Epoch Workshop 1 - Programming for AI (Notebook).ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# **Epoch + GDSC Workshop: Programming for AI**\n",
        "## Content prepared by: Donal, Ananya, Digjoy, Rudransh, Satwik, Vijay, Maharshi, Anirudh"
      ],
      "metadata": {
        "id": "N6sujYkV69b_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **NumPy**"
      ],
      "metadata": {
        "id": "JpNDV4Kh2Oaz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Stands for NUMerical PYthon\n",
        "- Faster because it is based out of C/C++"
      ],
      "metadata": {
        "id": "vubK1dnHiFSm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        " # Lists Vs Numpy\n",
        "\n",
        "\n",
        "*   Initialization\n",
        "*   Indexing\n",
        "*   Slicing\n",
        "*   Memory\n",
        "*   Time Consumption\n",
        "*   Few direct operations numpy can handle\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "h5a3O27HgefH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Initialization:\n",
        "We can initialize a numpy array using a list"
      ],
      "metadata": {
        "id": "pfYkf0AI3O-o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import time\n",
        "import sys"
      ],
      "metadata": {
        "id": "6zAxaxCmfLPC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# A Python list\n",
        "my_list = [1,2,3,4,5,6,7,8]\n",
        "# A numpy array that contains items of the same type\n",
        "my_numpy_array = np.array([1,2,3,4,5,6,7,8])\n",
        "\n",
        "# printing the list\n",
        "print(\"List:\",my_list)\n",
        "\n",
        "# printing the numpy array\n",
        "print(\"Numpy_array:\",my_numpy_array)"
      ],
      "metadata": {
        "id": "cmHKPPvthsZ-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "737d8789-f46a-4f7c-e8fe-27e5dcdda114"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List: [1, 2, 3, 4, 5, 6, 7, 8]\n",
            "Numpy_array: [2 3 4 5 6 7 8 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Both list and numpy are mutable\n",
        "my_list[7] = 9\n",
        "my_numpy_array[7]  = 10\n",
        "\n",
        "print(\"List:\",my_list)\n",
        "print(\"Numpy_array:\",my_numpy_array)\n"
      ],
      "metadata": {
        "id": "dQNg418IfjIN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "868174fe-414c-4685-fa6e-f5413efc178d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List: [1, 2, 3, 4, 5, 6, 7, 9]\n",
            "Numpy_array: [ 1  2  3  4  5  6  7 10]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Broadcasting\n",
        "my_numpy_array += 1\n",
        "\n",
        "# while you can't do the same thing with lists. my_list += 1 will give an error"
      ],
      "metadata": {
        "id": "oMgkwq8Aiksm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Both can be indexed and can be used for slicing operations\n",
        "# Accessing an element in an array through their index\n",
        "\n",
        "l = my_list[3]\n",
        "print(\"The fourth element(Numpy Array)\",l)\n",
        "n = my_numpy_array[4]\n",
        "print(\"The fifth element(Numpy Array)\",n)\n",
        "\n",
        "# Slicing operations\n",
        "\n",
        "l_s = my_list[:4]\n",
        "print(\"After the slicing operation(List):\",l_s)\n",
        "n_s = my_numpy_array[:4]\n",
        "\n",
        "\n",
        "print(\"After the slicing operation(Numpy_array):\",n_s)\n"
      ],
      "metadata": {
        "id": "wrpX5AirgHjO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "407ef031-7f17-439e-9767-5e8a26742760"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The fourth element(Numpy Array) 4\n",
            "The fifth element(Numpy Array) 5\n",
            "After the slicing operation(List): [1, 2, 3, 4]\n",
            "After the slicing operation(Numpy_array): [1 2 3 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Memory consumption o# Memory consumption of list and array\n",
        "list_memory = sys.getsizeof(my_list)\n",
        "print(\"Memory(in bytes) list consumes:\",list_memory )\n",
        "numpy_arr_memory = my_numpy_array.itemsize\n",
        "print(\"Memory(in bytes) NumPy array consumes:\",numpy_arr_memory )\n",
        "print(\"List consumes\", list_memory /numpy_arr_memory ,\"times memory than a numpy array of same size\")"
      ],
      "metadata": {
        "id": "LzKjFuFTgIma",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f9c67a75-f8ea-4f62-e9e4-7a9977b8bc47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Memory(in bytes) list consumes: 120\n",
            "Memory(in bytes) NumPy array consumes: 8\n",
            "List consumes 15.0 times memory than a numpy array of same size\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mathematical operations numpy can handle directly while lists cannot\n",
        "#List\n",
        "list_add = [i for i in range(10000)]\n",
        "\n",
        "start = time.time()\n",
        "list_add = [i+3 for i in list_add]\n",
        "end = time.time()\n",
        "\n",
        "t_add_list = end-start\n",
        "\n",
        "\n",
        "#Numpy_array\n",
        "my_numpy_add = np.array([i for i in range(10000)])\n",
        "\n",
        "start = time.time()\n",
        "my_numpy_add = my_numpy_add + 3\n",
        "end = time.time()\n",
        "\n",
        "t_add_np = end-start\n",
        "\n",
        "print(\"List after adding 3:\",list_add[:5])\n",
        "print(\"Numpy after adding 3:\",my_numpy_add[:5])\n",
        "\n",
        "#Time consumption\n",
        "print(\"Comparing the time consumed by lits and numpy arrays for addition operation\")\n",
        "print(\"Lists:\",t_add_list ,\"Numpy_Array\",t_add_np)\n",
        "print(\"Numpy arrays are\",int(t_add_list/t_add_np),\"times faster\")\n",
        "\n"
      ],
      "metadata": {
        "id": "LX291fvagYA2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24424025-c166-409d-ee6d-95f0f5384a37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "List after adding 3: [3, 4, 5, 6, 7]\n",
            "Numpy after adding 3: [3 4 5 6 7]\n",
            "Comparing the time consumed by lits and numpy arrays for addition operation\n",
            "Lists: 0.0015685558319091797 Numpy_Array 0.00015425682067871094\n",
            "Numpy arrays are 10 times faster\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "## Random Numbers\n",
        "\n",
        "\n",
        "*   np.random.rand() : Generates random values in a given shape from a uniform distribution\n",
        "*   np.random.randn() : Generates random numbers sample from a “standard normal” distribution.\n",
        "\n",
        "*   np.random.randint() : Generates random integer values\n",
        "*   np.random.choice() : Make random choices from a list/array\n",
        "*   np.arange() : Allows you to define the step size and infers the number of steps\n",
        "* np.linspace() : It allows you to define how many values you get including the specified min and max value\n",
        "*   np.shuffle() : Modify a sequence in-place by shuffling its contents.\n",
        "* np.random.normal() : Generates random numbers sample from a \"normal\" distribution.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "NyPhKvjzpkGZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## np.random.rand(shape)"
      ],
      "metadata": {
        "id": "y0RIFR6qcTu3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets initiate some randomness\n",
        "# np.random.rand() generates a random number between 0 and 1\n",
        "a=np.random.rand()  \n",
        "print(\"The random number generated is: \",a)"
      ],
      "metadata": {
        "id": "X68emi1Bph4Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "984258d3-804f-421f-d90a-10fa52c4d996"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The random number generated is:  0.30901895394083156\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lets initiate more randomness\n",
        "\n",
        "a = np.random.rand(5)  # Generates an array of 5 elements with random numbers between 0 and 1\n",
        "print(\"The random numbers generated are: \")\n",
        "print(a)\n",
        "a = np.random.rand(5,2)  # # Generates an array of size 5x2 with random numbers between 0 and 1\n",
        "print(\"The random array generated is: \")\n",
        "print(a)"
      ],
      "metadata": {
        "id": "E0-MXaEEdlp8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9469b3a9-2a67-403f-adae-65e5df8959c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The random numbers generated are: \n",
            "[0.95446821 0.95548362 0.3051654  0.44189875 0.95072201]\n",
            "The random array generated is: \n",
            "[[0.81286027 0.38747188]\n",
            " [0.46881388 0.64440485]\n",
            " [0.45295207 0.40746516]\n",
            " [0.68431635 0.28958445]\n",
            " [0.45885048 0.34813364]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## np.random.randn(shape)"
      ],
      "metadata": {
        "id": "LI_Bw3HtsvMQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# What is missing??\n",
        "# - No negative numbers !!\n",
        "# Yes, but numpy even got that sorted for you XDXDXD !!\n",
        "\n",
        "a=np.random.randn(2,2)  # It Generated random numbers between -1 and 1\n",
        "print(\"The random array generated is: \")\n",
        "print(a)"
      ],
      "metadata": {
        "id": "zdspbOW0eSBj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b78a61dd-7a18-442e-ffe8-07afe4ac7ecd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The random array generated is: \n",
            "[[-0.65392007  0.94451503]\n",
            " [ 0.79537131  1.39992722]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## np.random.randint(start=0, end, num)"
      ],
      "metadata": {
        "id": "zRsnxIRbsy9D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# But, what if you only want integer numbers\n",
        "# Numpy - Hold my beer !\n",
        "\n",
        "a=np.random.randint(5, size=10)  # It generates random integer from [0,5), and size defines the number of\n",
        "                                 # random numbers you wanted to generate\n",
        "print(a)"
      ],
      "metadata": {
        "id": "InabvllQehSZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "112d259a-1b1c-47bb-ee32-2d47f6594f4e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[4 4 2 0 3 1 4 1 1 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## np.random.choice(x, size = None, replace = True, p = None)"
      ],
      "metadata": {
        "id": "bUmDO6gYs2Bq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Favourite Subject\n",
        "\n",
        "subject = [\"Physics\",\"Chemistry\", \"Mathematics\"]\n",
        "a=np.random.choice(subject)  # Randomly choose one element from the list provided\n",
        "print(a)"
      ],
      "metadata": {
        "id": "sb6f1577fJg8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2c7eeef-49bb-4a72-b616-6e8606e7274f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Physics\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What if you know Mathematics is the best??\n",
        "\n",
        "a=np.random.choice(subject, p = [0,0,1])  # Here, p defines the probabilty of picking the corresponding element in the list\n",
        "print(a)"
      ],
      "metadata": {
        "id": "GqL5F-nXf1jf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f71ebf80-56dd-4d3c-992f-dcdee5b9446e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mathematics\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## np.arange(start=0, end, step)"
      ],
      "metadata": {
        "id": "L14G78BRs7WK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Return evenly spaced values within a given interval\n",
        "a=np.arange(12)  \n",
        "print(a)"
      ],
      "metadata": {
        "id": "YtPwc_PdgI5w",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5906753-cec1-4a65-80fe-f7d775124a7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 0  1  2  3  4  5  6  7  8  9 10 11]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## np.linspace(start, end, num)"
      ],
      "metadata": {
        "id": "pkk6Xnivs-Mh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 10 linearly spaced numbers\n",
        "x = np.linspace(1,10,10)\n",
        "print(x)"
      ],
      "metadata": {
        "id": "-2_xlBrRjamc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3be8dfe5-b11d-40c3-9386-963548718513"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 1.  2.  3.  4.  5.  6.  7.  8.  9. 10.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# difference between np.linspace and np.arange\n",
        "\n",
        "linspace = np.linspace(0,10,10 , endpoint = False) # uses number of equally seperated points\n",
        "arange = np.arange(0,10,1)  # uses distance between two points\n",
        "\n",
        "print(\"Linspace -> \" , linspace)\n",
        "print(\"Arange -> \" , arange)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aI-lE0-nfC7z",
        "outputId": "e7d0e907-d146-422e-c778-27bee2d5ed45"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Linspace ->  [0. 1. 2. 3. 4. 5. 6. 7. 8. 9.]\n",
            "Arange ->  [0 1 2 3 4 5 6 7 8 9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## np.random.shuffle(x)"
      ],
      "metadata": {
        "id": "gngI3t2EtAo1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Shuffle the given array for you\n",
        "np.random.shuffle(a)  \n",
        "print(a)"
      ],
      "metadata": {
        "id": "_2gEmV4wgMEF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f303a007-435a-482a-e968-e2fcb36a0a74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 1  4  6  0  5  9 11  3 10  7  2  8]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## np.random.normal(loc = 0.0, scale = 1.0, size = None)"
      ],
      "metadata": {
        "id": "bdorg8TFuv9X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Randomly generate a number from a normal distribution\n",
        "a = np.random.normal(0,0.2) # Here mu = 0 and variance = 0.2\n",
        "print(\"The random number generated using normal distribution is : \", a)"
      ],
      "metadata": {
        "id": "NAogcFcPhB1m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94b04d3d-3b41-4f53-9dca-5d51955d6b20"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The random number generated using normal distribution is :  0.02991613452198419\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Mathematical Functions in Numpy"
      ],
      "metadata": {
        "id": "_wFX0y7Nkgyf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " Trigonometric functions"
      ],
      "metadata": {
        "id": "HApYEd4ttb1m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "a=np.linspace(0,10)\n",
        "sin=np.sin(a)\n",
        "cos=np.cos(a)\n",
        "\n",
        "plt.plot(a,sin)\n",
        "plt.plot(a,cos)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ILtGYAKBkybk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "740a8688-b930-41dc-e5be-f8737d5e9a60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=np.linspace(-1,1)\n",
        "arcsin=np.arcsin(a)\n",
        "arccos=np.arccos(a)\n",
        "\n",
        "plt.plot(a,arcsin)\n",
        "plt.plot(a,arccos)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "1VSP_YaoIESu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "3f2bacfc-7d03-46aa-f400-bdb85d7a68b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=np.linspace(-3,3)\n",
        "sinh=np.sinh(a)\n",
        "cosh=np.cosh(a)\n",
        "\n",
        "plt.plot(a,sinh)\n",
        "plt.plot(a,cosh)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "G8CZ63SyJWUH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "48d26684-d800-488d-b40e-cf4f79a3c097"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAD4CAYAAADhNOGaAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAti0lEQVR4nO3deXxU1f3/8dcnKyGEhLAECAmL7IQ9RFQEF0SkKkK14r6jrX7VVqtVW7UuVduqtVVbqbs/RdxQVFBBcRchyL4HBJIAIZA9gSwzn98fd5BIw5ZkcjMzn+fjMY/M3LmZ+QzLfc8959xzRFUxxhgTusLcLsAYY4y7LAiMMSbEWRAYY0yIsyAwxpgQZ0FgjDEhLsLtAuqjXbt22q1bN7fLMMaYgLJ48eJdqtr+wO0BGQTdunUjMzPT7TKMMSagiMiWurZb05AxxoQ4CwJjjAlxFgTGGBPiLAiMMSbEWRAYY0yIsyAwxpgQ1yhBICLPi8hOEVlZa1uiiMwVkQ2+n20O8ruX+fbZICKXNUY9xhhjjlxjnRG8CIw/YNsfgE9VtRfwqe/xz4hIInAPcCyQAdxzsMBoMK8XfngZVs/yy8sbY4w/VeYsZ8/cB6GqvNFfu1GCQFW/BAoO2DwReMl3/yXgnDp+9XRgrqoWqGohMJf/DZTGs+g5+OgOqN7rt7cwxhh/2PnO7VR98xQ7Cksa/bX92UeQpKrbffd3AEl17JMMZNd6nOPb9j9EZKqIZIpIZn5+/tFXExYG4+6HkhxY+MzR/74xxrikbPU8Ugq+5cOEi+iY1KnRX79JOovVWQatQUuhqeo0VU1X1fT27f9nqowj03009BoHXz4KFQeewBhjTDPk9VL2wR3kaDuGTv69X97Cn0GQJyKdAHw/d9axTy6QUutxF982/xn7Z6gqha8e9evbGGNMYyhY+BodK9bzefJ19Evt4Jf38GcQzAL2jQK6DHivjn0+BsaJSBtfJ/E43zb/SeoPQy6EhdOgcLNf38oYYxqkei98ej+rtBsnnftrv71NYw0fnQ58B/QRkRwRuQp4GDhNRDYAY32PEZF0EXkWQFULgPuBRb7bfb5t/nXSnSDh8NkDfn8rY4ypr7xP/0Vi9Q6W9b2FLomt/PY+4jTfB5b09HRt8DTUn97nNA9N/Rw6D22UuowxptFUFFD+94Es8fYk7ba5JLSMavBLishiVU0/cHvoXll8wk3Qsi188icIwDA0xgS33A8eJMZTzvYRf2iUEDiU0A2CFvEw5nbY/BVkzXO7GmOM+Ym3YDMdVr/InIiTOWvcOL+/X+gGAcDwK6BNd5h7N3g9bldjjDEAbJv5RzwqyMl30iIy3O/vF9pBEBEFY++Bnath2XS3qzHGGKpzltIl+33eazGR04//n+Z8vwjtIADofw4kp8On98Pexr902xhjjpgqu97+HQXaiuSz7iQ8TJrkbS0IROCMv0JZHsz/i9vVGGNCWMn3r9CpcDEzE6/mhAE9mux9LQgAugyHEVc5cxBtW+p2NcaYUFRRQNjcP/KDtxcnXXALIk1zNgAWBPud8ieIbQ8f3Gwdx8aYJpf3zu20qCll5dB7OaZD6yZ9bwuCfWIS4PS/wLYlznTVxhjTRKo2fUNS1hu8EXk2vzrzjCZ/fwuC2tJ+CT1Odq46Ltl++P2NMaahPNWUvnUDOdqOLpP+3CTDRQ9kQVCbCPziUfBUwcd3uF2NMSYEFH76OG0rNvF+55sYPaCbKzVYEByo7TEw+lZYNRM22BXHxhj/0cLNtPzu78zTEUyaMtW1OiwI6nLCTdC2J8y+Bar3uF2NMSYYqZI/40ZqvJA/6s90jG/hWikWBHWJiIYzH3fWK/jy725XY4wJQntWvEuHHV/wWsuLOO+U41ytxYLgYLqPhkFT4JsnYMcKt6sxxgSTigKq37+V1d6uDD//TiLC3T0U+/XdRaSPiCytdSsRkZsP2OckESmutc/d/qzpqJz+F2iZCG9f46wUZIwxDaVKwRs3EFNVyGd9/sSwbvVcg70R+TUIVHWdqg5R1SHAcKACmFnHrl/t209V7/NnTUclti1MfAry18Cnf3a7GmNMENj7w3QSN3/IC1FTuPzcSW6XAzRt09CpwEZV3dKE79lwvU6DEVfDgqdh43y3qzHGBLKirfDhrSzy9mboBX+mVXSE2xUBTRsEU4CDzfV8nIgsE5E5IjKgCWs6MqfdD217wbu/gQr/L6lsjAlCXg+Fr15JjcdD5tBHGNHD/SahfZokCEQkCjgbeLOOp38AuqrqYOBfwLsHeY2pIpIpIpn5+fl+q7VOUS1h8jQo3wkf3mJLWxpjjlr554/TJn8Rz8Rex5VnjXG7nJ9pqjOCM4AfVDXvwCdUtURVy3z3ZwORItKujv2mqWq6qqa3b+9CkiYPg5P+AKvegRV15ZkxxtRNty8j+suH+NibwZmX3EJ0RNNPI3EoTRUEF3CQZiER6Si++VZFJMNX0+4mquvonPBbSDnWOSso2up2NcaYQFC9l5LXrqRAW5E3+mH6dGramUWPhN+DQERigdOAd2ptu05ErvM9PBdYKSLLgH8CU1SbadtLeARMegbUCzN/bdNVG2MOq/TDPxJfmsVzbW/l4lOGuV1OnfweBKparqptVbW41rb/qOp/fPefVNUBqjpYVUeq6rf+rqlBErvD+Idhy9fw5d/crsYY04x51nxI3NL/Ml1P55JLriKsiZaePFp2ZXF9DL3Yuer484dg3UduV2OMaY52baDmrWtY7u1Oi188RJc2Ld2u6KAsCOpDBM76B3QcCO9Mhd0b3a7IGNOcVJZS9vIUymrCmN3/b0zKOMbtig7JgqC+ImPg/FchLAxevwgqy9yuyBjTHKhS9sa1xBRv5PH4O/jtuae4XdFhWRA0RJuucO7zsGsdzLrBri8wxlD1xWO02vghT4RdzK+vvLLZDRWtiwVBQx1zCpx6t7OQzbf/dLsaY4yLNOszIj5/gPc9I8m44B6SE2LcLumIWBA0hhNuhv4TYd69sOlzl4sxxriicAuVMy5nvTeZ7Sf9nVG9m88UEodjQdAYRJxZStv1hjevsIvNjAk1VRWUv3IBlVU1vNL1Qa45Jc3tio6KBUFjiY5zOo+9NfDqebCn0O2KjDFNwVPD3hlXEFOwmodjfsftF03AN1lCwLAgaEztesKUV6FgE0y/wNY7NibYqVL1/m9psfEjHvJexhVXXEvrFpFuV3XULAgaW/fRzjQUWxfA21fbNBTGBLGa+Y8QtfRl/u2ZyIkX/5HeSXFul1QvFgT+kDYZxj8Eaz+A2b+3YaXGBCFd/BIRXz7EW57RtJ/4IKMDqHP4QM1jeZxgNPLXULodvnkCWneC0b93uyJjTGNZNwd9/2a+8Awmb/RfuT49xe2KGsSCwJ9OvRdKd8BnD0CrjjDsErcrMsY0VPZCamZczipvN+YP+iv3ju3rdkUNZkHgT2FhcPaTUJ4P798Ese2hz3i3qzLG1Ff+eqpeOY9tNfE8l/owj03OCLgRQnWxPgJ/i4iCX73sTFD3xqWw/hO3KzLG1Ef+Oqqfn0BxpfJg2wd56JJTiQgPjkNocHyK5i46Di6ZCR36woyLYN0ctysyxhyNnWuofm4CRXuq+V3MA/zlyrOJjQ6eBpWmWKFss4isEJGlIpJZx/MiIv8UkSwRWS4izXMJn4ZqmQiXvgdJaTDjEljzgdsVGWOOxI6V1Dw/gcK9Hm5q8QAPX3ce7eOi3a6qUTXVGcHJqjpEVdPreO4MoJfvNhX4dxPV1PRi2sCl70LnIfDmZbD6PbcrMsYcyvbl1LxwJrv3wk0tHuCv1/4yYCaSOxrNoWloIvCyOhYACSLSye2i/KZFPFz8DiQPd+YlWvnO4X/HGNP0ti2l5sUzya8M4/+iH+Bv105u1quMNURTBIECn4jIYhGZWsfzyUB2rcc5vm0/IyJTRSRTRDLz8/P9VGoTadEaLn4bUo6Ft6+C5W+6XZExprbcxXhePIu8yihujH6Qx66bFLQhAE0TBKNUdRhOE9D1IjK6Pi+iqtNUNV1V09u3D9wr+H4SHQcXvwVdT4B3roFvn7QrkI1pDtZ/jOeFM9le1YL/i36Ax66dGNQhAE0QBKqa6/u5E5gJZBywSy5Q+7K8Lr5twS8qFi56E/qfDZ/c5UxH4alxuypjQteiZ9HpU1hbncRvoh/iiWvPJiUxuEMA/BwEIhIrInH77gPjgJUH7DYLuNQ3emgkUKyq2/1ZV7MSGQPnvgjH3wiL/usML60qd7sqY0KL1wuf/Ak+vIXPPYO5M/4RnvnNL0IiBMD/VxYnATN9V95FAK+p6kcich2Aqv4HmA1MALKACuAKP9fU/ISFwbj7ISEV5twGL0yAC2dAXEe3KzMm+FXvRWdei6x+l1dqxjK7y295+bJjiY8JvOmk60s0ANul09PTNTPzfy5JCA7rPoK3roCWbZ1mow793K7ImOBVvhudfgGS8z0PVl/Itn5X8+j5Q2gR2fwXnK8PEVlc1zD+5jB81NTWZzxcMRs8VfDcOFg72+2KjAlOO1bgffZUanJ/4DdVN1Iz8gb+deGwoA2BQ7EgaI46D4WrP4XE7vD6BTD3HutENqYx/fAK+uxYiopLOH/vXQwdfwV3n9mfsLDAn0CuPiwImquEFLjyExh+BXzzD3j5bGdKa2NM/VVVwLvXw6wbyPT05hdVf+Gy83/FNaN7BMUsovUVPLMmBaPIFnDWPyB1JHzwW/jPiXDu89D9RLcrMybw7N7ozACct5KnPJOYEXsR/706g7TkeLcrc52dEQSCwVOcpqIW8c6ZwVePOcPdjDFHZvUsdNoYKnZt5fKq21jQ7de8939jLAR8LAgCRVJ/mDof+k+ET/8Mr0yEwi1uV2VM87a3GN67Ad64hA2eTowtf4D+o3/Ji1dk0CY2yu3qmg0LgkASHQfnvgBnPQG5P8C/j4fFL9rUFMbUZeN8ePp4dOmrvCCT+FXV3dx98ThuG9+X8BDtFD4Y6yMINCIw/HLocTK8d72zBOaa9+Gsf0L8/8zVZ0zoqSyDuXdD5nPkR6dyzd572Zs0lLcuHErPDnFuV9cs2RlBoGrTFS6dBRP+Dlu+haePg6XT7ezAhLbNX8O/j0czn+eNyImMKr6PEaNO493rT7AQOAQ7IwhkYWGQcQ30PNUZEvfudbDiTTjjr9Cup9vVGdN0ynfDZ/fB4hcpbtGFa6vvZnPUYF64ejDH92zndnXNnp0RBIPEHnD5hzD+EchZBE+PhHn3OqfIxgQzrwcWPQv/Gob+8AqzWk5mZNF9tBtwEh/fPNpC4AjZGUGwCAuDkddB2mQnBL5+HJbNgNMfgAGTnb4FY4LJ1u9h9i2wYwXZ8SO4tuxXZHu78tD5aUwc0jmkLxA7WnZGEGxadYBznoar5kKr9vDWlfDSWZC3yu3KjGkcpTtg5nXw/DgqS3ZxX8xtnJh3M937p/PpLWM4Z2iyhcBRsjOCYJWSAdfMhx9egk/vg3+fAIN+BSf9wWlKMibQVBQ40618Pw1VD/PbX8L12SeTmNCGFy5P4+S+HdyuMGBZEASzsHBIvxL6nwPfPAHfPwMr34ahl8Do39twUxMY9pbAgn/Dd0+ilaVsST6TG7ePZ1VuIleP6c5Np/aiZZQdyhrC1iMIJaU74KtHIfMFkDAYcTWc+DuItQ410wxV74GF/3X6u/YUsCvldO4qOouP8xMZlprAg5MG0q9Ta7erDCgHW4/Ab0EgIinAyzirlCkwTVWfOGCfk4D3gB99m95R1fsO99oWBA1UuAW++Cssew0iWjhnCMf9Btp0c7syY5wmoMzn4PtpUL6T8pQx/K36PF7cnEiXNjHcPr4vZw7qZP0A9eBGEHQCOqnqD751ixcD56jq6lr7nATcqqpnHs1rWxA0kvz1zretFW+CepwmpBNudNZDMKapFW6BBU/DD69AdTmV3U7hxbDJPLImkdjoCG48pReXHt+V6IjQWzimsRwsCPzWsOZbgH67736piKwBkoHVh/xF03Ta94ZJ/4ZT/+S0wS5+EVa9A91OhBNugmNOdYalGuNP25bAt/+CVe+CCHv6TuZlzuLxFZHUeJTLju/Kjaf0skni/KhJ+ghEpBvwJZCmqiW1tp8EvA3kANtwzg7qHOcoIlOBqQCpqanDt2yxmTcb3d5iWPySEwql25ymomGXwZCLIC7J7epMMKkscwYuLH7BCYKoOMoHXsK0qtP5z5I9VHu8nDM0mRtP6UW3drFuVxs0mrxpqNYbtwK+AB5U1XcOeK414FXVMhGZADyhqr0O95rWNORnNVWwZpZzhrD5KwiLgD4T9k92Z2cJpr62LXX+Xa14E6rKoEN/SgZcxFMFI3hxcQE1XmXS0GRuOLmnBYAfuBIEIhIJfAB8rKqPHcH+m4F0Vd11qP0sCJrQrg3OtQhLX4OK3ZDQ1VkoJ+1cp2nJmMMp3QGrZsKy12H7UoiIgbTJbO56Hk9uaMOsZdvxqPLLYclcf3JPura1APAXNzqLBXgJKFDVmw+yT0cgT1VVRDKAt4CuepiiLAhcUFPpTHf9w0vw41eAQsdBMPBcSPslxHdxu0LTnOwphNWzYOVbzoyg6oWOA/EOuYRvYk/lme9383XWLmIiwzkvvQtXj+pBatuWblcd9NwIglHAV8AKYN+6incCqQCq+h8RuQH4NVAD7AF+p6rfHu61LQhcVrLd+Ya38i3IXexsSz3OWT2t93hI7O5ufcYd5btgwydOAGTNA281JB4DA8+los85zMyO5fmvf2RjfjlJraO5/PjuXJCRQkJL6wRuKq71EfiDBUEzUrDJ6fRb+Q7s9A0Ia98P+oyH3mdAl3TnCmcTfFQhfy2smwPrP4LshYBC62QYMAkGnstKb3emL8rmvaXbKKusIS25NVeP6sGEgZ2IirC+pqZmQWD8r2ATrPsI1s9xFsvx1kDLttDzNOgxBrqPtiakQFdR4DT1/PgFbJgLRb7Re50GOwMKeo+nLHEA7y/fzvSFW1meU0x0RBhnDurMBRkpDO/axi4Ec5EFgWlae4qc5oH1H8HGz5yOZnCaCvaFQrfRENvW1TLNYVSWwdYFzoH/xy9g+3JAITIWuo3ynfmNx9OqEws27WbmklzmrNhOeZWHPklxXHhsKucMSSa+ZaTbn8RgQWDc5PU6zUY/fgE/fgmbv4GqUue5xGOcmVK7jHBuHfpDuE0g5gpV56wuZ5HTzJOzEPJWO1edh0dBlwwnwHuMgc7D0PBI1mwv5d2luby3NJe8kkrioiM4Y2BHpmSkMjQlwb79NzMWBKb58FQ748m3fA3Zi5wDTnm+81xkLCQPc5oaOg6CjgOhXS8It2+UjcrrhaLNsGOFc9u+HHIz95+5RcU5fw8pGc5AgNTjIMoZ1ZO1s4yPVm7n/WXbWZdXSkSYcFKfDkwamsyp/TrQItL6hJqrJp9iwpiDCo+ElBHODZxvooWb938Tzc10Zp30VPr2j4YO/aBjmtMR3a63Ew4JqdYRfTiqzjj+XeudW/46yFsJO1buPyuTMOfPtPd456wsJQPa9/3pz1ZVWbO9lI9WrmPOyh1s2OksgTq8axvuPyeNXwzsRKJN/xDQ7IzANE+eGti9wfeNdfn+b677vrGCExBtezqhkNgD2nR1wiGhK8SnQESIHJy8HijZBkVbnc7boq1Q8KPv4L9h/wEfIKoVJKU5Z1odfT879IfImJ+9ZI3Hy+IthXy2dicfr9rB5t0VhAmM6JbIGWkdOT2tI53iYzCBxZqGTHAo373/2+3uDc6BLn+dc/BTT60dBVp3dkYptUqCuE7OfEmtOkJcR2dby0SISYTIFq59nEPyVDsXZlUUQPlO55t96Q4oy4PS7VCaByU5UJzjjND6ie+zt+vlO3vqvf9+XKeDrl9dUF7FF+t38tnafL5Yt5OSvTVEhAnHHdOWM9I6MW5AEu1aRTfNZzd+YU1DJjjEtoXY46DrcT/f7qlxDo77vhHvuxVnO0Gx6QuoLK77NSNifKHQxrlFx0FUrPPted/P6FbO2g3hkU7HaXjU/vsSXvfB1esBT5XvVr3/fk0lVFdAZSlUlftuZc5tT6Hv4F/482/ytUW29IVZR0geDgMmH3A21AUiDn/ArvZ4WZZdxFcbdvHVhnyWZhfhVWjXKorTB3TklL4dGNWrHXEtrH8m2FkQmOAQHgEJKc7tYKoqfN+mfd+q9xTCnoL9B95994uyfQdm3wG6usI/NUe08AVNrNM5GxXrHNzb9/t5MMW0gVYdnG/zrZKcoKrHaBxV5cdd5XydtYuvNuziu427KausQQQGJcdzwym9OLVvBwYmxxMWZqN9QokFgQkdUS2d6S+OdgoMr8cJhZ++3df6hl9T6cyjUydx+il+OnuI3n8/KtbvI6FUlc27K1iwafdPt7wSpwM+JTGGswZ35sRe7Tj+mLY2zUOIsyAw5nDCwqFF818b1+NV1ueVkrmlkEU/FrBg0252ljoH/vZx0Yzs0ZaRPRIZ1bOdzfBpfsaCwJgAVVFVw9LsIhZvLmTRlkKWbCmktNLpNO7w04HfOfh3bxdrF3eZg7IgMCYAeL3Kpl3lLNlayJLsIpZuLWJdXikeryICvTvEcdaQzqR3bcOIbs4i73bgN0fKgsCYZiivZC/LsotYnlPMspwilmUXUbLX+bYfFx3BkNQEru93DENT2zAstY3N5WMaxILAGJcVlFexIreYlbnFPx38d5TsBSA8TOidFMcvBnVmaEoCQ1MTOKZ9KxvVYxqV34NARMYDTwDhwLOq+vABz0cDLwPDgd3A+aq62d91GeOG3WWVPx30nZ8l5Bbt+en57u1iObZHIoO7JDA4JZ7+neKJibJpNIx/+TUIRCQceAo4DcgBFonILFVdXWu3q4BCVe0pIlOAR4Dz/VmXMf6mquwsrWRFTjErtzkH/FXbitlevPenfbq1bcnQ1AQuO74racnxDOgcT3yMNfGYpufvM4IMIEtVNwGIyOvARKB2EEwE7vXdfwt4UkTkcOsWG9NcqCrZBXtYte3nB/1dZVWAc+1Xj3axZHRPJK1zPAOSW9tB3zQr/g6CZCC71uMc4NiD7aOqNSJSDLQFdtXeSUSmAlMBUlNT/VWvMYfk8TpX567a5jTv7Dvo7+vIjQgTeiXFcVKfDgzo3JqByfH069Sa2GjrjjPNV8D861TVacA0cCadc7kcEwJqPF425pf/1Ka/MreY1dtLqKhyJreLigijX6fWnDm4M2md40lLbk3vpDibj98EHH8HQS5Qe/KXLr5tde2TIyIRQDxOp7ExTcbjVTbll7EsZ39H7uptJeypdg76MZHhDOjcml+lpzCgc2vSkuPp2aEVkeG2ALsJfP4OgkVALxHpjnPAnwJceMA+s4DLgO+Ac4HPrH/A+JOqklO4h+U5xSzPKWJZThErcoopr/r5Qf/8ESkM6hLPwOR4erRvRbgN2TRByq9B4GvzvwH4GGf46POqukpE7gMyVXUW8BzwiohkAQU4YWFMoymrrGF5dhFLsotYsrWIpdmFP3XkRoWH0a9THJOHdWFQl3gGpzjj9O2gb0KJ3/sIVHU2MPuAbXfXur8XOM/fdZjQoKpsLaggc3MhmVsKWbK1kHV5pew7x+zRPpYxvTswJDWBwV3i6duxNVER1rxjQlvAdBYbU5caj5fV20tY+GMBi7c4B/9834ybcS0iGJrahvFpHRmSksCQlASbbtmYOlgQmIBS4/GyalvJT/PrZ27eP+NmlzYxnHBMW4Z3S2REtzb07hBnUzEYcwQsCEyzpqqszyvjqw35fJO1i0WbCynzHfh7tI/lrCGdObZ7IhndE20xdWPqyYLANDs7S/fyjW85xa837PppcZXu7WKZOKQzx/Zoy8juiXRo3UwXnTcmwFgQGNd5vcqK3GI+XbuTz9bmsTK3BIA2LSM5vmc7TuzZjlG92tGlTUuXKzUmOFkQGFeUV9bw1YZ8Pl2zk/nr8tlVVkmYwNDUNvz+9D6M7tWeAZ1bWxu/MU3AgsA0meI91Xy6Jo85K3fw5fp8Kmu8tG4RwZg+HTilb3vG9O5AYqyN6jGmqVkQGL8qLK/i41U7mLNyB99u3EW1R+nYugUXZKRy+oCOjOjWhgibpsEYV1kQmEa3t9rDvDV5vLskl8/X5VPjVVISY7jihO7OmP4uCdbkY0wzYkFgGoXHq3y/aTczl+QyZ+UOyiprSGodzZWjunP24M4M6NzaFlM3ppmyIDANsq1oD29kZvPGomy2Fe+lVXQEZ6R1ZNLQZI7t0dbm7DEmAFgQmKNW4/Hy+bp8pi/cyvx1O1HgxF7tuWNCP07rn2Tz8RsTYCwIzBHbWbqX/7dgK28symZHyV7ax0Xzm5N6cv6IFFISbYy/MYHKgsAc1prtJTz39Y/MWrqNaq+XMb3b8+eJAzilbwdbmMWYIGBBYOrk9SpfrM/n2a838U3WbmIiw7kgI4UrTuhOt3axbpdnjGlEFgTmZ2o8Xt5buo2nP89iY345Sa2juX18Xy7ISLEpnI0JUn4JAhH5G3AWUAVsBK5Q1aI69tsMlAIeoEZV0/1Rjzm8Go+XmUtyeWp+Fpt3V9C3Yxz/OH8IEwZ2soVbjAly/jojmAvc4Vuq8hHgDuD2g+x7sqru8lMd5jCqPV5m/pDLk/Oz2FpQwYDOrXnmkuGc1i/JLvoyJkT4JQhU9ZNaDxfgLEpvmhGvV3l3aS6Pz1tPdsEe0pJb899L0xnbr4Nd+GVMiGmKPoIrgRkHeU6BT0REgWdUddrBXkREpgJTAVJTUxu9yFDy7cZdPPjhGlZtKyEtuTX3XuaMALIAMCY01TsIRGQe0LGOp+5S1fd8+9wF1ACvHuRlRqlqroh0AOaKyFpV/bKuHX0hMQ0gPT1d61t3KMvaWcbDc9Ywb81OkhNieGLKEM4a1NmagIwJcfUOAlUde6jnReRy4EzgVFWt88Ctqrm+nztFZCaQAdQZBKb+dpdV8o95G3ht4VZaRoZz+/i+XHFCN7sC2BgD+G/U0HjgNmCMqlYcZJ9YIExVS333xwH3+aOeUOX1Km9kZvPQnLWUVdZwYUYqN4/tRdtW0W6XZoxpRvzVR/AkEI3T3AOwQFWvE5HOwLOqOgFIAmb6no8AXlPVj/xUT8jZkFfKnTNXsGhzIRndE3nwnDR6JcW5XZYxphny16ihngfZvg2Y4Lu/CRjsj/cPZXurPTz5WRbPfLmR2OgI/nruIM4b3sU6go0xB2VXFgeRrzfs4q53V7BldwWThyVz14R+1gxkjDksC4IgsLfaw0Oz1/DSd1vo3i6W164+luN7tnO7LGNMgLAgCHArc4u5ecZSsnaWccUJ3bh9fF8bDWSMOSoWBAHK41WmfbmJx+auIzE2ileuyuDEXu3dLssYE4AsCAJQdkEFt7yxjIWbC5gwsCN/mTTQZgY1xtSbBUGA+WTVDm55YxkKPHreYCYPS7YRQcaYBrEgCBAer/KPeev512dZDOoSz1MXDrPlIY0xjcKCIAAUV1Rz4+tL+GJ9PucN78L956RZh7AxptFYEDRza7aXcO0ri9levIcHJ6VxYUaqNQUZYxqVBUEz9t7SXG5/eznxMZHMuPY4hqW2cbskY0wQsiBohlSVx+Y6/QEZ3RJ58qKhdIhr4XZZxpggZUHQzFR7vNzxzgreWpzDlBEp3H9OGpHhtmawMcZ/LAiakfLKGn796g98uT6f347tzY2n9rT+AGOM31kQNBP5pZVc+eIiVm8v4ZFfDuT8EbYcpzGmaVgQNAOb8su47IWF7Cqt4r+XDueUvklul2SMCSF+a3wWkXtFJFdElvpuEw6y33gRWSciWSLyB3/V01wtyy7i3P98R3mlh+lTR1oIGGOanL/PCB5X1b8f7EkRCQeeAk4DcoBFIjJLVVf7ua5mYcnWQi59biEJsZG8cuWxdGsX63ZJxpgQ5PZwlAwgS1U3qWoV8Dow0eWamsTS7CIufW4hbWKjmDH1OAsBY4xr/B0EN4jIchF5XkTquhoqGciu9TjHty2oLcsu4pLnvqdNbBSvTx1J54QYt0syxoSwBgWBiMwTkZV13CYC/waOAYYA24FHG/heU0UkU0Qy8/PzG/JSrlqWXcTFz31PQstIplsIGGOagQb1Eajq2CPZT0T+C3xQx1O5QEqtx1182+p6r2nANID09HQ9ukqbh+U5+0Pg9anHkWwhYIxpBvw5aqhTrYeTgJV17LYI6CUi3UUkCpgCzPJXTW5akVPMxc9+T3xMJNOvGWkhYIxpNvw5auivIjIEUGAzcC2AiHQGnlXVCapaIyI3AB8D4cDzqrrKjzW5YlN+GZc+/z2tYyJ5fepIurSxdQSMMc2H34JAVS85yPZtwIRaj2cDs/1Vh9vySyu57IWFhInw6tXHWggYY5odu7LYjyqqarjqpUXkl1by+tTj6NrWhogaY5oft68jCFo1Hi83vLaElbnFPHnBMIakJLhdkjHG1MnOCPxAVfnTeyv5bO1OHpyUxtj+Nm2EMab5sjMCP3jysyymL8zm+pOP4aJju7pdjjHGHJIFQSN7MzObR+euZ/LQZG4d18ftcowx5rAsCBrR4i2F3DlzBSf0bMvDvxxki8oYYwKCBUEj2Vm6l9+8upjOCTE8fdFwoiLsj9YYExjsaNUIqn0jhIr3VPOfi4cTHxPpdknGGHPEbNRQI3hkzloW/ljA4+cPpl+n1m6XY4wxR8XOCBrog+XbePbrH7nsuK5MGtrF7XKMMeaoWRA0wIa8Um57aznDu7bhrl/0d7scY4ypFwuCeirdW821ryymZVQET180zDqHjTEBy/oI6kFVufXNZWwpqODVq48lqXULt0syxph6s6+x9fDKgi18vCqPO87oy8gebd0uxxhjGsSC4ChtzC/jL7PXMKZ3e64a1d3tcowxpsEsCI5CtcfL72YspUVkOH87164cNsYEB7/0EYjIDGDfRDsJQJGqDqljv81AKeABalQ13R/1NJan5mexLKeYpy8aRgfrFzDGBAm/BIGqnr/vvog8ChQfYveTVXWXP+poTEuzi/jXZ1lMGprMhIGdDv8LxhgTIPw6akictpNfAaf48338bU+Vh9/NWEpSXDT3nj3A7XKMMaZR+buP4EQgT1U3HOR5BT4RkcUiMvVQLyQiU0UkU0Qy8/PzG73QQ3lozho27Srn7+cNtnmEjDFBp95nBCIyD+hYx1N3qep7vvsXANMP8TKjVDVXRDoAc0Vkrap+WdeOqjoNmAaQnp6u9a37aH2xPp+Xv9vCVaO6c3zPdk31tsYY02TqHQSqOvZQz4tIBDAZGH6I18j1/dwpIjOBDKDOIHBDUUUVt721jF4dWvH7022RGWNMcPJn09BYYK2q5tT1pIjEikjcvvvAOGClH+s5an+ZvYbdZVU8fv4QWkSGu12OMcb4hT+DYAoHNAuJSGcRme17mAR8LSLLgIXAh6r6kR/rOSqLNhfwRmYOV5/Yg7TkeLfLMcYYv/HbqCFVvbyObduACb77m4DB/nr/hqj2ePnjzJUkJ8Rw46k93S7HGGP8yiadq8PzX//IurxS/ntpOi2j7I/IGBPcbIqJA+QW7eEf8zZwWv8kTuuf5HY5xhjjdxYEB7h31irnp104ZowJERYEtcxbncfc1XncNLYXyQkxbpdjjDFNwoLAp6KqhntmraJ3UiubXtoYE1KsJ9Tnn59mkVu0hzeuPY7IcMtHY0zosCMesD6vlGe/2sR5w7uQ0T3R7XKMMaZJWRAA972/mtjoCO6Y0M/tUowxpsmFfBB8uT6fr7N2cdOpvUiMjXK7HGOMaXIhHQRer/LQnLWkJMZw0chUt8sxxhhXhHQQvLs0lzXbS7h1XB+iI2xSOWNMaArZINhb7eHRT9YzMDmeswZ1drscY4xxTcgGwSvfbSG3aA9/OKMvYWHidjnGGOOakAyC4opqnpyfxZje7TnBVh0zxoS4kAyCp7/IomRvNX84o6/bpRhjjOtCLghyi/bwwjebmTQ0mX6dWrtdjjHGuK5BQSAi54nIKhHxikj6Ac/dISJZIrJORE4/yO93F5HvffvNEBG/D+R/7JP1ANwyztYgNsYYaPgZwUqcBep/tuC8iPTHWapyADAeeFpE6hqf+QjwuKr2BAqBqxpYzyGt2V7CO0tyuPz4bja7qDHG+DQoCFR1jaquq+OpicDrqlqpqj8CWUBG7R1ERIBTgLd8m14CzmlIPYfzyEdrad0ikutPsuUnjTFmH3/NPpoMLKj1OMe3rba2QJGq1hxin5+IyFRgKkBq6tFfBezxKr2T4hjTuz3xLSOP+veNMSZYHTYIRGQe0LGOp+5S1fcav6S6qeo0YBpAenq6Hu3vh4cJd9qkcsYY8z8OGwSqOrYer5sLpNR63MW3rbbdQIKIRPjOCuraxxhjjJ/5a/joLGCKiESLSHegF7Cw9g6qqsB84FzfpsuAJjvDMMYY42jo8NFJIpIDHAd8KCIfA6jqKuANYDXwEXC9qnp8vzNbRPZN7nM78DsRycLpM3iuIfUYY4w5euJ8MQ8s6enpmpmZ6XYZxhgTUERksaqmH7g95K4sNsYY83MWBMYYE+IsCIwxJsRZEBhjTIgLyM5iEckHttTz19sBuxqxHDcFy2cJls8B9lmaq2D5LA39HF1Vtf2BGwMyCBpCRDLr6jUPRMHyWYLlc4B9luYqWD6Lvz6HNQ0ZY0yIsyAwxpgQF4pBMM3tAhpRsHyWYPkcYJ+luQqWz+KXzxFyfQTGGGN+LhTPCIwxxtRiQWCMMSEuJINARO4XkeUislREPqk1G2pAEZG/icha32eZKSIJbtdUXyJynoisEhGviATkMD8RGS8i60QkS0T+4HY99SUiz4vIThFZ6XYtDSEiKSIyX0RW+/5t3eR2TfUlIi1EZKGILPN9lj836uuHYh+BiLRW1RLf/RuB/qp6nctlHTURGQd8pqo1IvIIgKre7nJZ9SIi/QAv8Axwq6oG1PSyIhIOrAdOw1l2dRFwgaqudrWwehCR0UAZ8LKqprldT32JSCegk6r+ICJxwGLgnAD9OxEgVlXLRCQS+Bq4SVUXHOZXj0hInhHsCwGfWCAg01BVP6m15vMCnFXeApKqrlHVdW7X0QAZQJaqblLVKuB1YKLLNdWLqn4JFLhdR0Op6nZV/cF3vxRYwyHWRW/O1FHmexjpuzXacSskgwBARB4UkWzgIuBut+tpBFcCc9wuIoQlA9m1HucQoAedYCQi3YChwPcul1JvIhIuIkuBncBcVW20zxK0QSAi80RkZR23iQCqepeqpgCvAje4W+3BHe5z+Pa5C6jB+SzN1pF8FmMam4i0At4Gbj6gNSCgqKpHVYfgnPlniEijNdsddvH6QKWqY49w11eB2cA9fiyn3g73OUTkcuBM4FRt5h0+R/F3EohygZRaj7v4thkX+drT3wZeVdV33K6nMahqkYjMB8YDjdKhH7RnBIciIr1qPZwIrHWrloYQkfHAbcDZqlrhdj0hbhHQS0S6i0gUMAWY5XJNIc3XwfocsEZVH3O7noYQkfb7RgWKSAzOoIRGO26F6qiht4E+OKNUtgDXqWrAfXsTkSwgGtjt27QgEEc/AYjIJOBfQHugCFiqqqe7WtRREpEJwD+AcOB5VX3Q3YrqR0SmAyfhTHmcB9yjqs+5WlQ9iMgo4CtgBc7/dYA7VXW2e1XVj4gMAl7C+bcVBryhqvc12uuHYhAYY4zZLySbhowxxuxnQWCMMSHOgsAYY0KcBYExxoQ4CwJjjAlxFgTGGBPiLAiMMSbE/X8nefN04UInrQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pi=3.14159265358\n",
        "print(np.degrees(2*pi))\n",
        "print(np.radians(360))"
      ],
      "metadata": {
        "id": "nkLeBxr8Kc8E",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "adf0ce29-3f64-46b3-985b-a6c47766446f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "359.9999999988778\n",
            "6.283185307179586\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "e=2.71828\n",
        "print(np.log(e**10))\n",
        "print(np.log2(4096))"
      ],
      "metadata": {
        "id": "N1cB0TVWMj0Y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "25b7c8a6-9729-4ef5-e3fd-604bdef4a72a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9.99999327347282\n",
            "12.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.exp(3))\n",
        "print(np.exp2(5))"
      ],
      "metadata": {
        "id": "dYZjI2JvM6Sx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0f5717a-e636-48a7-aa85-1a693b769c30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20.085536923187668\n",
            "32.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=1234.56789\n",
        "print(np.around(a,3))\n",
        "print(np.floor(a))\n",
        "print(np.ceil(a))"
      ],
      "metadata": {
        "id": "UJF0T3TcNRL7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52824048-c598-470f-98cd-1fb4646d2035"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1234.568\n",
            "1234.0\n",
            "1235.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(np.mod(25,11))\n",
        "print(np.mod(45,4))"
      ],
      "metadata": {
        "id": "UhoMpoP3wVuf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ea22ed6-6ae6-4e46-edc8-730da23f55ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n",
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=[1,2,3,4,5]\n",
        "b=[1,2,3,4,5]\n",
        "\n",
        "print(np.add(a,b))\n",
        "print(np.subtract(a,b))\n",
        "print(np.multiply(a,b))\n",
        "print(np.divide(a,b))"
      ],
      "metadata": {
        "id": "Nf0yqrQFvZpr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d69cdac7-3d49-40c1-8d3f-c3477371bc1d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 2  4  6  8 10]\n",
            "[0 0 0 0 0]\n",
            "[ 1  4  9 16 25]\n",
            "[1. 1. 1. 1. 1.]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=1234 + 56789j\n",
        "print(np.conj(a))\n",
        "print(np.isreal(a))\n"
      ],
      "metadata": {
        "id": "efH1OD1WtR7F",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50b80525-487d-4845-a011-3316cd7fc556"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1234-56789j)\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=np.array([1,3,2,4,6,5])\n",
        "b=np.array([6,5,3,4,2,1])\n",
        "print(np.minimum(a,b))\n",
        "print(np.maximum(a,b))"
      ],
      "metadata": {
        "id": "9lDJA3ZhOdNW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d5cbccb-a7ba-4c3a-dee5-294a432ce0d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 3 2 4 2 1]\n",
            "[6 5 3 4 6 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a= -1\n",
        "print(np.cbrt(a))"
      ],
      "metadata": {
        "id": "ruC_456-Sqq-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "19dba282-93e2-4943-dcbc-5c72648fb5f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-1.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a= -1\n",
        "print(a**(1/3))"
      ],
      "metadata": {
        "id": "fH_qq1x4TCzO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee0e81c1-c18a-4923-d337-cf5a5a4911cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0.5000000000000001+0.8660254037844386j)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=np.linspace(1,10)\n",
        "\n",
        "y=np.log2(x)\n",
        "\n",
        "plt.plot(x,y)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "nmWIWA_ClzKC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "2f3832dd-7154-4662-c288-2c0c2224a62b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=np.array([1,3,2,4,6,5,3,7,8,6,9,0])\n",
        "b=np.array([6,5,3,4,2,1,6,7,3,2,4,1])\n",
        "print(np.where(a>2*b,a,b))\n",
        "print(np.where(a>2*b,b,a))\n",
        "print(np.where(a+1>b,a,b))"
      ],
      "metadata": {
        "id": "QA4qNuraQ7KJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7afa2c90-27e6-4b47-afaa-1573eafc2c34"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6 5 3 4 6 5 6 7 8 6 9 1]\n",
            "[1 3 2 4 2 1 3 7 3 2 4 0]\n",
            "[6 5 3 4 6 5 6 7 8 6 9 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a=np.linspace(0,10,20)\n",
        "grad=np.gradient(np.sin(a),a)\n",
        "\n",
        "real=np.cos(a)\n",
        "\n",
        "diff=np.absolute(grad-real)\n",
        "\n",
        "plt.plot(a,grad,\"blue\")\n",
        "plt.plot(a,real,\"red\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "hc6_NW8elGEP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "9dad9802-b972-42cb-aa2f-e9b9e2fe7a09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Pandas**"
      ],
      "metadata": {
        "id": "FBKoubWT-h1W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Bq8lZxiEp4t2"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Pandas Series\n",
        "\n",
        "lis = [4,2,3,1]\n",
        "series = pd.Series(lis)\n",
        "print(series)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O8qcpNSofB3k",
        "outputId": "b9380aa5-380c-4e6d-9892-ef09df03b2db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    4\n",
            "1    2\n",
            "2    3\n",
            "3    1\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Series from np array\n",
        "arr = np.array([1,2,3])\n",
        "\n",
        "series = pd.Series(arr,index=[\"a\",\"b\",\"c\"])\n",
        "print(series)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XW3u_7dRgohb",
        "outputId": "b6a110c1-6df8-45a7-9465-5973d34a2cfc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a    1\n",
            "b    2\n",
            "c    3\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Series from dictionary\n",
        "d = {\"x\":1,\"y\":2,\"z\":3}\n",
        "\n",
        "series = pd.Series(d)\n",
        "print(series)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uo338ODxhGMC",
        "outputId": "161838d4-9e23-445e-8457-2e13f9701a72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x    1\n",
            "y    2\n",
            "z    3\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Series from np array\n",
        "arr = np.array([1,2,3,4,5])\n",
        "\n",
        "series = pd.Series(arr,index=[\"a\",\"b\",\"c\",\"d\",\"e\"])\n",
        "\n",
        "print(series[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wjeZwkVylECS",
        "outputId": "e6288c85-0cf1-4f83-837d-5b45f286ba5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(series[\"c\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iWgOcpT1lYoS",
        "outputId": "866efea8-764a-47bc-8cab-530f3eb4685c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(series[:3])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ve7ftqOPkWl8",
        "outputId": "344722a5-5594-4876-fb16-c5c0fae813ba"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a    1\n",
            "b    2\n",
            "c    3\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Frame\n",
        "data = {\n",
        "    \"Name\":[\"Sanjay\",\"Rahul\",\"Kumar\"],\n",
        "    \"Marks(100)\":[95,87,91]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data,index=[\"a\",\"b\",\"c\"])\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "bUOokrrFp9Fc",
        "outputId": "252ebe1c-0fd6-4f2f-84e3-e9a757ae878e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Name  Marks(100)\n",
              "a  Sanjay          95\n",
              "b   Rahul          87\n",
              "c   Kumar          91"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c23f3943-d598-4323-b0f4-8698b1c38e11\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Marks(100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>Sanjay</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>Rahul</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>Kumar</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c23f3943-d598-4323-b0f4-8698b1c38e11')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c23f3943-d598-4323-b0f4-8698b1c38e11 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c23f3943-d598-4323-b0f4-8698b1c38e11');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ncoNVUTBugM0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[\"a\"]"
      ],
      "metadata": {
        "id": "TCip1YQsrcn8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c2572271-8c8e-4582-c284-d8a7ad43a24c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name          Sanjay\n",
              "Marks(100)        95\n",
              "Name: a, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.iloc[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S2DGLOcuo0Iw",
        "outputId": "529f48a0-9203-473a-a051-e9eb00be0d31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name          Sanjay\n",
              "Marks(100)        95\n",
              "Name: a, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Series 1\n",
        "roll_name = {1:\"Sanjay\",2:\"Rahul\",3:\"Kumar\"}\n",
        "ser1 = pd.Series(roll_name)\n",
        "print(ser1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SWU6xp-IqZWu",
        "outputId": "96828ca2-2440-4039-b2d8-a444a587c49d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    Sanjay\n",
            "2     Rahul\n",
            "3     Kumar\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Series 2\n",
        "roll_marks = {1:95,2:87,3:91}\n",
        "ser2 = pd.Series(roll_marks)\n",
        "print(ser2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Heu4oo57vMdh",
        "outputId": "db2a73d8-9fce-43e2-973d-ddec184330a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1    95\n",
            "2    87\n",
            "3    91\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Combining ser1 and ser2 into a Dataframe df\n",
        "df = pd.DataFrame({\"Name\":roll_name,\"Marks(100)\":roll_marks})\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "fyD8IlETvO27",
        "outputId": "edfa8a6c-57bd-4b58-8166-cacf9a8ca2cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Name  Marks(100)\n",
              "1  Sanjay          95\n",
              "2   Rahul          87\n",
              "3   Kumar          91"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-53b504eb-9d32-4808-9155-85ad54a9b338\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Name</th>\n",
              "      <th>Marks(100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Sanjay</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rahul</td>\n",
              "      <td>87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Kumar</td>\n",
              "      <td>91</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-53b504eb-9d32-4808-9155-85ad54a9b338')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-53b504eb-9d32-4808-9155-85ad54a9b338 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-53b504eb-9d32-4808-9155-85ad54a9b338');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Dnf5pVU1ZFl",
        "outputId": "05def695-13ad-4f76-c393-47421e62d997"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([1, 2, 3], dtype='int64')"
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y-9mPe8b1aTK",
        "outputId": "90082762-a3db-45b8-abbf-a714f07ceb67"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Name', 'Marks(100)'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MK0QCwE_2XwH",
        "outputId": "b3e117d8-d02b-4370-ea57-c7e2a0ebc6a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Name          object\n",
              "Marks(100)     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HN3ch1Fq4jHP",
        "outputId": "8cdf9ef1-6efd-4de0-d92e-83e899c49cb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(df.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mKatZq5D2YZ5",
        "outputId": "dd3f91e6-f409-4f55-a977-0920cc6bbd4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['Sanjay' 95]\n",
            " ['Rahul' 87]\n",
            " ['Kumar' 91]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info(verbose=True)"
