CMB_Stage4(ii)_like.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "CMB-Stage4(ii)-like.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/M-torki/C-S-NN/blob/master/CMB_Stage4(ii)_like.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6JJP4gTEZE38",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee93ced3-9608-43ac-ff73-95565cdf93ce"
      },
      "source": [
        "%tensorflow_version 1.x"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 1.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VzrKNS0fM08_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec4bc51a-c126-4e7f-f57b-28a189058d65"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/gdrive')"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58fa_AauOQ7N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f28df457-b166-4650-9887-eaca1de179ad"
      },
      "source": [
        "cd /gdrive/My\\ Drive/"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/My Drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NRghSAwNOc6q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1582ac0-3174-4d81-ff2a-b08a81bb6cdd"
      },
      "source": [
        "!pip install git+https://github.com/vafaei-ar/ccgpack.git"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/vafaei-ar/ccgpack.git\n",
            "  Cloning https://github.com/vafaei-ar/ccgpack.git to /tmp/pip-req-build-bwz4xhfz\n",
            "  Running command git clone -q https://github.com/vafaei-ar/ccgpack.git /tmp/pip-req-build-bwz4xhfz\n",
            "Building wheels for collected packages: ccgpack\n",
            "  Building wheel for ccgpack (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ccgpack: filename=ccgpack-0.1.0-cp37-cp37m-linux_x86_64.whl size=910064 sha256=f9eded50fef86610b10d00777a1ff471b202dfd941b8fdf6a9f40386e37443ac\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-c3mgiwos/wheels/a8/3c/86/3720d002b7c606972520b7468f7b9daf3da3acc3052facac45\n",
            "Successfully built ccgpack\n",
            "Installing collected packages: ccgpack\n",
            "Successfully installed ccgpack-0.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LPuK_Jt4OgK1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e104ceb7-8ada-4689-aa43-4bd67a440436"
      },
      "source": [
        "!pip install git+https://github.com/vafaei-ar/Ngene.git"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/vafaei-ar/Ngene.git\n",
            "  Cloning https://github.com/vafaei-ar/Ngene.git to /tmp/pip-req-build-upon1lpm\n",
            "  Running command git clone -q https://github.com/vafaei-ar/Ngene.git /tmp/pip-req-build-upon1lpm\n",
            "Building wheels for collected packages: ngene\n",
            "  Building wheel for ngene (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ngene: filename=ngene-0.1.0-cp37-none-any.whl size=11659 sha256=ccade66140898faca4b3bc91be02c12e118602fbfd88c721d7a24d94295fe5dd\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-85n_588p/wheels/f7/d2/a8/1dae975d187b4a9790d83da45caaef0d018b41ffc0ab7fceb7\n",
            "Successfully built ngene\n",
            "Installing collected packages: ngene\n",
            "Successfully installed ngene-0.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvkgztZJOmwP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0cc409e5-5689-4319-b334-c878287d2e1d"
      },
      "source": [
        "import os\n",
        "import ngene as ng\n",
        "import numpy as np\n",
        "import pylab as plt\n",
        "from glob import glob\n",
        "import tensorflow as tf\n",
        "from random import choice,shuffle\n",
        "from sklearn.metrics import accuracy_score\n",
        "import cv2 as cv"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.7/tensorflow_core/python/compat/v2_compat.py:68: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NE2-ExHOOia7"
      },
      "source": [
        "import sys\n",
        "sys.path.append('/gdrive/My\\ Drive/git_reps/ccgpack')\n",
        "sys.path.append('/gdrive/My\\ Drive/git_reps/Ngene')"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BUXvNlu8OpKT"
      },
      "source": [
        "def conf_plotter(y_true, y_pred, classes,\n",
        "                      normalize=0,\n",
        "                      title=None,\n",
        "                      cmap=plt.cm.pink_r,\n",
        "                      mode = 'train',\n",
        "                      save = 0,\n",
        "                      name = 'conf.jpg'):\n",
        "\n",
        "    if not title:\n",
        "        if mode == 'train':\n",
        "            title = 'Normalized confusion matrix on train dataset'\n",
        "        if mode == 'test':\n",
        "            title = 'Normalized confusion matrix on test dataset'\n",
        "        if mode == 'prob':\n",
        "            title = 'Normalized confusion matrix on Non-Overlapping test dataset'\n",
        "            \n",
        "\n",
        "    from sklearn.metrics import confusion_matrix\n",
        "    \n",
        "    cm = confusion_matrix(y_true, y_pred)\n",
        "\n",
        "    if normalize:\n",
        "        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
        "\n",
        "\n",
        "    fig, ax = plt.subplots(figsize=(9,9))\n",
        "    im = ax.imshow(cm, cmap=cmap)\n",
        "    ax.figure.colorbar(im, ax=ax ,fraction=0.046, pad=0.04)\n",
        "    ax.set(xticks=np.arange(cm.shape[0]),\n",
        "           yticks=np.arange(cm.shape[1]),\n",
        "           xticklabels=classes, yticklabels=classes,\n",
        "           title=title,\n",
        "           ylabel= 'True label',\n",
        "           xlabel='Predicted label')\n",
        "    \n",
        "    for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] ):\n",
        "                    \n",
        "        item.set_fontsize(15)\n",
        "     \n",
        "    for item in (ax.get_xticklabels() + ax.get_yticklabels()):\n",
        "        \n",
        "        item.set_fontsize(12)\n",
        "\n",
        "                \n",
        "    plt.gca().invert_yaxis()\n",
        "    plt.setp(ax.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "             rotation_mode=\"anchor\")\n",
        "\n",
        "    fmt = '.2f' if normalize else 'd'\n",
        "    thresh = cm.max() / 2.\n",
        "    for i in range(cm.shape[0]):\n",
        "        for j in range(cm.shape[1]):\n",
        "            ax.text(j, i, format(cm[i, j], fmt),\n",
        "                    ha=\"center\", va=\"center\",\n",
        "                    color=\"white\" if cm[i, j] > thresh else \"black\")\n",
        "    fig.tight_layout()\n",
        "    if save == 1:\n",
        "        plt.savefig(name)\n",
        "    return ax"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsRWas-nO5c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d3fe3bd-f54e-4b49-8aa8-2fb01c981de2"
      },
      "source": [
        "g_files = sorted(glob('./data/Healpix_p/*.npy'))\n",
        "s_files = sorted(glob('./data/string_p/*.npy'))\n",
        "\n",
        "if len(g_files)*len(s_files)==0:\n",
        "    print('Somthing is wrong with initiation.')\n",
        "    #exit()\n",
        "\n",
        "print(len(g_files),len(s_files))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "409 12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LKJepo5vhAC8"
      },
      "source": [
        "# for i in range(200,205):\n",
        "#     print(np.std(np.load('./data/Healpix_p/'+str(i)+'.npy')))"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D1A8gpp4hKfa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7bc304d-ffc1-4d8a-e52d-1c5844ce9490"
      },
      "source": [
        "np.std(np.load('./data/string_p/0.npy'))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.552519"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZmSCGUh6D9xr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a37f520-5c34-4d00-e82a-131bc19d8871"
      },
      "source": [
        "np.std(np.load('./data/Healpix_p/0.npy'))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00010903541"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g4EVYjWeOuq8"
      },
      "source": [
        "def get_slice(data,nx,ny):\n",
        "    \"\"\"Slice matrix in x and y direction\"\"\"\n",
        "    lx,ly = data.shape  \n",
        "    if nx==0 or nx==lx:\n",
        "        slx = slice(0, lx)                \n",
        "    else:\n",
        "        idx = np.random.randint(0, lx - nx)            \n",
        "        slx = slice(idx, (idx+nx))       \n",
        "    if ny==0 or ny==ly:\n",
        "        sly = slice(0, ly)                \n",
        "    else:\n",
        "        idy = np.random.randint(0, ly - ny)            \n",
        "        sly = slice(idy, (idy+ny))\n",
        "    return slx, sly\n",
        "\n",
        "\n",
        "class OrganizedDataProvider(object):\n",
        "    def __init__(self,n_files,s_files,gmus,\n",
        "                 s2n, \n",
        "                 nx=0,ny=0,n_buffer=10,\n",
        "                 reload_rate=100,filt=None ,test=0 , noisy=1):\n",
        "\n",
        "        self.n_files = n_files\n",
        "        self.s_files = s_files\n",
        "        self.test = test\n",
        "        self.s2n = s2n\n",
        "        self.noisy = noisy\n",
        "\n",
        "        nmin = min(len(n_files),len(s_files))\n",
        "        if n_buffer>= nmin:\n",
        "            n_buffer = nmin\n",
        "            self.reload_rate = 0\n",
        "        else:\n",
        "            self.reload_rate = reload_rate\n",
        "\n",
        "        self.nx,self.ny = nx,ny\n",
        "        self.n_buffer = n_buffer\n",
        "        self.gmus = gmus\n",
        "        if filt is None:\n",
        "            def filt(x):\n",
        "                return x\n",
        "        self.filt = filt\n",
        "        self.counter = 0\n",
        "        self.reload()\n",
        "            \n",
        "    def reload(self):\n",
        "        print('Data provider is reloading...')\n",
        "        self.n_set = []\n",
        "        self.s_set = []\n",
        "    #        self.d_set = []\n",
        "        nind = np.random.randint(0,len(self.n_files))\n",
        "        \n",
        "        if self.test:\n",
        "            sind = (nind%12)\n",
        "        else:\n",
        "\n",
        "            sind = np.random.randint(1,3)\n",
        "            if sind==1:\n",
        "                sind = (nind%12)\n",
        "            elif sind==2:\n",
        "                sind = (nind%12)+12\n",
        "            \n",
        "#         print('sind' , sind , 'nind' , nind)\n",
        "\n",
        "\n",
        "\n",
        "    #        ninds = np.arange(len(self.n_files))\n",
        "    #        sinds = np.arange(len(self.s_files))\n",
        "    #        shuffle(ninds)\n",
        "    #        shuffle(sinds)\n",
        "        for i in range(self.n_buffer):\n",
        "            filen = self.n_files[nind]\n",
        "            files = self.s_files[sind]\n",
        "            self.n_set.append(np.load(filen))\n",
        "            signal = np.load(files)\n",
        "            self.s_set.append(signal)\n",
        "    #            if self.filt:\n",
        "    #                self.d_set.append(self.filt(signal))\n",
        "    #            else:\n",
        "    #                self.d_set.append(signal)\n",
        "    #            \n",
        "\n",
        "    def get_data(self): \n",
        "        self.counter += 1\n",
        "        if self.reload_rate:\n",
        "            if self.counter%self.reload_rate==0: \n",
        "                self.reload() \n",
        "    #        n = choice(self.n_set)\n",
        "        ind = choice(np.arange(self.n_buffer))\n",
        "        s = self.s_set[ind]\n",
        "        n = self.n_set[ind]\n",
        "#         print('ind' , ind)\n",
        "\n",
        "\n",
        "    #        d = self.d_set[sind]\n",
        "        return n,s#,d\n",
        "\n",
        "\n",
        "    def pre_process(self, n, s, gmu):\n",
        "        \n",
        "        sn = n + gmu*s\n",
        "        snslice = get_slice(sn,self.nx,self.ny)\n",
        "        sn = sn[snslice]\n",
        "            #    sslice = get_slice(s,self.nx,self.ny)\n",
        "            #    s = s[sslice]\n",
        "\n",
        "        if self.noisy == 1:\n",
        "            noise = np.random.normal(0, 0.000109704/self.s2n ,sn.shape)\n",
        "            sn = sn +noise\n",
        "\n",
        "        # sn = self.filt(sn)\n",
        "        #        d = d[sslice]\n",
        "        #        d = np.expand_dims(d,-1)\n",
        "        return sn #, (n*1e-6).std()#,d\n",
        "\n",
        "    def __call__(self, n, gmus=None): \n",
        "\n",
        "        if gmus is None:\n",
        "            gmus = self.gmus\n",
        "        n_class = len(gmus)\n",
        "        #        x,y = self.get_data()\n",
        "        X = []\n",
        "        Y = []\n",
        "        for i in range(n):                \n",
        "            n,s = self.get_data()\n",
        "            # print(n.shape)\n",
        "            inds = np.arange(n_class)\n",
        "            shuffle(inds)\n",
        "            gmu = gmus[inds[0]]\n",
        "            #            gmu = choice(gmus)\n",
        "            sn  = self.pre_process(n,s,gmu)\n",
        "            # print(np.std(sn))\n",
        "            sn = np.float32(sn)\n",
        "            sn = cv.bilateralFilter(sn,3,1e-3,1e-3)\n",
        "            # sn = (sn - np.min(sn))/(np.max(sn) - np.min(sn))\n",
        "            sn = (sn - np.mean(sn))/(np.std(sn) + 1e-30)\n",
        "            sn = np.expand_dims(sn,-1)\n",
        "\n",
        "            X.append(sn)\n",
        "            #            Y.append(-np.log10(gmu+1e-30))\n",
        "            lbl = n_class*[0]\n",
        "            lbl[inds[0]] = 1\n",
        "            Y.append(lbl)\n",
        "\n",
        "        X = np.array(X)\n",
        "        Y = np.array(Y)\n",
