Potential_snnTorch_broadband.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RBNKFLJIobYt"
      },
      "source": [
        "[<img src='https://github.com/jeshraghian/snntorch/blob/master/docs/_static/img/snntorch_alpha_w.png?raw=true' width=\"400\">](https://github.com/jeshraghian/snntorch/)\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3sfHnI586XV3"
      },
      "source": [
        "#Connection to the drive and loading of libraries."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fxg84USWDKyf",
        "outputId": "0f46de62-b9d6-4057-99d5-35f38618ed74"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "SdrMuBj5BA_5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1cb627af-daed-4cda-a3f1-2e68842ecb14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/125.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m125.3/125.3 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install snntorch --quiet"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "XRvvCXM-BA_6"
      },
      "outputs": [],
      "source": [
        "# imports\n",
        "import snntorch as snn\n",
        "from snntorch import surrogate\n",
        "from snntorch import functional as SF\n",
        "from snntorch import utils\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch.utils.data import DataLoader, Subset\n",
        "from torchvision import datasets, transforms\n",
        "import torch.nn.functional as F\n",
        "\n",
        "from IPython.display import clear_output\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import itertools\n",
        "import random\n",
        "import statistics\n",
        "import tqdm\n",
        "\n",
        "import scipy.io as sio\n",
        "import os\n",
        "import requests"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3jx-d_2-GgEa"
      },
      "source": [
        "#Training setting"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_setting = {\n",
        "\n",
        "    'path': '/content/drive/MyDrive/prova_github_Dataset/',\n",
        "\n",
        "    'decay_train'     :True,\n",
        "    'thr_train'       :False,\n",
        "    'bias'            :False,\n",
        "\n",
        "    '1beta4layer'     :False,\n",
        "    'beta_out_single' :False,\n",
        "    '1thr4layer'      :True,\n",
        "\n",
        "    'lr' : 1e-3,\n",
        "\n",
        "    'epochs': 10,\n",
        "    'batch': 10,\n",
        "    'training_ratio' : [0.8, 0.1, 0.1],\n",
        "\n",
        "    'hyperparams': [64, 128, 64],\n",
        "    'thr_init' : 0.1,\n",
        "    'bet_init': 0.9,\n",
        "\n",
        "    'reset': \"subtract\",\n",
        "}"
      ],
      "metadata": {
        "id": "Xl1MQKHTXpM9"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9k8YLY4-G-LK"
      },
      "source": [
        "#Loading dataset\n",
        "To ensure the script works, it is enough to have a dataset formatted in the following way:\n",
        "training_input [window_index, input_variables, window_length], training_output [window_index, output_variables, window_length]. The same goes for test_output and test_input."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "18BMipiBDHSz",
        "outputId": "942530be-81fe-4afc-a367-931b87d46211"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "training_input (135, 96, 3876)\n",
            "training_output (135, 2, 3876)\n",
            "\n",
            "validation_input (17, 96, 3876)\n",
            "validation_output (17, 2, 3876)\n",
            "\n",
            "test_input (17, 96, 3876)\n",
            "test_output (17, 2, 3876)\n"
          ]
        }
      ],
      "source": [
        "Dataset_input = np.load(train_setting['path'] + 'Dataset_input.npy')\n",
        "Dataset_output = np.load(train_setting['path'] + 'Dataset_output.npy')\n",
        "\n",
        "n_Windows = Dataset_input.shape[0]\n",
        "n_train   = round(train_setting['training_ratio'][0] * n_Windows)\n",
        "n_val     = round(train_setting['training_ratio'][1] * n_Windows)\n",
        "n_test    = round(train_setting['training_ratio'][2] * n_Windows)\n",
        "\n",
        "training_input  =  Dataset_input[0:n_train, :, :]\n",
        "training_output = Dataset_output[0:n_train, :, :]\n",
        "\n",
        "validation_input  =  Dataset_input[n_train:n_train+n_val, :, :]\n",
        "validation_output = Dataset_output[n_train:n_train+n_val, :, :]\n",
        "\n",
        "test_input  =  Dataset_input[n_train+n_val:, :, :]\n",
        "test_output = Dataset_output[n_train+n_val:, :, :]\n",
        "\n",
        "print(f\"training_input {training_input.shape}\")\n",
        "print(f\"training_output {training_output.shape}\\n\")\n",
        "\n",
        "print(f\"validation_input {validation_input.shape}\")\n",
        "print(f\"validation_output {validation_output.shape}\\n\")\n",
        "\n",
        "print(f\"test_input {test_input.shape}\")\n",
        "print(f\"test_output {test_output.shape}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5zGwy50JZoxN"
      },
      "source": [
        "#Saving train setting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "otG6uv03Zm1T"
      },
      "outputs": [],
      "source": [
        "import datetime\n",
        "data = str(datetime.datetime.now())\n",
        "data = ((data[0:16].replace(\" \", \"_\")).replace(\"-\",\"_\")).replace(\":\",\"_\")\n",
        "\n",
        "trained_folder = '/content/drive/MyDrive/Results/' + data\n",
        "os.makedirs(trained_folder, exist_ok=True)\n",
        "\n",
        "with open(trained_folder + \"/0parametri_training.txt\", \"w\") as f_save:\n",
        "  f_save.write(train_setting['path'] + '\\n')\n",
        "  for chiave in train_setting:\n",
        "    f_save.write(\"{} = {} \\n\".format(chiave, train_setting[chiave]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YB7gYrVCBA_8"
      },
      "source": [
        "#Create Datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-X7tJDJ6BA_8",
        "outputId": "74cb4d34-a06d-424a-ae37-d565f0c1051e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dimensioni dei dati: torch.Size([10, 3876, 96])\n",
            "Dimensioni del target: torch.Size([10, 3876, 2])\n",
            "Finestre Training : 14\n"
          ]
        }
      ],
      "source": [
        "class RegressionDataset(torch.utils.data.Dataset):\n",
        "\n",
        "    def __init__(self, target, spike_in):\n",
        "        self.num_samples = target.shape[0]\n",
        "        timesteps = target.shape[2]\n",
        "\n",
        "        target   = target.astype(np.float32)\n",
        "        spike_in = spike_in.astype(np.float32)\n",
        "\n",
        "        labels_target = []\n",
        "        lista = []\n",
        "        for i in range(self.num_samples):\n",
        "          target_i = target[i, :, :]\n",
        "          target_i = np.transpose(target_i, (1, 0))\n",
        "          labels_target.append(torch.from_numpy(target_i))\n",
        "\n",
        "          spike_ing = spike_in[i, :, :]\n",
        "          spike_ing = np.transpose(spike_ing, (1, 0))\n",
        "          lista.append(torch.from_numpy(spike_ing))\n",
        "\n",
        "        self.labels   = torch.stack(labels_target, dim=1)\n",
        "        self.features = torch.stack(lista, dim=1)\n",
        "\n",
        "\n",
        "    def __len__(self):\n",
        "        \"\"\"Number of samples.\"\"\"\n",
        "        return self.num_samples\n",
        "\n",
        "    def __getitem__(self, idx):\n",
        "        \"\"\"General implementation, but we only have one sample.\"\"\"\n",
        "        return self.features[:, idx, :], self.labels[:, idx, :]\n",
        "\n",
        "\n",
        "\n",
        "training_dataset = RegressionDataset(target=training_output, spike_in = training_input)\n",