      ],
      "metadata": {
        "id": "AOlmzC0V2lKE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dea9bd51-e7f8-4bb5-deac-7386cd664ff4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 3 entries, 1 to 3\n",
            "Data columns (total 2 columns):\n",
            " #   Column      Non-Null Count  Dtype \n",
            "---  ------      --------------  ----- \n",
            " 0   Name        3 non-null      object\n",
            " 1   Marks(100)  3 non-null      int64 \n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 72.0+ bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('imdb.csv')\n",
        "df"
      ],
      "metadata": {
        "id": "Z_Y2wxHb6gMG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel('imdb.xlsx')\n",
        "df"
      ],
      "metadata": {
        "id": "b7QvL4tQ_jy4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_csv('myfile.csv')"
      ],
      "metadata": {
        "id": "lpB__UxyFJvE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.to_excel('myExcelFile.xlsx')"
      ],
      "metadata": {
        "id": "4CIaNJkSGPH6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "id": "USo-ZCgIGVA8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail(3)"
      ],
      "metadata": {
        "id": "GfsZLDvsHkxF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecting Rows with IMDB Rating > 8\n",
        "df.loc[df.IMDB_Rating > 8]"
      ],
      "metadata": {
        "id": "W6xOICltHlv3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Selecting the Top 3 Movies\n",
        "df.iloc[:3]"
      ],
      "metadata": {
        "id": "5eutwwaXNeNR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Prints the Statistical Description of the Dataframe\n",
        "df.describe()"
      ],
      "metadata": {
        "id": "WQ2iEjRvNiwd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary of the Dataframe\n",
        "df.info()"
      ],
      "metadata": {
        "id": "BMqb45gPP3Vd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Sampling 5 Movies from Dataframe\n",
        "df.sample(n=5,random_state=0)"
      ],
      "metadata": {
        "id": "6E-zdL86QC_Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping the Column Poster_Link as it is irrelevant\n",
        "df.drop(columns=['Poster_Link'],inplace=True)\n",
        "df"
      ],
      "metadata": {
        "id": "-g3cMFhwR7vw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for Null Values(NaN) in df\n",
        "df.isna()"
      ],
      "metadata": {
        "id": "oPics0ZxUFlZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing the Number of\n",
        "# Null Values in each column\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "id": "k8Al2ASIVPgs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping Rows having NaN\n",
        "newdf = df.dropna(axis='rows')\n",
        "newdf.tail()"
      ],
      "metadata": {
        "id": "eXaMxJmjWeOj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Resetting the Index of newdf\n",
        "newdf.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "JIdZNl8-X3T2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mean of IMDB_Rating Column\n",
        "df['IMDB_Rating'].mean()"
      ],
      "metadata": {
        "id": "pD6tBg-kZTWm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Median of IMDB_Rating Column\n",
        "df['IMDB_Rating'].median()"
      ],
      "metadata": {
        "id": "DDlsi9owapkp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Mode of Director Column\n",
        "df['Director'].mode()"
      ],
      "metadata": {
        "id": "z9_vpceEa_uU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "id": "f0JmO_qQdZm9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes\n"
      ],
      "metadata": {
        "id": "npHfCihAdxpL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filling NaN Values of three Columns using Mean, Median, Mode\n",
        "df.fillna({'Certificate':df['Certificate'].mode()[0],\n",
        "           'Meta_score':df['Meta_score'].mean(),\n",
        "           'Gross':df['Gross'].median()},inplace=True)\n",
        "df.isna().sum()"
      ],
      "metadata": {
        "id": "M3ovSNS5bBv_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "id": "qOqo5G6VifYB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Finding Counts of each Unique\n",
        "# Element in Genre Column\n",
        "df['Genre'].value_counts()"
      ],
      "metadata": {
        "id": "yiVMe3MpkPYB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Grouping Movies by Genre and finding the mean\n",
        "df.groupby('Genre').mean()"
      ],
      "metadata": {
        "id": "8QZJ23qFdvAn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Unique Elements in Certificate Column\n",
        "df['Certificate'].unique()"
      ],
      "metadata": {
        "id": "L6ZdyHM_lzKe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Number of Unique Elements\n",
        "# in Each column\n",
        "df.nunique()"
      ],
      "metadata": {
        "id": "n1-n_35VhnQp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "id": "tzWkGB4uneNz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 10 Smallest Meta Score Movies\n",
        "df.nsmallest(10,columns=['Meta_score']).loc[:,['Series_Title','Released_Year','IMDB_Rating','Director','Gross','Meta_score']]"
      ],
      "metadata": {
        "id": "osjq7NQmhvYK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Matplotlib**"
      ],
      "metadata": {
        "id": "x74270-TbPkx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "syxVgMdqBUBW"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "Functional plot\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "PB1_uVWWHV9I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create x & y NumPy arrays (10 Floats from 0 to 5)\n",
        "# Any NumPy array will do for x & y points as long as you have \n",
        "# an equal number \n",
        "x_1 = np.linspace(0,5,10)\n",
        "y_1 = x_1**2\n",
        "# Display plot with x & y\n",
        "plt.plot(x_1, y_1)\n",
        "\n",
        "# Add a title\n",
        "plt.title('x vs x^2')\n",
        "# Add an X & Y Label\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('y')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "UB1VFPXOBgDx",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "c982eb10-5f3d-4732-d3f2-716991adb07a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_2 = np.linspace(-np.pi,np.pi,50)\n",
        "y_2 = np.sin(x_2)\n",
        "plt.plot(x_2,y_2)\n"
      ],
      "metadata": {
        "id": "Ck2lk-gCjfgR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "bd4de405-f8c4-4a8f-d3d7-c8f3a724f96a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f092c2d72b0>]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot a function of your choice:\n",
        "x_3 = \n",
        "y_3 ="
      ],
      "metadata": {
        "id": "Fp6C4GLbkZyv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multiple Plots\n"
      ],
      "metadata": {
        "id": "-KFQYKD_HcX3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(x_1, y_1,'b')\n",
        "plt.plot(x_2,y_2,'k')\n",
        "# Add a title\n",
        "plt.title('Multiple plots: sinx and x^2 ')\n",
        "# Add an X & Y Label\n",
        "plt.xlabel('x')\n",
        "plt.ylabel('y')"
      ],
      "metadata": {
        "id": "49TH5MnkHBnt",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "44fe9d9c-1eaa-4017-c5cb-8f01cf5441ca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'y')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " Figure objects"
      ],
      "metadata": {
        "id": "Gzlt2I1XMkyp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# A figure is an object that contains all the plot elements\n",
        "# It can contain many axes\n",
        "# Define width & height in inches\n",
        "# Dots per inch\n",
        "fig_1 = plt.figure(figsize=(5,4),dpi=100)\n",
        "\n",
        "# Adds axes with a left, bottom, width and height that ranges from 0 to 1\n",
        "# which is the percent of the canvas you want to use\n",
        "axes_1 = fig_1.add_axes([0.1,0.1,0.9,0.9])\n",
        "\n",
        "# Set lables and title\n",
        "axes_1.set_xlabel('x')\n",
        "axes_1.set_ylabel('y')\n",
        "axes_1.set_title('x^2 Vs x')\n",
        "# Plot on the axes (If you want a label associated with the legend\n",
        "# add it with label)\n",
        "axes_1.plot(x_1,y_1,label='x / x²')\n",
        "\n",
        "\n",
        "# You can plot to plots using the same axes\n",
        "axes_1.plot(x_1,x_1,label='x  / x')\n",
        "\n",
        "# Add the optional legend with a location number (best: 0, \n",
        "# upper right: 1, upper left: 2, lower left: 3, lower right: 4,\n",
        "\n",
        "axes_1.legend(loc=0)\n",
        "\n",
        "# You can create axis inside of others\n",
        "axes_2 = fig_1.add_axes([0.45,0.45,0.4,0.3])\n",
        "axes_2.set_xlabel('x_')\n",
        "axes_2.set_ylabel('y_')\n",
        "axes_2.set_title('x_ vs y_')\n",
        "axes_2.plot(x_1,y_1,'r')\n",
        "\n",
        "# Add text to plot from central point of 0,0\n",
        "axes_2.text(0, 40, 'Message')"
      ],
      "metadata": {
        "id": "_fqrjnG7M0fW",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 484
        },
        "outputId": "e7210846-e786-41cc-b1c7-ecbb9ed694f1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 40, 'Message')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Subplots"
      ],
      "metadata": {
        "id": "5qTG4eW0IhdZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# You can define multiple plots with subplots and it handles creating\n",
        "# the axes objects\n",
        "# axes_2 is a list of axes objects\n",
        "fig_2, axes_2 = plt.subplots(figsize=(8,4), nrows=1, ncols=3)\n",
        "#  What happens if nrows = 2 and ncols = 2\n",
        "\n",
        "# Put space between plots\n",
        "plt.tight_layout()\n",
        "\n",
        "# You can access the plots by index\n",
        "axes_2[1].set_title('Plot 2')\n",
        "axes_2[1].set_xlabel('X')\n",
        "axes_2[1].set_ylabel('X Squared')\n",
        "axes_2[1].plot(x_1,y_1)\n",
        "\n",
        "axes_2[0].set_title('Plot 1')\n",
        "axes_2[0].set_xlabel('X')\n",
        "axes_2[0].set_ylabel('X Squared')\n",
        "axes_2[0].plot(x_1,x_1**2 + x_1)\n",
        "\n",
        "axes_2[2].set_title('Plot 3')\n",
        "axes_2[2].set_xlabel('X')\n",
        "axes_2[2].set_ylabel('X Squared')\n",
        "axes_2[2].plot(x_1,-x_1**2)\n",
        "\n"
      ],
      "metadata": {
        "id": "q8NkQ6EjImTq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 344
        },
        "outputId": "4ca3a7b4-ec8e-4eaa-b8c2-4f9cc32e525f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0921f85c40>]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x288 with 3 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Appearance Options"
      ],
      "metadata": {
        "id": "nCny0hchGnOQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig_3 = plt.figure(figsize=(6,4))\n",
        "axes_3 = fig_3.add_axes([0,0,1,1])\n",
        "\n",
        "# Default colors (b: blue, g: green, r: red, c: cyan, m: magenta,\n",
        "# y: yellow, k: black, w: white)\n",
        "# color=\"0.75\" creates a 75% gray\n",
        "# You can use hexcodes color=\"#eeefff\"\n",
        "# You can use color names found next like this color=\"burlywood\"\n",
        "# https://en.wikipedia.org/wiki/Web_colors\n",
        "# alpha defines the percentage of opacity\n",
        "\n",
        "# The default line width is 1, so to double it put in 2 and so forth\n",
        "\n",
        "# There are many line styles \n",
        "# matplotlib.org/3.1.0/gallery/lines_bars_and_markers/linestyles.html\n",
        "# You can also provide a sample like '-.'\n",
        "\n",
        "# Markers can mark your provided points on the graph\n",
        "# https://matplotlib.org/3.3.0/api/markers_api.html\n",
        "# You can change the markersize as well\n",
        "\n",
        "# markerfacecolor changes the marker fill color\n",
        "# markeredgecolor changes the marker stroke color\n",
        "# markeredgewidth changes the markers stroke size\n",
        "\n",
        "axes_3.plot(x_1,y_1,color='navy', alpha=.75, lw=2, ls='-.',\n",
        "           marker='o', markersize=7, markerfacecolor='y',\n",
        "           markeredgecolor='y', markeredgewidth=4)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "8BazTmPmGmdJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "f50f6412-3ad3-445b-fa85-87308c04135b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0921f0d400>]"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig_3 = plt.figure(figsize=(6,4))\n",
        "axes_3 = fig_3.add_axes([0,0,1,1])\n",
        "\n",
        "axes_3.plot(x_1,y_1,color='navy', alpha=.75, lw=2, ls='-.',\n",
        "           marker='o', markersize=7, markerfacecolor='y',\n",
        "           markeredgecolor='y', markeredgewidth=4)\n",
        "\n",
        "\n",
        "# Set the lower and upper bound of x & y axis\n",
        "axes_3.set_xlim([0,3])\n",
        "axes_3.set_ylim([0,8])"
      ],
      "metadata": {
        "id": "KV7PbK2MmB8v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "4e1cd35e-7145-4590-cadb-eb52073474ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0.0, 8.0)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig_3 = plt.figure(figsize=(6,4))\n",
        "axes_3 = fig_3.add_axes([0,0,1,1])\n",
        "\n",
        "axes_3.plot(x_1,y_1,color='navy', alpha=.75, lw=2, ls='-.',\n",
        "           marker='o', markersize=7, markerfacecolor='y',\n",
        "           markeredgecolor='y', markeredgewidth=4)\n",
        "\n",
        "# Add a grid, color, dashes(5pts 1 pt dashes separated by 2pt space)\n",
        "axes_3.grid(True, color='0.6', dashes=(5, 2, 1, 2))\n",
        "# Set grid background color\n",
        "axes_3.set_facecolor('k')"
      ],
      "metadata": {
        "id": "HAz_VRqlmCJs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 336
        },
        "outputId": "f062ae82-46f9-4317-dd6f-b6d8bcb5859f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "TeX Markup"
      ],
      "metadata": {
        "id": "IbjacKWsG1Ns"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# You can use a subset of TeX markup by placing text between $\n",
        "# matplotlib.org/tutorials/text/mathtext.html\n",
        "fig_5 = plt.figure(figsize=(5,4),dpi=100)\n",
        "axes_5 = fig_5.add_axes([0.1,0.1,0.9,0.9])\n",
        "\n",
        "# All listed plus kappa, iota, zeta, nu, rho, eta, xi, omicron, gamma, tau, phi, \n",
        "# chi, psi, delta (Capitalize the first letter for uppercase)\n",
        "axes_5.text(0, 23, \n",
        "            r'$\\alpha \\beta \\sigma \\omega \\epsilon \\mu \\pi \\theta \\lambda$', fontsize=18)\n",
        "# Subscripts, multiletter superscript, sum, fractions, binomial\n",
        "axes_5.text(0, 18, \n",
        "            r'$\\delta_i \\gamma^{ij} \\sum_{i=0}^\\infty x_i \\frac{3}{4} \\binom{3}{4}$', \n",
        "            fontsize=18)\n",
        "# Another fraction, sqrt, cbrt, trig functions : \n",
        "axes_5.text(0, 13, \n",
        "            r'$\\frac{8 - \\frac{x}{5}}{8} \\sqrt{9} \\sin(\\pi) \\sqrt[3]{8} \\acute a \\div$', \n",
        "            fontsize=18)\n",
        "axes_5.text(0, 8, \n",
        "            r'$\\bar a \\hat a \\tilde a \\vec a \\overline {a} \\lim_{x \\to 2} f(x) = 5$', \n",
        "            fontsize=18)\n",
        "axes_5.text(0, 3, \n",
        "            r'$\\geq \\leq \\ne$', \n",
        "            fontsize=18)\n",
        "axes_5.plot(x_1,y_1)"
      ],
      "metadata": {
        "id": "R2bDg-1rG5Ex",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 437
        },
        "outputId": "ae7ea9b3-8883-4515-ec0f-a029b30f316a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f0921d7a940>]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Histogram"
      ],
      "metadata": {
        "id": "OgAxTPcdGGZU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "blood_sugar = [113, 85, 90, 150, 149, 88, 93, 115, 135, 80, 77, 82, 129]\n",
        "plt.hist(blood_sugar, rwidth=0.8) # by default number of bins is set to 10"
      ],
      "metadata": {
        "id": "tBnnfGDmoGsR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        },
        "outputId": "b07b914c-cc4a-4bba-e806-47cd5418be22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([3., 3., 1., 0., 1., 1., 0., 2., 0., 2.]),\n",
              " array([ 77. ,  84.3,  91.6,  98.9, 106.2, 113.5, 120.8, 128.1, 135.4,\n",
              "        142.7, 150. ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#bins parameter\n",
        "plt.hist(blood_sugar,rwidth=0.5,bins=4)"
      ],
      "metadata": {
        "id": "M3_-tBauoGil",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "outputId": "235a1e73-af32-4f15-b0c9-3bc99fd20907"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([7., 1., 2., 3.]),\n",
              " array([ 77.  ,  95.25, 113.5 , 131.75, 150.  ]),\n",
              " <BarContainer object of 4 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "*   80-100: Normal\n",
        "*   100-125: Pre-diabetic\n",
        "*   80-100: Diabetic\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "CCO0dRbkoW0F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel(\"Sugar Level\")\n",
        "plt.ylabel(\"Number Of Patients\")\n",
        "plt.title(\"Blood Sugar Chart\")\n",
        "\n",
        "plt.hist(blood_sugar, bins=[80,100,125,150], rwidth=0.95, color='g')"
      ],
      "metadata": {
        "id": "VmDhymY1oGXZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "outputId": "8bd54b22-4496-4399-add8-c8986b0afc6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([6., 2., 4.]),\n",
              " array([ 80., 100., 125., 150.]),\n",
              " <BarContainer object of 3 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bar Charts"
      ],
      "metadata": {
        "id": "jltqq-U3ITwr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "company=['GOOGL','AMZN','MSFT','FB']\n",
        "revenue=[90,136,89,27]\n"
      ],
      "metadata": {
        "id": "IeM5m4GgpgaD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "xpos = np.arange(len(company))\n",
        "xpos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oZ4yW1oXpguZ",
        "outputId": "c5540d8d-23e9-4fc0-aae1-d3f901706ae2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(xpos,revenue, label=\"Revenue\")\n",
        "\n",
        "plt.xticks(xpos,company)\n",
        "plt.ylabel(\"Revenue(Bln)\")\n",
        "plt.title('US Technology Stocks')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "0rFzPDtKpons",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "595eec84-aa4e-45f4-d74a-c1adacb63977"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0921b0f190>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "profit=[40,2,34,12]\n",
        "plt.bar(xpos-0.2,revenue, width=0.4, label=\"Revenue\")\n",
        "plt.bar(xpos+0.2,profit, width=0.4,label=\"Profit\")\n",
        "\n",
        "plt.xticks(xpos,company)\n",
        "plt.ylabel(\"Revenue(Bln)\")\n",
        "plt.title('US Technology Stocks')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "UKNoxQ4fprpB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "0881d000-7ebb-4013-d547-041951d61a93"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0921ac9af0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.barh(xpos,revenue, label=\"Revenue\")\n",
        "\n",
        "plt.yticks(xpos,company)\n",
        "plt.ylabel(\"Revenue(Bln)\")\n",
        "plt.title('US Technolog Stocks')\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "5xeL8Qzzpryo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "outputId": "71bfbea8-40e9-4cb2-8cf8-cc49db609f1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f0921a6dcd0>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Scatterplots\n"
      ],
      "metadata": {
        "id": "n3hFuRjNK4xk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Country array\n",
        "cnt_arr = np.array(['Australia','Brazil','Canada','Chile','France','Germany','Greece',\n",
        "                   'Iceland','India','Iran','Italy','Mexico','New Zealand','Nigeria',\n",
        "                   'Norway','Pakistan','Peru','Russia','Saudi Arabia','Singapore',\n",
        "                   'South Africa','Spain','Sweden','Turkey','UK','US'])\n",
        "# Death rate per 100k Coronavirus\n",
        "dr_arr = np.array([1.8,53,24.5,56.5,45.4,11.2,2.2,\n",
        "                   2.8,4,24.6,58.6,46.3,.5,.5,\n",
        "                   4.9,2.9,83.3,11,10.4,.5,\n",
        "                   21.5,61.6,56.9,7.3,62.4,52.9])\n",
        "# Daily confirmed cases (Tests)\n",
        "test_arr = np.array([110,7197,600,1862,1636,1103,35,\n",
        "                   10,295,1658,1226,2490,8,243,\n",
        "                   48,1395,1101,4447,1443,280,\n",
        "                   2830,1602,447,1205,1546,24988])\n",
        "# Dot size Confirmed cases\n",
        "cc_arr = np.array([24236,3456652,125408,390037,256534,229706,7684,\n",
        "                   2035,2836925,350279,255278,537031,1654,50488,\n",
        "                   10162,290445,549321,935066,302686,56031,\n",
        "                   596060,370867,85411,253108,323008,5529824])\n",
        "cc_arr_sm = cc_arr / 1000\n",
        "color_arr = np.random.rand(26)\n",
        "\n",
        "plt.title('Death Rate per 100k vs. Confirmed Cases')\n",
        "plt.xlabel('Death Rate per 100k')\n",
        "plt.ylabel('Confirmed Cases')\n",
        "plt.scatter(dr_arr,test_arr,s=cc_arr_sm,c=color_arr,alpha=0.5)"
      ],
      "metadata": {
        "id": "7P_wSslOLEqK",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "outputId": "32e2ca31-9281-4802-8862-20b671c18232"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7f0921a5e5e0>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3D Surface"
      ],
      "metadata": {
        "id": "q2U710IALI0n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Needed for creating 3D axes\n",
        "from mpl_toolkits import mplot3d\n",
        "\n",
        "fig_9 = plt.figure(figsize=(8,5),dpi=100)\n",
        "axes_9 = fig_9.add_axes([0.1,0.1,0.9,0.9], projection='3d')\n",
        "\n",
        "# Create a 3D scatterplot\n",
        "# Te darker points are represented that way to seem closer to you\n",
        "z_3 = 15 * np.random.random(100)\n",
        "x_3 = np.sin(z_3) * np.random.randn(100)\n",
        "y_3 = np.cos(z_3) * np.random.randn(100)\n",
        "\n",
        "axes_9.scatter3D(x_3, y_3, z_3, c=z_3, cmap='Blues')\n",
        "\n",
        "# You can create contour plots by defining a function for z based on x & y\n",
        "def get_z(x, y):\n",
        "    return np.sin(np.sqrt(x**2 + y**2))\n",
        "\n",
        "x_4 = np.linspace(-6, 6, 30)\n",
        "y_4 = np.linspace(-6, 6, 30)\n",
        "\n",
        "# Creates a rectangular grid out of 2 given 1D arrays\n",
        "x_4, y_4 = np.meshgrid(x_4, y_4)\n",
        "z_4 = get_z(x_4,y_4)\n",
        "\n",
        "# Change viewing angle to reorient camera 60 degrees and rotate 55\n",
        "axes_9.view_init(45,55)\n",
        "\n",
        "# Provide x, y, z, contours and color map\n",
        "#axes_9.contour3D(x_4,y_4,z_4,80,cmap='Blues')\n",
        "axes_9.set_xlabel('x')\n",
        "axes_9.set_ylabel('y')\n",
        "axes_9.set_zlabel('z')\n",
        "\n",
        "# You can create wireframes\n",
        "#axes_9.plot_wireframe(x_4,y_4,z_4,color='blue')\n",
        "\n",
        "# You can create surface plots which is wireframe with filled faces\n",
        "#axes_9.plot_surface(x_4,y_4,z_4, rstride=1, cstride=1,cmap='Blues',edgecolor='none')"
      ],
      "metadata": {
        "id": "tdJ3pEM_LLr3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 531
        },
        "outputId": "8c4cfe20-876f-4054-c002-7ea51d566fab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0, 'z')"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Heatmaps\n",
        "\n"
      ],
      "metadata": {
        "id": "GckCyc0kLaXG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# A heatmap is a color coded representation of data from a 2D list\n",
        "symptoms = [\"Coronavirus\",\"Influenza\",\"Pneumonia\",\"Dyspnea\"]\n",
        "dates = [\"Jun28\",\"Jul05\",\"Jul12\",\"Jul19\",\"Jul26\",\"Aug02\",\"Aug09\",\"Aug16\",\"Aug21\"]\n",
        "symp_per = np.array([[5.2, 5.5, 5.7, 5.6, 5.3, 5.1, 5.0, 4.9, 5.3],\n",
        "                    [3.5, 4.0, 4.3, 3.9, 3.5, 3.2, 2.7, 2.2, 2.0],\n",
        "                    [1.8, 2.2, 2.3, 2.2, 2.1, 1.9, 1.7, 1.4, 1.3],\n",
        "                    [1.0, 1.1, 1.1, 1.0, 0.9, 0.8, 0.8, 0.8, 0.7]])\n",
        "\n",
        "fig_10, axes_10 = plt.subplots()\n",
        "# Dfine data to use and color map\n",
        "im = axes_10.imshow(symp_per, cmap=\"Wistia\")\n",
        "\n",
        "# Add ticks at data points and labels\n",
        "axes_10.set_xticks(np.arange(len(dates)))\n",
        "axes_10.set_yticks(np.arange(len(symptoms)))\n",
        "axes_10.set_xticklabels(dates)\n",
        "axes_10.set_yticklabels(symptoms)\n",
        "\n",
        "# Rotate labels on the bottom so they don't overlap\n",
        "plt.setp(axes_10.get_xticklabels(), rotation=45, ha=\"right\",\n",
        "         rotation_mode=\"anchor\")\n",
        "\n",
        "# Loop over data dimensions and create text annotations.\n",
        "for i in range(len(symptoms)):\n",
        "    for j in range(len(dates)):\n",
        "        text = axes_10.text(j, i, symp_per[i, j],\n",
        "                       ha=\"center\", va=\"center\", color=\"k\",fontweight=\"bold\")\n",
        "        "
      ],
      "metadata": {
        "id": "sW5DMHgDLdnZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "outputId": "53630728-fa1f-4745-bb29-808d2d26f9d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Seaborn Tutorial\n",
        "“If Matplotlib “tries to make easy things easy and hard things possible”, seaborn tries to make a well-defined set of hard things easy too” – Michael Waskom (Creator of Seaborn)."
      ],
      "metadata": {
        "id": "9EKA5bJRYHzW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **'car_crashes' dataframe**\n",
        "#### Accidents in the states of the USA are examined. This is the data set of the cause of the accidents and the cost to the accident insurance companies.\n",
        "\n",
        "- total -> Number of drivers involved in fatal collisions per billion miles (5.900–23.900)\n",
        "- speeding -> Percentage Of Drivers Involved In Fatal Collisions Who Were Speeding (1.792–9.450)\n",
        "- alcohol -> Percentage Of Drivers Involved In Fatal Collisions Who Were Alcohol-Impaired (1.593–10.038)\n",
        "- not_distracted -> Percentage Of Drivers Involved In Fatal Collisions Who Were Not Distracted (1.760–23.661)\n",
        "- no_previous -> Percentage Of Drivers Involved In Fatal Collisions Who Had Not Been Involved In Any Previous Accidents (5.900–21.280)\n",
        "- ins_premium -> Car Insurance Premiums (641.960–1301.520)\n",
        "- ins_losses -> Losses incurred by insurance companies for collisions per insured driver (82.75–194.780)\n",
        "- abbrev -> USA states"
      ],
      "metadata": {
        "id": "i1zRJVjnadbj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###   **'tips' dataframe**\n",
        "#### It is a data set in which the values associated with the total paid account in a restaurant are kept.\n",
        "\n",
        "- total_bill -> The amount of the account paid in dollars (3.07$ — 50.81$)\n",
        "\n",
        "- tip -> Tip amount ( 1$ — 10$)\n",
        "\n",
        "- sex -> Gender of the person paying the bill (Male , Female)\n",
        "\n",
        "- smoker -> Is smoking on the table? (No , Yes)\n",
        "\n",
        "- day -> Which day people arriving (Thur,Fri,Sat,Sun)\n",
        "\n",
        "- time -> Time of day(Lunch, Dinner)\n",
        "\n",
        "- size -> Number of people sitting at the table (1–6)"
      ],
      "metadata": {
        "id": "FVoP7biIakdt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "qvxXljVNa2S2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Import Data"
      ],
      "metadata": {
        "id": "vzsXf4Eca-bH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# You can import custom data\n",
        "# cs_df = pd.read_csv('abc.csv')\n",
        "\n",
        "# Seaborn provides built in datasets\n",
        "print(sns.get_dataset_names())\n",
        "\n",
        "# Load a built in dataset based on US State car crash percentages\n",
        "crash_df = sns.load_dataset('car_crashes')\n",
        "crash_df.head()"
      ],
      "metadata": {
        "id": "izU7zhlYa8yL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "outputId": "05a86b22-e513-43b9-fcd6-f442514c5e49"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['anagrams', 'anscombe', 'attention', 'brain_networks', 'car_crashes', 'diamonds', 'dots', 'dowjones', 'exercise', 'flights', 'fmri', 'geyser', 'glue', 'healthexp', 'iris', 'mpg', 'penguins', 'planets', 'seaice', 'taxis', 'tips', 'titanic']\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total  speeding  alcohol  not_distracted  no_previous  ins_premium  \\\n",
              "0   18.8     7.332    5.640          18.048       15.040       784.55   \n",
              "1   18.1     7.421    4.525          16.290       17.014      1053.48   \n",
              "2   18.6     6.510    5.208          15.624       17.856       899.47   \n",
              "3   22.4     4.032    5.824          21.056       21.280       827.34   \n",
              "4   12.0     4.200    3.360          10.920       10.680       878.41   \n",
              "\n",
              "   ins_losses abbrev  \n",
              "0      145.08     AL  \n",
              "1      133.93     AK  \n",
              "2      110.35     AZ  \n",
              "3      142.39     AR  \n",
              "4      165.63     CA  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fabb8226-eb87-4781-ad8d-47a008743af2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total</th>\n",
              "      <th>speeding</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>not_distracted</th>\n",
              "      <th>no_previous</th>\n",
              "      <th>ins_premium</th>\n",
              "      <th>ins_losses</th>\n",
              "      <th>abbrev</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>18.8</td>\n",
              "      <td>7.332</td>\n",
              "      <td>5.640</td>\n",
              "      <td>18.048</td>\n",
              "      <td>15.040</td>\n",
              "      <td>784.55</td>\n",
              "      <td>145.08</td>\n",
              "      <td>AL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18.1</td>\n",
              "      <td>7.421</td>\n",
              "      <td>4.525</td>\n",
              "      <td>16.290</td>\n",
              "      <td>17.014</td>\n",
              "      <td>1053.48</td>\n",
              "      <td>133.93</td>\n",
              "      <td>AK</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18.6</td>\n",
              "      <td>6.510</td>\n",
              "      <td>5.208</td>\n",
              "      <td>15.624</td>\n",
              "      <td>17.856</td>\n",
              "      <td>899.47</td>\n",
              "      <td>110.35</td>\n",
              "      <td>AZ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>22.4</td>\n",
              "      <td>4.032</td>\n",
              "      <td>5.824</td>\n",
              "      <td>21.056</td>\n",
              "      <td>21.280</td>\n",
              "      <td>827.34</td>\n",
              "      <td>142.39</td>\n",
              "      <td>AR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12.0</td>\n",
              "      <td>4.200</td>\n",
              "      <td>3.360</td>\n",
              "      <td>10.920</td>\n",
              "      <td>10.680</td>\n",
              "      <td>878.41</td>\n",
              "      <td>165.63</td>\n",
              "      <td>CA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fabb8226-eb87-4781-ad8d-47a008743af2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fabb8226-eb87-4781-ad8d-47a008743af2 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fabb8226-eb87-4781-ad8d-47a008743af2');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data on tips\n",
        "tips_df = sns.load_dataset('tips')\n",
        "tips_df.head()"
      ],
      "metadata": {
        "id": "FOK7-h4RbNmi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "43537bb3-60c8-4693-e44c-21f116dc993a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_bill   tip     sex smoker  day    time  size\n",
              "0       16.99  1.01  Female     No  Sun  Dinner     2\n",
              "1       10.34  1.66    Male     No  Sun  Dinner     3\n",
              "2       21.01  3.50    Male     No  Sun  Dinner     3\n",
              "3       23.68  3.31    Male     No  Sun  Dinner     2\n",
              "4       24.59  3.61  Female     No  Sun  Dinner     4"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-41f71fee-be8c-46fa-8f71-9af008479a1f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total_bill</th>\n",
              "      <th>tip</th>\n",
              "      <th>sex</th>\n",
              "      <th>smoker</th>\n",
              "      <th>day</th>\n",
              "      <th>time</th>\n",
              "      <th>size</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>16.99</td>\n",
              "      <td>1.01</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10.34</td>\n",
              "      <td>1.66</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>21.01</td>\n",
              "      <td>3.50</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>23.68</td>\n",
              "      <td>3.31</td>\n",
              "      <td>Male</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>24.59</td>\n",
              "      <td>3.61</td>\n",
              "      <td>Female</td>\n",
              "      <td>No</td>\n",
              "      <td>Sun</td>\n",
              "      <td>Dinner</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41f71fee-be8c-46fa-8f71-9af008479a1f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-41f71fee-be8c-46fa-8f71-9af008479a1f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-41f71fee-be8c-46fa-8f71-9af008479a1f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The titanic dataset will be used for some exerises in b/w so , Stay tuned !!\n",
        "You can always write titanic.head() to visualise the top 5 rows of the dataframe"
      ],
      "metadata": {
        "id": "-d4bX127u9rI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "titanic = sns.load_dataset('titanic')        \n",
        "titanic.head()"
      ],
      "metadata": {
        "id": "82VFrdspu08P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Distribution Plots"
      ],
      "metadata": {
        "id": "6JQw38OxbSRQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# kde=False removes the KDE\n",
        "# Bins define how many buckets to divide the data up into between intervals\n",
        "\n",
        "# print(crash_df['not_distracted'])\n",
        "# sns.displot(crash_df['not_distracted'], kde=False, bins=20)\n",
        "# Visualise the distribution of alcohol impaired people who were involved in the car crash\n",
        "sns.displot(crash_df['alcohol'], kde=False, bins=20)          "
      ],
      "metadata": {
        "id": "OejQWenEbd_K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "75d32fdb-4fc3-4f97-dc9f-c72fe1a4c0b2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0921861310>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot(tips_df['total_bill'], stat = 'density', kde = True)  # KDE - Dark blue "
      ],
      "metadata": {
        "id": "n78A3nGAcdkb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "91fffd80-d9b2-403e-b201-2c73384df888"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7f0921848ee0>"
            ]
          },
          "metadata": {},
          "execution_count": 40
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise\n",
        "#### Say, we want to visualise the distribution of the fares in the titanic dataset using displot.\n",
        "\n",
        " Fill in the ( )"
      ],
      "metadata": {
        "id": "efTMQ9g6vi2I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.displot()                  # you can add color = 'red' in the arguments too"
      ],
      "metadata": {
        "id": "a0nrFNY1v_ds"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## KDE Plot\n",
        "- A kernel density estimate (KDE) plot is a method for visualizing the distribution of observations in a dataset, analogous to a histogram\n",
        "-  KDE represents the data using a continuous probability density curve\n",
        "\n"
      ],
      "metadata": {
        "id": "RuRNZuAwdUws"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get just the KDE plot\n",
        "# Among all the people involved in car crashes, most of them were drunk\n",
        "sns.kdeplot(crash_df['alcohol'])"
      ],
      "metadata": {
        "id": "TiBVt7Xydvmw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Joint Plot\n",
        "- a way of understanding the relationship between two variables and the distribution of individuals of each variable"
      ],
      "metadata": {
        "id": "3dkUG1HYdDqA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#  With kind you can create a regression line with kind='reg'\n",
        "sns.jointplot(x='speeding', y='alcohol', data=crash_df, kind='reg')  # Observe that they are almost linearly related"
      ],
      "metadata": {
        "id": "N9vm59qhdGi_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.jointplot(x='speeding', y='alcohol', data=crash_df, kind='scatter')"
      ],
      "metadata": {
        "id": "SnJByunddXO4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Pair Plots\n",
        "- Pair Plot plots relationships across the entire data frames numerical values\n",
        "- perform the jointplot for every possible pair of **numerical columns** in the dataframe."
      ],
      "metadata": {
        "id": "DSM0pWEcd0y4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.pairplot(tips_df)"
      ],
      "metadata": {
        "id": "Hng7S-wFd_nI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# With hue you can pass in a categorical column and the charts will be colorized\n",
        "# You can use color maps from Matplotlib to define what colors to use\n",
        "\n",
        "sns.pairplot(tips_df, hue='sex', diag_kind='kde')  # diag_kind='kde'     # size is a discrete variable"
      ],
      "metadata": {
        "id": "O1KBaN8Ed_dr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Styling"
      ],
      "metadata": {
        "id": "mGtCa3CoeimB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# You can set styling for your axes and grids\n",
        "# white, darkgrid, whitegrid, dark, ticks\n",
        "sns.set_style('white')\n",
        "\n",
        "# You can use figure sizing from Matplotlib\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "# Set the parameters that control the scaling of plot elements.\n",
        "# Change size of lables, lines and other elements to best fit\n",
        "# how you will present your data (paper, talk, poster)\n",
        "\n",
        "# sns.set_context('paper', font_scale=1.4)\n",
        "# sns.set_context('notebook', font_scale=1.4)\n",
        "# sns.set_context('talk', font_scale=1.4)\n",
        "# sns.set_context('poster', font_scale=1.4)\n",
        "\n",
        "sns.jointplot(x='speeding', y='alcohol', data=crash_df, kind='reg')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "O5Rc89CxetCz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Categorical Plots\n",
        "### Count Plot and Bar Plot\n",
        "These very similar plots allow you to get aggregate data off a categorical feature in your data.\n",
        "- **barplot** is a general plot that allows you to aggregate the categorical data based off some function, by default the mean:\n",
        "- **count plot** is like a bar plot, but the estimator is counting \n",
        " the number of occurences, which is why we only pass the x value:"
      ],
      "metadata": {
        "id": "kMs2X2bsfATn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x='sex',data=tips_df)"
      ],
      "metadata": {
        "id": "tvxo8D-HfeHF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#  x - categorical,  y = numerical \n",
        "sns.barplot(x='sex',y='total_bill',data=tips_df)  # mean is the default estimator\n",
        "plt.title(\"Based on Mean\")\n",
        "plt.show()\n",
        "sns.barplot(x='sex',y='total_bill',data=tips_df, estimator=np.median)\n",
        "plt.title(\"Based on Median\")\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "2HxfI2Cxfj3R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Q.) Let's say we want to know \"**how many**\" males and females survived. \n",
        "\n",
        "Which plot should we use ? Use it to get the below plot.  \n",
        "\n",
        "##### HINT : There is a column in the dataframe names \"alive\" which is either 'yes' or 'no'"
      ],
      "metadata": {
        "id": "DCiovoCfxrnD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.(x= '', hue ='', data=titanic)            \n"
      ],
      "metadata": {
        "id": "ArLMIPYOyOYT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Matrix Plots"
      ],
      "metadata": {
        "id": "G3k5i189fw7U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Heatmaps\n",
        "- Plot rectangular data as a color-encoded matrix."
      ],
      "metadata": {
        "id": "EsvKdiKif1qY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.set_context('paper', font_scale=1.4)\n",
        "\n",
        "# To create a heatmap with data you must have data set up as a matrix where variables are on the columns and rows\n",
        "crash_mx = crash_df.corr()   \n",
        "print(crash_mx) \n",
        "\n",
        "# Correlation tells you how influential a variable is on the result\n",
        "# Observe that n previous accident, alcohol is heavily correlated with accidents, while the insurance premium is not\n",
        "\n",
        "# Create the heatmap, add annotations and a color map\n",
        "sns.heatmap(crash_mx, annot=True, cmap='Blues')   # annot = True ensures the value of correlation is shown \n",
        "plt.show()\n",
        "plt.figure(figsize=(8,6))"
      ],
      "metadata": {
        "id": "B51CVG39f5U_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.set_context('paper', font_scale=1.4)\n",
        "\n",
        "# We can create a matrix with an index of month, columns representing years and the number of passengers for each\n",
        "# We see that flights have increased over time and that most people travel in July and August\n",
        "\n",
        "flights = sns.load_dataset(\"flights\")\n",
        "print(flights.head())\n",
        "print(\"\\n\")\n",
        "flights = flights.pivot_table(index='month', columns='year', values='passengers')\n",
        "print(flights)                                                 \n"
      ],
      "metadata": {
        "id": "4xKx_uOlgvWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## One final Exercise     \n",
        "Now that you have the data set up as a matrix can you plot a heatmap,using the previous example ?\n",
        "\n",
        "Fill in the ( )."