        "\n",
        "        return X,Y"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d9iLFRpPPBYa"
      },
      "source": [
        "dofilt = 'y'\n",
        "\n",
        "if dofilt[0]=='y':\n",
        "    import ccgpack as ccg\n",
        "    def filt(x):\n",
        "        return ccg.filters(x,edd_method='sch')\n",
        "else:\n",
        "    filt = None"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6r2VwysO2Pd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1d96812-3da0-4e7d-fced-5ebfcddc1672"
      },
      "source": [
        "gmus = [0]+list(5* 10**np.linspace(-8 , -5 , 10))\n",
        "n_class = len(gmus)\n",
        "print(gmus)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0, 5e-08, 1.0772173450159411e-07, 2.320794416806391e-07, 5e-07, 1.0772173450159411e-06, 2.3207944168063863e-06, 4.9999999999999996e-06, 1.0772173450159412e-05, 2.3207944168063865e-05, 5e-05]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2hLRJFD9PEtL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3e1f6a6-ecac-4724-c79c-51fa0bca080f"
      },
      "source": [
        "odp = OrganizedDataProvider(g_files,s_files,\n",
        "                              gmus=gmus,\n",
        "                              s2n = 20., \n",
        "                              nx=256,ny=256,n_buffer=10,\n",
        "                              reload_rate=1e5,filt=filt,test=1 , noisy=1)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data provider is reloading...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z26G61hdELoB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98cf993b-ddfd-4257-ccdf-f7cbba99ad35"
      },
      "source": [
        "x , y = odp(1)\n",
        "np.argmax(y)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k40t4ESgjXil"
      },
      "source": [
        "# c=0 \n",
        "\n",
        "# for i in range(120):\n",
        "#     c+= np.std(np.load('./data/Healpix_p/'+str(i)+'.npy'))\n",
        "# print(c/120)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HGxGZGEUjgAe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4bb3572-6af9-453e-9703-49da2e1f8acd"
      },
      "source": [
        "np.std(np.load('./data/string_p/0.npy'))"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "15.552519"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jICIPGBlBxEY"
      },
      "source": [
        "x , y = odp(1)"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pse5fDQr1khh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "outputId": "648556fb-4ba6-4e61-e00c-c6d69297e260"
      },
      "source": [
        "plt.figure(figsize = (5,5))\n",
        "plt.imshow(odp(1)[0][0,:,:,0] , cmap='jet')"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f4edd59f510>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dfuIlyLPHgw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6843dcd8-1c3d-44b6-f725-36bffdd0f7a2"
      },
      "source": [
        "g_test = sorted(glob('./data/test/healpix_p/*.npy'))\n",
        "# s_test = sorted(glob('./data/test/string_p/*.npy'))\n",
        "\n",
        "if len(g_test)*len(s_files)==0:\n",
        "    print('Somthing is wrong with initiation.')\n",
        "#     exit()\n",
        "print(len(g_test) , len(s_files))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "60 12\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qJTDEKb-PLB9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bff940e2-bcbb-4151-c4a6-8c9ae20cfc6b"
      },
      "source": [
        "odp_test = OrganizedDataProvider(g_test,s_files,\n",
        "                                 gmus=gmus,\n",
        "                                 s2n = 20. ,\n",
        "                                 nx=256,ny=256,n_buffer=10,\n",
        "                                 reload_rate=1e5,filt=filt,test=1 , noisy=1)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data provider is reloading...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PhwsSB5FPLD9"
      },
      "source": [
        "def arch_maker(x,n_conv,n_class):\n",
        "\n",
        "    print(\"\\033[91m ============================= Begin ========================= \\033[0m\")\n",
        "    \n",
        "    for _ in range(4):\n",
        "\n",
        "        x = tf.layers.conv2d(x,filters=8,kernel_size=5,\n",
        "                              strides=(1, 1),padding='same' , trainable=1)\n",
        "        print(x)\n",
        "        x = tf.layers.batch_normalization(x, trainable=1)\n",
        "        print(x)\n",
        "        x = tf.nn.crelu(x)\n",
        "        print(x)\n",
        "\n",
        "    for _ in range(n_conv-2):\n",
        "\n",
        "        x = tf.layers.conv2d(x,filters=8,kernel_size=5,\n",
        "                              strides=(2, 2),padding='same', trainable=1)\n",
        "        print(x)\n",
        "        x = tf.layers.batch_normalization(x, trainable=1)\n",
        "        print(x)\n",
        "        x = tf.nn.crelu(x)\n",
        "        print(x)\n",
        "\n",
        "        x = tf.layers.conv2d(x,filters=16,kernel_size=5,\n",
        "                              strides=(1, 1),padding='same', trainable=1)\n",
        "        print(x)\n",
        "        x = tf.layers.batch_normalization(x, trainable=1)\n",
        "        print(x)\n",
        "        x = tf.nn.crelu(x)\n",
        "        print(x)\n",
        "        x = tf.layers.max_pooling2d(x,pool_size=2,strides=1)\n",
        "        print(x)\n",
        "        \n",
        "    for _ in range(2):\n",
        "\n",
        "        x = tf.layers.conv2d(x,filters=8,kernel_size=5,\n",
        "                              strides=(2, 2),padding='same', trainable=1)\n",
        "        print(x)\n",
        "        x = tf.layers.batch_normalization(x, trainable=1)\n",
        "        print(x)\n",
        "        x = tf.nn.crelu(x)\n",
        "        print(x)\n",
        "\n",
        "        x = tf.layers.conv2d(x,filters=16,kernel_size=5,\n",
        "                              strides=(1, 1),padding='same', trainable=1)\n",
        "        print(x)\n",
        "        x = tf.layers.batch_normalization(x, trainable=1)\n",
        "        print(x)\n",
        "        x = tf.nn.crelu(x)\n",
        "        print(x)\n",
        "        x = tf.layers.max_pooling2d(x,pool_size=2,strides=1)\n",
        "        print(x)\n",
        "\n",
        "    x = tf.contrib.layers.flatten(x)\n",
        "    print(x)\n",
        "    x = tf.layers.dense(x, 40 , activation=tf.nn.crelu)\n",
        "    print(x)\n",
        "    x = tf.nn.dropout( x, keep_prob=0.5)\n",
        "    print(x)\n",
        "    x = tf.layers.dense(x, 20 , activation=tf.nn.crelu)\n",
        "    print(x)\n",
        "    x = tf.nn.dropout( x, keep_prob=0.5)\n",
        "    print(x)\n",
        "    y = tf.layers.dense(x, n_class, activation=tf.nn.softmax)\n",
        "    print(y)\n",
        "\n",
        "    print(\"\\033[91m ============================ END ============================= \\033[0m\")\n",
        "\n",
        "    return y"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QY-sUIFYPSqZ"
      },
      "source": [
        "restore = os.path.isdir('./models/model_s4ii')\n",
        "restore = 1"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NnJ2Fv_PVnP"
      },
      "source": [
        "def arch(x):\n",
        "    return arch_maker(x,6,n_class)    "
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GxJJd7mpPYaT"
      },
      "source": [
        "def loss(y_true,x_out):\n",
        "    return tf.losses.huber_loss(y_true,x_out)  "
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0E75Z-vqPcLe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "662e8a74-2146-448a-e94c-05dbde3e1ff7"
      },
      "source": [
        "conv = ng.Model(data_provider= odp,\n",
        "                 optimizer=tf.train.AdamOptimizer,\n",
        "                 loss = loss,\n",
        "                 restore=restore,\n",
        "                 model_add= './models/models/model_s4ii',\n",
        "                 arch=arch)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[91m ============================= Begin ========================= \u001b[0m\n",
            "Tensor(\"conv2d/BiasAdd:0\", shape=(?, 256, 256, 8), dtype=float32)\n",
            "Tensor(\"batch_normalization/FusedBatchNormV3:0\", shape=(?, 256, 256, 8), dtype=float32)\n",
            "Tensor(\"CRelu/Relu:0\", shape=(?, 256, 256, 16), dtype=float32)\n",
            "Tensor(\"conv2d_1/BiasAdd:0\", shape=(?, 256, 256, 8), dtype=float32)\n",
            "Tensor(\"batch_normalization_1/FusedBatchNormV3:0\", shape=(?, 256, 256, 8), dtype=float32)\n",
            "Tensor(\"CRelu_1/Relu:0\", shape=(?, 256, 256, 16), dtype=float32)\n",
            "Tensor(\"conv2d_2/BiasAdd:0\", shape=(?, 256, 256, 8), dtype=float32)\n",
            "Tensor(\"batch_normalization_2/FusedBatchNormV3:0\", shape=(?, 256, 256, 8), dtype=float32)\n",
            "Tensor(\"CRelu_2/Relu:0\", shape=(?, 256, 256, 16), dtype=float32)\n",
            "Tensor(\"conv2d_3/BiasAdd:0\", shape=(?, 256, 256, 8), dtype=float32)\n",
            "Tensor(\"batch_normalization_3/FusedBatchNormV3:0\", shape=(?, 256, 256, 8), dtype=float32)\n",
            "Tensor(\"CRelu_3/Relu:0\", shape=(?, 256, 256, 16), dtype=float32)\n",
            "Tensor(\"conv2d_4/BiasAdd:0\", shape=(?, 128, 128, 8), dtype=float32)\n",
            "Tensor(\"batch_normalization_4/FusedBatchNormV3:0\", shape=(?, 128, 128, 8), dtype=float32)\n",
            "Tensor(\"CRelu_4/Relu:0\", shape=(?, 128, 128, 16), dtype=float32)\n",
            "Tensor(\"conv2d_5/BiasAdd:0\", shape=(?, 128, 128, 16), dtype=float32)\n",
            "Tensor(\"batch_normalization_5/FusedBatchNormV3:0\", shape=(?, 128, 128, 16), dtype=float32)\n",
            "Tensor(\"CRelu_5/Relu:0\", shape=(?, 128, 128, 32), dtype=float32)\n",
            "Tensor(\"max_pooling2d/MaxPool:0\", shape=(?, 127, 127, 32), dtype=float32)\n",
            "Tensor(\"conv2d_6/BiasAdd:0\", shape=(?, 64, 64, 8), dtype=float32)\n",
            "Tensor(\"batch_normalization_6/FusedBatchNormV3:0\", shape=(?, 64, 64, 8), dtype=float32)\n",
            "Tensor(\"CRelu_6/Relu:0\", shape=(?, 64, 64, 16), dtype=float32)\n",
            "Tensor(\"conv2d_7/BiasAdd:0\", shape=(?, 64, 64, 16), dtype=float32)\n",
            "Tensor(\"batch_normalization_7/FusedBatchNormV3:0\", shape=(?, 64, 64, 16), dtype=float32)\n",
            "Tensor(\"CRelu_7/Relu:0\", shape=(?, 64, 64, 32), dtype=float32)\n",
            "Tensor(\"max_pooling2d_1/MaxPool:0\", shape=(?, 63, 63, 32), dtype=float32)\n",
            "Tensor(\"conv2d_8/BiasAdd:0\", shape=(?, 32, 32, 8), dtype=float32)\n",
            "Tensor(\"batch_normalization_8/FusedBatchNormV3:0\", shape=(?, 32, 32, 8), dtype=float32)\n",
            "Tensor(\"CRelu_8/Relu:0\", shape=(?, 32, 32, 16), dtype=float32)\n",
            "Tensor(\"conv2d_9/BiasAdd:0\", shape=(?, 32, 32, 16), dtype=float32)\n",
            "Tensor(\"batch_normalization_9/FusedBatchNormV3:0\", shape=(?, 32, 32, 16), dtype=float32)\n",
            "Tensor(\"CRelu_9/Relu:0\", shape=(?, 32, 32, 32), dtype=float32)\n",
            "Tensor(\"max_pooling2d_2/MaxPool:0\", shape=(?, 31, 31, 32), dtype=float32)\n",
            "Tensor(\"conv2d_10/BiasAdd:0\", shape=(?, 16, 16, 8), dtype=float32)\n",
            "Tensor(\"batch_normalization_10/FusedBatchNormV3:0\", shape=(?, 16, 16, 8), dtype=float32)\n",
            "Tensor(\"CRelu_10/Relu:0\", shape=(?, 16, 16, 16), dtype=float32)\n",
            "Tensor(\"conv2d_11/BiasAdd:0\", shape=(?, 16, 16, 16), dtype=float32)\n",
            "Tensor(\"batch_normalization_11/FusedBatchNormV3:0\", shape=(?, 16, 16, 16), dtype=float32)\n",
            "Tensor(\"CRelu_11/Relu:0\", shape=(?, 16, 16, 32), dtype=float32)\n",
            "Tensor(\"max_pooling2d_3/MaxPool:0\", shape=(?, 15, 15, 32), dtype=float32)\n",
            "Tensor(\"conv2d_12/BiasAdd:0\", shape=(?, 8, 8, 8), dtype=float32)\n",
            "Tensor(\"batch_normalization_12/FusedBatchNormV3:0\", shape=(?, 8, 8, 8), dtype=float32)\n",
            "Tensor(\"CRelu_12/Relu:0\", shape=(?, 8, 8, 16), dtype=float32)\n",
            "Tensor(\"conv2d_13/BiasAdd:0\", shape=(?, 8, 8, 16), dtype=float32)\n",
            "Tensor(\"batch_normalization_13/FusedBatchNormV3:0\", shape=(?, 8, 8, 16), dtype=float32)\n",
            "Tensor(\"CRelu_13/Relu:0\", shape=(?, 8, 8, 32), dtype=float32)\n",
            "Tensor(\"max_pooling2d_4/MaxPool:0\", shape=(?, 7, 7, 32), dtype=float32)\n",
            "Tensor(\"conv2d_14/BiasAdd:0\", shape=(?, 4, 4, 8), dtype=float32)\n",
            "Tensor(\"batch_normalization_14/FusedBatchNormV3:0\", shape=(?, 4, 4, 8), dtype=float32)\n",
            "Tensor(\"CRelu_14/Relu:0\", shape=(?, 4, 4, 16), dtype=float32)\n",
            "Tensor(\"conv2d_15/BiasAdd:0\", shape=(?, 4, 4, 16), dtype=float32)\n",
            "Tensor(\"batch_normalization_15/FusedBatchNormV3:0\", shape=(?, 4, 4, 16), dtype=float32)\n",
            "Tensor(\"CRelu_15/Relu:0\", shape=(?, 4, 4, 32), dtype=float32)\n",
            "Tensor(\"max_pooling2d_5/MaxPool:0\", shape=(?, 3, 3, 32), dtype=float32)\n",
            "Tensor(\"Flatten/flatten/Reshape:0\", shape=(?, 288), dtype=float32)\n",
            "Tensor(\"dense/CRelu/Relu:0\", shape=(?, 80), dtype=float32)\n",
            "Tensor(\"dropout/mul_1:0\", shape=(?, 80), dtype=float32)\n",
            "Tensor(\"dense_1/CRelu/Relu:0\", shape=(?, 40), dtype=float32)\n",
            "Tensor(\"dropout_1/mul_1:0\", shape=(?, 40), dtype=float32)\n",
            "Tensor(\"dense_2/Softmax:0\", shape=(?, 11), dtype=float32)\n",
            "\u001b[91m ============================ END ============================= \u001b[0m\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/tensorflow-1.15.2/python3.7/tensorflow_core/python/client/session.py:1750: UserWarning: An interactive session is already active. This can cause out-of-memory errors in some cases. You must explicitly call `InteractiveSession.close()` to release resources held by the other session(s).\n",
            "  warnings.warn('An interactive session is already active. This can '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Restoring parameters from ./models/models/model_s4ii/model\n",