        "train_loader     = torch.utils.data.DataLoader(dataset=training_dataset, batch_size=train_setting['batch'], drop_last=False)\n",
        "\n",
        "validation_dataset = RegressionDataset(target=validation_output, spike_in = validation_input)\n",
        "validation_loader  =  torch.utils.data.DataLoader(dataset=validation_dataset,  batch_size=train_setting['batch'], drop_last=True)\n",
        "\n",
        "test_dataset = RegressionDataset(target=test_output, spike_in = test_input)\n",
        "test_loader =  torch.utils.data.DataLoader(dataset=test_dataset, batch_size = 1, drop_last=False)\n",
        "\n",
        "\n",
        "for data, target in train_loader:\n",
        "    print(\"Dimensioni dei dati:\", data.shape)\n",
        "    print(\"Dimensioni del target:\", target.shape)\n",
        "    print(\"Finestre Training :\", len(train_loader))\n",
        "    break  # Uscire dopo il primo batch"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECWezOF0BA_9"
      },
      "source": [
        "#Construct Model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "bEM_frPwBA_9"
      },
      "outputs": [],
      "source": [
        "class Net(torch.nn.Module):\n",
        "    \"\"\"Simple spiking neural network in snntorch.\"\"\"\n",
        "\n",
        "    def __init__(self, timesteps, input = 1, target_variables = 1):\n",
        "        super().__init__()\n",
        "\n",
        "        l0 = train_setting['hyperparams'][0]\n",
        "        l1 = train_setting['hyperparams'][1]\n",
        "        l2 = train_setting['hyperparams'][2]\n",
        "\n",
        "        self.timesteps = timesteps # number of time steps to simulate the network\n",
        "        spike_grad = surrogate.fast_sigmoid() # surrogate gradient function\n",
        "\n",
        "        if train_setting['1thr4layer']:\n",
        "          thr_in       = train_setting['thr_init']\n",
        "          thr_hidden   = train_setting['thr_init']\n",
        "          thr_hidden2  = train_setting['thr_init']\n",
        "        else:\n",
        "          thr_in      = torch.ones(l0)*train_setting['thr_init']\n",
        "          thr_hidden  = torch.ones(l1)*train_setting['thr_init']\n",
        "          thr_hidden2 = torch.ones(l2)*train_setting['thr_init']\n",
        "\n",
        "        if train_setting['1beta4layer']:\n",
        "          beta_in      = train_setting['bet_init']\n",
        "          beta_hidden  = train_setting['bet_init']\n",
        "          beta_hidden2 = train_setting['bet_init']\n",
        "          beta_out     = train_setting['bet_init']\n",
        "        else:\n",
        "          beta_in      = torch.ones(l0)*train_setting['bet_init']\n",
        "          beta_hidden  = torch.ones(l1)*train_setting['bet_init']\n",
        "          beta_hidden2 = torch.ones(l2)*train_setting['bet_init']\n",
        "          beta_out     = torch.ones(target_variables)*train_setting['bet_init']\n",
        "          if train_setting['beta_out_single']:\n",
        "            beta_out   = train_setting['bet_init']\n",
        "\n",
        "\n",
        "        # layer 1\n",
        "        self.fc_in = torch.nn.Linear(in_features=input, out_features=l0, bias = train_setting['bias'])\n",
        "        self.lif_in = snn.Leaky(beta=beta_in, threshold=thr_in, learn_beta=train_setting['decay_train'], spike_grad=spike_grad, learn_threshold=train_setting['thr_train'], reset_mechanism=train_setting['reset'])\n",
        "\n",
        "        # layer 2\n",
        "        self.fc_hidden = torch.nn.Linear(in_features=l0, out_features=l1, bias = train_setting['bias'])\n",
        "        self.lif_hidden = snn.Leaky(beta=beta_hidden, threshold=thr_hidden, learn_beta=train_setting['decay_train'], spike_grad=spike_grad, learn_threshold=train_setting['thr_train'], reset_mechanism=train_setting['reset'])\n",
        "\n",
        "        # layer 3\n",
        "        self.fc_hidden2 = torch.nn.Linear(in_features=l1, out_features=l2, bias = train_setting['bias'])\n",
        "        self.lif_hidden2 = snn.Leaky(beta=beta_hidden2, threshold=thr_hidden2, learn_beta=train_setting['decay_train'], spike_grad=spike_grad, learn_threshold=train_setting['thr_train'], reset_mechanism=train_setting['reset'])\n",
        "\n",
        "        # layer 4: leaky integrator neuron. Note the reset mechanism is disabled and we will disregard output spikes.\n",
        "        self.fc_out = torch.nn.Linear(in_features=l2, out_features = target_variables, bias = train_setting['bias'])\n",
        "        self.li_out = snn.Leaky(beta=beta_out, threshold=1.0, learn_beta=train_setting['decay_train'], spike_grad=spike_grad, reset_mechanism=\"none\")\n",
        "\n",
        "\n",
        "    def forward(self, x):\n",
        "        \"\"\"Forward pass for several time steps.\"\"\"\n",
        "\n",
        "        # Initalize membrane potential\n",
        "        mem_1 = self.lif_in.init_leaky()\n",
        "        mem_2 = self.lif_hidden.init_leaky()\n",
        "        mem_22 = self.lif_hidden2.init_leaky()\n",
        "        mem_3 = self.li_out.init_leaky()\n",
        "\n",
        "        # Empty lists to record outputs\n",
        "        mem_3_rec = []\n",
        "\n",
        "        # Loop over\n",
        "        for step in range(self.timesteps):\n",
        "            x_timestep = x[step, :, :]\n",
        "\n",
        "            cur_in = self.fc_in(x_timestep)\n",
        "            spk_in, mem_1 = self.lif_in(cur_in, mem_1)\n",
        "\n",
        "            cur_hidden = self.fc_hidden(spk_in)\n",
        "            spk_hidden, mem_2 = self.lif_hidden(cur_hidden, mem_2)\n",
        "\n",
        "            cur_hidden2 = self.fc_hidden2(spk_hidden)\n",
        "            spk_hidden2, mem_22 = self.lif_hidden2(cur_hidden2, mem_22)\n",
        "\n",
        "            cur_out = self.fc_out(spk_hidden2)\n",
        "            _, mem_3 = self.li_out(cur_out, mem_3)\n",
        "\n",
        "            mem_3_rec.append(mem_3)\n",
        "\n",
        "        out = torch.stack(mem_3_rec)\n",
        "\n",
        "        return out\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4eWlGgnvBA_9",
        "outputId": "e41f00a5-fd4f-452c-ded6-dc9c43116e69"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "cpu\n"
          ]
        }
      ],
      "source": [
        "n_last = training_output.shape[1]\n",
        "steps  = training_input.shape[2]\n",
        "\n",
        "if torch.cuda.is_available():\n",
        "  device = torch.device('cuda')\n",
        "  print(torch.cuda.get_device_name(0))\n",
        "else:\n",
        "  device = torch.device('cpu')\n",
        "  print('cpu')\n",
        "\n",
        "model = Net(timesteps=steps, input = 96, target_variables = n_last).to(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s_iLz12RBA_-"
      },
      "source": [
        "#Construct Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "XkLtqAj2BA_-",
        "outputId": "7f54c4f9-aa7e-4dfc-de8d-4c1f875d83df"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 10/10 [29:09<00:00, 174.92s/it, loss=1.788e+03]\n"
          ]
        }
      ],
      "source": [
        "num_iter = train_setting['epochs']\n",
        "optimizer = torch.optim.Adam(params=model.parameters(), lr=train_setting['lr'])\n",
        "loss_function = torch.nn.MSELoss()\n",
        "\n",
        "loss_hist   = []\n",
        "v_loss_hist = []\n",
        "v_loss_best = 1e10\n",
        "epoca = 0\n",
        "\n",
        "# training loop\n",
        "with tqdm.trange(num_iter) as pbar:\n",
        "    for _ in pbar:\n",
        "        train_batch = iter(train_loader)\n",
        "        minibatch_counter = 0\n",
        "        loss_epoch = []\n",
        "\n",