      ],
      "metadata": {
        "id": "NahiW4HZuGZM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# sns.heatmap()\n",
        "\n",
        "# You can separate data with lines by adding (linecolor='white', linewidth=1) in the arguments\n",
        "sns.heatmap()      "
      ],
      "metadata": {
        "id": "QyiV_rgEt5nH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Regression Plots\n",
        "In the simplest invocation, both functions draw a scatterplot of two variables, x and y, then it fit the regression model y ~ x and plot the resulting regression line and a 95% confidence interval for that regression:"
      ],
      "metadata": {
        "id": "8V4ORG7eh63m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tips_df.head()"
      ],
      "metadata": {
        "id": "-XSU0dQOiEt-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(5,5))\n",
        "sns.set_context('paper', font_scale=1.4)\n",
        "\n",
        "# We can plot a regression plot studying whether total bill effects the tip\n",
        "# hue is used to show separation based off of categorical data\n",
        "# We see that males tend to tip slightly more\n",
        "# You can effect the scatter plot by passing in a dictionary for styling\n",
        "\n",
        "sns.lmplot(x='total_bill', y='tip', hue='sex', data=tips_df, markers=['o', '^'],    # different markers for men and women\n",
        "           scatter_kws={'s': 100})"
      ],
      "metadata": {
        "id": "55Vg988NiLJH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Scipy"
      ],
      "metadata": {
        "id": "nokChjuXDYUC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import scipy.stats as ss\n",
        "from scipy.stats import norm\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "781Czk1RD5mK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#gaussian distribution plot \n",
        "\n",
        "#lets define the mean and variance of the distribution\n",
        "mu = 3\n",
        "sigma = 1\n",
        "\n",
        "#lets define the distribution\n",
        "dist = norm(mu,sigma)\n",
        "\n",
        "#lets now display the probability density function for the distribution\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "x = np.linspace(norm.ppf(0.01),norm.ppf(0.99), 100)\n",
        "ax.plot(x, norm.cdf(x),'r-', lw=3, alpha=0.6, label='norm cdf')\n",
        "ax.plot(x, norm.pdf(x),'b-', lw=3, alpha=0.6, label='norm pdf')\n",
        "plt.legend(['cdf','pdf'])\n",
        "plt.style.use('ggplot')"
      ],
      "metadata": {
        "id": "PHCO2X7-DXXe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "902aceea-2556-4123-cf16-0a34b242f2b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#stats from gaussian distribution\n",
        "# mean, var, skew, kurt = dist.stats(moments='mvsk')\n",
        "\n",
        "#lets sample a few data points from the distribution\n",
        "num_points = 1000\n",
        "vals = dist.rvs(size=num_points)\n",
        "\n",
        "sample_mean = round(np.mean(vals),3)\n",
        "sample_variance = np.sum(np.square(vals - sample_mean))/(np.size(vals)-1)\n",
        "skewness = round(ss.skew(vals),3)\n",
        "kurtosis = round(ss.kurtosis(vals),3)\n",
        "\n",
        "print('The mean of the samples is = ',sample_mean)\n",
        "print('The variance of the samples is = ',sample_variance)\n",
        "print('The skewness of the samples is = ',skewness)\n",
        "print('The kurtosis of the samples is = ',kurtosis)"
      ],
      "metadata": {
        "id": "XLleCfEJDkY3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a470ddc-ccae-4fb6-b4ca-66f88d7c122c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The mean of the samples is =  3.014\n",
            "The variance of the samples is =  0.9589068383396178\n",
            "The skewness of the samples is =  -0.042\n",
            "The kurtosis of the samples is =  -0.11\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#find roots of single variable function\n",
        "from scipy.optimize import root_scalar\n",
        "#lets define an objective function\n",
        "def f(x):\n",
        "  return x ** 2 - 5*x + 6\n",
        "\n",
        "def fprime(x):\n",
        "  return 2*x - 5\n",
        "  \n",
        "#we need to provide an initial guess to the algorithm\n",
        "initial_point = 2.2\n",
        "\n",
        "result = root_scalar(f,x0=initial_point,fprime=fprime,method='newton')\n",
        "print(result)"
      ],
      "metadata": {
        "id": "bdVFAVztDhRO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a2c4bf74-258b-4983-b53a-4f1b87fc8668"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      converged: True\n",
            "           flag: 'converged'\n",
            " function_calls: 10\n",
            "     iterations: 5\n",
            "           root: 2.000000000000001\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#exercise - find a root of the following function x^2 - sin(x)\n",
        "from scipy.optimize import root_scalar\n",
        "from math import sin\n",
        "\n",
        "def f(x):\n",
        "  return x ** 2  - np.sin(x)\n",
        "\n",
        "def fprim(x):\n",
        "  return 3*x**2 - cos(x)\n",
        "\n",
        "init_point = 0.1\n",
        "\n",
        "result = root_scalar(f,x0=initial_point,fprime=fprime,method='newton')\n",
        "print(result)"
      ],
      "metadata": {
        "id": "-PX-pWaiY6ya",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b54bdcf6-eda2-49a1-feed-dba2c3953e0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      converged: False\n",
            "           flag: 'convergence error'\n",
            " function_calls: 100\n",
            "     iterations: 50\n",
            "           root: -7.360102093803626e-05\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#optimize single variable function\n",
        "from math import cos,sin\n",
        "from scipy.optimize import minimize\n",
        "#lets define an objective function\n",
        "def objective(x):\n",
        "  return x ** 2 + 2*cos(x) + 4*x\n",
        "\n",
        "#we need to provide an initial guess to the algorithm\n",
        "initial_point = 2 \n",
        "result = minimize(objective,initial_point,method='Nelder-Mead', tol=1e-6)\n",
        "print(result)"
      ],
      "metadata": {
        "id": "TgCCDzjrDqfx",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53744145-fcda-4e1c-f267-3cff9d2b6426"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       message: Optimization terminated successfully.\n",
            "       success: True\n",
            "        status: 0\n",
            "           fun: -5.3576393178310795\n",
            "             x: [-2.554e+00]\n",
            "           nit: 27\n",
            "          nfev: 54\n",
            " final_simplex: (array([[-2.554e+00],\n",
            "                       [-2.554e+00]]), array([-5.358e+00, -5.358e+00]))\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#exercise - minimize the following function x^2 - 2sin(x)\n",
        "from math import cos,sin\n",
        "from scipy.optimize import minimize"
      ],
      "metadata": {
        "id": "pKTpuClrY0nG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#optimize multivariable function\n",
        "from scipy.optimize import least_squares\n",
        "from math import cos,sin\n",
        "import numpy as np\n",
        "#here X = [x,y] is a list\n",
        "#The function is f(x,y) = x^2 + y^2 + 6\n",
        "def f(X):\n",
        "  return (X[0]) ** 2 + (X[1]) ** 2 + 6\n",
        " \n",
        "init_points = np.array([1,1])\n",
        "result = least_squares(f,init_points)\n",
        "print(result)"
      ],
      "metadata": {
        "id": "yPptyAxYDws9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7f62ea4-7969-45a7-ef5d-8e8649b65eb1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     message: `gtol` termination condition is satisfied.\n",
            "     success: True\n",
            "      status: 1\n",
            "         fun: [ 6.000e+00]\n",
            "           x: [ 1.110e-16 -2.220e-16]\n",
            "        cost: 18.0\n",
            "         jac: [[ 0.000e+00 -0.000e+00]]\n",
            "        grad: [ 0.000e+00  0.000e+00]\n",
            "  optimality: 0.0\n",
            " active_mask: [ 0.000e+00  0.000e+00]\n",
            "        nfev: 2\n",
            "        njev: 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Scikit-learn**\n",
        "\n",
        "* Train-Test Split\n",
        "* Confusion Matrix\n",
        "* Metric Score\n",
        "* Logistic Regression\n",
        "* K-Nearest Neighbours\n",
        "* Decision Tree\n",
        "* Random Forest(Bagging Method)\n",
        "* AdaBoost(Boosting Method)\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "UQ7kYvlm4Bax"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing scikit-learn"
      ],
      "metadata": {
        "id": "eCM-5jsHH13c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "GSJcHKP6lg9E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sklearn\n",
        "from sklearn.datasets import load_iris"
      ],
      "metadata": {
        "id": "PCLfvpCryerK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X, y = load_iris(return_X_y=True)"
      ],
      "metadata": {
        "id": "3QipJATR748Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_del=np.delete(X,2,1)\n",
        "X_del=np.delete(X_del,2,1)\n"
      ],
      "metadata": {
        "id": "KTo-heZLkqj1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
      ],
      "metadata": {
        "id": "xSVbe6Yj9RV2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "clf = LogisticRegression(max_iter=1000).fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "zuJ6wSjZ7VoA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "clf.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Ji6dv7r8P90",
        "outputId": "ae6bf10e-7994-4a7e-b3f0-3d4160323893"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.975"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=X_del[:,0], y=X_del[:,1], hue=y[:])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 285
        },
        "id": "kKXX4z6tluKT",
        "outputId": "468a373b-afa8-4ec8-962d-b4facccbc5d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dKVH4Gar98MR",
        "outputId": "8b43e193-22fd-403e-b2af-2d3ffc71339c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 2, 0, 2, 0, 0, 2, 2, 2, 0, 0, 2, 2, 2, 0, 2, 2, 2, 2,\n",
              "       0, 1, 0, 2, 0, 1, 1, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import metrics\n",
        "print(\"Accuracy of Logistic Regression model is:\",\n",
        "metrics.accuracy_score(y_test, y_pred)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iuUqMaVM-Btt",
        "outputId": "7c2493e8-b673-49f7-b6bc-6156f5d0521a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Logistic Regression model is: 93.33333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "confusion_matrix(y_test, y_pred, labels = [0,1,2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sJrTAWVr-TO5",
        "outputId": "46da1535-146a-425d-819b-f82e62f8b06b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11,  0,  0],\n",
              "       [ 0,  4,  1],\n",
              "       [ 0,  1, 13]])"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import ConfusionMatrixDisplay\n",
        "import matplotlib.pyplot as plt\n",
        "fig = plt.figure(figsize=(15,15))\n",
        "cm = confusion_matrix(y_test, y_pred, labels = [0,1,2])\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=[0,1,2])\n",
        "disp.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "V0YwJELX-i5-",
        "outputId": "fe054b5c-f60f-4940-b6be-4c6edadd78e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "clf_KNN = KNeighborsClassifier(n_neighbors=8)\n",
        "clf_KNN.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "53zIAXiNEKvu",
        "outputId": "a2de7786-3023-43eb-b1ab-2312dfbfd8d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=8)"
            ],
            "text/html": [
              "<style>#sk-container-id-16 {color: black;background-color: white;}#sk-container-id-16 pre{padding: 0;}#sk-container-id-16 div.sk-toggleable {background-color: white;}#sk-container-id-16 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-16 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-16 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-16 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-16 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-16 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-16 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-16 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-16 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-16 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-16 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-16 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-16 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-16 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-16 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-16 div.sk-item {position: relative;z-index: 1;}#sk-container-id-16 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-16 div.sk-item::before, #sk-container-id-16 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-16 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-16 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-16 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-16 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-16 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-16 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-16 div.sk-label-container {text-align: center;}#sk-container-id-16 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-16 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-16\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier(n_neighbors=8)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" checked><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier(n_neighbors=8)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_KNN.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nRIR45MSE5lP",
        "outputId": "42057161-469a-4654-bba1-64bb2dc5f007"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.975"
            ]
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf_KNN.predict(X_test)\n",
        "print(\"Accuracy of K-Nearest Neighbour is:\",\n",
        "metrics.accuracy_score(y_test, y_pred)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xIIEkedmE7J2",
        "outputId": "f12562b9-a36d-4266-8073-7beb7c5d9190"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of K-Nearest Neighbour is: 93.33333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cm = confusion_matrix(y_test, y_pred, labels = [0,1,2])\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=[0,1,2])\n",
        "disp.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "Q5b9-nJYFFhs",
        "outputId": "ccfd6d08-23ac-4d57-dea6-c9468b5bf73f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "clf_tree = tree.DecisionTreeClassifier()\n",
        "clf_tree.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "AQWmRi3F_-L9",
        "outputId": "62a06c95-950c-4fc1-8625-1ef116fa0570"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DecisionTreeClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-17 {color: black;background-color: white;}#sk-container-id-17 pre{padding: 0;}#sk-container-id-17 div.sk-toggleable {background-color: white;}#sk-container-id-17 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-17 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-17 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-17 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-17 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-17 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-17 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-17 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-17 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-17 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-17 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-17 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-17 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-17 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-17 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-17 div.sk-item {position: relative;z-index: 1;}#sk-container-id-17 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-17 div.sk-item::before, #sk-container-id-17 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-17 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-17 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-17 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-17 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-17 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-17 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-17 div.sk-label-container {text-align: center;}#sk-container-id-17 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-17 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-17\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" checked><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 154
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_tree.score(X_train,y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X7__7VTrARrf",
        "outputId": "f27c981c-2f26-43a8-d3f9-25e0bcb430bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,15))\n",
        "_ = tree.plot_tree(clf_tree, filled = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 846
        },
        "id": "kCiZBVS_AXgs",
        "outputId": "8239c48d-f69b-41d6-9e51-c8849bcd8c7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAM9CAYAAAB5Rim2AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAEAAElEQVR4nOzdZXRVRxeA4ffG3UOCuwR3d3d3KMVKS5EC/fDirgXaQotLcYfi7u5OPMSIJ8Ttfj8Cl4TchChB9rNW1jo9M2dmziWF7MzMHoVSqUQIIYQQQgghRPpo5PQAhBBCCCGEEOJLJMGUEEIIIYQQQmSABFNCCCGEEEIIkQESTAkhhBBCCCFEBkgwJYQQQgghhBAZoJXTAxBfB319fe/IyEibnB6HEOLro6en9zoiIsI2p8chhBBCfEghqdFFVlAoFMqosPicHoYQ4iuka6iBUqlU5PQ4hBBCiA/JMj8hhBBCCCGEyAAJpoQQQgghhBAiAySYEkIIIYQQQogMkGBKCCGEEEIIITJAgikhhBBCCCGEyAAJpoQQQgghhBAiAySYEkIIIYQQQogMkGBKCCGEEEIIITJAgikhhBBCCCGEyAAJpoT4TMyaMx1dQ40kX9VqVlJb987d28ycPY3mrRqTv5AthqY65MpjTp36NZgzbyZ+fn6p9tWsZaNkfQ0eMiA7XuuLktnPNT0iIyNZ9c9fNGvZiPyFbDE216NoiQI0b9WYufNn4eXllWV9CSGEECJ7aOX0AIQQ6TPq1xGs+vuvZPeDg4O5fecWt+/c4u9/VrJ9627q1qmXAyP8Mn3Kz/Xx40f07NsNe/uXSe67e7jj7uHOhYvnKVOmHB3adcxUP0IIIYTIXjIzJcRnpkD+Avi/DsH/dQgXz11NVh76JhSAqlWqsXTxcm5fv4+nmy/PnziyeOHvGBsb4+PrQ6eu7XB1c1Xbx6H9R1V91KldN1vf50uRFZ9rWtg72NOqbTPs7V+SL28+/li+kicPX+Lt7s/jBy9Y8/d6GtZvhIaG/PUshBBCfO5kZkqIz4xCocDIyCjF8sqVq9D/+4HJZkcsLS0ZMewXalSvScMmdQkJCWHBorms/OOfZG3o6+urrjU1NbNu8F+wrPhcP0apVPLj0EH4+PpgZ1eaMycuYGlpqSo3NzeneLHi9Puuf2ZfRwghhBCfgPzqU4gvzM8/DU91mVn1ajVo3aotAKdPn/xUw0q3gIAAdu3ekdPDUPkUn+vhI4e4cvUyCoWCdas3JgmkhBBCCPHlkWBKiCyybMVSVTKH4yeOqa0THBxM8VKF0DXUoH6j2sTFxWXLWEqVLAWAl/fnlcQgLi6OY8eP0vu7HhQqlpffpk7M6SGlS2Y/142b1gMJgVmVylWzbFxCCCGEyBkSTAmRRX4ZMZqGDRoD8OPQQWozv/0yejhur9wwMjJiw7ot2bbEztfXBwATE5NsaT+9nj1/xqTfxlO0RAE6dmnL3n27iYqKwsjIOKeHli6Z+Vzj4uI4e+40AI0bNUlSFhMTk/nBCSGEEOKTk2BKiCyiUChYu3oDZmZmeL/25ucRPyYp371nJ9t3bgVg0YKlFC1SNFvGER0drZoZq16tZrb0kRbBwcGsWfcP9RrWomKVMiz5fRFe3l5oa2vTtk17dm7bw/Urt9U+GxUVRWhoaIa/oqOjs/x9Mvu5Pnv2lIiICABKlbTD3cOdH4cOIk8Ba4zMdDG3NqJFqybs3bc7S8cthBBCiOwjCSiEyEL58+Xnj2Ur+a5/bw4e2s+mzRv4vt8APDw9GDHqZwDatmnPwP6Ds20My1csxfu1NwCDBv6Qbf2oEx8fz5mzp9ny7yYOHt5PZGSkqqxihUr07dOPnt17Y21tnWo7w0b8xJatmzI8jt8mTWXK5OkZfl6dzH6u7h7uqmsPTw+q1axIQECA6l54eDjnL57j/MVz9D12hLWrN6BQKDI/cCGEEEJkG5mZEiKLde/Wk57dewPw67hRODo5MnjIAAIDA8llnYtVf67Otr5v37nFzDnTAWjcqCltW7fLtr4Ss3ewZ+r0yRQvVYi2HVqyc/d2IiMjsbWxZdTIMdy58YAbV+8wYtgvHw2kPkdZ8bmGhASrrqfPnEJISAgzps3Cyf4VIQERXLt8i0YNE5b//bttM0uXLc6SsQshhBAi+8jMlBDZYMWyv7h67TJur9xo0Kg2vn6+AKxauYZcuXJlS5/e3t707N2V6OhorKysWPPP+mzpJzEnZycG/fA9V69dUd3T09OjXdsO9O3dj2ZNm2doX9ja1RtYu3pDVg41w7Lqc42Pj1ddR0dHM2fWfP43ZpzqXuVKVTh84CjValXi2bOnLF66gJ9/Gp4kjb0QQgghPi8yMyVENjA1NWXt6o1oaGioAqmBAwZn20zRmzdv6NC5Da/cX6Gvr8/eXQfJlzdftvSV2KtXbqpASqFQ8MuI0bg5efHvpu20bNHqiz/DKis/VwNDQ9W1paUlw4aOSFZHW1ubyROmAAmp4xMHqUIIIYT4/MjMlBDZpETxkhgbGxMcnLC8q1ePPtnST1RUFN16dub+g3toaWmxdctOataolS19fcjczBxTU1OCg4NRKpUs/+N3rl2/Sp/e39G9a08sLCwy1G5UVFSmMtzp6Oigo6OT4effjSErP1crSyvVdZnSZVOccapSpZrq+vmLZzRp3DTDfQohhBAie8nMlBDZZMjQQapACmD4L0NV2dyySnx8PN8P7Mu582cAWPXnatq8PVj2UyhfvgJuTl5s3rCVJo2boaGhwc1bN/hl9HAKFs1D915dOHj4QLoDo2EjfsLSxiTDXwsWzc3Ue2XH51qieEnVtY2NbYr1ctvmVl2Hhr7JVJ9CCCGEyF4STAmRDVb98xcnTx0HYNb0Oejo6PDixXMm/jbuI0+mz7ARP7L/wF4A5s5eQL/v+mdp+2mhp6dHj+69OHr4BPbPXZgxbRbFihUnOjqag4f2071nZwoWzcMvY4Zz6/bNTz6+jMiOz9XKyooC+QsA4OXlmWI9D08P1bWJiWmm+xVCCCFE9pFgSogs9uLlCyb9Nh6AwYOGMG7sRKa+TdP99z8rOX3mVJb089vUiazfuA6AMaP+x6+jx2ZJu5mRL28+JoybzJMHLzh76iIDvh+IsbEx/v7+/P3PSuo2qEm5SnbMXzgHt1duKbazdvUGosLiM/yVmbTo2fm5tm6dMLv1+MkjwsLC1Na5efO66rp8uQpZ1rcQQgghsp4EU0JkodjYWAYM+o7w8HCKFSvOwnlLAPh1zDjq1K6LUqlkyE8DCQwMzFQ/K/5cxqIlCwDo17c/8+YszPTYs1qd2nX5e+Va3Jy8WL9mEw3rN0KhUPDy5QumzZhCCbvC9OzTLaeHmUR2f65DBv2EhoYGQUFBLFuxJFl5ZGQkc+bPAqBA/gLUqJ5zhy4LIYQQ4uMkmBIiC82ZN5M7d2+jqanJhrWbMXybwU1DQ4P1azdjbGyMh6cHI0cNy3AfO3dtZ9yEX4GEM48WLVhKaGhoil9KpTJL3i2jDAwM6NP7O04cO8PzJ45MmTyNwoWLoFQquXv3do6OLbGs+FxnzZmOrqEGuoYauLi6JCsvU6YsgwcNAWD23JlMmTaJl/YvCQgI4OKlC7Rq2wwHB3sgYXmhlpbkCBJCCCE+Z/IvtRBZ5MbN6yxYNA+A8WMnUr1ajSTlhQoWYumi5fzw00B27dlB2zbt6NG9V7r7Wbt+teoH+bPnTmOTN/WMeS+eOlGoYKF095MdChUsxG+TpjF54lQuXrrA6TMnc3pIKp/qc126aDleXl4c/u8gCxfPZ+Hi+UnKFQoFs2fOo1vXHuluWwghhBCflsxMCZEFwsLCGDC4H3FxcVSpXJXJE6eqrdfvu/507NAZgF/GDE+SbOBbolAoaFC/IbNmZC7r3pdIW1ubPTv3s3HdFho2aIyVlRU6Ojrkz5efXj36cOXijSSH+QohhBDi86XI6SVA4uugUCiUUWHxOT2ML9qsOdOZPXcmBQsU5OUz50/Wb7OWjbh46QLf9fmetas3fLJ+hUgrXUMNlEqlIqfHIYQQQnxIZqaEEEIIIYQQIgMkmBLiM+Pq5qpKYlCtZqVs6aNZy0aqPi5eupAtfQghhBBCfO0kmBJCCCGEEEKIDJA9UyJLyJ6pzIuOjiY6OjrJPU1NTfT19bO8r4iICOLi4pLc09bWRldXN8v7EiKzZM+UEEKIz5UEUyJLSDAlhMguEkwJIYT4XMkyPyGEEEIIIYTIAAmmhBBCCCGEECIDJJgSQgghhBBCiAyQYEoIIYQQQgghMkCCKSFy2IWL51VnPrm4umR5+4OHDEDXUINmLRtledufwt59u2nVtjl5C+bC1NKAMhVKMnb8GDy9PLOsD08vT2bMmkr1WpWxyWuBubURduWK07VHJ/5a9QexsbGpPh8ZGcmqf/6iWctG5C9ki7G5HkVLFKB5q8bMnT8LLy+vj47h4OEDdO/VhWIlC2Jsrkf+QrbUrledsePHcO/e3ax6VSGEEEJkIa2cHoAQQqgTHx/PoB/6s23Hv0nuOzjYs+LPZWzb8S/7dh+iRvWamepnz95d/DziR4KDg5Pcd3JyxMnJkcP/HaRPr+8wMzNT+/zjx4/o2bcb9vYvk9x393DH3cOdCxfPU6ZMOTq066j2+eDgYPp+34uTp44nue/j64OPrw937t4mPj6eSpUqZ/gdhRBCCJE9ZGZKCPFZmjt/liqQGjhgMPfvPOGVszc7t+0hX958+Pn50a1HJ3x8fDLcx4GD++g3oA/BwcHUrFGLndv24PDCFa9Xfty8epc5s+ZjZ1c6xeftHexp1bYZ9vYvyZc3H38sX8mThy/xdvfn8YMXrPl7PQ3rN0JDQ/1ftZGRkbTv1JqTp46jr6/PxPGTuXn1Ll6v/HB86cbe3Qfp0a0XBgYGGX5HIYQQQmQfOWdKZAk5Z0pkJS8vL+zKFSMiIoKB/Qex6q81ScrtHeypVrMiERERjBw+ikULlqa7Dx8fH8pXtiMwMJAe3Xqxcf2WFIMedZRKJU2aN+DK1cvY2ZXmzIkLWFpapmsM02b8xvyFczEyMuLE0TNUrVItva/xTZBzpoQQQnyuZGZKCPHZ+XfrJiIiItDW1mb61NnJyosXK86A/oMA2LRlAzExMenuY+HieQQGBmJrY8vKP/9JVyAFcPjIIa5cvYxCoWDd6o3pDqR8fHxYumwxAL9NmiaBlBBCCPEFkmBKiCxy7PhRWrVtjm0+SyxyGVOtZiWWrVhKbGwsm7dsVCWZ+FBqCShcXF1UZRcunicqKorFSxZQpXoFzK2NyJXHnBatm3L8xLEUx/UlJqA4duIoAHXr1MfGxkZtna6duwMJe44uXb6Yrvajo6NVSwi/6/s9RkZG6R7jxk3rAaherQZVKldN9/PbdvxLdHQ0urq6DBrwQ7qfF0IIIUTOk2BKiCwwY9ZUOnZpy9lzpwkMDCQsLIyHjx4wfuL/aNOuBdEx0Znu403oG5o0b8DkqRN5/OQR4eHhBAcHc/7CWTp0bsPa9auz4E1ynlKp5P6DewDUqF4jxXpVKldFU1MTQFU/re7cvY2/vz8AjRs1TVKWllmuuLg4zp47/fb5Jul+HuDkqRMAVK1SDRMTE9X9j2UOFEIIIcTnQ4IpITLp4KH9zJ2fsBStWtXqHPvvFJ5uvjy6/5wJ4yZx6cpFFi9dmOl+fh07ihcvn7NowVKeP3HE082Xg/uOULhwEQDGjh+TqWQMKQkNDc3UV3r3ZXp4ehAWFgZA4UJFUqynp6dHbtvcANjbv0hXH3fv3VFdlyplx7XrV2nfqQ0WuYwxMtMlTwFren/Xgzt3b6t9/tmzp0RERCQ8X9IOdw93fhw6iDwFrDEy08Xc2ogWrZqwd9/uFMdw737CGEqWLEVkZCSz5kynZJmiGJnpYmSmS7WalVi8dKGqHyGEEEJ8fiQ1uhCZoFQqmfjbeABK25XhxNEzGBoaAmBpacmMabOxsrLmf+NGZ7ovd/dXnD11MUkq8JYtWrEn736q1KhAeHg4+w7s4achP2e6r8QsbUw+XikVL546UahgoTTXD3g7YwRgnStXqnWtrXPh7uGOf0BAusbk4eGuuj567D9GjhpGXFyc6p6/vz979+3mwMF9rFj2F4MHDknyvHui5z08PahWsyIBicYQHh7O+YvnOH/xHH2PHWHt6g0oFO/zJ0RERKjqa2tr06hpvSQBXkxMDA8fPeDhowfs3rOT/w4ex9raOl3vKIQQQojsJzNTQmTCjZvXcXR0AGD61JmqQCqxYUNHUDAdwURKunftqfZMpbJly1GhfEUA7txRP5PyJQkLD1Nd6+nqpVpXX18fSJg9S4/gkPdnSo3+dSSWFpZsWv8vPp6BBPi8Yd+eQxQuXIS4uDhGjhrGjZvXkzwfkuj56TOnEBISwoxps3Cyf0VIQATXLt+iUcOE5X//btusSjSh6j/RmVYbNq7j7r079OjWi/t3nvAmMJJnjx0YNnQEkLCEcfCPA9L1fkIIIYT4NCSYEiITrt+4BoCWlhYtmrdSW0dDQ4OWLdSXpUezps1TLCtWrDgAr31eZ7qfD0WFxWfqKz2zUp9KfPz7NP7R0dHs3X2Qnj16Y2pqiqGhIW1ateXYf6fQ0dEhLi6O2XNnpvr8jGmzmTBuMnnz5EVXV5fKlapw+MBR1RlVi5cuSLJcL16Z9PkWzVuxeeNW7ErZoaOjQ5HCRVi6eDkDBwwG4PiJo9y8dSNbPgshhBBCZJwEU0JkgpubKwD58uVHTy/lWZTixUpkui/bt/uD1DHQTzjUNTLyy99fY2jwfnYvMioy1brvApT0ZuNL3EeL5q2oXi15oovChQrzfb+EGaFz588kCYYMEs1AWlpaqmaREtPW1mbyhCkABAQEcPXaFbX9A0yZPE3tOKdMnq5K2X7k6OGPvpcQQgghPi0JpoTIhPC3S9I+/OH4QxlJvf2hd5nrUpMdh3B/6gQUFonOa/L9SEINX9+EcksLi3T1YWVlpbquVjXl852qVk4oi4mJUS3nBLCyfP98mdJlVcsNP1Ql0dlRz188U10bGxujo6MDJMxqVqpYWe3zeXLnIW+evG+ff57iOIUQQgiRMyQBhRCZYPA2iEq8z0ed9O7p+Zx86gQUefPkxdDQkLCwMJxdnFKsFxUVhZe3FwDFi5dM15hKJKpvY2ObYr3Es4FvQt+k+/nciZ4PTfS8hoYGxYoW5+mzJ1haWKKllfJfxba2uXnl/oo3b96kWEcIIYQQOUNmpoTIhAIFCgIJmfYiI1Nekmbv8PJTDemLp1AoqFihEgA3b91Msd7tO7dUGfje1U+rSpWqqK69vDxTrOfp5aG6NjUxVV1bWVlRIH+Bjz7v4fn+eZNEzwNUfjsGXz/fVM+m8nzbhqmpaYp1hBBCCJEzJJgSIhNq1qgFJBy0euLUcbV14uPjOX7i2KccVpbKiQQUrVq0BuDS5Qspnp317gwnU1NT6tWtn672CxcqTGm7MgDcuJlyYoebb7P4GRgYqJJ8vNO6dVsAHj95pDoXK6XnAcqXq5CkrM3b5+Pj47l955ba591eueH5NlgrX658iuMUQgghRM6QYEqITKhRvSZFihQFYOasaWoPWP179UpcXV0+8ci+bH37fI++vj4xMTFMnzUlWbmjkyMbNq0D4PvvBqCtrZ3uPn76MeE8rjNnT3Hp8sVk5S9evuDfbVsAaNemg2qP0ztDBv2EhoYGQUFBLFuxJNnzkZGRzJk/C4AC+QskS2vfpnU78ufLD8CMWdPU7i2bPmMKSqUSDQ0NOnXsmu53FEIIIUT2kmBKiExQKBTMmTkPSJihaN6qMefOn8Xf3x8HRwdmzJrK2PFjKFy4SA6P9MuSO3du/jdmHADr1q9h2MifeP7iOT4+Phw8tJ8WrRoTHh6OTS4bxv46QW0bzVo2QtdQgxJ2hdWWD+w/WDVb1K1nJ9ZtWIOnlyc+Pj7s3rOT1m2bERMTg7GxMVOnzEj2fJkyZRk8KOEw39lzZzJl2iRe2r8kICCAi5cu0KptMxwc7AGYO3tBsn1Rurq6zJuzEEjIFtitZ2du37lFYGAgjx8/4qefB7N1e0IwN2jgD5QqWSq9H6MQQgghspkkoBAikzp36sq4/01g4eL53Lx1g5ZtmiYpr1+vAd279WT4yKFpysgnEkyaMAVHR0e27fiXtetWs3bd6iTllpaW7N65n1y5cmWofW1tbfbtOUTrds15+fIFPw//MVkdExMTdmzdQ7GixdS2sXTRcry8vDj830EWLp7PwsXzk5QrFApmz5xHt6491D7frWsPnJ2dmDZzCof/O8jh/w4mq9OxQ2eWLFyW/hcUQgghRLaTmSkhssCsGXPZt+cQjRo2wdTUFAMDA8qWKcfc2Qs4evikKsGAsbFxDo/0y6GhocGGdZvZunkHjRo2wdLSEj09PYoWLcaIYb9w+8aDZEvn0it/vvzcuHKHWdPnULFCJYyNjdHX16dkyVL8MmI0D+4+pUnjpik+r62tzZ6d+9m4bgsNGzTGysoKHR0d8ufLT68efbhy8YZqhi0l48ZO5Oypi3Tr2oO8efKira2NlZUVzZu1ZPu/u9ixdTe6urqZek8hhBBCZA9FdpxLI749CoVCGRUWn9PD+Gz9b9xo/vhrOaXtynDv9qOcHo4QXxRdQw2USqUip8chhBBCfEhmpoTIZkqlkmMnjgKkeDirEEIIIYT48kgwJUQWCAgISLHsz5UrVIkIOneWjGxCCCGEEF8LWeYnssS3vsyvbMVSNGnclE4dumBXqjRaWlo4OjmwacsG1q1fg1KppGqValw8d1WSUAiRTrLMTwghxOdKgimRJb71YKpI8fx4eHqkWF6sWHGOHj5JwQIFP+GohPg6SDAlhBDicyXBlMgS33owdfT4Ef47coibN2/w+rU3gUGBGBsbY1eqNO3bdeTHH4air6+f08MU4oskwZQQQojPlQRTIkt868GUECL7SDAlhBDicyUJKIQQQgghhBAiAySYEkIIIYQQQogMkGBKCCGEEEIIITJAgikhRDLNWjZC11CDwUMG5PRQPnsenh4cOLiPSb+Np1nLRljkMkbXUANdQw1cXF0++vzr169Zs+4fevXtjl254phaGmBqaUCpssUYPGQAt27fTPNYzpw9TZfuHSlYJA8mFvqUKF2EocOH8NL+ZSbeUAghhBAp0crpAQghxJesUZO6uLq5ZujZW7dv0qBxHeLi4pKVOTs74ezsxL/bNvO/MeOYPXNeqm1NmTaJhYvnJ7nn6urC+g1r2bFzG5s3bqNdm/YZGqcQQggh1JOZKSGEyALm5uY0bdKc9u06pvmZ8PBw4uLisLayZvQvv3L8yGmcHdxxd3nNwX1HqFqlGkqlkkVLFrDiz2UptrPl302qQKp9u45cv3wbd5fX/HfwOGVKlyU8PJx+/Xvz7PmzTL6lEEIIIRKT1OgiS0hq9K9Ls5aNuHjpAt/1+Z61qzfk9HA+a/8dPUzJEqUoXqw4AAcPH6B7z84AvHjqRKGChVJ89v79e1y4dJ6fhvyMrq5usvKoqCgaNqnL3Xt3MDU1xc3JCz09vSR1IiIisCtbDC9vL5o2ac5/B4+hULzPIu7v70+lqmV57fOaDu07sWv73sy/9CcmqdGFEEJ8rmRmSgghMqFt63aqQCq9KlasxC8jRqsNpAB0dXWZMH4yAMHBwdy4eT1ZncP/HcTL2wuA2TPmJgmkACwtLRkzeiwAhw4fwNvbO0NjFUIIIURyEkyJb9q582fp1bc7RUsUwNhcDytbU0qVLUbLNs1YvHQh7h7uyZ5x93Dn79Urad+pDYWL5cPITBcrW1MqVyvP+In/w8PTI8X+Nm/ZqEpOAOD2yo1hI3+ieKlCmFoaULp8CabPnEJoaKjqGX9/f6ZMm0TZiqUwtTSgYJE8DB32Az4+Pmr7cHF1UfVx4eJ53rx5w7QZv1Gukh1mVobkLZiL7r26cOfu7Ux9dm/evGHR4vnUb1Qb23yWGJvrUaxkQQYM6sf9+/dSffbAwX107NKOgkXyYGSmS6485pSpUJIOndvy16o/CAgIyNTYvialStqprr3fBk2JHTt+FIAiRYpSqVJltW107dIdAKVSybETR7JhlEIIIcS3SRJQiG/WvAWzmT5zapJ70dHRvHnzBmdnJ86dP4OBgQE//zQ8SZ0q1csTFBSU5F5MTAxPnj7mydPHbNqygf17DlOrZu1U+7937y5tOrTA399fdc/R0YF5C+Zw5epljhw6gbuHO23at8DJyVFVxzvSm/Ub13Hx8kWuXLiBmZlZin0EBgZQt2FNnifaKxMREcHBQ/v578gh1v6zgd69+qY6TnXu3rtD567tVTMi77xyf8W2Hf+yY9c2Fi1YyvCfRyZ79qefB7Nh0/ok94KDgwkODsbBwZ7jJ45SqFBh2rRqm+5xfY18fd8HzcYmJsnK7z24C0CNajVTbCNf3nzkzZMXD08P7j9IPdAVQgghRNrJzJT4Jj1/8ZyZs6cD0KxpC479dwrHl254u/vz6P5ztmzcRreuPdDR0Un2bJnSZZk5fTYnjp7h4b1neLr58vDeM9av2URpuzIEBgbSu2/3JLNL6vTo0xVbm9zs23MId5fXPHvswM9DEwK3i5cusHb9avr260lcbCxbNm7DzckLx5duTJ86E4VCgYODPfMXzkm1j3ET/4eLizNzZs7j+RNHXjl7s2v7XooUKUpcXBw//DSQR48epuuzc3ZxpmWbpnh5e1G2TDm2bNyGwwtXvF75cfHcVTp17EJ8fDy/jh3F0eNJZ0FOnT6pCqR6du/NudOXcHHwwOuVH/dvP2bN3+tp2aI1Gor0/9UUERFBaGhohr9iY2PT3eencPDQfgA0NDSoVqV6krL4+HgcHR0AKFy4cKrtFCqUUP7ypaRJF0IIIbKKzEyJb9LpMyeJj4/HJpcN+/ccQltbW1Vmbm5OieIl6N6tp9pnz566mOyepaUlJUuUpFPHLlSrVQkHB3t27NrG4IFDUhyDpqYm505fwtTUFABra2t+X7yCJ08ec+HiecaOH4OJiQm3rt8nX958qucmjv8NBwcH/t22ma3btjB/7qIU+3B1dWHntj107NBZda9D+05Uq1aDajUr4ufnx6QpEzh84GjKH9YHfhk9nODgYMqVLc+l89fQ19dXldWoXpMdW3czeMgAtmzdxMTJ42jVorVqH8/JU8cBqFypCps2/JukXQsLC+zsStPvu/5pHkti7Tu15uKlCxl6FmDN3+sz3Hd2cXZxZvXav4GELH3W1tZJyt+8eUN0dDQA1ta5Um3rXXlAgH+q9YQQQgiRdjIzJb5J72YhrKyskwRSmWVgYED7th0AOH/hXKp1J46brAqkEuvUsYtqjMN/HpkkkHqnS5duAPj4+uD2yi3FPmrXqpMkkHonT+48jB75K5AQWKY1KYGDowMnTh4DYOWf/yQJpBKbPm0WAM+fP+PBg/uq+7FxCZ+7rW3uNPX3LYuOjqZf/95ERkair6/PrBlzk9UJCw9TXX+Y5e9D+noJf1ahYanPmAohhBAi7SSYEt+kCuUrAvDk6WNmzJqabA/Ux1y8dIGBg7+nTIWSWOQyViV80DXUYOmyxQDY26e+nKpJk2Zq7xcuVER13ahhk4/Wef065UCobSqHtLZ7ex5SfHw8N24lzxKnzoWLCQGiubk5dqVKp7hkzszUDGurhFmUu/fvqJ6vUK4iACdOHmPVP38RERGRpn7T4tTxc0SFxWf463OblfplzHBu3roBwKIFSylRvEQOj0gIIYQQH5JgSnyTGjVsTIvmrQCYO382+QrZ0LRFQ6bPnMK582dT3T8z5n+/0KxlI7Zu34KDgz1hYWFq64W8CUl1DLY2tmrvJ57tsUlDndQCkhIlSqZYVrxYcTQ03mYVdHNNdazvvHz5AoDAwECsbE2xtDFJ8cvXzxcAP19f1fN9en9H+XIViIuLY9SYEeQpYE2b9i2Zt2A2129cQ869SzBvwWzWb1gLwPCfR/LDoB/V1jM0MFRdR0ZGptpmRGTC94mRoVEWjVIIIYQQEkyJb9buHfuYPnUmefPkJSYmhkuXLzJvwRxatmlK4WL5+HPlimQ/3G/dtoW/Vv0BJMwa7di6m0f3n+Pp5ov/6xD8X4cw9tfxAMR9JKGBpqbmR8eYljqpBSCJf9j+kIaGhiooC0vj0q+QkOA01UssKjpKda2trc3pE+cZ/cuvWFpaEh4ezukzJ5k+cyoNGtehVNli7Ni5Ld19wNeTgGL9xrWqLJPdu/Zk8cLfU6xrbGysSpKSOOufOu/KLSwss2ikQgghhJAEFOKbpaury8TxvzFh3GSePn3CtRtXOX/hHMeOH8HH14dfx47Cx+c1M6e/z5i3dv1qAOrWqcex/04mOyAVUp8p+tQS76n5UHx8vGqshmmcrTB4G5wVL16Cx/efZ2hMpqamzJ+7iLmzF3D/wT2uXb/K2XNnOHnqOC4uznw/sC9h4WEMGvBDutr9GhJQHDi4j+EjhwLQtElz1q/dpPZ77B0NDQ2KFi3Gs2dPcXZ2TrVtF5eE8hIlZLmgEEIIkVVkZkp88xQKBWXKlGXwwCH8u2k7ji/cqFe3PgDL//id8PBwVd1HjxPSiHfs0DnFH3KfPnua/YNOo3fL8tSxd7AnPj4egAIFCqapvcKFE/Zqubu/IiYmJlNj09DQoHKlKgwbOoK9uw7w4qkTJUuWAmDe/NmZavtLdO78WfoN6ENcXBzVqlZn1/a9aUqOUqlCwkG9N2/fSLGOu4e76jDpihUqZc2AhRBCCCHBlBAfMjMzY8TwUUDCPhTXRPuJoqISlqzFxcWpfdbb25tLlzM+O5LVjhw9nGLZ4cMHgISgJrUDXxNr0qgpkDD7dvTYf5keX2J5cudRpZJ/5f4q3TN8X3ICirv37tCtZyeioqIoWbIUB/cdwdAw5SWaibVq2RpIOPA5cebExPbu2w0k/OKgVYs2WTJmIYQQQkgwJb5RDo4OqlkZdZycHFXXFuYWquuCBQsBcOx48nOZ4uPjGfXriEzP2GSlK1cvqw59TczTy5PfVywBEpaT2dqqT3TxITu70jRpnJCF8Nexo3D3cE+1/osPZsY+/O8POTknfO76+vofTfX9tbB3sKd9x9a8efOG/Pnyc+TQCSwt076vqV3bDuR+m2r+t2mTku2hCwgIYOnvCWeRtW/XMc1/1kIIIYT4OAmmxDdp/oI5lKlQkpmzp3Hh4nk8vTwJDAzk2fNnLF6ygOkzpwBQr259bGxsVM91fnsG1PkLZ/nhx4E8fvwIf39/rly9TMcu7dh/YC+lStnlyDupU7BgIb4f2JfFSxfi4uqCr68vBw8foEnzBvj5+aGlpcWcmfPS1eYfy1dibm7OK/dX1KhdmcVLF/LkyWMCAwN5/fo1d+/d4e/VK2nRuil16ldP8uzwkT9RtUZFFi9ZwLXrV/H29sbf35+HDx/w29SJ/LN6FQBdOnVLda/Q58Tdw50bN6+rvhKnxH/w4F6SMt9EmQ0hYSazTfsW+Pr5YmJiwo5tezA3M08xSca7A3oTS3wG1clTx+nZpxsPHtzHz8+P02dO0axlI7xfe2NgYMCMad/e8kkhhBAiO0kCCvHNcnJyZM68WcyZN0tteeHCRVjzz4Yk98b+OoEjR//j8ZNHbP53I5v/3Zik/Kcff8bK0orZc2dm17DTZcHcRUybOYXJUyYwecqEJGWampqs+Xs95ctXSFebRYsU5eTRs3Tr1RkXF2e1bb9jbm6e7N6jxw9Ve8/UqVypCgvnL0nXmHLSho1rU/zz7t6rS5L//jDJxclTx3F1dQEgJCSEOvVrpNrXb5OmMmXy9GT3v+v7PS/tX7Bw8XwOHNzHgYP7kpQbGBiwacNW7D6jQF8IIYT4GsjMlPgmzZk1n3WrN9K7Z1/KlimHlZUVWlpaWFhYUKd2XebPXcSdGw8oXKhwkueMjY05e+oio3/5lUKFCqOtrY2VlRWNGjZh25adLF/6Zw69kXoWFpZcPn+dsb+Op1ix4ujp6WFlZUX7dh25dP4avXv1zVC75ctX4OHdp/y5YhXNmrbAJpcN2tra6OvrU6RIUTp26Mwfy1fy5EHSg4vXrt7InytW0blTV+zsSmNubo6WlhbWVtY0btSUlX/+w8VzV9O1zE0kmDVjLkcPn6Rtm/bY5LJBV1eXggUKMrD/IG5cvUv7th1yeohCCCHEV0chh2SKrKBQKJRRYSnvQRKfjourCyVLJ2TdO3nsLA3qN8zZAQmRSbqGGiiVyi9j3acQQohvisxMCSGEEEIIIUQGSDAlhBBCCCGEEBkgwZQQQgghhBBCZIAEU0IIIYQQQgiRARJMCSGEEEIIIUQGSDY/kSUkm58QIrtINj8hhBCfK5mZEkIIIYQQQogMkGBKCCGEEEIIITJAgikhhBBCCCGEyAAJpsQ3Z/CQAegaatCsZaOcHsoXY9ac6egaaiT5qlazUrJ6cXFx7Ny1nf+NG02T5g0oWaYoFrmMMbHQp4RdYfr068nJUyfS1OfDhw/44ceBlChdBBMLffIUsKZxs/qsXb+auLi4rH7FJA79d5DJUybQonVT7MoVxzq3GcbmehQulo/O3TqwZ+8u0rLf1MnZiV/HjqJS1XJY5DLGyEyXQkXz0qlre/bu252t75CSocN+UP0ZlrArnGI9VzdX/ly5ggGD+lGtZiUKFsmDkZkuVramVK9VmYmTx+Hq5ppqXx9+z+gaarB5y8YsfiMhhBAi50gCCpElvqQEFIOHDGDL1k3Ur9eAU8fP5fRwvgiz5kxn9tyZSe6VL1eBW9fvJbkXFBSETV6Lj7bXs3tv1q7egLa2ttry5X/8zoRJY4mPV/89Vad2XQ7uO4KxsXEa3yB9cuUxJzg4ONU6DRs0Zs/O/SmOYf+BvQwY3I+IiIgU22jVsg07tu5GT08vU+NNq6vXrtC4WX1VIFiwQEFePnNWW3fFn8sYO35Mqu0ZGhqyetU6unbprrZc1zD57+vW/L2eft/1T9e4JQGFEEKIz5XMTAkh0qxA/gL4vw7B/3UIF89dTVauUCgoV7Y8Y38dz55dB7h17R7uLq95/sSR/XsP07BBYwB27NrG5CkT1Paxd99uxk34lfj4eKpVrc7RwyfxdPPl2WMHZs+Yi46ODleuXqb/oO+y7T2LFyvBiGG/sP3fXVy/fBs3Jy/sn7tw/MhpOnXsAsD5C2cZ/OMAtc87OTvx/cC+REREYGtjy19//M3jBy9wc/Li1PFztGjeCoBjx48wfeaUbHuPxGJiYhg+cihKpZJChVKekXrHwMCA1q3asmjBUk4eO8vjBy/wdPPl7s2HLFm0jDy58xAWFsb3A/ty//49tW28+17xfx2S1a8jhBBCfBZkZkpkCZmZ+rq9m5lKbSYjLZRKJV26d+TI0cPo6+vj4eqDoaGhqjw2Nha7ssVwe+VGubLluXT+Gvr6+kna2L1nJ32/7wXAkUMnaNqkWYbHk1G/jBnO3/+sBODZYweKFC6SpPy3qRNZtGQBGhoaXLl4g8qVqiQpj4+Pp0XrJly8dAFzc3M83XzR0Mje320tXrKAyVMn0qljF4wMjdiydVOm/jxd3VypVLUsYWFh9O3dj3VrNqZa/90slcxMCSGE+JrIzJQQ4pNRKBT06/s9ABEREbx4+TxJ+fUb13B75QbApAm/JQukALp17UG5suUB+Hv1ymwesXr9vxuoun7wIPmszKPHjwAoXrxEskAKQENDQ7U0LjAwEF9f32waaQIXVxfmzJ+FoaEhixYszZI2CxYoqJppvHf/bpa0KYQQQnxpJJgSn5Sfnx+GpjroGmqwfuPaVOs+f/FctWn92PGjScoePXrInHkzadS0HnkKWGNoqoNtPksaNqnLshVLCQ8Pz9D4mrVshK6hBoOHqF++BXDh4nnVuFxcXVKsd/nKJQYM6kcJu8KYWOiTK4859RrWYvkfvxMVFZWh8X0NYmNjVde6OrpJyh4+eqC6rle3QYpt1KtXH4DTZ06muicpu8TGvX8HHV3dZOXv9kApFClPprybidLU1MTc3DyLR5jUqDEjCA8PZ9L438ifL3+Wtfvuz1JXzWcghBBCfAskmBKflJWVFU2bNAdg587tqdbduWub6plmTZur7j98+ICqNSsyc/Z0rl67gr+/P7GxsQQGBnLt+lXGT/wf9RrW4vXr19n3IqmIi4tj+C9DadK8Adt2/IurmytRUVEEBwdz89YNxk34ldr1quPl5ZUj48tpe95msTM1NaVYseJJykJC3id9SC3AsDBPSHIRERHB8+fPsmGUqdu9ZyeQEAhVKF8xWXnFCgn37O1f8uTJ42TlSqWS/Qf2AVCvTn10dHSybaz7D+zl2PEjlCplxy8jU08okR7uHu5cv5Gwb65y5eSzb0IIIcS3QIIp8cn17J6w3+Xi5QupBhQ7d+8AoHOnrmhpaanuKxQKmjZpzh/LV3Lu9CWeP3HEw9WH29fvM3vGXKysrHj85BE/j/gxe18kBf8bP5o1a/9BS0uLkcNHce3yLbxe+WH/3IW//vhbNb6efbpmKMV3REQEoaGhGf5KPDP0qQQGBnLz1g2++743+w/sBWDi+N+SzWgYG5skeSYlAYEBqusPlwpmlzdv3vDw4QN+GTOc5X/8DsCQH34iX958yer++MPPWFhYEBcXR6eu7di9Zyfe3t6Eh4dz//49vuvfmzNnT2Fubp5ly+5SGvOvY0cBsGzJHylmT0yruLg4PDw92LV7By3bNCU4OBhjY2PG/qo+mYgQQgjxtdP6eBUhslb7dh0xMDAgPDycXXt28MuI0cnq3L5zC0dHByAhjXZi5cqV58ih48mesbKyoly58rRt24GqNSrw35FDvLR/SYniJbLnRdS4dfsmK1f9CcC61Rvp2eP92C0sLBg8cAh1atWlVr1qXL9xjX3799Cta4909dG+U2suXrqQ4TFmJAFARqzfuJahw4Yku29hYcGkCVMYMeyXZGWlStqpri9fuajKnPehy5cvqa79A/yzYLTqnTl7mtbtmie7b2hoyLChI5gxbbba5ywsLDh9/Dzde3fBwcFelTDjHW1tbXr16MOkiVOy9ftz+swpeHh60L1rTxo1bJzhdho1rcfVa1eS3a9UsTJr/l5PoYKFMjFKIYQQ4sslM1PikzM0NKRt6/YA7Ho7+/ShnbsSlgAWLFCQ2rXqpKt9u1J2VKyQcKDs+QtnMzHS9HsXSDVv1jJJIJWYnV1p1ezcjl2pL3X82mhqatK/30A6tO+ktrxO7bpYWloCMG/BHCIjI5PV2bd/T5K9VWGhodkz2FR079qD3r2+SzUDX5kyZdm9fR+VKlZOVhYTE4O7xytevU22kR3u37/Hqn/+wtjYmAXzF2d5+wULFGT4zyMpWbJUlrcthBBCfCkkmBI5okePhGDi9p1bOLydgXonPj6ePXt3AQmZ29Rt4o+Li2Prti107NKOIsXzY2Khr0oKoWuowe07t4CEPSuf0rvgrUH9hqkutStduiwA9+7dSXcfp46fIyosPsNfn2JWCqBf3/6qM4ZcHT05fOAYLZq3YumyxVSpXp6Tp04ke0ZPT4+J438D4MHD+zRv1Ziz587g7++Ps4szi5csoP+g75IccptakofMatigkeodXjl7c/rEefr27seGTeupXqsS/27drPY5pVLJzNnTqFStHIFBgWxctwUn+1d4vfLj3OlLdGjfiUuXL9KuYyu2btuS5eOOj49n2MifiIuL47dJ08iTO0+m2jt6+CT+r0Pw9QriycOX/PXH3yiVSgYN6U/LNk3x8/PLopELIYQQXxZZ5idyRItmLbGwsCAgIIBdu7czacL7g0svXb6Ip5cnkHyJH0BISAgdOrdRu+zoQ2/efLrDQkNDQ1XjnjxlQoqH0ibm65e9KbFzkpaWFkZGRgAYGRlha2tL82Yt+N+40fzx13J69O7CvduPky0RG/7zSOwdXvLP6lXcuHmdVm2TniNlYmLCvDkLGTbiJwBMzcyy7R00NTWTvEOuXLmoV7c+FStW4n/jRvPDTwMpUaIk1avVSPLc4iULmDNvFja5bLhw5gq2traqstq16lC7Vh3VeWfDRv5E40ZNyZ07d5aN+581q7h95xZlSpdl+M8jM91e4hT1JiYmFCtajE4dulC3YU2uXL3MoCH9Objvv0z3I4QQQnxpZGZK5AhtbW06d+oKvE808c6Ot1n8ypQuS7ly5ZM9O3b8aK5eu4JCoWDwoCEcOXSCl8+cee0RoJpFeLc0MDY2/QkeMio4USa6tIqOjk73M19iAorEZk6fg6GhIeHh4WxQkx5foVCw4ve/OHzgGO3adiCXdS60tbXJmycvA74fyM1r97ArVVpVP18WpvpOq+E/j6Rw4SLEx8erlna+Ex0dzeLfFwIw9KdhSQKpxKZNnQkk/Hnu2bcry8YWEhLCtBkJs3srlv2VJHlLVrK0tGTS21nE4yeOYu9gny39CCGEEJ8zmZkSOaZn996sXbea58+fcf/+PSpWrERMTAwHDu57W94r2TNhYWFs27EVgPFjJ6aYACA0g/to0rJkLKVgxNDAUHW9ecNWeqgZf1b4UhJQpMTAwIDSdmW4dfsm9x/cT7Fe82YtaN6shdqyM2dPqa6rVKqa1UP8KIVCQZXKVXF2duL+w6SH9j579pSgoCAAKlVMOWV4/nz5yWWdCx9fnyzNSBgQGEBwcEJg36R5ymd1Abi6uaJrmPA7teE/j2TJomXp6qtq1eqq64cP71P8g1T3QgghxNdOZqZEjqlbp57qANGduxMSMZw8fYKAgAAUCgXd1QQjL1++UM3mdOnUTW27sbGx2DtkbK+Urm7CXpyIyJQPgvX2Vp/O3czMDAuLhPOPnF2cMtT/t+JdQJrR/U6nzyQEUxXKV8TGxibLxpUeqncg6Tuk9r3zIaVSCZBqIovPWVyiXyxk5941IYQQ4nMlM1MixygUCrp17cHSZYvZvWcnc2cvYNeuhCV/NWvUUptuOSo6SnWd0hlNh/87SERE2n+gTczWJmFJlkMqS5bOnD2dYlmjhk3Yu283e/ftYcK4yRkaw8ecOn4uW9r9VAIDA3nyNOEg20KFCqX7eWcXZ44eS9ifM6D/oKwcWprFxsZy8+Z1AAoVKpykzMbm/bK+e/fv0LJFK7VtuL1yU+2ZK5CvQJaNLU/uPNy8ejfVOjNmT+PI0cPkts3NwX1HALCytk53X5evvk9R/+HnIIQQQnwLvsxfh4qvxrsEE6/cX3Hy1AkOHzkIkOISuYIFCqmuj504kqw8ICCASWlI/JCSKlUSlow9eHifp0+fJCu/dfsmu/aoT+cOqDb7P3z0gDnzZqbaV1RUFK5urhke6+fopf1L1WyLOkqlkv+NG62aXezYvnO62g8LC2PAoO+IioqiZMlSDBrwQ6bGq46jk+NH95XNnjtDlWyk4wdp3gsXKqwKLP7+ZyU+Pj5q25gxc6rquknjZmrrZISOjg4VKlRM9cvC3CJZ3bx58iZp58XLF6n288r9FQsWzgUS0qSrSwEvhBBCfO0kmBI5qkKFipQqlXBQ64hfhhIWFoaWlhZdO3dXWz937tzUqlkbgPkL57J46UIcnRx5/fo1Bw7uo0GTOnh5eVKwQMEMjadLp24YGBigVCrp2rMTJ08lLDt0cXXhj7+W06Z9i1QTHtSuVYdhQ0cAMHP2dDp1bc+x40fx9PIkKCgoYVbl+BHG/O8XipUsyN59uzM0zs/VwkXzqFytPIuXLODa9at4eXkRHByMs4szu/fspHGz+vy7LSGdeMcOnWnYoFGyNg4fOUTLNs3Yum0LDo4Oqs9t0+YNVKtViWvXr2JsbMyGtZvR0dFRO45mLRuha6hBCbv0z5Zs276FMhVKMnP2NM5fOIeHpwfBwcG8cn/F4SOH6NilHfMWzAGgWtXq9O3TL1kb48dOBMD7tTcNmtRh567teHp5EhAQwLXrV+nRu6vqc2jdqi2VKiUPRAYPGaBK9Z8TKlUtS/deXdi6bQvPnj3F398ff39/Hjy4z6LF86lZpwqeXp4oFAoWL1omy/yEEEJ8k2SZn8hxPbv3YvrMqapZmsaNmmKdypKjP5atpHHz+oSEhCRLQa6trc3afzawbsOaDM36WFtbs2jBUoaN+AlHRwfadUy6RKtG9ZqMHzeJzl3bp9jGogVL0dHRYdmKpRw99p9qSZo6KQUDX7Knz54weerEVOv06tGHv1euUVsWHx/PufNnOHf+jNry/Pnys3njNqpUzr7EEy4uzsyZN4s582alWKdJ42b8u2k7mpqaycoG9h+Mo6MDS35fhJOTI/0G9FHbRq2atVm/ZlOWjTsrxcXFcfDQfg4e2p9iHRMTE1b8/hft23b4hCMTQgghPh8STIkc16N7b6YnWvKkLotfYuXKlefKxZvMmTuTs+dOExQcRC7rXNSuVZdRv4yhSuWqrNug/gf1tBg8cAj58ubn9+VLuHf/DtHR0RQpXJTePfswcsRorl2/murzmpqazJ+7iL69+7F63d9cuHgeDw93IiIiMDU1pVjR4tSqWZv27TpSp3bdDI/zczR1ygxq1KjJ+QvnePr0CT6+rwkKCsLQ0JCCBQpRo0ZNvuvzPTWq10yxjepVa/DbpKmcv3AOJydH/Pz9MDY2pnixEnRo34kfBv2IsbFxquNw93BPaCuVflLy4w8/U6hgYc5fOMeDh/fx8XmNf4A/BgYG5MmTl2pVqtOjey+aNW2eajtzZs2nQ/tOrF33D1euXcHT04OYmBgszC2oWLEy3bv2oGeP3immLle9wwdnWH0qZ05e4Nz5M1y+fAnXV674+LwmJiYGczNz7OzK0LRxU/p9NyDHEoAIIYQQnwNFavsbhEgrhUKhjAqLz+lhiGwya850Zs+dScECBXn5zDmnh5MqVzdXStgVRlNTk/t3nlCieImcHlK6RUZGYpPXgsjISI79d4rGjZrk9JAy7d1yxYyk5tc11ECpVMo6QiGEEJ8d2TMlhPiqnD2XkG3xuz79vshACuDqtStERkbSsEHjryKQEkIIIb5WEkwJIdLs3SGvuoYaVKtZKaeHo9a5c2fR1dVl8qRpOT2UDHu3X2zWjDk5PJLMefe9klNJNIQQQojsJv/CCSG+Kps3biUkIIIC+bPu7KZPbdaMuUSFxefYfikhhBBCpI3smRJZQvZMfd2io6NVZ0O9o6mpib6+fg6NSHwJQkNDk93T09NLMelGSmTPlBBCiM+VBFMiS0gwJYTILhJMCSGE+FzJMj8hhBBCCCGEyAAJpoQQQgghhBAiAySYEkIIIYQQQogMkGBKCCGEEEIIITJAgikhhBBCCCGEyAAJpoQQQgghhBAiAySYEkIIIYQQQogMkGBKCCGEEEIIITJAgikhhBBCCCGEyACtnB6AEEII8TEKhUKZ02MQnwc9Pb3XERERtjk9DiGEAFAolfLvk8g8fX1978jISJucHocQ4utTIH9BHt5yzOlhiM+Ema0WSqVSkdPjEEIIkGBKiAxRKBT6gCdQWqlUeiW6rwDmAm2Bpkql8nUODfGLplAolEHesTk9DCHEZ0iCKSHE50SW+QmRMa2Bu2oCqSVAI6CRUqn0y6nBCSGEEEKI7CfBlBAZ0wvY/u4/FAqFBrACqA40ViqVgTk1MCGEEEII8WlINj8h0kmhUJgAzYC9b/9bA/gHqAw0k0BKCCGEEOLbIDNTQqRfR+CCUqkMVCgUmsA6oDDQQqlUvsnRkQkhhBBCiE9Ggikh0q8XsFmhUGgBmwBboLVSqQzL2WEJIYQQQohPSZb5CZEOCoXCGqgFHCVhz5QF0PZdIKVQKDQUCkUdhUKxRKFQ9MzBoQohhBBCiGwmwZQQ6dMVOAFsBnRJWPIXp1AomisUir8BD2AVEApcyalBCiGEEEKI7CfL/IRIn96APuAO/AusBdoAL4D9QH2lUmmfc8MTQgghhBCfigRTQqSRQqEoBtQl4bDeEoARsA+YoFQqPXJybOLrMm/RDBYsmZXkXtkyFbh85k6Se3Fxcew/tJs7925y/+FdPD098PPzITYullzWtlStXJ2+vfrTpFGLFPsaOnIg23dtTnKvTq36HNl/Nute6AsUHBLM3Xs3uXPvFrfv3uTO3Zv4+vkA8NeydfTp+X2W9vffsYPs2L2F+w/u4OP7GlMTM/LlLUCtmnXo3qUPFctXTvZMuapFeeXu+tG27157TpHCxbJ0vEIIIRJIMCVE2pUGXgLzgUNKpdI/h8cjvnFvQt8weGhftWWv3F155e7K/kO76da5FyuXr0dbW/sTj/DLNeG30cmCzOwQHBLMoB97c/rciST3ff188PXz4d6D28THK9UGU0IIIXKeBFNCpJFSqTwEHMrpcYhvR768Bbh+8SEAmhqaycoVCgVlSpeneZOWVKtai/z5CpDbJg+hYaG8ePmUv/5ZxsXL59i9bzu5rG2YM2NxsjaWLVrFonkrABgz7md27d2WvS/1hdHT06N82YqUKV2eDZtXZ2nbkZGRdOvdhpu3r6Ovr8+wn0bTvk1n8uctQHhEOA8f32Pv/p0Y6Oun2s6YkeMZ88vEFMsNDQyzdNxCCCHek2BKCCE+UwqFAiNDoxTLTU1MuXL2brL7VlbWFCpYmOZNW9Pr+04cP/kf6zf/w6RxMzA0TPqDta6uLrq6ugBoaso/Ce9833cQPw4aRpnS5dHW1iYoOCjLg6nFy+Zw8/Z1jAyNOLTnFJUrVVOVmZtbkDdPPlo1b/fRdrS1dVL9PhFCCJF9svVfTj1tDe+oWKVNdvYhhPj0dLUUryNj4m1zehwidQqFgt49vuf4yf+IiIjA3uE5FStUyelhJRMWFsbB//bSu0e/nB6KSs3qdbK1fV9fH1asXALA+P9NTRJICSGE+HJkazAVFau08ZhRKzu7EELkgLzTrskvSVLx59+/89v0sQDs3nqYZk1aJasTHBJMnUaVcPdwo1qVGhw/dBFNzeRL+TIrLjZWda2jo5vl7WeUUqnk6vVLbNu5mYOH9xAaFvpZBVPZbeferURHR6Orq8v3fQfn9HCEEEJkkJwzJYQQWWzYj6OoX7cRAMNH/4C/v1+yOmMnjsDdww0jQyNW/7U5WwIpgP2HdgNgYmJK0SLFs6WP9Hjl7sbCpbOpXKsUbTo1ZuuOjYSGhX5zy9TOvE04UbliVUyMTVT3YxMFv+kVHR2d6XEJIYRIHwmmhBAiiykUClYuX4+pqRmvfbz5ZexPScr3HdilSvQwd+YSChcqmqX9BwUFcvvuDQb91IdDR/YBMHb0ZNXeqE8tIiKCXXu30aFbc8pXK8rchdNxdnFEoVBQv24jVq3YwIuH6k8XiI6OJjQsNMNfUVFRn/ht0+b+w4S9biWK2xEZGcm8RTOoUL041vn1sc6vT90mVVj+5yIiIiI+2tb2XVsoViY3uQoYYJVPjyq17Rg7cSROzg7Z/RpCCPHNk93GQgiRDfLlzc/SBX8x6Kc+/Hf0AP9u30jfXv3x9PJgzIRhALRq0Y5+fQZlSX+bt65j5K8/Jrtvbm7BuDG/MfSHkVnST3rcuHWVbTs3s//gLkLehKjuFytagp7d+tKja1/y5yuQahtLls9LduZWevTq3o9VK9Zn+PnsEBERQWBgAADaWtq0bN+A+w/fnyEWExPD4ycPePzkAXsP7mLf9qNYWVmn2J7bKxfVdWxsLI5O9jg62bNp61oWzV0hywiFECIbycyUEEJkky4de9Ctcy8AJk4ZjbOLIz//MpCgoECsrXKxYvE/2dq/pqYm3/UaQNtWHbO1n8Q8vTxYumI+VeuUpkW7+mz6dy0hb0IwMzNnQL8hnPzvErevPOV/oyZ9NJD6WoW8CVZdb962jvsP79C1U09uXHyEj1s492+85MfBIwB4+OgeP/8yUG07pe3KMnv6Is4dv479I0983MK5d/0Fs6cvwszMnOjoaEaNHcqR43KigxBCZBeFUqnMvsYVCqUkoBDi65N32jWUSqUiu9pXKBTKIO+M7x35nCRONGFlaY2fvy8A2zfvTzHt9bxFM1iwZBb58xXk0W3HNPUTGxtLZFQkAGFhoTx+8oDV61dy4tQRTIxN2LB6O00atUi1jaEjB7J912bq1KrPkf1n0/GWEBQcxKCfenPuwmni4+MB0NLSoknD5vTq3o9WLdrl2DLDrBAUHEShklYA/LVsHX16fp/htry8PbGr+D6QbNa4Jbu3/Zes3i//+4lN/64F4PTRK1StXCPNfTi7ONKsTV38/H0pWKAwd64+Q0vr61iMYmarla1//wghRHrIzJQQQmQjUxNTVq1Yj4aGhiqQ+r7v4DSdH5QeWlpaGBkaYWRohE0uW5o0asHOLQcZOmQkIW9C+G5gN1zdXLK0z8SCg4M4c+6kKpDq3qU3T++5svPfQ3Rs3/WLDqSymsEHh+hO+N9UtfUm/G8qGhoJ/0wfP5k82EpN4UJFmTRuOgCubs7cvnsj/QMVQgjxURJMCSFENiterCRGRsaq/3639O9TmDJhNoYGhoRHhLNl27ps68dA34Bc1u8z5u/au40OXZuz/M9FeHqpTy6RFl9jAgpjI2N0dHSAhCC4QvnKauvlts1Dntx5AXhh/zzd/bRs3lZ1/fjJwwyMVAghxMdIMCWEENls+KjBhIS83yczZvywNGVpywoGBgaUKlkagIeP7mdbP9bWuXh6z5Xtm/fTtnVHdHR0ePbiCdNmT6RslcJ06tGSnXu2Eh4enq52lyyfR76iZhn+GjV2aDa9ccZpaGhQtHBCmnoLc8tUl9/Z5Eo4Gzs09E26+7G2yqW6Dg4JSvfzQgghPk6CKSGEyEZr1q/k9NszhaZOmo2Ojg4v7Z8zddaETzaG2Lg4ICFle3bS0tKiVfN2/Lt+D8/uuTFv1lLKla1IfHw85y6c5sfh31OiXB6GjhzIhctnVUsCv0XvZqP8/H2JiYlJsZ6XtycAJsam6e7jtY+36trUxCzdzwshhPi4r2M36lfoqnMw3TY+BeD6qErkN9fL0vZH7Xdg931fahUyYc+AMlna9qdw+Ik/W2+/5ol3GOHRceQ21aVpCXN+qp0HWxOdTLcfEhnLP1c9OfYsgFdBUehoKihmZUD3itb0qpwLDQ3Z+yw+zt7hBdPeBk0D+g1hzMgJKBQKZsyZzNoNK2ndoi2NGjTL1jEEBQXy7PljAAoUKJytfSVmaWnF0B9GMvSHkTx68oCtOzaxe+82/AP82L5rM9t3bSZf3vx079Kbnt2+o0TxUmrbmTh2GhPHTvtk4/5UWjVvy47dW4iPj+fu/VvUqFY7WZ1X7m6qYKpsmfLp7uNooix+5cpWyPhghRBCpEhmpsQXJT5eyYi99vy06yWXnIIJCI8lMlaJs38ka6550WzVA+68Sv9ymMSc/CNotuohyy548MIngvDoeIIi4rj96g3jDjvRe8szImO+3d+oi7SJjY1lyLDvCY8Ip2iR4syetgiAX4aNpVaNOiiVSoaNGkxQUGCG+3BwfElqGVmVSiUTpowhOjoagHatO2a4r8woV6YC82ct5fmDV2xZv4eWzduipaWFu8crlq5YQPV6ZWncsmaOjC2ntGzelnx58wMwd+F0tX+Os+dPRalUoqGhQYe2XZKUfWwf2kv758xbPAOAQgWLpCsToBBCiLSTYEp8UZZdcGffQz8A+lTJxfnhFXgwtiprepQgt4kOAeGxDNrxAr/QlJfNpCYqNp4B217gHhSFqZ4mSzsW5e7/qnDll0qMqJcXhQIuOQUz6YhTVr6W+AotWDKLew9uo6mpyT9/bsTQMCGDm4aGBn//sQljI2M8vTz4dcLwDPexZPl8ajesyLI/FnLj1lW8X3sRHBKMi6sz+w7solWHBuzYvQWAdm06Ub9uoyx5t4zS1tamXeuO7Nh8gKf3XJk1bSGl7coBcPf+7Rwd24dC3oRw68511df9B+/H5+zqmKTM2SV5+vpLV85jZquFma0WW3dsSlauq6vLzCkLALhw6Sx9BnTh7r1bBAUF8uTZI0aMGcLOPf8C0L/vD8lm7sZOGknL9vVZve4v7t2/ja+vj+rZRb/PoWnr2gQGBqChocGC2b+jqamZlR+PEEKIt2SZ32eqdmFTsvOMrmWdirGsU7Fsaz87vH4TzV9XEpa89K6ci4Xti6rKWpe2pJSNAc1WPcQ3NIY/L3swvWWhdPex5dZrHPwiUChgQ+9S1Chooiqb0LQA+toaLDz7il33fRlYIzdlcxum0pr4Vt26c52lK+YDMGbkhGSzAgULFGL+7GUMGzWIvQd20qpFO7p26pmhvp69eML0OZNSrdO9S29WLFmdofazSy5rG0YMHcOIoWO4d/8223ZuzukhJfHg4V3adWmqtmzx73NZ/Ptc1X/36t6PVSvWp7uPzh274+LmxOz5Uzl6/FCSZXnvtGvTiXmzlia7r1QquX7zKtdvXk2xfSNDI35ftIoWzdqke2xCCCHSRmamxBdj931fImPi0dZUMK5J/mTlRSz16V05IXvVzns+xMSlfyne5tsJG7abljBPEki9M7ROHswNtFAqYeud1+luX3z9wsLC+HF4f+Li4qhUoSrjf52itl6fnt/Trk0nAP43cUSG0odPGjed3xeupFP7btiVLIOlhRVaWlqYmJhStkwFBvb7kVNHLrP6r83o6WXtvsusVKliVRbNW5HTw8gRY0ZO4NjB83Tu0J08ufOira2NpYUVTRu1YNOanWxeu0vtGV1jRo5n7OjJNKzfhEIFi2BibKJ6tlbNukwaN507155/0jT8QgjxLZKZqWx25mUga6558cgrlOg4JYUs9OhawZpBNXKz96EvYw4kLA/5cBYqtQQUrwIjqbnsHgC7+5emSn5j1l7zYv8jP1wCItHWVFAutyE/1clD4+Lmasf1JSagOPMyYW9JjYImWBupTzLRrowl6294ExIZx3XXN9QrkvYMWC4BkTj6RaraUUdHS4OWpSzYfteHUy8CmddWbTXxDTM0NOTutbSdCbRl3e5M9ZU/XwEG9BvCgH5DMtWOSK5enYYEecd+kudrVKutNgFFaqpWriH7oIQQ4jMgwVQ2WnTWjWUXkv62+al3ODO9XTn9MpAOZa0y3UdodBxd1j/hnkeo6l5EDFxxDuGKcwgL2hWhb1WbVFr4MiiVSh57hwFQOZ9RivXK5zFCUwPi4uGJV1i6gqlHXmGq69T6qJzPiO13ffAKiSYgLAYLQ+009yGEEEIIIb4eEkxlk2PP/FWBVKW8RoxvWoAytgYEhsey54Evf132wCMoKtP9TDvmQmB4LNNbFqJFKXOMdDW57xHKb0eccQ2MYvpxF1qWssDKKGt/4A+LisvU8wY6Guk688YrJJrw6IRlewVTSROvp61BLiMdvEKicfRL36Go7+prKCCfWfJlNe8kniV09I+QYEpkm1furpjZJvw1XbZMBS6fuZPlfQwdOZDtuz6v/UpCCCHEl0KCqWygVCqZfdINgJK59NnVvzQGOgmZlCwMtBnfpACWBtpMO+6S6b48g6PZN7AMVfIbq+41Lm7Oul46NF35kIiYeP576k//6raZ7iuxEnNvZur59J6dFRjxfrmM5UeCFytDbbxCopM8k6Y+whPqm+proa2Z8nZCK8P3/9u8e0YIIYQQQnx7JJjKBnfcQ3EJSNh7M7ZxflUgldjAGrasve7Fq0zOTrUva5kkkHrHzsaQMrYGPPEO50GiJYBfqnezUgC6WqnPaOlpJwRCYdHpmz2LiIl7237qeVn0EpWntw8h0uLXXyYy4udfk9zT1Mie1NbLFq1Klvwhu/oSQgghvjYSTGWDd4fGamkoaFRMfQIIDQ0FjYubselW5jLCNSxmlmJZYUt9nniH4xeWsTOXUpOdaduF+Nbp6Oigo6M+yUpW09XVVZstTgghhBAfJ6nRs8G7vVB5THVUsyTqFLHUz3RfuYxT/oFL/23fETHpTxH+uTHQef85RsUqU60b+fZ9DdXMCKZGX1vzbfupf16RicrT24cQQgghhPh6yMxUNgh/+8O8QSqBFIChTuZjWc2053DIUp86AYW5/vtvVf+PzLS9m4lL/Eya+jBIqB8cEUtsnBKtFD5cv7DYZM8IIYQQQohvj/wkmA3eBVHhH5kRCov+cmeMPnUCitwmOhjoaBAeHY9rYGSK9aJi4/EJjQagqFX6Zv7e1Y9XwqugSAqnMHP4KlH/RbNgdlEIIYQQQnyZZJlfNsj7Nq22Z3C0asmZOk7+6Uvd/S1TKBSUtTUE4J57ygk1HniEEvf2Iy+T2zBdfZRLVP9eKkk77r7tP7eJjqRFF0IIIYT4hsnMVDZ4l10vNl7JeYcgWtpZJKsTH6/krH3QJx5Z1smJBBRNSphz0+0N111D8AuNUXt21n9P/AEw0dOkZsHkWQ5TU8hCj6JWejj6RfLfE386l7dOVic6Np6TLwIAaFZSfXIRIbLLpSvnadelKQAPbjpQsEChLG3/3ZlTdWrV58j+s1na9qdw4NAeNv67hkePHxAWHkrePPlp0aw1I4b+Sm7bPFne38hff2Tz1nUA5M9XkEe3HVOt7+LqxJ+rfufcxdN4ermjo6NLkULF6NyhGz8MHIaeXuqz9VFRUWzeuo4Dh/fw7MUTQkKCMTAwpFiR4jRv2pohA4dhYWGZZe8nhBDi42RmKhtUyWdEIYuE2anF516pUm4ntumWd6bTon9rulW0Rk9bg5g4JQvPuiUrdwmIZNtdHwB6VMqV6llRKelXNeE8rlMvArnlFpKs/J+rXviHxaJQQJ8qNuluXwiR9eLj4xky/Hv6D+nJ+Ytn8A/wIzIyEkcne1b+s5y6jStz6871LO3z+s0rbNm2Ps319x3YRc0G5Vm7cRWOTvZEREQQHBzEvQe3mTJzPA2bV8fTyyPF5318X9OoZU3GThrJlWsXCQjwJzY2lpCQYO7ev838xTOpUb8c9x9k/cHOQgghUibBVDZQKBRMbFoQgGevw+m28SmXnYIJCI/B2T+CRWfdmH7clYLmko44PWyMdRhWJ+G3y1vv+DD+sBMOvhH4hcZw7Jk/3TY+ISImHmsjbYbXzau2ja4bnpB32jVq/H5Xbfl31WwoZqVPvBL6b3vOrvs++LyJxjUgkgVn3FRBXPeK1pRN5zJCIUT2WLh0Nrv2bAXg+76DuXHxEfaPPNm8bjd58+TDP8CPPv274OvrkyX9xcTEMGbcMJRKJQULFP5o/Ru3rjJkeD8iIyMpVrQE/27Yi8NjLx7fcWbJ/D8xMTHl+cun9Pq+E7Gx6g8CHzKsH0+fPUKhUDCw34+cP3EDh8deXD13nwn/m4q+vj6+fj70HdiNiAhZQi6EEJ+KLPPLJm3LWDK8Xl7+vOTBPfdQemx6mqS8ViETOpS1YsJ/TmRgAuWbNapBPpwDItn30I9/b7/m39tJz+kyN9BiXc+SapcApoWulgYbepek1+ZnuAdFMXp/8mU79YqYMrdNkQy1L0Rm1KvTkCBv9T9sZ4VVK9azakXaZ1s+B96vvVj+1yIA+vUZxPLFf6vK2rfpRBm7stRtUhkf39f8/ucC5s5Ykuk+//r7d54+f0z7Np0xNDTC1c051fpTZowjNjYWW5vcHD94ASur90uIB/X/iTKly9GmU2MePLzL1h0b+b7v4CTP2zu84PzFMwD8OHgE82ctVZVZWVlT2q4sefLkY+SYIbh7uHH63Anate6Y6fcUQgjxcfJjfDaa2LQAG3uXom4RU0z0NNHX1sDOxoDfmhVgez87YuMTMiUYyVlFaaahoeCPLsVZ1a04dYuYYm6ghZ6WgkIWegyumZvTQyuo9qxlVBFLfU4NLc+oBnkpYa2PvrYGpnqaVM1vzIJ2Rdj2nV2q54cJIT6d7bs2ExERgba2Nr9NmJmsvGiR4nzXexAA/27fSExM5g4xd3VzYeHS2RgaGDJ35scDM3ePV9y8nbDEcNiPo5IEUu/UrF6H5k1bA7Bmw6pk5U+ePlJdd+3UQ20/XTr0QEMj4e8lR8eXH38RIYQQWUJmprJZs5LmKSYqcAlI2DNla5L84N3ahU1TTPKQ31wvTQkglnUqxrJOxdJd9iVoX9aK9mWt0v3cngFl0lTPRE+LsY0LMLZxgXT3IcTHnDx9lJWrl3P/4V1ioqMpXLgYPbv15afBI9i5ZyvDRiX88P/hLFRqCShc3VyoUD3h/+nDe09TvWotVq1ezq5923FxcURLW5uK5SszYugYmjVppXZcX2ICipOnjwJQu2Y9clmr38fYqX1XVq/7k5CQYK5ev0iDek0y3N+4SSMJjwhn+uS55Mub/6P1Hz95oLquU6t+ivXq1KrPsROHefzkAW6vXCmQv6CqTFfv/ZJwBerPv0t8bp+lZfKATQghRPaQX6/nEKVSyRn7QADZeyPEN2TOgml079ue8xfPEBQUSFh4GI+fPOC36WPp1KMlMTHRme4jNOwNrTs2ZPqcSTx99ojwiHBCQoK5ePkc3fq0Y+OWNVnwJjlPqVTy8NF9AKpVqZFivUoVqqKpmbAC4F39jDj43z5OnD5KyeJ2DPtpdJqeCXnzPpGNmVnKGUDNzd5nfb3/MOmezjJ25VWzToeP7lf7/H/HDhAfH4+WlhZ1a6cctAkhhMhaEkxlo8DwlJeTrLvujbN/wuGvbctIKlshvgWHjx5g0e9zAKhSqRoHd5/A6elrbl95yv9GTeTq9Uss+3NRpvuZ8NsY7B1eMHfmEu7fsMfp6Wt2bz1MoYIJe/0mTf01y5IxJBYaFpqpL6VSma7+PL08CAsPA6BggZT3Merp6WFrkxsA+wwugXsT+oaJUxICqIXzlqOtnbZ9mcbG75cdBwUFplgvMChAdW3v8DxJWYH8BenRtS8AK1YuYfrsiTg4viQyMhK3V66sXL2cX8cPA2DcmN8oXKho2l5KCCFEpskyv2zUfu1j6hc1pXVpS0pY66OpocAlIJKd93zZeichcULFvIY0KS7nFQnxtVMqlUybNQEAu5JlOLTnNIaGCbPSFhaW/DZhFpaW1kycMibTfXl4vuLYwfNUq1JTda9Zk1Zs3ZiPOo0qER4RzsH/9jJ4wNBM95VYvqJmmXo+vWdnBQT6q66t1exFSszKKhcenu5JnkmPOQum4enlQZeOPWhQt3GanytZ3E51feX6JSpXqqa23tXrl1TXAQHJx7hs0SqUSiU79/zLsj8XJQu6K5SvzC8//4/OHbuneWxCCCEyT2amslF4TDwbb76m+8anVFx0h3ILbtNuzWP+vf0apRIKW+rxT/eSaGqoXwMvhPh63LpzHSdnBwAmjZ+hCqQS+3HQcArkL5Tpvjp37JEkkHqnjF05ypWtCMDd+7cz3U9OC387KwV89MBb/bflYWGh6e7nwaN7rFn/F8ZGxsyenr6ZwyKFi2FXMmGv5qrVy9UGSrfuXOfEqSOq/w5L9F7v6OrqsmjeCkYMVR9s+/q+xtHZPsXU6kIIIbKHBFPZaEG7IvSpkgs7GwOsDLXR1lRgpq9F9QLGTG1RkFNDy5PPTM6aEuJb8C6jm5aWFs0at1RbR0NDg+YpJIdIjyYNm6dYVrRwQpIKX9/XKdbJqCDv2Ex9pWdW6lOJj49n9NihxMXFMf5/U8ltmyfdbUx+m2XQ08uDlh0acPTEYfz8fPHwdGfD5tV069MuybLBxMkk3rl15zqVa5bkr3+WMXrEOK6df4DLc19uX3nK9MlzCQoKZM6CafTp3znTGQuFEEKknSzzy0ZNS5jTtIQs4RNCwCv3hAOf8+bJn+osStGixTPdl62NbYpl+voGAEREfvkHuxoYvJ/di4yMTLVuxNtyQ0OjdPWxbuPf3L1/m9KlyvLT4BHpHyTQtlUHpk+ey4y5k3lp/5ze33dKUq6jo8Pyxf8wdOQAAExNTJOUO7s40rFbC8LCw/hr2Tr69PxeVWZmZs6oEeOoVKkqHbu14MTpo6zZsJKfh/ySobEKIYRIH5mZEkKIT+DdkjRDg9Szd6b3h311NDQ+fnZdepM9pMWnTkBhYf4+eY+vn2+qdf38fJI98zEhb0KYNX8KAIvn/4GWVsZ//zhqxDhOHblM1049yZM7Lzo6OuSytqFzh+6cOXaN+nUbqermzZM05fqfq34nLDyMIoWLJQmkEmtQtzH16jQEYPuuLRkepxBCiPSRmSkhhPgE3s2iqNsPk1hG9vR8Lj51Aoo8ufNiaGBIWHgYrm5OKdaLiorC+7UXAMWLlkhz+4GBAYSEBAPQumOjVOu+cnfFzDbhn9SffhjJ/FlLk9WpWrkGa1epT+F+9vxJ1XWlilWSlN24fQ2ACuUqpTqGShWqcPHyuWTZAIUQQmQfmZkSQohPIH++hAOgPTxfpbokzdHR/lMN6YunUCgoX64iALfv3kyx3t37t4iLiwNQ1f/cnD1/CgArS2sqlKucpCwyjUsy383saSjkn3YhhPhUZGZKqHTd8IRrLiF0q2jNsk7Fcno4n62wqDj+e+rPfY9QHnuF8fpNNP7hCRm0bI11qF7AmL5VbaiS3/gjLcF1lxA23vTm1qs3BITFYKKnRRlbA3pUzkWHslbZ/SriE6peNSG7XmxsLKfPnaBtqw7J6sTHx3PyzLFPPbQsE+T96TPJNW/amms3rnDl2kV8fX2wts6VrM6Bw3sBMDExpXbNtB9om9s2DxdPp571cO7C6Rw/+R+2NrnZtfUwkBAQpUdwSDA7dv8LQK/u3yU7wyqXtQ0Oji958Ohequ3ce3AHgHxvA3chhBDZT4IpIdLJOSCSMQcc1Za5BETiEhDJ7ge+/FgrN1NaFEqxnenHXVhzzSvJPb+wGC44BnPBMZj/nvizqmsJtDQldf7XoFqVmhQuVBRnF0fmLpxOk4bN0dfXT1Jn7YZVuL1yyZkBfqF6de/HwqWziYiIYM7CaSxbtCpJubOLI1u2rgOgb6/+aT5sFxISQ5R/m0o+JeZmFgBoa3+8rjpxcXEMHz0YP39frCytGT1ifLI69es24ur1Szg5O7Bj97/07NY3WZ0Ll89y6cp5ABo1aJbucQghhMgYWQsgRDppakDlfEb82jAfW/qW4szPFXg0riqXRlbk7+4lKJ/HEKUS/r7qxfrrXmrbWHXZQxVINSpmxsHBZXk8virnh1dgaJ08KBRw9GkAM064fMI3E9lJoVAwffJcAJ4+e0S7Lk24cPksAQH+ODk7MGfBNCZN+5VCBYvk8Ei/LLY2ufll2FgANm5Zw+hxP/PS/jm+vj4cPnqAdl2aEh4RTi5rG0YPTx6oALTp1BgzWy3KVS2aLWN89OQBTVvXZv2mf3jx8hlBQYG4e7xi/8HdNGlVm8NH9qOlpcVfy9ZiYZE8Qcbg/kMxM0vIDDt89GBmz5/C8xdPCQoOwtHJnuV/LqLXdx0BMDI0YtiPo7LlPYQQQiQnM1NCpJOdjSGHfyiX7L6FoTZFLPVpUdKc9msf88grjL+vejKwZu4k9QLDY/j9gjsADYuZsrlPKTTeHtxsbqDNb80LYq6vxdzTbmy65U2/ajYUtzbI/hcT2a5Duy6MGTmepSsWcPvuTTp0TXoeVJ1a9enaqSejx/2MpubHM/KJBOPG/IaTiyO79mxlw+bVbNi8Okm5hYUlWzfuVbsE8FNQKpXcvnszxX1d5uYW/LF0DS2atVFbbmVlzfZN+/luYDf8/H1ZvGwei5fNS1bP1NSMDf9so0D+glk6fiGEECmTmSkhspiOlgadyifsd/IIjiYgPOkBmmfsgwiLjgdgXOMCqkAqsR9q5cbCQIu4eNhyO+sPVxU5Z+qkOezYfIAG9RpjYmKKgb4Bpe3KMXPKfA7sOqE6cNXI6ON77kQCDQ0NVv+5iQ3/bKdBvcZYWFiip6dHkcLFGDpkJFfO3qNalZo5Nr5CBYswffJcGtZvQt48+dDV1cXU1IwK5Ssz/tcp3Lz4WO0eusRq1ajLjYuPmDRuOtWq1MDMzBwtLS1MjE2oWL4Kv/4ygesXHtI4lQObhRBCZD1Fdpw1ompcoVB6zKiVbe1n1GWnYDbf8uaueyj+YTHoaGlgaaBFfnM9GhQ1pWM5K/KY6iZ5xjM4ipMvAjnzMpCn3mH4h8eiq6VBPlMdGhQz44daucltoqu2v533fFR7bDxm1MIjKIoVlzw4Zx+If1gMtia6dChrybC6eTHUTfhtdEB4DP9c9eLoU388g6Mw0dOiaQlzxjcpgJVR8jX/rwIjqbksYXPy7v6lKZ/HiL8ue3DkqT8ewdEYaGtQo6AJI+rlpUJe9efYpCUBRWhUHBtvenPieQBO/hGER8djZaRNzYIm/Fg7D2Vzp3yGztGn/uy458MjzzACI2LR19bA0lCbwhZ6NCpmRqfyVpgbpH0/w+ds5WUP5pxKOKT1xaTqGOm+n2WYecKFf656YaCjwctJ1VEo1O+J6r/tOadeBJLfTJfroyurrZNT8k67hlKpzLbNXAqFQpkTyQw+BxOnjmHV6hXYlSzDtQsPcno4Qnx2zGy1svXvHyGESI9vbpnfsgvuLDr7Ksm96Lg4QqPicA2M4rJTMPraGgyokXRpVtOVDwiOjEtyLyYujuc+ETz3iWDnPR829rajWoHUf5v8yDOUXlueERj+/gdFl4BIll/04KbbG7Z9Z4dXSDS9tzzFJSBKVScyNIZtd3245hrCkR/KYaqf8h9dUEQsbdc8wt73fTrdyJh4jj0L4OSLAH7vWIwuFdKXbQrgoWco/bc95/WbpDMtnsHR7Hvox4FHfkxvWYhBHyxrA/jfQUe23/VJci8mLo6QyDic/SM5ax9EfnM9mpU0T/e4PjexcUqOPgsAoKiVXpJACuDN2+8jEz2tFAMpALO3f8avgqIIDI/5agJNkTKlUsnJ0wnZ/Mp/5EwhIYQQQuS8byqYcvCNYMm5hECqYTFTfqqTl6KWehjoaOIfFsNjrzCOPw9AWzP56seSuQxoVNyMKvmNsTHWwcJAi4DwWO57hLLysgcvfCL4adcLLo6opJpdUueHnS+xMdLm947FqJzPiNCoONZe92L9DW+uuYSw9c5rdt/3JTYeVnYtTu3CJsTEKdl135fF517h7B/JiovuqWaJm3nCFd/QaCY1LUC7spYYaGty61UIs0+64hIQxZgDjtjZGFDaNuVZpA+5BUbSY9NTQiLjsLMxYES9vFQtYIyBtgZO/pH8fdWTo08DmHrMhYIWejQt8T4ouuAQpAqkOpWz4vvqNhQw10NHU4FvaAz3PEL574k/ala7fVRETBzx8el/7h1dLY0sy5YXEBbDM59w/rzkwT33UBSKhGV8H3oXXIVExqJUKlMMqIIi3gfcDn6RVCsgwdTXIDAwAHNzC7Vlf6/9A0enhHOmOrbv8imHJYQQQogM+KaCqQuOQcQrwdpIm429SyUJmsz0tShqpU+HcurP9tk/qGyyexYG2hSz0qeNnQXN/n6Is38k+x/50beqTYpj0NRIaMtEL+GjtzTUZlbrwjx7Hc41lxCmH3fFSFeTU0PLJ1lqOKpBPpz9I9nzwJc9D/xSDaZeBUWxpkcJWpd+nxWqlZ0llfIa02zVAwLCY5l7yo1/v7NLsY0PTT7irAqkDv9QFn3t9wFjFQNt1vQoyaj9Duy+78vsk640KW6mChLOOQQBUD6PIX92LZ6kXXMDbUrkMqBHpYxtDP/u3+