            "\u001b[1;34;40m Model is restored! \u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vGy__HiBGOxP"
      },
      "source": [
        "#Correction of labels :"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78T5_dy7-g3_"
      },
      "source": [
        "String simulated maps are delta(T)/T . CMB simulations are just delta(T)...\n",
        "So we have to divide gaussian maps to T (ie. Sky temperature which is 2.7 kelvin)\n",
        "We could do this in our data provider but there is no difference whether we divide Gaussian map to T or divide gmu value to it because at the end we multiply G maps by the gmu. So : "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6cwKrkREGYx7",
        "outputId": "c9ac3702-12e1-4f4a-f8e9-36365e5a163e"
      },
      "source": [
        "gmus_ = [gmus[i]/2.7 for i in range(n_class)]\n",
        "print('\\n', gmus_)   \n",
        "labels = []\n",
        "for gmu in gmus_:\n",
        "    labels.append('{:3.1e}'.format(gmu)) "
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            " [0.0, 1.8518518518518515e-08, 3.9896938704294115e-08, 8.595534877060707e-08, 1.8518518518518516e-07, 3.989693870429411e-07, 8.595534877060689e-07, 1.8518518518518517e-06, 3.989693870429411e-06, 8.59553487706069e-06, 1.8518518518518518e-05]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LiagTI1r_9IS"
      },
      "source": [
        "We prefer non-overlapping patchs for validation of the model. So we re-write the dataprovider to a non-overlapping version: "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MGqRxM-N80gB"
      },
      "source": [
        "def get_slice_seq(data,nx,ny):\n",
        "    cp = []\n",
        "    for i in range(8):\n",
        "        for j in range(8):          \n",
        "            cp.append(data[i*nx:(i+1)*nx , j*ny:(j+1)*ny])\n",
        "    cp = np.array(cp)\n",
        "    return cp"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ENVNaDXX83sl"
      },
      "source": [
        "class non_overlap_dp(object):\n",
        "    def __init__(self,n_files,s_files,gmus,s2n,\n",
        "                 nx=0,ny=0,n_buffer=10,\n",
        "                 reload_rate=100,filt=None , test=0 , noisy=1):\n",
        "\n",
        "        self.n_files = n_files\n",
        "        self.s_files = s_files\n",
        "        self.test = test\n",
        "        self.noisy = noisy\n",
        "        self.s2n = s2n\n",
        "\n",
        "        nmin = min(len(n_files),len(s_files))\n",
        "        if n_buffer>= nmin:\n",
        "            n_buffer = nmin\n",
        "            self.reload_rate = 0\n",
        "        else:\n",
        "            self.reload_rate = reload_rate\n",
        "\n",
        "        self.nx,self.ny = nx,ny\n",
        "        self.n_buffer = n_buffer\n",
        "        self.gmus = gmus\n",
        "        if filt is None:\n",
        "            def filt(x):\n",
        "                return x\n",
        "        self.filt = filt\n",
        "        self.counter = 0\n",
        "        self.reload()\n",
        "\n",
        "    def reload(self):\n",
        "        print('Data provider is reloading...')\n",
        "        self.n_set = []\n",
        "        self.s_set = []\n",
        "    #        self.d_set = []\n",
        "        nind = np.random.randint(0,len(self.n_files))\n",
        "        \n",
        "        if self.test:\n",
        "            sind = (nind%12)\n",
        "        else:\n",
        "\n",
        "            sind = np.random.randint(1,3)\n",
        "            if sind==1:\n",
        "                sind = (nind%12)\n",
        "            elif sind==2:\n",
        "                sind = (nind%12)+12\n",
        "            \n",
        "        for i in range(self.n_buffer):\n",
        "            filen = self.n_files[nind]\n",
        "            files = self.s_files[sind]\n",
        "            self.n_set.append(np.load(filen))\n",
        "            signal = np.load(files)\n",
        "            self.s_set.append(signal)\n",
        "\n",
        "    def get_data(self): \n",
        "        self.counter += 1\n",
        "        if self.reload_rate:\n",
        "            if self.counter%self.reload_rate==0: \n",
        "                self.reload() \n",
        "    #        n = choice(self.n_set)\n",
        "        ind = choice(np.arange(self.n_buffer))\n",
        "        s = self.s_set[ind]\n",
        "        n = self.n_set[ind]\n",
        "\n",
        "        return n,s\n",
        "\n",
        "    \n",
        "    def pre_process(self, n, s, gmu):\n",
        "        \n",
        "        sn = []\n",
        "                    \n",
        "        sky = n + gmu*(s)\n",
        "        noise = np.random.normal(0, 0.000109704/self.s2n ,sky.shape)\n",
        "        sky = sky +noise\n",
        "        sky = np.float32(sky)\n",
        "        sky = cv.bilateralFilter(sky,3,1e-3,1e-3)\n",
        "        snslice = get_slice_seq(sky,self.nx,self.ny)\n",
        "        # print(snslice.shape)\n",
        "        for i in range(len(snslice)):                    \n",
        "            sn1 = snslice[i]\n",
        "            sn1 = (sn1 - np.mean(sn1))/(np.std(sn1) + 1e-30)\n",
        "            # print(sn1.shape)\n",
        "            sn.append(sn1[:,:])\n",
        "\n",
        "        sn = np.array(sn)\n",
        "#         print(sn.shape)\n",
        "            \n",
        "        sn = np.expand_dims(sn,-1)\n",
        "\n",
        "        return sn#,d\n",
        "\n",
        "    def __call__(self, n, gmus=None): \n",
        "\n",
        "        if gmus is None:\n",
        "            gmus = self.gmus\n",
        "        n_class = len(gmus)\n",
        "        #        x,y = self.get_data()\n",
        "        X = []\n",
        "        Y = []\n",
        "        for i in range(n):                \n",
        "            n,s = self.get_data()\n",
        "            # print(n.shape)\n",
        "            inds = np.arange(n_class)\n",
        "            shuffle(inds)\n",
        "            gmu = gmus[inds[0]]\n",
        "            #            gmu = choice(gmus)\n",
        "            sn  = self.pre_process(n,s,gmu)\n",
        "            #            rand = np.random.randint(0,2)\n",
        "            #            sn = sn-sn+rand\n",
        "\n",
        "#             sn = (sn - np.min(sn))/(np.max(sn) - np.min(sn))\n",
        "\n",
        "            #            Y.append(-np.log10(gmu+1e-30))\n",
        "\n",
        "            lbl = n_class*[0]\n",
        "            lbl[inds[0]] = 1\n",
        "            Y.append(lbl)\n",
        "            X.append(sn)\n",
        "\n",
        "        X = np.array(X)\n",
        "        Y = np.array(Y)\n",
        "\n",
        "        return X,Y"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RkdIxRepGY-P",
        "outputId": "e7457b78-e7bb-46ee-8136-409ff686bd49"
      },
      "source": [
        "nodp_test = non_overlap_dp(g_test,s_files,\n",
        "                            gmus=gmus,\n",
        "                            s2n = 20,\n",
        "                            nx=256,ny=256,n_buffer=10,\n",
        "                            reload_rate=1e5,filt=filt,test=1 , noisy=1)"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Data provider is reloading...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3_QkKnlWDKlG",
        "outputId": "b9bff0b5-3ab0-4adb-c1ce-1f14876e438c"
      },
      "source": [
        "x,y=nodp_test(1)\n",
        "print(x.shape , y.shape)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1, 64, 256, 256, 1) (1, 11)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "hm7rqWeFDNRD",
        "outputId": "03c85132-b5c5-47e7-97fc-e3bd6543540c"
      },
      "source": [
        "plt.imshow(x[0,0,:,:,0] , 'jet')"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f4e7ace8550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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U/XVcBGSufdp2MwSDV6P9g9KE8GaF1wY0efruc1vD02QcAyM43d5mxjS64vIANu4E/FxKtzDyAQsmT1emKXwH+DZcnMOdbRMEF+d2mdISul7aQ9Od6c0HmT3XrWSF21bTXEWAOufKpJFWoNXck4U88AXmY/cmhPfFGz9kHT0PIs/k1BF89GBaW6VeMojHk5CoyMjymsvxBeRdtu89uoKU1w5TaK60AxbhVVcZi7MhF2cDWznbfBXvhfBuS6nmWmH9fmonYYzElbuKYs6uL499rSZwNqfmZQ7psQ4IyT4FZcRx9Fwyam4zoyjWlP0eq5ORnU/n7ZPMCV230wQuVr2my/VReBF+7+89mBDiq3j38qdpN0MwdD7mewmEtvup7Zb0+4IJCIF0+v0tON/eogwPaIfTOFmSi+k4qrfaltc13WeYpvAnwFMTCMuVmQ2DDAbY/2DbwLgDDaHgyUuQMBBvMuiz/z9zv8/N21DniV4sVdwTlNqruARCRhXNJK8K27bk625PejNHijjAZXLoPN4v7+nTmlhDltQ7M+Z5RZ7XTIoE6PYoI74jtqVnPNqqaxRr0xYywxYkRKVGX7eYCMzTe3uF7YfPu+4YTnvY44hxEGDrIOAEyGZU8Xqldb3HfR5yFxHEvGA2HkQg5DFn1p+ymmKqp/CDqRsvU5IQG8Plo5H9LwF2ErZ/GPb3cyUIEuELWuiep90cweAfqswKMHsbkurdengNbMKvBJ4efMvs8kUxbKDusp0HgUCjh9yLp6wMWzgC3sewihyGI3uBaQhRGORmRsTmhZfwA6/xqLW1i/bKp/vMklvSk4G0kkkN1zatEpqAOXVY7fN4f/5YQGNl8eqwBzA98GhmgJkoXgtJrrnU19m2+fGNUl00hMKCIUYDTrhIFTWdKl5rltchqoJkHuj/9ruEgyaRXmpiPp5gE3EXeAm4B1/58tsc8BFAXG/PAtfDazbyZM2Z8DavR6Epdqf6wvrThN4TppSrnp1Pz1kawzTc03fCdWk8fBg+e4HowUhomNfdfnqOZtZ9HjUGaCL4Up/bLiXv/mubEfqtzBJ9H4TCfLvrNIGKKjxc2V42qM/CpVSRhZnXWDjYcTjeCDgI5ziD7mMTDFV45aFHB5gnJWIfMmmCcEEuWWkF3gTxgKX/TOInZI3rTqSb9gRvrxReQ/At7V/F40gLkDDwLsuPQ+c9V1+rpj7LBOkFfoD10zJO+naTIIvX2F8nrEJjQV4F3/xE8SbECU3tAtKE/CLwdejef8bBziH74YF77GURWJ0+Xkf7zJiaCVT2rDdzIxeBsSHVtwJiVye30+SWAJtC/6XHrP70jgkGgY5e49Fv9L5LEiC7RK3jYtVjveqxGA3pUTJsPadPajdDMGxIK6paWwvgmm3XffZuQYKmMNpikck+FrqeuxXNaKxFXZJVtRGnzjFk8QgTCt7rcW6vzQrm57AsTVOonGuxm4ffA12ZC3qNuWpaFO7lOf8exCxDIGB2Ge/vOhWxPVllZ0pNX9NjEIhb3syQSeDByTp89mZG+xxt88u7xkwomK4h7ojHO7xnpAmQOkZf6Pj1qpc0Bq8ZXCccvDDQuwSDdwNKKHwRmMJwvAgazCAuImI1CmuhwfzMechdCxIrh9SVeU2K/tr6N0smWsWAZTlMbEUf1xME2OpsaC7MD0kTXyaFBKHGxjRcNyThEBYs8oKqWkRTbszNiJV4vtbhakCQNy3aflqvevt9vT1ewMUosP6KXnQZCUADY9hBssGyqjbac4nFRByG1wdEYcA5bE7hOLAPn4XXoA6eCUxbWDxNl3sL2KuSGcCKJo2533qXtnAdXbr1xKKK7Wxdjw94AlH6zVXQ0LfkhsyR56CNIXzc7/2Et/dkOsh7outSPISPbPXHWbcmYCWm36oFSrXBaanb0hZOaAJ1mlwe+AtqfV1lrAsBrBoxC07Zif1i3gmLeJ3V0/g7IE76ujLAta4zyixoGXVhx185JqL3RKyAB30TBC+RFjm/n8Z/uO7xF39IVWWsxkO28tqwiEcEIbdEcbS3GzbUJ7ebIRh8q0msR+9OggbhKTYJBa20gS9wUcBy3GWeTSI6rMj6Mybc5SH3zr/P6egOIjANzy/t2KeYliDBoMCpsNp0RrA/gs05fO+xRYndwQSA3JdLCMYK7APPHsN9ghtTfIYCY0MKqPRmkDeRPPo+ioCzW+3r0BU+b0J1zXem0nuTQ9/nQQBIW2hrCBYzkTeEShXW9txdh9cGZJ7onBnVlQCsJQn38dfr4wvW9KjrMKEqFxshIDGSfkjjRlrCh+4lwXCftFrrGDNg6iZ26IOSHmWI0QATdPd4wNu8bi7IylyQeVazqIfkAvycja8Q6tjf0nq8Vqj70fW85Lb5seDNCODs0S7MOjDdMPniEU9XPci7/MSrDyOvwoPRn7bdDMGwRXPVl1SUjd52P7Vtcv/eNztc8QESCvagiziYT9glG9kgnjCnt7owpuEpNruPScLhMUlVVbh1bszEO980wfCYJBQUXy7B8BgDn289hn0JAr1KElVawsKDkCJzyfQIgVftVb+gDGp/GtgefFTocMX1pkAKCMpjIFTSEky11wBPx06cB3ERfKCQrqsi4yMOyNXXrDllh4fc5ZTdyCEBoqtUGkUU6CI1aeJoLIxpapReKOj1CHi0CU/mi6mv256I3Hz/Xrh5BqaE5du8zoIhu5xErbUii1reehX6JUtUbwlcH/TE+ALoNsPCPVFJY1ocB5k+fuF4FOIsph3m4wnj3RmDLy9jjAcYxtGmcH9SuxmCAa6aArLHffxHe7v/3gN2oSlIKEX+Wecol0BJz5KQlHP6TzFB4HGFY5KmoGv02kkFr9+F5UMbckryJE7DHDMtKow+fQrsn4djnpMEgh/YGgDe/SbsInhmqqzJu9fE9UE6ChH24ekCAhcOX/HeBKn0ntl4ncngjyUtxOcw0Gd5HB5yN3oYdLwFQ/6fd36R7q6BfT6XgQTVImp5OU9PpoktqD67DqCWFuDNhw/BKIZHwADOdkx4CKwLavnW9JxekcKjvWaT+AzmBZJ7W58VDp5lNfUoaVQ+DqSuMvK8NoJWvySFz7bGl9MIYqtI7kwJBp9opoLLkxG93VnDpJTQ/XxSomUy5q2XByS91hABFvdZrU62/MJhChqoct0JcJyePzWh8JgUC+H/b5stEkKrtG2ACYMlJggkDMBMjAFmZkSqdJuRF6772nv1rxBNCSmqsaAMpKI6DmgRWmZMo8BQMx5H5ZR+kZF6ztxqCgSfCER5nzxfIubFEFYTrmsQ0PgFQ3Jqhsxi3xeU/Iuv/SMGLLjNrIFdiC8hfsbsNAgFn4XpuqZ+kn//ESYgWGCi+3tEwSDQ8V54390w3Z1FjUYEOGEfFjliqrmSr2g8SejNmUStTKZQpEYHfKFc2ftWXnO5KqC/SZhJ203dBtTb33u2ZA5MV0yyeXxeygwld/HztJsjGLwKreZzD3jAscQ6w6H1QMqyNIJyrxfi98UALJkGDl3BmmG9YPL0IiVAOccG3ql7nZNU+etcpQ/h9DAJBGELajkmEBRXMQA2VZCDnt/Q5m70W59lcgDsmJlE3JQe+DAYAVIj/SohfCVhCXV0PbY1KuvKplfA+B2JXisTQU0sh+TZSN6JHU6YcZuMil1O2eOIN/h/eYO3eJf7IS1aivwsw3q9YMiyFNORj+eAeMRenxsmx2MM6v8B8AuGMdzHhMJL9urvPmGYLeJqK+Gg/8dOK5JwA1uV9f/QmVvqt5Iey3JIueqxXhVmDsVrr6DK02T35oIXAJAmv/YbX4TfVvH9J75wzD0exMQ4BaeALQbDxsj85HYzBMMW10cbQlS3lWeAxyTEvh1vEITGo1e2ecA9LFWcSXnFQwzPV6YhPCMlVZF6L0/EMUkoeA3M4wLhd1WdzAddhjSDgfvsTYyGluD/J9yT8AQdUKaHc3FqQttPZLun1VisPO2r/XxL5kgiOPlYgLaZINOjnUXLflfE7+oAVQrMNEFluoY0GjUJMWFAivjU/ouzIZHp2E7IouZBPD+iH4X+mnWxxOW/APe/Ar8IfI0oGLovPWN3+5QdTmO2KOWKqMhi3zb7IfWtp3d7gHYRLkbxHZerXprMQIwOlTCYkbQAtbZWHOdITXe8jMfv9Ute4X2mPGEZcoNK2H+SF+q6dnMEg7AEvQdt4aKw2AAI2ZzOYPOTFl1YBDNt3d/ipNiJvP5TdulR8nLIpzA8X9E/xCZXjk3q0o4VQ7LPMRNU+/nvoTmBV8AxXJQmXy7cLrfCZ59A+iK8LzBB0hAQ0oraiVy9uqjrDi7O3uqygb1IjbfuK6Mq7pv3KrSbj3/wq6TP5CQzYRASkfjgIeENEipadW3CW56LKbOIYXiNwHs/9Iqp5uoixAc496RfNQnv402cbFv9NXwNLn+xBw+6phn8wT68tA9/BPwc8G8RXJQX9KdzDrY/YhoyPuxz3KAzC/sQ49GDup45KnNDQLfuv8FbqHLzqqy6tuJLQPSDsOh3W0IgZKhqY1D9C/rjRcMDspNZVHBNzh7H8VoSm/fzynx0rsZNeOjtfAPZqIY9mDE1tXlk2oByC/rsw7dDTMDtxys6hyRhIHVUk1+rv8cWHH5wZUUPWsamgqOnkcPEBQYwzrGJvxfeb5EwiCGB7KRB3U7JpqYVRG7bIDSeHXSpMluRBRja6ru+shKntGgp07atgoPWpM5jn3m3oQZ7ylWY8kr7nIJS/Q1/WDvTJI/7KZuR0A8JcA9EVnEIJ8YlQH+8YCU3pVTtezah16uCnf1TpjyJ9yHgdcKc+ZcnnPy1HX6B3+ff57/hd/gVvsXP8IB7nJ7vUFUZ0+0ZU55wl4fsc8wOpxSUHLEfMzPVWICX8j7qHNYHRue2fknYgkKzB8UiRYT2S0uucl1wUF6zNQ0CJ6+TFybPUri623c4XjLNUjTwAYcMWMSMXCKxFQEned52MwRDhs0gjJRU5ymsuB2hVlIw5UmDSeelu94HLJiUczoyFZR9qSLNZpkSHmz04c4ViWdw7raV8PhpIjNJU3iGcRbUBiScYYA94+julFDwT8CzHgvTluZ7lrT2Aa8wZ8yUWSTYtElMoh0r3sDb/F4jENVYkxE8b0ARliV+tTEwcRE1iHWwW2UG+BwEahI0lma+cDBdip4sA6DpNZyosmcBwVcLQNv2S6fsFUfc3p41VkKfMl3xL9/gn/Dv/vb/BH8f/o3/5B/xn7/yH/Fb/OssRkOW5wP2OeY1vss+R9GU8LkcBUD6cHTvgclIGay8xuN5C72QTWkrr7mUcAgmRX9qumWe1/T6a3pZGXEg4270OKt2YdXhy3/lT3mTb/Ee91kwDMLMEgsJ0zlll4fcbXh5DFf7HHolNl14tp9WQ0iZcBYh6ccZVk/AkmU8adi56hQN6jFzbtezRFjyr7bWcEwSDNAgEkUV/9htK01bqJxGPsEm/qR1X0HWkQM7BdzaJtWNEKbi4yF8n4TPa4owwYya245k1OqlSbGmiDhDsnXNK6CAKG8jq7VTrEnQDEMsg4a+JuLAgW+94ENYOncn+MQvk3h8P+kacRCB+zBhzhmTiKQv+wMu8oGp2X3TFKaFCUfRfOVxGrBsmAT3eZdffvp7Zh7+MvzwlXE89/zpmOn2jNd5mzd4iylPmAQQY84Yy2S9iNqQ17J0DN2/CT4oGTe4IxLEyyojy6sQGZo1zR6MO9ErTKB5r0+ZFZzmO3Sncy5ObvEq7/IGb7FkyAk7UQBqHJywyzF74fksGtf5vO1GCIb1Vs5RltZar2IuGHDKbkRalYdRnWe2c/LjKxPT5OlF0hYkBIQrED4/pgk0qrUZaX7ilkko3BL46Vo3vPIshWJ382BCqB6FPDBe+Oj5CZcK2IkAwNvMGquxZzb6YijK1qPPCjOXur4IdjKYii9h0CRLJQ+DUPdkRiQA0q6nFw0ANZ9Fah0n1rCxaiUhVTlhYxNxHPgPAIvR0Fx8/TVZXjPZPmu4FPX/DidMsOIuO5zGLN/dY+CrsPkKvMUbWIGXGf/y9j/mTb7Nm3wr3p+EnejPyh5l/V1HjGTBMJpXHriVJnYdjpPnNXVeg2NEZnlNntcMigR4TjhD3JI5ExbFwDI+zeDb9fG1Fg4AACAASURBVM+wzopgFp418pHWpECuHU4iV6cOIt1rMJ+m3QzBgLkWoUnztdRggxhWfJeHvMp7UbLrwUCSkEOWDMtFYjHKhJC7S56AZzRdktDM04j7vu1ahBg0dSvsP+jDoAiUZ012NQGHbZdsW/C4bXkNs2IYbH2bkDKdxGFInIHENNytT+mtDA5d97umjgccQKnsfDCT9ZlyXw6jIBIXwjSSJj/B/9a6LY+DTxpexIXi86wi90EVrNRktijz05wJZQAgf/iDPeMw5Bt6u08iV0Peg3GYUKrwtBMWDssleQYjWB3Ae6Of5Ig9ajJe5V1e47v8PL/PlCfRzEp5K8fxnlOBGHswirJcBJ9TETxfKRWe1+YMkBVmIK1BeRKyvGIwWgbBtGbCGVOeMGQZs1lF3OgMHj844PDVGfscRfKY0vvL3JNAGLKI88aDqZ+23QjBcEnGCTsNVRUS2aWg5IBD3uRb7Jem15dFL6p2E+fHKurSAqGekTwIYlFCwguOSAIDt90H4+h7uSnDZL9wwiEKBB//4DUMT+9uCwXFSkh7gOiuXIy2KOqSMmtiCWsK5zlIWRJqcqZPzywqNNxLMbogG9XURRZdgBpEHl+wy7HVtwzH92CjNAUvILxJIpxBLEBNrnachl4LJzSGLDhij1N2WTDgPe7znR+8AR/2U06CrwH3Oky2zwLatAyuaONsqITehHkgrtl+a3p8/+Anguq9G8ysBT/P7/Orh79tY+SWmbEAJ9kOp+wi742OocS5dUMc+9XYKk75rNq+X9ekpCn5eBG9FPIq6FmqzN8up7zDa8DLJjTzmksXA6VztHEDCTJpLd7UeN52IwRDRc6M2xH4uRq2W8YVQWXr8toekzIi+7J2cSL7F6TJ/QHXCwJhD96UUJ2I4L149jSFWQ/6MLyDTWoRrnyEZO3efSSoi7dofN6m8UR0b2oamD7XglLPdZWoVt6UUaiPW1xSF00hkLgICbH2K53yJXj8wuc4BBuUPmOULzGnCSM1fBjR8bQqC4d4n3t8u/4Zq7b0Yde4B2I4hihBoyyvnKAyjaEgaTaKgpSdbo80i6aWaRVH/Ar/B1/47x7DnwFvAHtw69kF3IFy7ywIhmZ/qc909RLECZgswn0uYm5HtR5ryCyNv1L6E2Ipev1U4Efejpyad3iNf3D+b4b07xUXj27BPRi/dNK4T38O7x2y7+yZnbDLgjwC85+23QjBcMlWzIojf7B3P9kKERJ9jJIbzASBW5WqS6v9IJXfA4n6Xx4Jz6Zs05E9ozIjmiMXz+BZad6IW4QsTq4IbUMo6Fie3qwYD+3vcQbtX6RaEXldM88mcaWXtwGsjuOknpNVNV0xNw9pBuEEcphWfVWbIqyCOJPEezJEL8pQbcnEePQ0X+9a1K8Um6K8FxlVAJJT/IVStr3Lq3zvn/200ZfPMEHgGX73iMzE8XQetRZfXNYDdh6rEDYyfXpG9zF8+ekP4dt/Av+FPUt+GStXIBPzjh65Cbhe8KTomOuAaiVB6k0pBeo1zacrnoAsEMDyqwJfAuUBr/BPv/lz8LvAz8GFcjm+tGI8mkezS89V3gePhei8So/3udUYarYavmy1SF9GxTx6YeUKg7S6JHP+3bx9/xpgpXsXR8AlVYm9cB3jsE7/P35qFggEj8NBeH1ctKfXEnRb4ieoqtQtml6K8Pve6pL5dg+fGl4egiFLJuWc0XHQjhQm/phm+HkFWdXUBjyDcUAKoBIfQip0MiWajD8dSyul3n0xmsRlyKIqLuGmlPCn7PDg9JUQyxDuW9pByGakV/+lxwxGyzgp/BhpB2epDVgwOT+j+xD4NvA7wO/B6Qew8wYmNFUp7MB+IzDb7PS0MPkoUmk+vsn4FTip3/XCMdt93SuaZCOZYDOmHJ3uGwPyPg0hOZ6aqeTNSKWzFzAqTE6emVksJvQ5FQyXQTAIaBNApQGZ1M+CIqi4XlMQM7LrJ7ompmz30n32ZBHvjShpmhYSJivLBq35twfs/CLwJmlwaX9Vi/K5FWRiSGuQ6bFNswJSaEWp9zXDYoH86ZHS3XbBiovhmZROMJnKa0uPBronIHlPg1YhCeXMDURIlahTmnrL4KxEqdBkMfrPAhRVqq6usqvhxLj/x8DuRUyD7jWFRMQyG39CYmBqcseI2dNwjldg52vAT2G12NVf8ZGnMHPVE9E52kV8ZTYJsPX3CknQ2v5JrMoTo6aJvmDI/OnY+uQeLm5mw9Z4wWC0bNw/mPBTYeC3eZ0HvMJuiPo84JBv8WYU8M/bbohgyGLyTKHsn2QTWSm2S9Z9K8vWW11wUTjhoAkiWrOAPrg+Os/jAz6oKbg6lytb3P/aCIbfAP4zDKv4v0kDTOc8DOfadseGJCgERG6TYiMgxuV3gjAZVZewPWNQWCXw4fmlxYuIiOWFg46p0PNw73VO9EL0KKMAVtOEl61ekBK0SjhowpkG4AfzIIKa0gg0hSHFYoi/IJNiwYB1XVjsQDshjT4HXGF7d8akSO7LJBASmajN7JOgiy0HvoSxz265Z3OOmRPblkFcvy2CydDO9OyPmbl+9JGsvnn8RlmweiRBoT6N8SnBpamCtVQ53emcol82hDjhGewGt+y73OdtvkpNxg6nvMm3mDLjj/g6igp9Xq3hhgiGDpbC/bQROy6/slKTmiqqJK5JcKhUvDSHPHeFYL3WoKYV9ek130OyO2VKhO+/+CaGkP8cKcrzDklT0CTdIwGJ3kzxhCZhE9AM1FICkQBcDvNLRk9XV4WZhJY0Hh3fMSc3I2OQViRgynsDrpoYVeQFtHMstM2EFOzUQ/RmP8i98CkbQiMBZN3xkou8TnkWpqDirN3xksl0zjBbNExKr42U8X6qxvlqMopynfp12/WpGK59GmSzrLokL1LVJsMZ0hi0fulFV6u8McK+FKOiGqhy/w7jsWy195R0j0fU5AxGS8pVYfTovCbLA4ksV4atXkiOVwUMpMf73OOP+FmGLLnPNzngkCVDvsk3OORlJpwFrer52o0QDANWvMq70fOgyd9kmuXIBq6xPHpaXcqiR17X0X9fZSFQyYdL+5XJByVJ1fe4gia5fh+0Be5gq06B1ZiAJj7wlDTYJAR8XETm9vXn9NfkvAr0SSQt3O9KzHQ4c9u23fZwnsVoq7GiG6c/ZWuGJuOwHRvhV0g/sWU+qCR9JOM4U6IkoRR6foqT8KtXt78mG5uQz/OaclVQ9MugOieylk3YZVwB/XX6xL4SXzYQSNqaXNdPMS1Bi0bAlbKKAIkO4/UqaCp5IwwtMbKWjwtZxmjW5C7MogtTmIW04ESvTjEnNvQKhuMFC0JxGZKb0/NVREV/iJXPq8l5nbfZ44jbzFgw5D3uN57d55LHkIeVSh3XzFZ8/SWu6VF7d15WBslfGxXauwj7JCwArqr42sdOnqpBSc3vAz+JCYavAF8N+35AUyM4cJ/95Ndq7uMjtF3CR0VyJMi88BL3Akwb0X6aX9skEyV4Si5uWR0NTWS1BI55EKs5CQcsovu3LMRsbIKOvsS9kHyh89dVxNKUVRtmC3o7ydshk0Pp9ryrNOU7WDRMTWVk1PcJJykD8n/ZJK15j5TnleSEyuI1k2we79P6Zx37T/iFFie5jpW01uJHkiZTRlHSa0zMtkCw4zejYbdUjLZfhlJ56ffq+yELxszZ5SQSve7yEIsnMuLDnwdfgBsiGDps4uDAubW87zijioPPh70m9lcOmQmGKgsUZE1MqdzeS9FOCuM/t70EO5jKMAa+Bo9/oW8goBiU3jUKTS+EBIs3aby5p0m/ct+3uQ/ab4SteMJNBNrpPAG32GzDfLsfUQOPlHvKbjtqUmnSwfqxzjPyOnFFZMqpWK2EgFx1JZ5p4M2M1NFyK/qJ5SeKVG9fVu26KE+ZPHJlaxyoZVV9Nf2fx3b8s6/M9OwVF5Sj5Iz1SVcUp+KBT5kUEqoK+lNUpjAYjw8oLb3Ei4D22EcFLM6GXK56dEO1aq+5zRmzZBA1PVVUU1sw5CF3mcWadVx5Bp+m3QjBAM0KSO2mlUi4rlYeDT0grmhZZUZ3TOyiySWwznZuJnpR1KPHBM5pahRjbLXeM2R+yCqpp5C4Cnr3wVE6dptV6bUEH9YNSYj4Op1C8L0nxNfrDMKsLGis3D78We9+VZYKbF1Tkdd1BHTB1Nl1kbQEvXwVatWZFPg4ZxyxBDtuGuCJLJXo0r5NmMcJIzxE34t+PIh3aJ8ThTncW3WZnnVOM8GqOCR+9Lv//USryZzAsb7suf08vTuxH00A9yg5ayXMMTMieXjMQ9SsxTEcLyjziuF4SZYlLUWaGcABH0VwVPNDfTtj2sCI2jSAT9NuhGDQyp8GbAakZKZAANCKxmAWtVZgUbtQqvI6xHoO3pxQc+p31BSkXXwFMxceh213bb/p+VNzGX5AArIgqf3b7tjSFnzzCV81ub0AgKTZ6P+KZjk23UuNaTNhNRTgqH5VITnfz/pfardW25haPgvaWh7qbJxfUr5i4Nox+5yww4zbLBk4LaGIKrSiLNXkbtWEFhfDC6zaPWefjt4njDEG41k8o9LN9aJwS7kSChfXQo6ZgR8XR5SnmqDCQXQtas2w9BSFKlfmkgEFKd8izBsxKSYU1jHRzToIhCQwE1diUszpFUW8bz0zCVrFStRY4JRSEXii0z7HkVfxcUl9f1T7CwmGTqfzAMtNUgPVZrP5eqfTuQP8D5g39gHwtzabzZNPOFKc1FLjdDMp+s8rcrkbXPqmJq+DK6dOwmA1gv6KVDRGK4XnHewBO7C6QyRMrftbVpEqx7CDEVxYRKv5x/8Uq2cpIeIzT0nFDxW2r+ALmuQSCD64q40x6HPm9ltxVZjUds6ySAVYM5pZm+w9uR/VFHjjy91lZEHtXvOtvTd5j/vMmXDEXoPdCMkd2q43qe8STWsdsSRvQsiM9Jmh/ORv8ys0EprJfReNidTx5p3PDqZ5K1PRCQsVJlJfaMx9XF0GgYBiSEo4qQ/tPtYNzck3CW6ZVD5VX7sWBTSFppkq0k5MmCmgSjEbbZD5edqPQ2P465vN5sR9/nXgdzabzd/tdDq/Hj7/xz/qAFtcRlUQUqdCM4RX2zTgm/74Nb3VBf1zk/4X5vWxlT1UjYoBS5DuPOAHq22LRlSLBKpb9jrf2WJWTJmWM7rfuYS3MOLMDslMEG9CWoh/1yAU49IzL/3q5t2V3jRpR3j61TB4UlZ3lPEqawC40PT/ewRfq7X60qdbKyn4e8Xf5r98+h/w2vZ3eYO3Yv/4xCrtyeO1kh5NjMBP7uuEg9cWPBZSBc2waT7aMfQbHxFKO8N41frswcciCVRpPikxULLPvXvX/+9jM5rY2ABPLtN92u/anAdRy3vXPjsJAWEr3nzR/iVF7GNhDBIuNyFRy68CvxT+/2+B3+UTBENOdSUVmZBwLwT84PYZaoYsmZyfUZQpwUnUGqRyS733MQ0Ad0yrkFBQOTGAOr+EUOZuVphNfXD+2AhMFUaa8eaCJj7uXBIW6ul2YJc3bwqSmxRSVKh3uZ25/wWk3YXNAcxH48YAazeh5zIZ7HKSCu/BxRlT3uM+v8nfZL0qWG/3Qon36sqkFltV8RdmMyfXMiQzRu62JiaQBJTt64VDSj7bDhySgPARoEOMDNYwGbUY6DkIUwqvTVgUZMOv49ET90NCq50kR/9rHw+kKsx9wCK6cr2m5SuIe7eu3pWJ2z9L9YHmSkmKurVbLfFBbUpJ97ztLyoYNsBvdzqdDfBfbTab3wD2N5vNR2H7I5rZzmLrdDq/BvwawBe+tBWBMe+T9k1IdTMXgdmYVow2JMcJP2uoktAkAEEcHLItq8wQ+IbvPt+yNHOF5fS7Wz6k8z2SoNmmuXKrPSUJDGiaCBJWnr2IXUsUBH5fSCZDu8ZFn8itUDVvIeVeLfcagTfFku1rg2rBgBm3mTPmIw54m9eZMOe1/XfIqGNAjg3IIgpnH1CkFdS7mmU6pNDoZEr4RDNZ4zhJk/Dfe4+UtqUkcUGTEPDon7d3EUtjGMNmx/pukfns1ImQ1SZqtfNR6D7ToqV+b4a06xoJgK3yPCRyWIo41XFVjyLLrAbmNHvS8OiAEbBkRnhmq7xFupfDK/nFfnT7iwqGX9xsNj/odDp7wP/e6XT+1G/cbDabIDSutCBEfgPgp7+eb0CSN6XfTipVWoEkMbVqTTgjq2rW/a1IGb4o7MY60ETzvRsx3HmVGb1a7s44kbKaLAAORV0yOLug+xE2SQON9kqVZS8k/CAUJiABIVxB2oHnM9B6d3kmGy5XuWPDwO+tLqhGGmApe5JWi3ZQkC9Eo1XLtIUxM27HFW7KLGQ0yhokHnELPFlHdrEh4Sk1WnI1lhHs9OaF/+y1hPZK1xR2iY+hcQPOlhbWI2xH//fT/+bWtfqm7czaGneetAVE+70pGK6i/t688thDEhApu1XS8uy5LcuhuSkzJxCzspGxSWaLmq4nKZQpNuW03LEsUM/R/kKCYbPZ/CC8H3c6nd/EkvcfdTqdlzebzUedTudlUsbEj20dK/GJTAjwktg8FGrSGLTKFHVJb3VJnYdJHtTG3IN5/k5le7YDl+qSRdYcBADDckFWXdIVX8HHRAi7kOtT+RD8gFQWakjYgRcUiuj0uAI0hYEvqKN7EfU5CB6Bpn6QtvEZAVJrrsYcyPqXt8E8D9O4XWi3IvySyZcmtc4h7U5mg9iKSl3WrJiVErhenXBN294eXTMZa4r1SBhVjLj1gkACOmQhX4ysCvrSIR9ih8qrojR4mmRqln4taQi+eQ1CSVp0DDEg9VySk0m+OKuILbbjugzmRZWxVzyJ5pZPPltdc30ygeZMeHh6l4sPb318TY6PaX9uwdDpdEbA1mazmYf//wbwnwL/C/DvAX83vP/Pn3SsDVuNz/IHW0Yhu2ERRQaBLz9kybAOFNP+VlIfIZoVXX9QDY4WsUXzZpGldF1edVWwFrjfVxjO8BBT5bdJEY4rEp6hPAke+PK1LOR9cEFPDQHRFgzaJ95o+rd7DsPtZSTZiPTlhYT3uftJp3Rlinw85CDm2JyFIN5lOWRSzN2pU/boZqbmdbjUOn6WpqdkJMAVoeC9De2EqlrNc5rmpQchB0HY1GQhsO6SogwxM5kJg/KOmYfK/uUTDksoqCk4y9PzhW0pEEzeHF2Db/IW2KRNVPI6PAcgJrOVRgyWeyPLatZljzLQone3LetpG7NRP8sUUQ+qZ4/L/SQU2hW8PqH9RTSGfeA3O52OjvP3NpvN/9bpdP4Q+B87nc7fAb4P/K1PczChv7KtPP3VOiOlQleJud7qIhadgRREJbNi04eOz6mgldmBj6qKnZyeXv0rjU2Zz9mMLhNu8RgTCkekiasSd2IlCkNQ8y5GrxmotTERT3zyGIWP7dD/gSgltbbth9d9+RBh9bcmXk3GEXumeoZK1Icc8O7TV1k9ukP/pcfsFKb8DgILUAlUPXiYIgibZCZpee1SaYL6vEehbafLuyHw1AN/Yj62WYlqmz4xsW5ZJFEmKjfQUOc1kSGZSW113xe4VX96gDLHJ99NldaFI+j4PpWhTLBeVnJ2PrEKXBjZSfRmPbe218I0ulQFV1oQhDGirFjP0f7cgmGz2fwZ8NPXfH+K5cd57tYeTBBSVAVarieZ+JbX9vCzqlnXMRwsmQ+Zew/bbLD0IntPg9K78WbFlN7qsXEa1GMrjA6tiEoPLPq8jxJG0MQM/HZ/rdIavEnhz+tZm9DQIG4/XjG/Y6XVfBSjH7iQBrpPpvIkMBlP2eWIPY7Y452nP8Xq/7oD92zV8qnTLIwqZY+uEb3Zpoo9u+Rx0OT3xKqUKapqeDmuC95qmxk+TNwLFwOiL2NOCwmFRZHcjwLlfEtCNGt89n3VjhnRfXrTs1ljIg/HSNGn/r58q91xRYkGQvXqZLa1OR9e+HusSPUy47h6znYjmI8bOvGhR9puLYQ5cfbrLK0UCrWOYGOdNAdI/+ejVhHZANxt+gY8LYMK7dVINalnAMvxnLy+SCxKseniCUlmg3dBXocr6PdwlQXpj+f386Bp1ton5IzoAPvFD1mOhtRMuY7SoknhffQSjGfBdDAT4jark9vwEnz5r/xpTEnerOPomZRNz4Emr0wE+74Zyu3NDq8daMX12pvHM2Q6eKxpwryhRXYE1EqbJOEkVWuC1o3J3PbgJAFqLfWqT21neEIqFpyiUIdxv4wEqHpMQDjBmh7L84FVoFp16b/0mGk2i6HabSDZ/5+yV1shnbOTaRojIts9R7sRgqGDOS6uuKZWF9T5VozyUyvKdQSYNjlXUrqVRS/UtlxTV5dk2VU25LpvLqo5E06wupfe1gUPJFX0Vhd0pB1kpBoR+k6TVGHSXyVpChIOXlvQwIUmA1JNHo9bNE2SdkxHANSkjfTPYWd0impDtoFI+bg94KZU5bK3Ndi60zmTe/NYs0HawnVam5p0E6n40GTdyUTzRCa1ujXwvVovwZCSwS4bQiKjJqvqJBRcU47Fto3ejKBsVvq2RWEY9/XuUfWh0rXJxGkDg8uWp2PtBLE36wRILuqhmRCrAvoXTLdTbpIlwyhwUzBc8ohIKMyfjlmdDVNZO42zz6dguIy4gTI9R1UwZGkanq+o82QmxIkeJpqK30qArCmoixyKpH34egtlVjBjGoG2Hmv2OGI3lA7XgFV4a1aRaMnQpNfq+wpzZX4N0yaUmFa4gzAIcRzagVPtuTZy+0OiVvdb+8hDETwhRV2SZ80J55OkeOX7lJ0oQFRzUiDb3Z2Hzm9fRc+Bjql3v6JLKCjwyQta7e95KO2mlbPtjvP4RFtziXETq0vjsvSTcMjrwGLNlOLOawvJHSmS0MJpVLrXNhUZ9Fivug2FA3gOgfAFgaipv3uN57I4G3B5NoSzDv178wjC2z03k9RAwhJKeqxr+/3qbAgn/aYG2tZGP0W7EYIhqzfsPzafXoOYlEO3gm55ySY3ISGTIe5Xm1mgepceSQYzTZ5kUwrWVKNU21Eqs8yIKTOrd3X+mHW/y5NsGhNhRMZd223ozcQC0yIOMEakfyhPMfq0NAZpGtA0GTx1VxqD3Jne7ebpvLqO8N1qBFWWMQ31LefOPLBLv2rnigmZVkgbfqm4yzLGpXiCj8hUfkVtxzTEZ+yEgdc4EvbRZGq2gWBdp8/F0IgSLU0r6ToS2KY1um2iDeL96php4soD4YlH12MBXhNIMQ5VY6LLRJPHx2tofl8g9n13OqceZ0y2z2Lf+NqfkOjjKddFznrVM6Ew618vCPo8V7sRgqFTYWo6NPMSQJwAnSplZep64K0wQSE6sAfdbJdE+lmH1dHzzKUeK65dJobYZJY+3VnrJWnlr7k6QZUdCBLYKPNCfAafkcmbBhmJ7CSQUUKhlVgkCghX6GYVIitl6+5wQkYV4vdLPKt0yJIpM+7ykJqMQw6CGWG2qc+L4NV9j8L7id8Ohda+vllAlXWJVn/PrfDnkLDRc5LGcF2iWggktfwyagubfsKf6jyLAkD0bT82pKVIY/DAYZsKbUMg1bJsijtFTWZxnKlHlME5UaKT21jCRmHWuu82IKrvS1Lym0U9ZL3qmQniQTYtMs8pEPzP//KbADtoIvh+4ulKPZic2YNfjhMdWH58r94pZ39Sd63ztSqMg3CoyaLrskfJyxzGOIzuM1K+wGck216CQDjDU9Jk9ULhkCbG4O+lnTgErnIWoCEEGrUs7liVcFUIb3tX9jgO1NlBHFRyB6u0n+pFFqw5Yq8xScRq9C5ErWQ+nFp0Z/WvR9DbK69UdZGW7HHWziypovBIWksSCt5bpVBx+lDnNVlhZQV8l+Z1zTprAo+6DvEMFFvgJ55nWWr8AI1xJsECqrc6jAKimQQ3LUp2vHSFBSW9LNWwUKvwMUKpH6PpV2VUVWaApcbNdANV54pr/nnazRAM0AwO0kqb0cyADImUFBhsy3GitKrT5HaTQFD5Lw0oFYj9iAPnRkv0YK2oPUqKMsT2e76Cz8O4E96VdOU4XN8OzfTuPpuz2h6GRXjcwJsNGsM+zsNHbYYcEhcjZWwqGv2glqpKlZGn7wlIa3rcZsarvBv7asY0grJyTbY1BDVVmRYgCG1Q8fp8ANbXJgpkQ/us1TJdZMo1a0SaMPBm4yIbUGRrsqIOjNiQaCZ4tnRsT2gSCCuvjOdjputveirajFIdT5qCdxvWpAI7noNg95iiMsUo9b+z/lg3zp0wjgDKVhl1lbOV18YfzsOUHq+agGN1tf9/VLsZgmFDM6qwXUxWzeVwFK11kZnklhBYMOQB9yJZZ5dT9kKZmAELbocCHYccUFJE9VfN27PK7xBZlLLlfY4FpSFXvz8lkaCUQTrHBMUeTazggJRCXs1zG7zGVLiXBEQQDHVOtF1lTtmh6jjxpax7s+g6G3rIkp0AwFqg1DJqAX5gywxTPiczWYZxpW9PpnZTNKYHIT3AKKHQC6q/J0YlbSPZ8vpNTKWWQTkqAq+hjppgBPqiV6YXvAfJY5NQkGY4uY/y9ffWxhUkcKRJPGEaNbiY25KEs7wcXMGqz2GYgrV2SHc0W+qM9coK5mZ54H+4atpWQDdoOiG6MHGDP7ndHMHgg4zaSLy0BLdiLkZbzAubCGL6PeQu7/Aax1hGFaX+Vsy6HsgR+yjjk5B2H6CiFUmrYlcEKQmuO+F6VeLMJ2vZxsyGftheQricJhFKWaOhKQC823JEE0vw5oNbDdb9rltJfP6APA40DWA1rxb7wV9QouQqwhkkKPwE3wtZhMTKOwvl767Y/u486XPKwiVB4qtLQxJqyU3YtLt90FMTNK1iApdhvaDKMvN0UcX9NWHlOUip6a56JNqxJm2viRc2Uu/l9dFCpX6VUPD5Kw84ZMrMgcN5FKt2fh8IF8yHOqOuMnr9NetVjzyvyfO6USzXKmyH51tlrFcfl77q+nYzBMMlzdgA2eee9ecAXPMtxgAAIABJREFUt/NtEwp6mIcccMgB7/BTHLOPEHW5tNqSP6MK9StSYRup2BPmcdVRspZYyEbXJgFwjE1qTVjCZ5WtU9JZJXH11GYJmjNskre9E7jPhTuOL3FX2LVVmcf6r6L5V1e+JslGq6V+oxBptbt8EDUHP6g9rjNgiTgG7ag/T93VNWbxeCWNauXBRddMIJMoyGpS1zX5JIAmIaVaRk2WmUnheQyeaKRJXgeN0+deEMAoYXJdhilfZCeCgc6sSCZDilHx4+8uD5kyQ/kfJJQ9jpMFzUrp+kHmg71ievm8bvh16iAkypXhEJefS1OixiaOd9n5sGKpzSGpyrywtf49XuUdXuMt3kAcdp8+bBiAKrWSgqOQHmLKkziIUuabZdivx7BesCiGzBkzLJa8dP408Qo08T8AvgQXdwOv4gOSNrDCBIewB92b5z7oO6896IlUbpuEgz7j9ikMWPO8BetSTao0sNO2/Mq+loKsGbykybbPcdQafCIS/97EFsT/TynHdFw1CeGBe0ZafTOqBrko5VH0JfKKqB1pMmkCARFUva7pd1L5Rc5WP6VjNoHIjLrh0tQKnq69aPSt/1/mVQ+r3H6XhxxwSBWEiQcZvZlBELSR4VimehODwvJH1nXWCNFeng+oqixoDhUXZwOonm+q3wzBUGFIfxttl2DYxtKrBU3hXV7lLd7gm3yDY/apyRwrr4qrnn8vWDNnTE3OfiAyVWS8z734nQYywDIbcsJO3DbZmTPiEkpLodZXEtgA/BXlmlH/MtVJbHsgoOmW1OdWirHGb/z+2q7vg4YhAo+5uZLK7Gm8fmBDExhUH1lNhMWVfbSyaVL4+Avbzwq2NvM9NMFJnw1Z2IFISkpVJtu8ab/nJFdzAuG8R0BehJRnoqmhVFkWOQ7rrOmi1D5+lU4xB83oShumSbAp1Vo8TxTETQGY+tPA3ykz7vGAnVDNN1XJTviF+jT1m6szmtdNTYEq5g2RkKha2oKRfz6PgkGxBH5VdHiCsuzMswnv8Br/kH+Vt3gjJg6xlWcZwasiWHopKOdqirEea97mTd7irzLlSVQZSwomzBv5B7RiXYyMVZf51fwcJk9XFtmpieujKCFNeFGXQ/ageL9eEIxan7W/jxB1rXMOveySXmHZgWyX5iD1uINsdiBGRlrrNcDAgjVTZsFIO3QuuGa9RTVpDDqG4RTNDNTjgLwLm9jhtAFsjoMZp+OLA+Bt9TQJr6L94nNKkFi26DW91SVwSdafsyjSii+NQbEMAg/1v67Bm2Z6JY9KMo/S+EpbdC5pssaXqRrH9ceX6euFt/eQCHDsZanIr/pEXifbrzDzIboxn4/6eDMEwwUGzPnkJiEJyeoAZqNt5kx4izf4P/klvstrWOmvlDJMq5Ay9fkmG07YQ03GQ+7yTb4RfPapzLyPBxiyCCCb2cDd4DnpevxgBd1j6GpCn2LaT9uk8wQmR1+O715bUh+Iat0O09Z3Ie9DtyRWxl4wwBcw8RmWUvhwysDk3ZYCyGxyG6ioMut22uSv96uYfu9XxwFVQxPR80oJW+bxeeW1UZbNpLEcHD5TkgBC/1zFKqzIYiBcj5IDNBFLBmSW1esYuAXz7V78rc/t6LNcN+nhCb+w+0raiAK/kpehurKf95gI8wKvJSTPh36rwLAUn6nMz4OGFibMR7+TGTWvJiYUVj3TEvLqubUFu46b0IQxyPUnwlBgNL7Da3ybN3mH13jAvej3BeUTLKNN2U7mIWApeRusIx9yl3d5NaLjNfkVu9ce5hmT8zPLNu2jJHHvionwodiewQjNtPK3wuex26/dfD5J3HE8zyOkmbu4Y+y+5IFIIJkml69b2e4jSBmQ7GVCYxEyOTVzCjSDkHxwkWcq2qWmNOjtaEjvvaiytEIuggPZawlahZs05JS63udNXISUchYHssvkzpw75yujzQeV3QONygjtuQ1Nz0NTO5JA1T1680IakjflpJ36itNVEJ0Je0k9qHN4h6lM2ow6uCeb1HSZZxkVs+J2jM4k36T3z6XGAI00ZRH9r+GUHd7mq7zLq5yy01BNfZy/aCleBkMin/hksoccNOIgpB3shmrbYtpNmTEsF4YntIrcAs14hwpzU67cZ0+N9iaSL24DV4XDdfETaj7RSxiT3RKWzn2pAeYTiuj/YdAERHLy7syPa36S+GQk3iRRu47F2M7rmIrENAerT0nmJ5w3L9o1GlIuxXUcG4qBATjkZV65+4CXOWRNr5G/MsUvJG6DcCgfZSpt05KtDgLnYEhBM0FM8rg0Q9BFrlMfW2TmItxzMzrTmx86njifyrEw2T6LnhPr6yoK1OX5wKqH5yHV6jgM1s+lV0JNAiHDwMY3t3iLNzjkIPqagQhYNSskJuGgwWlJTRN4ZADWNK4Kexwz5YkFT3Ea8YTIeqzLUImJZpo1P+HVTjFSk1ZzuEpckgmhaEiZFbp3CQFpJmq1234NqLnJiVmOtcr60HFIk9dPVNvefO9FYZI3PDq2rRk+rXe/erUFjNckNLliwpZAICuzIqr0PrpRTU93xrRxDH9d7boJyt24cOQl5XmQQPBmiCo6tSM7l278yA16HVdDArbZl6mWhu8PjTFveiTg8apg1diui5xeYfcqkNT6ZxIJT4uzIfQvoMrZGi/I8toqZ38uwUfZ0qFwCjvAV+FBcY8FQ2ZMOQsrQDuIRoM8lRhP8dDKWygsIp2ujjTe3SAQJGxk+w7rBZOnF8nd6Fdqn8ZdQuOUFCSl1g52cvwD5Gnw2kE7TNa7LnUu9ZPjdlSZwLRBXH38wJVm0GbcXacleOzAcxI8ftDkKTQBXq8C63haLf01tVuqANWLq7Y9lXHDhPEmY1KhEyAoIFGTdcGQt3mdB9xjyiyaDz6yVv9716Rayp+ZJrHATkhamf8sbaItsDyw68OofbIX/2zWbiEUbtI2O0oKlueD5IEAuuMlRb8pKC8+lwQnUaL3sHqRX4IfvjJGefAS4kyk37YZZEZM6sWHXJNTOmTcx+57cFFIsUqcDVkyKeeMTi+b3pJ2Mk0fOanAKmjWlKi5PjgKUnQmJFPCx4ho8vvn2Q6nDdvqHDcMU31PSLEQ7UHXVH+vutd8yjb9xrbbrzx557oJLwGThMy6cV0KgJLWIEFwxD4PuRuFujFUz+LKKzPS+wP8BPO5HKWCH7PfWCB8LETybDS1FEgZya+u4nkDfEyPJ2ka7US5EmIDZ7IKUPVCR329YBgTH0tItPGLRT2krjJLBReEwlbgLkxHZjKm5C+dK8/oR7WbIRjAaMZvwOrrWKEXMg44JKNizhiVFvfpwQQWqtCpbNN51C5SWXdPwfU5AwZhwNl3AfTxlZKVO6FPojTnpKAoP8GFRbRXe8VDSFNQ8+ZDm/Og87c1CWlV2+ll/SU/eBUHbY+r8RFCy+FqTgFpXT6Ds2dUqlqYeQzaJkPKyZC+a7qJk7AKq2ydcKBDDniPVzlx2anVhC9kqF5lyreo5+vdm559KE1TnigBsW2GYU3eUPlrmuXy1EQXF6FOLEifQFb9LcelB14lFKbMolvcu5PbsRpLUgSs7xOZjEoaayfMyPpr8jz9pszC6vH/VzLYH2vrAW9g1aRDUzanRTHgLg/x2Wy04nn3l3IQmNqa7DdIKpxHxfW7hDKXcWBYtWhTEYoSOlr5PfAISUC0YxtcqrXIahSWoIxObXekLlfag55Mu+S9zhMAzNXIKmVJNfWDTH11XfMcfDttcrfJg2ETYByP54G1VDQl5SaAVOBW6q9/BnrP6zoO2AUD3uM+D7jHjGkMaEpd3BQuHnCz7rwavORX1Sr0yQIjrAln8PEN3uV4wEcsGHDKbhRmXjD4VV0TP1WWSpNbE1P7i7uxxxH7odSKhJ3nTthvMmqWVEGopCxTJpB8STsg8hW2QgBVr79ugJ22/dph8LHt5giGkFi1fw4XxSXrfqI+a0KrEyVPtarJbACiwLgeBKvjsYwIZUNIDzFlEupRj4LLL18zqi7T5PYUZw9EQtOLIEKTxri8FW1Sk28SEnbBze99IFngRKy2lZil11hRPW0YfGBY02yQaSXVVftbUdTbEWxLj6mM270AmnE7anNS5T3+o98p58EiM/Ve13jEXmOgyyOgaES7h5zrPBIShj4ASquwVnx5KnSd6pN23g5pNJ58NAgxNd5sWFM07qlgHQWiPbqEBIjoZcdasM9xSzPxXgyVrUvm4DKYIJ6PEvtj1bMoytw0hSyvKPprhtmi0Z+c9T+ngqFLKtICsV6kdxl5FNqDOOp0i3toRqS1V05NEK8ien+0nwhCmfO8ZjW6NB6Dmu81nwijnYRFXAX/G68NZNd8500HYRz6LKGgACxo+OT9vba/UxNxSGSmBdMGeSajjithWztQ86umH4A2YdZ4shOYSZewnxTAJWTduz6t69YogUuajL04leVpamQyclqAF1pyZfvJ6DN4pXAsubJfjveckyppWaRq8lB4CnOJQNYSH6Qlk8L8I8uGJyL1jUDztLBpbPu0+T3WETuryahrx5/olzGiclAs4vVlVBZbkW8g/zxiDLKl++Z6W/e7JIZC6jBoIuU+71/zcDWi74JsvQSCQdPHrwewIBF24rGyOvDSL9PqL+xBWZzgKlYgsyJz++i3dgHpdz7pa7vQjH9C0jaydJycVAZN9yN122sDwhZkf3sgC1IV5Y9LyyaBKoErTS1333scR5pZu3nMp7yyNRGGvFDwK7UmoPeeSGP0wKO/huvYgwIxxXY94BAV28lIJqe0HvEXUt6PQbwPicYkqPJ4jnEQiVOeRMGhvlJfmyY1bvRzHa/PStqnMO5QNi9kbqqrnKJf0uuvLZqU5K2bM6GuMrbGCy6/eLX6+Y9qN0MwdDCNQXkWskm0C9t57yCxHX0osKHOS+fMSXz0tiqoh2P5BwUiNVF3D5yVRY9hvrIEtF7gj0h2/3X8hbbJoM/iMUgQKOx65fbzNGgJj6K5vX8Og9EiagFqCtjxIBYkGrGfQOovTRrvI9fkaiZ5SVCmrN22h8Lb5n6ipurYSXtLdrXcj2ml9AlivZ0tk8+v4F5DklDxwixlwA7lAALYPGTBAYe8ynshW7ixQyUoZZ7J1TljyoMQeAfG7pR526YtZ9QRZPQU9CHL+JuSHmch45jIdsISPF09uWEtx0btCEsqgKsEdcoGNWHOZDTnZLTD2fnkufDHmyEYAmIvIM1nZPLmgE/e4Yujyr4bXrNCgSURsVXAZwEq4qRI7juVx1vGcwvpB5oYg5rMAMV6+PRtcjfW7rtWgFjMGdn2QJzSLEenbvAp4A7hTrWCg5OGmq57qlqf1Yd+wtgtZDGFm+ftQxqUwi5MoJZu8tr0TcM2aQp5bSvXIhs2TEH7jSH5wnjaWaw1QfSdwMx09U0zsZ0n0bQIyKKXokeTU1BF96HOY5m1TfPQOFNQnngv2jZjyik7lA7ENRr2JI5DcWM0thTop/F2wg5nTDhh14nw5FVSP2j8qXJ1qYzQq4Kt8cKiLYsKuVKHLNnnCMVPDFnwZDTle3z6dmMEw/m2io2mgemLgGgwatVRsJNtU7xD8s8rSq10K5KaJLFHuL2/WttlgmTULEZbBkLK46C4B9G41XxQFKSJLDNAdGgfZq13cSHkHm1zJ9r0aIDHcIcVxc6aRTGM/bdgGFVSX57e94OfYCnxyDIKFR+e7ME1ncPnZ5TZIq+D8i3W+RZZlipgW38v4zGl0SUhsw6TJ4/3oWvQOfyk0fV44ee9KKrgLUGjfc0zZRrmjGnIz6GiOol7IO1CWlDBmnu8T03OMXucsHMlv6jOJW6MPToTktJeU87r5MW5mkXM9nvIXT7igO/94LVEdw7JXi9XIxaYJ0I5OYYs2OE0PJ/dEEVbf/4Ew6ZDo+Co+OGQOsla6ViPVZSI3o9s3ydXkCS1V1+F+nphoKbtCr82u9we6iaHjia3GJCa2N5t6Vd5CQCfmi4Ij4sC8sBc7PoEL9Ckh6u1PSD67hxG9SXD0RnzbUt/n8J3BSVavgLzC/j8A8m8aluh7VgGCRalxVPfZdSNkoK91WWsDlZVl5SFpVtLOTSzRjyEJtE6HFfmh9eCvCDy7L+2sGubnka19qBhFm13uT/FhDQ+zLqBmdj1lfF6rOvtCkSjL4IGoTGoBLYyIVLxX+s78/gM4gKmLFZTnpBTxxCAh9zlj/hZ3jr9q1zMJjDrhDHVSYvT9IK9/aMoZhQEKC3QBG/5sdr0x7UbIRjqrBNtRAGOmtRt1dKvLrLptK8G8iKAYlJe2wMcZJPqHDly8HmKb0q3VTAvJmTV06gIxDoY0hhkTvTdZwkF76kI6dhUfLeuLlN+By+j2vhEm+/g8YegIXeACRewvaDM0uS3U6/jhJIdr+YneDp9+q6mSQv2z0AxD22B4GtHFuWasujFVHmK6wCiBggpxLvGBIdNuGZNB2iaG/47b3p6rVNuTH/98hL4XIxNV7gHdO2Yc3y2phR+7s2DLGhowhZUl0TmjohhJQUHHNKjZMbtEAdiWsk7vMZ7vMrbvM47R69x+e7InvfYjYsK2L3gzhePOeAwmHDLuOBp8ZOb38cMfZp2IwTDBpsk6myPTNfu4fvoySYqnYqCeFVTrW7dpk8KKoDL8IVBXDG0skgFnzMhG9XUeUDti+DCFKfBR1UqE7TPzBS0hYuRpbwHWzkL1olpCemJCNhssyIlHHL3vbQN9dPqgmK0pmYRNK/kgZF55W1Xb0sn1blu9IP1/zruD0SBADTvwd1HXpvwy/M6aoVaeSVgktqdrmnGbdZYJGzGXRR/APCEaUOw6d6axWdTwhq5ry1fw0dxwraL9nlhB8l8WoaAK0jJYXU+CRz1qT+WT4SjkTlkEbI4vc9tZhyxz0cccMRefA6WEuA+R/U+lx+O4ENgN4yh6QqqjO7ukns778fx6vvQ40TSh9p0709qN0IwQPJrS2vwzQ8YSKokNFc2j0k0c+hV7tgpU5O22WpRxONJ2i4x0ExkmTljFoW5rYq8BC4SjcFjBdB0LQbAcdNPmgLYxCqLHsPzVTpG3NjqoHa0pT+v3VDENbIKJuUcCvmz08onL4wSutCYEHUUEBLC3uXoIyJVY9SKDlut0bIgVR0P83Pd36LOs0b6dh3LTxqBlhL4liTV8jjscdR45occcBzKA/jkMWpyaVdBXTcqMtznvehdEMlNKL48BboejwGITyNGY4rjvT7rdttM0732gkmhzNCiOPvkuzucBFxkap4HmZQvwdbuOZPpnLrKmI5m0fswCM9MHjoJKAkiA3mvq33+8e1GCIZLOo2V3keRCT/IaQJNunlIQsCDjdr3/6PubWNrzbK8vt+5z+PznBefsu+12+5yt4tb01VT00U3dE+3GIRATJQRESNgxIcQiBQgQZlEAkWR+MAk+UCkCIkPeUNCQuooCCYivEh5I2gUkkFBk2gyg6aHgobq9HRV1+1xz622+/peu4597HN8Hp98WPu/19qPXVX3NgTcW6qy7/E5z3le9l57rf/6r//SQo+jVOVRxVKfYaKhLlPOmmQUbNdc5O8FWFR9+uMFbT0zlSDVTcS29trZARpbOG1dsazsu0fnl/QxkC7v+lrg3cXfpU1HQpQMUGsu/Bqwdn4NW1P6jbMRHUgc3ggnpEEoKnTMXmRQNhmF/uUV1dI8H4GMs7EZvGXyHNows0R/1vOQBxPTmxrxd+3wi9Q+UO7wHo95xEMO2OcJ2zkM1WftuBeo0tGW9kGxuMUjiD02JRGoOaKmOwIMJeoSDUgECiOAquuN3qqMbkuVuQnKrBmwfkRNy/vs8e1334B6afPpIQw+/ZT9jQM718a/Yz1hHJucBMPgdSkycl0s7ePGnTAM0EPU0OgCgTPewCv0wD0F/R7rKOzzavflVXSxgi6W08ZqOQcly7Jh4R1V8i7qBF5Vy2vjN4DLxMcRiFtlt+4277b5fVBiCN1wIRodOn/XCJTq8fE1o8El9UZLP9cmmNS7XN+WKlHIa7Z4kpB312dUfNxnnr2EpD2aw4jFYC3/LXpEYIZQHpvvWh7LR9CyratwnLV8HWJhTjjLz8UKsqd8nTdpqZIAi3MUVG2r61hS8RZfLErs9XxjfYeAyK/xeb7BDxex+22gp1Lc3UyG/i5C0hG72c+Qp7BMBuIsndXX+Dz/K7+ff/CPfjc8An5kjXufPmdr95hXeS83KpZxq/HeqlGKX94CkCXvuhqdHzc+1jD0er2/DPw+4Gi1Wn0uvfYA+JvAQ+wS/tBqtXrW6/V6wF8AfhKYAX98tVr96vOcSFTJEfAXrbBcv9vQdFnlrnGIo5ufj+w0/V23rnStnWLqD78EM1c19KLCc+JlZMHXKmkmBG9BC6CJBKkubTrWTsRUZaRhd+XkO7/3WhhWV8w3mhugYaTwSo2o6zGok5Odc+oJmcIFGTX9PRq6aAT1fbEkOYcRHYxCP/uXV/kYNbOiRBugXy1yMdImJzxhK6eztVBiI5rHvJyNoHZZhZZzGi4YcpjKsy8Y8h4Pecxe5gS0DDM24lTnGeJw+HU6vVv1u6qt8NDBSWRP2Mp9Ub7G5/nG6Q/bs3sI65/+Hi+PH+eKTCCn6WV4orhQlCTQujBvpJ8JVs87nsdj+CvAXwR+Nrz2M8DfW61Wf77X6/1M+vefAX4vpqjwOvBjwF9KPz9yrOghRSaV1R6zVdBz5f4pTo0S4jEGjIu6puWiMAaen9cNjPoM/bA4RJXtlnkXKTr1tYx3UylH6TkmV7+tbRHIMLR1Rf/yGql7F0YBnEkZ2ZC38RgiQzJqONzyZCM1PBbv+O7X5l2tomXUpvufF206Tsfuxr+3tVWkekhxnaXO2/qmOysvoa3vJTVnvS5DZK8Nl1e0weuqKmNS7nPAPgcWk6fnGslXynAAfIG3UB9P3QMZAgm2RGHgi/mIk8tNphuTwlPVbh2L9SKFWZuYjILKrFtcmVrVoCdscsA+X+VLfP3dLwKw9vADJptT9qrHbHFc4AcqA+j+W5uYP9MqRaULCKnl5x0faxhWq9Uv9Hq9h52Xfwr48fT7XwX+PmYYfgr42dVqtQJ+qdfrbfZ6vZdXq9X7H/UdPVbmTM2n9C+TO9qU6cjN81OqJZxsrBcIseK1A/YZccE+B9kiQ5nS8sniqkJ63fUYloFq7QYC3NILhOtfXntaTqMJP9PrCiPausoCJaDJj4ciGgoXZBTElrzs/D1+n7Ii4j40BnbOG6vAlBKScugysAqbonGQcZRXA2RPIY66DeeePJ61lElJgQBrNazq69SS/jpf88X6GsOzq5yhMSDTjY9+xu+oWzM4bQ113dKvFgzTsxhVZW9LOyXL5YuluMthXjxz+tlgqIw5pjhP2GR2NuLquy+xuGwYrc9YH5fcDX2X8WlGTJjmjUe79C5HeYPR4pUgjZb1++zxeL7HvfUZo/UZW+NjtniScAPv8q2+q1EyThsW3CTmQenpvsj4fjGG3bDYvwupvRN8CjgI7/tOeu0jDcM9rtk8Pw0VjNfALCH/MDy7Yu2bZCFVldgKEPo13uDn+Qn+IP9TXsRetmBxdLw5Qr9lbSMAFUEpkVd852yplpfp99DTMhKdoBR5TXd4dH7NdMNDiXnVeHEW6VgxPaku2Vt2zavaeAq3Vlt2Kz83jF4u2f3YPVmpt264Zaw8FUZ7P4a6NWAxYymx8lPXqHOPNSKBm9FLNPG1JdQD9zgWg3sZW9ColteFMVjV6dqDcZD3FS9BIU//0gzHyXgjL3ZpPexzwA6HGZA+oM5egjaSOaYtOWNkJc3b51mZ2TaFi5wNsMv34j41SY4pX20wduw+h+xkRamWimfJnxg2M6rtls3qhO1kFGLfjVJ60MWF4nDsw7NQyojcFl5/1PhnBh9Xq9Wq1+utXvRzvV7vp4GfBth/xXcILTDF4zmu3IXv7a/nNFUU5Kho+Ul+js/wbhEORG6/Uo/grjN0G6UsUdlwttbzGePT62Jy5iEMoFsT0QEE5024vjQEuoFN9LVuMdYRsAPnr99j1oxo2rmRl8ZpoSmNpfcrqzE2evlhs8Mx27fiLl1eh5SrsqLxfJEX6LyhcPGJBkzXXFF6MerS5QmIfE962Dn2L68cYMSYr818ke/DqrbrzPc83f9VOPWYPh2eXWWPZjG4h8RWRsx4yKNMnoo40S6HiMBmUnLrSNtixpC6biGUNKtKUmlO8ql506NIe7bb5ZWuKj1XRkLhC8B9ThhW9lnxLLT4xaRUOK3f9d1dD1o+YOwB8i8qXXmoEKHX670MCQWC36DQYeLT6bUbY7VafQX4CsCPfskMyyrE48vK+AwXVcWTjT7TDatAc2OwzDiDLKsooRVt1lYQfVVuXtfS6zZGJSch15PTS2syM08TWsCidkR5DBqV7+zFBO6g+MvKjN68aqCBzdNU97YENuCDrTVmnxvlnW6TE/arA5YPZtZYhmt68Ty0MIHVGJ40W5lNp1RrDB30Wh+yl5A1FVsPHyy9eo+2TsZhSSlfp7Al4hy6T/JiYkEZwNier4yCjH/dtozO3QD31JsjMDsp7qmRppQ6zRTsyq5/yiQvLmFUKoCKHuSSKoGLtrimLJCa0n3pJqaUp3bxyJaMKUrdQ+3cFj4s03HNM7lIgKSwsVniWii7oM1pGDwFeQky3BFXkJ5F5O3ouuK/u5vBx43v1zD8beCPAX8+/fxfwut/qtfr/Q0MdDz9OHwBYNXrsRj0sk1ra3swXvs+4pitHJfJWka9BjHOIjNND0lGwS+6FDMFNxDSgJycXrJ2hC2EFq9xiB5ZJ124kptcOXIf42Zzc69hYLRg5eVPNjazoXuSxOxl9cF3m3TCQDIO+m5VaA6sld9Z2pWkwyAHE2zCjJjlFJtCpiywkoBADe3kbZ1wg7hQhYN0OReRNiLPJvy9WppByKSlas7o/NJDFRmVTki2qpUNkSG48pAON8YjLrhIi1lhUzd9HTkG8hBlQCYCHISBAAAgAElEQVRMc8Yj7sAxqzPEWY4yBF3xV31fi6UtXWWqyc9em1bUCRH1X4YmApq3KZ53x4uGDbeN50lX/nUMaNzu9XrfAf4sZhD+Vq/X+xPAt4E/lN7+c1iq8h0sXflvP89JrOgxa0YopSV3zlHcYb5YexDet1DWfD3lrQVYbnF8gyceadTaPTQUC25zzNb5UzMKjzFfqAZetZ+XG56Wa+alezvPqs33aDugXS8toDWg3WmZVcNclTfNC3mI6jbEZhPy7O6heRnV8pIqgYxr89RbImlZRLHTbvGMZ1h898mCuqFlPJDZim1T09RzmvmVFZF1vaZuFSiURjPWlcyh3SA/Z40qhkT5xTBHat1bis+szf34vRrqMYzmMxaNdxuJAiraZeVqSyw2ovrSPIj3K6o+yVREj6EqfnexH81heWyxZqPryZZpUG+ipxAiZsUiITA+2/i9Czyd/88dY1itVn/kQ/70r97y3hXwJ1/oDNDNs8Ubuw6Vsm7TvDBUySajod32kN28qDzV6WFCJKnYxXsaUmmoPgvrPPUUMwpHuM7ChrnAz6pNRs0FcEaVvAJlHQQuxhqI3mX6/FNgx1zdI3aLvLmwgFHaW2QUJJmua8/07bHXKiwG5mWZF+IiLN0y3ipcb0x5VbSM5omvEejL0WuhMj7EmjQkFE6oLZ/mXeRVRONwSTYo1dL7R464YHSeQrbY2SsBl6txie1kYxBTtBH41PvSAtcGYzu+xGuGGX/S/dF7LhIdWYpN3BI2RB6BvqtkXbZ5TirsVVYoSr45ecx3/q4IkYRzhpzkcxCYGBmW0XPoMn2/n3EnmI9L6iymIusara9hB7bLHFP2T9RCieQTGYUyr+tWfJScbPtu+ylXbfP0zBaw2tnv4HqU5/DS4RWzPYsFxfiTQZDR6TO3Gojlpbm8yjBswDdf/TRv82aRMxdAqvNX+jC6pguMoKRy8paKfrWgrVw4xf5mhlAG1sMJ54LE3THeI7AQR9iE3GeJpFhqMRkH8MUZlbAVStThPcqkJLxm+iMDTtj0PLx2/W7KFTOqV2P3imrhGbqvEfQcJyM8vulOK5TQopEHoQ0mys9Hpau4myvW13wB31w8bCn7hsYO1JH8NEwnHT26ricij2EUnpv+3obfNWLZd0xC27G7yPlHjzthGDS8Oalb8q3kbLdUoVVdQ9dds11ykjIQXmIqlR6Bkl10fpgXoaUl1z7A42I1oE20ZhLQtnV6WuThBZRqp57T5357Ul7cBnz39Q3e5TPZS4CyktQLYOJuvswoQaS1iuaqa+qGTbPE1Gup2eRZzoPHCsm8S7Wz7CkcsoOIPwDbmDpURcu86jPbWNDfmHN/cEnvAWY4Y1gRf0YvgHQPX7eGwnZeJzTzhS3m21r/peMK2+h1j63PRUNUi4ZdLqLbXG+Fnj4fqrwTi2WrOp24sIZccD8xESN2pRoNM8iWDo89TrzGwjEJmy83BYNikZmebVkD4fM/ehs2R1y2z+bQR3cBu23cCcNQBVc5hhAS0+gz523e5C2+AJB3sthWDKRS3M+GREUqvkt6/0b7jBdGNe3cWtIpXt6B1Z6BXXJdBS7WrWUYolGIxswWUUOfK0PXa/j2q5/gXV7LEyW6oVH6XlmCSNGONR3C/fR968ngGUCYpMarBpLasT53nxN2OLqxQD519BSWcL51j6pa8mr7iGVV8S6vpbz7bg61FIZMmHLxYMHm/IT++No5GC2+60cvYAMLP16F7+2sB8KYcSUKo6D0r4yN1K6gNApxA9R3J7Cyf3nFfOy7ruZMlIWPvRm6i0wegDCaGILpbxOmxOKk6Jnqc1LRcl2Q9oaHoOFhxSI/cxmk+FPzyw2IhzQCWaO8fbymFxl3wjD0uMbjwRKUatnlgFf4v/hdfI3P02fBPgdULDlLZbe6TbHVuBqgahLmmxtUhHKTmnbO8OwqA4RswPkrxgWYcMZoPsslxkAGFmP4oHMvJllKJX57/xN8lS8Xbp2EQOJDU+ggS68FCdqZbDeo0mSTWO42x1TLa8ZH13AJy9efsMeEKWd5km5xzGg+SyBv+r52Du8BpzDeuGY8voQd+I0dAzCVb487kqoPG47tWMszj/nl4oMbWKlWPTCBmtE8pfTqyohr0rSwh1RWi6bfM4ekouzfqe+UN5deb2qYjcvdVT3RtQnpOcXwzIVm7VnIMyWfnhOWomSgjuWcBSc0ySOV9xFrGfTdUZkqUvXludxGZIr6EXotGn0PN17MIPi13oFxTZXTOTGtBlYVqRTeAftJYe9JzkiAk5TkFSzSlNAkHjJj0k4LHcJlVeVuVyLz5HvYmiitYRdQNamHRSL6yFOwt1aFcQIVcVuKr92Ao0QMFcMyvl+4QBMMwXowZjFujQVjujfaJSaDKQOuYGDl3Lvjw8JVfcweVbNkO2VrWmpm1YiXHpzCt4BTuPo98MsbP8ox2xyxA3BjAiorVJTyxjhf/9ZpKiSbw9oHsDZPZKmn6WeX9g3eCjCQxUR4yseOlPH0zPRar7J70NQL+k0/V09K2E67cVxwJqJ6kzQUd+M+C9aTdkMX8BO4qd8jFVmEp1g8pl1d+JB9h5+TcxdKkf3IxdGIzMauWI3O8597VuJfxFhS5d0/gmZbPKGmzTn5HQ7Z5pgpE97jYc4kaHFGN00stbwEl20u8hE5JsqR9UKnqMsdOGInx/YLGuq6Ze3pNasNsqaiRnQXjTtvMX4zmDOrbAHt8TjvKOYR+U4F7i34v2Mth/Pe9XndozkNh+yyWZ0wfOmUtTkMTmGzPsmKSS1WlqweByds5om/tXPK2hj4IvzPG7+Pd3gtX5fwGYDtJC7aTZe19T1Wg2tnYyruB/cW7CbZUIhxjPffrOHygWV8JqdXmbx11QHX12Lrv5pSvl/zPv170ALNNdX4knrQsqw2Mam4Ou/GYs06KSkJ/4YNKmoxCNh1T29ZGBE7jTbPifjvlip7r/Z+ZYzqwiuM4LBL2C/D5yjuvw1PzcaNStkuZaFeZNwJw0CqroQ+U/ocs80hOzxmjxaXkh8lHYHHvMwJm3SV8nWjFAeDhRQPecT41GsSLsdOoV1WySgANPDB7hrfqN4AyBNmTp/Ny1MYG4GoG79pUomHMKdhnxMmp1dU4ym7zSGiv7a4+Mxt6abbeAfRoOh9MUYWljLf6PPK0+/Rw4hJXTfyKHWS1nE3OeFk4z5f/QNf4hf5HawzzdJnsUinexydbz+lZHun+GKNMyrVbbDEQVww47EFT39kwPvsFdoP1bilaq6zEZ+NB9Rty/DsiqsmGQd5DMIV9PwUXoRiswFW1VknpWoVMek56N/yonR9VhR1lsOQ7hyz+266HDIewwQwejqyLG6yUyrrGKIHGOdBV3YuYlg6RizYKrE242lIN7PrHT3PuCOGYYWl06zTzyMe8g3eQPFxVHk+SXp/pSVv2eUo74jRhfs8X2P76VkGD3uX5FLpXD5cW33Bk8Z0/uX2yWOoaTkZb9CO3R2MVZJmHFSx2bLDEdtPz+idQ7+6pt+UsW5snhIx6DL11A+Tx+PHbu2D+kHIiI4eWBFPbF2n/eRJatQqhPwJW7zPHr+cKuN3OUoy6tO8c3U5D9G9rVjavdQs6hZzaT3tWCiQCWCpVcAB+0i+zY5XGW7RlJqe86phtmE736i+NJ6JuBGRRCVj0WDp4XRe/eaaqvFmNtq95RnIpVfIoYUUtSIjyNjm17wqVfNiSNkIN4YX3V27GzY7b8cl6N1rLHuFRKOg2iGXyvfQNJ7bi4w7YRhM3djq5L/Kl/gVvsxjXmbClG2OsxvnufqyA7J2t+1UM6GxzwGfPDrN7mcPrNpv6W7qYnCP6UY/FRypmWrI6wdeRRYxScSiXCmZXPsJUybt1IDMQA22h+KqRcq2CHyKuoxdQMlZoB5GKJOh18TemzHiMS/n48aKUWN1Psn9BkyAdJeKlh/mG5CewX1OCoJVFSa6PAgZiiickkcED9eBB6mR0HiQn5+wJN3rWMPg/RqdmKZuTQCb4xO2eOrGQZmQyLBU68CEWbS1L8IYc0dsSucjAywqeTTYUrcSVmSbmVfmRkPqpnyZbstHg4ARjOxmFXTe8Rz1mn6esMkhO9m4bXHMJm3xuRcZd8QwXNNS8cv8Nv4a/yZfO/4tjNZntI3Slhc3iDjxIQy5SKDQLE+wl3nMXvvY3UtNosqMggp5SgpxXUwchRFyD3PYsLSCn5PGmexqujI8u8rcfcnEl8ZmXnxHvJ7yO11w1F83A6ndQmw6eTiWk99Oi9eZkwo51hMFWsQdhUrR5RW4FsOIWIGq3WyZ7gOE+7uBL9LEATnfsOrQKeuZQBTjYHlEura4a+rvJ9zniJ38DDbrE2hTUdcZZYZC5yMp/2UCm9N97eIB+k/G/TaxH90b7b561naeHjZEL+smXdrbFMTn3Q3TRtz0Cp0NfFOEVuD8ITuccD8dY1Z8vvv784w7YRiu6PM3+Tf4Wf4o3/unrxhJZTBnkRBlccr9gsvUkOinQsr3eMyrPDJegu5H+nkVqvtm1Qhx5mOMdtvirFgyYcI2T6xjVlNxwCsA2VuZnHuV5Gpg3zNvXKo+6kx6iOBxbZwI+t6opRAnre0S9/Nrlqq9nxfTgn72Dvwz1kpeu1NLnUp4HWhz8Q9nPMpwKHyLRJtlhTEhY+owAYNXY5g2uVaVGUPOEvfEHomBdwoUoxBOV3AVDNCd0/dKTxGouunSCgc9xwQQ1qd7nwUXDPNGEsVxnTNQp8dZpdTmvDC4mn/d9HI0DroGXUfsQQFucNxYOpnNajas+lL1QlG1WkbzOMnauQH3nyrR/jiPpTvuhGE4ZYO/ze/n+HCLwaefsrlxUsS1AlJE75W7NEzhxTHbyZXa5V/h/2SfAzbniXlYOzEplvvOqlI5Wbt+XJxKoUY9wUc8pM+cMyb0WbDLYQ4hBo/TBWUX9h6RlxEX/23CouC9Newc+ojyrYmj3eQkCXxoQk6Z5IdvxVFnheGZMM3GE0rdwngO8hC6kynufvla6oq2vs5qTXk2NVbjMN0YYLoIW0gApdtjM8bDmvixUa50MbY4ToDz1NipqmeJFZ5iQMo4PUh9PJJHuEjGXiP2MnXgepH5IdGzWSSweCcpMtllzm/cl3jfyirIZTY40ZvQZ/UMY8AQw0fR/jUnIyFQ4bVo2dIj0YYqWbkXGXfCMLQYSafeLWO1mjaj+UfsMkw7syayHnKT3OhdjpK1bVLno0va8b2s6tPW95hXTQaV4s5chwerB6DqR8VvynWr5mAr8SkW9JlWE17aeApPbVHMxlZUpXMV9VYxtXaEuOPY7tznIixwFd1o0ZxgdOUnSRVZvAJlQwSaqd2bRuwqbbGsGVaXwPNYOLrQ0Wh1Xd9lVZkKVSolj231ZuN7GQAFmKY273Ydw2Dc9UxrpjT5724s3LDucGiiqKfYf5c4yBib+4wxfCM194khxIfF/VPWg6Fe5vdGb027dmyOa17ARTYSTkP2e+uYxe2t7wRkCxQWBUvnKM7OcS4QmOTvj/1Y5NnOGPGMTUZc8DKPGSVQ/0XGnTAMFc5Q1AVGt09um1xbjRjXGaJ+wowhj3hIw5yt8bEdvylrKlqqQh7OjmUPa4oJwhyzlfkTctVkBOLCmdPnPR7yhC2GO79Is7FIzXl9UkRgSQtCzV6MHTcrdrL4uXgcVf4ds5Xum9Nv42SOizeGH7qXVqouxW0X/OimtCLuEY2DjtxSUy0vXXYuhRTnW/eYNlaEJMMam7f47yP2eJzif+92bd+1SIbbPZsRF0xOL8sCLYGdA9wopN/NU9TTKhF/GYgFZd2NnYPaCdTZexEweswWWzzJqfJucZ7mpd9/tS/wMn/PejTFXIr3XUPh5BE7HGYFRbL3HPUltFnqnte0vME32OEwG9fnHXfCMIBXq5k7FQuk7KcafkaXTZV/fRZZJ0+yW7qh0BXNqAp3tgT3+sWNfRY0/4VRa0fWzi/rv8+Bfa4pewzE9FJMV8VFFy2+JL8E0ul1hQ4yCpErb1qDJQdfXoY6T8WFrckedz77nBOpYqVet2itSruYsTvvMeeaKtWQzBLYKBdXu3XEOeTxeL+OmqhTEMVPdJWRm0LEEMSSDEVU+m82vlcYXIGnGl2VI90fzyIZdX2ZsAifL6WXFcMwP0aZXXJK+81KR5W565y0CWkeaoMC5yjEArHiWMlbPGSHOf2c9n/RcWcMQ0R9oSQOySsQ+q7XomhrbCKqvoRxUkSXK8p3Qwk2akfTQ1GFoltk73expOJJWqgT9tLPKZLrltsOCaRqW4ZVlb9HQzLjsVZE53mRqiSfZXl0vw/5uJ3JYfdPNfvdxe8+QETLIebTS50C/b7AjWw2kI2ASAvXjEpexvICOh1HMbXqh7yXwMfbG67qXOUt2D2k1JOUIVAoscwfplpe02+sGqFJizIaHUmsl/fFnnamjaewpkpuvvp7dkOvaHz93tUZM3KcoiTHxfssz0SSb9oIrFiwX3wmPuf40+71Ba8mnUvd7x9Ij+E6NbXVDdYQWhytsfLb2r0ttXaRd4MGp38qlx+Zg1FE1hWC3VorLrb3NcQYU0CoWewRv8YbPGaPHQ4zQFZjwOkQk4eLEm+LwVqebF0Ayc7PHqCMwAVDniU3HLhxb8DdUhnK2PI86i9o9DsT2I5VZ0PXjVe7GQQ9J03kKRPa5sJUpTo7oZ2zXGbjm2hxvcY77HCY77uuJS5QGX5XsaotC+IHd86EaiXC5tjMYTFoaSoT+tF8ijoF4FJwwrZ0fRJriaGCyF/d7EOcnxrRQM7pF57kMPFAhHn5Z8xoTlkvNqY4IuAZ+S+6PnlWxgN6kmnwLzLuhGEANwoR