQMGtRcfvnlJbqiL3HXR3lt2bJFdKxebNwB4oUqbUZdgEWucF0BQ97+SUZ/t1hO6rSHPHr2geEanMejYS54DEzzgRJgPgSqXHTWQwECEA9xaRoBBBBAAAEEEDBUgAAkwcK0aNHCuoNh3rx50qBBg+JWxowZI71795YzzzxTRo8enWDryR+mwYY+yiorK0uGDBkihx12mPVYrttuu006d+4sX3/9tfVi9G7duln9POuss0qcdNiwYXLTTTdZ7wsp71Fd27dvF/2V6KZ+zZs3l9WrV7vyCKzPP//cstcxsyEQVQFd5GIuhKP6+tLz7vd/KVty8+U/Fx4sh7SqF46BpWgUzIUUQXOaQAgwHwJRJjqZAgENQPT/27jxCN4UdJdTIIAAAggggAACCLggQACSIKK+KPyuu+6SVq1ayQUXXCCNGjWyHhf13HPPWcHIxx9/LH369Emw9eQPa9euncydO1c0yNCXm9ubPqJL30WiC6Q5OTlWAPLaa69ZgU3sZgcgy5cvlyZNmpTZIX3Elj5qK9lN71LRvrAhgAACCOwSmLAiTd5cmCGNqxXKkE75ksa7z7k8EEAAAQQQSEpA724/55xzCECSUuRgBBBAAAEEEEAgWAIEIAnWq6CgwLqz4tFHH5Vt27YVt6JBiIYHGor4uekdH5MnT5Zp06bJgQfuemyKhjT6cnR9D8nJJ5+c1COwuAPEzwpzbgTKFuBbvuG4MgoLC+WvT0yWWSs2yk3HtZcLDi/7XU7hGK03o2AueONKq8EUYD4Es2702n0B7gBx35QWEUAAAQQQQAAB0wUIQJKskP6P6J9++klWrlwp+lgsfTl6tWrVkmw1+cMvueQSefrpp3cLQOz3ftx///3WnSD6LpABAwbIk08+WeKkxx57rCxYsICXoCdfClpAIKUCPOc9pdyenWza4rVy6hMTpUpmunx7Uy+pk5Pt2bnC2jBzIayVZVyJCDAfElHjmDAK8A6QMFaVMSGAAAIIIIAAAhULEICE9Ar57LPPpG/fvnL33Xdb7/KwtwcffFCuvfZa6x0g3bt3tx59NX78eOtdJjVr1rR205e0t23bVgYOHCjDhw/3TIiXoHtGS8MRFmCRKxzFv+6NGfLfaUvltM57ysh+ncIxqBSPgrmQYnBOZ7QA88Ho8tC5FAoQgKQQm1MhgAACCCCAAAKGCBCAGFIIL7rx17/+VTQIueWWW+TQQw+V77//3npnxzHHHCMffPCBdcpZs2ZJly5drBej67tC9HFe+n6TNWvWyPTp06Vhw4ZedM1qkwDEM1oajrAAi1zBL/66LblyyD1jZHtegbx9+eHSuUXd4A/KhxEwF3xA55TGCjAfjC0NHUuxAAFIisE5HQIIIIAAAgggYIAAAUgCRXjjjTekX79+CRy5+yFLliwR/XX44Ye70l5sI1u3brUCD33JuL7MfI899pBzzz1XbrvtNqlSpUrxrlOnTpVBgwbJpEmTJDMzU3r27CkjRoyQ1q1bu96n2AYJQDzlpfGICrDIFfzCPzdhodz54S+yT9Na8tFV3SWNt58nVFTmQkJsHBRSAeZDSAvLsOIWIACJm4wDEEAAAQQQQACBwAsQgCRQwvT0dOnYsaN1x8Rpp52W0Ds/Zs6cKY888oi89NJL1iOq9K6LqG0EIFGrOONNhQCLXKlQ9u4c+vLzXg98KQtWbZa7TtlXzjuUl58nqs1cSFSO48IowHwIY1UZUyICBCCJqHEMAggggAACCCAQbAECkATq98QTT8idd95pvfg8JydHTj75ZDn++OOla9eu0qZNmzJb3LRpk/VC8gkTJsjrr78u/8/efYBZVZ1rHH+nDwyD9CaIIqggoIKKgihiA6PGLmI30eQaSzCxJCZGY3LVCEaNscXELmrUqFdFRQUBBcUCiFIFpDeROgww5T57TwApw5xzZpe11v7P8/BY2HuV37e/m8t6PWd7AYh30HXqqadqyJAh2muvvTJYid23EIDYXT9Wb6YAh1xm1iXVVY395jud+49xKsrP0cc3Hat6Bbmp3sp12wnQCzwSCGwVoB94GhCoEiAA4UlAAAEEEEAAAQSSJ0AAkmHN161bp7vvvlsPP/ywFi5cuOUrSrwXibdo0UKNGjXyv2Zq5cqV/vs0FixY4Ace3i/vEyTeC8pvuummUL76KsMtRX4bAUjk5EyYAAEOuewu8pXPfq7XJy3SwB576H9P62L3ZmJePb0QcwGY3igB+sGocrCYGAUIQGLEZ2oEEEAAAQQQQCAmAQKQWsJXVFT4LxT3PtXhfbpj/vz5Ox0xPz9fBx98sI4//nhdcsklatOmTS1ntv92AhD7a8gOzBPgkMu8mqS6ouVrN+jw29/TpvJKvX7VEeq8+26p3sp1OxGgF3gsENgqQD/wNCBQJUAAwpOAAAIIIIAAAggkT4AAJOCaey80//bbb7V8+XKVlpb6nwRp1qyZOnbsuM2LxwOe1srhCECsLBuLNlyAQy7DC7SL5T048hvd+dZUHdCmgV79RS97N2LIyukFQwrBMowQoB+MKAOLMECAAMSAIrAEBBBAAAEEEEAgYgECkIjBmW6rAAEITwMCwQtwyBW8aRQjVlRU6qjBIzRvxXr95cyuOvtgPiVYW3d6obaC3O+SAP3gUjXZS20ECEBqo8e9CCCAAAIIIICAnQIEIHbWzYlVE4A4UUY2YZgAh1yGFSTF5XwwfZku+tcnKi7M1Se/PVZ18nNSvJPLqhOgF3g2ENgqQD/wNCBQJUAAwpOAAAIIIIAAAggkT4AAJHk1N2bHBCDGlIKFOCTAIZedxbz8yU/1ztdLdHHPPXXLKfvbuQnDVk0vGFYQlhOrAP0QKz+TGyRAAGJQMVgKAggggAACCCAQkQABSETQTLOjAAEITwUCwQtwyBW8adgjLlq1XkfcOULlFZUaPuhIdWheHPaUiRifXkhEmdlkigL0Q4pQXOa8AAGI8yVmgwgggAACCCCAwA4CBCA8FLEJEIDERs/EDgtwyGVfce95d7rueXeGDt2rkV742eH2bcDQFdMLhhaGZcUiQD/Ews6kBgoQgBhYFJaEAAIIIIAAAgiELEAAEjIww1cvQADC04FA8AIccgVvGuaIZeUV/qc/Fq8u1b0DDtSPD9w9zOkSNTa9kKhys9kaBOgHHhEEqgQIQHgSEEAAAQQQQACB5AkQgCSv5sbsmADEmFKwEIcEOOSyq5jDv16iy578VI2K8jX2N31VkMvLz4OqIL0QlCTjuCBAP7hQRfYQhAABSBCKjIEAAggggAACCNglQABiV72cWi0BiFPlZDOGCHDIZUghUlzGxY99opHTlulnR7bTb07smOJdXJaKAL2QihLXJEWAfkhKpdlnTQIEIDUJ8fsIIIAAAggggIB7AgQg7tXUmh0RgFhTKhZqkQCHXPYUa96KEh151whVVkojf91HezYpsmfxFqyUXrCgSCwxMgH6ITJqJjJcgADE8AKxPAQQQAABBBBAIAQBApCAUHv06KFLLrlEAwYMUIMGDQIa1e1hCEDcri+7i0eAQ6543DOZ9S9vTdUDI79R7w5N9NRPemQyBPfsQoBe4PFAYKsA/cDTgECVAAEITwICCCCAAAIIIJA8AQKQgGqenZ2trKws5efn68c//rEuvvhinXDCCf6/42fnAgQgPBkIBC/AIVfwpmGMuLGsQj3veE/L127UQ+d3U7/OLcOYJtFj0guJLj+b306AfuCRQIAAhGcAAQQQQAABBBBIqgABSECVf/HFF/XEE0/o7bffVllZmR98tGjRQhdccIEuuugidezId7tvT00AEtDDxzAI/ECAQy47HofXJy3Ulc9+oWbFBfrwxr7Ky8m2Y+EWrZJesKhYLDV0AfohdGImsESAT4BYUiiWiQACCCCAAAIIBChAABIgpjfUsmXL9PTTT/thyKRJk/zRvTDkkEMO8T8VwldkbQUnAAn44WM4BCRxyGXHY3DuI+M0dtZ3urpve117/L52LNqyVdILlhWM5YYqQD+EysvgFgkQgFhULJaKAAIIIIAAAggEJEAAEhDkzobxApDHH39cQ4cO1ZIlS7b5iizvUyH9+vVL9FdkEYCE+PAxdGIFOOQyv/Qzl67VsXd/oOwsafQNfbV7gzrmL9rCFdILFhaNJYcmQD+ERsvAlgkQgFhWMJaLAAIIIIAAAggEIEAAEgBiTUOUl5dr2LBh/qdCXn31VXn/7P14X5F14YUX6vLLL9dee+1V0zDO/T4BiHMlZUMGCHDIZUARaljCba9/rX+Oma1jOzbToxcdYv6CLV0hvWBp4Vh2KAL0QyisDGqhAAGIhUVjyQgggAACCCCAQC0FCEBqCZjK7d9//73/KRAvABk/frx/S05OzpYgJDc3Vz/96U/117/+VQUFBakM6cQ1BCBOlJFNGCbAIZdhBdluOaWbynXY7e9pZckm/evig9V3v+ZmL9ji1dELFhePpQcuQD8ETsqAlgoQgFhaOJaNAAIIIIAAAgjUQoAApBZ4u7r1h5/6eP3117Vx40ZVVlaqc+fOuuSSS/yXo3v/D/ijjz6qRx55RGvXrtU111yju+++O6QVmTcsAYh5NWFF9gtwyGV2DV/+fL6ufWGi/7VXo64/Wjne92DxE4oAvRAKK4NaKkA/WFo4lh24AAFI4KQMiAACCCCAAAIIGC9AABJwiSZOnOh/0uPZZ5/1X4juhR677bab//LzSy+91H8Z+vY/s2fPVpcuXVS/fn0tXLgw4BWZOxwBiLm1YWX2CnDIZXbtznjwI3327ff69fH76Mq+HcxerOWroxcsLyDLD1SAfgiUk8EsFiAAsbh4LB0BBBBAAAEEEMhQgAAkQ7jtb/O+vsoLPr788ks/9MjKylKfPn380OOMM85QYWHhLmfq1auXxo0bt+VrsQJaltHDEIAYXR4WZ6kAh1zmFm7KotXqf+9o5WZn6aMb+6pZ/V3/74K5O7FjZfSCHXVildEI0A/RODOL+QIEIObXiBUigAACCCCAAAJBCxCABCSanZ3tj7THHnvooosu8r/mas8990x59F/+8pfyPj0yYsSIlO+x/UICENsryPpNFOCQy8SqVK3p969M1lPjvtWJXVrogfO6m7tQR1ZGLzhSSLYRiAD9EAgjgzggQADiQBHZAgIIIIAAAgggkKYAAUiaYNVdfu655/qf9jj22GP9T3/wU7MAAUjNRlyBQLoCHHKlKxbN9es2lKnH/76ntRvK9MxPe6hX+ybRTJzgWeiFBBefre8gQD/wUCBQJUAAwpOAAAIIIIAAAggkT4AAJHk1N2bHBCDGlIKFOCTAIZeZxRz6yVz95uUvtVeTIr137VHK5uXnoReKXgidmAksEqAfLCoWSw1VgAAkVF4GRwABBBBAAAEEjBQgAAmoLN4fLBctWqSioiI1bty42lG9/6d73bp1atWqlXJzcwOa3c5hCEDsrBurNluAQy4z63PS30Zr8oLVuunEjrrsyHZmLtKxVdELjhWU7dRKgH6oFR83OyRAAOJQMdkKAggggAACCCCQogABSIpQNV32wAMP6KqrrtKQIUPkvc+jup977rlHv/rVr/Tggw/q8ssvr2lYp3+fAMTp8rK5mAQ45IoJfhfTTpq/Uqfc/6Hyc7M17jfHqFFRvnmLdHBF9IKDRWVLGQvQDxnTcaNjAgQgjhWU7SCAAAIIIIAAAikIEICkgJTKJX379tXo0aO1bNkyNWjQoNpbVq5cqaZNm6pPnz4aPnx4KkM7ew0BiLOlZWMxCnDIFSN+NVPf8OIkPf/pPJ16YCvdM+Ag8xbo6IroBUcLy7YyEqAfMmLjJgcFCEAcLCpbQgABBBBAAAEEahAgAAnoEdl9991VUFCgWbNm1Thiu3btVFZWprlz59Z4rcsXEIC4XF32FpcAh1xxye983lXrN+mw/31P6zeV698/P1yH7NnIrAU6vBp6weHisrW0BeiHtMm4wVEBAhBHC8u2EEAAAQQQQACBXQgQgAT0eBQWFuqAAw7Qxx9/XOOIPXr00KRJk7R+/foar3X5AgIQl6vL3uIS4JArLvmdz/vER3P0h9e+0j7N6+ntXx6prKwssxbo8GroBYeLy9bSFqAf0ibjBkcFCEAcLSzbQgABBBBAAAEECEDCfwa8T4B4f7hcunRpjZM1a9ZM2dnZWrx4cY3XunwBAYjL1WVvcQlwyBWX/I7zVlZW6oR7Rmn6krW69ZT9dVHPPc1ZXAJWQi8koMhsMWUB+iFlKi50XIAAxPECsz0EEEAAAQQQQGAnAnwCJKDH4vTTT9err76qp59+Wueee261oz733HMaOHCgTj75ZP/6JP8QgCS5+uw9LAEOucKSTX/c8XNW6KyHxqpOXo4+vukY1S/MS38Q7shYgF7ImI4bHRSgHxwsKlvKSIAAJCM2bkIAAQQQQAABBKwWIAAJqHzeC81POOEEFRUV6eGHH/ZDju1/hg4dqssvv1wlJSV644031K9fv4Bmt3MYAhA768aqzRbgkMuc+vzyuS/0yoSFOufgNrrzzK7mLCwhK6EXElJotpmSAP2QEhMXJUCAACQBRWaLCCCAAAIIIIDAdgIEIAE+EldeeaUeeOAB/zveW7VqpUMPPVQNGjTQypUrNX78eC1YsEDeV6JcccUVuv/++wOc2c6hCEDsrBurNluAQy4z6rNi3Ub/5ecbyyv02pW91LV1AzMWlqBV0AsJKjZbrVGAfqiRiAsSIkAAkpBCs00EEEAAAQQQQOAHAgQgAT8O9957r/70pz/J+3+ut/9p0qSJfv/73+uqq64KeFY7hyMAsbNurNpsAQ65zKjPI6O+0f++OVVddt9N/3fVEWYsKmGroBcSVnC2u0sB+oEHBIEqAQIQngQEEEAAAQQQQCB5AgQgIdS8tLRUH374oaZMmSLvkL+4uFj777+/evXqpYKCghBmtHNIAhA768aqzRbgkCv++lRUVOqYuz/Q7OXrdMfpXTTg0D3iX1QCV0AvJLDobLlaAfqBhwMBAhCeAQQQQAABBBBAIKkCBCBJrbwB+yYAMaAILME5AQ654i/phzOX67xHP1a9glx9/NtjVFSQG/+iErgCeiGBRWfLBCA8AwjUIMAnQHhEEEAAAQQQQACB5AkQgCSv5sbsmADEmFKwEIcEOPSNv5hXPPOZ3vxysS44rK1uO7Vz/AtK6ArohYQWnm3vVIB+4MFAoEqAAIQnAQEEEEAAAQQQSJ4AAUgINa+oqNCMGTO0YsUKeX/grO7nyCOPDGF2e4YkALGnVqzUHgEOueKt1dLVpep5x/sqq6jUsGt6q2PL+vEuKMGz0wsJLj5b30GAfuChQIAAhGcAAQQQQAABBBBIqgABSICVX7ZsmW688Ua98MILKikp2eXIWVlZKisrC3B2+4YiALGvZqzYfAEOueKt0f3vz9Dgd6are9uGeul/esa7mITPTi8k/AFg+9sI0A88EAgQgPAMIIAAAggggAACSRUgAAmo8t7HqQ855BB9++23at26tVatWqU1a9aoZ8+emjdvnhYsWKDy8nLVqVNHhx56qD/riBEjAprdzmEIQOysG6s2W4BDrvjqU15RqSP/MkILVq7X3WcfoNO7tY5vMczsfwLzzTff1Iknnqi8vDxEEEi0AP2Q6PKz+R8I8BVYPA4IIIAAAggggEDyBAhAAqr5DTfcoLvuuktXXXWV7r33XvXu3VsfffSRH3p4P97XYQ0ePFhDhgzRueeeq8cffzygme0dhgDE3tqxcnMFOOSKrzbvT12iSx//VA3q5mncb45RYV5OfIthZgIQngEEfiDA/zbwOCBQJUAAwpOAAAIIIIAAAggkT4AAJKCad+rUyf/0x6JFi1S/fv0dApDN0zz55JO65JJL9Le//U1XXHFFQLPbOQwBiJ11Y9VmC3DIFV99fvL4eL03dal+esRe+t1JneJbCDP7AvQCDwICWwXoB54GBAhAeAYQQAABBBBAAIGkChCABFT5oqIi7bnnnvrqq6/8EY866iiNGTNGpaWlO3z1hvcVWc2bN9dnn30W0Ox2DkMAYmfdWLXZAhxyxVMf72uvet/5vioqpfd+dZT2blovnoUw6xYBeoGHAQECEJ4BBLYX4BMgPBMIIIAAAggggEDyBAhAAqp5gwYN1KFDB40fP94f8aSTTtKwYcM0Z84ctWnTZptZvHeFTJs2TV4AkOQfApAkV5+9hyXAoW9Ysrse9+53pum+92eq596N9exlh8WzCGbdRoBe4IFAgACEZwABAhCeAQQQQAABBBBAAAECkICegc6dO2vlypWaP3++P+K1117rvwvkqaee0sCBA7fMsm7dOu2+++6qrKz0X5Se5B8CkCRXn72HJcChb1iy1Y+7qbxCve54X0vXbND9Aw/SSV1bRb8IZtxBgF7goUCAAIRnAAECEJ4BBBBAAAEEEEAAAQKQgJ6BSy+91A87li1bJu/TICNGjNAxxxyjli1b6oknntDhhx+uJUuW6Fe/+pVee+01HXfccXrrrbcCmt3OYQhA7KwbqzZbgEPf6Ovz1uRF+vnTn6tJvXx9dOMxys/Njn4RzEgAwjOAwC4E+N8GHg8EqgT4CiyeBAQQQAABBBBAIHkCBCAB1fzVV1/Vaaed5ocdF1xwgT+q98/ev8/Kytoyi/fJj4KCAo0ePVoHH3xwQLPbOQwBiJ11Y9VmC3DIFX19Lvjnxxo9Y7mu6LO3ru+3X/QLYMadCtALPBgIbBWgH3gaECAA4RlAAAEEEEAAAQSSKkAAElDlKyoqtGjRIhUXF6t+/fr+qN4fNm+//XY9++yz/rtA6tSpoyOOOEK33nqrunXrFtDM9g5DAGJv7Vi5uQIcckVbm1nL1qrvkA/k5dyjrjtabRrVjXYBzFatAL3Aw4EAAQjPAALbC/AJEJ4JBBBAAAEEEEAgeQIEIMmruTE7JgAxphQsxCEBDn2jLea1z0/Qy18s0DH7NdM/Lz4k2smZbZcC9AIPCAIEIDwDCBCA8AwggAACCCCAAAIIEIAE9Az88Y9/9L/q6oYbblB+fn5Ao7o9DAGI2/Vld/EIcOgbnfvMpWt1/F8/UEWl9H9XHqEurXeLbnJmqlGAXqiRiAsSJEA/JKjYbHWXAnwChAcEAQQQQAABBBBIngABSEA1z8vL0z777KOvvvoqoBHdH4YAxP0as8PoBTjkis786qFf6LWJC3Vcp+b6x4XJfqdTdOqpz0QvpG7Fle4L0A/u15gdpiZAAJKaE1chgAACCCCAAAIuCRCABFTN1q1bq2XLlho/fnxAI7o/DAGI+zVmh9ELcMgVjfn0JWt0wj2jVFkpvXH1Edq/FZ/+iEY+9VnohdStuNJ9AfrB/Rqzw9QECEBSc+IqBBBAAAEEEEDAJQECkICqefHFF+uFF17wX4S+224chKXCSgCSihLXIJCeAIdc6XllevUvnvlcb3y5SP07t9CD53fPdBjuC1GAXggRl6GtE6AfrCsZCw5JgAAkJFiGRQABBBBAAAEEDBYgAAmoOHPmzFG3bt3Up08fPfPMM6pTp05AI7s7DAGIu7VlZ/EJcMgVvv2URavV/97RysqS3rrmSO3bojj8SZkhbQF6IW0ybnBYgH5wuLhsLS0BApC0uLgYAQQQQAABBBBwQoAAJKAyPvnkk5o6daruuusuNWvWTGeccYY6duyooqKiame48MILA5rdzmEIQOysG6s2W4BDrvDrc/mTn+qdr5fopK4tdf/AbuFPyAwZCdALGbFxk6MC9IOjhWVbaQsQgKRNxg0IIIAAAggggID1AgQgAZUwOztbWVlZqvS+EF7y/76mn/Ly8poucfr3CUCcLi+bi0mAQ65w4b+cv0on3z/G//TH8EFHqn0zPv0Rrnjmo9MLmdtxp3sC9IN7NWVHmQkQgGTmxl0IIIAAAggggIDNAgQgAVXPewdIKqHHD6d77LHHAprdzmEIQOysG6s2W4BDrnDr85PHx+u9qUt16oGtdM+Ag8KdjNFrJUAv1IqPmx0ToB8cKyjbyViAACRjOm5EAAEEEEAAAQSsFSAAsbZ09i+cAMT+GrID8wQ45B/Uv+0AACAASURBVAqvJhPmrdSpf/9Q2VnSu9cepXZN64U3GSPXWoBeqDUhAzgkQD84VEy2UisBApBa8XEzAggggAACCCBgpQABiJVlc2PRBCBu1JFdmCXAIVd49bjoX5/og+nLdGb31hp81gHhTcTIgQjQC4EwMogjAvSDI4VkG7UWIACpNSEDIIAAAggggAAC1gkQgFhXMncWTADiTi3ZiTkCHHKFU4vPvl2hMx4cq5zsLI34VR/t0bhuOBMxamAC9EJglAzkgAD94EAR2UIgAgQggTAyCAIIIIAAAgggYJUAAUhA5XryySfTHunCCy9M+x6XbiAAcama7MUUAQ65wqnE+Y9+rDEzl2vAIW10xxldw5mEUQMVoBcC5WQwywXoB8sLyPIDEyAACYySgRBAAAEEEEAAAWsECEACKlV2dnbKL0GvrKz0ry0vLw9odjuHIQCxs26s2mwBDrmCr8/Hs77TOY+MU15Olt7/VR+1acSnP4JXDn5EeiF4U0a0V4B+sLd2rDxYAQKQYD0ZDQEEEEAAAQQQsEGAACSgKl188cXVBiDr1q3TzJkzNXHiROXl5enMM8/0//rYY48FNLudwxCA2Fk3Vm22AIdcwddnwCNjNW7WCp3XYw/9+bQuwU/AiKEI0AuhsDKopQL0g6WFY9mBCxCABE7KgAgggAACCCCAgPECBCARlujTTz+VF5Q0bdpU77zzjh+CJPmHACTJ1WfvYQlwyBWs7EffLNfAf3ys/Jxsjbyuj1o1qBPsBIwWmgC9EBotA1soQD9YWDSWHIoAAUgorAyKAAIIIIAAAggYLUAAEnF5ZsyYoY4dO+qmm27SrbfeGvHsZk1HAGJWPViNGwIccgVXR+/rCs9+eKzGz/leFx3eVrf+uHNwgzNS6AL0QujETGCRAP1gUbFYaqgCBCCh8jI4AggggAACCCBgpAABSAxl6dq1q0pKSvyvxUryDwFIkqvP3sMS4JArONlR05fpwn99ovzcbI2+/mg1r18Y3OCMFLoAvRA6MRNYJEA/WFQslhqqAAFIqLwMjgACCCCAAAIIGClAABJDWbxPgMyZM0fr16+PYXZzpiQAMacWrMQdAQ65gqml9+mP0x74SBPmrdSlvfbSzSd3CmZgRolMgF6IjJqJLBCgHywoEkuMRIAAJBJmJkEAAQQQQAABBIwSIACJuBzjx4/X4Ycfrt13313ffvttxLObNR0BiFn1YDVuCHDIFUwdR0xdqkseH6/CvGyNuv5oNSvm0x/ByEY3Cr0QnTUzmS9AP5hfI1YYjQABSDTOzIIAAggggAACCJgkQAASUDVGjRpV7Ujef0m8bNkyeeHHP/7xD61atUrXXnut7rrrroBmt3MYAhA768aqzRbgkKv29fH+b/Yp93+oLxes0uVHttNvT+xY+0EZIXIBeiFyciY0WIB+MLg4LC1SAQKQSLmZDAEEEEAAAQQQMEKAACSgMmRnZysrK2uXo3mHat7Pscceq1deeUV169YNaHY7hyEAsbNurNpsAQ65al+f4V8v0WVPfqq6+Tn+uz8a1yuo/aCMELkAvRA5ORMaLEA/GFwclhapAAFIpNxMhgACCCCAAAIIGCFAABJQGfr06VNtAOIFI0VFRWrXrp369++vfv36BTSr3cMQgNhdP1ZvpgCHXLWrS0VFpX70tzGasmi1ruizt67vt1/tBuTu2ATohdjomdhAAfrBwKKwpFgECEBiYWdSBBBAAAEEEEAgVgECkFj5kz05AUiy68/uwxHgkKt2rm9NXqSfP/256hXk+p/+aFiUX7sBuTs2AXohNnomNlCAfjCwKCwpFgECkFjYmRQBBBBAAAEEEIhVgAAkVv5kT04Akuz6s/twBDjkytzV+/RH/3tHa9qSNbq6b3tde/y+mQ/GnbEL0Auxl4AFGCRAPxhUDJYSqwABSKz8TI4AAggggAACCMQiQAASCzuTegIEIDwHCAQvwCFX5qavT1qoK5/9QsWFuRpzfV/tVjcv88G4M3YBeiH2ErAAgwToB4OKwVJiFSAAiZWfyRFAAAEEEEAAgVgECEACYn/iiSd06aWX6g9/+INuvvnmake97bbbdMstt+ipp57SwIEDA5rdzmEIQOysG6s2W4BDrszqU15RqRPuGaWZS9dq0LH76JpjO2Q2EHcZI0AvGFMKFmKAAP1gQBFYghECBCBGlIFFIIAAAggggAACkQoQgATEfeKJJ+qdd97R/Pnz1aJFi2pHXbhwodq0aaMf/ehHeu211wKa3c5hCEDsrBurNluAQ67M6vPqhAW65rkJ2q1OnkbfcLTqF/Lpj8wkzbmLXjCnFqwkfgH6If4asAIzBAhAzKgDq0AAAQQQQAABBKIUIAAJSLtt27aqrKzU3Llzaxxxjz32UE5OjmbPnl3jtS5fQADicnXZW1wCHHKlL19WXqHj/jpKs5ev03Un7KtfHN0+/UG4wzgBesG4krCgGAXohxjxmdooAQIQo8rBYhBAAAEEEEAAgUgECEACYi4sLNSBBx6ocePG1Thijx49NGnSJK1fv77Ga12+gADE5eqyt7gEOORKX/7Fz+br1/+eqIZ1vU9/9FW9gtz0B+EO4wToBeNKwoJiFKAfYsRnaqMECECMKgeLQQABBBBAAAEEIhEgAAmIuXnz5srLy/O/Aqumn9atW2vDhg1atmxZTZc6/fsEIE6Xl83FJMAhV3rwm8ordMyQDzR3RYlu7L+ffn7U3ukNwNXGCtALxpaGhcUgQD/EgM6URgoQgBhZFhaFAAIIIIAAAgiEKkAAEhCv9w6Qt99+W2+99ZaOO+64akcdPny4TjjhBB177LH+O0OS/EMAkuTqs/ewBDjkSk/2+fFzdcNLX6pJvXyNuv5o1c3n0x/pCZp7Nb1gbm1YWfQC9EP05sxopgABiJl1YVUIIIAAAggggECYAgQgAen++9//1jnnnKOmTZvqP//5j3r27LnDyGPHjtVpp53mf/LjmWee0YABAwKa3c5hCEDsrBurNluAQ67U67OxrEJHDx6pBSvX63c/6qif9m6X+s1cabwAvWB8iVhghAL0Q4TYTGW0AAGI0eVhcQgggAACCCCAQCgCBCABsp5++ul65ZVXlJWVpcMOO8z/1aBBA61cudJ/N4j3y3tR+qmnnqqXX345wJntHIoAxM66sWqzBTjkSr0+z3z8rW76z2Q1LS7Q6OuPVmFeTuo3c6XxAvSC8SVigREK0A8RYjOV0QIEIEaXh8UhgAACCCCAAAKhCBCABMjq/eHy+uuv1wMPPCDv770fLwzxQg/vx3tHyJVXXqnbb79d+fn5Ac6c2lCPPvqoLrvsMhUVFWnt2rXb3PT555/7a/dCmtzcXPXt21eDBw9Wu3bh/RfRBCCp1Y2rEEhHgEOu1LQ2lJWrz10jtWhVqW45uZMu7rVXajdylTUC9II1pWKhEQjQDxEgM4UVAgQgVpSJRSKAAAIIIIAAAoEKEIAEylk12KJFi/Tmm29qypQp8g75i4uLtf/++8t7T0iLFi1CmLHmIRcsWOCvwQs/Vq1atU0AMnXqVB166KE68MADdeONN6q0tFQ333yzvv/+e02YMMH/Wq8wfghAwlBlzKQLcMiV2hPw5Ng5uvnVr9SifqFGXteHT3+kxmbVVfSCVeVisSEL0A8hAzO8NQIEINaUioUigAACCCCAAAKBCRCABEZp9kAnn3yy/2mURo0a6cUXX9wmADn77LM1YsQIffPNN6pfv76/kW+//VYdOnTQoEGDdOedd4ayOQKQUFgZNOECHHLV/ACUbirXUXeN0JLVG3TbqZ11wWFta76JK6wToBesKxkLDlGAfggRl6GtEiAAsapcLBYBBBBAAAEEEAhEgAAkEEazB3n66ad1xRVX6Ouvv9bvfve7bQKQsrIyP/S48MIL9dBDD22zkRNOOEGzZ8/W9OnTQ9kgAUgorAyacAEOuWp+AP41Zrb++PrX2r1BHb3/66NUkMu7P2pWs+8KesG+mrHi8AToh/BsGdkuAQIQu+rFahFAAAEEEEAAgSAECECCUJQ0atQo3XLLLTrnnHP0s5/9rNpRvZDhhRde0G233aZevXoFNHv1wyxdulSdOnXSH//4Rz8Eufjii7cJQKZNm6b99ttPf//73/3f/+HPddddpyFDhqikpESFhYWBr5UAJHBSBkTAf/+Q9xV83lfuee8d4mdbgfUby9X7LyO0fO0G3X56F5176B4QOSpALzhaWLaVkQD9kBEbNzkoQADiYFHZEgIIIIAAAgggUIMAAUhAj4j3CYpnnnlGX3zxhbp27VrtqBMnTtRBBx2kSy65RP/85z8Dmr36Yc4880z/nSRjxozxvwJr+wDko48+8oOYoUOHasCAAdsM5L2s/be//a0WLlyoli1b7jDJhg0b5P3K9McLQNq0aaPly5dv+eqtTMfy/mA/fPhwHXfccRz6ZorIfU4I0Au7LuOjY+bozrenq3XDOnrnml7Ky8l2ou5sYkcBeoGnAoGtAvQDTwMCVQJeAOL9ucZ7J+Lmr/7FBgEEEEAAAQQQQMBtAQKQgOq7zz77+C8NX7ZsWY0jei8Vb9y4sbyXj4f589JLL2ngwIF+KON9CsT7qS4Aee655/xPr/zwZ3MA4gUoO3t5u/eJl1tvvbXWW3j22WdVt27dWo/DAAgggMCuBDaUS7d+nqN1ZVk6d+9yHdasEjAEEEAAAQQQSJCA98l2789HBCAJKjpbRQABBBBAAIHECxCABPQIFBUV+SHD+PHjaxzxkEMO8cOPNWvW1HhtphesXbtW7du31/nnn++/92Pzj/c1V6+99prmz5/vf1LC+2umX4HFJ0AyrQ73IRCeAP+Vb/W2D4+arcHDZ6hto7p66+qeyuXTH+E9iAaMTC8YUASWYIwA/WBMKVhIzAJ8AiTmAjA9AggggAACCCAQgwABSEDoDRs2VPPmzVP6VIcXOHifqvD+y6OwfubMmaO99tprl8P/+Mc/9t8H4n38+6KLLtKDDz64zfX9+vXTrFmzeAl6WEViXARCEOB73neOuqZ0k//uj5Ulm/TXcw7QaQe1DkGfIU0SoBdMqgZriVuAfoi7AsxvigDvADGlEqwDAQQQQAABBBCIToAAJCDrww8/XJ988ommTJki7+uwqvuZPn26/4mL7t27p/RpkUyXV1paqnHjxu1w+x133KEPPvhAw4YNU5MmTdS5c2f/q69GjhypmTNnqri42L9n7ty56tChgwYNGiTvnjB+eAl6GKqMmXQBDrl2/gT87b0ZGjJ8uto1LdLwQUcpJzsr6Y+K8/unF5wvMRtMQ4B+SAOLS50WIABxurxsDgEEEEAAAQQQ2KkAAUhAD8bgwYN1/fXX69BDD9Vbb72lBg0a7DDyypUr1b9/fz8o8d6v4V0f9c/27wDx5ve+jsv7Wq5u3brpxhtvlBee3HzzzVqxYoUmTJgg750lYfwQgIShyphJF+CQa8cnYNX6Tep95/taXVqm+849SKcc0Crpj0ki9k8vJKLMbDJFAfohRSguc16AAMT5ErNBBBBAAAEEEEBgBwECkIAeivXr1/uf6pg2bZqaNWumn/zkJ+rRo4cfhHjBh/dpjH/9619asmSJ/wmQzz77THXq1Alo9tSH2VkA4t3treeGG27Q2LFjlZubq759+8oLdfbee+/UB0/zSgKQNMG4HIEUBDjk2hHpr8On6973Zmif5vU07Joj+fRHCs+RC5fQCy5UkT0EJUA/BCXJOLYLEIDYXkHWjwACCCCAAAIIpC9AAJK+WbV3zJs3T6eddpo+//xzZWXt+PUqlZWVOvjgg/XSSy+pTZs2Ac5s51AEIHbWjVWbLcAh17b1WVmyUb3vHKE1G8r0wHnddGKXlmYXkNUFJkAvBEbJQA4I0A8OFJEtBCJAABIII4MggAACCCCAAAJWCRCABFyuiooKvfzyy3r11Vf994F4h/zeezX2339/nXrqqf6v7OzsgGe1czgCEDvrxqrNFuCQa9v6DH57mu4fMVP7tSjWm1f3Vjbv/jD7AQ5wdfRCgJgMZb0A/WB9CdlAQAIEIAFBMgwCCCCAAAIIIGCRAAGIRcVybakEIK5VlP2YIMAh19YqrFjnffrjfa3bWK6HL+iuE/ZvYUKJWENEAvRCRNBMY4UA/WBFmVhkBAIEIBEgMwUCCCCAAAIIIGCYAAFIxAVZsGCBnnnmGT399NOaNGlSxLObNR0BiFn1YDVuCHDItbWOdwybqoc++Eb7t6qv1686YqdfTehG1dnFzgToBZ4LBLYK0A88DQhUCRCA8CQggAACCCCAAALJEyAAiaDma9eu9d/78dRTT2nkyJHy3gXi/ZSXl0cwu7lTEICYWxtWZq8Ah1xVtVu2ZoOO/MsIrd9Urn9edLCO6djc3qKy8owE6IWM2LjJUQH6wdHCsq20BQhA0ibjBgQQQAABBBBAwHoBApCQSui9C+Sdd97xQw/vfSDr16/fEnwcdNBBOv/88zVo0KCQZrdjWAIQO+rEKu0S4JCrql5/ev1rPTpmtg5ovZte+UUvPv1h12McyGrphUAYGcQRAfrBkUKyjVoLEIDUmpABEEAAAQQQQAAB6wQIQAIu2RdffOGHHkOHDtXSpUu3hB4FBQW69tpr/eCjY8eOAc9q53AEIHbWjVWbLcAhl7R0dal6/2WENpRV6PFLDlGffZuZXTRWF4oAvRAKK4NaKkA/WFo4lh24AAFI4KQMiAACCCCAAAIIGC9AABJAiebPn++/18MLPqZMmeKP6H3NVcOGDXXWWWfpkUceUYsWLbRw4cIAZnNnCAIQd2rJTswR4JBLuuW1r/T4R3PUvW1Dvfjzw/n0hzmPZ6QroRci5WYywwXoB8MLxPIiEyAAiYyaiRBAAAEEEEAAAWMECEAyLIX3Xo8XX3zRDz0++OADP/DwftWpU0cnnXSSzjvvPPXv3195eXnKzs4mANmJMwFIhg8ftyGwC4GkH3ItWrVeR901UhvLKvTMT3uoV/smPC8JFUh6LyS07Gy7GgH6gUcDgSoBAhCeBAQQQAABBBBAIHkCBCAZ1ryoqEilpaV+6JGTk6O+ffv6ocfpp5+uevXqbTMqAcjOkQlAMnz4uA0BApBqBX7/ymQ9Ne5bHbpXIz1/+WF8+iPB3cKBb4KLz9Z3EKAfeCgQIADhGUAAAQQQQAABBJIqQACSYeW9UCMrK0sNGjTQ/fffrwEDBlR70EYAQgCS4WPGbQikLZDkQ64FK9erz10jtKm8Us9dfpgOa9c4bT9ucEcgyb3gThXZSVAC9ENQkoxjuwCfALG9gqwfAQQQQAABBBBIX4AAJH0z/44uXbroq6++8v/eC0Jatmypc845RwMHDlT37t23GZUAhAAkw8eM2xBIWyDJh1y/eflLDf1krnru3VjPXnZY2nbc4JZAknvBrUqymyAE6IcgFBnDBQECEBeqyB4QQAABBBBAAIH0BAhA0vPa5uqJEyfqySef1HPPPadFixZt+QRI+/bt/a/D8sIQ7+8JQAhAavGYcSsCaQkk9ZBr3ooSHT14pMoqKvXvnx+uQ/ZslJYbF7snkNRecK+S7CgIAfohCEXGcEGAAMSFKrIHBBBAAAEEEEAgPQECkPS8dnp1RUWF3n33XT3xxBN69dVXVVJSsiUM6datmz777DNegr4TOd4BEsDDxxAIbCeQ1EOu61+cqBc+na/eHZroqZ/04LlAQEntBUqPwM4E6AeeCwSqBAhAeBIQQAABBBBAAIHkCRCABFzzdevW6cUXX9RTTz2lkSNHygtHvB/vUyBHHXWULrjgAp1xxhkqLi4OeGb7hiMAsa9mrNh8gSQecs1Zvk7H3P2Byisq9fIVPdVtj4bmF4oVhi6QxF4IHZUJrBWgH6wtHQsPWIAAJGBQhkMAAQQQQAABBCwQIAAJsUgLFy7U008/7f+aPHmyP5P3vpDCwkKdcsopGjp0aIizmz80AYj5NWKF9gkk8ZDr2ucn6OUvFujofZvqsUsOta9orDgUgST2QiiQDOqEAP3gRBnZRAACBCABIDIEAggggAACCCBgmQABSEQFmzBhwpb3hSxevNgPQsrLyyOa3cxpCEDMrAurslsgaYdcM5eu1fF//UAVldJrV/ZS19YN7C4gqw9MIGm9EBgcAzkpQD84WVY2lYEAAUgGaNyCAAIIIIAAAghYLkAAEnEBva/Eeuedd7Z8MiTi6Y2ajgDEqHKwGEcEknbIdfXQL/TaxIU6tmNzPXrRwY5UkW0EIZC0XgjCjDHcFaAf3K0tO0tPgAAkPS+uRgABBBBAAAEEXBAgAHGhipbugQDE0sKxbKMFknTINX3JGp1wzyhVVkpvXH2E9m+1m9G1YXHRCiSpF6KVZTYbBegHG6vGmsMQIAAJQ5UxEUAAAQQQQAABswUIQMyuj9OrIwBxurxsLiaBJB1y/eKZz/XGl4vUv3MLPXh+95jEmdZUgST1gqk1YF3mCNAP5tSClcQrQAASrz+zI4AAAggggAACcQgQgMShzpy+AAEIDwICwQsk5ZBryqLV6n/vaGVlSW9dc6T2bVEcPCYjWi2QlF6wukgsPjIB+iEyaiYyXIAAxPACsTwEEEAAAQQQQCAEAQKQEFAZMjUBApDUnLgKgXQEknLI9bOnPtXbXy3RSV1b6v6B3dIh4tqECCSlFxJSTrZZSwH6oZaA3O6MAAGIM6VkIwgggAACCCCAQMoCBCApU3Fh0AIEIEGLMh4CUhIOuSYvWKWT/jbG//TH8EFHqn0zPv3Bs7+jQBJ6gbojkKoA/ZCqFNe5LkAA4nqF2R8CCCCAAAIIILCjAAEIT0VsAgQgsdEzscMCSTjk+ukT4/XulKU69cBWumfAQQ5Xk63VRiAJvVAbH+5NlgD9kKx6s9vqBQhAeDoQQAABBBBAAIHkCRCAJK/mxuyYAMSYUrAQhwRcP+SaOG+lfvz3D5WdJb177VFq17SeQ9VjK0EKuN4LQVoxlvsC9IP7NWaHqQkQgKTmxFUIIIAAAggggIBLAgQgLlXTsr0QgFhWMJZrhYDrh1wXP/aJRk5bpjO6tdaQsw+woiYsMh4B13shHlVmtVWAfrC1cqw7aAECkKBFGQ8BBBBAAAEEEDBfgADE/Bo5u0ICEGdLy8ZiFHD5kOuzb7/XGQ9+pJzsLL3/q6PUtnFRjNJMbbqAy71guj3rM0+AfjCvJqwoHgECkHjcmRUBBBBAAAEEEIhTgAAkTv2Ez00AkvAHgO2HIuDyIdf5j36sMTOX65yD2+jOM7uG4seg7gi43AvuVImdRCVAP0QlzTymCxCAmF4h1ocAAggggAACCAQvQAASvCkjpihAAJIiFJchkIaAq4dcH8/6Tuc8Mk55Od6nP/qoTaO6aahwaRIFXO2FJNaSPddegH6ovSEjuCFAAOJGHdkFAggggAACCCCQjgABSDpaXBuoAAFIoJwMhoAv4Ooh14BHxmrcrBU6r8ce+vNpXag2AjUKuNoLNW6cCxDYiQD9wGOBQJUAAQhPAgIIIIAAAgggkDwBApDk1dyYHROAGFMKFuKQgIuHXMO/XqLLnvxU+TnZGnldH7VqUMehirGVsARc7IWwrBjXfQH6wf0as8PUBAhAUnPiKgQQQAABBBBAwCUBAhCXqmnZXghALCsYy7VCwLVDrqWrS9Xv3tFasW6jfnZkO/3mxI5W1IFFxi/gWi/EL8oKbBagH2yuHmsPUoAAJEhNxkIAAQQQQAABBOwQIACxo05OrpIAxMmysqmYBVw65KqoqNRFj32i0TOWq1