dccwnJ0LMASzsWzXDfAAvOuLHiokey+7YmOkxtXpC2ZZJn5ipunZAOOzw6nkdeYZTPk3zZZUxcPHfd36j+OzAiJlkd8WVq1rYujeo3pV2bTpQqWRotTS2cXBzYumMTG7esAaByxao0b9I6h0cqhBBCiI/5phJQxMYn7A+zNNBSO/uUUfo6mrQslfCb5ivOwanWHVk/nyqQSqzt28AnNl7J4Jq2yfZsAbQrm1DHLywGj6CoZOXvVCtgnCSQesfWRIefaucBEgJLnzfRqY71HWf/CM7aBwGwsH2RJIFUYuMa5wfA3jeCJ97hqvtxbz/3XEY6aervS6enrUH/arbULWKqtrxxcXO0386GLTr7ivj45PsW1133xj/s/cxUeHQmpt/EZyU8PIw1G1bSvmszSpTLS5HSNjRtXYcNm1ejVCopWqQ4G9fslGx+QgghxBfgm5qZKvN2WdtznwgWnXVjSK08qe49+tA1l2C23/Xhrnsor99Eq/0B18k/MtU26hdV/wN2AYv3wVNKP4QXNH9fxyc0mrxm6hNetEhl31ELOwvmnnYjXgl33UNpaad+uVFiV50TZn7M9DUpYW1AWFSc2nqmelpYGmrhHxbLQ89QVTKKd5/7OYdANt7wpkdl6xQDsvTaM6BMlrSTUaMb5GN43bwABEfG8tAzIR36/DNubLzpxcbepSj3wcyTrYkO31ezZe11L87aB9F/+3N+qZ+Polb6+IXFsOueDyuveKKnpSAyNiHQkp3WX4/fF67k2Mn/uHXnBj4+3gQFB2JkZEypEna0adWRQd//lOwwXyGEEEJ8nr6pYKpuEVMaFzfjrH0Qyy548NdlT6rkM6JGQRNqFzalZkGTFPfPTD3qzLob3h/tIzQq9QxkKc3O6Gm9nymzTkOdyNiUZyqKWqX8g1gRCz00FBCvBPdUZrcSc/RPSGQRFBFHybnqz0n5UOJZlS4VrFh3w4un3uFMPurM7FOuVC9gTPWCJtQtYkqVfEapJmL4nOloaaDz9v8iQ11N8pjq0rykOT/uesnRZwH02/qcSyMrJUtCMblZAdyDIjn+PJAzL4M48zIoSXkuI22G1snDjBOuAJjoySzF16JFszYpnickhBBCiC/LNxVMAaztWZJVVzzZcvs13iHRXHd9w3XXNyy/6IGVoTYj6+dlYA3bJD/c73ngqwqk6hYxpV81G0rlMsDcQAvdt8sFV1x058/LnqQS4wCgmYYsC2nJh5BaRnt9nZR/8NbQUKCnrUF4dDxh0epnmD70JjJt9RKLjnv/QWhrarB3QBmWX3Bn531fAsNjueAYzAXHYBadfUUBc13GNc5Pp/LpzzD4OSWgeEdDQ8G0loU4+iwAn9AYDj32o3eVpPvodLQ0WNuzJIee+LPzrg8PvcIIi4rD1kSH5iUtGFk/L+ff7jUD1C77FEIIIYQQOeubC6Z0tTQY1SAfv9TPywufCG6/esMV52DOvAzELyyGqcdc8A2NYULT91nY/n17aGqNgsbs6GendhYl4mNR1CcUkUqQFB+vJDImYayGqQRdienrJASMRSz1uDQyY+maTfS0mNKiEJObFeSxdxi33d5wySmY8w5BuAVGMXyvA+HR8fRJJXmHOjmdgCIl+cx0sTLUxu9tlkh1FAoFHcpa0aGs+qQnL9+mtjfQ0aBYKrONQgghhBAiZ3xTCSgSUygUlLIxoG9VG1Z1K8GtMVWoWTDhjKg11zyTBCTPXickU2htZ5nicrSXPhFq7+cER7+Ux+IUEMm7fAf5Uthz9aGC5noAeIZEExOXuaBRQ0NB+TxGDKyZmw29S3FtVGVVoLDsonum2v7cvEt4ktEVjBcdgwCoX8Q0TTOaQgghhBDi0/rmZqZSYqqvxQ+18nDd9QWRsUrcg6Mobm0AQPTbWae4FNbW+byJ5rprxmdHstrJF4EMfZsU4UMn3h4mq6GAyvmSp+RWp97bhBiRMfGcfhlIK7vkmQIzytZEh75VbZh+3AXP4GgiYuLSlZwipxNQpOSlT7jqnKj8Znrpfv6W2xseeibMaPWqkr7ZOiE+N206NebKtYv06t6PVSvW5/RwPnshb0JYt3EVR48f4qXDC8LCQjE2NlElKRnY70cMDAzUPhseHs69B7e5c/cmd+7d4s69m7h7JJyvOP7XKUwcO+1TvooQQnz1vqmZKWf/CLVpqN9xCXific8sUZa/dzM4Z14GJnsmPl7J5KPOxMSlsonpE7vp9oZjz/yT3fcOiebvq54ANChqRi7jtKUqL5HLQJWFcOoxFzyDU09c4fDBzNiH//0h17efu562RpIkG5+rj71PdGw8U465AAnnijUvlXJ2RXX8QmMYc8ABgDqFTZIcgCyE+LrZO7ygdsOKzJgzmVt3bhAcHERsbCyBgQFcu3GF36aPpU7jSri6uah9fvlfi2jTqTFTZ03g4H97VYGUEEKI7PFNzUwtv+jBLbcQOpWzpnZhE4pY6qOnrYFvaDQnnwey9HzCPzo1CxonyajXprQlf1zy4IpzCKP3OzCkdm5sjHVw8I3gj0senLUPori1Pva+n8dSv/xmugzf68CvDSNpV8YSAx1Nbrm9YdZJFwLCY9HSUDCxWYGPN5TIvLZFaLP6IZ7B0bT4+yFD6+ShcXFzbE10iImLxyskmrvuoRx96s8DzzBeTKquenbCYSeCImLpVM6K6gVNKGiui5amAq+QaA498mPTrYTkHm1Lp7yM8nMy4bATUbHxdCxnRdX8xuQx1UFHUwO/sBhuv3rD6quePH+77HNQjdwUsUy+32njDW8uOgXRubw1FfIYYqynhX9YDBccgvjjkgc+oTHYGmuzpEPRT/16QogcEhsbS98BXXH3cENHR4fhP42mc8ce2ObKjae3B9t2bmb1uj9xdnFkwJCenD1+PcW2tLW1KW1XjioVq7Ft1yYiI1M/tkMIIUTGfFPBFIBLQBS/X3Dn9wvqywua6/J7p2JJ7g2vl5fTLwN59jqcXfd92XXfN0l5/+o2WBhos/T857HnZ0qLgiw884o5p9yYc8otSZmmRkLShXdnP6VVIQs9dvUvw+AdL3ALjFLb9jtm+smX6T17Hc6z1+rrA5TPY8i0lgXTNaacdNc9lLvuoSmWayhgcM3cTGmu/p1i4pWceB7IiefJZzsBSljrs6ZHSfKbp3+JoBDiy3Th0hle2D8DYPpv8/h5yC+qMisra8qXrYiRkRGLf5/L3fu3efDoHhXKJU0K1Lplexo3bEaFcpXR00v4+2PPgR0STAkhRDb5poKpyc0KUKewKRcdg3j2OpzXb6IJiYzDSFeTEtb6tChlQb9qNhh8kOXOSFeTfQMTUnsffRaAV0g0xrqalLY1pG9VG9qVsWTJuc9nKYW5vhb//VCWPy95cORZAF7BURjoaFK9gDEj6+ejQt607ZX6UBlbQ84Pr8iue74cf+7PE++EfUGaGgpsjbUpbWNIvaKmtC2TdE/Vsk7FOGcfxCWnIOx9I/AJjSY0Kh5TfU1K2xjSrowl3StZo635+S/xA5jfrgjn7YO44hyMk38kfmEJ72Okq0khC12qFzChR6VclLJRv6cBoHFxM3xD83DVOQT34CiCImIx09eiuLU+7ctY0aOSNTpfwJJHIUTWefL0keq6a6eeaut069SLxb/PBcDR0T5ZMPXhfwshhMhe31QwZW2kQ7eK1nSrmP7zjN6l9p7SopDa8l8b5efXRvnVlvWolOuj6bdrFzbFY0atVOvkN9f7aJ13jPW0mNisIBObpX22Jy3JHHS1NPiumg3fVUt7UoR8ZrrpfuZzVsxKn2JW+gyulTvDbRS10mdSOv5sxJfhwuWzrN/4D7fv3sDXzwddHV0sLa0pWKAQjRs0o2vnXuTNky/JMx6e7hw7cZgTp47w+OlD/Px90dPVI3++gjRu2IyffxxFntzqE8ps3bGJYaMGARDkHcsrdzeWrpjPqTPH8fP3IU/ufHTu2J1Rw8dhZJjwS5SAAH/+/Hsph47sx93DDVMTM1o0a8OUCbOwtk7+95SrmwsVqifM1h/ee5qKFaqw/M+FHPxvH+4ebhjoG1KrZl1+HTmBShWrZvizexP6hrUbVnL0+CHsHV8SHh5GLmsbateqz7AfR6UaJBw6sp+t2zdw/+FdAgL90dc3wNoqF0ULF6NJoxZ079Ibc3OLDI/tU9HVfT8TrUD9kmcNjfe/ZLG0Un+sghBCiE/nmwqmhBAiuyz6fQ5zFiTNlBYdHc2b0De4uDpx4dJZ9PUNGDJoWJI6tRtVJDg4KMm9mJgYnj5/zNPnj/l3x0Z2bjlIjWq1U+3//sO7dO7ZioCA98lnnJwdWPz7XK5dv8z+ncfx9HKnU49WOLs4qupERnqzees6rly7yJlj1zAzNUuxj8CgQJq2qq1aigYQERHBf0cPcOzEYVYuX0+Prn1SHafasT+4Q89+HfF+7ZXkvrvHK3bt2cqefduZO3MJPw0ekezZEWOGsGVb0gyBMTHBhIQE4+hkz8kzxyhYoBAtm7dN97g+tfLlKqquDx/dz8Dvf0xW5+B/ewEwNTWjYvkqn2poQgghUiDriIQQIpNe2j9n3qIZADRp1JyDu0/w5K4LLs99uX3lKev+3krnDt3R0UmeQbN0qTJMmTiLQ3tOcevyE5yevubW5Sf8/cdG7EqWISgokP4/9CQ0LOU9egD9BnXHNldudmw+gMNjL+7feMmQQcMBuHLtIhu3rGHAkN7Exsay7u+tvHzkwZO7LkwePwOFQoGjkz1Lls1NtY/fpo/F9ZUz0yfP5f4Ne+wfebJl/R4KFypKXFwcw0YN4vHTh+n67FxcnWnfrTner70obVeOdX9v5fEdZ5yf+XDqyGXat+lMfHw8E34bzYlTR5I8e/b8SVUg1a1zL44fusDzB69wfubD9QsP+WvZOpo3aZVkNietIiIiCA0LzfBXbGxsuvusVaMudWrVBxI+62V/LMTF1ZnIyEicXRyZu3A6C5bMQkNDg7kzlmBqYpruPoQQQmQthTKFs5OypHGFQpnWZWkic14FRlJz2T0AdvcvTe3C8o+syD55p11DqVRmW+pFhUKhDPJO/w+jOeXvtX8w4bfR5LK24cldF7S1tbOk3fDwcOo1rYKjkz3LFq2i/3c/JClPvMyvcKGinD95M9kP2G07N+Hy1QtoaWlhbGzC5TN3ky01/GnEAHbs3oK1VS7sH3smKUu8zA9g87rdtG/TKUkdL29P6jaujH+AH00btWDP9qRBT2rnTHXr3ZZTZ49TpnR5Th+5gr5+8uyXQ0cOZPuuzZQsbsf1iw9VWT8nTfuVlf8sp2L5Kpw/eSPFzzEj3o05o/5ato4+Pb9P93NBwUH8MLQvp84eV1tet3YDfv1lAo0aNEtzmwVKWBISEvzVnDNlZquVrX//CCFEesjMlBBCZNK7WQgrS+ssC6QADAwMaNOyPQAXL59Lte7/Rk1SO1PRoW0X1Rh/GjwiWSAF0Kl9VwB8/Xx45Z5y1s2a1WsnC6QActvmYcTQMQCcvXCK1z7eqY71HSdnB1XQsHzxKrWBFMBvE2YC8ML+GQ8f31fdj42NA8DGxjZN/X0JzEzNWLViAz269lVb7uXtiaOTwycelRBCiJRIMCWEEJlUrmwFAJ4+f8ycBdMI+mAP1MdcvnqBH4f3p0ptO/IWMcXMVkv1tWLlEgAcnOxTbaNRg6Zq7xcqWFh13aBe4xTqFFFd+6QSCLVu0T7FsjatOgAQHx/PrTtpmyW6dOU8AGZm5pQsUTrFJXOmpmZYWSYkDnrw8K7q+XJlEj7302ePs2b9SiIisu6svyP7zxLkHZvhr4zMSgEcO3mYCtWLcfjIPmb8No87V5/h8tyXa+cfMHrEOFxcnfjfxBGMGjs0y95VCCFExkkCiq9EejL9CSGyVoO6jWnWuCWnzh5n0e9zWPbnQqpVqUntWvWoV6chdWrWR0tL/V+3438bzT9r//hoH2/ehKRabpNL/eyMnp5+uupERKYckBQrVjLFsqJFiqOhoUF8fDyv3F1THes79g4vAAgKCiR/MfM0PePn//6cv57d+vLPuj95/OQBYyeNZNqsCdSsUYdaNerSoF5jqlWp+UUcBP7OjVtX6dO/C0qlkoO7T1K/biNVmZmZOdMmz6VokRIMHz2YjVvW0LRxS9q+DWKFEELkDJmZEkKILPDvhr1MHj+DPLnzEhMTw9Xrl1j8+1w6dG2OXcUC/L32Dz7co7pj97+qQKpBvcZsWruL21ee4vT0Ne6OQbg7BjF6xDiAjyY00NRMflj2hzQ0Pl4ntX20hgYpn52moaGB/tugLOwjyTLeCflIgKhOVFSU6lpbW5sj+88yYugYLCwsCY8I5+z5U8xZMI3mbetRsUYJdu/bnu4+IGcSUCz+fS7x8fHUr9soSSCVWJ+e31O4UFEAtu/anKF3E0IIkXVkZkoIIbKArq4uY0dP5n+jJvHs+RNu3r7GxcvnOHn6KL5+Pkz4bTS+vq+ZMnG26pmNW1YDUKtmXQ7sOqF2FiUiMvKTvcPHhIWHp1gWHx+vmtUyNEzbweAGBoYAFCtagttXnmZoTKYmpsyatpAZU+bz8NE9bty6xvlLZzhz7gSubs788PN3hIeH8X3fwelqt2vvNp88AcWN29cAqFi+cop1FAoFFctXxtnFUTWzJ4QQIufIzJQQQmQhhUJBabuy9P/uB9b/s43Hd12oXbMeAH/9s4zwRAHJk6ePAGjfpnOKy9Gev3iS/YNOI4dUfnh3dLInPj4egPz50nYg9bv9XB6er4iJicnU2DQ0NKhYoQo/Dh7O9k37eXDTgRLFSwEJZ4B9CSJTWWKZ2LvZQw2F/BMuhBA5TWam3hq134Hd932pVciEPQPK5PRwvghLzr1i6Xn3JPdK2xpwamiFJPfi4pUcfuLPPfdQHnmF4hUSjV9YDHHxSqyNdKiU14ielXPRsJjZR/t84h3G2mteXHMJwSc0GgMdTYpb6dOlgjW9KudCUyP790d4h0Tz91VPTr0IxDskCgMdTcrYGtKnqg3tylhme/8A9z1CWXPNk+uubwgIi8HCUJtaBU34oVZuKuRNeVag64YnXHNJurSqW0VrlnUqlsITIrPMTM34+cdRXL1+icjISF65u1KyhB0AUdEJS9bi4uLUPvvaxztTsyNZ7djJw4wc9j+1ZUeOHQQSgppqVWqkqb2G9ZoACUvqjp86QrvWHbNknJCQYbD/dz8waeqvuHu8IiIiIsVsgeoc2X82y8aSVrmsbXH3cON+oiQbH1IqlaryfHnzf6qhCSGESIH8Wktku9CoOIbtsWftdS9uuL7BLTCK8Oh4omKVuAdFcfiJP322PGP4Hnti4uJTbGf1VU9a/v2QXfd9eRUURVSsksDwWG66vWH8YSe6bnhCaJT6H0qzyu1Xb2i66gFrrnnhEhBJZKySgPBYLjkF89Oul4zcZ5/qnpOssOmmNx3WPubAI3+8Q6KJjlPiHRLN/kd+tF/7mM230paWWmQdJ2cH1ayMOs4ujqprc3ML1XWB/IUAOHn6aLJn4uPjGTtxZKZnbLLStRtXOHz0QLL7Xt6e/LFqKQCNGzRLMdHFh0qVLK3KQjhxymg8PN1Trf/hsraPLXNzdnECQF9fHz09vTSNKSe92yd18fI5Ll+9oLbO1h2bcHFNeK9GDdN+1pQQQojsITNTItPymupwblhFADTUhOcKBdjZGNCkuBlV8huT10wXW2MdwqLjeOkbweqrnlxxDmH/Iz+sjbSZ1rJQsjYOP/FnxomEDGGV8hoxrkl+yuY25E1kHIef+LPk3Ctuur1hxF57NvQulS3v6Rcaw6DtLwgMjyW3iQ4zWxWiegETAiNiWHPNi613fNj7wI/CFnqMbpg9vzG+7BTMb0ediVdCjYLGTGxagCKW+jj5RzDvtBs3XN/w21FnilrpU0fNwc1b+pbi3c/8ff99xk23N9kyzm/Not/ncv3mFbp17kW9Og0pVrQE+nr6vPbx5tiJw8xfknBOUu2a9chlbaN6rkPbzixZPp+Ll8/x8y+DGPbTKHLb5OGF/TOWLp/PqbPHKVncjhf2z3Lq1ZIokL8QQ4Z9x4Rfp9KxfTcMDQy5fusqU2aMwz/ADy0tLab9NjddbS5d8BeNWtbE3eMV9ZtWZeTPv9KsSSvy5M5LdEw0np7u3L57k0NH9nHv/m1eOQSqnh01dihBwUF069STWjXrUqhgEbS1tPHwcmffgZ2s27gKgA5tu34RWf1GDB3Dnv3biY6Opkff9kz431TatOqAhYUVXl4e7Nq7VRW05rbNQ78+g5K1ERUVxcPH95Lci3878+np5cGtO9dV942NTChVsnQ2vpEQQnz9JJgSmaZQKDDUTTlLmImeFqd/rpDsvqWhNgXM9WhS3IwB219w6kUgm2+/Zmzj/BjovG8vNk7JrBMuQEJQtntAafS1E8otDLQZXi8v+c11+Xm3PSdfBHLRMYj6Rc2y9B0B/rjkgV9YDHraGuzqX5oilglLhqyMtFnYvihKJWy768Oflz3pU8WGXMY6Wdq/Uqlk1klX4pVQKpc+2/uVRlcrIXq1NNRme7/StPrnIS98Iph1wpVjP5ZL9gPku88N+CRLIr8lzi6OLFw6m4VLZ6stL1SwCCuXr09yb9SI8Rw7eYSnzx6xbecmtu3clKT8hwE/Y2FhyYIls7Jt3Okxa9pC5iyYyrTZE5k2e2KSMk1NTf5atk519lNaFS5UlMN7T9N3QFdc3ZzVtv2OmVny9OlPnj7kydOHKbZfsXwV5s5YnK4x5RS7UmVYs/Jffh45gLDwMKbMHM+UmeOT1cuTOy/bNx/A2Mg4WZn3ay+atamrtv0t29azZdv778E6ternyHJGIYT4msgyP5HjFAoF3SsmHMgZGROPg1/STdh33N/gERwNwKgG+ZIEBO90KGuFnU1C2uaNN7N+mVt0bDw77/kA0LtyLlUgldi4JvnR0lAQGRPPnge+ycoz6657KI+9wgD4tVF+VSD1jq6WBv9rlDAj9sgrjPseaUtPLTJvxpR5rFqxge5d+1DarhyWFlZoaWlhbm5BrRp1mDVtIVfO3ktygC6AsZExxw6eZ8TQMRQsUBhtbW0sLaxoUK8xG1fvYNG8FTn0RupZmFtw+uhVRo8YR9EixdHT08PSwoo2rTpw+sgVenTtk6F2y5WpwM1Lj/l94UqaNGpOLmsbtLW10dfXp3ChorRr04mlC/7i7tXnSZ5btWIDvy9cSYe2XShVojRmZuZoaWlhZWlNw/pNWL74b04duYyFxafZy5gVOrTtzPWLj/hl2P+oUL4yJsYmaGlpYWZmTo1qtZg2eQ5Xz92nQrlKOT1UIYQQgCI793coFAplWg6SDQiLodLiO8TGK1nUvgi9q9ikWNfBN4IGf94HYHOfUjQp8f43lU+9wzjxPIALjsE4+EXwJjIOI10NilkZ0Lq0Bf2q2qCvo34GJbUEFO827ae2Uf+qczDdNiak9r0+qhL5zdWvz7/hGsK/t19z0+0NvqHR6GppUNRKn/ZlLPm+um2yH5A/Z+8SUOQz0+XG6JRT+abFocd+DN1tD8CZnytQyub9eTYbbnjx21EXAB6Oq4qlobbaNqYcdWb9DW/0tDV4PL6q2qAroy45BdNzU8Kf7/6BZahe0ERtvR6bnnLZKZjqBYzZP6hslvUPsPCMG8sveqCnrcGzCdXQUfO9Eh0bj938W0TGxPNL/byMa1IgxfbS8n2dkrzTrqFUKrNtakuhUCiDvNN/To/Ieq5uLlSonvD9cXjvaerVaZizAxLfPDNbrWz9+0cIIdLjs1jmZ2GoTf2ippy1D2L/I79Ug6n9jxJ+429hoEWDREu5nniH0XxV8qUeQRFx3H71htuv3rD7vi/b+9lhbZS1y6/SIi5eyeQjzmy5/TrJ/ajYOO65h3LPPZRd933Z+p0dNlm8POxLcPiJPwAmepoUtkwaiIZEvk8qYaqX8resmX5CWWRMPA6+EZTLk7azbtLisWfCLI+mBqlmy6ucz4jLTsE88Q77P3t3HR3l0T1w/LsadxcSIAQL7u60tFAKlFKoUPe+dTfavv3V3fvW3WiRCu7uGggS4u62m6z+/liyELIJJNkI9H7O6TnLM/M8czdQ2Lszc8dpY1c7cHJWqmeYh8NECkCrVtIzzIMdqWUcbIYYhBBCCCHEKW0imQKY3iuQ1ceK2ZpcSk6Zoc6EYuEB24fuKXEBqFWnvphSAKNjfJjUzZ9uIe6EeGnx1KrILjOw+lgxn27O5HCOjkcXn2i2AgX1eW5pMt/tzEGtVHDj4FBm9Aqkna8LOoOFNceLeXVVKodzdNz6yxEW3NSjwftZ9EYz9RQTOysXtbLGz7MlFOtNnCjQ8/mWLP45VAjAfaMia83OeZ22H6uk0lTnzFSx/tRMxvF85yZTiQW2g1NDvLT1zh5G+boAUGGwkFVaRZi3i9NiOFFgW/4Y5Vf/M9v5urAjtYzE/LZz2KsQQgghxIWozSRTk7r646ZRojdaWHQgn9uGhdfqszejnORC2wfE6T0Da7R1D/Xgx7m1qxL5e2joHurBxV39mPDRfpYfKSIxX09M4LmfN9JUe9LL+HKbbR/PO9NjmN4ryN7m5w7XDghhUJQXl/zvALvSyvnrUAGX9wis63EOXfd9Qq3zgxrirWkxXNU3uNH3n6sfd+XwyOITta77uql5YHQktwwNq9XWKejU79XWlFImd3e8/2Fryqn3X6Rz7hKxIp2tPHVgHYlctQDPU+22qn/OS6aq39PZYqhuL9a3nZLaQgghhBAXojazQcddq+KiLrb9T4sO5jvss/CA7XqkrwsDo2pXMapPbJA7PcJs+3A2JZU0IdKGq06kxnbyrZFIna5zsLs9QVy43/H7v1CplDCnXzCXdPN32D4oyhs/d1ve/+66dCqNtafg/oov4FC2zv7rCkMTpukc0J0c82x72lxPa2+1GDTKZhlfCCGEEELU1GZmpgCm9Qpk0cEC9mZUkFSgp8NpFdMsFit/nkyyLu8R4PDMELPFyoID+Sw+mE98to4inZEqU+0CGycKWnb50+aTyduwDt5U1HOobJdgW7K3P6vhe13OLJrRVs3qE2yfdaswmDmUo+Orbdl8vCmT73fm8Mmszozp5FvjHleNkvtHRTJvaTLx2Tqu/DqeR8dHERfqTnmVmT8PFvDm2jRc1QoqT/5+nwdHygghhBBCiPNcm0qmxnbyxddNTbHexMIDBTwwJtLetjWllOwy27KlaT1rL4ErqzRx3Q8J7DiHQ0jL60lonK2iymyP+/9WpPJ/K1LPek9hxYW7PEutUqBW2fZAebioCPbSMqaTL/OWJPP51ixu+fkIa+7uXasa4s1DQjlRoOebHTnsTi+3V9ar5uWi4umLonnsT9sSQm9X51XyA3A/OdtTZap/tqfytHYPrXMnft01SkrM5rPHcHIGy9nji3+n6Kj2SGVFIYQQwrE2lUxpVEqmxAXw/c4cFh3Mr5FMVS/x6xrsRvdQj1r3Prc0mR2pZSgUcE3/ECZ396djgBteLirUJ4s5XPP9YXaklmGyNF85+DOVVjX8Q4jB3PD4zscCFKd7fHw7ftydg85g4cfduTx2RklvhULBS1M6clFXf77dkc2utHJ7MYqxnXy5d1QE2aUGe/9wH+ftVQLwc7ftQ8o/S6JbUH6qvXppovNiUFNSaT5rDNXtvm71760SQgghhBBN06aSKbAVlvh+Zw7H8vQczKqgR5gHRrOFv09We5vWs/aeI53BzB8n9xn9Z2RErQ/i1SoMzTcjVVeC5n7aWUcfzox1OKvmDOdLAYq6uGlVdAlyZ0/GqYNpHRnTybfWMsBq6xNP7YXrHV474W6KmJPl2nPKDFSZLHXuW0otrgJss0LOLD4B0DHAjeTCKlKLqurtl3YyhphAx2edCSGEEEII52hz64AGR3sR7mMri149G7X2eAnFehMKBUzrWbuSW2K+3j6bMyXOcaU3k9na6L1S1UUFHBU+qJZb7ni2wMdNbT//KKVQSlXXpzohbex+p3WJxQDEhbo7/SyxHifLrJstsP/kmVOO7E4vPxmDc5M5sJ0vBbbzpoxmx38WjWaL/TyqHs0QgxBCCCGEOKXNzUwpFAou7xHIx5syWXQwn6cmRrHoZFLVP9Kr1l4agKrTlsVZ6pghWnaksN5kqD5BJ8tdJ9WTDK0/+UHekREdffgrvoC/DhVw3+jIOvs1xflSgKIuxXoTR3Jt1fiifBs+o5JaVMmqo0UAXN2v7kOfG2twlBdeLirKqsz8GV/AwCjvWn3yy41sO1mefeLJypTONL6zH++uz6DSaGHV0WImOah+uPJokf3PeXPEIGq7896b+OnXbxk+dBR/L1jd2uGcF15+/XleffO/Na71iOvNxlW76rzn2PEjfPDJ26xeu4LcvGx8ffzo13cgt998N2NGTWi2WPPz8/jznwXs3ruTAwf3kZuXTX5BHhq1hnaR0YwYNppbbryTrl1qH81xpiXL/+T7n75m954d9mdERkYxctgYbrv5brp07tZs78OR4uIiBo6IIy8/F4DHHnqGJx6Z57Dvuo2r2bhpHXv27iQ5NYm8/Fx0ugp8ffzoEdeL6VOvZM6suWg0jpcX//DzN9x9/821Y5D9eEKI81ybm5mCUwUmMksMrD1ezLIjtiV+Z54tVa2d76nlVCuPFtdqL9IZeWlFSqPj6R1hm5WIz66wf+A/3Z70MhYfLKjz/luGhAJwKFvH22vT6h2rymQhvbj+ZVznm8R8PVZr3fvArFYr85Yk22cXL+nuuER6XXQGM//5/ThVJiudAt24ur/zlytq1Ur7Msgfd+Xazzs73WurUzGarbhqlMzs7bgEflP0i/Skx8nZqTfWpGE4oxCFwWThzTXpgG0Wq0+E8w4tFqI1/b10MaMnDuSb7z8nLT2FqqoqcnKzWbLsT6bNmsSLrzzTbGNv3bGZBx69i+9+/JK9+3eRmZWBwWCgQldBwtFDfP71x4wY34+PP3uvzmeYzWZuves65sydzt9LFpGVnYnRaESn13H0WAJffPMJI8b349sfv2y29+HIvBefsCdSZ/PUvEd4/e3/Y+WaZRxPPEpJSTFGo5G8/FzWrFvJvQ/dzthJQ8jKzmzmqIUQom1pk8lUjzAPYk8e1Pr4X0noDBbUSgWX9XC8hC/ES8uAdrZzp97fkM5HGzNILqwkr9zAP4cKuPyLg2SXGYn0bdwelsviAnDTKLFa4aafjrD2eDFFOiNpRZV8viWLq787TLh33cvKBkZ5c/NgW0L1xpp0rv8hgVVHi8guNVCiN5FaVMnKo0U8+08Sg97azV/xdSdm56P3N2Qw/qN9fLghgx2pZeSUGSittL3vRQfzmf5lPPP35QFwaTd/hnfwqfWM5QmFXPXNIebvyyOpQG//uf2yJ5eJH+9nZ1oZni4q3pvRCW0d+5lmfhVPxLwtDH57d6Pex39GRhDooUFvtHDl1/EsOVxAfrmR43l6HvvzBD/ssn0ouWdEOMFejv88RMzbQsS8Ldy/4HiDx1coFDxzUTRKBRzO0THn20PsSC2jsMLIjtQy5nx7iMM5OlRKeObiaIfHBwjRlkRGRJGeWEx6YjEr/trosM+Ro4e59c5r0el1dO/agz9+/ofjB7NYu3w7ky+5HIA33nmZn379rlli1KjVjBoxlueeeomFvy5l+4aDnDiUw7b1B/jg7c+J6RiLyWTiiWceZMnyPx0+4+33X+W3P34CYNSIsSyav5yEfWns2XqED9/5gojwSIxGIw88cicHD+1vlvdxpm07NvPtD18QHdXhnPpHhEdy09zb+d+H37Lqn80c2pNCwr40lv25nrnX3IxSqeRg/D6uuWEGFgfVkK6aeY399/rt1z5y9tsRQohW0+aW+VWb1jOQ11en2WdpRnb0IcCj7upkL0/pwIwv4ymrMtcqQa5RKXh7Wgw/7Mpt1KxPgIeG5ya157E/T5BcWMk13x2u0d4v0pN7R0Vyw48JdT5j3qT2aFQKPt2SxcqjRaw8uSTNEU0rVtVrLkdy9by0sv6y8DN6BfL61BiHbRYrbDxRwsYTjg9cDvfR8tHMWPssYnMI9NTwxZwu3PBjApklBm75+WitPlf0DuT+ZlrKCbYloy9e2oFnlySzNaWMaV8crNGuVir476XtHSakQrQ1CoUCT4/6/5994eWn0el1BAeF8Ncfq/D3t32pFhgYxPdfzmfG7EtYs24lL7z0FNOnXomrq3MLr1w8cTIXT5xc67q/fwBdOndjyqXTGDamN5lZGbz/0VtcctFlNfpZrVY+/+pjAPr06s8fPy9BrT71T2+H9jH07zuQURMHYDAY+O7Hr3j1xbed+h7OZDKZeODRu7Farbz64tvMnjvtrPf88t0ih9dDQ8IYPHAYsTGdeeaFx9i9dycbN69j1IixNfqp1Wo81bbfa63WucV5hBCiNbXJmSmovaRvWq/6q+B1D/Xg79t6Mr1nIIEeGrQqBWHeWi7vEcCim3swvVfTll1dOyCE767tyvAO3ni7qnDVKOka7MaTE6L4/ca4s57po1IqeObi9qy4sxfXDwwhNsgNz5Nl2/3c1fSL9OT2YWEsuCmOm4eENSnWtuaRse14ZUpHLosLoEuwG/7uatRKBd6uKrqHunPdgBAW39KD96+IxVXj+OfYN9KTB8dEMjjai1Av2++vn7ua/u08eeaiaNbc3cfhPqbTZZXaEul+kY1PuAa082Llnb25ZUgY7f1dcVXb4hjR0YdPZnXmvRmxdc4IZZacSuSbEsP1g0JZdEsPLu8RQKi3Fq1KQai3lmk9A1h8Sw/mDgxt9LOFaEuyc7L4Z+liAO69+2F7IlVNoVAw76mXAMjKzmTJMsczQ83J18eXKZdOB2D/gT212gsK8snOyQJg2mVX1EikqnXt0p2ecb0BSEys/SWNs334ydscOnyASRdNYdJFU5zyzGvn3Gh/vc/Bz0EIIS5UbXZmKtrflYznhzbonphANz6YGVtne31FGt6Z3ol3pneq9/njYv0YF+t4U/+wDj7nFG+3EA9emtLxrP0uJBG+Llw3MITrBja+MESIl5aHxrbjobHtGnV/enEVyYVVqJTwcCOfUS3UW8vzl7Tn+UvaN+i+jUm2WbVoPxfm9Gvavq4+EZ58dGXnJj3jQlBQkE+X3pGYTCbee/NT5l5Te4N7taPHEhg0sgcAv36/mIsmXGpvO3hoP38vWcSadSs4evwIpaUleHp60SW2K5dNnsFNc2/H3d29wfFNnj6OTVvWM2fWXD5+z/F+mA2b1nLZFbYCCvu2Hyc6qr3Dfpu3buDr7z9ny9aN5OZl4+LiSudOXZh++SxuueFOXFwuvG/7l69aYt9vOX3qlQ779OnVjw7tY0hKTmTJir+Yfrnjfs3JbLIVUdA6+D1wOW2mrL6lt0ql7YukgADn77c8XWpaCq+++V/c3Nx49cV3nPZck+lUIQkXmXkSQvyLtNmZKSGcacPJaotX9g4iJtCtlWKwJVMPjm2HRiX/6zlDQEAg40ZPBLDvSalLdXuAfyDjxlxkv34gfh8jxvXj5defZ+v2zRQWFmAymSguLmLbji08/dwjTJg8nNy8nOZ7I/Uwm808+OjdXDptLL/O/8FegKG0tISdu7fz1LyHGTtpiH3240Kyf79thiM8LIKI8LqXzw7sPxiAAwf2tkRYNZSWlbJi9VIA+vbuX6vdy9OLDu1ty5f/XrrI4X6ipOREDsTvA2DMqPHNGC08+tR96PQ6Hrz38ToT98b4Y9Gv9td9+9T+OQghxIVKPtGJJksvrrIXVpj48b7WDsehjUkluKgVPDimabNSTbE5qYQuwW7MaKaDm89FdRGOiHlbmnTIc1syc8YcADZtWV9vQvH7wl8AmHbZzBpLrRQKBePGTOStVz9k6eJ17N12jMT4bDau3s28p/6PAP9ADh0+wH0P39G8b6QOTzz7EF9++ylqtZq7br+Ptcu2kXQ4lwM7T/DO6x/b45t785WYzQ0/mFyv11NeUd7o/06fkXC2Y4lHAIiOrr9IQtTJpOBE8nGHyYqzWa1WcnKzWbr8L6ZdeRGpacloNBoee8hxVcGH738SgO07t3LtTTPZvWcH5RXl5OXlsvjvBVwxZzKVlZWMGTWeq2Ze02xx//nPQpYu/4uOHTpx710PN/l5Op2tGuGLrzzDMy88Ctj2mA3sP6TJzxZCiPNFm13mJ4QzfTiz9ZfE7Xp4QGuHcEGaPOly3N3c0el1/L7wF+6+/f5afXbv2cGJJFsFxZkzZtdo69G9F3/8vKTWPQEBgfTo3otLL57K8HF9WbLsT44nHqVTTMv9Wdq1ezv/++IDAD5+7yuuPJk4Avj5+XPDdbcyZNBwxk4azPadW1n05+/MmDarQWPMvHoym7asb3SMH77zBdfMvr7R99ensMh2LEZQQP3LYqvbq6qqKK8ox9ur/v2TjXXb3XP59fcfa12P6RjLW699yKABjpd6XzP7ekpKi/nvy0/zz9LF9n1g1cLDInj68Re47+5H7Mv9nK28opzHn74fgNdferfRy0JPJB2n39Cuta5rtVrmXnMzr/3fu00JUwghzjuSTIlG+8/ICO4YFl7jWjN9DhBO8t21XTnzi3v1eV490sPDg0suvozfF/7C7wt+dphMzV/4MwDtIqMZMmh4g57fpXM3evXow+69O1m/cU2LJlOffvkhABPGXlwjkTpd1y7dmTl9Dt/9+CW/LfipwclUW6bTVQCctUKfm9uppbsVzZhMOeLvH8Adt/yHfn0G1tvv9pvvwc/Xn4ceuxudvuZ5hSUlxaSlp1JcXERQkPPPyQN46bV5ZGSmM3XyDMaPvdjpz584/hKuv+Zmp1dTFEKItk6SKdFoWrUSrfwJOq+4aVStHUKzmDljNr8v/IXde3dyIuk4HTucKiZjsVhYsOg3AK6YNsthEQCz2cxvf/zEgkW/ciB+HwWF+VRV1T5G4fiJ5q+0droNG9cAMHL4GMoryuvs162rrbjOvv0NP0Pt7wWrGxfcv9D7b33GW699hNVqpbCwgG07NvPO+6/xyJP38vlXH/PL94tp72BJYk5uNldfP51de3Zw2eTp3H37/XSJ7Ya+Us/Gzet48ZVn+eb7z9mwaS2L568gMsK5y5H3H9zLp59/gIe7By+98GaTntWhfQzpJ/egGqqqSEpO5LcFP/P5Vx/xz9LFPPPEf3nw3sedELUQQpwf5KOwEOK8N2HsJPz8/CkqKmT+gp959MGn7W2btqwnKzsTOLW/6nSlZaXMumYKW7dvPus4ZWVlzgv6LMoryu1xz3vxCea9+MRZ78kvyGvusFqUu7sHAJWVlfX20+v19tceZzm3qilcXFzsy+O8PL2IjmrP1MkzuOyK8ezYtY2rb5jBxlW7aizVMxqNTL/qEg4dPlCrqqMfMOuKqxk9chwjx/fnRNJxHn3qPn78+g+nxWyxWHjgkTsxm8088sBTTU7UapwN5uGJv38A/fsNYsSw0Vx74xW88NLTdGzfiWlTZzoheiGEaPtkUZYQ4ryn0WiYdpntw9vvC36p0TZ/gW2JX/euPejRvVete5989iG2bt+MQqHgxrm3seCXJezfkUjykXzSE4tJTyxmyKBhAM1abOFMpaWOD6iuj8FgaPA9bbkAhb+fPwB5Bbn19qtu12q1Zz0E2NlcXV3tZ10dOnyA9RtrzvT99c9CDh0+AMBTjz3v8BkhwaHccuOdACxd/hfFxXUf6t5QP//2Pbv27KBzbFfuvuMBpz33TFMuuZwRw0YD8Mnn7zXbOEII0dZIMiWEuCDMnG4rLHHk2GH7oaFGo5HFf9u+5T+z8ARARUUFv/7+AwAP3fc4b7/2EWNHTySqXTS+Pr54enji6eFJeUVFo2Kq71yhamaz42SkelYG4POPv6c423RO/zXUzKsnExnj2+j/fpn/Q4PHPFexMV0ASElJqrdfamoyADEdYputgEN9+vcdZH+9/4zy7Nt2bgEgKDC43lmh6rLqFovFqctJU1JtP7ujxxIIaueGb6i61n/VXn3zv/Zr+w/ureOJdevf17Zv7MyfgRBCXMgkmRJCXBCGDRlp/7D6+8nZqFVrllFUVIhCoeCKabWTqWOJR+yzOZdf5nhZkslkIrGRH25dXWyb8Ssr9XX2yc7Jdnjd18cXv5MzM8kpJxo1/vmuV6++AGRmZZCZlVFnvx27tgHQs2eflgirFtNpCfGZCXR9v/enqz6cGECpOD//aa6epTyXLxGEEOJCIXumhBAXBIVCwYzLZ/HeR2/yx6Jfef6ZV+xnSw0aMMThAaUGw6kiE3Wd0fT30sU19uQ0RHBwKACJJ8uyO7Jm3co620aPGMfCP+ez8M/f7WcVOVtbLkBx0fhLUCgUWK1WFv45n7tuu69Wn30H9pCUnAjAJROntHSIAGzZusH+OiqqZgGK4KAQAPLyc0nPSKtzdmrPvl321xFOLEBx0/W3M/mSy+vtM2qC7diGG+fexo1zbwNOzQo2xJZtGwGIjqr/XDAhhLiQnJ9ffwkhhAPVBSbSM9JYtWaZ/TyfmdMdlxWPatfe/nr5yn9qtRcVFfLcORR+qEu/PrYPqQcO7uVwQnyt9l27t/PHol9qXa92x63/AeBg/D5effO/9Y5VVVVFalpKo2Nti0JDwrh00lQA3vvwDYpOnjtVzWq18sJLTwEQFhrOJRdf5vQYjh0/Um97SWkJz71oS3Q9PTwZO3pCjfZRI8baX7/yxgsOn5GTm80XX38CQFz3XoScTMKdISQ4lF49+tT7X7XgoBD7tdPLzadnpKHT6Rw8/ZRvf/yS3Xt3AjDl0mlOi18IIdo6SaaEEBeMXj360CW2GwAPPHo3FboK1Go106de6bB/aEgYgwfaDlp9892XefeD10lKTiQ3L4fFfy/gostGkp2TSbvI6EbFM+2ymbi7uWO1Wrnmxivsyw5TUpP5+LP3mD77EiLC656FGDJoOLffYkuoXn79ea667nKWr/yHrOxMikuKSU5JYtmKv3ns6Qfo0b8Di/6c36g427Jnn3gRdzd3snOymDxjPGvWraCgIJ/9B/cy95ZZrFqz3Nbvyf9zeMZRSmqyfR/Qy687LgBRnyvmTGb6VZP45vvPORC/j/z8PIqLi0g4coj/ffEhI8b141DCQQCeeeLFWmdcjRg2mqGDbWebff/TV9xw62y279xCcXERmVkZ/Pr7j0ycPILcvByAGpUoT1f9Hu6896YGv4emWrdhNX0Gx/L0c4+was0y0tJTKSktISs7kzXrVnDbPddz30O3A7YvKO6588EWj1EIIVqLLPMTQlxQZs6Yzf+9Oo+0dNsszZiR4wkMDKqz/5uvfsill4+htKy0VglyjUbDR+9+ydfffWZ/XkMEBgbx0gtvcv8jd3Ii6ThXzJlco31g/8E8dN8TzJ47rc5nvPT8G2g1Gj745G2WrfibZSv+rrOvRqttcIxtXZfO3fjfR99x213XcejwAaZfdUmtPg/f/wRzZl3XLONbrVbWrFtZ73JMFxcXnn7sBW6/5R6H7V9/9gtXXnMZ+w/sOblss3bSq1QqefrxF7h8ygynxe5MuXk5fPDJ23zwydt19unTqz9ff/Zzix6aLIQQrU2SKSHEBeXKGXP4v1fn2X/t6Gyp0/Xo3ovVS7fy6pv/Ze36VZSUFhMUGMyQQcO5544H6NtnAF9/91mj47nhuluJCI/k/Y/fYu/+3RiNBjpExzDriqu56/b72baj/vOtVCoV/533GrNnXcdX3/yPDZvWkpmVjr5Sj7e3DzEdOjF44DAmX3I5QwePaHScbdmUSy5n3YodvP/xW6xZt5LcvGx8vH3p328Qt998N2NGTajz3sysdPvrgf0HN3js7778jTXrVrBx8zqSU5LIy8ux/ey9fIjt1JkRw8dw3ZybHO7JqxYSHMrKvzfx8/zvWfTnfA4c3EdRcSEatYbw8EiGDRnJzTfcQe+efR3en5HZtPfQVFMunYa7mzvrNq5mz75d5OZmk1+Qh4vWheDgUPr06sflU65gyqXTUKkuzIPBhRCiLorTKwg5/eEKhTXj+aHN9nwhROuImLcFq9XabCW7FAqFtTFlvsX54eXXn+fVN/9Lu8hoDuxMbNaxXnnjBV554wWGDh7OkkXrmnWs5vLjL99y13030T66Izs2xqPRaFo7pCb54edvuPv+mwEaVc7fN1TdrH//CCFEQzTrzJSLWpETMW9LSHOOIYRoeS5qRU5rxyDEuVi3YRVg2890vlp78j08/vCz530iJYQQF5pmTaYqjRbnlSQSQghxQUlLT7EfGtsjrjcbV+06yx0NU1FRwc7d25kw9mKGDRnp1Ge3pA0b19CtSxyzrri6tUNptNNno4QQ4kIie6aEEEJckDw8PMhLa9wZYW3J4b2prR2CEEKIOjTrnikhhGgM2TN1YTMYDBiMhhrXVEpVjbONxIXFZDJRWVVZ67qnh2eDnyV7poQQbYnMTAkhhGhRWq0W7QVYxl3UTa1W46lueOIkhBBtnRzaK4QQQgghhBCNIMmUEEIIIYQQQjSCJFNCCCGEEEII0QiSTAkhhBBCCCFEI0gyJYQQQgghhBCNIMmUEEIIIYQQQjSCJFNCCCGEEEII0QiSTAkhhBBCCCFEI0gyJYQQQgghhBCNoG7tAISopnLTZFsqTSGtHYcQQgghhBDnQpIp0WZYKk0hU7Pfau0wRBuwdsDL+IbKX09CiNpcXV1zWjsGIYSoprBara0dgxAAKBQKqyRTQlx4Foc+iNVqVbR2HBcahUIRAqwE/gKetJ72D7pCoegKrAbaWa1WcyuFKIQQFzzZMyWEEEKch6xWaw4wFpgEvKFQKBSntSUAOcCoVgpPCCH+FSSZEkIIIc5TVqs1HxgHjATeUygUp/+7/hMwp1UCE0KIfwlJpoQQQojzmNVqLQImAv2BT05LqH4GZigUCm2rBSeEEBc4SaaEEEKI85zVai0BLga6AF8qFAqV1WpNBRKAi1o1OCGEuIBJMiWEEEJcAKxWaxlwKdAO+FahUKiBH5GlfkII0WwkmRJCCCHOQwqF4nGFQvGlQqGYolAoXAGsVmsFMAXwx7ZnagEwWaFQuLdiqEIIccGSZEoIIYQ4P30N7AMeAbIVCsUvCoXiKmxnSE4DXICPgW3AZa0UoxBCXNAkmRJCCCHOQ1arNdtqtb5rtVpHA52BFcD1QAbwO/AnoAJCgGtaLVAhhLiAqVs7ACH+LRJeX8rRN5fXuOYdF86YVQ/X6lu8N43s5fEUbE2k7GgOxmIdKnctnh2DCJnYnfY3DMclwLPOsTZN/5CCLYk1rrWbNZC+78nWiTNZjGbWjX+DsqM5gHN+TiXxGZz433oKNidSmVuK2sMFz9hgImcOIPrqwShUdX+PtTj0wXMaY0r66yjVqibFKS4cVqs1F/gc+FyhUPhg2zs1Hds5Uyagl0KhCD7ZTwghhJNIMiVEG3PgyT9I+nJjreum0kqK96ZRvDeNpK82MfDzGwgY0rEVIrywHP9ojT2RcobET9cR//xisFjt1wxVJgq3JVG4LYn0+TsZ8sOtqD1dnTamEKc7WdnvJ+AnhULhhq2a31NAICDJlBBCOJEkU0K0MLcIP8aufxQAhVJRq91UXgWAb592RM4cQMCwGNxCfTCWVZKzPJ6EV5dgyC9n23WfM2b1w7i386/1jCE/3or15If5rVf/j8JtSc34js5fFSkFHHtnBWpvVxRKBcZifZOel7l4L/HzFgHg2zeKbk9cik+PCIxllWQu3suR15dSuC2J3Xf/wKBvbq73Wb1em0nkFf3rbJdZKXEurFarHlh08j8hhBBOJsmUEC1NAWoPlzqbfXtHEnX14FqzTlp/DzreOgq//tFsvOx9TGWVHHtvJb1fn1XrGSq3U2d0KpSyNbIuBx7/HbPeSM+np3D847VNSqYsJjPxz/8JgHf3MIb/cZf990Hr70Hsf8bj3s6fXXd8R/ayeHLXHSF4dJc6n6fUquv9cyKEEEKI1ifJlBBtTIebR9bb7tcvmpCJ3cleepDcNUdaKKqGMxRVkLfuKBHT+rZ2KA5lLNpL7poEfHpG0P6G4Rz/eG2Tnle0MwV9RhEAnR+4qEZCWy1iWl+OvbeS0kNZJH+1qd5kSjiHRumabbJWhbR2HEII51MrXHKMlsrQ1o5D/LtJMiXEOUr8ZC3xzy0GYPAPtxIyvlutPsZSPWvHvoE+owi//tGMWPyfeosNNJZnbDAsharcUqc/uymsZgu5axJI+2UH2cvjcQnyapPJlLGskvh5C0GhoOfLVzjl96g0PsP+OmBoTJ39AobGUHooi7x1RzDrDQ6TLuE8JmtVyPNDM87eUQhx3pm3JUK+KBGtTpIpIc5Rx9tHk7PyMPkbj7H3gZ8Zs+aRWhX19j/xO/qMIlQeLvT78JpmSaQADPnlAG2miEHZ0RzSftlO2vxdVOWcSvDUnm1zmVrCy/9QmV1K1NWD8R/Q3inPNJZV2l9rfN3q7KfxtZ2datYbKTuWi2+vyHqfazGYUGrlr2ohhBCiLZJ/oYU4RwqFgr7vzmbtuDeoyi1j3yO/MejLG+3tGQv3kPH7bgB6vHA5Hu0DmyUOi8FEzqrDAPj1j26WMc6FsVRPxsI9pP28naLdqfbrCo2KkHFdiZw1kNCJ3R3ea64yYTWZGz22UqNqdIJRvDeNpK83ofFzp9tTkxsdw5lOT2yNxXpcAh2XrjcW6+yvy4/n1JlMHXt/FfHzFmEs0aPUqvDoGETIhO50vGUkrqE+TotbCCGEEI0nyZQQDeAW4UevV2faigj8c4DUn7YTNWcQ+qxi9j8+H4DQi+OIvmZIs8WQ+Ok6qnLLAIi+bmizjeOI1WIhb/0x0n7ZTtaSA1gqTfY2n54RtJs1kIjp/epMJKrtf+Q30n7d0eg4Oj90EV0fmdTg+6xmC/se/Q0sVro9cWm9Z3U1lFfnU6tNCrYmEj6lt8N+BVtP2F8bCnUO+wBUJObZX1sMZsoSsilLyCb5m830+/AaQi+Kc0LUQgghhGgKSaaEaKCIaX3JXh5Pxh+7OfjMAgKGdmTfo/MxFuvRBnrS+43a1fWcpWhPKkdeXwpA4KjOLfaBuvxEHmm/7CDtt51UZhbbr7sEexE5oz/trhqAd7fwFomlKZK+2kjJ/nR8+7Qj+lrnJrz+gzqg9ffAUFjB0XdWEjKhOypXTY0+mX/uozQ+0/5rs66q1nP8BrSn3awB+PWPxi3cF5W7C7rkfDIW7+X4h6sxlVWy89ZvGL7wHvz6Rjn1PQghhBCiYSSZEqIRer1yBYXbktBnFLFh8nsYCmx7mPq8dRUuQV7NMmZlbik7b/kai8GM1t+Dvu/MbpZxTleRUsCe//xI4fZT51QpXdWEXtyDdrMGEjymS6P2hfV9bw5935vjzFDPqjK7hIRXloBSQa9XZjq9ZLzKVUPnByZy8JmFlB7MYPMVH9H18UvwiTvtnKk3lqF0VZ+a0VPUPmds5F/31rrm1SWUro9MImR8NzZf8RFmvZH45xYxYtF/nPoehBBCCNEwkkwJ0Qgabzf6vjeHzVd+bE+koq8d0mwzRabySrZd8xn6jGJUbhoGfXszbuG+zTLW6fTpRacSKYWCjreNostDF6HxrrvAQlt14OmFmMqriJ47DN8+7ZpljA63jKQ8MY/krzdRtCuFLVd+UqNd7eVK92cvY/8jvwE0+Ofo1y+aDjeP5PgHq23JfFYxbmG+zgpfCCGEEA0kyZQQjeTZKRi1pwumUlsVt4gZ/ZplHHOVie03fkXJgQwUaiX9P53rtAp0Z6PxdUPt7Wp7j1YrJz5dR9GOJCKvHEjEtD5o/Twa9dyWLkCRv/k4WX/tQ+vvQbcnL230uGejUCjo9coVhF4cR/I3myncmYyxxFaMInhsV2Lvm0Bldom9f2MS4pCL4jj+wWoASuMzJZkSQgghWpEkU0I00t77f7YnUgD7H5vP6OUPOvXcIKvFwu67vid/wzEAer8xq0ULD/jERXDx/ufJWnKAtJ+3k7fhGEW7UynanUr8vIUET+hOuysHEDKhO0qN6pyf29IFKHSphQAYCitY2vXpevum/brDHtvAr24k7JKeDY4veGxXgsd2ddiWt/6o/bVPI2bITi/uYSzVN/h+IYQQQjhP8xyCI8QFLunLjeSuSQCg25OXotSqKD+Wy6H//uXUcfY98htZf+8HoPszU4iaPcipzz8XKlcNkdP7MfSXO5i48xm6Pn4JHh2DsBjMZP9zgB03fsXy3s+x/4nfKdqd0uLxnW/y1h4BwLtHBK6N2F9XXckRGr5MUAghhBDOJTNTQjRQ+fFTSVP03KHE3jsBFAoO/9/fJH21iZCL4wge3aXJ4xz6v79I/WEbADF3jaXT3eOa/Mymcgv3pfP9E+l8/0QKtp0g7ZcdZC7ei6GwguSvNpH81SY8OwUTObM/kTMH4B7p5/A5LV2AIvTiOEavfKjePluv+YyqnFJCLoqj66O2WS/3KH+nxlGRUkDOykMARF89uFHPyF560P7ap0eEU+ISwpGkks18fehKAO7vuxU/V+fuNVxw/H725v1Ge++h3Bg336nPbgnxBX+yM+cHsiviMZh1+LiE0dlvAsPC78BbG9rk51eaStmc+SmHC5dQXJWGSqEl0L0TfYJm0S94DkqFfB8uRFsgyZQQDWAxmdl99w+Y9QY8OgYRN28qAJ3uHkvOykMUbkti7/0/M2bNI2h93Rs9TuL/1nH8fdu+mHZXDSTu2cucEr8zBQzuSMDgjvR4cTpZf+8n7eft5G9OpPx4LgmvLCHh1aWEXdqTgV/c0NqhovXzOOv+ruplilpf92ZJUkwVVey+5wcsVSY8Y4MdlmY/W0GJgq0nSPpqIwABQ2Pk8F4hWoHFamHB8fvYn/9HjesFlUlsyfqMfXm/c3XXr2nn1b/RYxToT/Dt4TkUV6WfdlVHWtlO0sp2El/wJ1d3/RqN0rXOZwghWoYkU0I0wNE3l1O8Lw2FSkm/D65G7eECgEKppN/717B23OtUZpVw4PHf6f/JdY0aI33BbuLnLQZsZ0n1eGEapora5xFVU7lrUTgosd1S1O5a2l05gHZXDkCXWmjbc/TbTnQpBRTvS2u1uJpDwutLOfrmcgAmbH+61sxV9rKDnPh8A+1mDcRvQDQu/p4YinUUbE7k2HsrqUjKR+3pQr/3r3FYQGP73C9wCfQi7LJe+PaJwjXEG4VCgS61kIzFe0n6fD0WgxmVm4a45y9vkfcshKhpXfo79kSqf/A1DA27FTeNH6ml21mS/Cylhix+PnIzd/ZeiacmsMHPN1mq+PHIjRRXpeOq8mFS+3l08h2D0aJnd+7PbMz4gBMlG/j7xJNM6/SWs9+eEKKBJJkS4hwV7krm2HurAIi9dzx+/aJrtLtH+dPjxensvf9nMhbuIeTiOCKnN7zCX8q3W8BqBSB//VGWdHmq3v6OPtS3Fvcof7o8fDGdH7qIgs2J5K070tohtSirxUr+hmP2giFncovwpd/H19VZmt1qsZK7JsG+H88RbaAn/T+6Ft9ekU6JWYi6dPAZxvNDM5rt+dM7vcP0Tu802/ObQ5khh02ZHwLQL/hqpsa8Zm/rHnApIe5d+Xj/RMqNeWzM+IBJ7Z9r8Bg7cr4jX38cBQqu7voV0d6nlgRPiHocjdKN1WmvsTfvVwaH3USYR48mvy8hROPJglshzoGpooo99/yI1WzBt3c7Oj90kcN+UbMHETbZVv3twBO/o88qbsEo2w6FQkHg8E50e3Jya4fSovz6RdP5oYvwH9IR11BvlFoVWn8P/Aa0p/uzlzF23aMEDOpQ5/1x86bS6e6xBAzpiFukHyp3LUqtCpcgLwJHdSbu+csZv/kJgkZ1bsF3JYSotjfvN4yWSlQKDePbPVqrPcCtI/2CrwZgT+4vmC3GBo+xM/tbADr7TaiRSFUbHn4n7mo/rFjZlfNDg58vhHAumZkS4hyoPVwYv+XJc+o78IsbmzTW8AV3N+l+0TgTdz5z1j5dH5lUb0l21xBvW/sjjYshaFRnSZSE0x0tWsWWrM/IKj+A2WrA37U9vYNmMjjsZvbn/c7CxAcBas1C1VeAoqgyjXf22Pb93dD9N9p59WdL1uccyF9AYWUyKoWGMI+eDA+/g1g/x8VzzscCFEeLbKsTor0H46kNctgnLuAytmV/SaW5lJSyrXT0GXnOzy+sTCa/MtH+HEfUSi1d/SexO/cnjhStYAovN/BdCCGcSZIpIYQQ4gK1OvV11mW8U+Natu4Q2SkvcLRoJT0Cm773zmAu58v4K8go32O/ZkRPUukmkko3cVnHVxkQcm2Tx2ltVquV7ApbNc1Iz7qXcId79kKJCgtmsiriG5RMZVUcsL+ub4xIz37szv2JUkMWFcZCPDRtY6m3EP9GkkwJ0cL06UUsDrV9E+wdF86YVQ87fYxN0z+kYEui058rhDh/HC5YYk+kIjz7MiHqMULd49CZitiXN5+NGR9SXNX0PVFLkuehMxUxqf1zdPW7GBeVJxnle/k76WmKqlJYmvwcXf0nNaoYQ32qzBVNul+rdG9Q8Z5SQxYGiw4AP9foOvtplK54aoMpNWSRr2/Y38PV/RUo8XWpe1/k6bOEBfpESaaEaEWSTAkhhBAXGKvVyvLUFwEIduvCDd1/RauyHdfgrvFnfNRjuGsCWJo8r8ljlRgyuSnujxqlwGP9xjFH+wUf7Z+A0aLnUMFfDAq9ocljne6l7U1bEtvQs7P0piL7aw9NQL19PTSBlBqyatxzLnQn+7upfVApNXU/X30qMdU1