/Bl2JlpcGHEhKCH2MtagQ5NcxVRaeF0hDig9CZAbranoUlxyBTVtVWi0nkhY+xzwJm+zdXpayI1RY3TgNBSrqgw4WnP1OlDRVYl/9DP3IGYullT5PpWZhUVAx+fZe4j0WHlKGl2vRdfrE9AWzZRJWuTNjZy5v191BV6cZM/UVJtLr6TOO64MvNKmseQ4KmvfOiL2kHgMKsEWxqO5o+s0j2VUbEoRi5KnoOeg9F+Xrqw0pOaQu/xtrvSVRoO+k8qFZr1ZbsM0cXh1PPN++3jXcsdLIqch1kRUtGzyjH0OiuzL8447YRjucZ1vsCioEY3WZO9OblW7AaFBh03CqNQT5eKi5Hy06HLd9X55DDIk4gQ0YVHtpi4Bb/I2r/EOm+enphUZR231GdafwdOu0XXUDm3mbT17Or6LK4Tync4JM2YAtJvpHkjVWEOI96SdZsZmHDKU3TDHPK5+DgfU3UgZEBnMPou8cFU4Bm7MZ4xSuHfGMVv5Hug9/mTcJLuhsIW8dXnlRjeWycgBEpchzereKTQbsBjMmVdNAP1irO+pPg3DZRZpp72PekVIJDZ6CTENqfsXvZG4wDWvH6e29wuaokFSRcsxWym92hDnvT8Lx3viecdwRtJw+xywx+M8h19k3AnD0GOFYmIH6MocsH42eTnbhDtjgroEx9p1yXWVRiHKepWdsbtAVESuX+YJuxwVk0ceibyVITMGTym1BdLhJdQaXcKYoZjmo0zyDloTad9lGjeCX7FnoWTgdT5yVzV0z/Jraf46EFtOni7rL/6ejxX+pgxAt/ZgSZUzSsqtx/sfn2fEZSK412BAJ2oC3O2pEfGFAEj2LqHfXLEcm/BNiZm47qPubZ953mEX9NnkWZ5DkwRFd+dJHJrHkdk5TwxVeVkzhrzLa3mOykhUtJnhCYY9KXD1JkmOkcR7pHum8xPmpmvtnufHjTthGECTcJ6dx7gzxiEyh90uR+ytksyr5aKnIJDRPu9dnr02PgrD3KzB2ONxjtf1nlnISLRYDjrn1qUrEHUJcIO0SEZB3oNSo9EYdUd3MmhnlVdgE+8oG4QJZ7mmA7jRbNY4FvWtk6abPQF3U7vU33jOUpGSDma/WuRsjBahG7U2h0XR/Y0gnmemkq+0DI1vukpOXer7nFyWPTiHtl4waxxkjeFjjM/13cJC+jRcpPkWG710n81tAPCCfibfRZxMwPZj9hJJzYzidgqDZZiUUhZhqZuajCB0BOmt/8WTHEJ2PaLnGXfCMKzoEYtV9MA8LHBegP5+waig16otej+5nZrGEWiap4ct2TR38ez/+g49KLUUt2Ygc8SMXAYLHgMCRMkVIDYwspM6XWsJzTA25RG7mVXZXRQlp9/dVccQpIYttFyl0vbaaD4rOkjFDtWSYDcZOLvaSHuO97vrtkoly0HhZTZAceH2L69S/wxJ91f5efk9W2a33L7LF4/ugUx2S5WMWvqOCDcIeIRS+EXzqw7HwCXe4q4e77VCM3C+gOaYgG+NqAImj2mWvkl05ZYqSfDZ58Rp2OEw3++yUM1DE6XTI+GpW4kbsSWFPBE0Vdr1RcadMAwamhTKXutSRQuWexoXmLyDn+DnWdDkvoB63Y9Z5YUt5p9APbloQDYcbYgrY6OP6F3oAVUsOWCf7Ve+Tu9bFI1t1c1Jcf2ztFMcsM8RO4hnIOEQuw9VsWAiB0LnoThS4Gs/GLBRO8sCNBDk15OtsQY4qd18Ja/nIk+6GNdGgxG9hcncWZXRG8lNiYFqaRkBZWB0faXYiXtIXQ8mehd9gj6khoRgu+GEKNHSm8QNc5STj+nDGMpUtNklHyZ4UK9Nzs9Mwj6Dy1FG6opVDdONOc+qzcz2VGFaVHLa5RABucp+yPDGdgdeHOep1vXgWWfjHLwa/Qclrf5Fxp0wDD1WhRunC40goVwp1QvElN8b/BoPecRj9rhgiMhNAs5us64xDBC6260PGGaD4GCT4mNNLgedksFIikSrsXea1nm+y2v8Mj+WDYIWX6xfiLX5XU69QDnhCBF7yHn+9Joaw1419pB7S/cawI1DU8+hEsZSUqSjRyTgTv0nui3rPUSxv5nc+zWzxoyMYmyFIN16Am0F0c2Pug86dk5VVli2R3iCRiQ6pZBuWZF7bug5NQnU7OI4Mn7qvD4azGjrk+wRNXNc1ToaKZGuajOIJshLBoMFkoOnxT0bsywMQyysimGdDJvqLbqkuSGz3F07siK7cm/PM+6EYZBQi0as4uuGEJGZCPCQR3yer7Ggf2PBCbPQMSO5RMfSbhRTOjJMSk851dWFVeLQ+5aVKwzNm1TWndKPj9njbd7kgH1ixWSsCbHJoIVZ5d07ljtLrSk2qBF/wz5nbv10w+TtbdGSmYlQ/qyWV7AObVUyDfU85ljNRd1an8i8MAgt+SA3vKExOfq6NU0K3StXVfZYONKTvZy4H55/WxgowBZfxBViRkLjAVlWbjUmezDd6tR8v/AYXuB2DIsKCUCFi6fhe9f9PHqX0G7Yc93lKBd+eRf2hjMmRSGW5lAE17sjNhHS3Im9LXQMAdG6triJvci4E4bhHqtbLZoVR908RTUC3eUwK0aLR67J1sUpILqmduNima++L6Z/ZL1v60mh9+g4m5xYKq02YZLpeB0VYj3iIe+lBrXxPMB7G4rDYUDmLO9c2mW1WzQseMIWNW0GHqMXlL2IZZsXRFubdkK7NMm07EmkyTw8u6JfnxYNdPI1ViZN37+8dvcZu04pN7f1vdyo5pBdptU6bz54mx86+m6+j9FT0CKQb6B7GEO1SHrKXAlhDNJ+OMVCiQ8owwh7mKbPOXaKto7vqdB5EbPXtwB0KsDLuqDnOKYhoxRTpw8sO6MQdMo6IrzVbcuzajMzXdtwjZHxG3EdbZDlplSCwzJ48f0xdIj3+XnHnTAM11aCA5SpObn5mkTCF0ZcMGHKFk9yCkjobtyFQTnped6h3P2/yQCMgqxRCHUYFmc8x7izTU4vszDLrBlljvsB+xlgFFlKxykzDeXPkk3n+7i+V16KC9Ro9126IUgLPPIWquW1d3TCOzytzWFwfpW0JK5z/4x+ExrHtrgQixZiY3oHUyYcssu7fIYZI97kbTvXds6sGiZSkPoyOMYg7oPJ+buCka61kFVL594L6tHpBpWK0QNgw7wZ02Lo33h2JR+m9CYiuFjgNG36T4ZAvImYol7C/ad2Mr1z+ER1VkjPLV936cEutmXnVaZqbeMyYxCxEc0fIPMsNB9ldOL1/ICCj9EwmOsYGX5ihPWT5VV9hEAaUWJs8bo7rpsRdyj7DltoIuvoNRmVuPN2MyUeAbeM2hnLqqKZpzZyg3upBf1m7k0Zi2AioSmOeG7x/LRT6PvjJBqmuFUptBhrzquGquqEYVVNVS2p1DYv3v04wWub0GpDN+hO/ui6pzBFE1GLa4sngHlOcDP9KZyiSh6MYR9XXKy3GSCNZeICDOtBS1u3jJfX5jEMsAWnHqOvAHvAS6Wc3E3VpmXhudjrZQgVRWhzWzxw7QfhDNGL2LCfvdP0foUbNbk8vH29Ti+VDZC6m9RtHBIZBqVPI2NUQ0zK7kYTn8HzjDthGK65V6SF+nmHFzg0TxG4F1XFhwpkMo0icyjpubftwF0hDVP9velydcGwSIYandvucLFuzW7fZy+30nPlIiuGEggZ3UIHNpfFBPioIZ6CkO5oyCIPJBoznbsJ1Vx5K/k5Za1BoBWvBiGjIZc9SqgNoGqiYpC0JY/NK6jvUS1bJstEtKmrjFMsq4Tsy3NpoG6vWD5wWnJ8hksqnlWb1FVLu2U9QpqkFNX7dfu5+oKpeM+rxjp3ozqXMv+fSXLtPHtWbRW6PCV9BPsb3nBX9yuqR6kvafJSaHCjEcOOS+AV033oFrbZ7Y3ebElmiyGP6nciIgReFzQMobD9tHn1A5mVuOZeXvTgcaXSW5LgkkSFC7Qu045iiLdq6MX6V0gA7m7pBnfz16IWx90lahTo5uqBCeQDi32fVZs84lWO2MllxQJKRTfuEnzcrV2ECeGq0D4JHEy6bVKZLPpFniqKO2PJd04/Vgtb0MsWLq/NM+i2fEu3YN4YaFl0mtLoAJn9BIDe54QtjrKHXL0AACAASURBVJnMp9kjUJ9PuM7GYC0ao/Sdy8avJ3oMej4zRpxhojjVuKUam7TZ6PfMcup6mycMmTGrhqh4S8+y3BiW6RpjP1CndTfzRT5/xE15itOxo1EA6wj4APeuTjv3dQNWpgR4I9Og0S14i9jR1ukp040BJ8R2CrMi06IweJTS1nGTezF/4Y4YhhW94gYppy8rOkxUZ+VztcBEG447eYylHJBRznd5A53VQjUv2nkD3p5M6S1XTc7HT23rThoLHY7Y4YD9Ih3VxRWK+DV4CLpeQ8an+fvtPf3sZZTnXjL39FqMRRWatXFVVzBazhxUiyk+zFMQ/wKuS03H4C2wlBz/gqZZBDruM6rlNYvBGv3LKxPI/SCcuHZQ2bhUJWlqznVxnbag7XmrDNny9WcofNFGMWHKMzbzvbRRZiI8v99AVbaGK+5tXeW0a/YQdN7nnf/GwA9b+DI4ovQoGiy82YInD9ZvLPzuz5iFkQcwOr9kbW73edpMOGYLp50P88Yowpg2l2gYbttQPmrcGcOgoQvwnLfnb7UL1rS3Wle43Q3XjYoqOr5gXPmm7RxDYQzB3Ih81FIVkugtFYcpLelOntSooqHxcVMT0JFlL1zSMlc7uxaVEMfr1/ujoIe+JXoSWX8g6Sgsl9fe1r4hEXS8BV9dtxmUzLthNBSNIfebnEBjZJ6GBdUSXjq+0smVCs7RU/AbYKFHBt2qbGDt3zWPeMiMYTIKnmGK3lcMB7u8jBojFAmT6WapWmraqs4hRsYWhCXcFkbUGLbxknUAK/gNyp7swgd7a0ga7jYgu7wVdt4mvzejmasvh4ULOxx2PE/3tEVmioC5CvBeZNwJw6BQIg5zibykV+kmqenEh66ouijoydPFY0yIdfG+YEpD4DljcfwtgxElzquMaURlnyjQ2r2WrnJQt1BI399SZU5DBEfl99jvdT6mk4Da7FYqBJPISFkaLfXhikWzYFQb52DZOFFJ1N50YC7HVwzkHmvGKC3YWgxet9fMm1nmO2TBVuEWsSQ6/kwsxZUEdHFBF9HeZ4w4ZIdjtjPt2xdYHZ6bN62dd5TAYtl41ArVfYnGc1kZFtKTEZCXcIbhLDFEeAX4LG7skrHMU24rkd0qAbSubt4NEzUE5krcZxmWxjCF2Ep5QqljEY8VN49R+wNYKxGHCkbgdi8gorURqIlAYxwKHaJxEFagEXcMfZ8WprT93EX35rv9sOhEf1V8H3d/7X6RrXbbThGvNf69eMC4GEvJGqyz8EwsOZe+hCZhzOS0zJhXfZ49KO9xLHkfMuNivDB3eZ/s2s7GA5r5gtH5Nb1LAyklTNOTy63dVgVN/lA83RkmfbeeRM9Oxly9TPVazPVrRKUj/RWkadBkYxDnTrzHqv1odN76T6lKpUVrLET4PGYEYin4ANhKr2+YB7bIz6BsliQMQefiYrBqpGxeV92mqtjKvZ8Fnq0S7qXiQt/QUnYrHed5x8cahl6v95eB3wccrVarz6XX/lPg3wW+l972H69Wq59Lf/uPgD+RbuN/sFqt/u7znIgmgasyzTLGsExc8ii2EQkikRnorLpFfh/Mi8USEVvF8B+2UA2AJH225oidTDBSuhDI4iVi0EVjpfOM0mldIxatflYP7kxe/X2Jh1bRRfTchv8Xy3QjhiK3WopSEZRV+jeek6cij9kcp1LkpmLYzNhujxO4GFKf4KXRXY+hS0ZKf+tfXrEcz4vqWJ2rxFEiGS2eV/QOhT3d9kz93rfFs4qKzZPTq7JJr/5TGCTB2b107hFs1LVuwdVLSaW6KntfxiI9e65lWBurV2Ph2LIqn+Pt2Y3w2TA3qpsFux85nsdj+CvAXwR+tvP6f7Varf7z+EKv13sT+MPAb8Zu28/3er0fXq1WH0m7WtHLeV3VAQhjWKZJG90/Cytudvtx1qK3Ba9wYhQ44Snu3BH00bDiJM9s9FlwzFYS2dilYc4+B8RCHDElZck1YctshMfQXQpsl/EWh0KFipYJ7t1IOchTcr4rxUyMrlPX7tiH7WRR0j4WismwuMpQP/XJDDUVVc3uusm0iQqd5eTXKV1scKNQkXUrlpWlUrUrzsPz9rqZkh7dUt+qsH2Tt1C62h5a1YWLLepzr+vtKCuhEGGMeQQ13glLOIRm+h6cbKwXm1dkc8rIdXuglrTtmllTUzXLQleja/DkPXY5DWrKe2srwY8ZH2sYVqvVL/R6vYfPebyfAv7GarWaA+/1er13gN8G/D8f90Et7GEqkQKKOCoKlETXyxfXTdmyCOTF/L7SkLM0ySLmoOOCTU6Vcm/yDFGvD9nNpdP3UzMPxbs6V43bwFC/XjOEMlKxtFtxpHVdnhY7Y3diCEiL9OlYk6AQonsukZobjUK5sHyHO2Nyq7FrWNCvrNqz31zRkOjDG+HLdEtkIFSaXhuwZsVXV0zH61lbI7L8nNp+MztTelRuCPPiC8Y/5vkrlpnzEF12xEEQy1PnPoDUfMyvIfIUCL8vXdE5VvWawXYsxrp6X3E8fsCUSap1iOGoGcFl5YxGeYJLhtljjRuOa30ub6ezP8f4Z8EY/lSv1/ujwK8Af3q1Wj0DPgX8UnjPd9JrN0av1/tp4KcBmld2Moq6oMmt2mOlmU1IxeiOQbjywoK4k2iUsbOX/sYHFj/jhT0WrV0wyhyBbY75MX6ZbZ7wDq/lpRzpqOtM8wOM8mUuJyYQzL9FUl4TfDdxUVqV3DpFO3pDMpJdtzIyOLWMI0QbhW67UvUaBVofnoTuH+l8JYPXsDDB27qlaq4TwcncYHnTXdalhHL1XnknqniNxJ7u89SI5KBuOGb3wmtOoucAFDqS2d2WhxOzJ2McJ4m8jsAYLcKkpzDZnVphWXg+N4rRWrjYs2I7lc/bvU+1PueXuXvaoipZsbdl5uKase9KKdcXy1Z+34bhLwH/GbBKP/8L4N95kQOsVquvAF8BWP/yGyvlqQ/ZSSmZozSNbzazlcWMtQ1RxkqAU5zkWh7+7zq/T2KgBjg6nyCWVJ8xYZ0pu+0hm5VZ9Uc85AnbqeJNbuoFitu7i06Tt6XKaLsYlzeb6Szyd7snYZ+eMcpdmbZOT1kM7lE1I2LBEVAsluhQqwOjzk8GLi58d2fL9uuKyWXoYhn6nL6BY1VLUyfhkqZkjUa3Vi50FjupxaBU7O8sVoVn8fzivIhENM0LjW45s6P9YT4kw9SLNRcV7uHIMMgoRPvUIWpxDvw6vLRzxXwnaDYmijUY7VxZm5eqK/ob3zbAE+OQyAipnF34gq5RjEjRoqNmZoXRzgUM8y/KMKxWq0P93uv1/hvg76R//gak7d7Gp9NrHzlaKo7YSbz/i4JDHn9CZINJwWhWgHWxnl+L5IIhMd2nmDXuegLbyio3B/s0tANscZzZdi0Vm5ywzpSjVB8xTQqB0vWbJE9CQGhcjBJYUaHRhCk7HHHETjZ6WoDqMbHNEypa1uYw3fAMwuT0Mnfb7tYLxNRe7AxlGMMwGxBdvwxHfE4KZQQC99M1nLCZ/p48v0r1khI1HeoBFgta97+iZVa5h2Xf530Zuh21ugZMcyLuqDpubuwapO7yfEqU6PFpUIYSpqDFftsqiSFGfE1eQzIOn1iecblBrlyFRLEWjpGMw+ADzBg0gmIurfdpXRXpY2FYUSE6ehCjdpa7sWcAlXAtzzm+L8PQ6/VeXq1W76d//kHgn6Tf/zbw3/d6vf8SAx9fB/7B8xxT9QdVykOANxjtAo0KJTQRBLKAM+eifHccIgqpmesSj5djtaS9txQVybsi5OpOLR6VQL+f1Jke8TC/17yIi+L8FPpspp5X2xzn4jD1QtznoAhVjGq9nQ3PIbu8ufN2Pl8Dbr8N79m1DvagrRdcNObqSzZfxkoYQ9dj6HposY6lyxKUUdCIXlrsylwa3bJ2RSFY17BrxDCiywCNhWVx04hh5qSdZgyhmXvZudD+ug3ds2OY0OKAqZrc6HWlLiN/ARx8rIED4AN7DoMHV1ymTutrLaWXoVV4CmzY+VUpxFpWFXXb0lYl/hONn11/SlkuWzNyXbbmC47nSVf+deDHge1er/cd4M8CP97r9b6AhRKPgH8PYLVa/dNer/e3gLfTKf3Jj8tI6CKtzGjGfU7yopG7GunPmhTyBlzNSJz4JmctulLoek/XI4l/0+4ySXu+UmURXNSQaMYWx7lF3iYniLH3Lp8h4gATpkh3cphKxyX5ropJlZRH3EK79yE7+byP2OWCUe4kLW3B37Hxi/zrl38H/l8MGW+8ulMAo9SvtACjPqbuc0zv2n3t58V2wSi75tGrML/gAoVHUbYsGgfJ0i3wxrsK53Qcga+R4SeD75hTyQeIBk0AXB8DFgeqaWiBCta0wKGslIyzVQAj+AKrKQ2CvIQucUu1ICJ5HcPgFTjfucdqfO3FaXU4pijUG7gu53zBrHGNShly+6hdsyqOm/nCjIJYmZF2/oLjebISf+SWl//bj3j/nwP+3IuchDISXsQkBL48Pa+d8HSkdty4yyi0mOKgn/QdYy5fLED3PKaoj8GIm7Ju7lu0SCRckmq7599jcAqf4ikP9x4x4oL/jn8LsLBDHgoYbVgSXKov0LFkFJRFkLjIlElaPMt8fTNGvMfDrGh1wD7/mM+z/7sP+O2f/Uf8xs6DlEHx7IMASBmD6JLLGHSNsO6Xnkm3zFt/lzeiAjjjfGyzzZPsETiAKml173Rlz3gRcBknnHU9ufhT74mv6fysGCq9SdWOonRHYxAXkaZdTK/Kk3hKaQzibtwVqLUTijfJflSprF1Ygo6VKjSvUp1KW1dFX0vyIW9p13h+ySAWdslbuK0A7jnGnWA+3uM6E1i0CGOzGV/EVbFjaTJ28QEAIe9lObf/fcmQSJrRAlcLslj6becRndyylr+ZL+yhpAn2iYMzftf+L6Cc/zws7E1OeMgjdjks3OLYW6GbARCWoSEux4I+J+zkhXTBiEftq/y56j/hJ3Z+noc8Ku5HN6PQ/V3frdFtZqv74L+XYJiwGvXMeMweT1LvxchPkbe0y2HwiOp0rfJiHFvw76iKZ3gzTHTjrX/nVJ2wg7P0n4xBDB80Ujl3Jix1PQp5CnDTU4ijwbtwb8DVA2N3rj1NeEaSAVyNU4aidkVrIGELjrNFvkY0CpP59KZRUN2G0pQvVipxdwxDgxM0uhZSoJ1+t06EEx6zxyNeZZsnvMzjovOSgL/obroCdJ3DCSjVfHKtflioccRUmLQYmm5cegq/9fybvLH/Td4a/1b+IV/ggFdoqdjjcSEbrtElV4FXec7Dzq7zlET+s+QpaOxWhgurmCtmbuIk83jcFLPVR8HNX53vt/IS+n4ZRih7csQeoY/Z4z0ecsaEYxa5AYqMg75PeEpc5N3mvrd5CPE6dL5SSc5GvZ2XWgoyCKIva+HE2gctZHkDsbGNFlk0DN0Rp65IURvk8OCl4ys71gN7y6o2D6IO2hd1C+3ymrpuUQXoRdjgZPzUJmB0fu0G4Wk671jQ9YLegk79X/qwFnVliimOuFNrN1KX6Tf4NYZJ+lwsRHkKcTFpiGHZ5SAUqZ6Q6Yijpkw9SS49vMENxCkMzuBLr/8j2CAbrdtEaW1ylzFyXMRy/bUgFdNXLDlil29+9bfyiS/9Oq/xDtKtMG9is0jVRXq0dldnSjrjsetdxB4FEprp7uTy8oQ5vMNneMSrhTfWvbYZI7ZSly/dH13vBcPCm4nfo6Fn7dfk7NW8YCJhSTjDh+2q2n/k3str0OKKG4Ak6u3k/PnHofclFmguya5ULelvrVsLIdbmJJDyXnq9zanKuA70/Efn16YYFa8pqkpF0doXGHfGMGymLtHg4FPMHERwSW75wySzKgJSRZsnikIOKEkhim2tZWuJaMdcdxxR76EQ2agr4NotvnaYFtsRWnvQL/P4BnC0xHt0lgBpt2ahSalEB2PFg18kI/DJL32L13i3cNeXeJWmFvecJqsIx6pN+7sMgZ+LwEiXXJfXVgq25vuR7t4B+xywz5R11ADnDO/arOerp7LH+/lZqxAsHjuWrMf+narx0POqKZv29LquddRS0M76FF/AsYuYX1RZci1vQaSmaBSCEC1jjA4u7+OM7I1c7pmXKe6EFK3q1gxGM4c511TLa2bjQZ4XcVMRJ6J33LnG6AHp9xcMI3Q5/9LHGlfcT1TQiMJ3U1OOJZgq8+f5GvscZAMAvhvVtFmyPDZN8Wq7sqdA9FZUrhxd6IiYXzCiX6XMySAZkctrMw5SLl4CL8H5xr18LifU4XucsynQ1VOnN5uxaAE04VoqWh7yKHX49roLYTXuihtUquyHmIC6v8poKIhSiFPmzvtULAtjG2N+fYeVSFvGJJKrlrhCVyRX9ZlnifWK5Q3gMVLB+8xzmKHv1KYhjzNL/p9flyXTwgeENYxxl5v0vNZxfoHITUus1FogY0dcJhuHWC3aLRiLnc8TTXw2VlMeNwqeRrXR1vcyU1LeaVs7Q3PtKFyT1KUCcer7AR017oRhqFnm/H03sxB3mSkTVBX4Br/Gm7yNQBktCfBJG3PqXnXmXklsMxdH1923PpVGY9bxLXNSWR+F80uXF5cbWsPV2LwKF5dxcpMdJ/pCfh7dnVjXo2tSV+yLtLAmOE4SAbiIK8Tr8e5Vxo3ohjAxw2P392Z8H3Uo3JTACZuJUFZ2ATMPqMFrCJ11+jZvMmPI/ZTqXRRHdE8tVtTqnvaDsdd1NvOFPYu40+stY4xhswE8Tn8/Tn+Lz0+KUwoh5HFEtatoHPT7vPNThsYeJMxFD7cQdDEow9G6TZJ6S3uP/lYtra/FWrwehULyGmTkuhmWj8JEPmTcGcPwMo/DxG6LSaV0WwwPhGirU7AmugzEEi+WEtDYZUjWeNYBKBZSLIMFX6wRJMyxeaoPGMemq8kmiXZbErjKVKC9vcQ0uiCsvAnrVbGVqeNDrNt1zO9H6q/+nXP6eDWnQhhdY2SDxhShzuMihBniI0TewwmbPOJhlreL6tzdbIY8DHkqZyHr0s3KxBAxHkPqC3KzFUr1L6/LdF1k/4navIFjCG9xc/Ho83LJj7mpzBSl9B/gbrs8RnAD01BwJOQBaMH3wt40OCdL/PeW9nsvGjn9/AALUY4oQ4jbMicvpgV7NwxDRZtaeV2EYpdlpu3a5HRCjHgBYhjuc8BmqnJsw/IAJ0LJcdf3DXEm322jC3TF1+VtSEBEi/98I7VnQ7noOMlrRBEuQdGyD2G8hrh7xrJqKTHbddni63Oz/6JqJ5SWtb/ZdbtkWhUMxDK/Bm6MvNWZ7+Kmj9FnmJ6LeBSH7LKgn0lhCv3iecXwTIZKz1nfH/kN3ZJ1Z6KqmlVGz7yFOoYM9gFNNMcBlKFosFZ3WvRadDH8iF6DvAPwdKQ8hDHu2iuE0OuBr7A2N7Zl1q6ICzlMRxVZ9SJmIEMXw4ZTXMGb8DMaso+lGZbjThiGmiXbHFs6q50yOb1i3sDj8SfzZPIqQ01mk/uqaHOq0jgNTWLfORAVSUQ35bximuymmEvDvOBCgO/mEiRtqaGSd7BgMSBz3BfBGMSwKHoss7TYNfpIrEQpuzpnY3QNTgZT6HOWz1vcA5Xwqvmt3euSrbhIx41l1JFZN2WSjUPkUlwwy/c89s1QgyDdZx1XGEIsLc8kpPScdHxhSQIoY7GQeQ/SBI3UbJeESyd/++/CDsB30j18142gXYzVY9l4TbkLRyyiDa+Bazhc4iDgMgnwiichXOLcO6z0biNfdc8vGoY5zrbUOV5Snu8LjDthGHqs2OHQCoDeB5Yw2IHN8QknbOadasaQE+6zoJ/l2bd4wj4H7J5/j4PxpwEBVrfrNMTJFJWWop6jQLkyC1AXu58t6GFmKOYQpDGsQLvsLHk9Atwc4XeRGCjp2fqsshIKpcoW8mYGov6lAMVYixABR3229BacKt29J/a3hinr+b67dL8b6yiwY02BQjot0aFloOz1NtHD7XoUwsiARiOl84zgpd+zm9NXmaIbLj2ULEUtapGZtOvLUxCmIN6DDIEEZrRAB5Ru+mX4u7wJGZa2czzC+7rnGtmaOp82/FtZlcjR0BD3Iorh/CAahpolk9Nr1g6wC03kD99h60SUsT6QJ2yyxFD2L/NVftM/+R40sPtDh0yqaV6EAvQaSvWcWIhixy/Lfx2wc31ATXB9zlWcS9JQXPxeqKTd3ok60SjEBjTeS8OrH8XLkMxczAjICDhBa94xDt7NSucUjWH0GAREyigY49KEU46xDlvT+YTpyYTr746tHuMh/NCP/VMe8h4iGek+yuUfhauR8YjDVaVKYlNMl1a0nFATCVtqKSDexgUjmmpBNZ4yvgzVknbTfbbHlOSAkhQU+0aAGxMtfh0ndTXP9lw79Dj8W8ZD2EaN99tUyKJjdA0M4RyihzDv/FtErJabRiCGED+I4OO91bX3HRjDassENIE8UVQopN3uVR7x4/x9fvR//7oBQ6/D5PSKajyl37hiE2gh2u9RM9J+9vMebN9XVq9p+KIUaFazpGxFLuPQba3nWgYOEMoLiVwN7wNR5YyB8RxGhdisxm1Vi5Hi3Q0j/Foa1B1L4Zf0IyLJSarXx2zxhG2+9+2X4Ttr8Aj43zAZns/Bt7bfZO8zj7lJOGoLbkW3nV70KE64j4hNpZith3SSR1OvySkTtpijJsfCKaqmpdo4Y6C0pBZQxBigXERKSV6G90cAbxA+P8Y2r/XweQGRg/BewvdGYBDc7dcx9Xr8exdP0H86bjQKup6YQqXz+guMu2EYFEdBbkYK5N1WxJx9Dhim2Pa38DV+81vfstgwtT3vLUlIr6veqAZ+noxF9Bg8e1EKusQ4XRM8VnrGQiZwY+Pove3A2ifd+NR5kcaYORqjhkXBy4jHjSBl3JFjAVIuqgkgbsRpIs4Rw5xYiSpm6RE7PGOT43abp9/ZgX+yBj+P/adC+7eAzR7/97//u/jcp74W0qZLVHvSpUPrWquMc3jvy6hF6ddXpj1nifBVYYI3ri+19Gczhmp5ZlWUUdnZbpQvWChTisvOe8F3c6UkNzCjoHBFQ8SmuHNHHkPMKMTjykDISCisucQNVsQu2vB3jWiIYpgUMYwXGHfCMOQLbaygZDG4x7xq8oJ9k7f5xDfP+OCH1viV6sv0mfMyj+0mvk6h428y29a5WTz0NiHJtoC8M1SMa6Nbb6ckJLxUIo47djQyLd5rQoDdRVh0GvH98ZjdVKveo7+Lxq1zioVhXfafriHK3ulvFzlEmGTDIM9B33vGhAP2ecweF/MRp0824Z1kFP7rFfA14PMw6JkUzxnwzoAnn9pil6PMdpxwlrJNs/xT4Y64IV3lqNuGiExxiEA1p8meQp8FF3jP02Z9ztr8qqyqBPca5OoLT9BcjDtwE34qu6B/N533DTqvxexANAox3tdlxe89C69pdI3XMrxHC38QXo9y/XXn388x7oZhgGzd5g2prbq5zkNmbD+1VuLLqgpqRrV5CvFBzBOqm4xML+0K1sDVFIKUExcHIe7WtghLpNsyT7eLsEacYJ4W9EVadKqojDu08yhm+TNxVxTDwURZojttr4sA1gXhImYSmZOReaj3iQsxS8ZAwKIUmIwjscvB4T7XZyO47MEJ5iHIS+A1+GQPPocZhk/b/Y/elJ6Ta1uostJIZaXMnvASV7Fui7/X2ThMWafBup4rszJjSL8TUkhmbrZzwSeXpzfj95iG1BCyH9+rRSXug/62wc1jxh26Tb+fhe+J9OQ6vKfBPYaYIoXSW6Dzu5ia3XNQg11dm879BcbdMAwrMoV4MVjL4qg2Ofo8fvCA/oNF0bOvYmlFJ9H6yvPoDIleRCS9y5L0HdwmYSTZRDl4ucpbHDNqZyyrKndMklFQ+k4ofiQC6bjK80dQc8RFATpqIYMbiOghxEUfC7yiwZJ3cZbEZXVv7fdhqjsZZizh8XyP00efJHWyt4n9XezfA+BhD5YjeIj991r6b9t29oZFNgRbPOE+J+wk7YnYnt3Tz4ucndDfJTPXBYdlNLrD9TDtzsSQqM+Caq9lcn5WirUo5hdxqe78F6nP0Sjo94j6i78AZcpQx9W/I/+hy5ysO8eIn+dDzmtO6SWo1kPGIknFZTLXC4y7YRggW8Z51eQdQyCU2r4pzdWwcFFRuWwRpwhjLf2slm221gIIFZPGzlS+5MpQQ5/rYySrydz4+NMN82REVX6S0HvvaVlnt1nXEEcUZZkGt1qLNxqo7mc1hOIL0Izlyvb7MKP+sxBKSJdSvz9hm9PvbpkR0E73CAfUBpgxqNPPTwOftP8Gn3yKVLg2U8frbY6zulXuaZnOSalLcTMsVDJ6dfQW3Bta5iyFPaeaKihxCbTViCHVjCGjesZqYJmKXhft1wLU4rnEjYDAxi5jUkOLO4Yn8gQiPRpuLnIdu5vx0Oe6AKjIUhoyCgIqlVUZp993uT3b8Rzj7hgGgApECLJ/LrPbqYyA9B37l1elFLkeSARhPuLqor6gRkwZanHFGFiTdMKUveZ9xqcWDF4wygi+FtpF4gBE1zjiFcNE0GlYJKLQOjN2DexLcm1Rh2GYFkHMMAiks8t1slP3b0DwFjyFqvj8CVv22rwPZ2tmFL4DvIOFEV/AJpgMAcB2+v0h8MkrNjcMYLzPCbscscshOxymIOUkC97qfsmziteirIbe163qtDnR5syGyuPlRek46hguAFlGaFnh/RW0I6ewM4cATeff69hCewlfyHA7zhAxAM1FpSqbzuvr4fXIpuxiEHEOy+BE0lLEKJLuQ+HhfB9Gofu1//JGD2vp1XF3Ig9BbmausyfwyyPSG42DXqop+iIKhCy/y+PaSOSJIYFSeJuc8CqPGG3MmFaTLM4aWYJaqJE4JUOjRS5fQBJoj3mZI3azYYKyviP2c+yKplpYYZ7QiOrG3+UpOM3a0X8ZjenJxO6jDMMjbPFvY7voNrY4pIfzabj3I+fs7B6yyxH7iERnlQAAIABJREFUHPBZ3uZVHmVjIOEUsU9H7Yx5dZbKsifZ+MchqnjXQ5LxULiiUEz3SOXo7hGK9brM82ANPC15jHsMMSMAZZgwwBdyxAiiUdDQcU7D37SQdcwWNzTyErqGBUo8IXrGEcuQZxNTn12AtFtK/hzjThiGVQ2XOyZ17gy3fk5tieuf48R0E67GloHodd2ueFXB+kZXU2ZCe1FMlSnGj/G+UnhTJuzxPo/ZY9bY37UnRnGYOGKaVDl4F0s1andLzZR1IsEI3DBIqEXHiTwMXU9UfoqdkKXpUNKzPWSZ02fWjrheVrboT9I9/O3AlzGDIALPEvMktmHwO5/yxY232OeAL/AWb/CNDDhK31EYShaMraoMzOrexHsWC+Xi9YhWLSHZWD4uAyK1KfDSeYWdxogE6kR8OsKqKxV+RqOgEXUV4u4eF3OXUBQ7WOmegRsUbX6DcLw4tPDPw99ielMkqpi+VJgTC7WiofuBTVdioKMWUIz55TY27dwk1IQlbFjD0MnplcdYuglyDzs3xEuF+9mtBwtR5qhTdL9YuOYl3OeCIUfs8pg9fo03mLLOF3krTErDLPSZ24bif0vT9SF5GfY3I0rNqBFjUqCahhc5eabD/+ZgXRwRr/HfnXchxuDJk024TFvgQ1j/w99jZ3wEWPr1ZHuT0boZ6tPfucVv+02/mPUwvsBbfJ5/jAGH3gk8Su9PaXjCVl70wl1i3wqlGY1WXTZ6BdeQuE1TQvcilpGPuLAsTMpGTS5THnCOGYYjygKnuFDjJqOMAZQeQ9zhYyoyhijgxkCLtWtYCMdR+KF/C+TsfreOqz6aen+slYjz/wfRMKzu+e+mJhQajdJmoC8r8gCcw0tcldRS3dAQV0lcM6rtOmLvGIIIMwoDZCQUlz9L4YJ4BtKclCISOJtSIwKGN91iS4UKMGzTMRV7tynPEIfCCR27e/yuQYqt8kRusl6YZ5xgcnHPEs25qluuB3N4DT7xqSPe4Bs5hBox44tb/5A3eZvP8C5f+E3/kPuccMguuxwmoRgxFJ0mHjMIs5S5kWq17sFt/BA116kSKCsQ1z0Rz+SAa0RGJueIGfscFNhD7hytBbmFxeIxTo8IP5SLV5mDjc77YqET3MyMxfSlRmRHRoBSmbXIkozKYLEOYgt4NXw+duTu0qN/EKsrr3u9rIgroKhprcVZM18wPgpqPLLEsdotWskIyIwNcIotwGOmwWsqYtY/MhabDAKqhDnGuPIuvITbd65YqRjFYeICEHYS+0LMA2LvhqysgIlgqGTbIqogQHOWvBx5MeJJSEjlJLEa22VFM5iztfUkn5May46Y8VnetkI1zGC8yiP6LNhvD7K6kKpJdX4lFlLl8+saL11FV+3J/6628NLUcLKWZVrM41I4J+P6Gu/QMM8e2YQpbV2xqq9hB3pfBn6dkv7cZSWCg4Zx1+92d4qLWHiFhha0vBHF/93dXwtZ4KgWuj4T050VZpw+a4RASJkW0b8j0KnzK6fQx447YRh62ELS7qmsw3B5ZTUUUa8P/CF2qacxNxw274t11V3c7uJr8gqcizG4hghGoCY3ZyEDUIKAKtOWIbhdrGSZ3ytC1DylJxXmRIn6yLLsji5/QQvsLKVPlc9X6PAs1EAADMcXhdHTMYdcZIPQUqcydzvvN/gGo+Ws6K3oKdpYUOZYhxmrUUrn9vN3dTkLUeEppitjjYXMoGorPPdhmY4+i/z7hGk+zpMH64ZVqd5BiymqM90GZuvf+j3u5vH9Xa9VCzpyFuj8rqEQJNZsxJRmDHM24Ool/+haNBrgfTb1/lv4PR817oRhAHvYddsyurz0TsDKzQpwjFmIU26y0aC48RJLkTciZqIdwgutYu7fz8fy4CqSksegSdwgolVJkAIn4uhbVcOgsu24CNStSecQe0ga7jDPnoG8kK7uYRxa/OpxqZhfC0iVksdssaRit/L+FrFDVJ8FmzyjYe4GJLEWpWE5bSb5M8puRDA0MjuB9N5ZTtlGoxfl6GfhGfl1OQtV1xkzRgJydZ72eZWqGxD6pDJWxcPxIwbnZwYqRqq05la3ZbxCiEhWghJPELYFt29YhNe6aUQdM85vZUu2ML2I+H1jYMfm9mKwlry29NXpGIvBPUbn164n+YJt6u6EYVjRyw1Hmzkuh73Ei0iiBe+CM3LPklu1SiHEYnAvpSld6ERAHJSZCK/o04LzztFRCk6LNubONZw05WxENySL4qdlIRz3EHYhg3TCJlX4Xp1nzCy4gbHjqbgoal0OmeUeG9qtD9lJocKCrSx46EOLVBqbOifzZBY8SxJuShkq+xB7THTL3CP2oOGaFN6Oz0OxMt0aPYmuslWU0x8GQ6zakxEzLhjxLGE3FcubO3usVdDc0qg6ryumX3b+gzItKWwBSnYl4fXogcR5PQA+i3FIZCiO0+uvmNK0vLW2vse8KY2oGd0FbV0xPLti7QcRfGyxoqnNszMzCsfYjYo6e9EIRMqn8szBCi8rDx9mlWsaxIkUac/dlJ/Kl+POJj2FMrVWKk1rl4zcgwovsb6tgY0+r8U8ZMYigWdAUWnZlYaL2QVhDRd4DYhIwi4gK12F+zTMs7q03hPBRg39u+RL1ElAR1TmERd4+Xh3xOuJSk92PC8RN6+mn98ng60Mjd/3eSaQ6RjyqSQrp6Fn9oxNalr2eMzktLN9djcaLf6o7lw+NH+/DEQsy44eg26H4n55HRELmOOFU+JOvArf/gOf4IwJv/koVREntanv7VgdUdNaE1tVDndl/RdNYtquA1zdeC4fNe6EYbimsq5BH2DG4IgybtMY4K7UOrncmrF7CUZmMkMDvpjkamqCS9jUVZG8FVsVdjhV7vlO74vSNQdLZl43++FS9CX3X15ClGyz8GGaqx4jDRgorkHfMac0GsO0SOf0k7cwRIxHkYpU7ahzEnA3YZo7ZUVPSiGQ3h89AvNSvGGuXWOpZCVae4t3iwJyl29Rst/lM0TGKFi15xlk49cPaeZcUJeGvMAjdlFNi0nKW8uBzXliZ3W9A3mlUV0p/r37e3T/BVTKc9Xo8mti+BAZuhpRI/KLdt07HDngvgNPd9RnYsm8arKBlVE+Y4IEbKRgNquGLDemvEjb6zthGMBEVvh1zChE1phGLEBZx5hjCV9YDTx0AJg1o5xuFD056iJ4U5pSSSi2cos8im5hlacM22Kx2GvR9W2LY4OLtZT6Dg4sRsCxTc7xbaXJ8d8xr6LekUptRgMmgFeiKUAmbxkjc5YhPKUj47GVo2lQ165SHUqcCJ1fDGkiGCuvapQUrita3uU13uIL6TuX2RD7sSxbE+XrbDq40VX2Q3/fShIzWWh4PjOxXjWZEY7Vhp8aXU5Bd8TQQZ5sl5X4UZ+Bm2XYMjav20b3mfNvWU/KBDaeb9zLz7JuW9qqZlZ5hakrcW3l5y2lMdtcfsAMwxoLet8Evo4ZBvAQQW6ZCEtCaRMnPLbzigKskbEob0G7SUwhevjgbj84YzCmGsvFuMzhQRd/AGfr3ZYJkXXvahzGz9vCKTGFiDP4ZzyPoiWr4WrNnhVRKtMavdzPwKKUp7eSKK/uhXpXDPFmtJJnk7eg+yWPINKt/ZqlSTlPYVrDhDP6zHnEq/wcP8l7PESMyW43bTXBlRq4np1+xr4a5vVYvcZe+5j+5VUqvb+FRq8MgIYwgi6P4cO8iEg3FkU5Lny4ucpuW5/n2Ma4Ze/vXcJA3zFWSzsPHSMvR/hZpOQDibMyTac2wjTsnm/cCcNQz1fwLaze/xirCuvywem8lrIOs/EAB1tctiwSeoRa224vV9duqlwxkWegDAdiy7O4+5K+zSalp9O0MG5LuYnR6d2Wqvx79C40jAF4gYqdZBQieCqgszs8ZegsybMgxmITyWJV7cIKL1R/cB8LqWKHatHThdiIcBRbzEUgV/dbZeEjPDTqM+erfJm/y7/Gr/DlfA8tvHLgdYejXKZ+m7KTcAqZ/U1O2OMx++ff8VJrOyEPF+QtdEPW2Ja+y0qMr0Vmo3CB+N6uwYipxpgJ0b/V11LHOQc2yh6XkWsTk+BO3XevWPdc86YLlH/cuBOG4bqPg4pK/cT5HlljndismS+YN043FmvRUH+Lr6VnKBBSezl4HYYdugQXFYN3d/3bQg6BlaXwatk5Kbq7MSMi6KzbAUsaBRfpGqI+pYyUk3imqHxb5zRhyjSxDQ/YL4A5GQzt/hJRifqMBikO8/vU3LY7NXXvInirjICeS/TGlskgHrHDezzkG7xR3GulRBU67PE4syGnTHKthI4r463QZJNn7LaHDNS2Lc6bSCDq8g8aHGOIZdSx9iHyGTS63ITlh/z7trBE79nBqziDIZmN7zFrXOo/tg/spmyVppZhiB3dIyD7PONOGIZ7ivGkvHubqESkPg+Ac2vcsRhYvLWoXPEIFO8Oka6BJqlGjFPd6b9ZzScsok0Ld54/7yEH2GSfYFJlm5wULr33cPCdVEpK8nbEcYiZDICLcHzpMwhojG386s4CjXUEj3mZk3aTrepJXtySfGtSmCBlhvXkFQyZscVxYUxH2aiU6VgtUoVeIy6y56DJHLUszQC0PGGbE+4nL8oa1K6n0u3NJPCiRS9PQZ5HV0ofzLuTxuTw7MpT3nXnZ5c30FLu5DWlclIUQ9FYTz9j+CCVpPhd4CSqSGcWe1fnIap1ommvNuDZg0E2rLGmJHqa+j1iRdocYrPk2zJiHzU+1jD0er194GcxB38FfGW1Wv2FXq/3APibWEX+I+APrVarZ71erwf8BeAngRnwx1er1a9+5JdcYDduh/LhxLMTvgCFdn+1vL6Rw42xdhdF10TWQp9wVqTj4g4PBm5FzUc7hsfVOmbs2+A4gX9W73M1ZjccMa6OfTAkVVbRZi1F7Qrqq1HR5hbyJVjX5NTgNsfcr05YZ5oNivMqvCBJbE5pNspzUGFSDNcEIA6DB9EFSaPoigxsJIr5Qn6W06f7HKRrepafZ/eZ6Fq93sQ8NaU2d2mtP0kMIxSCihMQsQYocQW9PxYiKa0Y52VkNMaiPYUj8fu6XkU8lo69Q67ofPZgkMPfyFuJxDxhC9Ew6F7H+SBtzRcZz+MxLIE/vVqtfrXX602Ar/Z6vf8D+OPA31utVn++1+v9DPAzwJ8Bfi8m0fo68GPAX0o/P3ys0plEK/xhLpjUdW45+y6dWYs0Cn9EElHZ/ansYRnThN3Mw83GL8twnHmx6PV3vVPNWeX62nuWeSHK5dNuvEiLVot/xoh3UkpvCqhuQ9/lgJSfg5B/gCglF3UN1JhmxOwGGLuVMgiajIt072RUI5Vd99LeX+djCHyMStE6ry2e5HPRe/W8PgzALesrtKsOGaVMDEsMr6opF7kox5Fg9GEgIbe8foveRyES281I6Hu69RERy6iAV8jaCZcbZBUweQamaencDjF15ZmJ2RqNQpPm4osahY+6FXmsVqv3gffT79Ner/d14FPATwE/nt72V4G/jxmGnwJ+drVarYBf6vV6m71e7+V0nNvHNQ7gdF0/naVSR8EorGrPSFTJPdai1AKNZB17XYj/RU65xUXvVvYmjhDBLo+1S+/hw+oZonGRW65dVIZBCyPiHxqRObibUjeR/BSzGxpWZXiRF7XfA6doi7WoRSvMQZTvCOq1VEzycWeFYV1gnAkBpLpPoGrJOh/TjdSswDjkUS1oMoFKIUpZZ1KCxPpOgWwHvMLkc2d84vFZWV/TDRE+LBXZ3dX9JjubMYqgdDNoeq8Wfhc8F54W53t6z2rLWjMes52fa0WbU5GxPiRm36KUoO69MCVppL7IeCGModfrPQS+CPwysBsW+3exUAPMaByEj30nvfbhhmHFTc66RCc0nxVG6Mam/hP9y2v6l5dObGrMxZXmQjcF2U1tuVdg6ce4YwkWBAfrbkN4Y+GRSbSXatMxNpYOAThhR55CVCOKn/XuVA4E6nx9cgiE0s7dz656RZu7dylMiYzDhnlOYUYg0u6BZ2R03d5DMsazjoxHSTq/R/PCK4vnoPsRG83o/kM/G7GYFo5YwwmbGVOZMeItvsAjHvLHvvC3rO+FjMGHhdlxFei9cS7GnR1KnccNbhdd6QKQWvx6b9R6DPN8urGWw4PYdySGaWKyyiDKKGijiPdJHtX/b1mJXq+3DvwPwH+4Wq0+MCjBxmq1WvV6vdWLfHGv1/tp4KcBXvkEftNldWXd687rIjg1eOVlDWtjON8hQ40LjPhiHy3b3ccdM4KQ4ISZ7u4bWXwxlu96JGUc7OCmFpBc9fh+gX/aheMi0LgNN1EGQBTvfn6vZx/UjTriIAIes1xeWtIRtIx1DHatTpJSJkQLWHRr7WSxsY3K0nUsSb/LW9nkBO+/0Q87n7vEXT3LWF35mJeZMmGfA1SgJUHeX935LD/6ytctFa70YgwhBADqkXU1EyocNLwN74rKSapmjB5D1+PVZyon5bW1tb2HlJqsmmwUvLhtke+gHdoB9nkwCmLRynOIbQHXU7j5vOO5DEOv11vDjMJfW61W/2N6+VAhQq/XexmnJv0GsB8+/un0WjFWq9VXgK8AfPm13qqwrBpdRdyotiOjEFy10fk182ZeuODaSeNijgg7+EKbhXi2xqXkzRLbQ4it5uPnI+koDhX2GOi3yG623ms6Tmf/X3tnF2NZdt313+lz6p6qW13T1V3larqdins8H7Y62JoYK1hxkgeQCDECg5BQQEoscAgPtvgQPDjOi6W8EEQcgYQiOSRSAgQrEkExyCgkgET4SMC2xh577LFnPJ103OMud83cdn3eW+fU4WHv/15rn3u7u1oZT1WLu6RW3b6f++yz99pr/dd/rcVlbmfWiioY6f36K3/Sj0FWxn706fusweDHbycQSpEIvU9KQViFzyLNq1SbddBHw+Xvyuf1OIXGoTHrGxUhkQ1gqI0pIW95WITInl9lxDrbXOUWAucUWr3JJptvv8mbtnfNZPdt5WDavfBrsOq9T3/9JpcVMEupaOguye/osdhQKeY3lDEeurhn5CuRuMxasLCv5l50ec2TTyxTXo/mYo07WejyJHKSqEQB/BLw5a7rPu5e+hTwAeAfx7+/6Z7/cFEUnySAjnfviy+AgY+zzLE++1GvKwPTWRfFHgzrQ6rFlrq0VGLvBmhBiersgUpvuinRZ9+ZZz5c56MbWs7eWuibbhX7Cfj0UtLwNr7Kyt4uk8WFRHFtKRnFU0Bxf29KvsDTVLQ8zQuEcni562MkorzLt5SmlIJcjVCW3/x/KTs99spPCxAsx8InqfmohCIR/Y5ZkLcglJ9syI391fzPytAMjMitxHMI7twonbYv8DT1e57jsa8f5eHBPo/Bu6zeDbhHSn8WkdDnfY0GicMijh4LyX2hDIDK1jW01TkOl4+ZLC4widEezdWYQWoG5HkvwforGZNnogpTAlPwE+rviCvxXuDHgOeKong2PvdRgkL49aIoPgj8AfDX4mufJoQqXySEK//mA3/hHBYD9hGJ/nN+tJ64oht5N7pwy0cM6rtZZSGvHDxVF5hSHlqMfTRXn+sXGOkv1umsS9+7wsKEY2rWuMObnt2FRag3QlPecT1Iadcq3qKx6fdGXGSdO9lrfly+67WPGHgRqzF8RjkG/QY8OQXbJ36Jau7dCLlofk7CQjaXSgs7fF8+x407ISEHdT0QDCQLR9fscSXd5xGrPF9eZ/Opm1x99dVQ6ciHD7V+ZDlAngzlp83XXuxHO7z0LY2oSA7OB8Xv19+YAZN6MlXmXYfBfuae2drUmlUpwJKWDW6zEkPSwoTCWlmdwq4eJCeJSvwPYue3GfJnZ7y/Az70UKM4R16BVzFlTwjxz6vVl26INH/8f3EIixXAMdTHUB1xFCd+sniOQW21GcATj2zi7xU/FxHJA45gJrKv5+DF++vyswdMeMvWtwJH/ioUlwIvo62r7LvD55tUQm6Nbb6f/5nGCWYZ2LkdTv++kgrXO0ifKd319K0FEZOEgMtaEJdC9R083jDArBFPne4rJmX/6bdk9QQMY5JOwlkiBaHFHyJQOcvS3KAq1eccXxowvBQA3uHeIYuiIe8R0ggWsYxdHy7XupwVqsS9B6Yp1vH5w2XYKfMMX12Hupx7DKvfzcxT4SUKgy9FduhVXkEuncd5gNfflXhDRKFKTxzxj6NiOFqGBcWdL5DX64OAO/hq0ZEhSQ0LEQ1uq+MATtY2ybpBnmMehhXwhT6fwXP9+36wjxb4BeoBR1GPq3YUxv4OUkJYqJadrz6h9yI8CXRU6ArM1LaS+wfp9PZ+fnBnSmcpzGZ76kp3nNug3AdfC1PumvxaKSSNUa6LIiBeOeoUW8LqXeRkM1ueTRq7z1spWWVE6IthWa8COWG6FaGU1OryiIvfHRiSVQuFsiN974g4NamuosDwxl4D8pRtyEPt2/H/G6TIkKQPZmvNKF3e05s1Pz7C1lDGDNJtnuBFVthhm3V3/0KeyVVusZZ6Dp5MzoZiiA1nkmhitcEr0okvEAeVd9N+23V/fXhI749SxnJvAr9CQ9T8dLImMdVMt8BKlisdu5xazD4SkROgJkl7N2XJ16+GCxeBqWptIy+RU5DD5VvERAst+NX5ltbG91GOAFItZWalj6z4helDX75kW5ifQbr+J3iJlpIbXEvX5xvY9gFO7z97haUoxYiL6Xf1W7oOv5GkJOQuePejxtijElVyCi5XwIZ2yhX2L4TvuHhhlO5JmuvWdn9Tlgz3DkOWZtw1hc/A7DMZbbLgEMpG7o5ZX14m7nnrZDa7hJ/ngqxEYFEVsb0FWdKwyU2u8grD8SOYK8E5cnKIyB5VCOmA1VqAYxY8zVRMtlfd52UtyA3xWAVERLhMJrA2sSjHfaAmZ9mZr2v+uxFzPEgH0MQTXSdkiFAM0jdI0ulYGl4gX7x/aqh4DFjGZj7e6fJzvqqTf58WWR9YtI7g5ZRZqmte5w4loZeGMizllijsOYt+rfH2wd4DhtFHHqQ5KLF6A36cYTyTZNXongzwNTKND6EsVeESQUm2iUh1UOYNfibUrJR5iG+yHOakbschlTvevgUdQj5cWQYG4+IF4LZVFNOa8+sOggIUiCtFJwlKzvIdRCsHOM8O62wn5a8iPAKUN7nJxVcP0z46qZwNxRAnsa3OMTg8pmotxgvBvJZU7XH+WWEOQprj9wHGX4cMvKzHE9o6xHkN1CqzsGa/kKme12L3fqJHhT0yb6ZrAB3l8+Li07lFIkpxOB11o/X7PvdCYBSETdNXZnIZfIUqW4TWPDY/YcL/d7HeDP46DXwNhCjVkxQ3w5Kyghsjc96Kq5ir4S0HIe6B4DXhgCWW2E/VrVXufhxHmidT+bCf1X3QmCU+s1RgaOgutp6Unyp16HumN2cEc8uSZnlMPZ4wODwO1mwNVQQnVVrwtXKV4fIB68u7vFaGvE8P0nqcSsl+++4g6buomjetGzFTZTmBsUzXuMPFdsRj20fQ8GjWfGyqgtHyY2ErNbssjKGtSVEFyeAw1q0Tkixfz1sQymmfxYsguhHNMdQ5UUnbWyaz32j9VGWPpPsYs7W4s6y2kOIzSUCePuMXhY+K+N/R4peJqI0g68RqNNRUWPUlj29YU137fr85PQAIJA5m65SSL7jqMxj1eZXV94vdJ4P5x3Va2Bbq1Rz3mxnPIqD56luaB31GG15z13dXLGwX7pkK46qGg5SBhRJzYpqYqklh1NBWLWVjLfDkiqgf6C4rbF0KvUnFPQi9RPJq2rqvUobedZP7KUUg4pJIavoeT0ob7h2GWhSR2NU9iorhiAVGrLLBbcBAOB9mHI73Q4s6yJWBWn+ruKaPNff++cnRCWobKGxeH8j0rwdTzXolyPzt14HwQJ3+CoBUZ6U+Qm9YxfTtyBOKcrKP4LhBPMEvczvbpEqY2mYdVZAOU2MbNixgS8BRvoNMe20OHxq0MnV1vGJLoupnnIJFKbyrIcXkr8+iIjYXUlpLHMSrNTcqKExT4NMFXMyNspEbwSyY9RWD5F6UqRdFn8MR5sCiUumUL2FQGv7hXQMgdi9fTweAIlIeSLT8kIvss5TmWvdJOTRWNMeyUzVfclWHe679wrdJ2Fshd/qEciYUwzGmZeXH9c36uhrTlMdGg5Yy6Ke1xpr7ok2nf3FP1WOriGO+bl6B2IsnmoD56kY/tWpKfUCy/3068cuoZAR6SnHoPbpm/d/7zArneSUS2G3byYQX80/RAs/yzDkbptR8zcAG69epyk1XuJUUoheZshqrV6ZasB4I9XNdtS11E0qcl2WTTvRB9Lct+hIiDtusJxAzzM8gWUQex/DiXSH165LJ7rMzteb2WcoqXSkRTwlgcv08V8MTvvxvjljlJpvsRgxG60lYCpDWkVxDbyn5+pYXI27g8SR/qA0Irs2iL0TzGMGC9iD9CeVMKIaCLt1sHwHIKMRlSdUeWUUd32tCbsXV+E+JKrEupCyJolETmnMBZ6jarK2aQl8SH1HQ4pQ/7MWbxJ4tqRs9ZjosCCSXo3SLt3GPZcJbDoFhF8qN8NmJ4fcnadH2FywYgOlBPJ+ddxA34PNcT6fXk7yU5XHIElA2JlhPDVkR/TyPOimkSVrQZTMNmsqU1++oGO42a9zgGk/zQqZIIXf7vLui3w7f3STFpuvUaTxhwBYb6R5Jeaiz1Soj1tjOFOoYO1C0bn226z5DbrLJNusp4qS1o1wTYUVWcWk/WQRS7GGOd90BknNSsvlrAt6x4C1rCBG/x6befl85E4oBiCeEWQlKFV2NYZbU1HaPYCJBuHDfyFNde/qciMhjD2naEWOIsrQbcAtFPYbNIfvLi6kBTluFwpsy/XTzfVUdbxX4jReGaK5D6zaij2xoYfmTV6e3L+cmsRM5b+6qxzr1+0VkffVoXUNfyYVScBuoga96dO6wwlqKKoSSc+pLoY3cRHO5b9JL4ZXkvTnH9YB6PKFs2tQtTOPyohDRqLHHAAAgAElEQVTxNW6kzSjFKBMdcpakV7beipHiMVZocCl80V8wi21MHRvxhm37ZOQLLGEl8/Ub/uDYYoMRq9SM8d3FFK700Rg1MQ5raZIwj5pxSn4S3CilqP+vsZ3uNcuwcvcwKAeB8TG/6HBWVbT7yJlQDB1FZjJ781RRikLRh34EwhfdvABcNc7Dwl4gRVVRc4ZIxznK5piygXp8nBJX2iooi7KB4V4IUNdj2F8mmrk5dKZNINPemtzW2SKRP+gBQQgJT8FUthOgz7ZUZMI/70lVXun0cxH0Hp/wFKyInJrsT3nVh9x1ZrwAzWFMMVO23hrbDNlPFo1kFqFK5rMk4A2xca+jrPtN7M10fXaNO73vabLfzLGHnFot8Qo43IfgaskVNN6GNSY6YBj7dl7ngCU22OIaL6fIQb+AimjKvvOVB19FStN99iXvAFTZW7RxYUC6Zq3BAZN0oKJckQsVdTtmsthSNsdxDatu5C4nlTOhGI4pkob22r8eT3KlIDdCUmPZlZHIJOAymKkW+pSUzXGMdkC1d5wUTCrVXQVTrIuuhyIYOr39Zg0+9DiZfxp/APXWkokoVEIbyOoYWm4B2EbKk76qqcWcrsVtBMt1sP4X+g4jJOWnoQfMzKWQ0puw5PAKA8wmibPQb+Jj/vp0qrYUkG9F11JCOU41O73bIzP7NhsJEPVWU+WuXe/31pN3u6SchEX0s22DxTDOlHqeBh7maYsNdjifUrwFluv3+lWU5I4IePXKy98nU/rmEorKbUyQQTxoLEHKE9N0bQNCg5m6nKR0cHPRHznF4ENJFrOFmIo6q8x3P6W1JIQgG6Z8177FsFPHhVbt8NjhkUU5IDxuoKjs+3SC6Kb7Ran4vBbVkIOs+pBnHXoE3YvvvFTF7+qDWbIO/BxJzFpoUJ6BdxP6MXkpBH/Sj2IDXL1f5eRCHciwaVbiwmopU8NcH8aEQLjpZ7N6xTbA2JWeAerRf312m/VU4qzvW+fKqMQYCObOyYILz9lmHbi51vzPqqUoSrJ+S8Sybda5xdXE7AzvN6wALOrhO597Jqa3jDwhbpt1BoQeH8KP5C5V8TARB2Xs3MxgediM92t+fMcKtXwn5ZhzafKtDkLL4NCRmZT9VhE2r5iNyqWIlkTVwgRiXDl0+12QUol+lszxlbsuFVfz5krLdcuwc2ExA0X9iS514U9z36WpyXLmbSGIzisGJFglaSmPmklaVBa9CLddp65ueGBUGv3Yiz9l/QnVvx4l7TSUKYd/PTa8vRXbLStMtxNJTX4MUkraeKL0grEzwzX7grAN9TgCeEpyiyb8FhupCIsUlTaCtxL8GHzNCp38/bCjgL/Q51MY0SQrnSZ3oO/GSdnss5Qo4JvczNZueF+Z3AnhOp6h6u+J/i/X43muU9FyjRtscjMBvD5d3tibVkNU9wdIDF6vpPsh8gfJmVAMikp4tLtux7RVwAmyzSusLFoIiI6qrDiC+T84PKatAk5ATHBZGMPC8jHD6jC4J30rRBmakUrdlLaZtCDCTxtF2pNqGreAwuYNhUi8362/nlnpTxMPEkr8wvSnqmLiK+yk98tKWWI/uirheZV0l/RNbW0iVYlejz0fVQvgvPsN/zkbo0UcFO7TgjXQM4BwIgtJKZTNMVXVUpXh2tRvYsTFDMSFPE/Ag4lAAlZ9VqLfhFJmJW0Kh4IxR31+Qr93qJ/3EMo14Fk1NWX0v8JVnuOd7HA+WZXTlpMxG2WBfZW38c1vXOVNb95in6U099PWUlDivm+l5kKHhvAH1dugB+g+SM6EYhDiD0aeacqSlfGRpbpqw3o2Y+Wec5GIEI6MORXKpVBvwgYKWRt9JSqlkzIx7aV7hcfCa6rWaxtgFt1Z1yb3wl+/vsebrXpull8qRarwpDAA4QWK8csFUWqvfkMn8AHDtIFL2nQ6ycc3l8LQcZGNvP3hG8Z4jod8Y12DckCGHCQQWPMs8/s2l5N/7WtjyMUcY7iFpH/iy0T39GNfGm8Q/29Zqv3+pj7fxZv9dbo/N9lkwIRNbqJwbEvLyzzOSzyRMANhFp7D4RsKy+XZGa9wYX0Uy+mbMtHv+SY7SxEw9XkzXoH4YsP+t04qZ0IxQJfMUZ2Yw3bf+gxKAcxq6OGKah4tWziyULkuD1zqu6RsvILBvccX/ozSZ/WBuA1LjLA0a6spME5muRamtzT8jfIncB+Q8kpBIkgM8jwEvT+5Ykzuq4AEpPrvBePpe9KQz9mw68mToeQeBUzDwDgfkbnGDS6zFSo4E2ofSjEE9F9Koc6UYxifcBG5Bwam9qs/aaOHz1oNROWA2Nybq+Bp7d7SyMlMvjRfyTZriSIuxTJiNdW1BHPFPGEpXI/YjAdsA6v1KM1/YOHWMdpVp9/TPdAh1F9HUmAbrqqVXNKHkTOhGEqOHYDWpgntqlghRtGHHq25aCwDs2oDF0EhmgRY7pK7DL4ZCORKoZ8ySx4S64NH2lz+RMyLoJR4ss39ACB/uobh9DMhm/TXc+L9ieDHYaZrw4Ebm81wCK8FEzrn74ffsc/oVFUR2T7LUPTqcB3nU0UnX+FpxCrv4DnrcRHZqk2MIt0p1yIhaI1QtcgiKR5DyN2qJlMIvsp0P4zqLbf+nOZU9txa6IdfdW/C71bc5nK2LibRUltnmxGr3OAaO6xkzXglFVZaT2QmDzT28Svvvs1