PL+vrPL3qqIDcnZmGmt0XApV6wxZx1mitAP5hbG1YWrQABSLTezIYAAggggAACCJggQABiQhUSugYCkIQWnm2HKuDSIdc/Rs3Sn9+cosK8bL1+VW+1b1YvVDsGd0vApV5wqzLsJg4B+iEOdeY0UYAAxMSqsCYEEEAAAQQQQCBcAQKQcH0ZfRcCBCA8HggEL+DKIdfkBat02gMfalN5pW4/vYvOPXSP4LEY0WkBV3rB6SKxucgE6IfIqJnIcAECEMMLxPIQQAABBBBAAIEQBAhAQkBlyNQECEBSc+IqBNIRcOGQq2RjmU66b4xmLV+nE/ZvrofO766srKx0GLgWAbnQC5QRgaAE6IegJBnHdgECENsryPoRQAABBBBAAIH0BQhA0jfjjoAECEACgmQYBH4g4MIh140vTdJz4+epRf1CDbumtxoW5VNjBNIWcKEX0t40NyBQjQD9wKOBQJUAAQhPAgIIIIAAAgggkDwBApDk1dyYHROAGFMKFuKQgO2HXG9+uUhXPPO5vA98PPPTHuq5dxOHqsNWohSwvReitGIu9wXoB/drzA5TEyAASc2JqxBAAAEEEEAAAZcECEBcqqZleyEAsaxgLNcKAZsPuRauXK9+94zS6tIyXdFnb13fbz8rzFmkmQI294KZoqzKZgH6webqsfYgBQhAgtRkLAQQQAABBBBAwA4BAhA76uTkKglAnCwrm4pZwNZDrvKKSp37j3H6ZPYKHdB6N734Pz2Vl5MdsybT2yxgay/YbM7azRWgH8ytDSuLVoAAJFpvZkMAAQQQQAABBEwQIAAxoQoJXQMBSEILz7ZDFbD1kOv+92do8DvTVZSfozeu7q09mxSF6sTg7gvY2gvuV4YdxiFAP8ShzpwmChCAmFgV1oQAAggggAACCIQrQAASri+j70KAAITHA4HgBWw85Ppi7vc686Gx8j4FMuSsA3RG99bBwzBi4gRs7IXEFYkNRyZAP0RGzUSGCxCAGF4glocAAggggAACCIQgQAASAipDpiZAAJKaE1chkI6AbYdca0o36Uf3jdHcFSU65YBWunfAgcry3oDODwK1FLCtF2q5XW5HYJcC9AMPCAJVAgQgPAkIIIAAAggggEDyBAhAkldzY3ZMAGJMKViIQwK2HXJd+/wEvfzFArVuWEdvXtNb9QvzHKoGW4lTwLZeiNOKud0XoB/crzE7TE2AACQ1J65CEeYPrAAAIABJREFUAAEEEEAAAQRcEiAAcamalu2FAMSygrFcKwRsOuR65YsF+uXzE5SdJf3754ere9tGVhizSDsEbOoFO0RZpc0C9IPN1WPtQQoQgASpyVgIIIAAAggggIAdAgQgdtTJyVUSgDhZVjYVs4Ath1xzvyvRifeN1toNZRp07D665tgOMcsxvWsCtvSCa+7sx0wB+sHMurCq6AUIQKI3Z0YEEEAAAQQQQCBuAQKQuCuQ4PkJQBJcfLYemoANh1xl5RU66+Gx+mLuSh2yZ0MNveww5eZkh2bCwMkUsKEXklkZdh2HAP0QhzpzmihAAGJiVVgTAggggAACCCAQrgABSLi+jL4LAQIQHg8Eghew4ZDr7nem6b73Z6q4MFfDrumt1g3rBg/BiIkXsKEXEl8kACIToB8io2YiwwUIQAwvEMtDAAEEEEAAAQRCECAACQGVIVMTIABJzYmrEEhHwPRDro9nfadz/zFOFZXS/QMP0kldW6WzPa5FIGUB03sh5Y1wIQIBCNAPASAyhBMCBCBOlJFNIIAAAggggAACaQkQgKTFxcVBChCABKnJWAhUCZh8yLWqZJP63ztKC1eV6qzurXXXWQdQNgRCEzC5F0LbNAMjUI0A/cCjgUCVAAEITwICCCCAAAIIIJA8AQKQ5NXcmB0TgBhTChbikICph1yVlZX6xbOf680vF2uvJkV6/aojVFSQ65A8WzFNwNReMM2J9SRDgH5IRp3ZZc0CBCA1G3EFAggggAACCCDgmgABiGsVtWg/BCAWFYulWiNg6iHXC+Pn6fqXJik3O0svX9FTXVs3sMaUhdopYGov2KnJqm0XoB9sryDrD0qAACQoScZBAAEEEEAAAQTsESAAsadWzq2UAMS5krIhAwRMPOT6ZtlanXTfGK3fVK4b+++nnx+1twFSLMF1ARN7wXVz9meuAP1gbm1YWbQCBCDRejMbAggggAACCCBgggABiAlVSOgaCEASWni2HaqAaYdcG8sqdPqDH2rygtXquXdjPf2THsrOzgrVgMER8ARM6wWqgkCcAvRDnPrMbZIAAYhJ1WAtCCCAAAIIIIBANAIEINE4M8tOBAhAeCwQCF7AtEOu29+coodHzVLDunkads2RarFbYfCbZkQEdiJgWi9QJATiFKAf4tRnbpMECEBMqgZrQQABBBBAAAEEohEgAInGmVkIQHgGEIhEwKRDrjEzluv8f37s7/uRC7rr+P1bRGLAJAh4Aib1AhVBIG4B+iHuCjC/KQIEIKZUgnUggAACCCCAAALRCRCARGfNTNsJ8AkQHgkEghcw5ZDru7Ub1P/e0Vq6ZoPO67GH/nxal+A3y4gI7ELAlF6gSAiYIEA/mFAF1mCCAAGICVVgDQgggAACCCCAQLQCBCDRejPbDwQIQHgcEAhewIRDrsrKSl325Kd6d8pStW9WT/935RGqk58T/GYZEQECEJ4BBFISMOF/G1JaKBchELIAAUjIwAyPAAIIIIAAAggYKEAAYmBRkrIkApCkVJp9RilgwiHXU2Pn6PevfqX8nGy98ote6tSqfpQEzIWAL2BCL1AKBEwRoB9MqQTriFuAACTuCjA/AggggAACCCAQvQABSPTmzPhfAQIQHgUEgheI+5Br2uI1OuX+MdpQVqGbT+qkS4/YK/hNMiICKQjE3QspLJFLEIhMgH6IjJqJDBcgADG8QCwPAQQQQAABBBAIQYAAJARUhkxNgAAkNSeuQiAdgTgPuUo3levH93+oaUvWqM++TfXYxYcoKysrneVzLQKBCcTZC4FtgoEQCEiAfggIkmGsFyAAsb6EbAABBBBAAAEEEEhbgAAkbTJuCEqAACQoScZBYKtAnIdct7z2lR7/aI6a1MvXsGuOVNPiAkqDQGwCcfZCbJtmYgSqEaAfeDQQqBIgAOFJQAABBBBAAAEEkidAAJK8mhuzYwIQY0rBQhwSiOuQ6/2pS3Tp45/6ko9fcoj67NvMIVW2YqNAXL1goxVrdl+AfnC/xuwwNQECkNScuAoBBBBAAAEEEHBJgADEpWpathcCEMsKxnKtEIjjkGvpmlL1v2e0vlu3UZf22ks3n9zJCisW6bZAHL3gtii7s1mAfrC5eqw9SAECkCA1GQsBBBBAAAEEELBDgADEjjo5uUoCECfLyqZiFoj6kKuiolIXPfaJRs9Yro4t6+uVX/RUQW5OzApMj4AUdS9gjoDJAvSDydVhbVEKEIBEqc1cCCCAAAIIIICAGQIEIGbUIZGrIABJZNnZdMgCUR9yPTp6lv70xhQV5mXr/648Qh2aF4e8Q4ZHIDWBqHshtVVxFQLxCNAP8bgzq3kCBCDm1YQVIYAAAggggAACYQsQgIQtzPjVChCA8HAgELxAlIdckxes0mkPfKhN5ZX682mddV6PtsFviBERyFAgyl7IcInchkBkAvRDZNRMZLgAAYjhBWJ5CCCAAAIIIIBACAIEICGgMmRqAgQgqTlxFQLpCER1yFWysUwn/22Mvlm2Tsd3aq6HL+iurKysdJbKtQiEKhBVL4S6CQZHICAB+iEgSIaxXoAAxPoSsgEEEEAAAQQQQCBtAQKQtMm4ISgBApCgJBkHga0CUR1y/eblLzX0k7lqXr9Ab11zpBoW5VMGBIwSiKoXjNo0i0GgGgH6gUcDgSoBAhCeBAQQQAABBBBAIHkCBCDJq7kxOyYAMaYULMQhgSgOud6avEg/f/pzeR/4eOYnPdSzfROHBNmKKwJR9IIrVuzDfQH6wf0as8PUBAhAUnPiKgQQQAABBBBAwCUBAhCXqmnZXghALCsYy7VCIOxDrkWr1qvfPaO1av0m/U+fvXVDv/2scGGRyRMIuxeSJ8qObRagH2yuHmsPUoAAJEhNxkIAAQQQQAABBOwQIACxo05OrpIAxMmysqmYBcI85CqvqNR5j47TuFkrdEDr3fTi//RUXk52zDtmegR2LhBmL2COgG0C9INtFWO9YQkQgIQly7gIIIAAAggggIC5AgQg5tbG+ZURgDhfYjYYg0CYh1x/HzFTd709TXXzc/Tm1b21Z5OiGHbIlAikJhBmL6S2Aq5CwBwB+sGcWrCSeAUIQOL1Z3YEEEAAAQQQQCAOAQKQONSZ0xcgAOFBQCB4gbAOub6Y+73OfGisvE+BDD7rAJ3ZvXXwi2dEBAIUCKsXAlwiQyEQmQD9EBk1ExkuQABieIFYHgIIIIAAAgggEIIAAUgIqAyZmgABSGpOXIVAOgJhHHKt3VCmE+8drbkrSnTyAa1034ADleW9AZ0fBAwWCKMXDN4uS0NglwL0Aw8IAlUCBCA8CQgggAACCCCAQPIECECSV3NjdkwAYkwpWIhDAmEccl37wgS9/PkC7d6gjt68prd2q5PnkBhbcVUgjF5w1Yp9uS9AP7hfY3aYmgABSGpOXIUAAggggAACCLgkQADiUjUt2wsBiGUFY7lWCAR9yPXqhAW65rkJys6Snv/Z4Tpkz0ZWOLBIBILuBUQRsFmAfrC5eqw9SAECkCA1GQsBBBBAAAEEELBDgADEjjo5uUoCECfLyqZiFgjykGveihL/q6/WbCjTNcd00KDj9ol5d0yPQOoCQfZC6rNyJQJmCtAPZtaFVUUvQAASvTkzIoAAAggggAACcQsQgMRdgQTPTwCS4OKz9dAEgjrkKiuv0NkPj9Xnc1eqe9uGev7yw5Sbkx3auhkYgaAFguqFoNfFeAjEIUA/xKHOnCYKEICYWBXWhAACCCCAAAIIhCtAABKuL6PvQoAAhMcDgeAFgjrkunv4dN333gwVF+T67/1o06hu8ItlRARCFAiqF0JcIkMjEJkA/RAZNRMZLkAAYniBWB4CCCCAAAIIIBCCAAFICKgMmZoAAUhqTlyFQDoCQRxyfTJ7hQY8MlYVldJ95x6kUw5olc4SuBYBIwSC6AUjNsIiEAhAgH4IAJEhnBAgAHGijGwCAQQQQAABBBBIS4AAJC0uLg5SgAAkSE3GQqBKoLaHXKtKNqn/vaO0cFWpzujWWkPOPgBaBKwUqG0vWLlpFo1ANQL0A48GAlUCBCA8CQgggAACCCCAQPIECECSV3NjdkwAYkwpWIhDArU55KqsrNSVQ7/QG5MWqW3junrj6t6qV5DrkA5bSZJAbXohSU7sNRkC9EMy6swuaxYgAKnZiCsQQAABBBBAAAHXBAhAXKuoRfshALGoWCzVGoHaHHL9+9N5uu7FScrNztKL/9NTB7ZpYM2+WSgC2wvUphfQRMA1AfrBtYqyn0wFCEAyleM+BBBAAAEEEEDAXgECEHtrZ/3KCUCsLyEbMFAg00OucbO+06WPj1fJxnJd329fXdGnvYG7Y0kIpC6QaS+kPgNXImCPAP1gT61YabgCBCDh+jI6AggggAACCCBgogABiIlVCWBN77//vp5++ml99NFHmjdvnho0aKCDDz5YN998s7p3777NDJ9//rmuv/56jRs3Trm5uerbt68GDx6sdu3aBbCS6ocgAAmVl8ETKpDuIdfGsgr99d3peuiDb1RZKfXcu7Ge+kkP5WRnJVSQbbsikG4vuLJv9oHAzgToB54LBKoECEB4EhBAAAEEEEAAgeQJEIA4WvOzzjrL/3/wvb926tRJy5Yt05AhQ/Tpp5/q7bff9kMO72fq1Kk69NBDdeCBB+rGG29UaWmpH5J8//33mjBhgpo2bRqaEAFIaLQMnGCBdA65vlm2Vtc894UmL1jti51zcBvdfHInFfHejwQ/Qe5sPZ1ecGfX7ASBnQvQDzwZCBCA8AwggAACCCCAAAJJFSAAcbTyS5cuVbNmzbbZ3dq1a9W+fXt17txZ7777rv97Z599tkaMGKFvvvlG9evX9//dt99+qw4dOmjQoEG68847QxMiAAmNloETLJDKIZf3svNnP5mr217/WqWbKtSgbp7uOL2L+nVumWA5tu6aQCq94Nqe2Q8C1QnQDzwbCBCA8AwggAACCCCAAAJJFSAASVjlvU9+LFiwQNOmTVNZWZkfelx44YV66KGHtpE44YQTNHv2bE2fPj00IQKQ0GgZOMECNR1yfbd2g2546Uu9O2WJr3RE+yYafNYBarFbYYLV2LqLAjX1got7Zk8IEIDwDCCwawG+AosnBAEEEEAAAQQQSJ4AAUiCar5q1Sq1bdvW//qrl19+2Q9B9ttvP/3973/XFVdcsY3Edddd539lVklJiQoLwzkYJQBJ0MPHViMT2NWh78hpS3Xdi5O0bM0G5edk+y87v7TXXsrmfR+R1YeJohMgAInOmpnMF6AfzK8RK4xGgAAkGmdmQQABBBBAAAEETBIgADGpGiGv5fzzz9fzzz/vv+zcexG694L0Xr16aejQoRowYMA2s99+++367W9/q4ULF6ply51/Lc6GDRvk/cr0xwtA2rRpo+XLl2/5+q1Mx/L+YD98+HAdd9xxysvLy3QY7kPAeoGd9ULppnLd9c4MPTlurr+/9k2LdPdZXdWxZbH1+2UDCFQnwP8u8GwgsFWAfuBpQKBKwAtAvD/beP9h2Oav/8UGAQQQQAABBBBAwG0BAhC367tld7///e/1pz/9SX/729905ZVX+v9+cwDy3HPP6ZxzztlGYnMAsmjRIrVo0WKnSrfccotuvfXWWgs+++yzqlu3bq3HYQAEENhRYME66ckZOVq8Psv/zd4tKnTKHhXKz0ELAQQQQAABBBBIloD36faBAwcSgCSr7OwWAQQQQAABBBIuQACSgAfACym8sOLPf/6z/6mOzT+1/QosPgGSgIeHLVonsPm/8j3m2GP17KeLdNc707WpvFKNi/J1x+n7q88+Ta3bEwtGIBMB/ov3TNS4x1UB+sHVyrKvdAX4BEi6YlyPAAIIIIAAAgjYL0AAYn8Nd7mDzeGHF4D84Q9/2ObazS9Bv+iii/Tggw9u83v9+vXTrFmzeAm6488H23NPwDvkGvrKm3p7ZXN9+M13/gaP2a+Z7jyzq5rUK3Bvw+wIgWoEeOcBjwYCWwXoB54GBKoEeAcITwICCCCAAAIIIJA8AQIQh2t+22236eabb9bvfvc7eX+/sx/vq69GjhypmTNnqri46n0Ac+fOVYcOHTRo0CDdcccdoQnxEvTQaBk4wQJvTJyv6/89QevKslSYl63f/aiTzuuxh7Kyqr4Cix8EkiLAgW9SKs0+UxGgH1JR4pokCBCAJKHK7BEBBBBAAAEEENhWgADE0SdiyJAh+vWvfy3vkxzbf/LD2/Jhhx3m73zq1Kk65JBD1K1bN914440qLS31Q5MVK1ZowoQJato0vK/LIQBx9OFjW7EIrNtQptte/1rPjZ/nz9+pZbHuO7eb2jerF8t6mBSBuAU48I27AsxvkgD9YFI1WEucAgQgceozNwIIIIAAAgggEI8AAUg87qHP2qdPH33wwQfVzlNZWbnl9z777DPdcMMNGjt2rHJzc9W3b18NHjxYe++9d6jrJAAJlZfBEyQwcd5K/fL5CZq9fJ28D3r0bVmh+y47XkV1+MqrBD0GbHU7AQ58eSQQ2CpAP/A0IFAlQADCk4AAAggggAACCCRPgAAkeTU3ZscEIMaUgoVYKlBeUakHR87UPe/OUFlFpVruVqi/nN5ZK6aO04knnqi8vDxLd8ayEai9AAe+tTdkBHcE6Ad3aslOaidAAFI7P+5GAAEEEEAAAQRsFCAAsbFqjqyZAMSRQrKNWATmf1+ia5+fqE/mrPDn/1GXlvrf07qobp705ptvEoDEUhUmNUmAA1+TqsFa4hagH+KuAPObIkAAYkolWAcCCCCAAAIIIBCdAAFIdNbMtJ0AAQiPBAKZCbw6YYF+95/JWrOhTEX5Obr1x511Rrfd/Redc8iVmSl3uSdAL7hXU3aUuQD9kLkdd7olQADiVj3ZDQIIIIAAAgggkIoAAUgqSlwTigABSCisDOqwwOrSTfr9K5P16oSF/i4P2qOB7jnnQLVtXLRl1xxyOfwAsLW0BOiFtLi42HEB+sHxArO9lAUIQFKm4kIEEEAAAQQQQMAZAQIQZ0pp30YIQOyrGSuOT+CT2Ss06PkJWrByvbKzpKuP6aArj26v3JzsbRbFIVd8NWJmswToBbPqwWriFaAf4vVndnMECEDMqQUrQQABBBBAAAEEohIgAIlKmnl2ECAA4aFAoGaBTeUVuvfdGXpg5ExVVEptGtXRPeccpO5tG+70Zg65ajblimQI0AvJqDO7TE2AfkjNiavcFyAAcb/G7BABBBBAAAEEENhegACEZyI2AQKQ2OiZ2BKB2cvX6ZfPfaGJ81f5Kz6jW2vdckonFRfmVbsDDrksKS7LDF2AXgidmAksEqAfLCoWSw1VgAAkVF4GRwABBBBAAAEEjBQgADGyLMlYFAFIMurMLtMXqKys1PPj5+nW//ta6zeVq35hrm4/vat+1LVljYNxyFUjERckRIBeSEih2WZKAvRDSkxclAABApAEFJktIoAAAggggAAC2wkQgPBIxCZAABIbPRMbLPD9uo268eVJevurJf4qD2vXSHeffaBaNaiT0qo55EqJiYsSIEAvJKDIbDFlAfohZSoudFyAAMTxArM9BBBAAAEEEEBgJwIEIDwWsQkQgMRGz8SGCoyesUy/emGilq7ZoLycLP36+H11We92yvbeep7iD4dcKUJxmfMC9ILzJWaDaQjQD2lgcanTAgQgTpeXzSGAAAIIIIAAAjsVIADhwYhNgAAkNnomNkygdFO57np7mv45Zra/snZNi3TfgIPUeffd0l4ph1xpk3GDowL0gqOFZVsZCdAPGbFxk4MCBCAOFpUtIYAAAggggAACNQgQgPCIxCZAABIbPRMbJDB9yRpdPfQLTV28xl/V+YftoZtO7KQ6+TkZrZJDrozYuMlBAXrBwaKypYwF6IeM6bjRMQECEMcKynYQQAABBBBAAIEUBAhAUkDiknAECEDCcWVUOwS8F50//tEc3T5sqjaWVahxUb7uPKOrju3UvFYb4JCrVnzc7JAAveBQMdlKrQXoh1oTMoAjAgQgjhSSbSCAAAIIIIAAAmkIEICkgcWlwQoQgATryWj2CCxdU6rr/j1JH0xf5i/6qH2a6q6zuqpZcWGtN8EhV60JGcARAXrBkUKyjUAE6IdAGBnEAQECEAeKyBYQQAABBBBAAIE0BQhA0gTj8uAECECCs2QkOwS8T328O2Wpbnhpklas26j83GzddGJHXXh4W2Vlpf6i813tlkMuO54FVhm+AL0QvjEz2CNAP9hTK1YargABSLi+jI4AAggggAACCJgoQABiYlUSsiYCkIQUOuHb9EKPKYvWaNjkRXrzy0X6Ztk6X2S/FsW679yDtE/z4kCFOOQKlJPBLBagFywuHksPXIB+CJyUAS0VIACxtHAsGwEEEEAAAQQQqIUAAUgt8Li1dgIEILXz425zBbzQY/KC1Xpz8iIN+3KR5nxXsmWx+TnZuqhnW/36hH1VkJvZi853tXMOucx9LlhZtAL0QrTezGa2AP1gdn1YXXQCBCDRWTMTAggggAACCCBgigABiCmVSOA6CEASWHSHt1xRUakJ81f6gcewyYs1//v1W3ZbkJutPvs21YldWqrvfs1UXJgXmgSHXKHRMrBlAvSCZQVjuaEK0A+h8jK4RQIEIBYVi6UigAACCCCAAAIBCRCABATJMOkLEICkb8YdZgl4ocdnc7/3v9rqrcmLtWhV6ZYF1snL8cOO/l1a6Oh9m6moIDeSxXPIFQkzk1ggQC9YUCSWGJkA/RAZNRMZLkAAYniBWB4CCCCAAAIIIBCCAAFICKgMmZoAAUhqTlxllkB5RaU+mb3Cf6eHF3osXbNhywKL8nN0TMfmOrFLCx21TzPVyQ/+K65q0uCQqyYhfj8pAvRCUirNPlMRoB9SUeKaJAgQgCShyuwRAQQQQAABBBDYVoAAhCciNgECkNjomThNgU3lFfp41gr/nR7vfLVYy9du3DJCcWGujuvUXP07t1TvDk1UmBd96PHD7XDIlWZxudxZAXrB2dKysQwE6IcM0LjFSQECECfLyqYQQAABBBBAAIFdChCA8IDEJkAAEhs9E6cgsLGsQh9+s9x/p8fwr5fo+5JNW+5qUDdPx3uhR5eW6rV3E+XnZqcwYjSXcMgVjTOzmC9AL5hfI1YYnQD9EJ01M5ktQABidn1YHQIIIIAAAgggEIYAAUgYqoyZkgABSEpMXBShQOmmco2Zsdz/pIcXeqwpLdsye+OifB2/fwv/660Oa9dYeTnmhB4/JOKQK8IHhqmMFqAXjC4Pi4tYgH6IGJzpjBUgADG2NCwMAQQQQAABBBAITYAAJDRaBq5JgACkJiF+PwqB9RvL9cH0pRo2ebHem7JUazdsDT2aFheo3/4t/BeZH7pnI+UaGnoQgETxpDCHbQIc+NpWMdYbpgD9EKYuY9skQABiU7VYKwIIIIAAAgggEIwAAUgwjoySgQABSAZo3BKIwLoNZRoxbamGfblY709dqvWbyreM26J+oR94eO/06N62oXKyswKZM6pBOOSKSpp5TBegF0yvEOuLUoB+iFKbuUwWIAAxuTqsDQEEEEAAAQQQCEeAACQcV0ZNQYAAJAUkLglMYE3pJj/sePPLRRo5bZk2lFVsGXv3BnX8r7by3ulxYOsGyrYs9PghEodcgT0yDGS5AL1geQFZfqAC9EOgnAxmsQABiMXFY+kIIIAAAggggECGAgQgGcJxW+0FCEBqb8gIuxZYVbJJ705ZomGTF2nU9OXaWL419GjbuK7/KQ8v+Oiy+27KyrLrkx7V7ZxDLroCgSoBeoEnAYGtAvQDTwMCVQIEIDwJCCCAAAIIIIBA8gQIQJJXc2N2TABiTCmcWciq9Zs0Z/k6fb1otd7+arE+nLlcm8ort+yvXdMi/ahLSz/46Niy2JnQ44cF5JDLmceZjdRSgF6oJSC3OyVAPzhVTjZTCwECkFrgcSsCCCCAAAIIIGCpAAGIpYVzYdkEIC5UMfo9lGws05zlJZrz3TrNXr71lxd8fLdu4w4L2rd5sf9OjxO7tFSHZvWcDD0IQKJ/DpnRfAEOfM2vESuMToB+iM6amcwWIAAxuz6sDgEEEEAAAQQQCEOAACQMVcZMSYAAJCWmRF60saxCc1eU+AGHF2zM+u9fvX9evLp0lyZNiwu0V5MiHdmhifp1bqn2zeolypBDrkSVm83uQoBe4PFAYKsA/cDTgECVAAEITwICCCCAAAIIIJA8AQKQ5NXcmB0TgBhTilgWUl5RqQXfr9ds75Mcy9ZqznclW4KO+d+XqGLrN1ftsL7d6uT5IUe7JkXas0mR//feL+/v6xXkxrIfUyblkMuUSrCOuAXohbgrwPwmCdAPJlWDtcQpQAASpz5zI4AAAggggAAC8QgQgMTjzqySCEDcfwwqKyu1ZPUGzVq+1v/aqtnL12r2f/86b8X6bV5Kvr1G3fycLaGGH3Q0LtJeTYu0V+MiNSzKdx8vwx1yyJUhHLc5J0AvOFdSNlQLAfqhFnjc6pQAAYhT5WQzCCCAAAIIIIBASgIEICkxcVEYAgQgYahGP6YXcqxYt9F/J8esZet+8G6OEv/rq9ZvKq92Ufm52WrbqO6WT3Bs/hSHF3h4X2WVlZUV/YYsn5FDLssLyPIDE6AXAqNkIAcE6AcHisgWAhEgAAmEkUEQQAABBBBAAAGrBAhArCqXW4slALGrnl7QsWDlek1bvEZTF6/RjCVrtryEfHVpWbWbycnOUpuGdbb5qio/6GhcpFYN6sj7fX6CE+CQKzhLRrJbgF6wu36sPlgB+iFYT0azV4AAxN7asXIEEEAAAQQQQCBTAQKQTOW4r9YCBCC1JgxtgJUlG/2QY3PYMX3JGk1fvEZrNlQfdLTardD/iir/q6p+8F6ONo3qKi8nO7S1MvC2Ahxy8UQgUCVAL/AkILBVgH7gaUCgSoAAhCcBAQQQQAABBBBIngABSPJqbsyOCUDiL0XppnLNXLr2v2HHak1bslZWVi75AAAgAElEQVTTFq/239uxs5/c7Czt3bSe9m1R7P/a2ws8mhSpbaMi1cnPiX9DrIBDX54BBP4rwIEvjwICBCA8AwhsL0AAwjOBAAIIIIAAAggkT4AAJHk1N2bHBCDRlaK8olJzV5T44Yb3yQ7vEx3eX713dFRU7nwduzeoo/3+G3R4Ycd+Ler7n+zw3tvBj7kCHPqaWxtWFq0AvRCtN7OZLUA/mF0fVhedAAFIdNbMhAACCCCAAAIImCJAAGJKJRK4DgKQ4Ivuvadj2doN/ldXbfP1VUvWqHRTxU4nbFA3T/s29wIO71Md9f1PduzTvJ6KC/OCXyAjhi7AIVfoxExgiQC9YEmhWGYkAvRDJMxMYoEAAYgFRWKJCCCAAAIIIIBAwAIEIAGDMlzqAgQgqVvt7Mp1G8o0bUlV0LHl15I1WrFu404HLsjN1j7Nq766ygs8qj7VUaymxQXKyuJF5LWrhjl3c8hlTi1YSbwC9EK8/sxulgD9YFY9WE18AgQg8dkzMwIIIIAAAgggEJcAAUhc8syrOAMQ7yuhvlu3QUtXb/ADg+ysLGVnSzlZWcrJ9v4+a8vfe//s/7v//p73HozNv//De7a/zhvLu662P5vKKzR7+bqt7+lYvFbTlqzWvBXrdzq0N6X3IvLN7+nYHHa0bVzk74MftwU45HK7vuwudQF6IXUrrnRfgH5wv8bsMDUBApDUnLgKAQQQQAABBBBwSYAAxKVqWraXMAKQfv36a82mSi1ZXeqHG/5f11T91Xux99I13l9LtXztRnkhSNg/3gcrNgchW8KVLCk3J/u/gcrW3/9hsLI5cPHCj2+/K9HG8p1/fVWz4oItn+Twv76qebE6NK+nwjxeSB52bU0dn0MuUyvDuqIWoBeiFmc+kwXoB5Orw9qiFCAAiVKbuRBAAAEEEEAAATMECEDMqEMiV5FOAFJRUanvSzZuCTE2hxtL1lQFHYtXr9fcpau0tixbZSkGG96HIZrUK1Cjonzfv6Ky0r/Xm6u80vur/JDE+3v/rz/4vc3/7P1eZfg5iuoV5Prv5fBCji0vJm9erIb/XXsiHyA2vVMBDrl4MBCoEqAXeBIQ2CpAP/A0IFAlQADCk4AAAggggAACCCRPgAAkeTU3ZsebA5A5C5dpfVa+H254n85YtuUTG1Wf2vD+2fvkxqby1JIG71MXjYsK1Ly+96tQ3qckmtUvrPrn4kI1+++/b1yU738So7Y/3ovHNwclXmhSVlFRFZ78999vE6xsCVeqgpWy8ko/ePHDFf+v296/+eusWjesw3s6aluohNzPIVdCCs02axSgF2ok4oIECdAPCSo2W92lAAEIDwgCCCCAAAIIIJA8AQKQ5NXcmB1vDkDa/PIFZRfUTWldTerlq2nx1jDDCzWa1i9Uk7q5mvHlpzqtX1+1aFCkvACCjZQWxEUIGCbAIZdhBWE5sQnQC7HRM7GBAvSDgUVhSbEIEIDEws6kCCCAAAIIIIBArAIEILHyJ3vyHwYgTRo12PpJjeKqT2744cbmsMMLOeoVKD9355/Y4A/2yX6W2P1WAXqBpwGBKgF6gScBAf63gWcAge0FCEB4JhBAAAEEEEAAgeQJEIAkr+bG7HhzALL0uxVq2qhhrdbFQVet+LjZIQF6waFispVaCdALteLjZscE6AfHCsp2MhYgAMmYjhsRQAABBBBAAAFrBQhArC2d/QtP5yXoNe2WP9jXJMTvJ0WAXkhKpdlnTQL0Qk1C/H6SBOiHJFWbve5KgACE5wMBBBBAAAEEEEieAAFI8mpuzI4JQIwpBQtxSIBDLoeKyVZqJUAv1IqPmx0ToB8cKyjbyViAACRjOm5EAAEEEEAAAQSsFSAAsbZ09i+cAMT+GrID8wQ45DKvJqwoHgF6IR53ZjVTgH4wsy6sKnoBApDozZkRAQQQQAABBBCIW4AAJO4KJHh+ApAEF5+thybAIVdotAxsmQC9YFnBWG6oAvRDqLwMbpEAAYhFxWKpCCCAAAIIIIBAQAIEIAFBMkz6AgQg6ZtxBwI1CXDIVZMQv58UAXohKZVmn6kI0A+pKHFNEgQIQJJQZfaIAAIIIIAAAghsK0AAwhMRmwABSGz0TOywAIdcDheXraUlQC+kxcXFjgvQD44XmO2lLEAAkjIVFyKAAAIIIIAAAs4IEIA4U0r7NkIAYl/NWLH5AhxymV8jVhiNAL0QjTOz2CFAP9hRJ1YZvgABSPjGzIAAAggggAACCJgmQABiWkUStB4CkAQVm61GJsAhV2TUTGS4AL1geIFYXqQC9EOk3ExmsAABiMHFYWkIIIAAAggggEBIAgQgIcEybM0CBCA1G3EFAukKcMiVrhjXuypAL7haWfaViQD9kIka97goQADiYlXZEwIIIIAAAgggsGsBAhCekNgECEBio2dihwU45HK4uGwtLQF6IS0uLnZcgH5wvMBsL2UBApCUqbgQAQQQQAABBBBwRoAAxJlS2rcRAhD7asaKzRfgkMv8GrHCaATohWicmcUOAfrBjjqxyvAFCEDCN2YGBBBAAAEEEEDANAECENMqkqD1EIAkqNhsNTIBDrkio2YiwwXoBcMLxPIiFaAfIuVmMoMFCEAMLg5LQwABBBBAAAEEQhIgAAkJlmFrFiAAqdmIKxBIV4BDrnTFuN5VAXrB1cqyr0wE6IdM1LjHRQECEBeryp4QQAABBBBAAIFdCxCA8ITEJkAAEhs9EzsswCGXw8Vla2kJ0AtpcXGx4wL0g+MFZnspCxCApEzFhQgggAACCCCAgDMCBCDOlNK+jRCA2FczVmy+AIdc5teIFUYjQC9E48wsdgjQD3bUiVWGL0AAEr4xMyCAAAIIIIAAAqYJEICYVpEErYcAJEHFZquRCXDIFRk1ExkuQC8YXiCWF6kA/RApN5MZLEAAYnBxWBoCCCCAAAIIIBCSAAFISLAMW7MAAUjNRlyBQLoCHHKlK8b1rgrQC65Wln1lIkA/ZKLGPS4KEIC4WFX2hAACCCCAAAII7FqAAIQnJDYBApDY6JnYYQEOuRwuLltLS4BeSIuLix0XoB8cLzDbS1mAACRlKi5EAAEEEEAAAQScESAAcaaU9m2EAMS+mrFi8wU45DK/RqwwGgF6IRpnZrFDgH6wo06sMnwBApDwjZkBAQQQQAABBBAwTYAAxLSKJGg9BCAJKjZbjUyAQ67IqJnIcAF6wfACsbxIBeiHSLmZzGABAhCDi8PSEEAAAQQQQACBkAQIQEKCZdiaBQhAajbiCgTSFeCQK10xrndVgF5wtbLsKxMB+iETNe5xUYAAxMWqsicEEEAAAQQQQGDXAgQgPCGxCRCAxEbPxA4LcMjlcHHZWloC9EJaXFzsuAD94HiB2V7KAgQgKVNxIQIIIIAAAggg4IwAAYgzpbRvIwQg9tWMFZsvwCGX+TVihdEI0AvRODOLHQL0gx11YpXhCxCAhG/MDAgggAACCCCAgGkCBCCmVSRB6yEASVCx2WpkAhxyRUbNRIYL0AuGF4jlRSpAP0TKzWQGCxCAGFwcloYAAggggAACCIQkQAASEizD1ixAAFKzEVcgkK4Ah1zpinG9qwL0gquVZV+ZCNAPmahxj4sCBCAuVpU9IYAAAggggAACuxYgAOEJiU2AACQ2eiZ2WOD/2zsTsN2m8g8vMiVDQoN5SmZKKkpIlKQMmY4MHXM6pUROFFKmoiJJVOYxhcoUpaQyRabIPKUQToaSof91b9c6/322/b57eIdvv/u713W5fHx7r73W/axn7W8/v7WeZZCrxca1a5UI6AuVcHlxywnoDy03sN0rTUABpDQqL5SABCQgAQlIQAKtIaAA0hpTjl5HFEBGz2a2uPkEDHI130a2cDgE9IXhcPYpo0FAfxgNO9nKwRNQABk8Y58gAQlIQAISkIAEmkZAAaRpFhlH7VEAGUfGtqtDI2CQa2iofVDDCegLDTeQzRsqAf1hqLh9WIMJKIA02Dg2TQISkIAEJCABCQyIgALIgMBabTEBBZBiRl4hgaoEDHJVJeb1bSWgL7TVsvarDgH9oQ4172kjAQWQNlrVPklAAhKQgAQkIIHuBBRAHCFjRkABZMzQ++AWEzDI1WLj2rVKBPSFSri8uOUE9IeWG9julSagAFIalRdKQAISkIAEJCCB1hBQAGmNKUevIwogo2czW9x8Aga5mm8jWzgcAvrCcDj7lNEgoD+Mhp1s5eAJKIAMnrFPkIAEJCABCUhAAk0joADSNIuMo/YogIwjY9vVoREwyDU01D6o4QT0hYYbyOYNlYD+MFTcPqzBBBRAGmwcmyYBCUhAAhKQgAQGREABZEBgrbaYgAJIMSOvkEBVAga5qhLz+rYS0Bfaaln7VYeA/lCHmve0kYACSButap8kIAEJSEACEpBAdwIKII6QMSOgADJm6H1wiwkY5Gqxce1aJQL6QiVcXtxyAvpDyw1s90oTUAApjcoLJSABCUhAAhKQQGsIKIC0xpSj1xEFkNGzmS1uPgGDXM23kS0cDgF9YTicfcpoENAfRsNOtnLwBBRABs/YJ0hAAhKQgAQkIIGmEVAAaZpFxlF7FEDGkbHt6tAIGOQaGmof1HAC+kLDDWTzhkpAfxgqbh/WYAIKIA02jk2TgAQkIAEJSEACAyKgADIgsFZbTEABpJiRV0igKgGDXFWJeX1bCegLbbWs/apDQH+oQ8172khAAaSNVrVPEpCABCQgAQlIoDsBBRBHyJgRUAAZM/Q+uMUEDHK12Lh2rRIBfaESLi9uOQH9oeUGtnulCSiAlEblhRKQgAQkIAEJSKA1BBRAWmPK0euIAsjo2cwWN5+AQa7m28gWDoeAvjAczj5lNAjoD6NhJ1s5eAIKIINn7BMkIAEJSEACEpBA0wgogDTNIuOoPVOmTAmvfe1rwwMPPBDmmGOOnnrOh/0ll1wS1l133TDjjDP2VJc3S2CUCegLo2w9295PAvpCP2la16gT0B9G3YK2v18EHn/88bDooouGJ598Msw555z9qtZ6JCABCUhAAhKQgAQaTEABpMHGaXvTHnzwwbDgggu2vZv2TwISkIAEJCABCUhAAhJoEIG77rorLLbYYg1qkU2RgAQkIAEJSEACEhgUAQWQQZG13kICL730UlhyySXDddddF6abbrqu16+yyirhmmuu6XgN6bQQU/qxm6Sw4Q24oIjHsJo46Hb0s/5e66pzf5V7yl5bdJ2+MKzRP+1ziuzSj1b18xm91FXn3qr3lL2+23X6Qj9GXb06ytqvXu0h9LP+Xuqqc2/Ve8peX3Sd/lB3tPV2X5Fdequ9Ob5AP+r0tco9Za8tuo4d6AsttFB44oknkp3oFglIQAISkIAEJCCB9hNQAGm/jRvdw2WWWSbceuuthW0suq6f54kUNqYBFxTxGFYTB92Oftbfa1117q9yT9lri67TF4Y1+qd9TpFd+tGqfj6jl7rq3Fv1nrLXd7tOX+jHqKtXR1n71as9hH7W30tdde6tek/Z64uu0x/qjrbe7iuyS2+1N8cX6Eedvla5p+y1RdeNN1/odYx5vwQkIAEJSEACEmgDAQWQNlhxhPtw9NFHh912262wB0XXjbePmSIehUD7dMGg29HP+nutq879Ve4pe23RdfpCnwZ3xWqK7FKxutzL+/mMXuqqc2/Ve8pe3+06faEfo65eHWXtV6/2EPpZfy911bm36j1lry+6Tn+oO9p6u6/ILr3V3hxfoB91+lrlnrLXFl033nyh1zHm/RKQgAQkIAEJSKANBBRA2mBF+xD8mHEQSOBlAvqCI0EC+oJjQAJZAr4bHBMS8N3gGJCABCQgAQlIQALjlYACyHi1fMv67Yd9ywxqd2oT0Bdqo/PGlhHQF1pmULvTEwH9oSd83twiAvpCi4xpVyQgAQlIQAISkEBJAgogJUF5WbMJ+DHTbPvYuuER0BeGx9onNZuAvtBs+9i64RLQH4bL26c1l4C+0Fzb2DIJSEACEpCABCQwKAIKIIMia71DJeDHzFBx+7AGE9AXGmwcmzZUAvrCUHH7sIYT0B8abiCbNzQC+sLQUPsgCUhAAhKQgAQk0BgCCiCNMYUN6YWAHzO90PPeNhHQF9pkTfvSCwF9oRd63ts2AvpD2yxqf+oS0BfqkvM+CUhAAhKQgAQkMLoEFEBG13a2PEXAjxmHgwReJqAvOBIkoC84BiSQJeC7wTEhAd8NjgEJSEACEpCABCQwXgkogIxXy7es337Yt8ygdqc2AX2hNjpvbBkBfaFlBrU7PRHQH3rC580tIqAvtMiYdkUCEpCABCQgAQmUJKAAUhKUlzWbgB8zzbaPrRseAX1heKx9UrMJ6AvNto+tGy4B/WG4vH1acwnoC821jS2TgAQkIAEJSEACgyKgADIostY7VAJ+zAwVtw9rMAF9ocHGsWlDJaAvDBW3D2s4Af2h4QayeUMjoC8MDbUPkoAEJCABCUhAAo0hoADSGFPYkF4I+DHTCz3vbRMBfaFN1rQvvRDQF3qh571tI6A/tM2i9qcuAX2hLjnvk4AEJCABCUhAAqNLQAFkdG1ny1ME/JhxOEjgZQL6giNBAvqCY0ACWQK+GxwTEvDd4BiQgAQkIAEJSEAC45WAAsh4tXzL+u2HfcsMandqE9AXaqPzxpYR0BdaZlC70xMB/aEnfN7cIgL6QouMaVckIAEJSEACEpBASQIKICVBeVmzCfgx02z72LrhEdAXhsfaJzWbgL7QbPvYuuES0B+Gy9unNZeAvtBc29gyCUhAAhKQgAQkMCgCCiCDImu9QyXw3HPPhYMPPjhMnjw5zDzzzEN9tg+TQJMI6AtNsoZtGUsC+sJY0vfZTSOgPzTNIrZnrAjoC2NF3udKQAISkIAEJCCBsSOgADJ27H2yBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACAyKgADIgsFYrAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpDA2BFQABk79j5ZAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCBARFQABkQWKuVgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhg7AgogIwde58sAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpDAgAgogAwIrNU2n8Bzzz0Xdtlll3DppZeGKVOmhGWWWSYcccQRYbXVVmt+422hBPpM4JhjjgnHHXdcuOmmm8I+++wT9t9//z4/weok0DwCjz76aNhuu+3C5ZdfHuaff/5w9NFHh3XWWad5DbVFEhgCAd8DQ4DsIxpPwO+DxpvIBkpAAhKQgAQkIIHKBBRAKiPzhrYQeOaZZ8Lhhx+eBL8WWGCBcPLJJ4c99tgj3H///WHWWWdtSzfthwRKETj33HPDq171qnDSSSeFZZddVgGkFDUvGnUCm222WZh99tnDUUcdlYjh2267bbjzzjvD3HPPPepds/0SqEzA90BlZN7QQgJ+H7TQqHZJAhKQgAQkIIFxT0ABZNwPAQGkCbzuda8Lv/71r8OKK64oGAmMSwI77LBDIgi6A2Rcmn9cdfrpp58OzPl33XVXWHDBBZO+r7nmmmGbbbYJEydOHFcs7KwE0gR8DzgeJDAtAb8PHBESkIAEJCABCUhgtAkogIy2/VrR+qeeeioceOCB4YYbbgjXX399eOyxx8J+++2XG4AlYLXvvvuGs846Kzz++ONhqaWWCnvvvXfYYostemZx2223hbe+9a2BlCizzTZbz/VZgQSqEmiCLxj4qmo1rx8WgX77B++btddeO3mXxDJp0qQw00wzJbsDLRJoMoF++4MCSJOtbdu6ERikL/Bcvw8cfxKQgAQkIAEJSGD0CSiAjL4NR74H9957b1hppZWSXRdLLrlkOP744zsKIOuuu2645pprwiGHHJJce9pppyXXn3rqqWHChAm1WTz77LNhjTXWCBtuuGFy/oFFAmNBoAm+oAAyFpb3mWUI9Ns/rrjiirD11lsH6o2F+R8R/thjjy3TJK+RwJgR6Lc/KICMmSl9cI8EBukLfh/0aBxvl4AEJCABCUhAAg0hoADSEEOM52b873//S7o/3XTTJYGneeedN1cAueCCC8L666+fiB5bbrnlVGSIIrfccktydgdnGFBY1XvllVfmYt1zzz2THSexPP/882GjjTZKcr6fcMIJSTssEhgLAmPtC/RZAWQsLO8zyxDot3+4A6QMda9pKoF++0O6n74Hmmp125VHYFC+4PeB400CEpCABCQgAQm0h4ACSHts2YqedBNAdtxxx3DGGWeEJ554IswwwwxT+3v66acnuz8QPFZbbbVKHF566aWw1VZbBVZ4nXPOOdPUW6kiL5ZAnwkM2xdi8w189dmQVjcQAv3wj3gGyN13352ce0NZa621kl0hngEyELNZ6YAI9MMfFEAGZByrHSqBfvmC3wdDNZsPk4AEJCABCUhAAgMnoAAycMQ+oAqBbh8uq666anjxxRfD1VdfPU2V7P5YbrnlkpQlO+20U5XHBUSVO+64I1x00UVhlllmqXSvF0tgkASG7QsvvPBC4J9dd901zD///MlZOzPOOOPUXVWD7Kt1S6AqgX75x6abbhrmnHPOcNRRR4XLLrssET94J8wzzzxVm+T1EhgzAv3yB98DY2ZCH9wnAv3yBb8P+mQQq5GABCQgAQlIQAINIaAA0hBD2IyXCXT7cOHMj8UWWywRK9Ll4YcfDvPNN1846KCDwuTJk0ujvO+++8IiiyySCB8xdRY3X3jhhWH11VcvXY8XSmAQBIbpC7R///33DwcccMA0XfnRj34Utttuu0F0zzol0BOBfvnHo48+Grbddttw+eWXJ8Lf0UcfHUiraJHAKBHolz/4Hhglq9vWPAL98AW/DxxbEpCABCQgAQlIoH0EFEDaZ9OR7lHRh8viiy+eCBR5AsjBBx8c9t5775Huv42XQCSgLzgWJNCZgP7h6JDA/xPQHxwNEniZgL7gSJCABCQgAQlIQAISyCOgAOK4aBSBfm1db1SnbIwEahDQF2pA85ZxQ0D/GDemtqMlCOgPJSB5ybggoC+MCzPbSQlIQAISkIAEJFCZgAJIZWTeMEgC3T5cON+DA8+zh6BzMPqWW25Z6xD0QfbFuiXQCwF9oRd63tt2AvpH2y1s/6oQ0B+q0PLaNhPQF9psXfsmAQlIQAISkIAE6hNQAKnPzjsHQKDbhwuprz70oQ8FBI/NN9986tPXW2+9cOONN4b777/fA5sHYBOrHBsC+sLYcPepo0FA/xgNO9nK4RDQH4bD2ac0n4C+0Hwb2UIJSEACEpCABCQwFgQUQMaCus98BQHEjWeeeSY89dRTYeLEiWHTTTcNm222WXIdosess86a/MzhtNdee2049NBDwxJLLJHsCDnuuOPCKaecErbaaivJSmDkCegLI29COzBAAvrHAOFa9cgR0B9GzmQ2eEAE9IUBgbVaCUhAAhKQgAQk0BICCiAtMeSod2ORRRYJ9913X2437rnnnsDvKU8//XTYZ599wllnnRUef/zxsNRSS4XJkyeHLbbYYtQR2H4JJAT0BQeCBDoT0D8cHRL4fwL6g6NBAi8T0BccCRKQgAQkIAEJSEAC3QgogDg+JCABCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISaB0BBZDWmdQOSUACEpCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkoADiGJCABCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSKB1BBRAWmdSOyQBCUhAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIACiGNAAhKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCCB1hFQAGmdSe2QBCQgAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACCiCOAQlIQAISkIAEJCABCUhAAhKQgAQkIAEJSEACEpCABFpHQAGkdSa1QxKQgAQkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCSiAOAYkIAEJSEACEpCABCQgAQlIQAISkIAEJCABCUhAAhJoHQEFkNaZ1A61lcCaa64ZfvOb34Rf//rXgZ8tEhhFAvfee29YdNFFw8ILLxz42dJ/ApdffnlYa621whprrBH4uUrp5d5Oz1lkkUXCfffdF+65557Az7Fst9124cQTTww/+tGPAj83sYzSvPvII4+ExRdfPLz73e8OF110URNx2qYQkvd3nXf5s88+G7785S+Hc845Jzz44IPhhRdeCNtuu2044YQTRpIr7f7EJz7xij74jhicOU866aSE93HHHRd22GGHwT3ImiUgAQlIQAISkIAEJNAwAgogDTOIzRkfBGJAMN3bWWaZJcw333xJ0HKPPfYIyy677DQw6gZN6hLdf//9k1vjv+vW08T7svynm266MNtss4U555wzLLXUUuGd73xnmDBhQlhmmWX62vwnn3wyfOtb3wqvfe1rw+67797XuqtWRqCbfxhXwxTU6ga36vhMVSb9uD4G9ahrxhlnDA8//HCYe+65c6smgInPP/roo8nv99tvv774WzcRo8ivx5MAUsYHhj3v9jIGd9ttt/Dd7343XHnllWG11VbrWNU//vGPsMACCyQBdK7jesvwCNQdU1tttVU47bTTwqyzzpq8m2aeeebwoQ99KHzxi18cXuMLnjRp0qTwne98J7nqkksuCeuss07HO/otgESu/ZpHGwO1jw3B59/ylreE//znP+GOO+5IxpJFAhKQgAQkIAEJSEAC44GAAsh4sLJ9bByBGMx985vfHF7/+tcn7SM4zgfpf//73ySwcfbZZ4cNNthgatvrBk3qdh5RgPK///2vbhWNvS+PPwGBxx57LFmpHssmm2wSjj322I4B7KodrBv8r/qcMtcTCD/ggAP6FnQv80yuqcugjs+UbVM/r0sLINR79NFHh09+8pO5j/jFL34RPvzhD0/9Xb8Cd91EjCK/vvrqq9mMDhoAACAASURBVMM222wT3vGOdwRWC/ejdNoBMnny5PDTn/40HHzwwWGjjTbqx6Mq1VHGB2ABE1jApKnl7rvvTsRbdn+wS7BbQYT97Gc/O/WSO++8M9k5YhkOgTrv8ieeeCLMM888gYUSt912W1hwwQWH09gKT3n++ecTQZf3KGXrrbfuOocogFSA28dL+Ztml112CYccckj4whe+0MearUoCEpCABCQgAQlIQALNJaAA0lzb2LIWE4gBwWzqF1bmfvzjHw+XXnppEnQnWMzOBEqdoEkvCIsCpb3UPdb3duJPuwjenHrqqeGrX/1q8jNBxT/+8Y/J7pBeS93gf6/Pzbu/TPB3EM+ty6COzwyi/UV1xqAe4iaBZXYT/eEPf8i9bYsttghnnnlmsiL39ttv75sY1YsAUtS/Or/vJIDUqauf94yVD/SzD7EuApmHHXZYqXRiK6+8cvjTn/6U7ERDeIcD4ptlOATqvMsR4ZhLEOGuuuqq4TS04lN+9rOfhY985CNTx9VrXvOawN80/DuvKIBUBNyny6dMmRLe8IY3hDe+8Y0B4XT66afvU81WIwEJSEACEpCABCQggeYSUABprm1sWYsJdAvA//3vf0/y5D/33HPhJz/5ydSV0XWCJr0gHK8CSGTGTpBVV101SWHE+QSIVb2WusH/Xp+bd/9YBX/rMqjjM4PgVlRnDOqtvfbaSYohcv2zs2uJJZaY5tannnpqahCK8zp++MMfKoAUwe3z78fKB/rcjcDKe1JaEdgk4NxNrL311luT9IqvfvWrk1RF22+/fbL7A7HOMhwCdd7lzCPcV+dcn+H0KoTNNtss2bm6zz77hHPPPTfccsst4eSTT04WdeQVBZBhWeaVz9lwww3Deeedl5wV9IEPfGDsGuKTJSABCUhAAhKQgAQkMCQCCiBDAu1jJJAm0C2Yy3XLL798uPnmm8Ohhx4a9tprr+TWdNCElXtf+tKXklQnzzzzTJIPnBXABCDyCgGy733ve0kwgvQZL774YhKQ5XpSoaTzQMegYCeLZQ8y/v3vfx++8Y1vJHnkSdNBSq/3ve99gfQ2Sy+99CuqqdsP+nn44YeHH//4x+Guu+5K+jDvvPMmwbsPfvCDybkpnLlQphTxj3UQxCE1zwwzzJCslMymHSE9GCv4f/CDHyQrqp9++ukw//zzJ2mNyMuOnWKJBz53al821Rh2YkX3r371q0SEYSfQu971rqSf8M0r1AEfxJprr702CYiy0pOA5+abbz71oOkobuXVkT1Ut0of0/URsDvwwAOT9EE8721ve1vChJ0RdQ5BL7JZns/Qnqoc07sVGOvY4Jprrgn//Oc/E38rOi8lLYCww2PHHXfMFTaw0cSJE8O+++6b2JcxlE2BVRSg7xRAzNsBUtavO+0eSQtXcCF4/v3vfz/xRcYmQbSvfe1rYaGFFnrFsKp7CDq7YvB5fOChhx5K5inqwr9I4fKmN71p6rN++ctfJgG9K664IjzwwAOBA6PxxXXXXTeZi7LtKusD3YLV+Aa7xThQ+M9//nMiWi+88MLhox/9aDIfv+51r3sFi7SwfOGFFybpv66//vpkFTbncRx00EHhrW99a5lpbOo1nLUAf0Q3dg92K7Ag9c2mm26apCdifvjXv/4VmMcRfGPBvpznsP7664ef//znuVU+/vjjyRwHB4T79Fk38D/qqKOSgPhf//rXRAxccsklA+dYfPrTn07SPKZLeqxzlgnp+XgudueeeNB3VTvHZ5BakvfUiSeemKQ5pK2kmGSOIk1dt3SAVecQnsnuQQ4sZ0wydzAuEAL23nvv5FyMsoegR78r8z6u8p6nvqwYzTjGp+kv77Iq6S951zAWSCWJ8MG7EyEE/7v44otzmz/WAgi7nzhQ/vzzz0/azFibaaaZkl2fjDlSF/Luj4X5iN8xdv72t78l1+aV+C5i/OI/sVR9l6bnYvz6iCOOSP6GY87l7yzsR2E8M2/gG7EP/G204oorJu993kPZcvzxxyfvJg6hR3y3SEACEpCABCQgAQlIoO0EFEDabmH710gCRcHc5ZZbLvkgzxNACOIQLCKQRkDp/vvvn3qIct5qy3//+99JwJAgIgVRAqEAgeWll14KK6200tSUW/yej2H+iQfjklM+XQiwx8D+McccEwhW8WHPBzlBRlYSE1ggVznXpgMA1BMDilX6QfBs9dVXT1JRESgkiD777LMnQQgCb/QD8YWULmVKEf9YB/UievAcAgaslo6FYBNBEgJ8FHKfE3RgtT/BP4KzBDCwEYUABecdIEwQ/Hv7298+TVN/97vfTf3vs846K8mfTtCOfiLy0E/+we7f/va3k+BkunAtgQ6eQeH5rAqn7fyDjWJA6z3veU8ybggU0790cDh9qG7VPsb2nHHGGQkb+BEsQvBAQGJcwIEgIAHBGMDph83yfKYOxzg2aCfiBKvpEQsRKfCvKgIIwTV8BTswLtKFYDU+SbDx61//+sAFkLJ+XUYAYYzg+4wbxjxzFYFPfkaAIKVXutQRQBAWEIgY1+xWYN7CrxBfEBqy6QMJVDLeaAO+yJzBtQinjMHf/va3iVAcS1kf6CSA4EsEtDmUmrLYYosl8w/zKm1mfGNf/n+6RAEEfgRYGR/4KsFV2oqYhOBGoLVsYZwiPiFuMG47FfhgC/yeeYJV4ARACUTvuuuuyQHqsTz66KMJR9rLvJMn5hAs33nnnZODuDnPJhaCsAS+2W2CXXgm7xx8ALvAHtEGu8YSBRCYROED4ZbCXIlASKlqZ+7hmbwDYyCe8cmzsRW+iRCCWJN3Bk+dOQRevDeZ82gvcxO2pf+0A8GJ8VhGUKWuj33sY4mYTXvnmGOOZIFELPF9XPU9z/1pAWS99dZLFknwPkAU4z3OO7VsQTzZaaedkqD7DTfckNTN2Od9zXhLi5WxzrEWQE455ZTkPYuQQfv4GwaxirbjK/ztgjiSThGFSElKQ+b2jTfe+BV4rrvuumS84tcPPvhgeNWrXpVcU+