cAwhhHNJMiVEC+l83wQ63TW2xjWFsnlKmg/58VasFmvNsdSqZhlLCNH2pJfvorAyGYBx7R6xJ1KnGxx6E1uzPqe4qmlHGPQImOrwTKUQj26EuseRrYsno3xfk8ZoC6pnpQDUCpd6+1af/2Ro4OyZ0ay3PV9Z//PVp50v1dAxhBDOJcmUEC1EqVU7PFuoOajctC0yjhCibUor2wWAUqGmk99Yh32UCiWxvuPYkfNNk8bq5DumzrYAtw5k6+KpMOY3aQxHmrNsuxBCnCspjS6EEEJcYKr3Qvlow+2zJI4EuHVs8lhe2uA62zRKNwCMFn2Tx2ltWuWp2T2Tte6D1AGMlkrbPSqPBo2hUdl+XiZL/c83nXx+Y8YQQjiXzEwJIYQQFxjjySVpGmXt5X2n0yqb/kFcQessIW7pAhRuaj/76wpjQb19q2fiTr/nXLif7K83lWC2mlApHH9MqzDl17pHCNE6JJkSQgghLjDVSZTxtH0+jhgs5+9+m5YuQOGtDUOrdMdg0VFUmVJnP5OlinJDLgCBbjENiqm6vxULxZVpBLg5PuS7qPLUPreABo4hhHAuWeYnhBBCXGB8XSIAW6U942lLws5UoD/RUiGd9xQKBaEePQBIP+1MrTNllO/DghmAMI+4Bo0R5tHztOfUPUZ6+W7AluBJWXQhWpfMTAnRyvI3HWfzFR8BMGH707hHOfcfxj33/kTarzsIGBrD8AV3O/XZLSFz8V6Sv99K6cEMTLoq3ML9CJnYnU53jsE11KfRzzXpDJTsS6NodwpFe9Io3pOCPqMYgM4PXUTXRybVe39Dz/Lq885somYPqnFNl1ZI1pID5G88TumhTKpyS1GoVLhF+hI4rBMdbh6JV+eQBr83Iaqr61msJo4Xr6Wbf+0/zxarhWPFq1s6NKdpjQIUnf3Gk1q2nZTSrZQb8x2enRVf8BcAripvor2GNOj5/q7tCXSNIb8ykfiCv+gVNKNWH5PFwJHC5QB08ZvYiHchhHAmSaaEEG2S1WJhz70/kT5/V43rFSfyOPHpOtJ/28mg727Gv3/7Rj3/+IerOfrmcidEem68u4bV+HXmn/vYedu3YLWe0dNM+bFcyo/lkvLjNno8N5UON49ssTjFhSHSsz/+Lu0prEpmTdobdPIZbS9uUG1H9jdNLov+b9Mn6ErWpb+N0VLJ6tTXmBrzWo32wspkduf+CEDf4KvqPSuqLgNC57I0eR5HilaQWrqDKO+BNdo3Z31KhakABQr6h1zT+DcjhHAKSaaEEG3S0bdW2BOp6GuH0PG20Wj93CnYnsTBZxZSmVnMjhu+Yszqh3EJ8mr0OAqNCu9uYfj2iSLt1+1YKk3ndJ+js7zOtHbcG+hSCvCMDca3T829GaaKKrBacYv0o92sgQSN6YJHh0CwWinceoLDryyh4kQeB55agGuYD2GX9mr0exT/PgqFggnRT/Dr0dvJ0R3m60NXMj7qcULdu6M3lbAvbz4bMj/AzyWaoqq69/+Imry0IQwPv5u16W+yK/cHFAoFQ8JuxU3tS1rpDpYkP4vRosdTE8SIiHscPuOr+Jkkl27B1yWSB/ptq9U+MOQ6duZ8R77+OD8m3MCk9vOI8R2D0aJnT+7PbMj4AIA+QbMIO7nsUAjReiSZEqKVBQ7vxNTst5rt+X3fm0Pf9+Y02/ObQ2VOKcc/tC0/irpmML3fmGVvC5/cC+9uYawb/wZVeWUc+2A1PZ6/vMFjhE3qQfCYLvj0jETlavv2OGPh7nNOps52llfR7hR0KbaKX5FX1D7Q1DXEmz7vzKbdrAEolDW3r4ZP7UPgyFjWTXgLfUYRh1/6R5Ip0WBxAVMYGXEPGzI+IL18D98cuqpGe3vvofQIvJy/TjyOspUq8p2PRkfeT2FlEvvz/2BnzvfszPm+Rru72o/ZXb5wuATwXKiVLlzd5Su+PTyH4qp0FiQ+UKtPR5+RTO74UqOeL4RwLilAIYRoc9J+3YFZb0ShUdH18UtrtXt2DCLq6sEApP60DYvR3OAxfHpG4j+wgz2Rcrb0308uT1QoiJxRO5kKHtuVqNmDaiVS1bR+HnS8fRQA5cdz0aUWNkuc4sI2IeoJru76NR19RuCq8kajdCPEvRsTo55mbrefsFhsXx5oVZ6tHOn5Q6lQckXs+1wZ+zEdfUbgrvZDrXDF37U9Q8Ju4c7eK+171horwK0jd/ZaweiI+wly64xG6Yaryod2XgO4rOOrXNftx3rPDxNCtByZmRLCSXJWHiLxf+sp2Z+GxWDGo0Mg7a4cQIdbRpI+fxd77/8ZoNYsVH0FKHSphawc9CIAw36/C78B7Tnxv3Wk/7EbXXIBCo0S317tiLlzDCHjuzmM63wsQJGz8hAAAUM64lrHEr7wqX1I+mIjptJKCrYmEjSyaWWSncliMpOxaC8A/oPaN7qoiFfsqeITlbmlTi9OIv4duvhNrLNQQWFVMgDe2tBabR18htVZ5MHPtd05FYCY3ukdpnd6p8Ft54MegVPpETi1wffdGDf/nPq5qr0ZF/UI46IeafAYQoiWI8mUEE6Q8OoSjr69osa10vhM4uMXk7PiEOHT+jZ5DFNFFZumfUDxntRTF/WQv/EY+RuP0ev1K2l/3dAmj9ParFYrJQdsH9L8+kfX2c+3dzsUKiVWs4WSAxltKpnKW3sEQ345AJEzBzT6OVUnnwGg9pRvoYVzWa1WjhWtApC9N0II0UiSTAnRRFn/7LcnUr59o+j25KX4xEVgKKog7bedHH9/Nbr0oiaPc/DpBRiKdMS9cDmhF/dA4+VK0Z5UDjz5B7qUAuKfXUTYpB5NKsbgiKmiqkn3q9y1KBSKc+5fmVWCWWcAwCMqoO7numpwCfGmMrOY8sTcJsXobNVL/JQuasIv693o52QtOQCAxscNr9hgp8Qm/l10xiLcNX4O27Zmf0FBZRJg218lhBCi4SSZEqIJrFYrh/5rO1PEq0sow+bfidrDBQCtvwfdHr8UlwBPDj6zsMlj6TOLGb7onhqlwEPGd8Pt6xtZO/YNzHoDmX/tp8ONw5s81un+iXmiSfc39OwsQ1GF/bU2sP59HC6BnlRmFmMo0jU6PmczVVSRvTQegJAJ3dD6ujfqOYU7k8lechCAdrMHoVDJFlfRcJ8fnEqMzyi6B1xKkFtnlAoVhZXJ7Mn7hV05PwAQ4dGHWL/xrRypEEKcnySZEqIJinalUJGUD0DXxybZE6nTdbh5BCf+tx5dWtMKCERM6+vwTCXvbuF494ig9GAGxXtTAecmUy2telYKOGtxiOp2cxNnz5wp6+/9mPW29+Coit+5MJbo2X3X92C14hLkRey98kFXNI7RomN7ztdsz/naYXuAawdmdfkUpUKq+QkhRGNIMiVEExTtTAZAoVYSPM5xAQiFUknw+G4kf72pSWMFjelSZ5tnh0BKD2ZQlVfWpDEcac6y7Rei6iV+Gl83QiZ0b/D9VrOFXXd8Z6vep1TQ553ZuARIpTXROJd1fJWEwuWkl++m3JhHpakErcqDILfOdPOfxMCQubUO8xVCCHHuJJkSogmq90K5hfvWO4viGRPU5LFcQ7zrbKs+88hcaWzyOK1N5X7q/KazvZ/qdpWDGcHWUJlTSv7G44Ct2qBS2/C/Yvc9+hu5axIAiHtuap1VGoU4F539JtDZb0JrhyGEEBcsSaaEaILqJWmnJwCOqDzqbz8XdZ1HVIO1ycPU0tIFKLR+HvbXhtOq2TlSXe1O69e4fUnOlrFgN1azBWjcEr/Dr/xD6g/bAOh0zzhibhvt1PiEEEII4VySTAnRBNVJ1On7fBwxV9Tf3pa1dAEK1zAfVO5azDoDFakFdfYzV5moyikFwDOmbVS6q17i5x7lj/+gDg2698Rn6zn2zkrAVnCi+9NSXU0IIYRo66Q8lBBN4B5pKzmszyyud0laeWJeS4V03lMoFPj0jACgaHdqnf2K96baZ4Gq+7emsqM59vOxIq/o36DZuLT5Ozn47CIAQi+Oo8+bs5olRiGEEEI4l8xMCdEEfgPaA2A1Wchdk0DYJT1r9bFaLOSuOtzCkTlPaxSgCJnQncJtSRRsSaQqr8zh2VmZf+4DQO3tSsCQmJYOsZb0+TvtrxuyxC9nxSH23v8zWK0EDOlI/0/nShl08a/1VfxMkku30CfoSqZ3eqe1w2mzqswVHCr4i4zyvWRVHKTMkIPOZJvJ99KGEuU1iAEh19LO6+x/FyWXbmV79tekle2gwliIq9qbUPc4+gVfRY/Ay5v7rQhx3pN/sYVoAr/+0bi3tx0sm/DaUntJ7NMlf7WpyWXR/23azRqIyk2D1Wgm4bUltdorkvPte4ui5gxGqWndss5Wq5X0P3YD4Nu7HZ6dzm3ZYeGOJHbe9g1WkwXvuHAGfXvzWcvBCyFEYWUSCxMfZEfOt6SX76bEkIHRUonRUklhZTJ7837li4OXsyz5v/U+Z2nyc3wVfwXxBX9SasjGbDVQYcwnsWQdvx27i1+O3IrZamqhdyXE+UlmpoRoAoVCQfenprDz1m8oO5zF5is+ouuTk/HpHo6hREf6rzs59v4q3KMD0KXUvf9H1OQa4k2nu8dx5I1lpHy3FRQKOt46Cq2vO4U7kjj4zELMeoPtDKZ7xjl8xqbpH1KwJRG3SD8m7nymVru5ykTJwfQa16xmWwWPyqwSCncl269rPF3x6hJaZ7yFW0+gP1nZMfLKc5uVKjuaw7a5X2DWG3EN92XA59ejUCnrLPihdFGjVMtZQEIIUKIi0rMfnXzHEunZB2+XcDw1wVSaisnWxbMx4yMyK/azOesTfFwiGBJ2U61nbMz4mC1ZnwHQyXcsYyLvJ8A1hgpjPnvyfmFz5iccKvyHZcnPc2mH+pMyIf7NJJkSoonCL+tN7L3jOfbeKop2p7Jl5sc12gOGxhAxvS/7H50vy7caoPODE6lIzid9/i5Svt1CyrdbarRr/T0Y+PWNDpcAnouqnFI2Tn7PYVvqj9tI/XGb/dcBQ2MYvuDuOp9VXXhCoVYScXnfcxo/Y9EejEU6ACozi1k99OV6+/d5ZzZRswed07OFEBe2EI9u3Nrzz1rXPTT+BLh1pIvfxXx+cCpZFQfYnPlJrWRKZyxiXfrbAHTyGcM1Xb9FqbD9++Su8eOi6KdxU/uxMvUldmR/w8CQuQS5xzb/GxPiPCSf7IRwgm5PTmbQtzcTODIWtbcrKjctXt3C6P7MFIb+egdWoxkAtWfbOA/pfKBQKun3wTX0/3QugSNj0fp7oHRV49EhkI63jWLM6ofx79++tcPEXGUi8y/b/q2gUZ0bndwJIYSzqJVaegVOB6DEkIHOWHOp+bHiVRgsFQCMi3rUnkidbmjYrbir/bFgZmfOd80ftBDnKYXV2gwH0wjRCAqFwtoaxQ5awsFnF3Lif+vx6hLK2HWPtnY4QrSoxaEPYrVaz728oRMpFArr80MzWmPoep0o2ciObNt+lwpjAWqlFnd1AH6u7YjxGU3PwGn4uITXuKekKpMjRcs5WrSK7IpD6EwFqJUu+Ggj6eQ7mqFht+LtEuZwvD25v7Aw8UEAnh+aQXFVBhsy3uNY0RoqjAV4u4TSI+ByRkTcjYvKdtabzljI5qxPOVTwDyVVmbiqvensN4HxUY/hqQmsNUZRZRrv7BkCwA3dfyPcsxcbMz7kUMHflBgy0CjdifYezMiI/xDh2dthnOdSgKLKXM727K9JKFxGgf4EBosOT00g0d5DGBZ+O2EePer8uR8q+Ic9uT+TWXEAvakIjdIND00A/q4d6OQ7ll6B03HX+NV5//lkY8ZHrEj9PwCeHHQEF5WnvW1Z8gtszvoUrdKdJwcdrbP66I8JN3CkaAW+Lu14oN/WFom7IeZtiWi1v1uEqCbL/IRoZlarlZyVtmp+Pj0jWzkaIURrW5f+DqvTXq9xzWw2UGUup6gqhRMlG9Eo3RgcdmONPh/tm0ClueSM+4zk6hPI1SewJ/cXru72NVFeA+sdP7P8AN8dnoPOVGS/VliZzPqMd0kt28513X6k1JDFd4euprAq2d6n3FjJ7twfSSndwq09/8ZN7VPnGHpTMZ8dmEKe/pj9mtFSyeHCJRwpXM60Tm/TO+iKeuN0HPt+fky4gTJjTo3rJYZM9uf/wYH8hUxq/xxDwm6ude+ixIfZnftTjWtms5FKcykFlUkcK16Nn2s7uvhNbHBcbY3ZauJw4T8ABLrG1EikACrNZQC4qr3rPcbBTe0LQHFVGjpj0QWTaArhTJJMCeEEhqIKtH4eDtuSPt9AxQnbOVPhUx1/GyuE+HfI0x9nTdqbgG2vyvCIOwhwjUGrcqfCWEBWxUESCpeiUtau6hjs3oVY37G08+qPlzYEd7U/OlMhGeV72ZjxEbn6I/x65A7+03e9fXbJkV+O3oqnJoRpnd4m0rMfVeZytmZ9zrbsL0ku3cKunB/Ym/cbFkzMjP2IDt7DMFuN7M37lTVpb1BQmcT69Pe4uH3twi7VlqW8QLkhjwlRT9Ij4DI0KnfSSnewPOVFCquSWZj4ICHu3Qj16H7OP7uiylS+OXQVleZSQty7MTLiP0R5DUCjdKeg8oS9YMKS5Gfxd42ms98E+73Hi9fZE6megdMZFHI9fq5RqBRayo15ZJTvIb7gLxSN2P1gNOuxYGnwfdXUShdUCud8HKswFpKrO8yGjA9IL9+DAgXjomqvhqhOripNpVit1joTKr2p2P46v/I4UZr6E3Uh/o0kmRLCCTZMeY+gUZ0Jn9wLzy6hKFVKW/nun7fbqtEBvn3aETK+WytHKoRoTYnF67BiwVMTxNVdv66RNLmpfQl0i6FnHWf73NxjQa1r7hp/At060c1/Mp/sn0hBZRIH8hcwIOTaOmNQouLmHgtwVXsD4KEJ4NIO/yVHd5jk0i0sTXkOF5Und/ZaUWOp4ejI+ymoTGJf3nz25c+vN5kqrkrjqs6f0T3gUvu1bgGXEOHVl4/3TURnKmRF6ktc1+37un9YZ/g76Sl7InVrjz/RqNxO+zn056oun7Hg+P3szfuN5SkvEus73p4kHC9eA0C4Ry9mxn5wxs/Qj2D3zvQNvuqcYznd9wnXkVy65ewd6zAt5q1Gjw2wKvVV1mfULqbjrQ3louhniQuYUqstyM1WTMJg0ZFVcYBwz161+pgtRtLLdtt/rTcW1eojhJACFEI4hVlnIPmrTWye+THLe85jafdn2HDpu7YKdFYrHh2DGPDZ9VLNT4h/OcvJM3vc1QEOZ58aS6tyo6v/JACSSjbV23dU5L32ROp03U9+6LZYTQwJvaXWni2AHgGXAVBhzKe4qu69aFFeA2skUtW8taEMC78DsCWWZYbcemOtVqC3LcMDmNrxtRqJ1OnGtbPNwuTpj5Gti7dft1htRYA8ted2Btz5TqN0ZWDoDXT0GeGwPdZvHCqF7c/f6rTXsVhrz6xtzf6CCtOpIz0MFl3zBCvEeU5mpoRwgt6vXUn28oMU7UqhMrcMY4ketacLXp1DCLukJ+2vH4bKTdvaYQohWlmoRxwAufoEVqe+ztDw2+rde3Sm5JIt7M79ifTy3ZQZchx+wC2oPFHvM2J8Rjm87u8SZX9d14dwP5do++tyQy6+LhEO+3Xxu7jO8bv5X8zK1JewYiG9fDfdTiaB9Ukq3QyAm8qXIPfOVJkrHPZzVfvgoQ6gwlRAZvl+ezGK6p/78aI1bMv+mn5BV9WZkDXUjXHznfKcxhod+QAjIu4BoNJUYjtfKvMTVqW+wvasr7m669eEe/ascY+3NpSBodezNetzjhWv5qeEGxgVeR+BbifPmcr9lU2ZH6FWuGKyVp68S+o8COGIJFNCOEHIxO6ETDz3tf9CiH+njj4jiPUdx7Hi1azLeIeNmR8S6dmfaO/BdPAZRrT3kDr3z/yT9Czbsr846xhVpvJ62+uanVErXU/rE3TWPiZLpcM+AIFuMXW2+bt2RIESKxaKq9Lr7He6An0iAHpzMS9t73JO95w+q9I78Aq2ZX1Btu4Q/yQ9xYqUF4nyGkS09yA6+owg0rN/vYUY2jK1Uosa25d1LioPfFzC6eJ3Eb8evZ3Dhf/wQ8Jc7u27oVYRiolRT1FcmU5C0VKOFq/iaPGqGu2emmCGh9/JspTnAXBV1Z7NFEJIMiWEEEK0qNldPmdT5sfszPmOUkM2KWVbSSnbyvqMd/HQBDIq4l4Gh95U48P9vrz59kSqo88IBobMJdi9K+5qP1RK2/l169PfY2PmB1gw1Tu+UqE6a4wKzt7HSt1Hq2jrmfVRKpRolK4YLDoMdcwwnam6+lxDmC0G+2uVUsONcb+zLv1d9ub9gs5URGLJOhJL1rE67XX8XKIY1+5RegVNb/A4bakARTWlQsmk9vM4XPgP5cZcDuYvpn/I1WeMq2V2l8+JL1jM7txfyKrYT5W5Am9tKF38L2JUxL0cL15r7+9o2acQQpIpIYQQokWplS6MjryfURH3kas/QlrZTpJKNnG0aBUVxnyWJD9LuTGPCVGP2+/ZmWMr1BDtNZi53X52OItisuhb7D2cjcFcdywWqwXjyVktbT1VB0+nVdqSswDXjtzbd0OjYnJVe3Nx+2eYGP0U2RUHSS3byYmSDRwvXktRVSq/H78Hg0XHgJBrGvTc1i5AURdfl0g8NIFUGPPJqjjosI9CoaBH4OX0qKPoSZ7+KABapTuBbp2cHqMQFwLZDS+EEEK0AoVCQYh7VwaEXMuVnT/mwf47iPayHXq7JfOzGglJjs52Vl23gEvrXI6We/KDb1uQf3JZniOFlSewnpzJ8XU5t7P3/Fxte7VKDZmYLcYmxaZUKAn37MWQsJu4uutX3N9viz1RWJ/+TpOe3dZUFzxRNHK/U2LxegA6+ow6pxlNIf6NZGZKCNFgm6Z/SMGWRNrNGkjf9+a0djjnHUOxjtUjXsGQb9vb0vmhi+j6iONN+Lq0QrKWHCB/43FKD2VSlVuKQqXCLdKXwGGd6HDzSLw6h7Rk+KKZuKl9GBp+KylHtmKyVlJSlU6Qu62EtenkkjXryap0Zyoz5JJSurXFYj2bI0XLGRFxp8O2w4XLAFCgJNKz3zk9r6PPSMB28O/RopV0C7jEOYFiK8YwIORaliY/R4khE6NZ36DiFK1dgKIuubqj9nOifF3bNfj+1LIdZFbsB6B/iPw9L0RdZGZKCCFa2OEX/7InUvXJ/HMfKwf9H/HPLiJneTz69CIsBjNmvYHyY7kkf7OZtePfIOmLxi17Ei2vQJ/ksAx1tcLKZPtrN7Wv/XX1DM7RolWcyWK18E/SU5itTZuxcabUsu0cLlhS63qpIZvNmZ8AEOM7Gq9zLFUe7N7ZXoVwSfKzlFRl1ts/X3+83l+fqbAyBbCVFD+9yEZbdbb3Y7IYWJJsOwdMiYqufhc16PnlxnwWHn8QgA7ew2scgCyEqElmpoQQogUV7kgi5YdtuEf5o0strLevqaIKrFbcIv1oN2sgQWO64NEhEKxWCree4PArS6g4kceBpxbgGuZD2KW1D94Ubcv6jHdJLd1Bz6DpdPAeRoBbRzRKV8oNeSQULWdt2lsARHsNqVFRr3vAZDZkvE9S6SYWHH+AYWG34aUNIU9/nA0Z73OseDVBbrHk6Y+11lurwdelHb8fv4cxlQ8RF3AZWpU7qWU7WJ78X3SmQpQKNROjnmjQM6d0fJn/7Z9MiSGTT/ZfzPDwO4n1G4e3NhSzxUipIYv08t0cKviHzIp9PDnoiP3eP088jt5UTM/A6UR7D8LPJRqVQk2pIYsD+YvZkf0NYDtr63yo6vfniccxWaroGTiNdl4D8NGGo1JqqTDmk1a2k82Z/yNXnwDA4LCbCXDrWOsZ27K/5kTxenoFzSDcozeuai8qjAUcL17Hhoz3KTfm4qUJ5fKYN1v67QlxXpFkSgghWojFZGbfo/PBaqXHi9PZPrf+MteuId70eWc27WYNQKGsuZAgfGofAkfGsm7CW+gzijj80j+STJ0nCquSWZf+Nut422G7n0s00zvVbBsZcQ9Hi1aSozvM3rxf2Zv3a432QSE34K7xZ236W80Wd0NcHP0Mq9JeY0Xq/7Ei9f9qtClRMS3mLfvZT+fK37U9N8T9ys9HbqGoKtXhs6u5qXxrXcvRHSYn9XCdzw/36MWk6HkNiqk1pZfvJr18d53tCpQMCbuFi6KfcdhusRpJKFpGQtEyh+1Bbp25qstn+DViiaAQ/yaSTAkhRAtJ/GQdZYezCLkojtCLzv5BMnhs13rbtX4edLx9FPHPLqL8eC661ELco/ydFa5oBhOjnqKDz3ASi9eToztMmSGHSnMpLipPgtw609X/YgaGzEWrcq9xn4vKk5vi/mBd+rscLvyHUkMWLiovQj26MyDkWuICLmNNWtuZQXBT+3Frj7/YkPEBhwv/pqQqC63KnSivQYyKvJcIz96Nem6oRxz39FnLnrxfSShYSrYuHr2pGKVChZcmlBCP7sT4jCQuYEqN+6Z3eodjxWs4UbyBPP0xyg25VFnKcVP5EOLRnbiAy+gbNAuVUuOMt9/sLuv4CseK15JUsomCyhNUGPKpspTjovLE36U9Ud6D6Bt8FSHudf8dEus7jvLwPJJKN1NSlY7eVIyb2pcgt1jiAqfSN+gq1Eo5bF6Is1FYrXWfEyFES1IoFNap2W3jW9VqeRuPkfz1Zop2p2DIL0OpVaMN8MQ9yp+g0V2InNEPt3DfGvfoM4vJXhZPzop4W8GAggpULmrcIv0IHtOVjrePwi3M1+F4qT9vZ+/9PwMwNfstdOlFHHtvJbmrEqgqKMMtzJeIaX3pdM841B62s2UMhRUkfrKWzL/3o88oQuPtRsjE7nR7/FJcgrxqjaFLLWTloBcBGPb7Xfj2juTYB6vJ+st2v8pNi/+QjnS+dwK+fRx/I3kuBShM5ZUkfbWJ7KUHKU/Mxawz4BLkRcDQGGJuH41Pz7qreGX+vZ+0n7ZTvD8NQ5EOlZsGl0AvPDoEEjy2K5FX9EPrd24lldsKXVoha0a9BlgZu+4x3KP8WRxq25NQXwGKs8ldk8DWOf8DYMRf9+I/oL2TInaexaEPYrVaW2XtlEKhsD4/NKM1hv7XKapM4509tmqEN3T/jQ4+w1o5InGhm7clotX+bhGimsxMCVGHo2+vIOHVmhuoLQYzpvIqdCkF5G84htpNQ4ebR9bos3bs6xhLap6xYjKaKUvIpiwhm9SftzH4u1vwH9ih3vGL96ezdfanGApPHWpZkZTP0bdXULD1BEN/uR19VglbrvoEXXKBvU9VZRmpP2yjYMsJRi25H41P3VWpDMU61l/yLuXHcuzXzHoj2f8cIGdZPH3enU27mQPqjdNh7PvS2Db3C6pySmtc12cUkz5/F+l/7KbHC5fT8ZZRte7d++AvpP64rcY1k9GMqbSSihN55K46jHuU/znN7LQlB55agFlvoOtjlzh19qjqtEIWas+2v3FeCCGEuJBIMiWEA2XHckh4fSkAQWO70OmusXjGBKNy12IoKKfkQAZZSw6g0Nb+X8irayjB47rh3z8a11AftP4eGAorKNqTyvEPVlN2JJudt37DuE1P2GeXHNl589e4BHvR553Z+PWLxlRRxYn/rSPpi40UbEkk5bstpP26E6vJQv9PriNgeCesRjNpv+wg4bWlVJzI4+g7K4ibN7XOMQ49t5jKvFK6PTWZ8Kl9ULtrKdyRRPwLf6JLLmDv/T/j3T0cn+7h5/yzq0gpYPOVH2MqrcSrWxid75uA/8D2qNy1lJ/II/GjtWT9vZ+DTy/EIzqQkInd7ffmrj1iT6QiZvSj/Q3D8YgOQKlVUZVbRtGeVDL/3IdC2fAvIs16A1ZL42filS5qlOrGnbOS9c9+cpbH49EhkJi7xjY6BofPXnIAAI2PG16x51YZTQghhBDOIcmUEA7krTsCFisuQV4M/vYWlJpTH6K1vu54xgQTMa2vw3tHLPpPrWtafw88OwUTNrkX6ya8ScWJPNL/2E3764bWHYRKwYjF/0HjbZtZcgn0pOf/zaD0cBYFmxM5OG8Rai9Xxqx6uMZSw84PTKT8RB7pv+0k7bed9SZTurRCBnxxA+GTTxUuCLu0F379olk77g0MhRUc/u9fDPnptrrjPMOBJ37HVFqJd/cwRv59Hyq3U2vu/ft74P/FDey59yfSft1B/At/Ejyhm716Vu4aW/Upn16R9P/o2po/Qz8PvLqEEjV70DnHcrqtV39GwZa6DxI9mz7vzG7U2KaKKg48vRCAni/NQOXivL92C3cmk73kIADtZg9CoZLTLoQQQoiWJP/yCuGA1WQ7B0Yb4FEjkWoqtbuW0Ek9AMjfWH8J4873T7QnUqcLn9LbHmPHW0bW2rMFEDG1DwCG/HJ06UV1juE/qEONRKqaa6gPMXeOASB33REqc0tr9XGkPCmP3NW2hKj3G7NqJFKn6/q47cDN8mM5lB48tZ/FarIdSOoa4n1O450PEl5bSmVmMWGTe521oERDGEv07L7re7Dakv7Ye8c77dlCCCGEODcyMyWEA949IgAoS8gm4dUlxNwxpt69R2fK33yc1B+3U7Q7hcrsEsw6Q60+FSfy6n1G0OjODq+7RwfYXweOPHufqtxS3CP9HPYLvbjufUdhl/Tk8P/9DRYrRbtSCLukZ73xAhRssh0kqfF1w6tziO2cJAc0Pm5oAzwxFJRTvD/dXozCJ872c89dnUDSlxuJmjOozoSsoYYvuNspz2mIkoMZJH2+AZW7lh4vXO6051rNFnbd8Z3tnCqlgj7vzMYlwNNpzxdCCCHEuZFkSggHgkbEEjyuK7mrEzj69gqOfbAa//7RBAyNIWB4JwKGdKxz/8yBpxeQ9PmGs45hLKust9012PHsjMpVc1qf2tX6zuxjrjTWOYZnp7r32Hh0DASlAixW9PXMbp2u/LgtQTQW6/mn05PndI+h4FQBhcgrB3Diiw2Uxmdy4Mk/OPTfv/Af3IGAwR0JHBmLX//o8+JATQCrxcK+R37DarbQ+YGJuEU4TmgbY9+jv9mXRMY9N5WQ8d2c9mwhGsvPtR1SOVEI8W8jyZQQdRj41U0kfrSG5G83U5lVQsHWExRsPQFvr0Ab6Enn+yfQ4eaRNT7cp/22055IBY6Mpf31w/DuFobWzwPlyb0yR99ZwfH3V9uXEtblXPa/nHmQq0P11FxQudc966NQKlG5ajDrDHXOMJ3JWKY/e6czmKtM9tdKjYrhC+7m6NsrSPtlB4bCCvLWHiFv7RF4dQnuUf50ffwSImf0b/g4LVyAIu23XRTvScUzNpiYO8Y0etwzHX7lH1J/sBXp6HTPOGJuG+20ZwshhBCiYSSZEqIOKhc1nR+YSOz9EyhLyKZwZzL5G4+Rs/IwhvxyDj69kKq8cro9can9npTvtgDgP6QjQ3+9w+EsiqWemaKW5mj5YTWrxWKf1aqv6uDp1CeTM4+YIMZveqJRMWm83YibN5Xuz0yh5EAGhTuSyd9wlNw1CehSC9l91w+YdQair62neIcDLV2AQpdqK1dffiyXv9o9Um/fo28u5+ibywEYvfIhfE4uMz3Tic/Wc+ydlYCt4ET3p6c47CeEEEKIliEFKIQ4C4VCgXe3MNpfN5QBn87lot3PEjCkIwCJn67FdFpCUnooE4Dwyb3qXI5WeiTH4fXWUH48t862ihP5cHImx62OPVdnqt6rVZlZjMVoblJsCqUS397t6HjLSAZ9czMTtj+N58nS30ffXtGkZ5+P0ubv5OCziwDbXrc+b85q5YiEEEIIITNTQjSQxseNjrePpmDrCSyVJvTpRXh1DgHAYrAtWbOaHS/hq8wtbdLsiLNlL4+n093jHLZVn1+EUoFf/+hzel7QyYIYZr2RnBXxhF1au1JgY7mG+hB93VDin12EPqMYs97QoOIULV2Aov31w85atGPdhDcBiJ47lPZzhwHgGRNUq1/OikPsvf9nsFoJGNKR/p/OlTLoF6gFx+9nb95vtPceyo1x81s7nPPCmrQ3WZv+Vo1roe7dubN33V+65OuPsznzfySWrKPckIer2ocIzz4MDr2RGN/ah4k3h8Ti9WzL/oqM8r1Umkrw1AYT4zOKYeG3E+gW02zj5uqOcqx4NRnle8jTHafClE+lqQSN0p1At47E+o5nYOj1eGjqP1zcZKliZ873xBf8SZ7uGEZLJV7aYGJ8RzEk7FaC3DrVee9X8TNJLt1S41qfoCuZ3ukdZ7xFIVqUJFNCOFCelIdHdECde5IqkgvsrzV+7vbXbu38qUjMI2fl4Vr7ZKwWCwee+ANrE2dsnKlwWxJZ/+yvlfRUZpeQ+PFaAIJHd6mzGMaZvLqEEjS6M3nrjnLwmYX49olyWLq9Wvnx3BpFMM789Zl0J3/uKjcNytOKbLRFrsHe5/xzcwnyqnNpX+GOJHbe9g1WkwXvuHAGfXtzjQIjQoiGSShcxvxjd2O0nNrjWW7M5UjRco4ULWdUxL2Mj3qsWWNYmfoyGzI+qHGtuCqNXbk/sD//D2bGfkRX/4uaZexdOd+zNfuLWtfN5hLSy/eQXr6HbdlfclXn/9Hex/Fy6pKqDH5IuJ4c3eEa14uqUtmZ8z17c+czrdOb9Ayc1hxvQYg2RZIpIRw49vZKCrafIHJGfwKHd8IzJgilq4aq3DKylx3kyJvLAAgY0hHXoFMV9cKn9ObYuyvJ33iMPff9RMwdo3EN8aHsWA7H3l1J7uoEPGNDKD/WNpb6ubfzZ/fdP9D5oXwipvZB5a6lcEcS8c8vxlBYgUKtpNvTkxv0zF6vzmT9pLfRZxSzbsKbxNw1lpDx3XAN88FiNFOZWUzR7lSy/t5P8d5ULj3+sv3efY/8hrFER8T0fgQM6Yh7dABKtQp9VjEZC/eQ9PUmAMKm9D5vqvo1RdnRHLbN/QKz3ohruC8DPr8ehUpZZ0GQhhbJEOJC4qON4O4+awBQ1rGLIU93zJ5IBbt15eL2zxLm0YOSqkzWpb9DQtFS1me8R4BbR/oEXdksce7N/dWeSHX1m8ToyPvxcQknq+Igy5JfIFefwPxjd3F7zyUEucc6fXwXtTdxAVNo7z2MEPdueGtD0ao8KTNkkVC0nC2Zn6EzFfLjkRu5p/cavF3Catxvshj46chN5OgOo1JoGB15Pz0CLsdN7UOO7jCrUl8lrXwXC47fj69LO9p51S4YdG3X77BgW8Hx/eFrSS3b7vT3KURLkWRKiDrokgs4+tZyjr613GG7e3QAfd6dU+Na7H/Gkb08nrLDWaT9soO0X3bUaG9/43C0/h72YgOtrfu8y0h4dQmHX/yLwy/+VaNNoVLS553Z9rOfzpVH+0CG/X4XO278Cl1qocNnV9P41j67q/RQFqWH/q7z+T69IunxvPPObGrLMhbtwVikA2z70FYPfbne/g0tkiHEhUShUOCi8qi3z8rUVzBa9Hhqgrgx7jfcTy5l89AEMLvL53x3+GoSS9azMuUV4gIuQ6N0dWqMRrOelamvABDjM5rZXT63fzHUyXc04XG/8eG+cZQb81iV9iqzu3zu1PEBxrV72OF1D40/oR5xxPqO5bMDU6gyl7Ez94da/ffm/UpWxUEApsW8Ra+gGfa2Dj7DuT7uVz4/MJVsXTzLUl7glh6Lao2lUZ36u1+pkC+AxPlNFt0L4UC3Z6bQ9705RM7sj1e3MLT+HijUSjR+7vgP7kD3eVMZs/phPE47HBdA7enKiEX3EHPnGNyj/FFoVGj9PQgcGcuA/82l18tXtNI7ckzr58HIf+6n03/G4dExCKWrGq2/B6GX9GDk3/fRbuaARj3XJy6CsRsep9drMwka2wWXIC8UGhUqNw3u7QMIm9yTXq/OZPzmmmdR9X1vDr1em0nYlN54dQ5B4+uGQq1EG+BJ4KjO9H7jSkb+fR9a//o/MAkhxJnKDDkcKbKtKhgefqc9kaqmUCiYEGX7O6nMmM2RQud/6ZVQtJwyo21lwoSoJ2rNsLtr/Bkefqetb+FSygx1FwlqLhGefQh27wpAVsWBWu0H8xcDEOzWpUYiVU2jdGVM5AMApJXttCdeQlyoZGZKCAdcg7xoN2sg7WYNbPC91aW94+ZNddje9ZFJdH1kksO2qNmDzjqzEDi8E1Oz36q3j3uU/1n72OP1cqX7U1Po/tS5l9k+l2IOKhc17ecOsxdWOBfu7fwbfM/57Gy/R/X9WRHOV2Es5I1dfbFYTUzt+Dr9Q66us2+e/jgf7LWd8XVN12/p7Dfe3pZdcYiEwmUklqwjX3+cSnMZLkpPAt070d3/UgaEzEWrqj0rezbVm/br26ifVLKZrw/Zlqfd33crfq7tHPZLKd3GzpzvSS3bTrkhD7XShUC3GOICpjIo9HrUynM7DuF8crRoNdaTB+/FBVzmsE+4Z0/8XdpTWJXMkaIV9Ah0/Pd442OwHW3g79KecE/HBWriAi5jWcoLWLFytGgV/UPmOOzXnCxWWzEltaJ2kZ8c3SEAor2H1Hn/6W0JhcsJ8+jh5AiFaDskmRJCCCGwLXOK8RnFseLVHMhfUG8ydSBvAQDuan9ifE8dnJxdEc/H+2sXDtCbi0kr20la2U725v3G3G4/4amtXbmxuVmsZv5OeoqdOd/VuG4yV9mLD+zN+5Xruv2AlzakxeNrTtWzLN7aUHxcwuvsF+nVj8KqZLIr4pshhoP2Meri4xKOtzaUUkM22a0wq5NdEU++3lZ1Ntyzd632SnMZAG5qnzqf4ab2tb/Oqtjv3ACFaGMkmRJCCCFO6hU4nWPFq0ku3UqZIafOhOJAwUIA4gKmoFKc/k+pghif0XTzn0SIeze8tCEnN/dnc6x4NZszPyVHd5jFJx7l6q5fNf8bOsPS5OfYmfMdSoWawaE30itwBr4u7TBYdBwvXsOq1FfJ0R3mlyO3clOPBQ3ez2I06+2FBRpDrXQ54+fpPAWVtgTBz6X+ox58XWyzeYWVyVisFpQK5+yIsFgtFFYmn4wh6iwxRFFqyCa/smWO0jBbjJQZczhevJa1aW9jxYKXJpSBIdfX6uui8kRvKkZvKqnzeXpTsf11vv54c4QsRJshyZQQQghxUlf/SWiUbhgteg7kL2JY+G21+mSU77V/KO4ZOL1GW6hHd+Z2/7HWPbbN/d3p6ncxH+2fwJGi5eTrE5v1PKEzpZfZSl4DTI95h15Bp2J3x48BIdcS5TWI/x24hLTyXRwq+IsegQ0r9vJ9wnW1zg9qiGkxb9E3+KpG318fnbEIsBWbqI+HJhAAk7UKg7kCV7VXvf3PlcFcjtlqqDHG2WLQm4qcMnZd3tzVn1JDdq3r7b2HMr3Tuw7fe5BbLKllO0gp3Vbnc1PKTrXpmvk9CNHapACFEEIIcZJW5U4XP9syvYP5tauQARzIXwiAr0skUV4N21cZ5B5LmLtt/0hSyabGB9oI1YlUJ9+xNRKp0wW7d7YniPtPvs8LhdFiq4ypPkuFvtMr+BksFU4b33ByfFsM9e9Jq47BYHbe+OcqxL0bg0NvwkfreClkV3/bPs5cfQIHHPw/YrJUsS79HfuvDWZdrT5CXEhkZkoIIYQ4Ta/AaRwsWERGxV4K9EkEuHWwt1msFg7m/wlAj4DLHZ53ZrGaOZC/gIP5i8nWxaMzFmGy1j4brKDyRPO9CQeSSjYD0MF7GFX1fEgPdu8CNG6vy41x8xsXnGgV/+mzESsWLFYTZYYcjhevYUPG+/xy9Fa6+V/CjE7v1yqW0j/4GrZlfUmJIYMFx++nqDKFHoGX46ryIkeXwOq018iqOIBa4YrJWomCC/9MQPHvJsmUEP8yDan0J8S/USffsbipfdGbijlQsNBe5hkgpXQrZUbbsqiegdNq3VtpKuOHhOtILdtRq+1MVeZyp8V89rEq7HGvSP0/VqT+31nvqTAWNndYLUqjdAfAZKmst5/xtHat0nnHMGhPjm+LwfHB22fGoD3LuVlNjum0RMlN7UOwe2e6+k/ifwcu5XDhEpan/JcpHV+qcY+r2ouru37F94evo8yYw6q0V1mV9mqNPt39JwNWDhX+g6vau1nfgxCtTZIpIYQQ4jQqpYa4gCnszPmeg/mLaiRT1Uv8gt26EurRvda9S5OfI7VsBwoU9A+5hu7+kwlw64iLygvlycIK3x++htSyHfby0y2hylTa4Huq9/c0RFsuQOGu8QOgwlhQb78KYz4AKoXWqcmMVuWJSqHFbDXYxzhbDG5qP6eNf678XaMZHHoza9PfZHfuT1wU/TRalXuNPqEecdzVeyVbsj7nSNFyCiuTUaAk2L0z/UOupW/QVXx7eDYA3nUsFxTiQiHJlBBCCHGGnoHT2ZnzPXn6Y2RVHCTMowdmi5FDhX/b2oOm1brHYNaxP/8PAEZG/IfxUY85fHZz7oOpK0HTnPZheGbshw5n1ZyhLRegCHCN4UTJRoqqUurtV1yVdrJ/B6dV8gNQKpT4u7YnT3+UoqrUs8Rgaw90bbkCJaeL9OwD2BLqPP1RIk7++nTuGn/GRz3K+KhHHT4jT3cMgHDPXs0VphBtghSgEEIIIc4Q7TXYvgG/ejbqeMla9KZiFCjoGTCt1j35+kT7bE5cgONDsM1WU6P3SlUXTjDWs0yt3Jjr8Lqb2sd+9k9hZf3JxIUqzMN2SG6pIZvSqqw6+6WX7QZssy/Oj8FWfCS9fHedfUqqMu0V9kJb6bDbmkl5w/c85egSKDPmABDrO9ZJUQnRNsnMlBCNsOfen0j7dQcBQ2MYvuDu1g7nvJDw+lKOvrm8xjXvuHDGrHq4znvKj+eS+MlactceoSqvDI2PG359o+hw80iCRnVutlir8svJ+mc/xXvTKDmYQVVeGYaCchRqFe6RfgQM60SHG4fj1SX0rM/KXh5P6k/bKN6TSlVBBUq1CrdIXwKHdaLDzSPx6tyyB6MainWsHvEKhnzbfp3OD11E10cmOey7YsB/0afXLGtcX/8LiUKhoEfg5WzK/JiD+YuYGPWUvXJZpFd//Fzb1brHfFqRCYvV8VK3I4XL6k2G6uOpsR3yW1iZVGefxOL1dbZ19BlBfMFfHCr4i9GR9zUqhrNpywUoOvuNQ4ECK1biC/9iaNittfpkVRyksCoZgC5+E5shhgnsz/+Dwspk+4znmeIL/gJAgYLOfuOdHsO5SCnbbo+h+tythtiWZasc6aUJIbaV3oMQLUVmpoQQbVLW0oOsm/gWKd9vRZ9ehKXKRFVuGdnL4tky6xMOv/JPs41duCOJ/Y/OJ/XHbZTsT6cyqwSLwYxZZ6DsaA7JX29i7fg3OPFZ3R9crWYLu+76nu1zvyB7yUEqs0uxGs2Y9QbKj+WS/M1m1o5/g5Qftzbb+3Dk8It/2RMpUb/qpXAlhkyOF6/lSOGyk9cdlxU//UPn0eKVtdp1xiJWpLxU6/q5ivDsDUB2RTy5uiO12tPL9nCwYHGd9w8JvcV2v+4Qa9Perncsk6WK4qr0RsfaFnlpQ+jidzEAmzI+tp87Vc1qtbIy5WVbX00oXfwvcnoMXf0uwktj+wJlZeorWK3WGu06YxGbMj+29fWfhJc22KnjG8y6emflwPbna0f2NwBEeQ3GQ+PfoDEOFfzN7tyfAJgQ/USz7YEToq2QP+FCiBblFuHH2PW2NfYKpePlI2VHc9h95/eY9Qa8uoYS99zl+PSIQJ9ZzNG3l5O95CDH3lmJZ8cg2s1q2Dk/50KpVhE4Ipag0Z3x7d0O1zAfXAI8qcovp2hXCsfeX0XFiTwOPrMQ9+gAQi+qvRzo2PuryPjDtpQncEQssfdPwCs2BLPeQMHWEyS8tpTKzGL2PzIf3z5R+HRv/k3ahTuSSPlhG+5R/uhSz16pbez6x+Dkh701o15Dn/HvOnwzzKMHQW6x5OmP8VfS4xgsOpQKNT0CLnPY30sbQjuvAaSV7WRD+vuoFS50D7gUrdKD1LIdrEp9hTJjNr4ukY1KVOICLmN5yosYLXp+OnITkzv8H+Eevakyl5NQtIy1aW/irQ2vc09QlPdABofezLbsL1iT/gYZFXsZGDKXUI84NEo3Kk0l5OmPcrx4PQcLFjE8/E6Gh9/R4DjbsglRj5NYso4yYw5fH7qSi6OfJdSjB6WGTNalv8vxkrW2ftGP1zhvqlpRZRrv7BkCwJjIBxnb7qEGja9RuTEh6nEWJD7A8eI1/HL0NkZH3oe3NpzsioMsS3mBcmMuGqUb49s53nO3J/cXFiY+CMAN3X+jg8+wcx6/wljAB3vHEBcwmS7+Ewl1j8Nd44/FaqaoMpWEomVsy/oCg0WHWuHCxe2fcfic7w5dQ7B7V7r5T8LfrQMKlBRUJrIvbz67cn7EioWegdPpE3Rlg34+QpyPJJkSQrQsBag96j+w8vDLf2PWG3AJ8mL4H3ej9bdV1HIJ9GTglzeydfan5K07yuGX/iZ8ah9UrhqnhhgysTshE2tXatP6e+DVOYSwS3uyZsxrVGaVkPjRmlrJlNVqJekr24GsPr0iGfLzbSjVKnu7R/tA/PpGsW7im1gMZlJ/3EbPFx3PdjiLxWRm36PzwWqlx4vT2T73i7Peo3bXnvrFv/SomJ6B01id9ro9+enoMxIPTUCd/ad0eJkv42dQZS6rVYJcpdAwLeZtduX+0KhkykMTwKT2z/HniccorEzmu8PX1GiP9OzHqMh7+THhhjqfMan9PFQKDVuyPuVo0UqOFtWeQTs93gtNkHssV8R+wO/H7iFHd5hvD8+p1WdUxL3NmgT0CZ5FfmUiGzI+4HDhPxwurDnLrlG6MTP2Q4LcY5tlfJO1kn35v7Mv//c6+3hqgpnR6V2HhSfAtjfveNZaNmd94rC9f/A1TO7Y+FlYIc4nkkwJIdqUypxSspfGA9Dp7rH2RKqaQqGg21NTyFv3FpXZpWQviyfi8j4tGqPGx42wS3uS9MVGig9k1Go3FFRQlWMrRR1+We8aiVQ1ry6heMdFULwnlYrEvGaPOfGTdZQdziLkojiHM2nCsZ6B01md9rr9173OUgUv1KM7t/X8m7Xpb3OiZAOVplI8NAFEeQ1iWPjtRHj2ZlfuD42OZ0DItfhow9mU+QlZFQcwWw34ubSnV9AMhobdSlrZznrvVypUXNz+GfoEzWRHznckl26m1JCF0VKJq8oLf9cORHkNpKv/JKK9BzU6zrasm/8k7ui1lE2Zn3KiZD3lhjxc1d5EePZlcOiNxPiOqvPeUsOpJXKRnv0aHcOEqCfo4D2cbdlfkVG+h0pTKZ7aIDr6jGJ4+B0EutVdxa86Bq3Sw37A8rnydgnjum4/klSyidSy7ZQasqgw5mO1grvGl2D3bnT2HU/voCvqPR9qTOSDHC5cQnr5HsqNeZgtRry0wUR7D6Zf8NUX7J8dIRyRZEqcd6oKylne+zmsJgu935xF9DVD6uxbdiyHNSNthwkO/v4WQiacmm0oOZRJ9pID5K07SvnxXIyletSeLnjGhhA+uRfRc4fV/Gb+HG2a/iEFWxJpN2sgfd+r/a0nQP6m42y+4iMAJmx/Gvcox2vSC7aeIOX7LRRsPUFVXhlKFzVenYIJv7wv7W8YjsrlwvtfOGfVIfvSsvCpfRz28e0ViXv7AHTJBeSsaPlkCsBqshUYcPR7oHI97Zqi7imd6mWO2gBP5wZ3Bl1aIUffXI7KTdPsM2AXGn/XaJ4fWjthrk+gWwwzYz+os72+Ig3TO73D9E7v1Pv8WL9xxPqNc9jWwWfYOcUb4tGt1mGs/yaBbp24POb1s3c8w4mSjQBEeQ0i1q9pVepifEfVm7idLYYhYbfUO0vqiEqhppPvaDr5jm7wuKfrFnAJ3QIuadIzhLhQSAEKcd5xCfAkaLTt27jqPSl1qW7X+nsQNObUN3gl8RmsG/cGR15fRuH2JAyFFVhNFozFeop2JBP/3GI2TH6Xyryy5nsj9bCaLex7dD6bpn1A+vxd9gIMptJKinanEj9vEesnvU1lTsMP4mzrSvbbPgi6hvngFu5bZz+//tG2/g5mhpqbsayS3NUJAPj0jqzVrvZ0xb297UNO9tKDWC21K7tVJOdTEm+LPWhU8yznqXbgqQWY9QZi751QZ+IuhDi7EyUbANveq9ZgMOtJL9uNm9r3gtvPJsT56sL7Wlv8K0TO6EfuqsPkb0mkMqcU1xDHyxEyFu4BHCy1UigIGtOFsEt64tUtDNcQb9SeLlTmlJK76jCJH6+l7HAW+x/+lUHf3NwSb6mGg88uJOXbzSjUSjrcPJLIGf1wj/LHVGEgd00CCS//Q9nhLHbc/DUjFt2DQtWw70XMegNWi/XsHeugdFE7XLrmDOWJtnNy3KPr/8bVI8rWXpGcj9ViQaFs3u+GrFYrVXllFO9N4+hby9GlFaLQqOjy0MUO+3e+fyJ77/+Zop3J7Ljpa2Lvm4BX5xDMOgMF25M4/OJfWCpNBI7qTOTM/s0Wd9Y/+8lZHo9Hh0Bi7pLzXsSFq7gqnXlbIgAIde/Onb1XOPX5BrOOjPI9dPIdS7T3YKc++1yllm3HZK1iTPhD9S7Da+u+ip/ZpMOdhWhLJJkS56XQST1QuWkx6w1kLNxDzO21lywU7UmlIikfgIgZNde2+3QPZ+jPt9e6xyXAE5/u4YRe3IO1414ne1k85Ym5eMY4tzxtfYp2p5D0hW0ZR9/35hA549QHba2fB+2vG0rAoA6sn/Q2RTuTyfxzHxHT+jZojK1Xf0bBlsRGx9jnndlEzW6eNfGGIh1g+72oT/XSOEuVCVOFAY1X7cpbzrDr7u/J+L32DKhHxyB6vTYT/wHtHd4XNXsQxlI9CS//Q/bSg2QvPVij3TXMh66PX0Knu8c1WyJoqqjiwNMLAej50owLclmoEC1Fq3Ln2SHJrRpDJ9/RDV52KoRoXvIvqzgvqT1cCL04joyFe8hYsNthMlU9K+UW6Yf/oA4Ner5X5xB8ekRQvDeN/I3HWzSZSvrSlkgFj+1aI5GqEV+XUCKm9yP1x21kLNjd4GSqLTPrDAAoXev/60nldqrSmLmiqtmSKUe0/h50vGUkfn3qP8yy480j0fq6s/+x3zHrDTXajCV69OlFGIt1uAR5NUuc1eXXwyb3Inhs12YZQ4jWNjLiPww7Y8mbUnYxtGnXdv0OCzWXP8t5VOJ8JX9yxXkrYkY/MhbuoXhvGuVJeXh2CLK3WS0WMhfZkqmIaX1ROCgCYDVbSP9jN5mL9lISn4GhsAJLlalWv/ITzV9p7XT5G48BEDC8E6aKqjr7eXUNBaB4f8NLLA9fcHfjgvsX6vPWbHq/diVYrRgKdRTuSOLY+6s48OQfJH21icHf34KHgyWJlbmlbL/+S4r3pBI2uScdbx+DV2ww5kojBZsTSXhlCSnfbyV/03GGzb8Ttwg/p8ZdcjCDpM83oHLX0uOFy536bCHaErVSi5qGFwsSrUejcmvtEIRwGkmmxHkreGxXNH7uGIt0ZCzYQ5cHT51WX7DlBJXZtuIMZy7xA1sBgW3XfEbh9qSzjmMqq3Re0Gcbq6LKHvfhF//i8It/nfUeQ0F5c4fVolQnKyhaKmsntqcz642n7jnLuVVNisdFDSeXx6k9XXGP8idsci82X/ERRbtS2H7Dl4xZ9VCNpXoWo5ktV31K2eEsh1UdI6/oT+DIWNaNf5OKpHwOPLWAQV/f5LSYrRYL+x75DavZQucHJjo9URNCCCGEjcyDi/OWUqMi/LLeAGQs2FOjLWOBbY+LV9dQfLqH17o3/tmFtkRKoSB67lCG/HI7E3Y8zSVH/o9LE1/m0sSX7UsDq0tgtwRjqb7B91gM5gbfY9YbMFVUNfo/i6nhY54rrZ87YCuBX5/qJFKpVaH2aNlvpVWuGro9NRmAssNZ5G88XqM9658DlB22nQXT9bFJDp/hGuxN+xuHA5C9PB5Dsc5p8aX9toviPal4xgYTc8cYpz1XCCGEEDXJzJQ4r0VO70fKt1soP5ZDyYF0fHpGYjGayfx7v63dwayUqaKK9N93ARB733i6PX6pw2fXt8SuXnUfK2RnNTtO0NTup2ZY+n18LZHTG38oZH3acgEKz5hg8jccQ5dSUG+/ilRbu0eHoGav5OeIX98o++uSA+kEjeps/3XRTtuMpzbQs95ZId/eJ/dcWaxUnMhD2y/aKbHpTv5syo/l8le7R+rte/TN5Rx9czkAo1c+hE+PCKfEIIQQQvwbyMyUOK/5D+mIW4QvAOknZ6dy1yRgLNKBQkHEtNrJSHlinn02p3pm60wWk5mKRu6VUrnYCiOYK4119qnrfCiNjxuakzMzZ0smLlQ+vWwf5iuzStBnFdfZr2hXiq1/z9b58F8jIT5jT159v/c1H3Jaefp6DvcVQgghRNskM1PivKZQKAi/vC+JH60hc9Eeuj8zxV7Fz29AtMMDSi2GU3txrGbHZy1lLz1YY09OQ7gE2yqzVSTVnYzlrTtaZ1vQiFgy/9xH5p/76Hz/xEbFcDZtuQBFyPjutsTCaiXzz33E3Fa7UmPJgXR0ybZkM2RiXEuHCEDB1hP212f+OXMJsp3/YsgvR59RVOfsVPG+NPvr6i8FnKH99cMIu6RnvX3WTXgTgOi5Q2k/dxgAnjFB9d0ihBBCiDPIzJQ471Uv5dNnFJO3JsF+nk9dS+Tc25364Juz8lCtdkNRBYfOofBDXXz72JZ/lRzMpDQhu1Z70e4UMhbtqXW9WodbRwFQGp/JkTeX1TuWucqELq2w0bG2Ra4h3oROsiVIxz9cg6Gooka71Wrl0Et/2/qGehN6sfOTqfLjufW2G0v19j8jKg8XgkZ3qdEeOKKT/fWRNxz/HlbmlpL89WYAvLuH4RrsvAM4XYO98ekRUe9/1VyCvOzXVG5SEU0IIYRoCEmmxHnPp0cEnrEhAOx7dD5mnQGFWkn41D4O+7uGeOM3sD0Ax95dybEPVlORnE9lXhmZf+9n42XvU5VTiltk4yqghV/W2/ah1Gplx41fkrsmAUNRBbrUQk58tp4tsz/FLdy3zvsDBnWgwy0jATjy+jK2Xfc5OSsPUZldgrFET0VKATkrDnHg6QWs6P8CmX/ua1ScbVm3JyajctNSlVPKphkfkbvuCFUF5ZQczGDnLV+Tt+aIrd+Tk1G5amrdr0stZHHogywOfZCE15c2ePytc/7Hlqs+IeX7LZTEZ1CVX46hWEfZkWySvtjA2nFvUHYyUe72xKW1zrgKHNYJ/8G2AiapP21n563fULgzGUOxDn1WMem/72Lj5PeoyisDoPNplShPV/0e9tz7U4PfgxBCCCGanyzzExeEyBn9SHh1Cfr0IgCCRnbGJdCzzv69Xp3Jpss/wFRWWasEuUKjou+7s0n5bqv9eQ3hEuhJ3AuXs/+R36hIymfrnP/VaPfrH03sfRPYPveLOp/R4/nLUWpUJH6yjpwVh8hZUXsGrZpSq2pwjG2dV+cQ+n10Dbvv+oGyw1lsverTWn1i759Au1kDm2V8q9VK3rqj9S7HVLqo6frYJXQ8mfieacBn17Ptms8oOZBhX7ZZ+yEKuj1+CeFTHO/dE0IIIUTbJsmUuCBEnEymTv91fXy6hzNq6f0ceXM5eeuPYirVow30RxbdWgAA75hJREFUImBQB2LuGINvn3akfLe10fG0v24obuG+JH68luL9aViNZtyjA4i8oj8xt4+mcEdyvfcrVEri5k2l3awBJH+zhfxNx6nMKsZcaUTj7YZHh0D8B3Yg9JIeBAzu2Og427KwS3oyesWDHP94DXnrjlKVV4bG2w2/flF0uHlkjep5Zzq9cIV//4ZXyBv45Y3krTtC/ubj6FIKqcors/3svVzx7BRM4PBORM0Z7HBPXjXXYG9G/n0fafN3kvnnPkoPZmIo1qFUq3AN9yFgSEc63DAcn56Rjt9D5qn34NeI9yCEEEKI5qewWh1vwBeipSkUCuvU7LdaOwzRTBJeX8rRN5fjFunHxJ3PNOtYR95YxpE3luE/uAMjFv2nWcdqLqm/7GDvfT/hHh3AuI2Po9S07gzkigH/RZ9eROeHLqLrI47PzqrL4tAHsVqtrVKuUKN0zTZZq0JaY2whRPNSK1xyjJbK0NaOQ/y7ycyUEOKCk7fBtjyv2xOTWzmSxss/+R66PHxxqydS5zP5oCWEEKI5STIlhGhR+vQiFoc+CIB3XDhjVj3s1OebKqoo2p1K8NiuBAw5f5dA5m88hleXUCKvaJ6Dm89F9WyUEEIIIRyTZEoIcUFRe7hwWdrrrR1Gk12097nWDkEIIYQQZyF7pkSbIXumLmwWgwmL0VzjmkKpkLON2jCTzgBn/Buh1KhQahv2PVxr7pkSQgghmpPMTAkhWoRSq27wh3DRutTukugKIYQQ9ZFDe4UQQgghhBCiESSZEkIIIYQQQohGkGRKCCGEEEIIIRpBkikhhBBCCCGEaARJpoQQQgghhBCiESSZEkIIIYQQQohGkGRKCCGEEEIIIRpBkikhhBBCCCGEaARJpoQQQgghhBCiERRWq7W1YxACAJWbJttSaQpp7TiEEM6ldFFjqTK1dhiiDdCoXHMMJn1oa8chhBDOIsmUEI2kUCgWA99ardb5Z1x3AX4G1MCVVqu1sjXi+zdTKBTWr+/Kb+0whBBnuOGjQKxWq6K14xBCCGeRZX5CNIJCofAHRgPLzrjuCvwOWIErJJESQgghhLhwSTIlRONcASy3Wq1l1RcUCoU7sAgoB66yWq2G1gpOCCGEEEI0P0mmhGicOcBP1b9QKBQewF9ADnCt1Wo1tlZgQgghhBCiZUgyJUQDKRSKcKAv8M/JX3sDS4Ek4Ear1So77YUQQggh/gUkmRKi4WYBi6xWa6VCofAFlgMHgFutVqu5VSMTQgghhBAtRpIpIRpuDvDTySIUK4GtwN1Wq9XSumEJIYQQQoiWJMmUEA2gUChigPbAPmD1yf8esJ48Y0ChULRTKBT3KhSKtQqF4j+tF6kQQgghhGhu6tYOQIjzzGxse6VWAguAZ4EuCoViOjAD6AAsBt4EVrRWkEIIIYQQovlJMiVEw1wHuGFLqBRAPOCNLbF6DFgvBSiEEEIIIf4dJJkS4hwpFIpRQBegGJgA/AHcCOyQ/VLCmRZsf5VFO1+vca1dQA/+e9XaGtcsFjPbExdyImc3yXn7KKrIolSXj9lqwsc9mJjgfozsdg09o8bVOdZnq+5h05Gfa1zrEj6MJ6Ytdtr7OR/pqko5kbubEzm7OZG7ixM5uynV5wFw87j3Gdl1TpOen1eayiPf9ztrPz+PMN6+/oDDtpcXTuVI5uazPuORy34nrt3oBscohBDi7CSZEuLcVQE/A/8HxFfvkxKiteiN5Xyy4naHbQVlaRSUpbE9cRFDYq/glnEfoFZpWjjC89cPG5+slWQKIYQQZ5JkSohzZLVat2Gr5CdEiwjwjOT/5mwEQKlQ1WpXoKBdQBy9oibQKXQAAV7t8PUIpcpYQUbhEZbt+5jDGRvYeux3fNyDmTP8v7WeccOYN7lu1CsAfLP2YbYcm9+8b+o8o1G5Eh3Yk8iA7qw99E2zjPHg5J/pHD7EYZviHOpEDY2dyfVj3qizXatya3RsQggh6ifJlBBCtFUKBa4azzqb3V28+e9V62o3uAUS5B1N7+iJvLvkWvYmL2NN/NfMGPQ4LhqPGl01Khc0KhcAlEr5J6HamO7XMbHnLUQGxKFWaaioKmm2ZEqjdq339/lslEp1k+4XQgjReM36L6fSVZNtrTKFNOcYQoiWp3BR51gqjaGtHYeon0KhYESXOexNXobBpCer6Bjtg/u0dli1VBkr2JH4JyO6zm7tUOxiwwa3dghCCCHOA82aTFmrTCFDM55vziGEEK1gS8Q8+ZKkHkv3fsTPm58FbEu4ekVPqNVHV1XKM7+MoqA8nZiQATw1/W+UytpL+ZrKclpxSfXJGai2wGq1cjRrCxsTfmZH4iIqjRVtKpkSQgghzoWs6RBCCCe7uPed7EtZweGMDXyx5l5evGoDXm4BNfp8t/5RCsrTcdV4cPuEj5slkQLYfnwRAG5ab0J8OzbLGA1RUJbOxiM/synhF3JLk+zXXc9YfvhvZDIbUKu0TbjfiEqpRqFQODEqIYQQ9ZFkSgghnEyhUHDLuA945tdRlOhy+Xrtg/znklP7bbYdW2Av9DBn+IsE+3Rw6vgVlcVklySyfN8n7DzxJwBTBzxk3xvV0gwmPTtP/MXGhJ84nL4BK7ZCmAoUdI0YwYgusxkQM8XhvSazAZPF0OixVUpNq73vc/Xd+scoKEunylSBRuVKpH9X+na4hPE9bsbD1fes98enreGBb3pSVJGFUqHCzzOcbhEjmNDzFtoH9W7+NyCEEP9ikkwJIUQzCPCKYO6o1/lkxW3sSvqbDYd/ZGS3qykqz+Lb9Y8A0Lf9JEZ3v84p46079B1frX2g1nUPFz8uH/AwF/V2XEK9OR3L2s7GIz+x/fhC9IYy+/VQ3xiGdb6K4V1mEeAVWe8z/tz1dq0ztxpieJfZ3Dr+g0bf3xIyi47YXxvNlSTl7SUpby+rD37JPZO+oVPogHrvL9bl2F9brGYKytLYmPATmxJ+ZuqAh5k+6LFmi10IIf7tJJkSQohmMiR2BnuTl7H12O/8sPFJuoQP5Zt1D1NRVYy3WxA3jnm7WcdXKlSM6nYN/TtObtZxTldUnsWmI7+w8chPZBcn2q97uPgyMOZyRnSdTafQgS0WT1ulVCjp0W4cQ2OvIDqoF/6eEaiUarKLj7Mx4WdWHfyCYl0Ob/89h+euXEWQd1StZwR7tycucjTdI0cR4BWJl2sAJbo8DqStYvHONyksz2DRztfxdPVnYq9bW+FdCiHEhU+SKSGEaEZzR73Osaxt/D979x3fdLU+cPyTpOnee0BbOlhlg2xkyxYRXLjQK1e94va6xb1F+HFx4MSJgsqQJSB77w2lk+69Z5Imvz9CQ2uT0klbeN6vF68bvt/zPedJ8EKennOek12UxJt/TKCwNAuA+0bOx9neq8nGGdr5DgaETwOgTFtMYtZpNp/8ivXHFrHtzHf854av6R44qsnG+6fi8nw+2zib00nbMBj0AKiUVnRrP5IhnW6nd4fxDVpuN63/c1flzIqHUzuembKsxvUgrx4EefWgc8BQFm24l+LyXP448C4PjvmsRtt/jVpopt8ARnS9h74dJvH2iomk5cXwx4F3GNzxljotGRRCCFE/lz8NUAghRIPZ2zjzwOhFKBRKUyI1vOvd9A4e36TjqC6eNWSrdsTV3ofugaN4ctLP3NDjQUo1hfxvwywyCxKadMyqSsrzOZW4xZRIDQqfwcf3nODJSUvpHza11e9bam36hkzkurCpAByJXYeuon77xpzsPJg59B0ASjWFHE/Y1OQxCiGEkGRKCCGanZ9rWLVDVQeFz7hiY08f8BI2Vg5odCXsOPtjs41jY2WHs5236fd7o37jg9U3s+7oQnKLUhvcr65CQ5m2qMG/tBXlTfH2WkSv4HEAlOuKSc+Pu0zrmiLaDUetsgUgMet0k8YmhBDCSJb5CSFEM/t66+OUagpMv/9u+zO8fusWrK3smn1sG7U9Ae6diM04woXME802jrO9F/PvPcGJhM3sOruU4xc2kZxzjmV732D5vrfoGnA9QzrdSt+Qydio7evc77VQgMISZ7tLy0BLyvPr/bxKaYWDrSt5xWmUaOr/vBBCiMuTmSkhhGhGm09+zcmEvwGYMeBlrJTWpOZF8eveK3egecXFg3ub+/whldKK3sHjeXTCd8y/9yQzh7xNoGd3DAY9p5O28cXf/+HxJV348u85nEnagf7ikkBhXn5Jhum1vY1LvZ/XVWgpKss1Pm9d/+eFEEJcnsxMtVL5e+I4c8sSAHrvewLb9m5N2n/0EyvIXH4M50HBRPx2X5P2fSVk/3ma9J8OUXw6jYoSDTZ+LriN6Yj/Q4Ox9nVudP+6gjJSFu8hZ/1ZyhPzUFirsA/zxOvWXnjf0QeFUn4OIS4vNTeKZReTphFd72Vy3ydAoeC3fW+y5eTX9A4eT7f2I5o1huKyPJKzzwHg6RzUrGNV5WTnwQ09H+SGng+SkHWKneeWsu/8bxSWZbM78hd2R/6Cu2MAgzvewuBOt+HvFm62n6u1AEVdHI1bD4CNlQM+DTiL7FTiFnQXlzkGenZr0tiEEEIYyTdC0aYY9HqiHv2d8w8tI39nLLqcEgxlOsriskn9ci/Hx35G4eHERo1RGpvNibGfkbxgO6WRGehLNFTklVJ4KJHYZ//k7Mwf0Jdpm+gdiatVhV7HF5sfRqMrwcclhDuGvAHAxN6P0tFvIAYMfL3lUYrL8ho8RlpeNAaDweJ9g8HAz7tfMh162+8KlkivKtCzG3cOfZv5957i0fHf0St4HCqlFTlFyaw5soAXlw7i9d/GtkhsLeVy+8gORK/kcOwaAPqFTsFKZV2v5/NK0vlp10sA2Fk70yPo2vp8hRDiSpGZKdGmJC3YTtYfxn0f3nf2xW/2INRudhQcSCB+7no0qQVE/usXem5+GLWn42V6q0lfriPyvp8pT8pD5WJL8KvjcR0Rhr5US8YvR0hetIv8nbHEvriWsI9vauJ3J64mqw59SFzmMZQKFf8e8xk2agfAeL7Q7NGf8sqv15NbbDzA9+EbvmzQGH8eXkB85jEGd7yVjn4D8XIOxNrKnuLyXGLTj7D55FdEpe0HoG/IZLoEDGuy99cQVio1fUMm0TdkEvklGew5v5zd534lKecMcRlHWzS2fyrVFJKcc+kw3XJtsel1Rn480WmHTL93tvPA+x8zR2eTd/H+qpsA+Neo/zGs8x3V7s9dNoLOAUPp3WE8QZ49cLH3Qm/Qk5YXze7IX9lx5kcMGHCy8+Tm/i/UiG/dsUWcS97NoI7TCfcdgJdLEGqVLQUlGZxM2MLao/9nWiZ4y8BXcGjAMkEhhBCXJ8lUK+UyuAODkptvT0XYgmmELZjWbP03B016ISmf7AbAe2YfQj+40XTPY2JX7Dv7cGLsZ2gzi0hetIvg1+pfejr9h4OURmeBQkHnb2fiPODSsqjA58egtFOT+MEWMpcdw+/+ATh082v8GxNXnei0Q6w5vACAyX2fINSnb7X7Xs6B3DnsXb7e8ij7o1fQu8N4BoZPb9BYyTnnWL7vjVrbDAqfwX0jFzSo/+biYu/NhF6PMKHXI8RlHGXXuV9aOqRq4jOPm5Khf/rz8Dz+PDzP9PuGFLnQ6bUcjFnFwZhVFtt4O3fgkfHf4OEUYPZ+YvYpEveesvi8SqlmxsCXGdWt7S3lFkKItkKSKdFmZC4/hr5Mi0Ktov2zo2vctwvxwHtmH9K+2U/Gr0cJfGksSrWqXmOkfW/8abPbmI7VEqlK/g8PIfXLfehyS0j/6TAh705u2JsRV61ybTFf/v0wekMFHbx6MbXff822G9b5Do7F/8Xh2DX8sOM5OvkNxs2xfsn5zf2fJ8y3H2eTdpKcG0lBSSYlmnysrezxdAokzLcfQzrdTphvv6Z4a82mg3dvOnj3bukwrqh/jVxIZOpeYtMPk1ucSlFZDnqDHidbdwI9u9Gnw0QGdZxhseLjiK5342znSXTaAdLyoiksy6FcW4yt2hEflxA6BwxlRNd78HYJvrJvTAghrjGSTDWz3L/Pk/rlXopOpmLQVGAb7I7XjJ74/WsAmb+fIOaplQA1ZqFqK0BRlpjL0YELAOi6fBZOfduT+tVeslacpCw+B4VahUN3P/wfGoLbKPObuttiAYrcv88D4DwgCGsv80v4PKZEkPbNfioKyijcdwGXYSF17r8sPoeymCxTP+Yora1wH9+ZjKVHyN0UCZJMiX+wUTvw/p0H69T20fFLGjWWh1M7RkbMYmTErEb1I2rqEjCUJf/Jarbn+4VOpl9ow//+CHDvTIB75wY/L4QQomlIMtWMEj7cQvKC7dWulZxJ48IbaeRuPo/n1MZXV6oo0nB6+jcUHU2+dLFUS8HuOAp2xxHy/hR87mrdP5WuC4PBQPGpNAAc+7Sz2M6xhz+olFChp/h0ar2SqeKTlzZ01zpGn3ZkLD2CJrUAbU4xaneHOo8hhBBCCCGuHpJMNZPs9WdNiZRj7wACnxuDfYQvutwSMn87TvInuyhPzmv0OPGvrkeXW0Lwa+NxG9cZlaMNRceSiXt5LeUXcol/bQPu4zs3qBhDbSqKyxv1vNLeul5n3mhSC9CXGCuS2QZZLhOvtFVj7e2IJrWA0pj6/VTZ1F6pwKadq8V2VWcJS2OyJZkSzSa7MJFZn3oC0N6jG2/etq3Jx/jy7znsjmxd+5WEEEKItkKSqWZgMBhIeGsjAHadvOm6bBYqe2NZW7W7PYHPjUbtYU/8qxsaPZYmJZ+IP+7HqW970zW3UeFYf30HJ8Z8ir5US/aaM/jO6t/osao60PGdRj1f37OzdLmlptdqj9qTF7WnA5rUgmrP1G2MEgCsXOxq3Wtl5Xlp/MpnhBBCCCHEtUeSqWZQdDiJsvgcANr/d5QpkarK9/4BpH61j/LEvEaN5XFjt2qJVCWHLj7YR/hScjqNouPJZp5sWypnpQAUNrX/Z6u0VQNQUayptd0/VZQaz45SXrb/S/frO4YQdTGl75NM6P1ItWtKRf2KqdTVrBHzuPv6967IWEIIIcTVRpKpZlB5aKzCSonbyDCzbRRKJa6jwkn/rm4b1S1xHWG+fwC7Dh6UnE5Dm1VssU1DNWfZdiGudVYq6xqHtDYXtcoGtcrmiowlhBBCXG2ULR3A1ahyL5S1v4tplsQcuxCPRo9l7e1k8Z7Szji2/uKMS1umrDK7ZyjX1dpWX2Z8vyqH+n0ZVVV+Xpft/9L9+o4hhBBCCCGuHjIz1Qz0JReXi9lbTqQAlE3xRVxV9yIOTelKF6Cwcrt01oo2u/aZtsqZuKrP1G0MewB0+aUYdBUorMwvddJVmemrfEYIIYQQQlx7JJlqBpVJVGVSZYm+De+3udIFKKz9nFHaW6Mv0VB2IddiO325Dk1GEQB2oZ71isnUXm+gLDEPuw7mZw7LEnOrPNP42UUhhBBCCNE2yTK/ZmAT4AoYK+1VLjkzpzQ2+wpF1PYpFAocuvkCUHQ0yWK7ouPJUKEHwCHCr15jOHS/1L7auV3/HOOIcXxrP2cpiy6EEEIIcQ2TmalmUFldz6DTk7ctGvfxXWq0Mej15G2JutKhNZmWKEDhNrojhQcSKNh3AW1Wkdmzs7LXnAZA5WyL08CgevVvG+yObagnZTFZZK85jdfNPWq00Wt05GyMNMYztlMD3oUQzeds8i7eX3UTAB/edQQv58Am7b/yTKpO/oN54abVTdr3lXAgehXbz3xPQtYpyrTFuDv60yv4Bsb3egQ3h/r98KWqM0k7OJeym7iMY2QWxFNQmkW5tgQHG1fae0TQP2wqQzrdjpXK/NJvXYWWM0nbOZ6wmdi0Q6Tlx6LRlWBv40qgRwT9w6YxpNNtFp8XQgjRcmRmqhk49m2HTbA7AIkfbTWV3K4q7buDjS6Lfq3xuqUXSls1Bm0FCR9sqXG/LD6HjJ+PAOB9W+9az4qyxPeefgDkboqk4GBCjfupi/egyy4GhQKfO/vWu38hxJWnN+hZvPlhPt34L04nbaewLBttRRnp+bH8dfxz5v46gui0Qw3uf+nuuaw+NI+TCX+TlhdDSXk+FXotBaWZnE7axrfbnuT138aSW5xq9vnXlo/i47W38/fJr4jLPEappoAKvY7C0ixOJ23n221P8NYf48krTmtwjEIIIZqHzEw1A4VCQdALYzj/4DJKzqZz5pYlBD4/GvuuvujyS8n87Tgpi3ZiE+RGeS37f0R11j5O+D8yhKR528j46TAKhQK/2QOxcrWj4GAi8XPXoy/VovZyJGDOULN9nJ7xLQV747Fp50qf/U/WuO9z93Wk/3CI0ugszs36meBXx+M6IhR9qZaMX46SvGgnAF639sKhW8N/ki2EuHJWH/qIveeXAzC8692M6/EwjrZunE/dz8+7XiSnOIWF6+/hrdu242zvVe/+3R39CfPtR7jfAHxdQnFz8EOhUJBZkMDOcz+z89zPJGafYuH6e3hl+l8oFdV/jlmqLUKpUNEreBz9QqcQ6tMXBxs3cotT2X7mB/4++RXxmceZv24mr07fhFIp54AJIURrIclUM/GYHEHAnGEkL9pJ0dEkztz2XbX7zoOC8Zzajdjn14BKJgjrqt0TwymLyyHrjxOk/3iI9B+r/zTZys2eTl/fbnYJYF0obazo9O1Mzt7xPeVJecQ8uaJGG5dhIYS8M6lB/QvRnLoEDGXJf7Karf/Zoxcxe/SiZuu/OeQVp7Hu6P8AuL7LXdw3Yr7pXr/QybTz6MLcZSMoKM1g7dH/444hb9V7jCcn/Wz2uquDL+F+/fFzDePXva8Rl3GUyJTddAkYVq3dgLBpjIy4Fy/n6kuTHW3duGvYu3g4BvDr3te4kHmCAzErGRg+vd4xCiGEaB7yLb4ZBb4whs5LZuIyNASVsy1KOzX2XXwIfHksXZbeg15nLJSgcpSziupKoVQS/r/phH92Cy5DQ7Bys0dha4VtsDt+Dwyk5+aHTXvWGsouxIMemx4m4Inh2HX0QmmnRuVii1O/9oS8P4UuP99d6/lhQojWY1fkr2h0paiUaqYPeLHGfV/XUK7vchcAO87+jK6i6c/lG9blTtPr+MwTNe7fOmhujUSqqht6PoSjrXHp+MmErU0enxBCiIaTmalm5ja2k8VCBeXxOQBY+zrXuOcyuIPFIg+27d3qVAAibME0whZMq/e9tsDzxm543tit3s9F/HZfndpZOdsS+N9RBP53VL3HEKKxjl/YxMbjnxOfeRxdhRZvlw4M6XQrY3v8mz3nl/P1lkcBasxC1VaAIrMggf/+2AeA56auJMz3OjYeX8ze87+RURCPSmlFsFdPJvR6hB5BY8zG1RYLUJy4sAmATv6DcLH3Ntumf9hUNp/8klJNAedT99K13fVNGoNef+mgb7XKpt7Pq5RW+LiEUFSWQ35JelOGJoQQopEkmWohBoOB3L+N1fxk740QotIfB95l9aF51a4lZp/ilz2nOH5hIwPCGv9DkDJtEe+umEJsxpFq188m7+Rs8k5mDZ/HiIh7Gz1OSzMYDFzIPAlAqE8/i+2CvXqhVKjQGyq4kHmiyZOp/dErq43VEAWlxsTZ1tqpCSISQgjRVCSZakba3BLUbvZm76V9vY+yOOM5Ux6TI65kWEKIVupw7FpTIhXi3YcZA18m0LMbRWU57I5cxtoj/0dWoeVz1urq510vUVSWwx1D3qJPhwnYWTsRm36EH3Y+T2ZBPD/vfoU+HSY2qBhDbcq0RY163sbKAYVCUef2ucWplOuKAWpdRmdtZYurvQ85xSmk5kU3KsZK5doSsouS2Xt+OeuPGveZ9Qy6gTBfy0mdJYlZp8ksiAcg1EeqiAohRGsiyVQzOnXjV7hcH4rHxK7YdfRCoVJSFp9D5q9HSf/pMAAOvQJwGx3ewpEKIVqawWBg2d7XAAhw78xzU1dgozYeCu1o6870AS/iZOvBz7tfavRYOUXJvHDTmmpf7HsEjeExx+955dfr0ehKOBj7J6O73d/osap66MvgRj1f37OzispyTK+d7Txrbetk70lOcQrFZQ2vsJqeH8tzP/Wvcd1Kac31Xe7irmHvNajfX/cal3VbW9kzuOMtDY5PCCFE05NkqhnpS7SkLzlA+pIDZu/bdvCg0+JbUUg1PyGueTHph0jPjwPg5v4vmBKpqsb0mM3GE4vJKqx5Blp99A+bZnaGpL1HVwI9u5OQdZK4jKONGqM1KNeWmF6rVba1trVW2QFQpi1u8jh6BI1hRNd7sLaqPQZzNh5fzKlE47l6k/s8bnHflxBCiJYhyVQzCnl/Cjkbz1F0JAltZhG6/DJUDtbYdfTCfXwXfO65DpWdVIUTQkB02kHAWGyge+Bos22UCiU9gsaw5dQ3jRqre/uRFu/5uISQkHWSgpLMRo1hTnOWbW8NvJ078PnseAB0FRoy8uPZG/U7W059zdG49Uwf8BKT+z5R5/7OJu/i14uzleG+A5jcp+7PCiGEuDIkmWpGbmM64jamY0uHIYRoA7Iv7oVydwyodQbD1yW00WO5OvhavGdtZZyh0ehKGz1OS7NRX9qzqq0oq7WtpsL4fm3NzAjWlUKhwFZ98Yw7tXF5ZohPHzr7D+F/G+7ht/1v4e3Sgf5hUy/bV1L2Wf634V4q9Fp8XDrw6ITv5LBeIYRohSSZEkKIVqCyUIKNlfmiNZXMLf+rL4Xi8kuLDRgaPc4/XekCFJVnM8GlaniWFJYY7zvYujUsuFr0DZlIZ/8hnEvZzaYTX1w2mcoqTGTemlspKc/Hxd6bZ6b8dtk9X0IIIVqGJFNCCNEK2FgZk6RyXUmt7cqbYU/PlXKlC1C4OfhhY+VAua6YzIILFttpK8rJu3h+k59rWKNitCTEpw/nUnZzIetkre0KSrP46M8Z5BanYmftzNOTl9VaiVAIIUTLksoHQgjRCng4tQOMlfY0OstL0tLyY65USG2eQqEgyKs7ALHphy22i8s4it5QAUCQV49miaXi4sG9CizPrJVqCpm35lbS8mJQq2x5YuJPBHrW/3ByIYQQV47MTAmT0zO+pWBvPF639CJsQeMPBr1aVRSXk73mDEXHkik+lYomvRBdtnE2wdrXCaf+gfjc1Q+nvu3NPl+WmMvRgQvqNeag5NcbG7Zo5cJ8rwOMX7pPJmyhb8jEGm30Bj0nLmy+0qE1mZYoQNEjaCznU/dxLmUPBSWZZs/OOhi9GgA7a2c6+g1qljjOp+4DsDizpq0o5//W3cWFzBMoFSr+c8OXdPJvnliEEEI0HZmZEqKeyuJyiHlqJenfH6ToSBKa5Hz0ZVr0ZVrjOWLLjnFq6tfEv/lXk4xn10lKIV8LQn364e3cAYAVB94zWwBiy6lvGl0W/VoztNNtWFvZUaHX8seBd2vcz8iPY/vZHwG4vstMrFT1q7CaXZhcrQS7OdvP/GgqNd83ZFKN+3qDns83Pci5lN0A3DdiPr07TKhXHEIIIVqGzEwJUV8qJY592uE6MgzHXu2w8XdG7e2ILq+MktNpJH+6i+ITKaR+vgebABf87h9Y7XGbdq70P/9irUMUHk7i7B3fA+B1c/MsOxKti0Kh4JZBr/DJX/eTlHOG91bdxIwBLxHo2Y3i8jx2R/7K2iP/h5dzMJkF8S0dbpvh6uDLxN6PsvLgB2w78z2g4IaeD+Fo40pU2gF+2vUiGl0JznbeTOr9uNk+3l15I5Epe/Bwas+8u6ufv3UmeQfL977J4I4ziGg/An+3jthZO1OuKyY5J5Ldkb+y7/xvAHg6BTK+1yM1+v9hx7Mcjl0DwNR+z3Bd2I0Wi3UoUFarUiiEEKJlSTIlRD05dPGh+5+za1xXuztgF+KB27hOnLrxK4pPppLy+Z4ayZRCoUDlYFPrGDnrz1Y2xnOaJFPXiutCb2RynydYc2QBsemH+WD1zdXud/IfzMDwm/lu+zMoFVImu65u7PcM6flx7D2/nG1nvmPbme+q3Xe0deexCd+bXQJYFwWlGWw4/ikbjn9qsU2wV08eGfcNdtZONe5tPb3E9HrVoY9Ydegji/2YS+iEEEK0HEmmhGhiSmsrPKf1oPhkKprkfLQ5Jajd6/6TZL22guw/TwPgPDAImwCX5gpVtEIzBr5MmO91bDyxmLiMY1TodXg7BzO40y3c0OMhUyJga+3YwpG2HUqFkgfHfEbv4HFsO/M9CVmnKNeW4O7oR8+gG5jQew5uDn4N6rtvh0nY3GDHmaQdxGUeI78kg8LSbNQqa1zsvQny6sl1oTfSt8MkOSdKCCGuQtdkMpW/K5a0i/tdtNnFKK2tsPKwx7a9Gy7DQ/G8qTs2/tW/wJan5JO7MZLcv89TfCYNXXYJShsrrNu54Do8DL/Zg7DxczY7XsavR4l5aiVgLCRQnpxH8sKd5G6NQptdjI2vMx5TuxHwyFDTjIU2p4TUxXvIXneG8pR8rJxtcRvTkcDnRqP2rPklqmpRg67LZ+HYw5/kT3aRvfYMmuR8lPZqnAcEEfDoMBx7BjT4s6soKidtyQFy/jpHaWw2+hINak9HnAcG4f/gYBy6Wf5Ckr3uDBm/HKX4ZAq63FKUdmrUHg7YdnDHdWQYntN6oHa7OpavGCr0ptdK6/p9gcrbGoUu17gHw3O6zEpdi3oFj6NX8Diz9zLy4wDMfvnvEjDUYpEHL+fAOhWAmD16EbNHL6r3vbagf9hN9A+7qd7PvXDTaov37G2cG9xvpZYozCGEEKJpXHPJVNKC7SR+uKXatQpNBRVF5ZRfyCV/VyxKOzV+9w2o1ub4mE+pyK9errhCW0HpuQxKz2WQ8etRuiyZidN1tZ+BUnQyhbN3/GD6sgxQFp9D8v/toPBAAl1+vhtNagFnZv5AeXyOqY22rIiMn49QsPcC3dfOxsrFzuIYurxSTk7+ktKoTNM1fZmWnPVnydkYSdj8m/Ca3rPWOM3GfiKFc7N+RpteWO26JiWfrD9OkLXyJMGvjcfvXwNrPBvzzCoylh6pdq1CW0FFQRllcdnkbYnCtr0bbmM71Tuu1sagqyBnnXGZnm2oJyrH2pf0/VPW7ycAUNha4TEposnjE22XwWDg+IVNAAR5SqIthBBCtLRrKpkqjc4kcd5WAFxGhBHw0BBsQz1Q2VujzS6m+FQqORvOoVTXnEmw7+SN68hwnPq2x9rHCSt3e3Q5JRQdSyb5012URmYQ+dAyeu94tNb9MOdn/4rax5Gw+Tfh2KcdFUXlpH61j7Rv9lOwN570nw6TufwY6PSEfzoD58EdMGgryFx2jMSPtlIWl03Swh0Ev2L+p9YAF974C01mEYEvjsFjSjdU9moKDiZy4a2NlMcbK9HZd/HBoatvnT+7soRcztz2HRUFZdh38SHg0WE49QtEaa+mLDablM/3kLPuDPFz12Mb5I7bmI6mZ/O2R5sSKc9p3fG5tz+2gW4orFVoM4soOppM9prToLR8/oolFaVa0Osv39ACpY0VCqumWXqjzSmm5GwGyYt2UnQ0CRQKAp8dVa8+dIVl5GyOBMB9TCesnG2bJDbRdhSV5eJo62b23qYTX5CeHwvAdWE3XsmwhBBCCGHGNZVM5W2PAb0BtZcjnZfMrJY0WbnaYRfqiefU7maf7bbiXzWuqd3tsQvzxH1SF06M/ZyyuGyyVpzE565+loNQKem24l+mL8lqDwc6vDmRkrPpFOyN58JrG1A52tBj08PVlhq2e2I4ZXHZZP52nKzfjteaTJUn5tHxy9vwmNjVdM1jQhecegdwfOxn6HJKSHhnE11+vNtynP8Q99JaUyLV7c/ZqOwulQ9W97Wn05e3Ef3ECjKXH+PCWxtxHR2OQmFMjvK2RgPg0MOf8EUzqn+GbvbYd/TG+7bedY6lqnN3/0jB3vgGPQsQ+vFNDR4bIOH9v0leuKPGdWtfZ4Lm3oDH5PrNLOWsPYOhzHi4p2cDZg9F2/fWH+OJaDeCfqGT8XfrhEppRXp+HDvP/sz2M8YKjx28e9MzcGwLRyqEEEKIa+qcKYPOOINh5WFvdvapoVR21riP7wxA/u64Wtu2e+x6s7MNHpO7mmL0fWBgjT1bAB5TugGgzSqmPDnP4hhO1wVWS6QqWfs64//QYMCYWGoyCmu0Maf04jI8gJAPbqyWSFXV/uIsTGlUJiWn00zXK/cPWXtfGxvmlbZqfGddh8vQkHo/m3lxiZ+Vuz2uI8OaOjTRBpRrS/j71Ne8v2oajy/pypxvOvLm7+PYduY7DBjwcQnhkXHfSDEDIYQQohW4pmamHCKMy9pKz2WQ8OEW/P89qNa9R/+UvzeejKVHjAe1pheiL9HUaFMWm11rHy7Xh5q9bhPofqmNhS/hNkGXlv5oMoqwCXA1285tnOV9R+7jupDwzmbQGyg6koT7+C61xgtQsMeYIKpc7bDv6EVFcbnZdlYutlh5OKDLLqboRIqpGEXl5567NZq0Jfvxuq2PxYSsviJ+u69J+mmodk8OJ2DOUAB0+WUUn0gh5fM9JLz3N6lLDtB5yUwcu/vXqa/ylHwK9sUD4DElokkTftF23Dt8HsfiNxCTfpj8kgyKy/OwVTsS4N6JPh0mMqrbfVhb1f3vLSGEEEI0n2sqmXIZGoLrqHDytkSRvGA7KZ/swrFvO5wHBOEyuAPOA4Ms7p+Jm7uOtK/3X3YMXZH5RKOSpdkZpe2lPwprr8u30V9cCmaOXainxXu2Ie7GvUl6A+VJebXGWqk0xpggVuSVcqDTO3V6RpddbHrtOb0nqV/vp+RMGnEvrePCW5tw6h+Ic/8gXIaG4Ni3nWlJYFujtLYCa+Ofi8rBBht/F9xu6MT5B5eRs+4s5+75id47H6tTEYqslSdBbwBoUIEQcXXoFXwDvYJvaOkwhBBCCFEH11QyBdDpq9tJ+Ww36T8cQpNWQOG+CxTuu0Dy/+1A7elAwGPX43v/gGpf7jN/O25KpFyGhuBzz3XYd/bGys0epY0x+UpauIOURbtAV3sxBIWqDisrVXVILAwGy4/bWVseX6lEaatGX6KhorjmzJo5FYVll2/0D3pNhem1Uq0i4vf7SPq/7WT+egxdbgn522PI3x5D4odbsAl0o/2zo/BqwOG0rakARSWFUknwq+PJWXcWbUYRWatP4TOz72Wfy/rDuMTPNtgdp77tmzQmIYQQQgjR9K65ZEppY0W7J4YT8Pj1lEZmUHgokfzdceT+fR5tVjHxc9ejzSwi8PkxpmfSfzwEgNOAILr8co/ZWRR9qeWZoiutotRykmTQ69GXaQFQOVhOuqpSXkzObEM86L3zsQbFZOVsS/Ar4wh6aSzFp9IoPJRA/s5Y8rZFU56QS/Sc39GXaPC5s5biHWa0dAEKS2zauaL2dECbZawSeTnFZ9IoOZsOgOfNUvJaCCGEEKItuKYKUFSlUCiw7+yDz1396PjZLfQ9+BROA4MASPlyb7WEpPJLrsfELhaXo5Wez2j+oOuoNMbyAZBlsTmmpWQ27Vzr1J/txb1ampQC9NqKy7SunUKpxLGHP373D6TztzPps/cJ7MKMyxKTFtSsiteWVRY8oQ5LGCtnpUCSKSGEEEKItuKam5myxMrFDv/Zg4jcdwFDmY7ypHzsw70A0GuMs06GCvNL6zQZhRTsu3DFYr2c3I2RBDw81Oy9nL+Mh8miVODYp12d+nMZZiyIoS/Tkrv5PB4TLl+0oq6sfZ3xuasf8a9tQJOST0Wptl7FKVq6AIUlJecz0OWVAmDb3rXWtga93rhfCnDs0w67Dh7NHZ4Qrcq7K28kMmUPQzrdzuzRi1o6nFZt1qeW98RW9fVDaaiU8k+8EEI0t2tqZqo0LhtDLftryuJzTK+tXC9Vy6qcwcn9+3yNZwx6PXEvrcPQyBmbplR4IIHs9WdrXNekFZDy+R4AXIeHYu3tVKf+7Dt6m6oQxs9dT3lKfq3tS6Ozav39P5VdMH7uSlt1tSIbrdXl3o9eoyP+lfXG36iUuN3Qudb2BXvi0aQWAOA1XWalhBBCCCHaitb/zbUJJf/fDgoOJuA1rTvOgztgF+KB0laNJrOI3I3nSPx4GwBOA4OqVdTzmNSV5P/tpGB3HNFPrsDv34Ox9nGiNDqT5P/tJG9LFHbhXpRGZbbQO6vOpr0r0XN+p+zpEXhMiUBlb03hwQTi39yILqcEhZWSwBfqd+BnyLuTOTHpCzQp+ZwY9zn+Dw/BbVQ41r7O6LUVaFILKDqSRPa6MxQfT6F/5IumZ2Of/xNdXime07rj3D8ImyA3FFYqNKkFZK0+Sdp3BwHjWVttoapf7PN/oi/X4XlTd5z6tcfa3wWltQptVjGFhxJJ+WIPpeeMyz79/jUAu5DaZ5oyLy7xU6hVeNzYrdnjF0K0ffcO/4hBHWdYvC+zUkIIcWVcc3/blsfnkDR/O8zfbva+TZAbYfOnVbsWMGcYuZvPU3I2ncxlx8hcdqzafZ9Z/VG725N0MRlraUGvjCPxg79JeHsTCW9vqn5TpST045tMZz/VlW2wOxHLZhH5wC+UJ+Sa77tyCNeaZ+CUnE0n4eLeM3McevgT9Or4esXUkoqOJFF0JMlyA6UCvwcGEvRK7SWu9WVactadAcB1RBhqd4emDFMIcZWyUtlgq742DkIXQojW7JpKpgJfGovLkA7k7Yih5Gw6mvRCKgrKUDnaYNfRC/dxnfG55zpU9tWr3KkcbYj4436S/m87OevOokktQOVkg0NXX3zu6ofHlAgS521toXdVk5WbHd3WzCZ50U5y1p6lPDUflb01Tv0DaffY9Tj2DGhQvw4RvvTaNofMZUfJ3nCOktNp6PJKUaiUqH2dcOjqg8uwUDwmR1R7LmzBNPK2RpG3M5bSqEw0GUXoi8pRudjh0NUHjykReN3au80cUhvy3hTytkWRvzuOsthsNFnFxvfjaINNsDvO/QPxvq039p19LttXzsZIKgqNZ5NJ4QkhhBBCiLZFYajlvKJGd65QGAYlv95s/YtLyhJzOTpwAQBdl8/CZXCHlg1IXNX2BryKwWBotWsyFQqFYcl/at/b1hLOJO1gy+klxKYdoqA0CyuVNU52nng5B9Kt/QgGhs/A3dG/2jM5RSkcjd/A8fiNJGSforA0G7XKBk+n9nRrP5JxPR/GzdHP7Hg7zy3l6y2PArDkP1lkFybx5+H5nEz4m4LSLNwd/egfdjOT+jxqmuUoKsthw7FPORT7J9mFydjbONMz6AZmDHgJZ3uvGmNkFiTw3x/7APDc1JUEe/Vk3dH/cTBmNTlFydhY2dPRbyCT+z5BB2/zxxDUpQBFqaaQv099w9G49aTlRVOuLcHZ3otO/oMZ3/Nhgrws/zDiUMwadp77ifjM4xSV5WJtZYeznSc+LiF0DxzNoI4zcLR1s/h8a1JZgOJfo/7HsM53tHA09TfrU89W/XeHEELU1zU1MyWEEC1l9aF5/HHg3WrXdHoNZdoiMgviOZO0A2sre8Z0f6Bam5d/HUZJefWiLxV6LUk5Z0nKOcvOcz/zxMSfCffrX+v48ZnH+ejPWygqu1RoJz0/jj8Pz+N86l7+O+U3copS+OjPW8goiDO1yS8pY8fZH4lM2cPcGZtwsHGxOEZxWR5v/j6OlNxLxXo0ulIOx63laPwGHhj1PwZ3urXWOM3GnnGMBevuJK+k+lLhnKJk9p5fzr6o35k55C3G9vh3jWe/2fo4O87+VO1aqUZLqaaA9PxYTiRsxss5kF7B4+odV2ugq9BgparbmYFCCCGaniRTQgjRzFJyo1hx8H0AurUfxcTec/B1DcPGyp7CsiziM09wJG4dVsqaxwK0c+9C98DRhPn2w9XeFyc7DwpLs4nNOMq6owtJzjnHJxvv572Z+2rdQ7Now3242vvwwKj/EeLTlzJNERtPLGbzyS+JTNnDtjPfs/vcL1QYdDw09gu6BAylQq9j17mlrDjwHun5sfx5+GNuH2x5tcEve+aSX5LBLQPn0j9sKjZW9kSlHeDXPa+RURDH11sfo71HBO09Iyz28U+ZBRd4f/XNlGoKaOfelcl9nyDcdwA2anvS8mLYcOwTDsX+yU+7XsTLOZhewZf2KZ5K2GpKpAaGT2d0t/vxcg7CSmVDfkkGsRlHOBi9CoWi/oVtNbpS9IaGV3FVq2wbVSRi7ZEFLN39MiXl+VgprfF1DaVH0FjG9piNm4P5mUohhBBNT5IpIYRoZqcTt2Iw6HG28+aJiT9hpbqUNDnYuuLrGsbA8JvNPvvitDU1rjnauuPnFk6/kMnMXTaC9PxY9p3/nRER91qMQalQ8eK0tdjbOAPgbOfJXcPeJSn7DOdSdrN098vYqp1487bt1ZYa3tjvadLzY9kd+St7IpfVmkxlFSYwZ9wS+oVONl3rGzKJEJ8+zP11BIVl2Szb9wZPT/7V8of1Dz/seI5STQHtPSJ4ZfoGrK0uFbgJ8+3HnPHf8uXfc9gd+QvL9r5Gz6CxpqqgJxP/BiDYqycPjV38j8/QjQD3Tg1eKjdvzW1Epuxp0LPQ+GV6aXkxptc6vcY0U7n19Lc8OObzNjvTJoQQbc01dc6UEEK0hAq98eBvZzuPaolUY9mo7enTYSIAZ5J31tp2Sr+nTIlUVf1CbzTFeEOPf9fYswVwXehUAApKM8kutFzFMtx3QLVEqpKbgx/jez0CwKnErTWW61lSuQwPYNaIj6slUlVNH2A8iiEl9zwJWSdN1yv0xpkjF/vLF4NpK8J8ruPe4R/xxq3bWHR/FF8+mMzbt+9mar//olbZUqopZNFf9xObfqSlQxVCiGuCzEwJIUQzC/Q0nh+WlHOWPw68y7ie/6l179E/nUvezY6zPxGTfoi84nTKdcU12qRXmakwp1u7EWavezsHmV53bXe9hTbBptf5JRl4OLUz2653B8vHG/TpMIHl+97AYNATk3aYviETa40X4GzyLgAcbFzxd+tImbbIbDt7G2ec7DwpLM0iPvO4qRhF5ed+MuFvNp/8muu7zLSYkNXXCzetbpJ+6uvl6etrXAtw78S0/s/RI2gM76+6CY2ulF/2zDU7qymEEKJpSTJ1lbBt74ZUThSidera7np6BI7hRMJmVh+ax9ojCwnz7UdHv8F0CRhCJ//BFvfP/LTzRTad/OKyY5RqzCcalVzsvc1eV1vZ1quNRldqcQw/1zCL93xcQ1EolBgMerILE2uNtVJabjQAxeV5PPxV3SqUFpZmm14P7ngrm058SWL2KX7c+RzL9r5OR78BdPQbSNd21xPq069NHBReV6E+fRnTfTbrji7kfOo+cotSLVZ6FEII0TQkmRJCiCvg0Qnfsf7oIraeXkJucSqRKXuJTNnLn4fn4WznxZS+TzKm++xqX+53Ry4zJVJd213PyIj7aO/RBUdbd1MFtz8PzWft0f+jwqCrdXyl8vLnuCkUl29jwPJxGjZqy4dOKxVKrFV2lOuKKdPWnFkzp0RTUKd2VWkryk2vrVRqXrhpNasPz2PXuaUUleVwKnErpxK38seBd/FyDuLm/i8wqOOMeo/T0gUoLOkdPI51RxcCkJB9SpIpIYRoZpJMCSHEFaBW2XBjv6eZ0vcpknPOEZ12gDPJOzlxYRMFpZn8tOtF8ksymTHwJdMz205/B0BHv4H8d8rvZmdRNBVlV+w9XE55LUmS3qBHU2Gc1bKtJemqqjI583UN5b2Z+xsUk72NM7cPfp1bB71KQuYJotIOciZpOycTtpBZcIHFmx+iXFfCiK731Kvfli5AYYmTnafp9T9L6gshhGh6UoBCCCGuIIVCQTuPLoyIuJf/3PAV8+45QSf/QQD8dfwzyrUlpraJ2acB6BcyxeJytJScc80fdB2l5kVbvJeeF4PBoAfAw6l9nfqr3M+VU5SCrkLbqNiUCiXB3r0Y22M2j0/8kQ/vPoyfazhgPAPsapFfkmF6bV+PfXlCCCEaRmamLop+YgWZy4/hPCiYiN/ua+lw2oTEeVtJ+nhbtWv2XX3puelhi8+URmeR8sUe8rfHoMkswsrFFsdeAfjeNwDX60ObLVZtdjE5685QdDyF4tNpaDOK0OYUo7BSYdPOBedBwfjO6o99R/N7RvL3xHHmliV1Hs+mnSt99j/ZRNHX1NDPsfK/86rkv/mW5WDjwg09HiYyZS/aijKyC5Pwd+8IGA9kBSwuJ8srSedcI2ZHmtqx+L+Y2PtRs/eOxBkLJygUSkJ9+9apv67thgPGJXXHL2ykb8ikpgkUY4XBERH3snT3y+QUJaPRldarOEVLFaC4nKNxlwpUVBbgEEII0XxkZkpcMTl/nePE+M/J+Okw5Ul5GMp1aDOKyN0Yydk7vifh/b+bbezCgwnEPr+GjKVHKD6RgiatAIOmAn2JhtLzmaR/d5ATYz8j9au9TTKeXSfzSVlTaMnPUTRMen4s+ouzMuZkFMSZXjvYuppeezobZ3COX9hU4xm9Qc+PO56nQt+4GZumdD51H4dj19a4nlucyoZjnwDQrf1IXOtYqjzAvRMRF6sQ/rTrRXKKUmptn5obVevv/ymjIB4Aays71CrbWtu2BrlFqbXej0zZy+ZTXwPQyX+wHN4rhBBXgMxMiUazDnCh11bjGTIozefnJVGZRD3yG/pSLXadvQmeOw6Hbn6Up+STtGA7uRvOkbxwB3YhHnjd0qvJY1RYqXAe0gHX4aE4dPfHxt8ZK3cHtNnFFB1JIvl/OymLyyb+1Q3YBLrjfkOnas87Dwii//kXax0ja/UpYp8x/rTaa3qPJn8P0PjPMeT9KXR421iSOvb5NWT9caJZ4hTVrT70MVGp+xnUcTqdA4bi6xqKtcqO/JIMjsZvYOXBDwDo5D+oWkW9fiE3subIfM4m7+SrLY8yrufDuDn4kpJ7njWHF3AiYTP+bh1JyT3fUm+tGk+nQBZvfoibrvsv14XehI3anqjUA/y651UKy7JRKa24ZeAr9erz3uEf8vpvY8kpSubVZSOZ0PsRegSOxc3BD51eQ25RCjEZRzgU8ydxGUf5fHa86dkl25+mpDyfgeHT6eg3EC+XIKyUanKKUtgftYItp74B4LrQG9tEVb8F62bibO9Nv5AphHj3xtXBF4VCQWbBBQ5Er2LTiS/Q6TVYW9lxx5A3WzpcIYS4JkgyJRpNoVCgcrCptU3Ce5vRl2pRezkSsfw+1O72AKg9HOj01e2cnfkD+TtiuPDeZjymRKC0bbqDTQHcxnTEbUzHGtfV7vbYh3vhPr4zx0d9iiatgJTFu2skUwqV8rLvMWfdWQBUjja43dC56YKvorGfo9LGCmyM/7dXqGRi+krKKIhj1aGPWHXoI7P3vZyDeWDUomrXJvV5jGPxf5GUc4Zd55ay69zSavdHd/sXjrburDr0YbPFXR+3DX6dP/a/w7K9b7Bs7xvV7ikVKv41cmG9l555u3Tguakr+d+Ge8ksuGC270oONq41riVmnzbtPTMn2Ktnm0k89AYDJxP+5mSC5dlnZzsvHhzzOcFePa9gZEIIce2SZEo0O016Ibl/RQLg//AQUwJQSaFQEPjiGE7uiEGbVkjOxkg8b7yya/2tXOxwn9iFtG/2U3yy9qU05mizisjfYTw01X1iF1R2TZsMQtv4HIV5tw6aS9eAYZxK2kZi1mnyS9Ip0eRjq3YiwL0TvTtMYFTErBqlxe2snXhx2hpWH57H4dg15BSlYG/tTHvPCEZ0vZf+YVNZceD9FnpXNTnaujF3xkbWHF7Aodg/ySlKwVbtQLjfAKb0fZIO3r0b1G+gZzfeuWMPu84t5XDsOhKyTlFcnotKaYWrvS/tPSOIaDec/mFTqz03e/QnnEz4m9NJO0jJiSS/JJ0ybRH2Nq4EekRwXdhUhna6AytV0///tTncPvh1TidtIyb9EFmFSRSV5lCh12Jv40p7jy70CBrDsM53Ym/j3NKhCiHENaNVJFPanGIO9/4Ig05PyIc34jPT8ubk0uhMjg03/vS28/d34jb60mxD8Zk0cv46R/72GEqjs6goLEPpaIN9mCfuE7vic08/VHbW9Y7v9IxvKdgbj9ctvQhbMM1sm6oFCnrvewLb9m5m2xXsv0D6j4coPJCAJrMIpY0VdqGeeNwYge+9/Y0zB1eZ3C3nwWA8m8ZjSoTZNo7d/bEJdqc8PofcTS2TBBh0xj0tSuv6/xlkrTplet7r5ub5iXBb+RxFTS723gzpfBtDOt9W72crS3vfPtj8odzT+j/HtP7Pmb03rPMdly2/3SVgKEv+k1VrGy/nwMu2qWRn7cQtg17hlkF1X85Xl2IOapUNIyNmMTJiVp379XRqX+9nWrOI9sOJaD+8pcMQQghRRav45q52d8Dl+lDytkSRteJkrclU5oqTAFi52+M6/FLVsuLTaZy44bMa7SvySik8lEjhoUQylx+jy9J7sPZybPo3cRmGCj1xL60l/YdD1eMr11F0NImio0lkLjtGl5/uxtrH6YrH15wqZ3qsfZ2x8bdcqtepTzvK43MoPp12pUIz0RWWkbfVuFndoad/vZ/PvLj3yNrXGechwU0Zmklb+ByFEEIIIa4lrSKZAvCc1oO8LVEU7ItHk15oMaHIXmlMpjwmR6CwUl26oQCX4aG4j++CfRcfrH2cUDlao0krJG9LFCmL91ByNp3YZ1fT+duZV+ItVRP/2gbSfziEwkqJ730D8Ly5BzbtXdGXaMjbGk3C+39TcjadyNm/0m3F/fXez1JRqgW95Wphl6O0sar+eTahsphsAGyCzM/WVbJp72psH5+DQa9HYaGYRVMxGAxoM4soPp5C4oLtlCfmoVCraP/kiHr1UxqTRfGxZAA8p3Vvtrhb6+cohBBCCHGtajXJlPv4zijt1OhLtWStOon/vwfXaFN0LJmy+BzA+KW1KoeuvnT9ueYJ9mp3Bxy6+uI2rjMnxnxK7sZISmOysAv1rNG2uRQeTSLtm/0AhC6Yhte0KpXe3OzxuasfTv0DOTnhC4oOJ5K95gyeU+u3POvc3T9SsDe+wTGGfnwT3rc1bD/D5WhzjYeQqj0cam2n9jTeN5TrqCjWYOXUPKWKox793WwVO9sOHoS8NxmnfnU7ULRSZpW+PKc336bv1vY5CiGEEEJc61rNj6xV9ta4XayglrXqlNk2WRdnpWzaueJ0XWC9+rcP98K+m/HMjfzdcZdp3bQqEynXkWHVE6kq7Dt6mxLErJVXV7lqfYnxHBylbe25e9XKc/piTbPG9E9Wbvb4PTAAx14B9X42a4Xxz8u+iw8OXep2fk5DtIXPUQghhBDiWtJqZqYAPG/qQfaqUxQfS6Y0Lhu7Dh6mewa9nqw/jUmWx9RuZs8EMVToyVpxkqzVpyg5nYY2twRDua5Gu7LY7OZ7E2bk7zEmb86DO1BRXG6xnf3Fg16LT9S/mlzEb/c1LLhrUOhHUwl5bzIYjLM9hQcTSf5kJ3EvrSNtyUE6f38ntoG1L6WrVHgwgfILuQB4NtPZUkIIIYQQonVqVcmU68gwrFzt0OWVkr3yJO2eHGG6V7DvAtq0QgA8b+pe41ldYRnn7v6JwoMJlx2noshyQtPUKorLTXEnvL2JhLc3XfYZbU5xc4d1RSntjTMl+rKaiW1V+jLtpWcc6l91sc7xVDlrSeVog217NzwmduH0LUsoOpJE5P1L6bHxoTrtNTIt8VMq8LypeZOp1vY5CgH1q/QnhBBCXG1aVTKlVKvwmBxB+o+HyFp1qloyVbnEz66zNw5dfWs8G//aBmMipVDgc2df3Cd1xS7EA5WTDQor45fis3f+SOHBBFMJ6ytBV1D/xM2gqaj3M625AIXazXgekja79iRRm2W8r7BWobrCSYDSVk3gi2M5M+NbSs6mk78rDtfrQ2t9Rq+tIPtP42GgLoM7YOPXvGe7tIXPUQghhBDiWtKqkikwFpZI//EQpVGZFJ9KxaGbH3ptBTlrzwDgZWZWqqJEYyooEPDoMAKfG22274pm3D9iKUFT2V/avxL+yQyzs2pNoTUXoLAN9SB/V6xpOZwl5Yl5xvYdPFqkAl3V/VLFp1Mvm0zlbY1Cd7EohOfNzb/Er618jkIIIYQQ14pW903LaUAQ1hfP0KmcjcrfFo0urxQUCjzMJCOlMVmm2RyPyeYPMzXoKhq8V6pyw3/V5VP/pM0oMnvdysUOK1c7AMou5DRo/LbOobux8IcmrYDy1AKL7QqPJBnbR9ScebwiKi4lxOb25P1T1u/GBF5pq8ZjUtdmC6tSm/kchRBCCCGuEa1uZkqhUOA5tRspn+0ma9UpAl8aS9YqY1Ll1Lcdtu1rFgYwlF9aFmewsNQt56/IWpOh2qgvHvJbFmc5GcrbEWPxnsvQELLXnCZ7zRnaPd48p9e35gIUbqM6gkIBBgM5a07jN3tQjTbFp1Ipv1j23m1spysdIgAF+y+YXtuY+e+sKl1hGTmbIwFwG9cJlaNNs8YGbedzFPX35d9z2B35C538B/PCTatbOpw2YcWB91l16MNq19p7dOPN27ZZfCY1N4oNxz/lVOI28ksycLBxIcS7D2O6zyaiffP83VwpIz+O2IwjxKQfITb9MBeyTqKrMC4Db8k9Z3vOL+eLzQ+bfv/hXUfwcq5ZLfds8i7eX3VTjeuW2gshxLWi1c1MwaUCE5qUfPK2RZPzl/FL6z/PlqpUeUgpQN7m8zXua3NLuPDO5Qs/WOLY07j8q/h0GiWRGTXuFx5NInu1+XLuAL4PDASg5EwaifO31TqWvlxHeVJeg2Ntjax9nHAbZ/xin/zZbtN5SZUMBgMX3t0MgNrXCfcbmj4JKI2u/cuKrqDM9N+I0sEal+tDam2fs/YMhouFILxubr6zpapqDZ+jEG3Vkbh1vLp8NNvP/EB2YSK6inLySzI4Gr+BD/+czu/732nW8Z/96To+3/Qgm04sJib9kCmRaknFZXn8sntuS4chhBBtWqubmQJw6OaHXbgXpVGZxD2/Bn2JBoWVEo8p5g+ytfZxwqlfewoPJZL0v50obKzwmNgVpYM1hQcTSHjvb7Rphdi0c21QouIxJYILb21EX6ol8v6ldHh7Eg49/akoKif3r3MkztuGtb+zxb0sztcF4vuvAaR9vZ+kj7ZSfCwZn3uuwyHCF6WdGl1+GaXnM8nbEU32qlP4PzwE/4eG1DvO1izw+THkb49Bm17ImVuWEDR3HA7dfNGkFJD0f9vJ3xYNQNDzY6qdk1SpLDGXowMXANDuqRG0f3pkvcY/e9cP2HbwwGNyVxx7tcPa1wmFSokmo5D83XGkfr7H9N9G4POjL3vQbebFJX5WHg64jqh9b1VVRwbMpzwpD+dBwQ2aTWzs5yjE1cbDsR1v37ELAKXCfBGdlJzzfL7pITS6Etq5d+H2wW8Q6NWdnKJkVh+ax5G4dfx5+GN8XUIZ0vm2Zo3Xxd6bDt690ehKOZO0o1nHupzl+96goDQTL+cgMgsu1Nq2k98gPp8dD8D5lH18vPb2KxChEEK0fq0ymQLj7FTih1tMX3BdhoWg9nCw2L7Du5M5ffM3VBSW1yhBrlCrCJ1/Exk/HW5QMqX2cCD4tfHEPvcnZfE5nL3zh2r3Hfu0o91j13Nu1s8W+wh+dTwKtYrUxXvJ3XyeXDMzaFXjvdrYh3sRvmg6UXN+p+RsOmfv+L5Gm4DHrsfrll7NE4AB8nfEkF/LckyFjRWBz47C7/6BtXZVnpJPwb54ADyndmu2KojmtPjnKERro1Bgq3astclv+99CoyvB2c6b529ahaOtOwDOdp48Ov47PvrzFk4nbWP5/je5Lmwq1la1/zClIf5zw9eE+PTB06k9AH8d/7xFk6notINsP/MDHo7tGN/rEX7Y8Wyt7ZVKFbZK4+esbobPRwgh2qrWm0xNMyZTpt9f5gwfh66+dF/7b5LmbyN/Zyy6gjLUHg449Q/E/8HBOPYMIOOnww2Ox+euflj7u5Dy+W6KT6Zi0FRgE+yG18098Js9iMJDibU+r1ApCX5lHF4zepH+w0EK9sSjSS1AX6ZF5WSLbQd3nK4LxH18Z5z7BzU4ztbMfXwXemx4iJTFu8nfEYsmswgrZ1scewfge9+AWqvnaaoUXHDs067eY3f86jbyt8eQvzee8gu5aLOK0JfpUDnZYBfqifPgYLzv6GN2T94/Za08CXoDUL8qfnqNzlTWvCHvoVJjPkchrjV5xWkcjVsPwMTec0yJVCWFQsEtg17h9PJt5BWncSx+A/3DbmryOPqHTW3yPhuqQq/ju+3PYMDAHUPeolRb2NIhCSFEm9VqkynbIHcGJb9er2fsQj0JXzTD4v3allWFLZhG2IJptfbvNioct1HhZu+5DO5Qp3gduvgQ8s7ky7a7WtmFeRL6Yf2/VOTvigXAqX8gbiPN/xnUxrG7P47d/QmYM6zez/5TwH+GEvCfofV+rvBwEvpSLSpnW/wfbtwyzoZ+jqKmwtJsnvguggq9jvtGzGd417sttk3JjeLFpcbCH09OWkrPoLGme4lZpzkSt45TidtIzYuiVFOArdoRP7eO9AuZzMiIWdio7esd37srbyQyZQ9DOt3O7NGLzLapWhygtoIAkSl72Xbme86n7iO/JAO1ygY/t3AGhN3EqG73o1Y1fyGVK+34hc0YMP7ww1KSFOzVE2/nDmQUxHE0/q9mSaZak43HPycx+zTdA0fTL3QyO88tbemQhBCizWq1yZQQVeXvNCZTgc+PaeFIGi5/p3GJof+/B5kO4BUtz8nOg4h2IziRsJl9Ub/Xmkzti/rN+IytB93aX9q3l5B1irnLRtRoX1yeR3TaAaLTDrDr3C/898bfcLH3bvL3cDl6fQU/7HyOraeXVLuuqygnNv0wsemH2Xl2Kc9MWYarw9VVUv9ClnF/o5uDH+6O/hbbhfr0JaMgjoSsk1cqtBaRXZjEyoMfYqWy4a5h77Z0OEII0eZJMiUarTwpj70BrwJg39WXnpsevswT9VNRoqHoaDKuI8NwHtB2l0Dm74rDysMBv3/XLGl+pUQ/sYLM5cdabPzWamDH6ZxI2My5lD3kFadZTCj2R/0BwHWhN6JSXvrrU4GCbu1H0qfDRNp7dMXVwRdbtSN5xWkcT9jMX8c+JSnnDEu2PcXjE3+8Iu+pqp93v8zW00tQKa0Y0/0BBoXPwNM5iHJtMScT/ub3/e+QlHOGRRvu48Vpa1Aq67cPUKMrRW+ouHxDC9Qq22qfZ1NKyzMWZfFyrv3vDs+L99Pz49Ab9CgVrbLYbaP9uPN5ynXF3NjvaXxcaq9aKoQQ4vIkmRKtnsremoHxbb98b/fVD7R0CMKCvh0mYm1lj0ZXwv7oFYzrWfMHArHpR0jPjwOMyVdV7T0jeGbK8hrPONl50N4zgj7BE3hl2fUcjd9AWl40vq5hzfNGzIhNP8Lmk18C8MCoRQzqeGkptKOtGyMi7iXcbyCv/zaG6PSDHIxZzYDw2pc8/9O8NbcRmbKnwTH+a9T/GNb5jgY/X5uiMmOVVWc7r1rbOdt5AsbZunJtMXbWTs0ST0s6HLuOo/Eb8HIOYnKfJ1o6HCGEuCpIMiUaLODRYfg/NLj6ReXV+dPcq0XI+1Po8PbE6hflzwwbtQO9g8exP3oF+6L+MJtM7Y82zkp5OLUn3HdAvfr3d+9IoGd34jKOciZp5xVNpjZdTKS6B46ulkhVFeDeiYHhN7Pj7E/si/q93slUa1auNRZ9udx+sKoV/Mq0RVddMlWuLeanXS8AcOfQd7C2smvhiIQQ4uogyZRoMKW1FVjLf0JtidLGCmzkz8ycgR2nsz96BXEZR0nPj622BEpv0LM/eiUAA8KmoVAoajyv11ewN+p3DkSvICHrFIVlOWYPZk3Pt1yevzmcTd4JQJeAIZRpiyy2C3DvAkB85vF6j/HCTasbFpy4Yv448B45Rcn0Ch5Hr+BxLR2OEEJcNeRblRBCAN3bj8bBxo3i8lz2Rf3B1H7PmO5FXtxLBTAofHqNZ0s1hXy85nai0vZfdpxSzZUrQ12mLTLFvWzvGyzb+8ZlnykszW7usK4oG7XxfEKtmcS2Ko2uzPT6cudWtTUJWafYfPJLrK3suHOoFJ0QQoimJMmUEEIAVio114XeyLYz37H/H8nUvqjfAWjn3oX2nhE1nv1518tEpe1HgYLhXe+hX+gUfF