        "        for feature, label in train_batch:\n",
        "            # prepare data\n",
        "            feature = feature.to(device)\n",
        "            label   = label.to(device)\n",
        "\n",
        "            feature =torch.swapaxes(input=feature, axis0=0, axis1=1)\n",
        "            label   = torch.swapaxes(input=label, axis0=0, axis1=1)\n",
        "\n",
        "            # forward pass\n",
        "            mem     = model(feature)\n",
        "\n",
        "            loss_val = loss_function(mem, label) # calculate loss\n",
        "            optimizer.zero_grad() # zero out gradients\n",
        "            loss_val.backward()   # calculate gradients\n",
        "            optimizer.step()      # update weights\n",
        "\n",
        "            # store loss\n",
        "            loss_epoch.append(loss_val.item())\n",
        "            minibatch_counter += 1\n",
        "\n",
        "            avg_batch_loss = sum(loss_epoch) / minibatch_counter # calculate average loss p/epoch\n",
        "            pbar.set_postfix(loss=\"%.3e\" % avg_batch_loss)       # print loss p/batch\n",
        "\n",
        "        loss_hist.append(avg_batch_loss)\n",
        "        epoca = epoca + 1\n",
        "        torch.save(model.state_dict(), trained_folder +  '/network')\n",
        "\n",
        "        model.eval()\n",
        "        vloss_sum = 0\n",
        "        with torch.no_grad():\n",
        "          for v_feature, v_label in validation_loader:\n",
        "            v_feature = torch.swapaxes(input=v_feature, axis0=0, axis1=1)\n",
        "            v_label   = torch.swapaxes(input=v_label, axis0=0, axis1=1)\n",
        "            voutputs = model(v_feature)\n",
        "            vloss_sum += loss_function(voutputs, v_label)\n",
        "          vloss = vloss_sum.mean()\n",
        "          v_loss_hist.append(vloss)\n",
        "          if vloss < v_loss_best:\n",
        "            best_epoch = epoca\n",
        "            v_loss_best = vloss\n",
        "            torch.save(model.state_dict(), trained_folder + '/network_best')\n",
        "\n",
        "\n",
        "        clear_output(wait=True)\n",
        "        plt.figure(figsize=(10, 6))\n",
        "        plt.semilogy(loss_hist, label='Training Loss')\n",
        "        plt.semilogy(v_loss_hist, label = 'Validation Loss')\n",
        "        plt.title('best epoch {}'.format(best_epoch))\n",
        "        plt.legend()\n",
        "        plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 545
        },
        "id": "6pe1dMgFWYDd",
        "outputId": "6dd070f5-8474-44d1-f693-979acf827de2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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iIiIi4jIuZVmp9sqCO/65u95oj49H7v3o/MYbb3DXXXflPC5WrBhRUVE5j//v//6PWbNmMWfOHIYNG3bN9+nXrx+PPPIIAG+//TZjxoxhw4YNdOjQ4R+Pz8rK4tNPP6VixYoADBs2jDfeeCPn+U8++YQXX3yRrl27AjB27NicVSNn7N+/nzlz5rB69WqaNGkCwNSpUwkPD2f27Nk8+OCDHDt2jG7dulGzZk0AKlSokPP6Y8eOUadOHerVqwc4VuvyI61U5WO5/RsREREREckf/iwJf0pJSWHUqFFUrVqVIkWK4Ofnx+7du2+4UlWrVq2cbV9fXwICAoiPj7/m8T4+PjmFCqBkyZI5xyclJREXF0eDBg1ynrdYLERHR9/S13a53bt34+bmRsOGDXP2FS9enMqVK7N7924ARowYwZtvvknTpk159dVX2bZtW86xgwcPZvr06dSuXZvnnnuONWvWOJ0lL2mlKp86nXSJ7hPW0rJyMK0ig2laKQhfT/3jEhERkcLN293CrjfaG/K5ucnX1/eKx6NGjWLhwoW8//77VKpUCW9vb7p3705mZuZ138fd3f2KxyaTCZvNdkvHG/2L/P79+9O+fXvmzp3L77//zjvvvMMHH3zA8OHD6dixI0ePHmXevHksXLiQtm3bMnToUN5//31DM/+dVqryqWV7z3Iy8RLT1h9j4JTN1HljIY9+sZ4vVh7iQHyK4X/5RURERIxgMpnw8XC7439MJlOefl2rV6+mX79+dO3alZo1a1KiRAmOHDmSp5/5d4GBgYSGhrJx48acfVarlS1btjj9nlWrViU7O5v169fn7Dt37hx79+6lWrVqOfvCw8MZNGgQM2fO5F//+hcTJ07MeS44OJi+ffvy7bffMnr0aD7//HOn8+QVLX3kU11ql6ZEgBdL98azdG88x89fYtWBBFYdSODNubsJL+ZNq8gQWlcJpnGFILw9cve3JyIiIiJy50RERDBz5kw6deqEyWTi5Zdfvu6KU14ZPnw477zzDpUqVaJKlSp88sknXLhw4aZK5fbt2/H39895bDKZiIqKonPnzgwYMIDPPvsMf39/XnjhBUqXLk3nzp0BGDlyJB07diQyMpILFy6wdOlSqlatCsArr7xCdHQ01atXJyMjg19//TXnufxEpSqf8vaw0LpKCK2rhGC32zmUkMqyvWdZtjee9YfOc/z8JaasO8qUdUfxcDPTqEJxWlcOplXlEMoH+d74A0REREQk3/jwww95/PHHadKkCUFBQTz//PMkJyff8RzPP/88Z86coU+fPlgsFgYOHEj79u2xWG78C/wWLVpc8dhisZCdnc2kSZN46qmnuO+++8jMzKRFixbMmzcv51REq9XK0KFDOXHiBAEBAXTo0IGPPvoIcNxr68UXX+TIkSN4e3vTvHlzpk+fnvtf+G0y2XUe2RWSk5MJDAwkKSmJgIAAo+P8o9SMbNYePMfSvfE5pwlerlxxH1pVDqFV5WAaVSiOVy6fAywiIiJyJ6Snp3P48GHKly+Pl5eX0XEKJZvNRtWqVenRowf/93//Z3ScPHG9v2c32w20UuWCfD3daFctlHbVQrHb7RyIT3GcJrjnLBuPnOfIuTQmrznC5DVH8HI306RiEK0qB9O6cgjhxXyMji8iIiIi+dTRo0f5/fffadmyJRkZGYwdO5bDhw/Ts2dPo6PlaypVLs5kMhER6k9EqD8DW1TkYnoWqw+cY/k+R8k6k5zOkj3xLNkTD+ykYrAvrSqH0LpyCPXLF8XTTatYIiIiIuJgNpuZPHkyo0aNwm63U6NGDRYtWpQvr2PKT3T639+4wul/N8tut7PnzEWW7T3L0r3xbD56Aavtr3/cPh4WmlQMonUVx7VYpYt4G5hWRERE5Eo6/U/uBJ3+J9dlMpmoWjKAqiUDGNyqIkmXslh9IIGle+JZtu8sZy9msGh3HIt2xwEQGepH68ohtKwcTL2yxfBw08R9EREREZEbUakqRAK93bmnZknuqVkSm83OrtPJLPtj2MWWYxfYF5fCvrgUPltxCD9PN5pVclyL1apyCCUC9dshEREREZF/olJVSJnNJmqUDqRG6UCGtYkgMS2TFfsTWLY3nuV7z3IuNZPfdp7ht51nAKhaMiBn2EXdMkVws2gVS0REREQEVKrkD0V8PLg/qhT3R5XCZrOz/WRSzrVYW08ksvt0MrtPJzNh2UH8vdxoERFMq8rBtKwcTIi/VrFEREREpPBSqZKrmM0mosKLEBVehKfaRXAuJYOV+xNYujeeFfvOciEti7nbTzN3+2kAapQOoPUf98WqHV4Ui/nGd9wWERERESkoVKrkhor7edKlTmm61CmN1WZn64lElv0x7GLbiSR2nExmx8lkPllygCI+7n+tYkUGU9zP0+j4IiIiIiJ5ShfGyC2xmE3ULVOUZ+6uzJxhzdj4n3a8/2AU99UqSYCXG4lpWczZeopnvt9KvbcW0Xncaj5auI/Y44nYbJreLyIiInKzWrVqxciRI3MelytXjtGjR1/3NSaTidmzZ9/2Z+fW+xQWWqmS2xLs70n36DC6R4eRbbURczyRZXsdNx7edTqZrccT2Xo8kY8X76eYrwctIx2rWC0iginq62F0fBEREZFc16lTJ7Kysvjtt9+uem7lypW0aNGCrVu3UqtWrVt6340bN+Lr65tbMQF47bXXmD17NrGxsVfsP336NEWLFs3Vz/q7yZMnM3LkSBITE/P0c+4ElSrJNW4WM/XLFaN+uWI8274KccnpLP9j2MWq/QmcT81kVsxJZsWcxGyC2uFF/rgWK4TqpQIw61osERERKQCeeOIJunXrxokTJwgLC7viuUmTJlGvXr1bLlQAwcHBuRXxhkqUKHHHPqsg0Ol/kmdCA7zoUT+cCY9Gs+WVu5g+sBFPtqxAlRL+2Oyw5VgiHyzcR6exq2jw9mJG/bCVX7edIikty+joIiIiIk677777CA4OZvLkyVfsT0lJ4YcffuCJJ57g3LlzPPLII5QuXRofHx9q1qzJd999d933/fvpf/v376dFixZ4eXlRrVo1Fi5ceNVrnn/+eSIjI/Hx8aFChQq8/PLLZGU5ftaaPHkyr7/+Olu3bsVkMmEymXIy//30v+3bt9OmTRu8vb0pXrw4AwcOJCUlJef5fv360aVLF95//31KlixJ8eLFGTp0aM5nOePYsWN07twZPz8/AgIC6NGjB3FxcTnPb926ldatW+Pv709AQADR0dFs2rQJgKNHj9KpUyeKFi2Kr68v1atXZ968eU5nuRGtVMkd4W4x06hCcRpVKM6LHatyKvESy/aeZdneeFYfSCAhJYMfN5/gx80n/rhuqwitKofQunIIVUv6YzJpFUtEREQAux2y0u7857r7wE3+POLm5kafPn2YPHky//nPf3J+jvnhhx+wWq088sgjpKSkEB0dzfPPP09AQABz586ld+/eVKxYkQYNGtzwM2w2Gw888AChoaGsX7+epKSkK66/+pO/vz+TJ0+mVKlSbN++nQEDBuDv789zzz3HQw89xI4dO/jtt99YtGgRAIGBgVe9R2pqKu3bt6dx48Zs3LiR+Ph4+vfvz7Bhw64ojkuXLqVkyZIsXbqUAwcO8NBDD1G7dm0GDBhwU9+3v399fxaq5cuXk52dzdChQ3nooYdYtmwZAL169aJOnTpMmDABi8VCbGws7u7uAAwdOpTMzExWrFiBr68vu3btws/P75Zz3CyVKjFEqSLe9GxYhp4Ny5CZbWPTkfMs3RvP0r1nORCfwsYjF9h45ALvLdhLaIAnrSJDaF0lmKaVgvD3cjc6voiIiBglKw3eLnXnP/ffp8Dj5q9nevzxx3nvvfdYvnw5rVq1Ahyn/nXr1o3AwEACAwMZNWpUzvHDhw9nwYIFfP/99zdVqhYtWsSePXtYsGABpUo5vh9vv/02HTt2vOK4l156KWe7XLlyjBo1iunTp/Pcc8/h7e2Nn58fbm5u1z3db9q0aaSnp/PNN9/kXNM1duxYOnXqxLvvvktoaCgARYsWZezYsVgsFqpUqcK9997L4sWLnSpVixcvZvv27Rw+fJjw8HAAvvnmG6pXr87GjRupX78+x44d49lnn6VKlSoARERE5Lz+2LFjdOvWjZo1awJQoUKFW85wK1SqxHAebmaaVAqiSaUg/nMvHD+fxrJ9Z1m2J57VBxOIS85gxqbjzNh0HDeziXrliuZcixUZ6qdVLBEREcl3qlSpQpMmTfjqq69o1aoVBw4cYOXKlbzxxhsAWK1W3n77bb7//ntOnjxJZmYmGRkZ+Pj43NT77969m/Dw8JxCBdC4ceOrjpsxYwZjxozh4MGDpKSkkJ2dTUBAwC19Lbt37yYqKuqKIRlNmzbFZrOxd+/enFJVvXp1LBZLzjElS5Zk+/btt/RZl39meHh4TqECqFatGkWKFGH37t3Ur1+fZ555hv79+zNlyhTatWvHgw8+SMWKFQEYMWIEgwcP5vfff6ddu3Z069bNqevYbpZKleQ74cV86N2oLL0blSU9y8qGw45VrOV7z3IoIZV1h86z7tB53pm/h1KBXrSqEkKrSMcqlq+n/kqLiIgUaO4+jlUjIz73Fj3xxBMMHz6ccePGMWnSJCpWrEjLli0BeO+99/j4448ZPXo0NWvWxNfXl5EjR5KZmZlrkdeuXUuvXr14/fXXad++PYGBgUyfPp0PPvgg1z7jcn+eevcnk8mEzWbLk88Cx+TCnj17MnfuXObPn8+rr77K9OnT6dq1K/3796d9+/bMnTuX33//nXfeeYcPPviA4cOH50kW/QQq+ZqXu4UWkcG0iAyGTnAkIZVlex03Hl578BynktKZtv4Y09Yfw8NipkH5YrSqHEzbqqGUD8rdkaMiIiKSD5hMt3QanpF69OjBU089xbRp0/jmm28YPHhwzhk2q1evpnPnzjz66KOA4xqiffv2Ua1atZt676pVq3L8+HFOnz5NyZIlAVi3bt0Vx6xZs4ayZcvyn//8J2ff0aNHrzjGw8MDq9V6w8+aPHkyqampOatVq1evxmw2U7ly5ZvKe6v+/PqOHz+es1q1a9cuEhMTr/geRUZGEhkZydNPP80jjzzCpEmT6Nq1KwDh4eEMGjSIQYMG8eKLLzJx4kSVKhGAckG+9AsqT7+m5bmUaWXdoXOO+2LtPcux82msOpDAqgMJvDl3N1HhRehauxT3RZUiyM/T6OgiIiJSyPj5+fHQQw/x4osvkpycTL9+/XKei4iI4Mcff2TNmjUULVqUDz/8kLi4uJsuVe3atSMyMpK+ffvy3nvvkZycfEV5+vMzjh07xvTp06lfvz5z585l1qxZVxxTrlw5Dh8+TGxsLGFhYfj7++PpeeXPTb169eLVV1+lb9++vPbaa5w9e5bhw4fTu3fvnFP/nGW1Wq+6R5anpyft2rWjZs2a9OrVi9GjR5Odnc2QIUNo2bIl9erV49KlSzz77LN0796d8uXLc+LECTZu3Ei3bt0AGDlyJB07diQyMpILFy6wdOlSqlateltZr0cj1cVleXtYaF0lhNc712D5s61Y/K+WvHxfNZpVCsJiNrH1eCKv/bKLhm8v5rFJG/g59iSXMq//mxgRERGR3PTEE09w4cIF2rdvf8X1Ty+99BJ169alffv2tGrVihIlStClS5ebfl+z2cysWbO4dOkSDRo0oH///rz11ltXHHP//ffz9NNPM2zYMGrXrs2aNWt4+eWXrzimW7dudOjQgdatWxMcHPyPY919fHxYsGAB58+fp379+nTv3p22bdsyduzYW/tm/IOUlBTq1KlzxZ9OnTphMpn4+eefKVq0KC1atKBdu3ZUqFCBGTNmAGCxWDh37hx9+vQhMjKSHj160LFjR15//XXAUdaGDh1K1apV6dChA5GRkYwfP/62816LyW632/Ps3V1QcnIygYGBJCUl3fJFfJJ/nL2Ywa/bTjE75iRbTyTl7Pf1sNC+Rgm61ilNk4qO8iUiIiL5U3p6OocPH6Z8+fJ4eXkZHUcKqOv9PbvZbqDT/6RACvb35LGm5XmsaXkOnk3h55iTzIo9yfHzl5i55SQzt5wkxN+TzrVL0aVOaaqVDNAUQRERERFxilaq/kYrVQWX3W5ny7ELzIo5ya/bTpOY9tcdviND/ehSpzSda5emdBFvA1OKiIjIn7RSJXdCbqxUqVT9jUpV4ZCZbWPZ3nhmx55k0e54MrP/GvfZsHwxutYpTceaJQn01o2GRUREjKJSJXeCTv8TcZKHm5m7q5fg7uolSLqUxW87TjMr5iTrDp1n/WHHn1fm7KRtlRC61ilNq8oheLhprouIiIiIXE2lSgq9QG93Hqpfhofql+Fk4iXmxJ5iVswJ9sWlMH/HGebvOEMRH3furVmSrnVKE122qK6/EhEREZEcKlUilyldxJvBrSoyqGUFdp++yOzYk/wce5K45Aymrj/G1PXHCC/mTZfapelSpzQVg/2MjiwiIlLg2Wy2Gx8k4qTc+Pula6r+RtdUyd9ZbXbWHjzHrJiT/LbjNKmX3euqVlggXWqXplNUKYL9dYNhERGR3GSz2di/fz8Wi4Xg4GA8PDx0tojkGrvdTmZmJmfPnsVqtRIREYHZfOXlHhpU4SSVKrmeS5lWFu6OY3bMSZbvO4vV5vjXx2I20TwiiK51SnNXtVB8PLQILCIikhsyMzM5ffo0aWlpRkeRAsrHx4eSJUvi4eFx1XMqVU5SqZKblZCSwdxtjgEXsccTc/b7eFjoUL0EXeqUpknF4rhZNOBCRETkdtjtdrKzs7FarTc+WOQWWCwW3NzcrrkCqlLlJJUqccbhhFRmx5xkduxJjp776zdpwf6e3B9Viq51SlO9lG4wLCIiIuJKVKqcpFIlt8Nxg+FEZsec5Ndtp7hw2Q2GI0L+vMFwKcKK+hiYUkRERERuhkqVk1SqJLdkZttYse8ss2JPsnBX3BU3GG7wxw2G76lRkkAf3WBYREREJD9SqXKSSpXkheT0LH7bcYbZMSdZe+gcf/5b52Ex06ZKCF3qlKZ1lWA83SzGBhURERGRHCpVTlKpkrx2OunPGwyfZM+Zizn7A7zcuLdWKR6oW5roMkUxm3X9lYiIiIiRVKqcpFIld9Lu08k5Ay7ikjNy9ocV/esGw5VCdINhERERESOoVDlJpUqMYLXZWX/IcYPh+TvOkJKRnfNczdKBdKlTmk5RJQnx9zIwpYiIiEjholLlJJUqMVp6lpVFu+OYtcVxg+HsP24wbDZBs4hgutYpxd3VSuDrqRsMi4iIiOQllSon5atSlXgcioQbm0EMdS4lg7nbHTcYjjmWmLPfx8NC+z9uMNxUNxgWERERyRMqVU7KN6Uq4QCMjYbwhlC7F1TvCl5aOSvMjiSkMjv2JLNjTnLkshsMB/n9dYPhGqV1g2ERERGR3KJS5aR8U6pipsKcYWD/495G7j5Q9X6o8yiUbQpmrUwUVna7ndjjjhsM/7LtNOdTM3OeqxjsS9c6pelcuzThxXSDYREREZHboVLlpHxTqgCST8O26Y6CdW7/X/uLlHWsXtV+BIqUMS6fGC7LamPl/rPMijnF7zvPkHHZDYbrlytK1zph3FtTNxgWERERcYZKlZPyVan6k90OJzZCzLewYyZk/nlvIxOUb+FYvaraCdy9DY0pxrr45w2GY0+y5uCVNxhuXSWYrnVK07pKiG4wLCIiInKTVKqclC9L1eUy02D3LxD7LRxe8dd+zwCo8QDUfhTC6oGuqynUziSlM2frSWbFnGL36eSc/Y4bDJekS+3S1C9XTDcYFhEREbkOlSon5ftSdbkLR2HrdxA7FRKP/bU/qDLU6QW1Hgb/UOPySb6w50wys2NO8XPsSU4npefsL13Em861HQMuIkL9DUwoIiIikj+pVDnJpUrVn2w2OLLSUa52zYHsS479JgtE3OW4/iqyA7h5GJtTDGWz2Vl/+DyzYk4wf/sZLl52g+EapQPoUrs090eVIiRANxgWERERAZUqp7lkqbpcehLsnOUYbnFiw1/7fYpDzR6OFawSNY3LJ/lCepaVxbvjmRVzkmV743NuMGwxm7i3ZkkGtqhAjdKBBqcUERERMZZKlZNcvlRd7uw+x+rV1umQcuav/SVqOYZb1HwQfIoZl0/yhfOpmY4bDG85wZbLbjDctFJxBjSvQMvIYN37SkRERAqlQl+qEhMTadeuHdnZ2WRnZ/PUU08xYMCAG76uQJWqP1mz4eASx3CLPfPAluXYb/GAyvc4ClbFNmDWVLjCbsfJJL5YeYhftp3G+sfqVeVQfwa0qMD9UaXwcNP90URERKTwKPSlymq1kpGRgY+PD6mpqdSoUYNNmzZRvHjx676uQJaqy6Weg+0/OArWme1/7fcvCVEPO6YHBlUyLp/kCycTL/HVqsNM33CM1EwrACUCvHisaTkeaViGAC/d90pEREQKvkJfqi53/vx56taty6ZNmwgKCrrusQW+VF3u9DbH6YHbvodL5//aH97QMdyielfwKuDfA7mupEtZTFt/jEmrDxN/MQMAP083HmkQzmNNy1OqiO6NJiIiIgXXzXaD2zqX57///S8mk4mRI0fezttcZcWKFXTq1IlSpUphMpmYPXv2Px43btw4ypUrh5eXFw0bNmTDhg1XPJ+YmEhUVBRhYWE8++yzNyxUhU7JWtDxXfjXHujxDUS0B5MZjq+HX0bAB5Vh1iA4vNIxYVAKnUBvdwa3qsjK51vzXvdaRIb6kZKRzcSVh2nxv6U8PSOWXaeSb/xGIiIiIgWY06Vq48aNfPbZZ9SqVeu6x61evZqsrKyr9u/atYu4uLh/fE1qaipRUVGMGzfumu87Y8YMnnnmGV599VW2bNlCVFQU7du3Jz4+PueYIkWKsHXrVg4fPsy0adOu+XmFnpsnVOsMvb6HZ3ZDu9eheARkpTnug/X1fTCmNix798r7YUmh4elm4cF64SwY2YJJ/erTqEIxsm12ZsWc5J4xK+n95XpW7U+gECx8i4iIiFzFqdP/UlJSqFu3LuPHj+fNN9+kdu3ajB49+qrjbDYbdevWJSIigunTp2OxOAYh7N27l5YtW/LMM8/w3HPPXT+gycSsWbPo0qXLFfsbNmxI/fr1GTt2bM5nhYeHM3z4cF544YWr3mfIkCG0adOG7t27X/fzCtXpf9djt8OJjRDzLeyYCZkX/3jCBOVbOIZbVO0E7jr9q7DadiKRz1ccYt720/wx04JqJQMY2KIC99YqibtFQy1ERETEteXp6X9Dhw7l3nvvpV27dtc9zmw2M2/ePGJiYujTpw82m42DBw/Spk0bunTpcsNCdS2ZmZls3rz5is83m820a9eOtWvXAhAXF8fFi44ikJSUxIoVK6hcufI133PcuHFUq1aN+vXrO5WpwDGZILwB3D8GRu2Drp87yhR2OLwcZg6A9yPhl6fg+EZHCZNCpVZYEcb2rMvyZ1vTr0k5vN0t7DqdzMgZsbT831K+WHmIlMtuMCwiIiJSULnd6gumT5/Oli1b2Lhx400dX6pUKZYsWULz5s3p2bMna9eupV27dkyYMOGWw/4pISEBq9VKaGjoFftDQ0PZs2cPAEePHmXgwIHY7XbsdjvDhw+nZs1r3/R26NChDB06NKeNymU8fCDqIcefC0cdpwTGTnWcCrh5suNPUGXHjYVrPQz+oTd6RylAwov58Nr91RnZLoJv1x1l8pqjnEpK5825u/l48X56NSzLY03LERrgZXRUERERkTxxS6Xq+PHjPPXUUyxcuBAvr5v/AalMmTJMmTKFli1bUqFCBb788ss8v5logwYNiI2NzdPPKJSKloVWL0CL5+DISke52jUHEvbCwldg0esQcZdjemBkB3DzMDqx3CFFfDwY1iaC/s0rMDvmJJ+vPMShs6l8uvwgX646ROfapRnYogKRof5GRxURERHJVbd0+t/mzZuJj4+nbt26uLm54ebmxvLlyxkzZgxubm5YrdZ/fF1cXBwDBw6kU6dOpKWl8fTTT99W6KCgICwWy1WDJ+Li4ihRosRtvbfcJLMZKrSEBz6HUXuh08cQ1gDsVtj3G3zfGz6sAvNfuPJ+WFLgeblbeLhBGRY93ZIv+tSjQbliZFnt/Lj5BHd/tIJ+kzaw5qCGWoiIiEjBcUsrVW3btmX79it/QH7ssceoUqUKzz//fM4gisslJCTQtm1bqlatyg8//MC+ffto1aoVnp6evP/++06F9vDwIDo6msWLF+cMsLDZbCxevJhhw4Y59Z5yG7wCIbqf48/ZfY7Vq63TIeUMrJ/g+FOilmO4Rc0HwaeY0YnlDjCbTbSrFkq7aqHEHLvAxJWH+G3HGZbtPcuyvWepWTqQAS0qcE+NErhpqIWIiIi4sNu++W+rVq2uO/2vYcOGhISEMGvWLDw8HKeCbd26lTZt2vDSSy/946pVSkoKBw4cAKBOnTp8+OGHtG7dmmLFilGmTBnAMVK9b9++fPbZZzRo0IDRo0fz/fffs2fPnquutboVmv6XS6zZcHAJxH4Le+aB7Y+x+hYPqHyPo2BVbAPmq4u4FFxHz6XyxcrD/LD5OOlZjnufhRX15olm5elRLxxfz1u+zFNEREQkz9xsN8jTUgWwcOFCmjdvftU1WDExMQQHBxMWFnbVa5YtW0br1q2v2t+3b18mT56c83js2LG89957nDlzhtq1azNmzBgaNmx4O1+OSlVeSD0H239wFKzLTwX0LwlRD0PtRyGoknH55I47n5rJlLVH+XrtEc6nZgKOGw0/2qgMfZuUI8RfQy1ERETEeHesVBU0KlV57PQ2x+mB276HS+f/2h/e0DHconpX8NL3vbBIz7Ly4+YTfLHyEEfOpQHgYTHTtU5pBrSoQKUQP4MTioiISGGmUuUklao7JDvDMdAiZiocWAh2x6lguPtAtc6OglW2qWMghhR4Vpudhbvi+HzFQbYcS8zZ365qCANbVKR+uaJ5PjFURERE5O9UqpykUmWAi2ccgy1ip0LCvr/2FynrKFe1H4EiZYzLJ3fU5qPn+Wz5IRbujsu5p3RUeBGebFGB9tVLYDGrXImIiMidoVLlJJUqA9ntcGITxEyBHTMh8+IfT5igfAvHcIuqncDd29CYcmccPJvCl6sO8+PmE2RmO1YyyxTzoX/z8jwYHY63h4aciIiISN5SqXKSSlU+kZkGu39xDLc4vOKv/Z4BUOMBx3CLsHqgU8IKvISUDL5Zc4Rv1h0lMc0xRbKojzu9G5ejT+OyBPl5GpxQRERECiqVKiepVOVDF47C1u8cpwcmHvtrf1BlqNMLaj0M/s6P0RfXkJaZ/cdQi8McO+8YauHpZqZbdBj9m5WnQrCGWoiIiEjuUqlykkpVPmazwdFVjuEWu36G7EuO/SYLRNzluP4qsgO4eRibU/KU1Wbntx1n+HzFQbaeSAIcC5Z3VQ3lyZYViC6rm0uLiIhI7lCpcpJKlYtIT4adsyDmWzix4a/9PsWhZg+o2xtCqxuXT/Kc3W5nw+HzfL7iEIv3xOfsjy5blAHNK3BXtVANtRAREZHbolLlJJUqF3R2n+PUwK3TIeXMX/vLt4BGQyHibo1mL+AOxF9k4orDzIo5SabVMdSifJAv/ZuXp1vdMLzcNdRCREREbp1KlZNUqlyYNRsOLnFMD9wzF+xWx/7ilaDhIKjdEzx8jc0oeSo+OZ2v1x5hytqjJKdnA1Dc14M+jcvRu3FZivnq1FARERG5eSpVTlKpKiASj8OGz2DzN5DhuO4GryIQ3Q8aDITA0kamkzyWmpHNjI3H+XLVYU4mOq6983I306NeOE80K0/Z4irXIiIicmMqVU5SqSpgMlIcpwaumwAXDjv2md2gWhdoPARKRxsaT/JWttXGvD+GWuw4mQyA2QQdapRgQPMK1ClT1OCEIiIikp+pVDlJpaqAsllh32+wdrxjguCfwhs5ylWV+8Cs624KKrvdztpD5/h8xSGW7T2bs79BuWIMbFGBNlVCMGuohYiIiPyNSpWTVKoKgdNbHeVqx09gc9xMliJlHNdd1ekNXvrnXpDtPXORz1ccYs7Wk2RZHf/3VzHYlwHNK9ClTmkNtRAREZEcKlVOUqkqRC6egQ0TYdNXcOm8Y5+Hv2Mce8MnoWg5Q+NJ3jqTlM6kNYeZtu4YFzMcQy2C/Dx5rGk5ejUsQxEfDbUQEREp7FSqnKRSVQhlXXKMY183ARL2OvaZzFD5Hmg8FMo0dtxdVgqki+lZzNh4nK9WHeZUUjoAPh6WnKEW4cV8DE4oIiIiRlGpcpJKVSFmt8OBxbBuPBxc/Nf+UnWg0RCo3hUs7sblkzyVZbXx67ZTfL7iMLtP/zXU4p6aJXmyRUVqhgUanFBERETuNJUqJ6lUCQDxux0rV9tmQLZj9QL/ktBgAEQ/Bj7FjM0necZut7PqQAKfrzjEyv0JOfsbVyjOwBYVaFU5GJNWLkVERAoFlSonqVTJFVITYNMk2DgRUuIc+9y8ofYjjtWroAhj80me2nUqmYkrD/HL1lNk2xz/VxkZ6seA5hXoXLs0Hm5mgxOKiIhIXlKpcpJKlfyj7AzYMRPWjYMz2//aH3G3o1xVaKXrrgqwU4mX+GrVYb7bcIzUTCsAoQGe9GtSnp4NyxDordNCRURECiKVKiepVMl12e1wZJXjuqu984E//vUJqQ6NBkPNB8Hdy9CIkneSLmXx3YZjTFp9mLjkDAB8PSw83KAMQ1pVpLifp8EJRUREJDepVDlJpUpu2rmDsP5TiJkKWamOfb7BUO8JqP8E+IUYm0/yTGa2jTlbTzFxxSH2xl0EIKyoN5Mfa0ClED+D04mIiEhuUalykkqV3LJLF2DLN7D+c0g+4dhn8YCaPaDxEAitbmw+yTN2u53l+87y6pydHD2XRqC3O5/3jqZhheJGRxMREZFcoFLlJJUqcZo1C3bPgbXj4eSmv/aXb+m431Wlu8CswQYF0bmUDPp/s4mYY4l4WMy83yOK+6NKGR1LREREbpNKlZNUqiRXHN/oGGqxaw7YHYMNKB4BjQZB1CPg4WtsPsl16VlWnpoew4KdjimRL3SswpMtKmj8uoiIiAtTqXKSSpXkqsRjsOFz2PwNZCQ59nkVgeh+0GAgBJY2Mp3kMqvNzltzd/PV6sMAPNqoDK91qo6bRSuUIiIirkilykkqVZInMi5C7DTHDYUvOH7gxuwG1bo4rrsqHW1oPMldX646zJtzd2G3Q9sqIXzSsw4+Hm5GxxIREZFbpFLlJJUqyVM2K+z7zXHd1dFVf+0Pb+QoV1XuA7PFuHySa37bcZqnpseSkW2jVlggX/StR4i/xu2LiIi4EpUqJ6lUyR1zKtaxcrXjJ7BlOfYVKQMNB0Gd3uClv3+ubvPRC/T/eiMX0rI0cl1ERMQFqVQ5SaVK7rjk07DxC9j0FVw679jn4Q91e0PDJ6FoOUPjye05nJDKY5M2cEQj10VERFyOSpWTVKrEMJlpsG2GY/UqYa9jn8kMVe6FRkOhTCPQJDmXpJHrIiIirkmlykkqVWI4mw0OLnGMZD+45K/9peo4ylX1LmBxNyyeOEcj10VERFyPSpWTVKokX4nfDevGw9YZYM1w7PMvBQ0GOMay+xQzNJ7cGo1cFxERcS0qVU5SqZJ8KTXBcc3VhomQGu/Y5+7juJFwo8EQFGFsPrklGrkuIiLiGlSqnKRSJfladoZjWuDa8RC3/a/9Ee0dI9nLt9R1Vy5CI9dFRETyP5UqJ6lUiUuw2+HISke52vcb8Me/xqE1HCtXNbqDu35Az+80cl1ERCR/U6lykkqVuJxzBx0TA2OnQlaaY59vMNR7Auo/AX4hxuaT69LIdRERkfxLpcpJKlXisi5dgM1fw4bPIfmkY5/FA2r2cJwaGFrd2HxyTRq5LiIikj+pVDlJpUpcnjULds9xnBp4ctNf+8u3hMZDodJdYNa0ufxGI9dFRETyH5UqJ6lUSYFyfAOsHecoWXabY1/xCGg0yDE50MPX2HxyBY1cFxERyV9UqpykUiUFUuIxWP8ZbPkGMpId+7yKQL3HoMFACNCpZvmJRq6LiIjkDypVTlKpkgIt4yLETIX1E+DCEcc+sxtU7wqNhkDpuobGk79o5LqIiIjxVKqcpFIlhYLNCnvnw7rxcHT1X/vLNIF2r0KZRsZlkxybj15gwDebOJ+a+cfI9fpUCvE3OpaIiEihoVLlJJUqKXROxThGsu/4CWzZjn3Vu0K716FoWWOzCUcSUumnkesiIiKGUKlykkqVFFrJp2DZfx3XXWEHiyc0GQbNngZPrY4YSSPXRUREjHGz3UAjpUTEIaAU3D8GBq2Ecs3BmgErP4BPomHLFMcpg2KI4n6efDegEe2rh5JptTHiuxg+XX4Q/U5MREQkf1CpEpErlagJfX+Bh6dBsQqQEgdzhsHnreDIKqPTFVpe7hbG94rm8ablAfjv/D28/PMOsq02g5OJiIiISpWIXM1kgir3wpD1cPdb4BkIZ7bB5HthxqNw/pDRCQsli9nEK52q8fJ91TCZ4Nt1x3hyymbSMrONjiYiIlKoqVSJyLW5eTiuqxqxBer3B5MZdv8C4xrC7y9DepLRCQulJ5qVZ0Kvuni6mVm8J56HPltH/MV0o2OJiIgUWipVInJjvkFw7wcwaDVUbAPWTFgzBsbUhU1f6XorA3SoUZJpAxpRzNeD7SeTeGD8Gg7EXzQ6loiISKGkUiUiNy+0Gjw6E3r+AMUjIC0Bfn0aPm0Oh5YZna7QiS5blJmDm1CuuA8nLlyi24S1rD90zuhYIiIihY5KlYjcGpMJIu+GIWuh4//AqwjE74RvOsO0hyHhgNEJC5VyQb78NLgJdcoUIelSFr2/3MCcraeMjiUiIlKoqFSJiHMs7tDwSRgRAw0HgdkN9s2H8Q3ht3/DpQtGJyw0NHJdRETEWCpVInJ7fIpBx3dh8FqIaA+2bFg3znG91YaJYNVkujtBI9dFRESMo1IlIrkjOBJ6fe+45iq4Klw6D/NGwadNYf8io9MVCn+OXH9FI9dFRETuKJUqEcldldrCoFWOaYHexeDsHpjaDb7tDmf3Gp2uUHhcI9dFRETuKJUqEcl9FjfHfa1GxEDjYWB2hwMLYXxjmPcspJ03OmGBp5HrIiIid45KlYjkHe8i0P4tGLoeqtwHdits+BzG1Ia14yE70+iEBZpGrouIiNwZKlUikveKV4SHp0KfORBaA9KTYMGLML4R7J0PmlKXZzRyXUREJO+pVInInVOhJTy5AjqNAd9gOH8QvnsYpnSBuJ1Gpyuw/mnk+oRlGrkuIiKSW1SqROTOMlsgui8M3wJNR4LFAw4tg0+bwS8jIeWswQELpr+PXH/3N41cFxERyS0mu35VeYXk5GQCAwNJSkoiICDA6DgiBd/5w7DoVdj1s+OxZwC0GOW4obCbp7HZCqivVh3m/+buwm6HtlVC+KRnHXw83IyOJSIiku/cbDfQSpWIGKtYeejxDfSbByWjICMZFr4C4xrA7l90vVUe0Mh1ERGR3KVSJSL5Q7mmMGAZdB4PfiXgwhGY8ShMvg9ObzU6XYGjkesiIiK5R6VKRPIPsxnq9ILhm6HFs+DmBUdXwWct4eehcDHO6IQFyt9Hrj8wfo1GrouIiDhBpUpE8h9PP2jzEgzbBDW6A3aI+RY+qQsrP4AsnaqWWy4fuZ6cnq2R6yIiIk5QqRKR/KtIOHT/Eh7/HUpHQ2YKLH4DxtaHHTN1vVUu+XPkeofqJTRyXURExAkqVSKS/5VpCE8sggcmgn8pSDoGPz4GX3WAk1uMTlcgeLlbGNerrkaui4iIOEGlSkRcg9kMtXrA8E3Q6kVw94Hj62Bia5g1CJJ1ytrtsphNvNKpGq/cVw2TCb5dd4wnp2wmLTPb6GgiIiL5mkqViLgWD19o9YLjeqtaDzv2bf0OPomGZe9CZpqx+QoAjVwXERG5NSpVIuKaAkvDA59B/yUQ3hCy0mDZ2zC2Hmz7Hmw6be12aOS6iIjIzVOpEhHXFhYNjy+A7l9BYBlIPgkzB8CXd8HxjUanc2kauS4iInJzVKpExPWZTFCjGwzbAG1eBndfOLkJvmwHPz4BiceNTuiyygX5MnNIU+pq5LqIiMg1qVSJSMHh7g0tRsGILVDnUcAEO350nBK45C3ISDE6oUsq5uvBNI1cFxERuSaVKhEpePxLQOdxMHAZlG0K2emw4n+OYRax03S9lRM0cl1EROTaVKpEpOAqVRv6zYUeU6BIWUg5A7MHO8awH11rdDqXo5HrIiIi/0ylSkQKNpMJqt0PwzbCXW+Ahz+cjoVJHeD7vnDhiNEJXY5GrouIiFxJpUpECgc3T2j6lON6q+h+YDLDrtkwtgEseg3Skw0O6Fr+PnK96ziNXBcRkcJLpUpEChe/EOj0MTy5Esq3BGsGrPrIcb3V5q/BZjU6ocu4fOT6yUSNXBcRkcJLpUpECqcSNaDPz/Dwd1CsIqTGwy8j4LOWcHiF0elchkaui4iIqFSJSGFmMkGVe2DIOmj/NngGQtx2+LoTTO8F5w4andAlaOS6iIgUdipVIiJuHtB4KIyIgfoDwGSBPb/CuIbw+0uQnmR0wnzvn0auvzRbI9dFRKRwMNn1q8QrJCcnExgYSFJSEgEBAUbHEREjxO+BBf+Gg4sdj32KQ+v/QN2+YHEzNpsL+GrVYf5v7i7sdmhTJYRPHqmDr6e+byIi4nputhtopUpE5O9CqkDvmdDrRwiKhLRzMPcZ+LQZHFxidLp8zzFyPRpPNzNL9sTz8OcauS4iIgWbSpWIyLVE3AWD10DH98C7KJzdDVO6wrSHIGG/0enytQ41SvDdQI1cFxGRwqHAlqrExETq1atH7dq1qVGjBhMnTjQ6koi4Ios7NBwIw7dAw8FgdoN9v8H4RjD/BUg7b3TCfKtuGY1cFxGRwqHAXlNltVrJyMjAx8eH1NRUatSowaZNmyhevPh1X6drqkTkuhL2O4ZX7PvN8di7KLR6Eeo9oeutruF8aib9v97IlmOJeFjMvN8jivujShkdS0RE5IYK/TVVFosFHx8fADIyMrDb7RrvKyK3LygCes6A3rMgpBpcugDzn4OfhxqdLN/SyHURESnobrlUTZgwgVq1ahEQEEBAQACNGzdm/vz5uRpqxYoVdOrUiVKlSmEymZg9e/Y/Hjdu3DjKlSuHl5cXDRs2ZMOGDVc8n5iYSFRUFGFhYTz77LMEBQXlak4RKcQqtoEnV8I97ztGsG+bDtt+MDpVvqWR6yIiUpDdcqkKCwvjv//9L5s3b2bTpk20adOGzp07s3Pnzn88fvXq1WRlZV21f9euXcTFxf3ja1JTU4mKimLcuHHXzDFjxgyeeeYZXn31VbZs2UJUVBTt27cnPj4+55giRYqwdetWDh8+zLRp0675eSIiTrG4QYMB0OJZx+O5/4LEY8ZmyscsZhOvdKrGK/dVw2SCqeuPMXTaFqw2rViJiIhry5VrqooVK8Z7773HE088ccV+m81G3bp1iYiIYPr06VgsFgD27t1Ly5YteeaZZ3juueeuH9BkYtasWXTp0uWK/Q0bNqR+/fqMHTs257PCw8MZPnw4L7zwwlXvM2TIENq0aUP37t2v+3m6pkpEbpk1G75qDyc3Qdlm0HcOmC1Gp8rXfttxhhHTY8jMtjG8TSX+dXdloyOJiIhc5Y5cU2W1Wpk+fTqpqak0btz46jc3m5k3bx4xMTH06dMHm83GwYMHadOmDV26dLlhobqWzMxMNm/eTLt27a74rHbt2rF27VoA4uLiuHjRMb43KSmJFStWULnytf+jPW7cOKpVq0b9+vWdyiQihZjFDR74HNx94egqWDPG6ET5XocaJXi3W00APllygN92nDY4kYiIiPOcKlXbt2/Hz88PT09PBg0axKxZs6hWrdo/HluqVCmWLFnCqlWr6NmzJ23atKFdu3ZMmDDB6dAJCQlYrVZCQ0Ov2B8aGsqZM2cAOHr0KM2bNycqKormzZszfPhwatasec33HDp0KLt27WLjxo1O5xKRQqx4Rej4rmN7yVtwKtbQOK6ga52wnGus/vX9VvbH6T5WIiLimpwqVZUrVyY2Npb169czePBg+vbty65du655fJkyZZgyZQozZszAzc2NL7/8EpPJ5HTom9GgQQNiY2PZunUr27Zt48knn8zTzxMRoc6jUOU+sGXBzAGQmWZ0onzvxXuq0KhCMVIzrQycspnk9KuvwRUREcnvnCpVHh4eVKpUiejoaN555x2ioqL4+OOPr3l8XFwcAwcOpFOnTqSlpfH00087HRggKCgIi8Vy1eCJuLg4SpQocVvvLSLiNJMJOo0BvxKQsA8WvmJ0onzP3WJmbM+6lAr04nBCKk9Pj8WmwRUiIuJicuU+VTabjYyMjH98LiEhgbZt21K1alVmzpzJ4sWLmTFjBqNGjXL68zw8PIiOjmbx4sVXZFi8ePE/XtslInLH+BaHLuMd2xsnwr7fjc3jAoL8PPmsdz083Mws3hPP6MX7jY4kIiJyS265VL344ousWLGCI0eOsH37dl588UWWLVtGr169rjrWZrPRsWNHypYtm3PqX7Vq1Vi4cCGTJk3io48++sfPSElJITY2ltjYWAAOHz5MbGwsx479Nar4mWeeYeLEiXz99dfs3r2bwYMHk5qaymOPPXarX5KISO6q1BYaDnZs/zwEUs4am8cF1AwL5J2ujutexyzez+87zxicSERE5Obd8kj1J554gsWLF3P69GkCAwOpVasWzz//PHfdddc/Hr9w4UKaN2+Ol5fXFftjYmIIDg4mLCzsqtcsW7aM1q1bX7W/b9++TJ48Oefx2LFjee+99zhz5gy1a9dmzJgxNGzY8Fa+nKtopLqI5IqsS/B5azi7GyI7wiPfOU4PlOt6bc5OJq85gp+nG7OHNqVSiJ/RkUREpBC72W6QK/epKkhUqkQk15zZDhPbgDUT7hsN9bSSfiNZVhu9vljPhsPnqRDsy+yhTQnwcjc6loiIFFJ35D5VIiJyHSVqQts/hlUs+DckHDA2jwtwt5gZ36suJQO9OHQ2lWdmaHCFiIjkfypVIiJ5qdFQKN8CstJgZn+wamT4jQT5efLpo9F4uJlZtDueMUs0uEJERPI3lSoRkbxkNkOXT8GrCJyKgWX/NTqRS4gKL8JbXWoAMHrRfhbuirvBK0RERIyjUiUiktcCS0On0Y7tVR/C0bWGxnEVD9YLp0/jsgA8MyOWg2dTDE4kIiLyz1SqRETuhOpdIeoRsNtg1kBITzI6kUt4+b5qNChXjIsZ2Qz8ZhMX03X6pIiI5D8qVSIid0rH/0GRMpB4DOY/b3Qal+BuMTOuV11KBHhx8Gwq//p+qwZXiIhIvqNSJSJyp3gFwAMTwWSGrd/BjplGJ3IJwf6efNo7Gg+Lmd93xTF2qaYoiohI/qJSJSJyJ5VpBM3/5dj+dSQknTQ0jquoHV6EN/8YXPHRon0s3q3BFSIikn+oVImI3Gktn4dSdR3XVc0eBDab0YlcQo/64TzaqAx2O4ycHsshDa4QEZF8QqVKROROs7g7TgN094HDK2DdOKMTuYxX7qtOvbJFHYMrpmwmJSPb6EgiIiIqVSIihgiqBO3fdmwvfgPObDc2j4vwcDMz/tG6hAZ4ciA+hX99H6vBFSIiYjiVKhERo0T3g8r3gDUTfhoAWZeMTuQSQvy9mPCoY3DFgp1xjF+mwRUiImIslSoREaOYTHD/J+AbAmd3w6LXjU7kMuqWKcobnasD8MHCfSzdE29wIhERKcxUqkREjOQbBF3GO7bXT4ADi43N40IeblCGng0dgytGTI/hSEKq0ZFERKSQUqkSETFaxF1Qf4Bje/ZgSD1nbB4X8lqn6kSXLcrF9GwGTtmkwRUiImIIlSoRkfzgrjcgqDKkxMEvI8Cu4Qs3w8PNzIRedQnx92RfXArP/rAVu753IiJyh6lUiYjkBx4+0G0imN1hz68Q863RiVxGSIAXEx6ti7vFxPwdZxi/7KDRkUREpJBRqRIRyS9KRkGblxzb85+HcyoHNyu6bDFev78GAO//vpdlezW4QkRE7hyVKhGR/KTJcCjbDLJSYeZAsOoaoZvVs2EZHmkQ7hhc8V0MR89pcIWIiNwZKlUiIvmJ2QJdPwXPQDi5CVa8Z3Qil/La/dWpU6YIyenZDPxmM6kaXCEiIneASpWISH5TJBzu+9CxveI9OL7B2DwuxNPNwqePRhPs78neuIs89+M2Da4QEZE8p1IlIpIf1ewONXuA3QozB0DGRaMTuYzQAC8m9HIMrpi7/TSfrThkdCQRESngVKpERPKre96DwHC4cAR+e8HoNC6lXrlivNqpOgD/+20PK/adNTiRiIgUZCpVIiL5lXcR6PoZYHKMWN81x+hELqVXwzI8VC8cmx2GfxfDsXNpRkcSEZECSqVKRCQ/K9cUmo10bP8yApJPGxrHlZhMJl7vXJ2o8CIkXcpi4JRNpGVqcIWIiOQ+lSoRkfyu1b8d97C6dAFmDwabzehELsPL3cJnj0YT5OfJnjMaXCEiInlDpUpEJL9z84AHvgA3bzi0FDZ8ZnQil1Ii0IvxveriZjbx67bTTFypwRUiIpK7VKpERFxBcCS0f9OxvfBViNtpbB4X06B8MV7tVA2A/87fw8r9GlwhIiK5R6VKRMRV1HsCItqDNQN+GgBZ6UYncimPNirLg9FhOYMrjp/X4AoREckdKlUiIq7CZILOY8EnCOJ3wpL/MzqRSzGZTPxflxpEhQWSmJbFwCmbuZRpNTqWiIgUACpVIiKuxC8EOo9zbK8dC4eWGRrH1Xi5W5jwaDRBfh7sPp3M8z9pcIWIiNw+lSoREVdTuQPUe9yxPWswpJ03No+LKVXEm3E9HYMr5mw9xZerDhsdSUREXJxKlYiIK7r7TSheCS6egl9HglZbbknDCsV56d6qALw9bzdrDiQYnEhERFyZSpWIiCvy8IUHJoLZDXb9DFu/MzqRy+nbpBzd6joGVwydtkWDK0RExGkqVSIirqp0XWj1omN73rNwXqex3QqTycRbXWtQs3QgF9KyeFKDK0RExEkqVSIirqzZ01CmMWSmwKwnwZptdCKX4uVu4bPe0RT39WDX6WRenKnBFSIicutUqkREXJnZAl0/A88AOL4eVn1kdCKXU6qIN2N71sViNjE79hRfrT5idCQREXExKlUiIq6uaFm4533H9rJ34MRmY/O4oMYVi/Ofey4bXHFQgytEROTmqVSJiBQEtXpA9QfAboWZ/SEjxehELuexpuV4oE5prDY7w6bFcOKCBleIiMjNUakSESkITCa470MIKA3nD8GCfxudyOWYTCbefqAm1UsFcD41k0HfbiY9S4MrRETkxlSqREQKCu+i0PVTwARbvoY9c41O5HL+HFxRzNeDHSeT+fes7RpcISIiN6RSJSJSkJRvAU2GO7bnDIeLccbmcUFhRX0Y27MOFrOJmVtOMnnNEaMjiYhIPqdSJSJS0LR5CUrUhLRz8PMQ0ErLLWtSMYgXO1YB4M25u1l36JzBiUREJD9TqRIRKWjcPOGBL8DNCw4sgg0TjU7kkp5oVp4utUthtdkZOnULJxMvGR1JRETyKZUqEZGCKKQK3PWGY3vhyxC/x9g8LshkMvHOA7WoVjKAc6mZDJqiwRUiIvLPVKpERAqqBgOhUjvITneMWc/OMDqRy/H2cAyuKOrjzvaTSfxn1g4NrhARkauoVImIFFQmE3QeBz7F4cx2WPqW0YlcUngxH8b2rIvZBD9tOcE3a48aHUlERPIZlSoRkYLMvwR0GuPYXj0GDq80No+LalopiBc7VgXg/37dxXoNrhARkcuoVImIFHRV74O6fQA7zHoSLl0wOpFL6t+8PPdHlSLbZmfotC2cTtLgChERcVCpEhEpDNq/A8UqQPJJmPsvjVl3gslk4t1utahaMoCEFA2uEBGRv6hUiYgUBp5+8MBEMFlgx0+w/QejE7kkbw8Ln/eOpoiPO1tPJPHybA2uEBERlSoRkcIjrB60esGxPfdfcEEDF5wRXsyHTx6pg9kEP2w+wbfr9H0UESnsVKpERAqTZs9AeEPISIZZg8Cm09ec0TwimOc7VAHg9V92sfHIeYMTiYiIkVSqREQKE4sbdP0MPPzg2BpYPdroRC5rYIsK3FerJNk2O4O/3cKZpHSjI4mIiEFUqkRECpti5aHj/xzbS9+GUzHG5nFRJpOJ/3WvRZUS/iSkZDDo281kZGvlT0SkMFKpEhEpjGr3hGqdwZYNPw2AzDSjE7kkHw83Pu9dj0Bvd2KPJ/LK7J0aXCEiUgipVImIFEYmE9w3GvxLwrn98PtLRidyWWWK/zW4Ysam40zbcMzoSCIicoepVImIFFY+xaDLBMf2pi9h72/G5nFhLSKDeba9Y3DFa3N2skmDK0REChWVKhGRwqxia2g01LH981BIiTc2jwsb1LIC99YsSZbVzuCpW4hL1uAKEZHCQqVKRKSwa/sKhFSHtAT4eRjomiCn/Dm4onKoP2cvZjBYgytERAoNlSoRkcLO3Qu6TQSLB+xfAJu+MjqRy/L1dOOz3tEEeLmx5Vgir83ZZXQkERG5A1SqREQEQqtDu9cc2wv+A2f3GRrHlZUL8mXMI3UwmeC7DceYtl6DK0RECjqVKhERcWg4GCq0guxLMLM/ZGcanchltaocwqi7KwPw6pwdbD56weBEIiKSl1SqRETEwWx2TAP0Lgqnt8Ly/xqdyKUNaVWRjjVKOAZXfLuZeA2uEBEpsFSqRETkLwGlHPevAlj5IRxdY2gcV2YymXj/wSgiQ/2Iv5jB4KlbyMy2GR1LRETygEqViIhcqXoXqN0LsMPMJyE9yehELssxuKIe/l5ubD56gdd/2Wl0JBERyQMqVSIicrWO70LRcpB0DOY9a3Qal1Y+yJcxDzsGV0xdf4zpGzS4QkSkoFGpEhGRq3n6Q9fPwWSGbTNg+49GJ3JprauE8K+7IgF45eedxBzT4AoRkYJEpUpERP5ZmYbQ4o9VqrnPQNIJY/O4uCGtKtG+eiiZVhuDvt1M/EUNrhARKShUqkRE5NpaPAulox3XVc0aBDYNWnCW2Wzigx61qRTiR1xyBkM1uEJEpMBQqRIRkWuzuMMDE8HdF46shLWfGJ3Ipfl5uvF572j8Pd3YeOQC//frLqMjiYhILlCpEhGR6yteETq849he/H9wepuxeVxchWA/Rj9cG5MJpqw7yvcbjxsdSUREbpNKlYiI3FjdPlDlPrBlwU/9IeuS0YlcWtuqoTzdzjG44qXZO4g9nmhsIBERuS0qVSIicmMmE3QaA36hkLAXFr5qdCKXN6x1Je6q9sfgiimbOXsxw+hIIiLiJJUqERG5Ob7Foct4x/aGz2D/ImPzuDiz2cSHPaKoGOzLmeR0hk7dQpZVgytERFyRSpWIiNy8Su2gwZOO7Z+HQGqCsXlcnL+XO5/3qYefpxsbjpznrbm7jY4kIiJOUKkSEZFbc9frEFwFUuJgzgiw241O5NIqBvvx0UO1AZi85gg/bNLgChERV6NSJSIit8bd2zFm3ewOe+fClm+MTuTy7qoWysh2EQD8Z/YOtp1INDaQiIjcEpUqERG5dSVrQdtXHNu/vQDnDhqbpwAY0SaCdlVDyMy28eSUzSSkaHCFiIirUKkSERHnNB4G5ZpDVhrMHADWLKMTuTSz2cSHD9WmQrAvp5M0uEJExJWoVImIiHPMZuj6KXgFwsnNsPx/RidyeQFe7nze2zG4Yv3h87w9T4MrRERcgUqViIg4LzAM7vvIsb3yfTi23tg8BUClED8+6BEFwKTVR5i55YTBiURE5EZUqkRE5PbU6Aa1Hga7zXEaYHqy0YlcXvvqJRjRphIAL87czvYTSQYnEhGR61GpEhGR23fP/yCwDCQedQyukNs2sl0kbauEkJFtY9C3mzmnwRUiIvmWSpWIiNw+r0B44DMwmSF2KuycbXQil/fn4IryQb6cTLzEsGkxZGtwhYhIvqRSJSIiuaNsE2j2tGP7l6cg+ZSxeQqAQG93Pu8dja+HhbWHzvHO/D1GRxIRkX+gUiUiIrmn1YtQqg6kJ8KsQWDTysrtigj1zxlc8eWqw8yOOWlwIhER+TuVKhERyT0Wd3hgIrj7wOHlsH6C0YkKhA41SjKstWNwxfM/bWPHSQ2uEBHJT1SqREQkdwVFQPu3HNuLXoMzOwyNU1A8fVckrSsHk5FtY+A3m9hw+LzRkURE5A8qVSIikvuiH4PIjmDNdIxZz0o3OpHLs5hNjH64DuWDfDmVlE6Pz9by1PQYziTpeysiYjSVKhERyX0mE9z/CfgGQ/wuWPy60YkKhEBvd34a3IRHGpTBZIKfY0/R5oNlTFh2kIxsq9HxREQKLZUqERHJG37B0Hm8Y3vdeDi4xNg8BUQxXw/eeaAmc4Y2o26ZIqRlWnn3tz10GL2SpXvjjY4nIlIoqVSJiEjeibwb6vd3bM8eAmm6Dii31AwL5MdBTfjgwSiC/Dw5nJDKY5M20v/rjRw9l2p0PBGRQkWlSkRE8tZd/wdBkXDxNPwyAux2oxMVGGaziW7RYSwd1ZIBzcvjZjaxaHc8d324gvcW7CEtM9voiCIihYJKlYiI5C0PH8eYdbMb7P4FYqcanajA8fdy5z/3VuO3kc1pHhFEptXGuKUHafvBcn7Zegq7iqyISJ5SqRIRkbxXqja0/o9je/7zcP6QoXEKqkoh/nzzeAM+fTSasKLenE5KZ/h3MTwycR17ziQbHU9EpMBSqRIRkTuj6VNQtilkpsDMJ8GqU9PygslkokONEix6piUj20Xg6WZm3aHz3DtmFa/N2UnSpSyjI4qIFDgqVSIicmeYLdD1U/AMhBMbYOUHRicq0LzcLYxsF8miZ1rSoXoJrDY7k9ccofX7y5i+4Rg2m04JFBHJLSpVIiJy5xQpA/f+UaaWvwvHNxqbpxAIL+bDp72j+faJhlQK8eN8aiYvzNxO1/GriTl2weh4IiIFgkqViIjcWbUehBrdwW6FH/rp+qo7pFlEEPOfas5L91bFz9ONrSeS6Dp+Dc/+sJWzFzOMjici4tJMdo0EukJycjKBgYEkJSUREBBgdBwRkYLpUiJ80RbOHQD/UtB3DgRFGJ2q0Ii/mM678/fy05YTAPh7ujHyrkj6NC6Lu0W/bxUR+dPNdgOVqr9RqRIRuUMunoFvOsPZPeAbAn1+htBqRqcqVDYfvcBrc3ay/WQSABEhfrx+f3WaVAoyOJmISP6gUuUklSoRkTsoNQG+6QJx28G7GPSZDSWjjE5VqFhtdr7fdJz3FuzlfGomAPfULMF/7q1G6SLeBqcTETGWSpWTVKpERO6wtPPwbTc4tQW8AuHRWRAWbXSqQicpLYsPF+5lyrqj2Ozg5W5mSKtKDGxRAS93i9HxREQMoVLlJJUqEREDpCfB1Afh+Hrw8IdeP0DZxkanKpR2n07m1Tk72XD4PADhxbx5+d5q3FUtFJPJZHA6EZE7S6XKSSpVIiIGyUiB7x6GIyvB3QcemQ4VWhqdqlCy2+38su00b8/dzZnkdABaRAbzaqdqVAz2MzidiMido1LlJJUqEREDZabBjF5wcAm4ecFDUyGindGpCq3UjGzGLT3AFysPk2m14W4x8XjT8gxvG4Gfp5vR8URE8pxKlZNUqkREDJadAd/3hX3zweIBD06GKvcanapQO5KQyv/9uovFe+IBCPH35MV7qtCldmmdEigiBZpKlZNUqkRE8oHsTJjZH3b9DGY36PYFVO9qdKpCb+meeF7/ZSdHzqUBUK9sUV67vzo1SgcanExEJG+oVDlJpUpEJJ+wZsPswbD9ezCZocsEiHrY6FSFXka2lS9XHWbskgOkZVoxmaBngzKMursyRX09jI4nIpKrbrYb6LbpIiKSP1ncoOunUKc32G0waxBs/troVIWep5uFIa0qsfhfLbk/qhR2O0xdf4zWHyxjyrqjWG36Xa2IFD5aqfobrVSJiOQzNhvMfxY2fuF4fM/70GCAsZkkx/pD53h1zk72nLkIQLWSAbzeuTr1yxUzOJmIyO3T6X9OUqkSEcmH7Hb4/SVYO9bx+K7/g6YjjM0kObKtNqZtOMb7C/aSnJ4NQJfapXjxnqqEBngZnE5ExHkqVU5SqRIRyafsdljyJqx83/G49UvQ8lljM8kVzqdm8t6CvUzfeAy7HXw8LAxvE8Hjzcrh6WYxOp6IyC1TqXKSSpWISD63/D1Y+qZju/koaPMSaKx3vrL9RBKvztnBlmOJAJQP8uWVTtVoXTnE2GAiIrdIpcpJKlUiIi5g9RhY+LJju/EwuPtNFat8xmazMyvmJO/M30NCSgYA7aqG8PJ91Shb3NfgdCIiN0elykkqVSIiLmL9544BFgD1+0PH98Csobb5zcX0LMYs3s+k1UfIttnxcDMzsHkFhrSuiI+Hm9HxRESuS6XKSSpVIiIuZPPX8MtTgN0xer3Tx2DWtTv50YH4i7z+yy5W7k8AoFSgF/++tyr31iyJSauMIpJPqVQ5SaVKRMTFbJ0Bswc57mVVs4fjJsEWrYDkR3a7nQU74/i/X3dxMvESAI0rFOe1+6tTuYS/welERK6mUuUklSoRERe0cxb81B9s2VCtMzzwBbh5GJ1KriE9y8qnyw8yYdlBMrJtWMwmejcqy9N3RRLo7W50PBGRHCpVTlKpEhFxUXvmwg/9wJoJkR2hx9fg5ml0KrmO4+fTeGvubn7beQaA4r4ePNehMg9Gh2M265RAETGeSpWTVKpERFzY/kUwoxdkp0PFtvDQt+DhY3QquYGV+8/y2pydHDybCkBUWCCvd65B7fAixgYTkUJPpcpJKlUiIi7u0HL47mHISoNyzeGR6eDpZ3QquYEsq42v1xxh9KL9pGRkA/BgdBjPdahCsL9WHEXEGCpVTlKpEhEpAI6uhakPQuZFCG8IvX4Ar0CjU8lNiL+Yzrvz9/LTlhMA+Hu6MfKuSPo0Lou7RSPzReTOUqlykkqViEgBcWIzfNsV0pOgVF149CfwKWZ0KrlJm49e4LU5O9l+MgmAyFA/XutUnSaVggxOJiKFyc12gwL7K5/ExETq1atH7dq1qVGjBhMnTjQ6koiI3Elh0dD3F/AuBqe2wNf3Q2qC0ankJkWXLcrsoU1554GaFPP1YF9cCj2/WM+QqZtzxrGLiOQXBXalymq1kpGRgY+PD6mpqdSoUYNNmzZRvHjx675OK1UiIgVM3C74pjOkxkNwFegzB/xDjU4ltyApLYsPF+5lyrqj2Ozg5W5mSKtKDGxRAS933exZRPJOoV+pslgs+Pg4Jj5lZGRgt9spoP1RRESuJ7QaPDYP/EvB2T0w+R5IOml0KrkFgT7uvN65BnNHNKdB+WKkZ9n4cOE+7vpoOb/vPKP/vouI4W65VL3zzjvUr18ff39/QkJC6NKlC3v37s3VUCtWrKBTp06UKlUKk8nE7Nmz//G4cePGUa5cOby8vGjYsCEbNmy44vnExESioqIICwvj2WefJShI52GLiBRKQRGOYhVYBs4dgEkd4cJRo1PJLapaMoAZAxsx5pE6lAjw4vj5Swycspm+kzZy8GyK0fFEpBC75VK1fPlyhg4dyrp161i4cCFZWVncfffdpKam/uPxq1evJisr66r9u3btIi4u7h9fk5qaSlRUFOPGjbtmjhkzZvDMM8/w6quvsmXLFqKiomjfvj3x8fE5xxQpUoStW7dy+PBhpk2bds3PExGRQqBYeUexKloeEo86itW5g0ankltkMpm4P6oUi//VkiGtKuJhMbNi31k6jF7BO/N254xjFxG5k277mqqzZ88SEhLC8uXLadGixRXP2Ww26tatS0REBNOnT8dicZz3vHfvXlq2bMkzzzzDc889d/2AJhOzZs2iS5cuV+xv2LAh9evXZ+zYsTmfFR4ezvDhw3nhhReuep8hQ4bQpk0bunfvft3P0zVVIiIFXPJp+OZ+SNgHfqGOa6xCqhidSpx0JCGVN37dxZI9jl+qhvh78uI9VehSuzQmk8ngdCLi6u7YNVVJSY5Rp8WKXT2m1mw2M2/ePGJiYujTpw82m42DBw/Spk0bunTpcsNCdS2ZmZls3ryZdu3aXfFZ7dq1Y+3atQDExcVx8eLFnIwrVqygcuXK13zPcePGUa1aNerXr+9UJhERcREBJaHfPAipDilxMPleOLPd6FTipHJBvnzVrz5f9atHueI+xF/M4OkZW3nw07Xs+GMcu4hIXrutUmWz2Rg5ciRNmzalRo0a/3hMqVKlWLJkCatWraJnz560adOGdu3aMWHCBKc/NyEhAavVSmjoldObQkNDOXPmDABHjx6lefPmREVF0bx5c4YPH07NmjWv+Z5Dhw5l165dbNy40elcIiLiIvyCod+vUDIK0hJg8n1wcovRqeQ2tKkSyoKnW/Bs+8p4u1vYdPQC949dxX9mbedCaqbR8USkgHO7nRcPHTqUHTt2sGrVquseV6ZMGaZMmULLli2pUKECX375ZZ4vyTdo0IDY2Ng8/QwREXFhPsUcp/5N7Q4nNjrGrj/6E4Q3MDqZOMnTzcLQ1pV4oG5p3p63h1+2nmLq+mPM3X6af91dmZ4NymAx65RAEcl9Tq9UDRs2jF9//ZWlS5cSFhZ23WPj4uIYOHAgnTp1Ii0tjaefftrZjwUgKCgIi8Vy1eCJuLg4SpQocVvvLSIihYh3Eeg9C8o2hYxk+KYLHLn+Lwol/ysZ6M0nj9Rh+sBGVCnhT2JaFi/P3kGnT1axfN9ZjWAXkVx3y6XKbrczbNgwZs2axZIlSyhfvvx1j09ISKBt27ZUrVqVmTNnsnjxYmbMmMGoUaOcDu3h4UF0dDSLFy/O2Wez2Vi8eDGNGzd2+n1FRKQQ8vSHXj9ChVaQlQrfdocDi2/4Msn/GlUozq/Dm/H6/dUJ8HJj1+lk+n61ge6frmXV/gSVKxHJNbdcqoYOHcq3337LtGnT8Pf358yZM5w