ddGufi9773vcnfAfgYfwswzzDfMh5o6yqrrJIIbgjE/J5n8n5HoIz2yDb0xhtvTH7n7q+yI9zrJCABCUhAAhKQgARGnYACyKhb0PaPJIGy6XzSH9kxEEcgiaATQVNEBgJxBL4QSwhY8eEbP7qB8/nPfz5ZRc3vyNHNSnwKAQ4CPwRg2QnCToZ0KUqBRZCDD2+CS6ySZ2cCgQKCk5/73OeSYBrB47/85S/TBD/q9AMOBIH4YCc4RtAqFgJ1BCFZzZjeydJtYJQVQKiD5/J8mBMgiiWupGa1NsFlhCQKgSiY038CIQQzYymT/onABHne4X/kkUcmoksMwGA/gq4E0wi0wCMWdvJwuDEH5RKoZ1dMLAggBCsJMsZStLuA6+r0keAnwVtWECN0sMKaICABIFbFs7Kcn/spgKTTxkWfqcsxjg18iBXc7Fqh/fgZge3syvXsOEvvAGHVbkwLk15hT2CM/jM+yOe/ww47DFwAie0s8usiAQQW1MHugbiymKAhP9Nfxi5CZXqHRVUBBJGQQCPjhB1w2CGeI8D/w8bMAQTSY2F8E1xmnosFX/zmN7+ZrERn3skeDl7GBzoJIHGHBIFJ2sOqfkoMWCMgc2YDLPLmVeYqdh6wM4xCSjR2m1122WXJqm1ExLKFHWH0jWD4Jpts0vE2BBmEt7nmmitpJ4FfbEbbETgQ+dKr2plDEA04NJngdraQuo3xQiAZwZNC4BixmvHOmGBOIqBOYdxPmDAhEcmYI3mHZecj/I4xhPAQ53mCvbzrKHXsjNi+5557Jn1kDmVsUQjMs1uHQDLjKiuA1J1DCIwjMPGuZSdE3DkIf0Qn+sPzygggkU+3c324ps57Pr6PYA5f3qOc4UHBd9ICVdFYJEiPXflbANYU/BM/gD9/H2TLWAsg2Jcx8P73v3+aeR3hCmEQkYo2sisylrhzAk7s7skWdjchpmXHd513abQ59kHEZSzFXVrRJ1gIwVxEG/l3PDOOdvG3HX3I81120DJ3YWf8Pr1btcjW/l4CEpCABCQgAQlIQAKjSEABZBStZptHnkCnAPwjjzySBJIIShGkIrUBH6mUGIgj6E26pfSqRIIprMYmqMXvYgoVVpqyspGV03HFbxoewfkYbGd1KqsBYykKlBKIZ5U2ASQ+zNOFYDGrVFkZTqqur3zlK1N/XacfpGwhgMDOBwIMvZYqAkgUFghOciYLBdGFoBbBcPqYFmT4PUFA0lXBlwAEAUFKGQGEACbP6dTXGHhldXxcFY3AQZ8YB+nndeNUFPyt20fszQHyiGOkv8oWxi+Bp34JIJ18pg5H2hrHBuIgq3+rlqwAQh34SHqFPWIl4hACFwGsURJA4IEPMj7TBTtgUwJzBHoJjsdSVQBh5fUFF1yQ7ACJY7yqHdLX43/ssCIAT1qsWIp8ID3vpoPVzG/0leApAsvuu+8+TfMQAVn9jmCGoJFOWRfnVeyO/dPlpptuCiussEIiHLNbqmxh3iZoS7A5Bvfz7o27PRhvrNiPcxVMeHdk3xGIXARW88Qj5hzmQALnnDsSA6/xIGz8HxEknUKI5xFsjbviGDMxyB5twZxKX9JCVlkOeXZmLubdiE3SQk2sk4UACLYEhLMCSJ05hProH2MEYSXuYonPY7ywQIDSLwGk7ns+nV6LRRKxXWV5x+vibg/+m79ZouDDIgB2iHbahTDWAki3fpJmip1/iIPsVooFoZK/qfBtxhQ7zmLh/cu4ZTdG2vZ136VRAKH+TjtOokhJCj7mjiolzsuItIi1FglIQAISkIAEJCABCbSZgAJIm61r3xpLIH54ksqJtAsUAl6IEHxYs8vj9NNPn2Y1bxQOOgXG44dwehUwQUSCiQSACFLk5b2PKR1IyUJe/ViKBBDazYc9Yg2rirMlpkfJroKu0w+EFgQX0qoQpCu706PTAKgigMT0MqwSJcc2hTzyrNwmjz4rlfMKog8BNXZAUAelSADB9gQ/CaSQvoJ0J9nCDh+Cr+nUFXFVKqIL6TnKlKLgb90+Iqgh/LArhoBrtrCanHFWVwAp4zN1OdLWODZIbcbun6olK4DEoBgBUYK/+DbiIKtzCSITQBs1AYR0TTGIneZDOhmCzOz0QbSMpYoAwork6AMwyqbT6mYPdo4w/5F6iZRBBLUpzKsE25kPSfUTS5EPcF3eDpB4kDjBf4KdcXdKum3sdGAOz7KI82qngCWCACIS9abP1OjWb3gRBGe3XafUWXBlJwYB3KxAFUVedi4QaI2Fa7mHXX2IPWlRIgbys7tVosjC7hbSWeWVuGOFHQNxF0+0RbczR2JdVexMIBp/w0bMqXnnNsSdLGkBpO4cEoP+7IjgnI9sgSm79Ki/XwJI3fd8WgDhXU676hQEb4TvbJ8Zw4wZ5kBE73TqLp7TBAGEsc2Yxxa8W1ksElNFIijydw4CX7rE87zY3fSZz3xm6q/imWHZnZ9136VRAMG/2WWX3tkbH8rOV97/8Occm25nG2Vtyw4lzh9CtGT3nEUCEpCABCQgAQlIQAJtJqAA0mbr2rfGEogBwXQDCcyQhoAgAukiYkqleE0MxGUP1oy/32abbZLUR+mUDXygE9zqdhBp/IDmOg7ZjKWbAIJYww4VCkHcvPzeBA8IbnEdgade+kHKJ/KoE7AhGIDYw2pfmGRX2JYxehUBhNXdiE7p4GBMN5I9PyP9bIImrAYm2I+4RCkSQGKwjrHACuq8QnCGldUEXwlqUhgv2I62EpgsU4qCv3X7SH5ygs8IMQgy2cIOlTXWWKO2AFLGZ+pypO44NvJWb5fhmhVAuIdV0ARGCZAh/LBDKx3oHSUBBAGHAH16B1rkwvkTpJtK75ZKM+VMDvjGEgOJ6fM84i4IxlHZMwjwiU996lPTnGGRZ6vsDoAiH6COPAEkBjoRgRCD8gpnciB8ZlnEeZUUcXnCCWI1YgNzBWOlTGHXBAF1dk5w3k5eQYxBlCEgTf1p+yEoMN8w77ATJM7t1BNTuDG/8I6IJQqdsGCHUywEf0nPx3zNXJ1XOBSdgDtpvhBQKNEW2dRB6fvr2BnBnLl75ZVXTs5fyisEsdmNkxZA6s4h8X2RTZmYfi67g/CFfgkgdd/z8X2E8IE96hZEN/yAFJH0O10IrHN2BWmxSI+VLmMtgCB48LdRJx+mrTF9Y7rd8R3GPM6O21jwdfyBXZrM+bHUfZdGASQrqKTbQipSfDGmlYx/GyHqFe2i4pwa/pZgEQeLOSwSkIAEJCABCUhAAhJoMwEFkDZb1741lkCVAHzsRKdc9PH3ecHEuDKT3ROII3kl7nBIp0Xhum4CCKlkYpoLzgDJW5lI8J/V+tkAQp1+0B6eyVkArPBmFW0sHGxMSqEqKxir8I/53NNCRhSNygwwUsgQ6KEUCSBRNCpTL9fElaqxPQQRCeKVKUXB37p9xN6svMf+6ZRqsU0EmwiY1d0Bkj38Oq+vdTlSV6fdCmWYck2eAEKKEXK3s6OEfpNuJh38HSUBBJGWnSx5hXRV9CW9W6ob07w5K9oOOxAkLlNiqiYEBc6VIG0NaZ1ieqWYri87dop8gGfnzVfx8GQEXnYx5JW4KyvLomhnXZ3xhwCdTX+YbRMHlV944YVJmiPGX7aw0wZhIhvEjmJPOqVdTA+ESIXQm95VwZyP75cpaXuUsUUdO0db5aXxim2M78C0AFJ3Dom+TJ3s/ssrzAXMCf0SQOq+54veR2VsSJpDdnkijDIGOWclXaLwhj8iOKSFt7EWQOJOJNrP7gkWndB++sLfNfw7/Z5N9wvxk51lcWdL3O2CfzM/pjnUfZcWnfsS28NYYuyys4t2U2gH8yDi2NJLL51rSv4/u+yyO8LK2N1rJCABCUhAAhKQgAQkMGoEFEBGzWK2txUEqgTgY4frCAd1V4bGD+hOH//92AHSKfiTFxRNG50PfFYYExxADGFVLwJMPHS4zAApy5/88TE/fjqlU0wZw2p3gk9lS1HAibQ4BGF4JoJP2UJQM55F0K8dIHX7WLQDhIAxu5wGKYDU5QjvOgHotJ3yBBB+T8CMACB82LlDsDAG6DsJIATlCAwj/PFztpBmiJ0PaZGNa7oFzooC8EWHoHfbAXLwwQcnh8b3sgOEgCJnBlTZAfKBD3wgydOftwIdHuw4YtV2vwSQfuwAieJl1qZ1xl88V6dTOkJECuaUmBKs27zCqnDOS4mFFEGIXjFFI+cixID79ttvn6TfSRdWxbMqnXSBiD9lSxkBpI6d4w6Qbqvo83aA1J1DyuwAiWe29EsAqfueL3oflbEd8w/zUJmSHRNjKYCwcxWfIJ0mO6Kywg3iKzt1Ov0NFHe7sfuSQ945+JyzkRC5SZ+YLnXfpWUFkPgsdpXxdxDjigPt6Rd9ZDcT82m2kN6O1IDsumPHlkUCEpCABCQgAQlIQAJtJqAA0mbr2rfGEigbgE93oI4AEnODs1uDw0mbdAZIXQEka9Qtt9wyWU2/0047Bc6XKFPK8ucwcg7CJehLQCQeoMwBwjwv7wD4bs/HBjy7U/Cf/OMEKghUkpIkG5TpVHdceV/lDJAYXM8e/BufUbePMTVOp50a8WyYQQogdTnS9zoB6LRdOgkgkTfXck4CglosnQQQAmukjkkfoJ4XWBumAMLz2SnASv9siWn4ej0DBB8gpVPZM0Di7oW8cwZIDYPf4k/ZMVnkA/Sv6AwQ6o0HgKd5FJ0B0k8BBPuzO4IgLAHpbInndZAqKy8QGq+PZx2wwyMGf/kdQgfjlXONOGuAtIOcg5I94J1rt9pqqyT4mnc4fLe5sYwAUsfOt9xySxLcxUakYoyr+tNtiTsB0nNh3TkkngGC6EYAO1sIUnO2yyDOAKn6nu9VAEkf+k2fsgfex76TwpJ+Mz9wHkYsYymAkKKR8884B4MFFdkSRU7+f56vIqCQro4znBAa2EVCOqy8FKV136VVBZB0Hxi/HIqOL+OP/J2ULpwpQuozBCDEzTy/6Oav/k4CEpCABCQgAQlIQAKjRkABZNQsZntbQaBsAD7d2ToCCAfjkh6Fj2FWwm644YbT8Iu53xFGSOeQTlnEhzEr1bk3rlRP3xwPPM4TAQgY8PHNysPs6vU6/ehmdM7nYNUtKbA4zLNMKcMfsYJUJaSzyK50JvhBrn36SYAtLxic1w5WW7LqMu9g1Xj9BhtskARROgkTefXSRgQFAlKs3mYVd1EhHzuB6r322itJIZYtdfsY08kQECI1R7bEFeKDFEB4Zh2O3DcoAYTzGRDNKKTGYWzF0kkAiQJc3up1goqsxmcnSRUBpMivi3aA0Oa8s2YQAggIcj5INjBe5RD0tO2yafk6jWl2TbFiP2/XQRTcuDcrgBT5APfkzVf4PX1iR09eoD/6DkHubHqZoh04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      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Plot model accuracy with decreasing photon budget (photons per multiplication)\n",
    "\"\"\"\n",
    "fig, ax = plt.subplots(1, 1, figsize=(16,6))\n",
    "ax.set_position([0.1,0.15,0.5,0.8])\n",
    "ax.set_xscale('log')\n",
    "#ax.set_yscale('log')\n",
    "ax.set_ylim(1, 100)\n",
    "ax.set_xlim(0.008,100)\n",
    "ax.set_xlabel(\"Photons Detected Per Multiplication (Averaged for All Layers)\")\n",
    "ax.set_ylabel(\"Accuracy (%)\")\n",
    "ax.grid(True)\n",
    "ax.tick_params(length=12)\n",
    "line_dict = {0: \"solid\", 1: \"dashed\", 2:\"dotted\"}\n",
    "\n",
    "df = df_state\n",
    "avg_trans = [0.48, 0.42, 0.39] # The average transmission for the matrix for each fc layer.\n",
    "x = df[\"max p/mul\"] * (0.48*784*100 + 0.42*100*100 + 0.39*100*10)/(784*100 + 100*100 + 100*10)\n",
    "y = df[\"accu_ref_fullres\"]\n",
    "ax.plot(x, y)\n",
    "\n",
    "#ax.legend(bbox_to_anchor=(1.03, 1.0))\n",
    "plt.title(\"The Photon Noise Resilience of the Trained MLP Model\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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