aS0aUOkjvl5K6GElWJ5UzoRgKjjMUFUJkYX/5XAovSvbrIXUbF+ThUXrN90ZOkYxdjDatyIXm09drIACNxZ77kjZEJCRN3ORe5LP7MKEWSd90Ex6hkJKnI4fQ1Pk4LEOevYiaq5PDMuuapBT8iT9mkGUo+jH4U6TEWvDNWmxqOyclZNZBk12jNuhB9M1VGPYOazzHO9liIymqd/NZ1te+SFMGZb5ThgpLd1hLtQi8v68EqX5417suPn/FU571PgMfc4BW1pXuse7nLJ+8b9Fp4wkgDN2zBpH41YaqUdSx5NpuvDdGq5ZVN2KVXbeWbE0ZthH4C3VSYnVvnflDQXiS8mVa8lyXk8iZUAznCJXn/Q0S8QWHVqflGGnMzbJtHjEVa9+CrMXcCZWHA0uqcuSowgOPTmEITVY6K9hp3K85KPXk6xkAmemq08DqOAQWxA4r2ekk4k3fpbDT2tdzjK9FhVk2LWU9fWIKc5CSKWkZUaE2dF70nSoSW2cLtknXaWSivOSb+j3cZJMtNmgoeZlrLLHPk7zEy5f+RJrPEatscTlFRjwXwJv1fVDWbzB7v7c7q3Sdcod0OhuQau/vX7++3wBQA6IN4xiwzRpXuZXcgivc4oBholNbUVZjPIoSLddDOIZ3y4QVGOnKDpIxFnrV2PRZn92rdRju73/lpHImFANYSwpNyWvlahaW02tSED405unLTXkccs89uOixhIhJdMs58WlBroSUiejT2elUpdNaIo3vAR9g6mTqU4d1alv59Sor3zXLcvBl2TUmQ6HHKS09WDr71NWYQaJzT9LCWmMb37xGIKOP+/sxh98epO9Ywkwpf1r3G6/KtN/kZhx7CAve4FrKYhR6HpTCUraolT2Zu21hMwk8DHM9dmPRiTrGrCtTpv36lmDmvyUy5REXfbcK1pgysrnRRpcigG3ex6e5zQY3eDyBqQMmbLOWwEMBiN66Uei5378jjyQtpQNG90rKxZiUTUbHfhg5E4pBLC8fsgvatJ66UZoMJbEcMGRQTiij3T9ZbKnaSHX2tfeUVFWHTsIyY4WMg7Ed9Vj1F7WZgzmd5wLsx1JpCh22lKm2QP9myMQX4eggcQjz0z1sTqNL6zsVffBt46QY02+MwzUsjI/pqmMG9RH14pj9Mvj6FxmlxbaElbQPPTJtc+dlxXJmprduvPmu9+hUFaV6hd10Wg6Y8Bqr3Ikt2DxOYGvhPN5k98rHlMQgneizAEPNt35Dp7Z4H/otu4/DzGJrZv62WYx2PeF+yeLyIK0aBa9xJ1kHbVTMwmGMqxIwKVmhGmMfyNQ1ziK6SXyYVu7mI0twajItmIfoINxEq8ITiEG60Z711lYt4/qYRSkCcR+ctaAKO34EEIlQEeDcXz6XGHlWhNXqCMhKubx3l+Lwm3SLob0YqJtQmzaLP3lrJrEUmXXl9ixFfxOlkPR/AX9eKaiKUNVGjMSVQi8qWFyGsjmiXNxJXcE9WCYiladCh/kX/98AS+Nx+M1jS0inn64lEJnyRK+Kli0uZ/dYj31D35zWbY13xtmcTlPFpcilQryFIOq6cT7ysnh9yxTGWRjUuzf+98Rx0RoBq7HgafB6v5SOnyewilaGWYwYM0j1HLxIKQtDMtdwNYGd6kIGg6l5epCcCcXQUWSJJNLV3kzyN7NOpA27maqt2JYlLO5TNkfBpVCGWQxPHtXW9MOftEBWYh7sRPH+ter9LaomxKvA3VD6/bHqCC7A3tUW6rw8VxNNPBFwZDrvcD5LDBowjTgbeNgk0HE43mdweBwIYHr7HtOt3MehfR8cs1LvsAOsQDpxlSbuefkyqcU6bKncplFzWgO/ZL7np7q6Yhn9uG95eMUS3DCLlnh/2pvSdmm5K6Cx9MFfKVAD8qoMuJOy826qN8lljWg8cvG8Ulpx4/HUat1rPx5TOv0ISZvmS6S11WjdHbCfwES5cTo0vOun71eEQ3P2sKHK8P1nQDSJWlQ+Th8mdJA0vv9Mv2qOtGhblowvxPTgWNFGIc22IqZRN+Fv2VDF8lw+AqJSXYYex14Eqho1JjTlU+TjkFAcpgop4m01drUeAuFIC2wnhvSkFPwpKMtHC0mmr7pzr7DLyt5uGIdKlguG6HdydmtmoYQVDkP2HXk9QV/NSQtZ45JfLWyjr7Q0577UnCwQzZ2UgN7rwcXw+wEUDVhNSEyb9BRNzsdoUoSnDz7q3T6k65WDwq+2dnLeiJRanmtiv90HRkPOx77DDGo3J3nVLYGVEj+Xwqc8w1SUalMakxRW9S5OGJdZg1e4Ey3TlbSOfNvGk8iZUAzHnGObtcwK8KQYhV0kCtd4lDcpBYTaRnrockVb7cdNHz7vFwZE07Uq2a+V8GLTUhKqAi/tHuXVo7Yxi0EK+TJwyXj/w71Q28GfHiGJ+WKmFHx2nPIIdHLYyRdPkr1dq+UnVidYFMYTu3wOfh0Kgg7H++zUK8n6acoyerrBZBYN9xZXklmqzFDfAVtAqFfoikp4M1knGJC9V4y9sLBXpu6zryERvicHfsUy9G6R3AR/+uqxZBBBa69sPH/D09ohLzmn35WC0T1Vlebw/WN8wt0s8QqzwtyPpaj8vXXhxyflINDac2iuuoI9mldfWKcfdXmQnAnFoNMClHpqVkO/GpBOJ5lSfgGWvfcBqMfB4p5hB1Xb0pbOrC1l+ubNYlJDlNiUJCmELYJC2Ma6KEdW5uEF2Fk+HxiRy6u8wtV0w3ZY4bVY4UCmuzaZ3ARvCuumiqZ8sR0FoNT31PBsTblOCs/KjcKNk7B4V+4exvTyI8Y1rDSHIRV9OWQPbrPOTTaTpebvgZKowHzpMYOUCZnmFctv8Cez7pmBb3ay6/PT4dMmo/nq/oqi7ecLjI2puRRG0k8LV36I/z0pJJnulqQU6foENqXwgMDXCDiAj6SICm3FfWwjlzSJ26DxyALS3Eq5qPmvLDh9l+7DNW7wtljZyrcL9FbTI6kYjjmXstm0kX1DVJjOSPN0WW9mengOQnGPt9/6AxhD8RgMymMOztui0qLxSLOXejwJoORd4DZBKfwh8Ep8DoJSWANqGC1f4CabvMDbuME1xgy4zFbsZj1JC9TAtLAAlWwlyyBkTB6keghDDoLV4pPKdNlSDiJy+eI1YZKSRVE2oRPXwrfDcwWxnd8hLADlUy03uMaLPJEl4fh7IRTfL3wRmvy90sbxyVSKOkiZ+AVrdSrySt3+t72F4LGXPFHNIgPeTcinxH5XEYkD9z5fts3XyVS5OUtOCorvqzyNbzfvazhKvKLyitY/9ia/kJjw2DXKifUjVmm5yi02uZnGaNBmkynHnAL4YDkTimGBo8Q1v8wWa9FH8uLj1l7j9zf0iFXusJZ83id5EZY/F1qQ5zhNZrJ5QEyvKWMQFdTcIyiGm8DXCcrhEHgceDt885kL/C4/yG0us81asgRUxU+S06vzPpJSBr68vMZTBfBaX5Jng/rkstq9z1O9GwI2sXdsBXTreG3bwFrg9T/PdbZZR0VoPE/AR0n6TW39NfqsQ4+X9IvD+AxGH7KVK+jzQPScAYl2usrC9ElXs2jNfk79Y2EgihAYKBqiKn7cciU85rXPkBd4mlVGrHMnwwCm07cD1tBPBJOr0Ld+Pb7QULJKG6nlTaJRK4Ts359bTI8gj6Gk5R08h8pkg4Ffswga/ZyCA4aJ/qtNqVNtxCpf2XhLIJ60+0YCwjINtYDyeHrw+etqDByHU/dV4BZBKXwFnv8aXH8ceCt86yfO8/P8A7ZZYy3WMQBQ1t0knjzCE2bFlsMNPk9Jww7iaSyhakL1hTGPNa63hnclvJXgeyyKCi43Y1aqefz/q39ykd/lBzPGnoqFaNOFUmuGEwTFcD4lPoFVsfInqt8Y3go0sNNcBD0v69Gfgvqcxw78a7amPI5koULPlvSZlPY5UdfzPIXw+Sq97tsa6u8BIePSYyEey/KRFm/5+GvwBWw8y7SKatDPX7iyeuZjf72PbFTiHC2b3ORye5umNLxBWhx8UlCbnUyyFqr4vMJwntRym8vhzaXRqPs+V+sWiLcamrKE6sgiAHIfDuH6nwH+Jfzrx/8qv8bfYIeVVFxD3+HDYXdiRSKwE1UAnhacNpji71ZfoKYsW17bCJv10tahnfrKAfFsT1eYJolPJAO7+y1wFT7NX+B5rjNgklKtBYpqPnw16TF1AlGt+tM0MzR8NvjrYhD6exrmyWdDWgahwLww3Db7q9/pb+Dp+xpMKM9DyRme+rxVqprll7fxHskSMmq0pau37vpmuaYak92SsHpVJUuHh2FstrGF9+igbAil3mR9eYU3a988jJwJxdDFCk5Lu0e01RHt8m6iCXvNDgbI+NNC6azK3FNcXZq5H4ryvpu9VqXvD3+1YGI+RE0owf3dhErUfwk+/xNP8bN8hOe5Ts041QIUSCSAaMQqVlXZCqr634fpBi5t7+YqfDWhpt0YsXp3l7YidexKGISshZYQTlVbd0lUJt2iFQp9lmeSKezj4DJtPb9B16XHFkGw3pDCS3ynJCleAWkS//m2jfejKWMa8k6mLLxbYIzUYIHJJekrGL1XkQoVsNE/hRgbp9SE+QA9C8GUQZV9t0VUpCjFLJX4xDm/5nTtAnkVPvaKwGe/6rt1H3Qg+uQqn5bdV4QnkTOiGApKWhZeBS7Nzou/F/MMSBe/ioWYluKilXvS9m6QXInwfhUBMe1qN60K5bgXCQrhMrAMe8+c43mu8zZe4DK3M99bohvnE2a8uexDbgKL5BpJdOKGxrFhMQRLYon2gjWKGTBh9cKI+nwgYJVN4G00VwKh67HtI7N2olIY17BTr3CTTXZY4XFupEVqCVpKmKrJ/eS8PqM2nfzbgfsX3h9Cnp7KO+a8cxLc/Wmm769P1tJJ2idOSeH409LWR1hVchO02YUVCMXX+yUWkrUcF7kpXrmsOCvJ339/33Wv/Ph0gPmq3JrDNe5wma2kXLWWjI5epfUuRa3XBeSLVu/xkJPImVAM5+LGJhZaucVVbnAtnT6NuwXGPhu4CfKmVJMmo4rAmWnO3N+SJvXa2KO7EE3RxXMs1MfIIznchJv1ZrQQDthiI5UKl1LT6bPKaylDcSm6DgLUdD3hNLJbIfNdyktU5YOInvuTQwk8iRpeTiiX8yrNJQ3lhS2GVejNkZTC8vnEW5AC9dECnfAKrfYVsg9H6kTK+2KO03NhHG3215+YFS2TNrhLZRlOyNBbYTL1OUnInsxzAaaTpyxikSdP5T0qZUVqk+oeiGCk69X36PdtzRmg7KsqScRXCa9bMpbW8Ura9jvJzbzKK1zmNiUt27HquNaNMlEV+jb+yCBZHQe0KaLlcZSTyBlRDF1QAq9CXcGt5atZrQPwHPMqaWKl1aqIaXhflamRfl47hPRksR/1vN4j/xEMoATgQuBB7FxY4FZ5NTYTUS/Bg/T7Or0Vl/f+qI8rh+/PcwW0KCWKivgFrJCnypt5XMSiNmZp6frH9YCyOaQsg/LdX15MroFf1BqXZlBEnuBbT6eB+//bHE4Xpm3cmKTIdY1JwZRmxqufxTRt2FKnh07R9pUWSSlZURmPfcj9UMaoXCZPkJISGOOUF0wpzllRD79JBdpq/J6rURLwtSd5Kbsmgb9SKL4qtywE/88TnqTUDG9rUb2Pk8oDFUNRFJvArxLOyw74RNd1/6woio8Bfxv4VnzrR7uu+3T8zE8BHyR4uX+367rfut9vNFRcfHUH/hCKZRhe2mc/kjT8hs8XlhXAhPubtqAEnyWGHLBfDlEiVFsKIR5QRcuhv3FH9So7G3kLPZXcEhCq9nZ2ygTFYMVSTJPPcoXCX18+zYermuRzmjuSJxnpGsGKgfhAWdW2LO6Fvhv7y4tpEen3fL0CnyylhakF6wll/Q0hRalTz9+DPmJu19qkTQnGXpSVJfHKdhAVbj8pKt+A9n0+8cxUiyknW4d2TWY52vUdJOZmld1DI2n5StVBecgt9LUn9d1D9nmaF/jT/J/4/UtxzAfxPgwThuArYikSNOIich90hfqOURzfa6zyTp5jk5s8jJzEYmiAf9h13eeKolgBPlsUxW/H136+67p/6t9cFMV14EeB7yF45b9TFMXTXdfdExY9xzHjGhYfh29tBs0m88dnvLVYeKtOG8UXovChrCbDFrRFslOsLDFf2pJuPF7gi65oMSv1+QbXmDhF4okwFaNsU3m8xCa2TBuuT8UNjxtkxSg/35vHPjrjKyv5UmsytVfuhjBnW53LFrY2r/z/Np6SOz3UXsSrwOA8j+8LHqo8kQBW/znNoa7XJ0/1FaGANq9YpPB1fdroUkBeiZZYBMVbgR4YbMlzbLTGBJzq/VV2H2ZzALxl5V0OK5tv61dZjkaBnnCZ27yT51jnDre5nEU8ZBnIjQDjjYgjIw6Iz+QUUXCHFba311lbu8MP81u8m8/OvIZ7yQMVQ9d1rxCoPHRdt1MUxZeBN9/nI+8HPtl13Rh4uSiKF4HvA/73vT6wwBE7y+fZfmrALa6mCS+dGSa/DMhOrv4JrMe+S4+x18IkhpPbxGfnebO6TRvXQCmlOseBJBzEc/9V/ERprz5HwG+Kvj+rz2tTW/KPnXHeCpJv7/GQkGiV5/Sn0KZyOMb7lLWZ6BPqxLgT3XfAOClbkWhGXEyL3jMFdRKq8G0fLwkKZzo8HMzdsEn8BvZFU31mq0folxJt7CD7Tphd6akPGEqpeh6Ml9w18ITuME4LuVbJdfI8Bc++VUhX9PYxA1bY5Qq3aChTON0TwoTpeCvhIKnmYfp+WQvexRhTs3P3PEeHA0Z3V7lx4XEe5wYPIw+FMRRFcQ34XuD3gfcCHy6K4seBzxCsitcISuP33Mf+iBmKpCiKnwR+EuC7vhsmnE8VgpXb7je8fD2P2FuoyjS3HvvekUodBh+P9w10rav2sI0bxFkT4T3B9VjaPWJhDHsXziUlImRbro1Ody0S8QFkivrxQw48yurwCL9OyH6VYcgJNPInV8Y7DPeCFVaPCcQsSJmf45rQZQsPpOVdvD2YKJdJQJfemzAbh+OE+W0MDO2NUSemV4K6XitbY+3clCujx+qM1e+n4efUJ1ppLD5hym8675op76LvTuTfPciUgpR2XxkZ5rSUKONrsVeHXCU/z94t0yGokn9iiHqmqbcqdE2SIfu050vapmJ43jqjPYycWDEURXEe+HfA3++67ttFUfwC8DME3OFngJ8D/tZJv6/ruk8AnwD4U99bdCNWo4mUdxDup8X2GWP+tT4A1HdDvNkn0Xu0WJUq7UVWQuq4XYa0bJmi3sJRdSUbh08e2km+/ay6hgoJalwDclBPLoJf6J5hKFLX8t3jQH9W6XzHhCya2AC8mtDWnsk3ia6PYDoz/xVK1VhBfREspKn08HtFEfrPrfJaPEXN95bi0/eAWWT2PYabaD3oO8NBkIOiut+ad1HlpRzMQsrBV8M+zIUMnzmIqjrMSF8ZiPbt6d9+HeoQ8WMJr9ka8nUxPOAopWCKwRSIrk8KkxL2q6DARFl/GDmRYiiKYoGgFP5N13W/AdB13W33+i8C/zH+9xvApvv4d8Xn7vMDpIuG6UxJPdYi9eJNc8+N12mkm+SBur4vCCQrpQ+ohd9vQq+KyCzc2zjHi+WT3GYjjU8Ly5u7XpRwJO1vZcmszqKnHfvfFuHI5x94sXyB8G2pDbqqXvs8iqgchhwD+7ELsi3a0CfjYlpIAlD1/V4JeQBSY/MEId0fid/sJcMEBmr+ZAn48HK/FZ7Ny4x7hIUmNUc+2a7fpaoPXHtnT25c+GyV/bZqfF6MOJK/Pq07WYdyEyvaKUtIc67xKxStqlFSCr52h1KufbjzgCE747B3lur9dL2Hu0MO/+gSz37PM3w//4uHkZNEJQrgl4Avd133cff8lYg/APwV4Ivx8aeAXyuK4uME8PEpiLDrvaSz9OhZCSf9jQB5jL7/uqeYDhOzvx9OqqY+J0XifXyBSqGAyzEsw436WuLE+5st4C+cfHbq6US2ugvjDEHuj8UDZ8OYVJXcilhDoaVKWZhT81HBgrF88ySrllTeTqJFH9yFi9ziChNq1riTagToHxiQpkXvTXw/17omT2rSdYisFZ439qpZHxPWucOACTfZjCDb0M1zuC+KNHmuhDaeb9Ird8i2fpnda6/QpIy99eNT5P2GVp0KW2cByxAeI6Ug4FTj04GlUHA/spLhBTEXRbU8ZKmlSMTeErujoBhW3zxiB4Ki+OYifAVG11ZZX77Dw8hJLIb3AsBdnfoAAATrSURBVD8GPFcUxbPxuY8Cf70oimcIrsQN4O8AdF33paIofh14nrAkP3S/iATAcVnQEqrXeLqtQjy6OTKhc154bpJ760JmpV7z9FkPBuq7PeFGiw5ivkMN7VooqvpaLMvmT8sKQ8z94q/a4J5MMJB0RG5+6nktPm0ygVUys+XqVG00pUtL79VmHXLASnMY8icWSYoA5VToxrcBcanbcSrHD3CLK7zCVa5wK0U0RKYBMRat8KlXgN4vN0vA/HQrbltFRWkbTxaRn8dneJYtNniRJxLY6XNjBCJrrXiF7AvyioMp83tCjchCssC0UU0Z76Y5B6sPUTNOZfE32GI9Kk+AdcLmu8kmL/EEANci6Cfl4bkguueAUyvh6rwbIWznNhscjIcs1QE3m4yjuxe1/IX1UTrc2qYMFcWehL+4/B94B8/xMFJ0Xffgd32HpSiKbxEM34dTa6cj6zwa44RHZ6zzcb7+Mmusb+m67k0n+fCZUAwARVF8puu6d5/2OB4kj8o44dEZ63ycr7/8ccd67sFvmctc5vL/m8wVw1zmMpcpOUuK4ROnPYATyqMyTnh0xjof5+svf6yxnhmMYS5zmcvZkbNkMcxlLnM5I3LqiqEoij9fFMULRVG8WBTFR057PH0piuJGURTPFUXxbFEUn4nPXSqK4reLovha/HvxFMb1y0VRbBVF8UX33MxxFUH+eZzjLxRF8a4zMNaPFUXxjTivzxZF8T732k/Fsb5QFMUPv4Hj3CyK4r8VRfF8URRfKori78Xnz9S83mecr9+cdl13av8IlJuXgLcS6qh/Hrh+mmOaMcYbwHrvuX8CfCQ+/gjws6cwrh8C3gV88UHjAt4H/CdCG4n3AL9/Bsb6MeAfzXjv9bgOakJh/peA8g0a5xXgXfHxCvDVOJ4zNa/3GefrNqenbTF8H/Bi13Vf77puAnySkLZ91uX9wK/Ex78C/OU3egBd1/13LG9Scq9xvR/41S7I7wGrRVFceWNGes+x3ktS2n7XdS8DStv/jkvXda90Xfe5+HgHUImBMzWv9xnnveSh5/S0FcObISstMzNF+5SlA/5zURSfjaniAJc7yxP5Jqka5KnLvcZ1Vuf5w9EE/2Xnjp2JsfZKDJzZee2NE16nOT1txfAoyA90Xfcu4EeADxVF8UP+xS7YamcutHNWx+XkF4AngGcIhYB+7nSHY9IvMeBfO0vzOmOcr9ucnrZiePgU7TdYuq77Rvy7Bfx7ggl2WyZj/Lt1eiPM5F7jOnPz3HXd7a7r2q7rjoFfxEzbUx3rrBIDnMF5vVcphNdrTk9bMfxf4KmiKB4vimJAqBX5qVMeU5KiKJZjnUuKolgG/hwhvfxTwAfi2z4A/ObpjHBK7jWuTwE/HlH09wB3nWl8KtLzxftp+z9aFEVdFMXjnCRt//Ub08wSA5yxeb3XOF/XOX0jUNQHIKzvI6CqLwE/fdrj6Y3trQQ09/PAlzQ+Qm/r/wJ8Dfgd4NIpjO3fEszFI4LP+MF7jYuAmv+LOMfPAe8+A2P9V3EsX4gL94p7/0/Hsb4A/MgbOM4fILgJXwCejf/ed9bm9T7jfN3mdM58nMtc5jIlp+1KzGUuczmDMlcMc5nLXKZkrhjmMpe5TMlcMcxlLnOZkrlimMtc5jIlc8Uwl7nMZUrmimEuc5nLlMwVw1zmMpcp+X/+FDS+/FiFrAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bxCeZRvzLZVH"
      },
      "source": [
        "Xs = {\n",
        "'x0' : [],\n",
        "'x1' : [],\n",
        "'x2' : [],\n",
        "'x3' : [],\n",
        "'x4' : [],\n",
        "'x5' : [],\n",
        "'x6' : [],\n",
        "'x7' : [],\n",
        "'x8' : [],\n",
        "'x9' : [],\n",
        "'x10' : []\n",
        "}\n"
      ],
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 618
        },
        "id": "m4aZr9wSGZKv",
        "outputId": "9047bea0-9545-4495-a4d9-614734d66099"
      },
      "source": [
        "Y_t  = [] \n",
        "pr_t = []\n",
        "\n",
        "for _ in range(500):\n",
        "    \n",
        "    x, y = nodp_test(1)\n",
        "    b = 'x{}'.format(np.argmax(y))\n",
        "\n",
        "    pred = []\n",
        "    \n",
        "    for i in range(6):\n",
        "        pred.append(conv.predict(x[:,i,:,:,:]))\n",
        "#     print(np.array(pred).shape)  \n",
        "\n",
        "    preds = np.prod(np.vstack(pred), axis=0)\n",
        "    preds = preds/(sum(preds)+1e-30)\n",
        "#     print(preds)    \n",
        "\n",
        "    (Xs[b]).append( float(np.dot(preds , gmus_)) ) \n",
        "# Xs\n",
        "        \n",
        "    Y_t.append(np.argmax(y))\n",
        "    pr_t.append(np.argmax(preds))\n",
        "\n",
        "\n",
        "conf_plotter(Y_t , pr_t , labels , normalize=1, cmap = plt.cm.YlGn ,\n",
        "             title ='' , save=1 , name='Final CM_s4ii.png')"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f4e7ada77d0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 648x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eWqyW4SvO0qt",
        "outputId": "9f9b21a5-8bab-418a-af68-348874b8926f"
      },
      "source": [
        "print(len(Xs['x0']), len(Xs['x1']),len(Xs['x2']),len(Xs['x3']),len(Xs['x4']))"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "42 48 48 48 47\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pgcRefs3KgPv",
        "outputId": "39620f97-d736-49b5-f1db-0fd564acfc4c"
      },
      "source": [
        "from scipy.stats import ttest_ind\n",
        "\n",
        "dist_0 = Xs['x0']\n",
        "pv_d = [1]\n",
        "\n",
        "for i in range(1,n_class):\n",
        "    dist = Xs['x{}'.format(i)]\n",
        "    tt , pval = ttest_ind(dist , dist_0 )\n",
        "    pv_d.append(pval)\n",
        "    \n",
        "    if pval<= 0.05:\n",
        "        print('{:3.2e}'.format(gmus_[i]) , ':' , pval)\n",
        "print('pv_det:' , pv_d)\n",
        "\n",
        "\n",
        "from scipy.interpolate import interp1d\n",
        "\n",
        "f = interp1d(pv_d[:7],gmus_[:7], kind='linear')\n",
        "pv_det = f(0.05)\n",
        "print('pv_det:' , pv_det)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "8.60e-08 : 0.04193612513354885\n",
            "1.85e-07 : 1.3797141112787132e-20\n",
            "3.99e-07 : 5.5326974268696555e-111\n",
            "8.60e-07 : 6.788700941330878e-136\n",
            "1.85e-06 : 8.072678588696734e-195\n",
            "3.99e-06 : 4.726874967154256e-205\n",
            "8.60e-06 : 2.904704089021984e-233\n",
            "1.85e-05 : 8.191910609947418e-278\n",
            "pv_det: [1, 0.844323147360659, 0.6931391696769978, 0.04193612513354885, 1.3797141112787132e-20, 5.5326974268696555e-111, 6.788700941330878e-136, 8.072678588696734e-195, 4.726874967154256e-205, 2.904704089021984e-233, 8.191910609947418e-278]\n",
            "pv_det: 8.538500552907196e-08\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "cRcbJv-7H2zF",
        "outputId": "0c10f219-a0c2-4ba9-cd98-9b5ff8f2884b"
      },
      "source": [
        "plt.axes(xscale='log' , yscale='log')\n",
        "plt.xlabel('$G\\mu$' ,   fontsize=15)\n",
        "plt.ylabel('$\\it{P}$-value' , fontsize=15)\n",
        "plt.plot(gmus_[2:] , pv_d[2:] , 'bo' , gmus_[2:] , pv_d[2:] , 'g--')\n",
        "plt.axvline(8.5e-8, color='red', ls=':')#, label='$\\it{P}$-value = 0.05' )\n",
        "\n",
        "plt.text(1e-7, 1e-240 ,r'$G\\mu$ $\\geq$ $8.5\\times 10^{-8}$' , color='r' ,fontsize=14)\n",
        "plt.tight_layout()\n",
        "plt.savefig('final_pv_s4ii.png')\n",
        "plt.savefig('final_pv_s4ii.pdf')"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "htCvaWOYPLDn"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}