5c4ZLly5ddazNZqNjx46ULVuWGTNm4ObmRrVq1Vi4cCGTJk3io48++sfPSElJITY2Nuf0vcOHDxMbG8uxY8dyjnnmmWeYOHEiX3/9Nbt372bw4MGkpqby2GOP3eqXJCIihZ2HDzwyAyLuhuxL8N3DsPc3o1NJLnCzmOnbpBxLR7XiiWbl8XQzs/noBR79cj09PlvLmoMJRkcUkQLglkeqX+taqEmTJtGvX7+r9i9cuJDmzZvj5eV1xf6YmBiCg4P/8dTBZcuW0bp166v29+3bl8mTJ+c8Hjt2LO+99x5nzpyhdu3ajBkzhoYNG97Kl3MVjVQXESnEsjPhx8dgz69gdofuX0G1+41OJbkoPjmd8csOMm3DMTKzbQA0qlCMp9tF0rBCcYPTiUh+c7Pd4LbvU1XQqFSJiBRy1iyY9STs+AlMFuj6GdR60OhUksvOJKUzYdkBvttwnEyro1w1qVicp++KpH65q28TIyKFk0qVk1SqREQEmxXmDIfYqYAJOo+FOo8anUrywKnES4xbeoDvNx0ny+r4kah5RBAj20USXbaowelExGgqVU5SqRIREQBsNpj7DGye5Hh874dQ/wljM0meOXEhjXFLD/LDpuNk2xw/GrWMDObpuyKpHV7E2HAiYhiVKiepVImISA67HX57Edb/ccP69u9A4yHGZpI8dfx8GmOXHODHLSew/lGu2lQJ4el2kdQMCzQ4nYjcaSpVTlKpEhGRK9jtsOg1WD3a8bjtK9D8X0Ymkjvg6LlUPllygJlbTvBHt6Jd1VBGtougRmmVK5HCQqXKSSpVIiJyFbsdlr8Ly95xPG75PLR6Ea4xEVcKjsMJqXyyeD+zY0/mlKv21UMZ2S6SqiX1c4JIQadS5SSVKhERuaZVHzlWrQCaPgXtXlexKiQOnk1hzOL9zNl6ij9/cupYowQj20VSuYS/seFEJM+oVDlJpUpERK5r3QT47QXHdsNB0OG/KlaFyP64i3y8eD9zt5/Gbnf8o7+nZklGto0gIlTlSqSgUalykkqViIjc0Kav4NenHdvR/eDej8BsNjSS3Fl7z1zk48X7mLf9DOAoV51qlWJE2wgqhfgZnE5EcotKlZNUqkRE5KbETIU5w8Bug6iejntZmS1Gp5I7bPfpZEYv2seCnXEAmE3QuXZpRrSNoHyQr8HpROR2qVQ5SaVKRERu2vYfYeZAsFuh+gPwwOdgcTc6lRhg56kkRi/az8Jdf5WrrnXCGNG2EmWLq1yJuCqVKiepVImIyC3Z/Qv88BjYsqDKfdD9K3DzNDqVGGT7iSRGL9rH4j3xAFjMJrrVLc3wNhGEF/MxOJ2I3CqVKiepVImIyC3btwBm9AZrBkTcDT2mgLuX0anEQLHHExm9aB/L9p4FwM1s4sF6YQxtXYmwoipXIq5CpcpJKlUiIuKUg0vhu0cg+xKUbwmPfAceOu2rsNt89AKjF+1j5f4EANwtJh6sF86w1pUoVcTb4HQiciMqVU5SqRIREacdWQ3TekBmCpRpAj1ngJf+WyKw6ch5Plq0j9UHzgHgYTHzcINwhrSqRIlArWqK5FcqVU5SqRIRkdtyfAN82w0ykqF0PXj0J/AuYnQqySfWHzrHR4v2se7QeQA83Mz0bFCGwa0qEhqgciWS36hUOUmlSkREbtupGJjSFS5dgJJR0Hs2+BQzOpXkI2sOJjB64X42HHGUK083M70almVQqwqE+KtcieQXKlVOUqkSEZFccWYHfNMZ0hIgpBr0+Rn8QoxOJfmI3W5nzcFzfLhwH5uPXgDAy91M70ZlebJlRYL8NEVSxGgqVU5SqRIRkVxzdi98fT+knIHiEdB3DgSUMjqV5DN2u52V+xP4cOE+Yo8nAuDtbqFPk7I82aIixXw9jA0oUoipVDlJpUpERHLVuYOOYpV8AoqWdxSrImWMTiX5kN1uZ9m+s4xeuI+tJ5IA8PWw0LdJOQY0r0BRlSuRO06lykkqVSIikusuHIVv7ocLRyAw3FGsilUwOpXkU3a7nSV74vlo0T52nEwGwM/TjcealqN/swoE+rgbnFCk8FCpcpJKlYiI5Imkk45ide4A+JeEvr9AUITRqSQfs9vtLNwVx0eL9rP7tKNc+Xu68Xiz8jzerDyB3ipXInlNpcpJKlUiIpJnLsY5hlec3Q2+IY7hFaHVjE4l+ZzNZuf3XWcYvWg/e85cBCDAy40nmlXgsWblCPBSuRLJKypVTlKpEhGRPJV6DqZ0hjPbwbsY9J4FpWobnUpcgM1mZ/6OM3y8eB/74lIACPR2Z0Dz8vRrWh4/TzeDE4oUPCpVTlKpEhGRPHfpAkx5AE5tAc9A6D0TwuoZnUpchM1mZ+7204xetI+DZ1MBKOrjzoAWFejbuBy+KlciuUalykkqVSIickekJ8PUB+H4OvDwg14/QNkmRqcSF2K12fl12yk+XrSfQwmOclXM14MnW1Sgd+Oy+HioXIncLpUqJ6lUiYjIHZORAt89DEdWgrsPPPIdVGhldCpxMdlWG3O2nmLM4v0cOZcGQJCfB4NaVqRXw7J4e1gMTijiulSqnKRSJSIid1TWJZjxKBxYBBZPeHgqRNxldCpxQdlWG7NiTvLJkgMcO+8oV8H+ngxuWZGeDcvg5a5yJXKrVKqcpFIlIiJ3XHYG/NAP9s4Dszv0+Bqq3Gt0KnFRWVYbM7ec4JMlBzhx4RIAIf6eDG1diYfqh6tcidwClSonqVSJiIghrFnwU3/YNRvMbvDARKjxgNGpxIVlZtv4cfMJxi7Zz6mkdABKBHgxtE0letQLw9NN5UrkRlSqnKRSJSIihrFmw89DYdt0MJmh83io/YjRqcTFZWRb+X7TCcYvPcDpP8pVqUAvhrWJoHt0GB5uZoMTSmGUnmXl7MUM4i9mEJ+c7vjfi+nEJzv2jWwXQZ0yRY2OqVLlLJUqERExlM0Kv46ELd8AJug0GqL7GZtJCoT0LCszNh5n/LIDxCVnAFC6iDfD21SiW3QY7haVK7l9aZnZxCdnEJdTlBxl6WxyBnGXlaakS1nXfZ8Pe0TxQN2wO5T62lSqnKRSJSIihrPZYP5zsHGi43HH96DhQGMzSYGRnmVl2vpjTFh+kLMXHeUqvJg3w9tE8ECd0ripXMnf2O12LmZkO1aUkjOuWlWKS07PWXVKyci+6ff1sJgJ9vckNMCTEH8vQgI8CfF3bDeqUJwyxX3y8Ku6OSpVTlKpEhGRfMFuh4Uvw5pPHI/vegOaPmVsJilQLmVambr+KJ8uP0hCSiYA5Yr7MLxNBJ1rl1K5KgTsdjsX0rKuKEh/bV+5Lz3LdtPv6+1uySlKwZcVpb+Xp0Bvd0wmUx5+hbdPpcpJKlUiIpJv2O2w9G1Y8T/H49b/gRbPQj7/IURcS1pmNt+uO8qnyw9xPtVRrioE+TKibQSdokphMevvm6ux2uycS80gPjnjjxUkR0G6/PS7s3/8ybTefFny93LLKUiXryqF/K0s+Xm65fuydLNUqpykUiUiIvnOivdgyZuO7eb/gjYvq1hJrkvNyOabtUf5bMVBEtMc17tUDPblqXaRNKsUhLvFhIebGQ+LucD8wOxqsqw2ElIyrlhBikvO4OzfVpUSUjKx2m7+R/yiPu45pSj4GqtKIf5ehfJG0ipVTlKpEhGRfGnNWPj9P47thoPhrtfBzdPYTFIgpWRk8/WaI3y+4tA1hwl4WMyOgvVHyfpz2/2Pbc9rPH/F42sc43nZY/e/HeP5T+9TAIpeRrb1shUkR1G68vQ7x/5zqZnc7E/uJhMU9/2jEP1RjEIDvAjx9yT4srIU7O+p8frXoVLlJJUqERHJtzZMhHmjHNsBpaHFKKj9KLh5GJtLCqTk9Cwmrz7C5DVHck4LzM/cLaZrFDhLTtFzd7v8GEvOtuffitrlZe6WC+Kfjy1m0i8rS3GXjQ0/+7eVphtNwrucxWwi2M/zslJ02apSToHyIsjPQ9fF5QKVKiepVImISL62YyYs+A9cPOV4XKQMtHweaj0MFjdjs0mBZbXZybLayMi2kZltI9P6x//mPLaSmW2/cr/VmrOdcdlrsq445rL3/Nv75nye9Z+fz76F09tcwZ+T8P6+qvT3YQ/FfT0w6zq3O0alykkqVSIiku9lpcPmybDyA0iNd+wrVgFavgA1u4NZp/JIwWez/VHirip4lxW5a5S5jH8shX9u2y97nfWqMpfx53v9w+dmWa/+sdrb3fLPQx0uW1UKDXCNSXiFkUqVk1SqRETEZWSmwcYvYPVoSDvn2BdUGVq9ANW6gFmn/ojcSX8vep5u5gI1Ca8wUqlykkqViIi4nIyLsP4zxz2t0hMd+0JrQKsXocq9mhQoIuKkm+0G+hWWiIiIq/P0dwytGLnNUaQ8AyBuB8zoBZ+3gn2/c9Mjw0RE5JapVImIiBQUXoGOU/+e2uq4n5W7L5yOhWkPwpd3wcElKlciInlApUpERKSg8SkGbV9xrFw1GQ5u3nBiI0zpCpPugSOrjE4oIlKgqFSJiIgUVL5BcPebjpWrhoPA4gnH1sDke+Hr++H4BqMTiogUCCpVIiIiBZ1/KHR8F0bEQL0nwOwOh5c7Tgn8tjuc3GJ0QhERl6ZSJSIiUlgElob7PoThm6FObzBZ4MBCmNgavnsEzmw3OqGIiEtSqRIRESlsipaFzmNh2EaIegRMZtg7Dz5tBt/3gfjdRicUEXEpKlUiIiKFVfGK0PVTGLIeanQDTLDrZxjfGH7qDwkHjE4oIuISVKpEREQKu+BI6P4VDF4NVTsBdtj+A4yrD7OHwPnDRicUEcnXVKpERETEIbQ6PPQtPLkCIjuA3QaxU2FsPZgzAhKPG51QRCRfUqkSERGRK5WMgp4zoP9iqNgWbNmw5Wv4pC7MHQXJp41OKCKSr6hUiYiIyD8Lqwe9Z8Jjv0G55mDNhI0TYUxt+O3fkHLW6IQiIvmCSpWIiIhcX9nG0O9X6PsLhDeC7HRYNw4+rgULX4W080YnFBExlEqViIiI3JzyLeDx3+DRn6BUXchKg9WjYXRNWPImXEo0OqGIiCFUqkREROTmmUxQqR0MWAKPTIcSNSEzBVa8B6NrwfL/QXqy0SlFRO4olSoRERG5dSYTVO4IA1dAjykQXBUykmDpW47TAld9BJmpRqcUEbkjVKpERETEeWYzVLsfBq+Bbl9C8Qi4dAEWvQYfR8HacZB1yeiUIiJ5SqVKREREbp/ZDDW7w5B10OVTKFoOUs/Cgn/Dx7Vh/eeQnWF0ShGRPKFSJSIiIrnH4ga1H4Fhm6DTGAgMh5QzMP9ZGFMXNk0Ca5bRKUVEcpVKlYiIiOQ+iztE94XhW+DeD8C/JCSfgF9HwifREDMVrNlGpxQRyRUqVSIiIpJ33Dygfn8YEQsd/gu+IZB4FH4eAuMbwrYfwGY1OqWIyG1RqRIREZG85+4FjQbDU7Fw1xvgXQzOHYCZ/WFCE9g5C2w2o1OKiDhFpUpERETuHA9faPoUjNwGbV4Cr0A4uwd+6AeftYA9c8FuNzqliMgtUakSERGRO8/TH1o8CyO3Q8sXwMMf4rbD9J4wsTXsX6hyJSIuQ6VKREREjOMVCK1fdKxcNXsG3H3hVAxM7Q5f3g2HlqlciUi+p1IlIiIixvMpBu1ehae2QuNh4OYFJzbAN51h8r1wZLXRCUVErkmlSkRERPIPv2Bo/5ajXDV4EiwecHQ1TL7HUbCObzQ6oYjIVVSqREREJP/xLwH3/A9GxED0Y2B2c5wK+GU7mPqg4xRBEZF8QqVKRERE8q/AMOg02nET4TqPgskC+3+Hz1vB9F5wZofRCUVEVKpERETEBRQtC53HwbCNUOshwAR7foVPm8L3fSF+j9EJRaQQU6kSERER11G8IjzwOQxdD9W7Ovbtmg3jG8FPA+DcQUPjiUjhpFIlIiIirie4Mjw4GQathir3AXbY/j2MrQ+zh8KFIwYHFJHCRKVKREREXFeJGvDwVBi4DCLag90Ksd/CJ9Hwy1OQdMLohCJSCKhUiYiIiOsrVQd6fQ9PLIIKrcGWDZsnw5g6MO9ZuHjG6IQiUoCpVImIiEjBEV4f+syGx+ZD2WZgzYQNn8PHUbDgP5By1uiEIlIAqVSJiIhIwVO2CfT7Ffr8DGENIDsd1o51lKtFr0HaeaMTikgBolIlIiIiBZPJBBVawRO/Q68fHacIZqXCqo9gdC1Y+jZcSjQ6pYgUACpVIiIiUrCZTBBxFwxYCg9/B6E1IfMiLH8XPq4FK96DjItGpxQRF6ZSJSIiIoWDyQRV7oEnV8CDX0NwFUhPgiVvOlauVn8MmWlGpxQRF6RSJSIiIoWL2QzVu8DgNfDAF1CsIlw6DwtfcVxztW4CZKUbnVJEXIhKlYiIiBROZgvUehCGboDO46FIWUiNh99ecIxi3/gFZGcanVJEXIBKlYiIiBRuFjeo0wuGbYL7RkNAGFw8BXP/5biJ8JZvwJpldEoRycdUqkREREQA3Dyg3mMwYgt0fA/8SkDSMZgzHMbWh63TwWY1OqWI5EMqVSIiIiKXc/OEhgPhqVi4+y3wCYILh2HWkzC+Eez4CWw2o1OKSD6iUiUiIiLyT9y9ockweGortH0VvItCwj748XH4tBns/gXsdqNTikg+oFIlIiIicj2eftD8GXhqG7T6N3gGQPxOmPEofN4S9i1QuRIp5FSqRERERG6GVwC0eh5GboPmo8DDD05vhWk94It2cHCJypVIIaVSJSIiInIrvItC25cdK1dNRoCbN5zcBFO6wqR74MgqoxOKyB2mUiUiIiLiDN/icPf/Oa65ajgYLJ5wbA1Mvhe+vh+ObzA6oYjcISpVIiIiIrfDPxQ6/hdGxEC9J8DsDoeXw5d3wbfd4eQWoxOKSB5TqRIRERHJDYGl4b4PYfhmqNMbTBY4sBAmtobvHoEz241OKCJ5RKVKREREJDcVLQudx8KwjVDrYTCZYe88xxj27/tC/B6jE4pILlOpEhEREckLxSvCA5/BkPVQ/QHHvl2zHTcQ/mkAJBwwNJ6I5B6VKhEREZG8FBwJD06CwWugyn2AHbZ/D+MawOwhcOGI0QlF5DapVImIiIjcCaHV4eGpMHA5RLQHuxVip8In0fDLU5B0wuiEIuIklSoRERGRO6lUbej1PTyxCCq0Bls2bJ4MY+rAvGfh4hmjE4rILVKpEhERETFCeH3oMxsemw9lm4E1EzZ8Dh9HwYL/QMpZoxOKyE1SqRIRERExUtkm0O9X6PMzhDWA7HRYO9ZRrha9BmnnjU4oIjegUiUiIiJiNJMJKrSCJ36HXj9CqTqQlQqrPoLRtWDp23Ap0eiUInINKlUiIiIi+YXJBBF3wYCl8PA0CK0BmRdh+bvwcS1Y8R5kXDQ6pYj8jUqViIiISH5jMkGVe+HJlfDgZAiqDOlJsORNx2mBqz+GzDSjU4rIH1SqRERERPIrsxmqd4Uha+GBiVCsIqSdg4WvOMrVugmQlW50SpFCT6VKREREJL8zW6BWDxi6ATqPgyJlIDUefnvBMYp94xeQnWl0SpFCS6VKRERExFVY3KDOozBsM9z3EQSUhounYO6/HDcR3vINWLOMTilS6KhUiYiIiLgaNw+o9zgM3wId/wd+oZB0DOYMh7H1Yet0sFmNTilSaKhUiYiIiLgqdy9o+CSMiIW73wSf4nDhMMx6EsY3gh0/gc1mdEqRAk+lSkRERMTVefhAk+Hw1DZo+wp4FYGEffDj4/BpM9j9C9jtRqcUKbBUqkREREQKCk8/aP4vGLkNWr0IngEQvxNmPAqft4R9C1SuRPKASpWIiIhIQeMVCK1egKe2OkqWuy+c3grTesAX7eDgEpUrkVykUiUiIiJSUPkUc5wOOHKb4/RAN284uQmmdIVJ98CRVUYnFCkQVKpERERECjrfIMcgi6e2/n979x4b45rAcfw3M73N2mlDpaVRTuOPdSvK0NCs2LBEsLHHIYTEZSPZpC41iaQk+MOlSohQSkXsJlgk1pIm/pDKabFsS1V0XbM2tiFVdkXrsnRnZv9wOW216njPzDPe+X6Sycw877z1a/sQvzzzPiNl/15yxUv/+qv0h8nSH38j1VWaTgh81ShVAAAA0cKTKk0qlJZelby/k5yx0j/Lpf2/lg5+Jz2oNp0Q+Co5gkHeUNtSY2OjkpKS9OzZMyUmJpqOAwAAEDpP70sVm6WaP0nBd59r9YvJ0q9WST0Gmc0G+woEpP/9993tdYvHLZ6nDJB+nmI66Wd3A0pVG5QqAAAQdf79D6l8s3T9mBR897lWA6a93UEwpZ/RaAiBYFDyN7cpNW3LTTvHmjsY76gYtbxvfvXD80Bz5xm/OyAN+jb0P4tOfG43iAljJgAAAESi5L7St3ulX/qk7zdJf/+zdOMv0o2TUuYMKWOM5HC2uTnaGbP4GqcrtH9OJGl3tabl/auOy0tzJwXmc85VhKyrOGOkmAQpJr71fezPTCf7UVipaoOVKgAAEPXqa6XvC6RbpaaT/LRalay2Ba6zYvYFx4P+jsuN/43pn8YPXC0KTWxCm5LT9nE7BSgmXop1tz/e7nktHrsie42HlSoAAAB8mR6DpFmHpIdXpb+VSK/+8/ZtgR/dgq2fB/yfPt7Z+Z883vZrB3789/Wl54Waw/l2u/uPVms+UWLae33L0vK557riJCd711lFqQIAAED70rKk3xabTtGxTkvZ5xS3wNu34ln9Gu29xuF4V3Dcan/l513BifDVGnSO3yAAAAC+Tg7H27fxyWU6CaIca30AAAAAYAGlCgAAAAAsoFQBAAAAgAWUKgAAAACwgFIFAAAAABZQqgAAAADAAkoVAAAAAFhAqQIAAAAACyhVAAAAAGABpQoAAAAALKBUAQAAAIAFlCoAAAAAsIBSBQAAAAAWUKoAAAAAwAJKFQAAAABYQKkCAAAAAAsoVQAAAABgQYzpAJEmGAxKkhobGw0nAQAAAGDS+07wviN0hFLVRlNTkyQpPT3dcBIAAAAAkaCpqUlJSUkdHncEO6tdUSYQCOjhw4fyeDxyOBxGszQ2Nio9PV11dXVKTEw0mgX2x3xDuDHnEE7MN4Qbc84egsGgmpqalJaWJqez4yunWKlqw+l0qlevXqZjtJKYmMhfRoQN8w3hxpxDODHfEG7Mua/fp1ao3mOjCgAAAACwgFIFAAAAABZQqiJYfHy81q5dq/j4eNNREAWYbwg35hzCifmGcGPORRc2qgAAAAAAC1ipAgAAAAALKFUAAAAAYAGlCgAAAAAsoFQBAAAAgAWUqgi1a9cuffPNN0pISFB2drYqKytNR4JNFRQUaMSIEfJ4PEpJSdG0adN0+/Zt07EQJTZt2iSHw6G8vDzTUWBjDx480Ny5c5WcnCy3263MzExdvnzZdCzYlN/v1+rVq5WRkSG3262+fftq3bp1Ym84e6NURaCjR4/K5/Np7dq1qq6u1pAhQzRx4kQ1NDSYjgYbKi8vV25uri5duqQzZ86oublZEyZM0IsXL0xHg81VVVVp7969Gjx4sOkosLGnT58qJydHsbGxOn36tG7cuKGtW7eqa9eupqPBpgoLC1VcXKyioiLdvHlThYWF2rx5s3bu3Gk6GkKILdUjUHZ2tkaMGKGioiJJUiAQUHp6upYsWaL8/HzD6WB3jx8/VkpKisrLyzVmzBjTcWBTz58/17Bhw7R7926tX79eQ4cO1fbt203Hgg3l5+frwoULOnfunOkoiBJTpkxRamqq9u/f/2Fs+vTpcrvdOnjwoMFkCCVWqiLMmzdvdOXKFY0fP/7DmNPp1Pjx43Xx4kWDyRAtnj17Jknq1q2b4SSws9zcXE2ePLnVv3VAKJw6dUper1czZsxQSkqKsrKytG/fPtOxYGOjR49WWVmZ7ty5I0m6du2azp8/r0mTJhlOhlCKMR0ArT158kR+v1+pqamtxlNTU3Xr1i1DqRAtAoGA8vLylJOTo0GDBpmOA5s6cuSIqqurVVVVZToKosC9e/dUXFwsn8+nVatWqaqqSkuXLlVcXJzmzZtnOh5sKD8/X42NjerXr59cLpf8fr82bNigOXPmmI6GEKJUAfggNzdXtbW1On/+vOkosKm6ujotW7ZMZ86cUUJCguk4iAKBQEBer1cbN26UJGVlZam2tlZ79uyhVCEkjh07pkOHDunw4cMaOHCgampqlJeXp7S0NOacjVGqIkz37t3lcrn06NGjVuOPHj1Sjx49DKVCNFi8eLFKS0tVUVGhXr16mY4Dm7py5YoaGho0bNiwD2N+v18VFRUqKirS69ev5XK5DCaE3fTs2VMDBgxoNda/f38dP37cUCLY3YoVK5Sfn69Zs2ZJkjIzM3X//n0VFBRQqmyMa6oiTFxcnIYPH66ysrIPY4FAQGVlZRo1apTBZLCrYDCoxYsX68SJEzp79qwyMjJMR4KNjRs3TtevX1dNTc2Hm9fr1Zw5c1RTU0Ohwk8uJyfno4+JuHPnjvr06WMoEezu5cuXcjpb/xfb5XIpEAgYSoRwYKUqAvl8Ps2bN09er1cjR47U9u3b9eLFCy1YsMB0NNhQbm6uDh8+rJMnT8rj8ai+vl6SlJSUJLfbbTgd7Mbj8Xx0vV6XLl2UnJzMdXwIieXLl2v06NHauHGjZs6cqcrKSpWUlKikpMR0NNjU1KlTtWHDBvXu3VsDBw7U1atXtW3bNi1cuNB0NIQQW6pHqKKiIm3ZskX19fUaOnSoduzYoezsbNOxYEMOh6Pd8QMHDmj+/PnhDYOoNHbsWLZUR0iVlpZq5cqVunv3rjIyMuTz+bRo0SLTsWBTTU1NWr16tU6cOKGGhgalpaVp9uzZWrNmjeLi4kzHQ4hQqgAAAADAAq6pAgAAAAALKFUAAAAAYAGlCgAAAAAsoFQBAAAAgAWUKgAAAACwgFIFAAAAABZQqgAAAADAAkoVAAAAAFhAqQIAAAAACyhVAAAAAGABpQoAAAAALKBUAQAAAIAF/weMqsF4DTzKkQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.semilogy(loss_hist, label='Training Loss')\n",
        "plt.semilogy(v_loss_hist, label = 'Validation Loss')\n",
        "plt.title('best epoch {}'.format(best_epoch))\n",
        "plt.legend()\n",
        "plt.savefig(trained_folder + '/loss.png')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hlBQz0mMBA_-"
      },
      "source": [
        "#Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ImVcUNZTkwuy"
      },
      "source": [
        "Inferences computing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "7WjfcAPEBA__",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f68e4d95-cb17-4a13-be42-e909970eb99a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-0bf49fec4d37>:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
            "  model.load_state_dict(torch.load(trained_folder + '/network_best'))\n"
          ]
        }
      ],
      "source": [
        "model.load_state_dict(torch.load(trained_folder + '/network_best'))\n",
        "\n",
        "inference = []\n",
        "\n",
        " # pause gradient calculation during evaluation\n",
        "with torch.no_grad():\n",
        "\n",
        "    # loop over data samples\n",
        "    for feature, label in test_loader:\n",
        "\n",
        "        feature =torch.swapaxes(input=feature, axis0=0, axis1=1)\n",
        "        label   = torch.swapaxes(input=label, axis0=0, axis1=1)\n",
        "\n",
        "        # prepare data\n",
        "        feature = feature.to(device)\n",
        "        label = label.to(device)\n",
        "\n",
        "        # forward-pass\n",
        "        mem = model(feature)\n",
        "        inference.append(mem.detach().squeeze().numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yycuR9Z8kzB5"
      },
      "source": [
        "Storing model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def CC(gt, inference):\n",
        "  return np.corrcoef(gt, inference)[0, 1]\n",
        "\n",
        "def Rsquare(gt, inference):\n",
        "    num = np.square(gt - inference)\n",
        "    den = np.square(gt - np.mean(gt))\n",
        "    return 1 - np.sum(num)/np.sum(den)\n",
        "\n",
        "trained_output = np.array(inference)\n",
        "\n",
        "test_windows = test_output.shape[0]\n",
        "\n",
        "R  = np.zeros((test_windows, n_last))\n",
        "Cc = np.zeros((test_windows, n_last))\n",
        "\n",
        "for var in range(n_last):\n",
        "  for i in range(test_windows):\n",
        "    R [i, var] = Rsquare(test_output[i, var, :], trained_output[i, :, var])\n",
        "    Cc[i, var] = CC(test_output[i, var, :], trained_output[i, :, var])\n",
        "\n",
        "print(f\"Mean R^2 value: x-axis {R[:, 0].mean()} y-axis {R[:, 1].mean()}\")\n",
        "print(f\"Mean CC value: x-axis {Cc[:, 0].mean()} y-axis {Cc[:, 1].mean()}\")\n",
        "\n",
        "with open(trained_folder + '/Results.txt', \"w\") as fw:\n",
        "  fw.write(f\"Mean R^2 value: x-axis {R[:, 0].mean()} y-axis {R[:, 1].mean()} \\n\")\n",
        "  fw.write(f\"Mean CC value: x-axis {Cc[:, 0].mean()} y-axis {Cc[:, 1].mean()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bv9GhkC4lSi4",
        "outputId": "07b65d4b-5072-4eb2-fb9f-3fef999437a5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean R^2 value: x-axis 0.46256597240897807 y-axis 0.5474998933422431\n",
            "Mean CC value: x-axis 0.786253989068313 y-axis 0.8157080332709684\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KJP4fpWlAzj"
      },
      "source": [
        "#Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 872
        },
        "id": "f01K02dXOUZq",
        "outputId": "b841e2b9-9744-4463-f699-ef788fe74432"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1000 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(20, 10))\n",
        "\n",
        "sel_index = 0\n",
        "\n",
        "for var_out in range(n_last):\n",
        "\n",
        "  axs = plt.subplot(n_last, 1, var_out + 1)\n",
        "  axs.plot(test_output[sel_index, var_out], label='GT')\n",
        "  axs.plot(trained_output[sel_index,:, var_out], label=\"Inference\")\n",
        "  axs.set_ylim([-400, 500])\n",
        "\n",
        "axs.legend(loc='upper center', bbox_to_anchor=(0.5, 2.35), ncol=2, fontsize = 15)\n",
        "plt.savefig(trained_folder + '/behave.png')"
      ]
    }
  ],
  "metadata": {
    "celltoolbar": "Raw Cell Format",
    "colab": {
      "collapsed_sections": [
        "3sfHnI586XV3",
        "5zGwy50JZoxN",
        "YB7gYrVCBA_8",
        "ECWezOF0BA_9",
        "s_iLz12RBA_-"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.15"
    },
    "vscode": {
      "interpreter": {
        "hash": "2a3056c17c3c31a88ffeb08a28ff32bf922ba3f6fa0343ca62cc95241e30c809"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}