1DsbN2RnWxmMO8P28jKm0/FfqGF2qor9Ly+iduOr2m3s+05gIUjrbGs+MKSjNrbVdQmgWAldLalIBdLX7e9RIVeh039nvGYtl8IYQQDSPJlBBCXDSo43S2nfmOlNzzXMg8QZBXD3QVWg7FrAFgoJlZqXJtMXvPG4tPTO77JNMHvGi277KLe3fqS0HNJYX/pLeQoFlXOdfqobGLzcbfFFpzAQpf1zDOJO0gs+BCre2yLt73cQ256ir5ZRYmALDiwHusOPBerW3/+2MfANp7dOPN27Y1d2hCCNHmXV3/YgghRCN09BuEu2MAAPsulkE/lbiF4vJcFCgYEH5zjWdS86JNsznXhd5ott8Kva7Be6XUFwsjaHWlFtvklaSbve5g44KDjXFmJiO/9mTiahXk2QOA3OJUcotSLbaLST8MQKBn9ysSlxBCiKuDzEwJIcRFCoWCAWHTWH9sEfujV3DroFdNSVWo73Vml0jpKi4ti7O01O1I3Ho0tSRDtak85Dc9P9Zim9OJ2yze69rueg7GrOJgzGpu7PdUg2K4nNZcgKJn0BgUKDBg4EDMKsb1fKhGmwuZJ8goMJa9730VFmd4atIvtS7fPBa3gRUH3wfgyUlLcXXwxVpla7G9EEKIS2RmSgghqqg8QyqnKJmTiVs4GrfBeN3CEjlPp/am18cvbKpxv6gsl+V1KPxgSQfv3oCxiEByzrka92PTj7A/eoXF52/o8W8AErNPsergh7WOpa0oJ6swscGxtkauDr707jABgPVH/2c6d6qSwWBg+b63TG17BY+/4jE2twD3TgR5drf4y92pnamtv5uxrZ9beAtGLIQQbYckU0IIUUWQZ3f83ToC8N32ZyjXFaNSWtE/bKrZ9q4OvoT59gfgz8PzWXd0IRn5ceSXZHAoZg1v/TGBvJI0PKokXfVxXeiNWFvZY8DAwvV3czJhC0VluWQWJLDx+GI+/HOGaWmiOeF+Axjb3ZhQrTj4PvPXzuT4hU3kFqdSXJ5PZsEFjsVv5KedL/L09704GL2qQXG2ZjMGvIy1lT15Jem8t3IqpxK3UViazYWskyz66z5OJW4B4JYBr1Q7b6pSZkECsz71ZNannqw48H6DYsjIjyM67ZDpV3aVpLXq9ei0Q2YrPu48t9QUw9nkXQ2KQQghRNOTZX5CCPEPA8On88eBd01feLu2G46znafF9vde/yHvrJxMqaawRglylVLNA6P+x7Yz31f7Al1XznaezBzyJku2P016fhzz1txa7X6oTz+m9H2SBevutNjHHUPeRKVS89exTzl+YSPHL2y02NZKZV3vGFs7f/eOPDjmcxZvfoiknDN89GfNw4un9H2KIZ1va7YYVh2ax+7IX8zee+uP6rNhz01dSZeAoc0WixBCiKYjyZQQQvxDZTJV9fe1ae8ZwaszNrHy4IecSdpBSXk+znaehPsNYHyv/9DBuzfbznzf4HhGRNyLu2MA6499QnzmcSr0WrydgxnU8RbG9XyIqLQDtT6vVKq4ffDrDOl0G1tPL+Fc8i5yilLQVpRhZ+2Mj0sI4b796RMykY5+AxscZ2vWN2Qir9/yN+uPfcLppO3kl2Rgb+1CqE8fxnSfTUT74RafzS1OMb0O9e13JcKtGUORMQZbtQMB7p1bJAYhhBA1KQwGQ7N1rrRVpxnKdT7NNoAQokUobKzS9WVa35aOwxKFQmFY8p+slg5DNKMVB95n1aEP8XBqz7y7jzbrWCsPfsDKgx/Q0W8gL05b06xjWfLeyqmcS9nNlL5PM33ACy0SQ6Wzybt4f9VNAHx415F6nV0161NPDAbD5ev9CyFEG9GsM1Ot+cuWEEIIURdnknYAMH3ASy0yfrm2hOj0QzjYuDGh1yMtEoMQQgjzZJmfEEKINiu7MJFZnxr3szXHQbPl2mJi0g/TPXA0nfwHNWnfdRWVuh9dRTnTrnsOexvnFomh6myUEEKISySZEkIIISywUTvw9UOWD/u9EroFjkSWrQohROvUrHumhBCiJcieqaufrkJT4yBapUKFtZVdC0V0ddPrK9BU1Dx42sbKAYWi7lugZM+UEOJqIzNTQggh2hwrlfVVWca9tVIqVdgqHVs6DCGEaHXk0F4hhBBCCCGEaABJpoQQQgghhBCiASSZEkIIIYQQQogGkGRKCCGEEEIIIRpAkikhhBBCCCGEaABJpoQQQgghhBCiASSZEkIIIYQQQogGkGRKCCGEEEIIIRpAkikhhBBCCCGEaACFwWBo6RiEEKJJWVvZpWkrynxaOg4hRHVWKht0FeUtHYZoI2xtbdNLS0t9WzoOIWojyZQQQjQzhUIxFFhkMBh6mbl3C7AImGgwGA5f6dgEKBQKg/xbKETro1AoMBgMipaOQ4jayDI/IYRofncAy/95UaFQ3AksBG6QREoIIYRoe2RmSgghmpFCobACUoBBBoMhpsr1WcDbGBOp0y0UnkBmpoRorWRmSrQFVi0dgBBCXOVGA3H/SKT+DbwCjDIYDJEtFpkQQgghGkWSKSGEaF53AEsrf6NQKOYA/wVGGgyG6BaLSgghhBCNJsv8hBCimSgUClsgFYgwGAwpCoXiKWAOxhmp+BYNTpjIMj8hWidZ5ifaApmZEkKI5jMROHoxkXoBuB8YbjAYEls4LiGEEEI0AZmZEkKIZqJQKJYDGwF/4HZgtMFgSLl4zx2YAtwM9AVCDQaDHMDTAmRmSojWSWamRFsgpdGFEKIZKBQKZ+AGoAswAxhx8fp/FArFZiAOmIqxZHp3SaSEEEKItkeW+QkhRPOYCmQB4zAmTCuBTsBa4BPgL4PBUNJi0QkhhBCi0WRmSgghmsdcIBjwBHyBVwFfg8Fwt8FgWCGJlGgqr732GgqFotqvXr16mW176NAhXn31VUaOHImPjw9qtRpXV1f69+/PG2+8QVZWVq1jjRgxosZYs2bNavo31cYEBwfX+FzM/YqObtoCnj/99FO1/uPj4y22rUt8CoUCnU7XpDEKcbWTmSkhhGgePwL7gM0Gg6GipYMR4tFHH2XRokU1rufn53Pw4EEOHjzIJ598wm+//cawYcNaIEJRH7m5uTz11FMtHYYQ1zyZmRJCiGZgMBheNxgMf0kiJa6UwMBACgsLKSwsZO/evTXuFxYWAnDdddexcOFCjh8/TlZWFrGxsSxYsAAnJycyMjKYPHkyFy5cMDvG+vXrTWMMHTq0Wd9PW/TCCy+YPh9zv0JDQ5tsrOeff56MjAw6dOhQr+c+//zzWmO0spKfswtRH/L/GCGEEOIqoFAocHR0tHi/X79+/Otf/6ox6+Th4cHjjz/OwIEDGTJkCAUFBbzzzjssXry4Rh92dnam1yqVqumCv0pYW1vX+mfQVPbu3cuXX35JYGAgzzzzDI888kidn7WxsbkiMQpxrZBkSrQZaqVtms5Q7tPScQghqrNS2KRr9WW+LR2HqN2cOXNqvT9gwAAmT57MqlWr+Ouvv65QVPWXk5PDxo0buf3221s6lBah0+l46KGHMBgMzJ8/n4KCgpYOSYhrmiRTos3QGcp9Xh+U3NJhCCH+4dW9AfJDjkb4+OOPefrppwFYt24dEyZMqNEmPz+fHj16kJCQwMCBA9m1a1ezzAx16dKFVatWkZqa2uR9N0ZFRQV//fUXS5YsYfXq1fj6+l6zydSCBQs4ceIE48eP5+abb2bJkiUtHZIQ1zTZMyWEEEK0oCeffJJRo0YBcP/995utqPfII4+QkJCAo6MjP/74Y7MtscvIyADA2dm5Wfqvr7Nnz/Lcc8/Rvn17Jk2axPLlyykvL8fJyamlQ6uVRqNpln4TEhJ47bXXsLGx4X//+1+j+mquGIW41kgyJYQQQrQghULBkiVLcHV1JS0tjX//+9/V7v/666/89NNPAMyfP79JixhUpdFoWLduHQADBw5sljHqIj8/n8WLFzNw4EC6du3KBx98QGpqKmq1mhtvvJHff/+dw4cPm322vLycoqKiBv9qbILx3Xff4e3tjY2NDWq1mo4dOzJnzpwmK4n+6KOPUlxczLPPPktYWFiD+nj33Xdxc3PDxsYGGxsbunfvzvPPP09KSkqTxCjEtUaSKSGEEKKFtW/fns8++wyAFStW8O233wKQnJzMww8/DMCNN97IAw880GwxfPzxx6SlpQHUSOiam16vZ+PGjcycORNfX18eeugh9u/fD0Dv3r1ZsGABycnJrFq1iptvvhlra2uz/Tz44IM4OTk1+Nc777zTqPcRHx9PZmYmYNzbFBUVxSeffEJERARffvllo/peuXIlq1evpkOHDrzwwgsN7uf8+fPk5eUBxgT61KlTvP/++3Tp0oU1a9Y0KkYhrkWSTAkhhBCtwO23387MmTMBePzxx4mJiWHWrFnk5ubi7e3d6C/jtTl48CCvvvoqAGPGjGHKlCnNNlZVUVFRvPTSSwQFBTFu3DiWLl1KWVkZvr6+PP3005w4cYIjR47w+OOP4+XldUViaoju3bszb948Dh48SHp6OuXl5URHRzNv3jzc3NzQaDQ8+OCDrFq1qkH9FxcX89hjjwGwcOHCalUV62rQoEF8/vnnHDt2jOzsbMrKyjh9+jSvvvoqtra2FBQUMGPGDA4ePNigGIW4VkkBCiGEEKKV+PTTT9m1axcJCQkMGjTINMvx1Vdf4e3t3SxjpqWlMX36dDQaDZ6enqZZseYUGxvLPffcw+7du03XbG1tmTp1Kvfeey833HBDg/aFLVmypEUKMvz55581roWGhvLUU08xdepU05/lk08+yaRJk+p9ltPcuXNJTExkypQpTJ48uUEx7tmzp8a1rl278tprrzFhwgRGjhxJaWkpTz31FDt37mzQGEJci2RmSgghhGglXFxc+O6771AqlaZEavbs2c02U1RYWMjEiRNJTEzEzs6O1atX065du2YZq6qEhARTIqVQKHjqqadIS0vjl19+YcKECVfVGVahoaG88cYbAMTFxZmWL9bV8ePHTbNRCxcubI4QGTBggGnma9euXSQnS+VcIepKkikhhBCiFenUqVO1anV33nlns4xTXl7OtGnTOHr0KFZWVixbtoxBgwY1y1j/5ObmhouLCwAGg4GPP/6YcePG8emnn5KTk9Pgflu6AIUlVZPh48eP1+vZJ554Ap1OxwsvvEBwcHATR3ZJY2IU4lomyZQQQgjRitx///3k5+ebfv/QQw9RWlrapGPo9XruvPNO/v77bwC+/PLLBi8fa4iePXuSlpbGzz//zNixY1Eqlezfv59HHnkEPz8/br75ZlauXIlWq61Xvy1dgMKSqks0K4s/1FVcXBxgXOqnUChq/LrvvvtMbTt06IBCoaBXr15XNEYhrmWSTAkhhBCtxCeffMKGDRsAeOedd7C2tubcuXM8++yzTTrOgw8+yO+//w7ABx98wKxZs5q0/7qwtbXljjvuYOPGjVy4cIG33nqL8PBwNBoNK1asYNq0afj5+TFnzhwOHDhwxeNrSpVVEgFcXV1bLpBatIUYhWiNpACFEG1cXP4elpy5BYAneu/DzbZ9k/a/IvoJjmUuJ9h5EPdF/NakfV8Jp7P/5FD6T6QVn0ZTUYKLjR8d3cYw2P8hnK19G91/ma6APSmLOZuznrzyRFQKazztw+jldSt9vO9AqZCfWYm6iYyMNCVNDz74IC+88AIKhYIXXniBTz75hBtvvJGxY8c2epwXXniBr776CoD//ve//Pe//210n43Vrl07XnrpJV566SV27drFkiVLWLZsGdnZ2XzyySd88skndOrUibvvvpu7776bwMBAs/20VAGKy6laxa++s0br1q2rdfnh6tWrTZUY165di7+/f4Oq/TUmRiGuZZJMCSGuSnqDnhXRj3Mi649q17PL4tib+iXHM39nZucltHfq2+Axsktj+f7sHeSVJ1W5WkJi4SESCw9xOvtPZnZeglpp2+AxxLVBp9Nx1113UVJSQnh4OPPmzQPg2WefZe3atezatYv77ruPkydP4ubm1uBxFixYwHvvvQfArFmz+OCDD5ok/qY0dOhQhg4dysKFC/n999/59ttv2bZtG5GRkbz88su88sor3Hzzzfz2W+v44U5ycjIBAQEW7587d86U7ISEhDBgwIB69d+1a9da7x87dqxaW3P7qi4X486dO1m0aBEAw4cPx9/fv14xCnEtkx+ZCiGuStuTFpgSqb7edzKn5zb+2+84t3X8EmdrP0p0OfwS+S+KtFkN6l+nL+fnyPvIK0/CVuXCTaEf80zfIzzeezfDAh5FgYLY/J2sjX2xKd+WuEq98cYbHDp0CJVKxQ8//ICDgwMASqWSH374AScnJ5KTk/nPf/7T4DGWLl3KU089BRjPkpo/f36txRgMBkOTvLeGsre35+6772bLli3Exsby2muvERISgsFg4NChQy0aW1Vz5sxh6NChLFq0iEOHDpGRkUFubi4nT57krbfeYsCAAeTk5KBUKlm4cKHZSoWvvfaaaQ9UfHx8k8c4ZcoUJkyYwNdff82JEyfIysoiOzubQ4cO8eyzzzJmzBjKy8uxs7Pj448/bvLxhbiaycyUEG1cB5fBvD6o+crYTgtbwLSwBc3Wf3Mo1KSzO+UTAPp4z+TG0Es/fe/qMREf+858dmIsRdpMdiUvYnzwa/Ue42D6D2SVRqNAwczO3xLkfOmnzWMCn0ettGNL4gccy1zGAL/78XPo1uj3Ja5O+/btMxU+ePHFF2vMXAQHB7Nw4ULuu+8+fvnlF2688UbuuOOOeo+zePFiU4K0efPmy85wxcXFNWv1uPoIDg7m1VdfZe7cuWzfvp2NGze2dEgmBoOB3bt3Vzsz658cHR1ZvHgxkyZNuoKRXaLX69mwYYNpP5453t7e/PTTT/Tp0+cKRiZE2yczU0KIq86xzOVo9WWoFGpGt6+5cd/DLoQ+3jMBOJrxKxX6+lUMAziU9j0AHd3GVEukKg3xfxh7KzcMGDic/lO9+xfXhuLiYu6++24qKiro168fc+fONdtu1qxZ3HzzzQA88sgj1+w5QAqFghEjRjRb1b2GeOGFF3jllVcYM2YMISEhODs7o1ar8fT0ZNiwYbzxxhtERUUxc+bMFovxo48+4tlnn+X6668nKCgIBwcHrK2t8fHxYcyYMXz88cecP3+eMWPGtFiMQrRVMjMlRCtxPvdv9qZ+SWrRSSoMGtxtg+npNYMBfv/iRObvrIwxLs/55yxUbQUocssSWXB0IACzui6nvVNf9qZ+xcmsFeSUxaNSqPFz6M4Q/4cIdxtlNq62WIDifK6x3HOQ8wAcrb3MtonwmML+tG8oqyjgQuE+QlyG1bn/nLJ4sspiTP2YY6W0prP7eI5kLCUydxOTebee70JcCxwcHIiKiqpT28rqew21bdu2Rj0vzBswYEC990H902uvvcZrr73WoGdnzZp12WqMY8aMkURJiGYiyZQQrcCWhA/Znryg2rW0kjOkXXiD87mb6eY5tdFjaCqK+Ob0dJKLjpquaSklrmA3cQW7mRLyPv187mr0OC3NYDCQVnwKgHaOlper+Dv2QIkKPRWkFp+uVzKVWnzS9Lq2Mdo59uFIxlIKNKkUa3NwULvXeQwhhBBCtH6STAnRws5mrzclUgGOvRkT+By+9hGU6HI5nvkbu5I/Ia+88Ut61se/Sokul/HBr9HZbRw2KkeSi46xNu5lcssvsCH+NTq7j8dR7dnosaoqryhu1PPWSnsUCkWd2xdoUtHoSwBwsw2y2E6ttMXR2psCTSpZpTH1iqmyvQIlrjbtLLarOkuYXRojyZRoVhcuXDD9f6Vnz57Vqrw1lREjRrB9+/Ym71cIIdoqSaaEaEEGg4GNCW8B4G3XiVldl2GtsgfAXu3O6MDnsFd7sCH+1UaPla9J4f6IP6qVAg93G8Ud1l/z6YkxaPWlnMleQ3/fWY0eq6p3DnRs1PP1PTurVJdreu2g9qi1rYPakwJNarVn6qLkYns7KxdUSrXl/q0uJaYl9RxDCCGEEK2fJFNCtKCkosPklMUDMKr9f02JVFUDfO9nX+pX5JUnNmqsbh43mj1TycehC772EaSVnCa56HijxmgNKmelAKwUNrW2rTz/SVPP2TNtRamxf2Xt/VtVOV+qvmMIUVcvvvgizzzzTLVr5spvN4X169dTUVFR7ZpabfkHCkIIcbWTZEqIFpRYeBgApcKKMLeRZtsoFUrCXUdxMP27Ro0V5jrC4j0Puw6klZymuIFnLtWmOcu2CyHA2toaa2vrKzKWnZ3dFRlHCCHaCimNLkQLqtwL5WLtb5olMcfDLqTRYzlZe1u8p1YavyBp9aWNHqelWSsvze7pDOW1ttXqy4zPqBzqNYZaZfy8dPra+9dd7L8hYwghhBCi9ZOZKSFakPbikjS1subyvqqslY3/Iq6geZb9XM6VLkBhZ3XpINJibXatbStn4qo+Uxf2F9uX6vKpMOhQKcz/VVqsy6rxjBBCCCGuHpJMCdGCKpMobZV9PuZo9G13v82VLkDhbO2HtdIejb6E3LILFtvp9OUUaTIA8LQLrVdMle0N6MkrS8TDroPZdrlll/a5edRzDCGEEEK0frLMT4gW5GoTABgr7WmrLAn7p+zS2CsVUpunUCjwdegGQFKVM7X+KbnoOHqMG+n9HCLqNYafQ/cq/VgeI6noCGBM8KQsuhBCCHH1kZkpIVpQZXU9vUFHdN42uriPr9FGb9ATlbflSofWZFqiAEVHt9EkFB7gQsE+irRZZs/OOp29BgBblTNBTgPr1b+7bTCetqFklcVwOnsNPbxurtFGp9cQmbMRgE5uYxvwLoRoWdu2bWPkSGNhnLi4OIKDg5u0/1mzZvHdd98xfPhwtm3b1qR9XwnLly/niy++4NixYxQVFdG+fXsmT57MM888g7+/f4P7rfxcLufLL7/kgQceaPA4QoimITNTQrSgdo59cbcJBmBr4kemkttVHUz7rtFl0a81vbxuQa20pcKgZUvCBzXu55TFcyTjZwB6e99W61lRlvTzvQeAyNxNJBQcrHF/T+piinXZKFDQ1+fOevcvhGid9Ho9d999N7feeiubN28mKyuLsrIyoqKimD9/Pj179mTfvn0tHaYQ4gqRZEqIFqRQKBgT9AIA6SVnWXLmFmLzd1GizSG7NI4tCR+y4cJruNkEtXCkbYuTtQ9D/B8B4HDGT/wZ+xyZpdEUabM4m72eJadvQasvxVHtxdCAOWb7+Pb0DF7dG8D8IwPM3r/O52487cIwoOfnc7M4lrGMQk0GOWUX+DvhfVMS18vrVvwuLjsUQrR9b775Jj/++CMAs2fP5syZM6Snp/P777/Trl07srKyuOmmm8jIyGjUOEOHDqWwsNDir1mzZjXBuxFCNJYs8xOihUV4TGZYwBx2Ji8iqego3525rdr9YOdBdPOcyprY51G2UEW+tmh4uyfIKYvjRNYfHEr/kUPpP1a7b2/lxu2dvja7BLAurJQ2zOz0Ld+fvYO88iRWxDxZo02IyzAmhbzToP6FaGkjRozAYDA0W/9LlixhyZIlzdZ/c0hNTeX9998H4IEHHuCLL74w3bv55pvp3r07PXv2JD09nffee4+PP/64wWOpVCocHR0bHbMQonnJzJQQrcCYwBeY2XkJIS5DsVU5o1ba4WPfhbGBL3NPl6Xo9ToArFXyD2tdKRVKpof/j1vCPyPEZSj2Vm5YKWxxtw1moN8DPNxzs2nPWkN52IXwcI9NDA94Ai+7jqiVdtiqXGjv1I8pIe9zd5efaz0/TAjRtnz33XeUlpaiVqt56623atwPDw837WP65ptv0Gq1VzpEIcQVpmjOnzoJ0ZQUCoWhJYoZtAbr419lX+pXeNt14pFebbcYhbg6vbo3AIPBUPfDwFoZhUJhuJr/LVy3bh3z58/n8OHDaDQawsLCuOeee3jsscf48ccfue+++wBqzELVVoAiPj6eDh2MRwJs3bqVQYMGsWDBAn766SdiYmJQq9X07duXZ555hgkTJpiNqy0WoBg2bBi7du1i9OjRbN682WybXbt2MWzYMAA2b97M6NGj6zVGW/xcmotCoWjTf7eIa4PMTAnRyhkMBqJy/waQvTdCiHqZO3cukyZNYvPmzeTm5lJcXMzx48d5+umnueGGG9BoNI0eo7CwkOuvv57nn3+ekydPUlJSQn5+Plu2bGHixInVlsK1ZQaDgaNHjUchDBxouQJov379UKmMS7Ir2zdGRUUFFRUVje5HCNE8JJkSohUo0eZavLcv7Wuyy+IA4/4qIYSoixUrVvDmm28C0L9/f1PlucjISF566SV27Nhh2v/TGI8//jjnzp1j/vz5xMbGkpWVxbp16wgJCQHgySefbHQxBnOKiooa9au+s5HJyckUFxsPUK98b+bY2tri5+cHQGRkZIPf38mTJwkNDUWtVqNWq/H39+e222675merhGhtpACFEK3AV6duJNTlerp6TMTLriNKhYqcsniOZv7K4fSfAAhw6EW4W/2Wiwghrk0Gg4Fnn30WgIiICLZs2YKDgwMAHh4evPXWW3h5efHEE080eqzExER27txZbbZmwoQJrFy5kh49elBSUsJvv/3Gf/7zn0aPVZWTk1Ojnq/v2VnZ2dmm197e3rW29fb2Jikpqdoz9ZWTk0NOTo7p96mpqSxbtoxly5Yxe/ZsPvvsM9MMmBCi5UgyJUQroNWXcCB9CQfSl5i972HbgVs7LUapkH84hRCXt2/fPqKjowFjKe/KRKqqRx99lAULFhAfH9+osW6//Xazy966d+9Or169OHbsGAcP1jyLra2pnJUC4+xTbezs7ADj7Fl9+fr68vzzzzN+/HhCQkLw8fEhJyeHrVu38tZbb3HmzBm+/PJLnJycmDdvXr37F0I0LVnmJ0QrMCXkffp634mPfRcc1J6oFGrsrFwJdOrPuKC5PNxjE6427Vo6TCFEG7F3714ArKysLBaAUCqVTJw4sdFjjRs3zuK98PBwANLT0xs9zj8ZDIZG/arPrNSV9N577/Huu+8yfPhw2rdvj7W1Nb6+vtxxxx0cPHiQIUOGAPB///d/nD9/voWjFULIzJQQrUBHtzF0dBvT0mEIIa4SFy5cAKB9+/a1zqJ07Nix0WNV7g8yx97eHoDS0tJGj9PSqs7ulZWV1dq28v029TlR9vb2fPnll3Tt2pWKigqWL1/OSy+91KRjCCHqR5IpIYQQ4ipTuSTN3PK+qpriy35d9u00R+n5hiyhq8rBwQGFou5Vtz08PEyvL1dQo/J+1WeaSpcuXQgLCyM6Oprjx483ef9CiPqRZEoIIYS4ylQmUVX3+ZjT2ISkJV3pAhQBAQE4ODhQXFxMbGysxXbl5eWkpqYC0KlTp0bFaIm3tzfR0dHk5eU1S/9CiLqTPVNCCCHEVSYoKAgwVtqrbUma7LmpO4VCQe/evQHYv3+/xXYHDx40nQtV2b6ppaWlAeDq6tos/Qsh6k5mpoQQTe7b0zOIL9hLL69bmBa2oKXDabXKK4o5k72G5KJjpBafolCTTonOWErZydqXQKf+9PO5i/ZOfVs4UtHWDBo0CACdTseGDRu46aabarTR6/WsW7fuCkfWdJpj6eDlTJo0iV27drF9+3YyMjLMlkhfvnw5AC4uLlx//fVNHsPJkydNM2O9evVq8v6FEPUjM1NCCNFCcsriWBnzFAfTvyep6Aj5mmS0+jK0+jJyyuI5lrmMr09N5a/4N1s6VNHGDBw4kNDQUADmzp1rtgDEp59+2uiy6Neae++9Fzs7O7RaLa+88kqN+zExMXz11VcA3H///ajV6nr1n5aWZprVMqeoqIjZs2cDxkqNt956a736F0I0PUmmhBCihShR0c6xDyPaPc1dnX/gPz3/5tl+J3ms105u7fg5/g49MGBgT+rn7Ev9pqXDFW2IQqHgvffeA4wzGSNHjmTLli1kZ2cTHR3N3LlzefLJJwkJCWnhSNsWPz8/nnvuOQC++OILHnroIc6dO0dGRgYrVqxg5MiRlJSU4OPjw/PPP2+2jxEjRqBQKMzu1/rll1/o1KkTr7/+Otu2bSM5OZm8vDxiYmL4+uuv6d27t2mJ4dNPP01YWFizvVchRN3IMj8hhGghPg5dmN39zxrXHdTueNiF0MltHF+dupHU4pPsSfmcgX73t0CUoq2aMWMGL7zwAu+++y779+9n9OjR1e4PHz6cO+64g4ceeqhOFfmE0SuvvEJ0dDQ//vgjixcvZvHixdXue3h4sHLlSrNLAOsiJiaG1157zeJ9hULBk08+yTvvvNOg/oUQTUtmpoQQopWyUlrTw3MaAPmaZEq0OS0ckWhr3nnnHf78809Gjx6Ni4sL9vb2dO/enQ8++IBNmzah1WqBxlfGu5YolUp++OEHfv31V0aPHo2Hhwe2traEhYXxxBNPcOLECQYOHNigvqdNm8ZHH33EtGnT6Ny5Mx4eHlhZWeHi4kLPnj2ZM2cOx44dY968eSiV8hVOiNZA0RIbOIVoCIVCYXh9UHJLh1FNbP4uDqYZ97sUa7OxUlpjb+WBm217Ql2G093zJlxs/Ks9k1+eQmTuRs7n/k1a8RlKdNlYKW1wsW5HmOtwBvnNxtnG/CGYRzN+ZWXMUwC8PiiZvPJkdiYvJCp3K8XabJxtfOnmMZWhAY9gozKWRi7R5rAndTFnsteRX56CrZUzHd3GMDrwORzVnjXGyC1LZMFR4xeBWV2X4+/Yg13Jn3Amey35mmTUSnuCnAcwLOBRAhx7mo2zLgUoyiuKOJC2hHM5f5FdGotGX4Kj2pMg54EM9n8QP4duFj/3M9nrOJrxCynFJynV5aJW2uGg9sDdtgNhriPp4TkNe7Wbxefbkl3Jn7Ip4W0AXuwfiY2qaQ8BbQqv7g3AYDDU/cCeVkahUBiu1X8Ln3zySRYsWEBERASnTp1q6XCEqEahULTpv1vEtUGW+QnRQNuTFrAl8cNq1yoqNJRXFJFbfoHY/F2olXYM8LuvWptPj4+hrCL/H89pySg9R0bpOY5m/MrMLksIdLqu1vFTik7yw9k7KNHlmq7llMWzI/n/SCg8wN1dfqZAk8oPZ2aSUx5valOkLeNIxs9cKNjL7O5rsbNysThGqS6PL09OJrM0ynRNqy/jbM56InM2clPYfHp6Ta81TvOxn+Dnc7Mo1KZXu56vSeFE1h+czFrJ+ODXGOj3rxrProp5hiMZS6tdq6jQUlZRQHZZHFF5W3CzbU8nt7H1jqu1qTDoOJtjrLbmaRvaKhMp0XYZDAbWrl0LQJ8+fVo4GiGEaJskmRKiATJLo9maOA+AMJcRDAl4CA/bUKxV9hRrs0ktPsW5nA2olDUrOXnbdyLcdSTtnfriZO2DvZU7JbockouOsSv5UzJKI1kW+RCP9t5hml0y59fzs3FU+3BT2HzaOfahvKKIfalfsT/tG+IL9nI4/SeOZS5Hj44Z4Z/SwXkwFQYtxzKXsTXxI7LL4tiRtJBxwTUrUlX668IbFGkyGRP4It08pqBW2ZNYcJCNF94ipzyelTFP4WPfBV+HrnX+7HLLEvjuzG2UVRTgY9+FYQGPEujUD7XSnuyyWPakfM6ZnHWsj5+Lu20QHd3GmJ6NzttuSqS6e06jv8+9uNkGolJYU6TNJLnoKKez16BowApmbUUpevT1fq6SldIGlaJp/kot1uaQUXKWncmLSCo6igIFowKfbZK+xbUlJycHd3d3s/cWLlxIVJTxByW33HLLlQxLCCGuGpJMCdEAMXnbMaDHUe3FzM5LqiVNdlaueNqF0t1zqtln/9VtRY1r9mp3PO3C6OI+ic9PjCW7LI6TWSvo53OXxRiUqPhXtxXYWjkD4KD2YGKHN0kvOUt8wV42XHgNG5UjD/fYVG2p4fB2T5BdFsfxzN84nvVbrclUXnkit3X8kq4eE03XunhMIMCpN58dH0uJLodNCe9wd5cfLX9Y/7A27iVTIjW725+oVXZVPoe+3NbpS1ZEP8GxzOVsvPAW4a6jUSiMqzyi87YC4O/Qgxnhi/7xGbrhbd+R3t631TmWqn48dzfxBXsb9CzATaEfN3hsgL8T3mdH8sIa152tfbkhaC4RHpMb3Le4dg0aNIixY8cyffp0unbtipWVFdHR0Xz77bd88cUXAFx33XVMnDjxMj0JIYQwR3YvCtEAeoMOAHsrD7OzTw1lrbKjs/t4AOLyd9fa9vp2j5kSqaq6XvzSrTfoGOj7QI09WwDdPKYAUKzNIq/c8j60QKfrqiVSlZytfRns/xBgTCwLNRm1xlopu9S4DA/gxpAPqiVSVY1qb5yFySyNIq3ktOm63mA8f8XRumFVstoatdKW63xnEeIytKVDEW1UcXExn3zyCaNGjcLX1xdPT08GDhzI4sWLMRgMhIeHs3z5cqnmJ4QQDSQzU0I0gK9DBAAZpefYkvAhg/z/Xeveo3+Kz9/LkYylJBUdoVCTjkZfUqNNdllsrX2Eulxv9rq7TaDptaUv4W42QabXRZoMXG0CzLbr5DbO4vhd3MexOeEdDOhJKjpCl4tJYG3iCvYAYKdyxcu+I+UVxWbb2Vq54GDlQbEum5SiE6ZiFJWfe3TuVvanLaGP120WE7L6ui/itybpp6GGt3uSoQFzACjT5ZNSfII9KZ/zd8J7HEhdwszOS/B37N6iMYq2Z/HixaxevZp9+/aRlpZGbm4uTk5OdO3alWnTpvHwww9jZ9c0/x8SQohrkSRTQjRAiMtQwl1HEZW3he3JC9iV8gntHPsS5DyADi6DCXIeaHH/zLq4uexP+/qyY5Trimq9b2l2xkppW6WN12Xb6PRlFsfwtAu1eM/dNgQFSgzoyStPqjXWStmlMQCUVuTxzoFOdXqmWJdtet3Tczr7U78mreQM6+JeYtOFtwh06k+Qc39CXIbSzrGvaUlgW2OltMYKawBsVA642PjTye0Glp1/kLM56/jp3D081nunFKEQ9TJp0iQmTZrU0mEIIcRVS5IpIRro9k5fsTvlMw6l/0CBJo0Lhfu4ULiPHcn/h4Pak+sDHmOA7/3Vvtwfz/zNlEiFuAzlOp978LbvjL2VGyqlDQA7khayK2URenS1jq9UXH5ZjoLLtzFguSS0dS2zPkqFErXSFo2+BI2FGaZ/KqsorFO7qir0GtNrlVLNfRG/sz3p/ziW+Sslulxi8rcTk7+dLYkf4mYTyKj2z9LDa1q9x2lNBSgqKRVKxge/ytmcdRRpMziVtZq+PjObdAwhhBBCNJwkU0I0kJXShuHtnuD6gMfJKI0ksfAQcfm7OZ/7N8XaLNbHz6VIm8mYwOdNzxxKNxZqCHIawD1dfjE7i6LTl16x93A5mgrLsegNerQXZ7Wsa6k6WJW10picediG8FjvnQ2KydbKmXHBrzA26CXSik+RUHiI2PydROdtI7c8gd+j56DRl9DP58569dvSBSgscbVph4Pak2JtFqnFcg6QEEII0ZpIAQohGkmhUOBj35l+PndxS8fPeKrvQYKcjIfe7k35slpCkl5yFoAuHhMtLkfLKD3f/EHXUdbFZXnm5JTFYrg4k+Nq065O/bnZGvdqFWhSqNBrGxWbUqHE37EHA/3uZ2bnb3miz1487cIA2JG0oFF9tzaVBU8UtM0ljEIIIcTVSmamhGhidlYuDPKfzYXIfegMZeSXJ+FlHw6A7uKSNcPFqnT/VKjJ4ELBvisW6+VE5m5kaMDDZu+dzfkLAAVK2jnW7cDPEJdhgPHg3/O5m+niMaFpAsVYYbCfz11siH+NfE0K2orSehWnaOkCFJZklJynVJcHgKtt+5YNRogWNmLECLZv3869997LkiVLWjqcVkuv17Nz507Wrl3L7t27OXfuHAUFBTg7OxMREcG0adOYPXs2jo6yB1OIxpKZKSEaILs0Dr3B8v6anLJ402s7K1fT68oZnPO5f9d4Rm/Qsy7uJSoMjZuxaUoJhQc4m72+xvUCTRp7Uj4HINR1OE51LFXubd/RVIVwffxc8stTam2fVRpd6+//KafsAmAsKV61yEZrdbn3o9NrWB9vPAdMiYrObjdcibCEEG3c1KlTGTFiBB9++CF79uwhJycHnU5HTk4OO3fu5KmnnqJXr16cPXu2pUMVos2TmSkhGmBH8v+RUHCQ7l7T6OA8GA+7ENRKW4o0mZzL3ci2xI8BCHIaWK2iXlePSexM/h9xBbtZEf0kg/3+jZO1D5ml0exM/h9ReVvwsgsnszSqpd5aNa427fk9eg4jyp4mwmMK1ip7EgoPsjH+TUp0OSgVVowNfKFefU4OeZcvTkwiX5PC5yfGMcT/YcLdRuFs7UuFXkuBJpWkoiOcyV5HSvFxXuwfaXr2z9jnKdXl0d1zGkHO/XGzCUKlsKJAk8rJrNUcTPsOMJ611Raq+v0Z+zw6fTndPW+ivVM/XKz9USmtKdZmkVh4iD0pX5BReg6AAX7/wsMupIUjFkK0BYWFhSgUCkaOHMnMmTMZPHgwPj4+ZGZmsnTpUt59911iYmIYN24cp0+fxsnJqaVDFqLNkmRKiAbKKY9ne9J8tjPf7H03myCmhVW/NyxgDudzN5NecpZjmcs4lrms2v3+PrOwV7uzLenjZou7PsYFvcLfiR+wKeFtNiW8Xe2eEhU3hX5sOvuprtxtg5kVsYxfIh8gtzzBbN+V7FSuNa6ll5wlPcHyT1P9HXowPujVesXUkpKKjpBUdMTifQVKBvo9wA1Br1zBqIQQbdnYsWNZuHAhPXr0qHbd3d2d1157jW7dunHLLbeQmJjIokWLeOGF+v1QTAhxiSRTQjTA2MCX6OAyhJi8HaSXnKVQk05ZRQE2Kke87DrS2X0c1/ncg7XKvtpzNipH7o/4g+1J/8fZnHUUaFKxUTnh69CVfj53EeExha2J81roXdVkZ+XG7G5r2Jm8iLM5a8kvT8VaZU+gU3+ub/cYAY49G9Svr0MEc3pt42jmMs5lbyCt5DSlujyUChVOal98HLoS6jKMCI/J1Z6bFraAqLytxObtJLM0iiJNBuX6IuxULvg4dCXCYwq9vW5FpVQ3xdtvdlNC3iMqbxtx+bvJLoulWJNFub4IG5Uj7jbBBDr3p7f3bfjYd27pUIUQbchLL71U6/0ZM2bQs2dPjh8/zl9//SXJlBCNoDAYLJ8xI0RrolAoDK8PSm7pMK56uWWJLDhqrEY4q+tyOrgMbuGIRGv36t4ADAZD619XaYFCoTC0tn8Lt2zZwmeffca+ffvIyMjAxsYGLy8vOnTowA033MDMmTNp1656Fc2kpCRWr17NmjVrOH78OJmZmdja2hIUFMS4ceN48sknCQgIMDvekiVLuO+++wAwGAwkJCTwzjvvsH79ejIyMmjXrh233347zz33nKloQXZ2NvPmzeP3338nISEBV1dXJk+ezNtvv423d819lPHx8XTo0AGArVu30rdvX95//31+++03EhIScHBwYNiwYbz44ov069fPbJx1KUBRWFjIJ598wqpVq4iMjKS4uBgfHx+GDx/OU089Re/evS1+7n/88QfffPMNhw8fJjs7G3t7e7y9vQkPD2f8+PHceeeduLu7W3y+Lbnjjjv45Zdf6NixI5GRkZd/oAUoFIo2/XeLuDbIzJQQQgjRirz11lu88kr1ZZ0ajYbCwkJiY2P5+++/sbe3Z86cOdXadO/enby8vGrXtFotp06d4tSpU3zzzTesWbOGwYNr/wHJkSNHuOGGG8jOzjZdi46O5q233mLnzp1s3LiRpKQkbrjhBmJiLh2fkJaWxldffcX27ds5cOAArq6uFsfIyclhwIAB1QoglJaWsmLFClavXs2SJUu46667ao3TnMOHDzNlyhRSU1OrXU9MTOTHH3/k559/Zv78+Tz22GM1nn3ggQf4+uuvq13Lz88nPz+fqKgo1q1bR4cOHZg8eXKNZ9uijIwMAJydnVs4EiHaNqnmJ4QQQrQS586d49VXjXv+xo0bx+bNm0lMTCQnJ4fIyEiWLl3KbbfdhrW1dY1nu3Xrxttvv82WLVs4d+4cWVlZnDt3ju+//56IiAhyc3O55ZZbKCoqqjWG6dOn4+fnx59//klGRgYxMTE8+uijAGzfvp0vvviC2267DZ1Ox9KlS0lLSyMxMZE333wThUJBVFQUb79tfh9kpaeffpq4uDjee+89YmNjSU9P548//iA0NJSKigruu+8+Tpw4Ua/PLi4ujtGjR5Oamkr37t1ZunQpCQkJZGdns3fvXqZPn45er+fxxx9n7dq11Z7duHGjKZGaOXMmu3btIiUlhezsbE6fPs23337LxIkTUSrr/7WptLSUoqKiBv/S6XT1HvNysrKy2LVrFwADBw5s8v6FuJbIMj/RZsgyvytDlvmJ+pJlfk1n4cKFPP744/j4+JCYmIha3TT7/0pKSujVqxdRUVEsXryYf//739XuV13mFxoayuHDh3FxcanWZuTIkWzbtg0rKyucnZ05fvx4jaWG9957L99//z3e3t6kp6dXu1d1mR/A77//zs0331ytTUpKCj179iQrK4vx48ezfn31oxlqW+Y3ceJE1q9fT48ePdi3bx92djXPmZs1axbfffcdXbp04fTp06aqn0899RTz58+nb9++HDp0yNLH2CCVMTfUt99+y6xZs5ouIOChhx5i8eLFKBQKjh07VqNQRWshy/xEWyAzU0IIIUQrUTkL4eXl1WSJFIC9vT033XQTYNyPVZuXX365RiIFxqIFlTE+/vjjNRIpgFtvvRUwLiFLSEiwOMaQIUNqJFIA/v7+PPPMM4BxtigtLa3WWCtFR0ebEq8vvvjCbCIFxiWUAGfPnuXYsWOm65Wfu5+fX53Ga8tWrVrFF198AcD999/fahMpIdoKSaaEEEKIVqJXr14AnDp1irlz59bYA3U527dv55577qFjx444OjqiUChMvz788EMAzp8/X2sfY8eONXs9JOTSOWejR4++bJvaEqGpU6davFeZ9On1evbt21dbqCZbt24FwM3Nja5du1pcMufq6oqXl/Hsv8OHD5uer/zc169fzyeffEJpaWmdxq2Lbdu2YTAYGvyrKWelzpw5w7333ovBYCA8PJz5880f7SGEqDtJpoQQ1bjZtuf1Qcm8PihZlvgJcYWNGjWKCRMmAPDmm2/i7e3N8OHDeeWVV9iyZUut+2cef/xxRowYwQ8//EBUVBTFxcVm2xUUFNQag6+vr9nrVWd76tKmtoSkU6dOFu+Fh4eb9iZduHCh1lgrVVajy83NxdnZGScnJ4u/MjMzAUz/C3D33XfTs2dPKioqmDNnDp6enowbN4633nqLvXv30lqWgTZGSkoKEyZMID8/H09PT9auXSuH9QrRBCSZEkIIIVqRFStW8OabbxIQEIBWq2XHjh289dZbjB49moCAABYuXFjjy/0PP/zAwoULAeOs0W+//UZkZCRZWVkUFhZSWFjI888/D3DZggYqleqyMdalTW0JiIODg8V7SqXSlJRdrlhGpfz8/Dq1q6q8vNz0Wq1Ws337dp555hk8PDwoKSlh48aNvPLKKwwePJjQ0FB+/vnneo8BraMARW5uLuPGjSMhIQFHR0fWrVtHeHh4o/sVQkhpdCGEEKJVsbGx4eWXX+all17i9OnT7Nmzhy1btrB27VoyMjJ4/PHHSU9Pr1Yxb/HixQAMGzaMTZs2mQorVNWUS9cay9KsGRiX91XGWnmm1eVUJmeNOTPJxcWFDz/8kPfff5+jR4+yZ88eNm/ezIYNG4iLi+POO++kuLiY2bNn16vfCRMmtGgBipKSEiZPnsypU6ewtrbmjz/+4Lrrrmtwf0KI6mRmSgghhGiFFAoF3bp149///je//PILiYmJXH/99QB8/PHHlJSUmNpWlhGfPn262UQK4PTp080fdB3VlvBERUWh1+sBCAoKqlN/lXu1EhMT0Wq1jYpNqVTSt29fHn30UVatWkVcXBydO3cGjEsv2xKtVsuMGTPYs2cPSqWS77//3uKeOCFEw8jMlBDNYEX0ExzLXE6w8yDui/itpcNpE7YmzmNb0sfVrvnad+XhnpssPpNVGs2elC+Iyd9OkSYTWysXAhx7McD3PkJdr2/ukAGIydvB/rRvSS46RpkuH0drb0Jdrmew/4N42oU227gZJeeJyttCctFRMkuiKdZlUabLR620x9MuhHDX0Vzney8Oanezz1ctgV+VlMNvvVxdXXnyySfZsWMHZWVlXLhwgS5dugCXlqxVVFSYfTYtLa1RsyNNbfXq1fz3v/81e2/lypWAMamp6xlIY8aMAYyzb2vWrGHatGlNEicYKww++OCDPPnkkyQmJlJaWmqxWqA527Zta7JY6qOyeEVllcOFCxdy2223tUgsQlzNZGZKCNEmncv5i89PjOdwxk/klSehM5RTpM0gMncj35+9g78T3m/2GDYnvMv3Z+8gMncjRdoMdIZy8soTOZzxE5+fGMe5nI3NNvbh9B/ZeOFNTmevIaP0HMXaLCoMWsoq8kkqOsrWpI9YdGw48fl7my0G0fSio6NNszLmxMTEmF67u19KlIODgwFqHEYLxmVzc/6/vTsPj6o6Hzj+vbMlM9k3EhJI2CHsm2wKuKBSKwpVXHCptnWre9tftbUt2FZr64atrVq14r6g4oLIoqwiIEvYQ4CQkH2dyT773N8fQ4aEzAQyJISE9/M8PE9y77n3vDPGmXnnnPOe++477RGb9vTdd9+xZMmSFseLiop45plnALjssssCFro40dChQ30jLg8++CAFBQWttj9xZOxkUwMbn3ej0UhoaOgpxdTZHn74Yd86r/nz53Pvvfd2ckRCdE8yMiWEOKtEGVK4d7S3zLEmwPc95Q2H+PjQvTg9VnoYh3B5nz/RM2w41fYi1hUs5IBlOesL/0mcsR+jE+Z2SJw7yz5iQ+GLAAyJmcn0Xg8RFZJMcf1eVuT+mTLrAT4+9EvuGvE1Cab2X+gdootkWNyV9ImcQqIpnUhDEgZtOLWOYg5YVrKp6FUaXGbey7qd+0atITKk+f450SG9+P0Eb4nsansh/951UbvHKNrur3/9K9999x033XQTF110EYMGDcJoNFJSUsIXX3zBggULAJg2bRqJiYm+66699lqefPJJVq9eze23386vfvUrkpOTyczM5Mknn+Trr78mPT2dzMzMTnpkzfXp04ebbrqJBQsWcN111xEWFsbGjRv5zW9+Q0VFBTqdjqeeeqpN93zppZc477zzyM/PZ8yYMfzf//0fV1xxBSkpKTgcDgoKCtiyZQuffPIJW7dubVbV8K677sJisTBv3jymTp1Kv3790Ov1FBQU8MEHH/Cf//wHgLlz5wacRnk2+fvf/84LL7wAeDdS/s1vftNqMY9TXZsmhGhJkikhxFlFURRCtIErfQF8k/cUTo+VcH0Ctw9bjOnYVLYwfRw3DH6NtzPnkV29nm+OPsWwuFnoNe37TbLTbeWbPO8Hvf5R07lh8Gu+D1gDoqeTPGwx/951MXXOcr7N/zs3DH6tXfsHuLj3b/weD9PHkhQ2jIHRF/Hqniuxu2vZVvZui/ZNn2e95tSnLImOl52dzZ///Gf+/Oc/+z3fr18/Fi1a1OzYo48+ypdffsmePXtYtGhRi/P33nsv8fHxPP744x0Udds888wz/OEPf+CRRx7hkUceaXZOq9XyxhtvMGrUqDbds3///qxZs4Y5c+aQk5Pj996NYmJiWhzbvXu3b+2ZP+PGjeO5554LeP5s8tJLL/l+fvPNN3nzzTdbbd8dSr8L0Vlkmp8QokupdZSSZVkBwPnJ9/gSqUaKojAj9ffets4Ssjpgqt0By0pqnaUAzEj9XYtvqk36WM5Pvsfb1rycWkdZu8dwMinho+lh8i6aL67fc8b7F8H5+9//zptvvsnNN9/MiBEjiI+PR6fTERsbywUXXMAzzzzD7t276du3b7PrIiIi2LBhA7/5zW/o27cver2e+Ph4LrnkEj766CNefPHFTnpE/sXFxbFlyxYeffRRBg4cSGhoKPHx8cyePZvNmzdz8803B3XfUaNGkZmZycsvv8zll19OYmIier0eo9FI//79+clPfsJLL73EoUOHml335ptv8vLLL3PttdcydOhQYmJi0Ol0JCQkMGPGDP773/+yadMm4uLi2uPhCyG6EUW+jRBdhaIo6uOTC0/art5p5pntY/CoLq7q9zTjEucFbFtuPcyLO6cDcNOQtxgUc4nvXEn9fg6YV5BdvY4K62Fs7lpCNOHEmwYwNPYKxifeikHr/xv91gpQvLHvWnJrNjE6YS5zBiz0e31O9fcs2u+dnvbQmM3EhPb22+5ozRa2lb5DXu0P1DnK0WlCiDf2Z1jcVUxI+ik6TUjAx362aSxAER3Si4fHbgnYbnvp+3xxxDvK8quxW4kKSfbb7oUd52O25zIy/idcM/Bf7RrrJ4fuZ3fFp8SG9OHBsRv9tqm2F/HcDm/54av6PcO4xBvbNYZT8eLOCym3HmJo7BVcP/jVgO2aFqMIpgDF/E0pqKp69s99CkBRFFXeCztebm6uLwlcs2YNF154YecGJM56iqJ06dcWcW6QaX6i2wnTx9I/ahqHqlazp2JJq8nUnnLvAmiTLpb+0dN9x0vq9/HS7statLe6q8iv3UZ+7TZ2li/m1vT3CTcktP+DOAmP6uarnMfYVvp2s+Mut52CugwK6jLYWf4Rt6S/S4QhMcBduqbGUZZIQ1LARAqgV8RYzPZcSurbvxx0cf1eXx+BRIUkE2lIosZRQsmx9mdSSf0+KqzeRfPJ4W2bLiWEEEKIUyPJlOiWRsbP4VDVanJrNlPrKA2YUOyp/AyAYXFXolWa/u+g0D9qOumxM0k0pRNhSDy2uL+EQ1Wr+b7oFUobMvniyG+ZN+SNjn9AJ1ieu4BtpW+jUXRMTLqdkfE/ITqkNw5PA4er1vBt3t8pbcjkw6w7+NnwJWgUbZvu73Rb8RC4otjJ6DQhJzyf7afS5k0QYkJa338mOsQ7mme25eJRPWiU9pnV7FE9mG25x2JIPUkMqdQ4SqiwZbfarr24PU5qnaUcrlrL2vznUfEQoU/ivMSfnpH+hRBCiHONJFOiWxoSOxO9xojTY2VPxedMSb6zRZvCup2+D8Uj4pvvSZIUNpRbh77X4hrv4v6hDIm5nP/snkGWZSUV1uwO3U/oRAW1GWwp+R8Ac/ovZGTC8dhNxDA+8WZSIybw3z0/Ir9uO/srlzI8/uo29fHOgVvIrQm+pPbs/s8xpkfH7GfS4LQA3mITrQnTxwPgUu043PWE6iLapX+Huw636mjWx8lisLos7dJ3IM9uH0eNo6TF8T6Rk5kz4IV2e+xCCCGEaE4KUIhuyaA1MTjGO01vb8XnftvsqfgM8JaITo04r033TzANpKdpOAA51f7XzHSUxkRqQPRFzRKppnqYBvkSxN3HHmd34fQ0AKA7SYW+phX8HJ76duvfcax/bwytr0lrjMHhbr/+T1WiKZ2JST8jyhB4KqQQQgghTo+MTIlua2T8bPZWfk5h/U4qrTnEGY9Xv/KoHvZWfAnA8Lir/e4b4lHd7KlYwt6KLyhp2EeD04JLtbdoV2k70nEPwo+c6u8B6Bs5BXsrH9J7mAYDUFwfuNRvICcWzRBnt/tHf4eKB4/qotZRyuGqNWwo/BcfHryD9Ngf8ZMB/wpYLEUIIYQQwZNkSnRbA6IvwqiLxuqqYk/lZ1zY62HfuaM1m6l1eqdFjYif3eJam6uWdw/cQl7t1pP2Y3cH3gixvdnd9b64V+U9waq8J056Tb3T3NFhnVF6jQkAl8fWajtnk/MGTev7VrWF4Vj/3hhaJtf+YjCcZN+s046pSaJk1EXRwzSIIbEz+e+eK8g0f83Ko3/hyn5PdmgMQpxMnz59ZD8jIUS3I8mU6La0Gj3D4q5kW+k77K34vFky1TjFr4dxCElhQ1tcuzx3AXm1W1FQGJd4E0Njf0ycsR8h2gg0xworvJN5E3m1W/GorjPyeADsrpo2X9O4vqctzuYCFCa9d7PNemdlq+3qnRUAaBVDuyYzBm04WsWAW3X4+jhZDEZdyw1CO1psaBoTk37O2oJn2VH2Ppel/QGD1nTyC4UQQghxyiSZEt3aiPg5bCt9h3LrIYrr99IzbDhuj5P95q+85xNmt7jG4W5gd8WnAExNuZ9LUh/xe++OXAcTKEHTN/kwfO3Af/sdVWsPZ3MBirjQ/hyp/g6L/Wir7ars+cfa9223Sn4AGkVDbGgfyq0HsdjzThKD93x86JkrUNJUr/DRgDehLrceJOXY70IIIYRoH1KAQnRraRETfQvwG0ejDlevxeqqQkFhRNzsFtdUWLN9oznD4q70e1+36gp6rVRj4QRnK9PU6pxlfo8bdVEYddEAmG2tJxPdVc+wEQDUOEqosRcHbFdQuwOApLBhHRCDt/hIQd2OgG2q7UW+CntJx9qfac2Tctn3UgghhGhvMjIlujVFURgefzUbi15ib8XnXJr6GHuOVffrFTGOmNDeLa5xNyky4VH9T3XLMq9oNRlqTbjeu8mv2ZYTsE121fqA5/pFXcC+yqXsr1zK9F4PBhXDyZzNBSgGxVyMgoKKyj7zUib3vKNFm+L6vZjtuQAMjrm0A2KYwe6KTzHbcn0jnifaV7kUAAWFQTGXtHsMp+Jo7Q++GBr33RJd22233cabb77J9OnTWbt2bWeH0yUsWLCAxx9/vNmxUaNGsXPnzoDXZGVl8eyzz7Jy5UpKSkqIiYlhwoQJ3H///cyYMaND483OzuaHH35gy5YtbNmyhYyMDOx27/vSmVhz5na72bNnj6//H374gczMTDwezyn93eXm5tK3b98Wx9esWcOFF17YMUEL0YlkZEp0e41T4aodRRyuWkuWecWx4/7Lijf90Hmw6psW5xucFlYdDX4xf0r4KABK6vdR1pDV4nxBbQZ7K78IeP2kpF94r2/Yz9r851vty+WxU2UvCDrWs1GEIZHBMZcDsLHwJd++U41UVeWbo3/zttUnMTj2snaPYUjMZUTovRtBf5P3VIsPOA1OCxuLXvK2jZ1JhKFHu/bvcDe0OioH3r+vrSVvApAaMZEwfWy7xiBEd/X5558zduxYXn31VY4ePYrdbqekpIQvvviCSy+9lD/84Q8d2v+AAQOYN28eL7zwAps3b/YlUmfKhg0bGDNmDHfffTdvvPEG+/btw+MJfg2tEN2dJFOi2+sZNpwE40AAluY8isPTgEbRMTxult/2EYZEekeMB2BDwb/4rvA/mG251DnK2V+5jNf3Xk2ts4TokF5BxTMsbhZ6jREVlfezfsbhqrU0OC1YbPlsKn6NtzPnEdnK3kCpkecxMennAKwpeIZ3D/yUg5ZvqXGUYHVVY7HlcdDyDcty/sRzOyb4Rki6kxmpj6LXGKl1lrJo/1yyq9ZT7zRTXL+XDw/eyeHqtd52aY8222+qkcWWz/xNKczflMKa/Gfb3L9ea2RG6qMAHK5aw4cH76S4fi/1TjPZVetZtH8udc4y9Bojl/T2v+Yuo+xDXwyN5e5PVb2zkhcyLuDTQw+wr/JLKq1HsLqqqHdWUlCbwTd5T/H63tk4PA3olBAu7/PHNj9GIbqb1NRUamtrqa2tZdMm/2tCMzMzmTdvHg0NDQwfPpwVK1ZQVlbG9u3bmT17NgBPPPEEb731VofHm5SUxKxZs7jkks4Z2QZvYnfTTTcxePDgU74mLS3N9zzv27evA6MT4uwg0/zEOWFE/GxW5z/tG6XpFzWVMH1cwPZX9v0b/9v3E+zu2hYlyLWKntn9n2d72btBjfqE6eOY2WcBXx55BLMtl7czb2p2vlf4WKb1eoD3DtwW8B4z+8xHq+jZVPwKBy3fcNDScgStabzdTYJpINcMfJFPDt1HaUMmb2Xe2KLNtJQHGJ0wt8NiGN3jOips2WwofJFM8zIyzcuanddrjFw78N8kmAZ2SP8u1cauik/YVfFJwDbh+h78ZMALUnhCCLzTvsPDw1tt8/vf/56GhgYSExNZu3YtcXHe94mEhAQ+/fRTLr/8clatWsXvfvc7rrvuOkJDW988PBgfffQREyZMIC0tDYCFCxfy7bfftns/gQwaNIjly5czYcIEYmK8lUhnz55NVlbLmRT+NH2eTSapICq6P0mmxDlhRPwcVuc/7ft95Emq4CWFDeXOEV+xtuB5jlRvwOaqIUwfR2rEBKYk30VK+Ci2l70bdDzjE28mypDMxqKXKa7fg1t1EBPSh5EJP2FyzzvIr93W6vUaRcvlff7I6IRr2Vr6Nrk131PjKMbpsRGqjSA2tC+pEecxJHYmaZETgo7zbJYeO5O7Ry5nY9ErHKleT52jnFBdJCnhY5iYdDv9o6cFvLbGcXyKXK/wsUHHMCP1d/SNPJ8tJW9QWJeBzVVDuCGBflHTOD/5buKNgav4NcZg0IT5Nlg+VZEhPbkl/T1yqjeSV/sDNY5i6p0VqCqY9NH0MKUzKPoSRiVcQ6guMujHJ8S5pLi4mM8/966p/e1vf+tLpBopisJTTz3FqlWrKCoq4osvvuC6665r9zjmzu24L4FORXJyMsnJgWdHCCGak2RKnBNiQ9N4fHJhm66JN/bn2oEvBjzfWpGGOQMWMmfAwlbvPzDmYgbGXOz3XN+oKacUb2JY+jm9GWu8cQBX93/65A1PcKT6OwBSIyYwMOai04qhf/S0VhO3k8UwqecvWh0l9Uer6BgQPZ0B0dPb3K8IXkVFBT179sTlcvHqq6/yi1/8ImDbAwcOkJ6eDsBXX33FFVdc4Tu3e/duPvvsM1auXMmBAweorq4mIiKC9PR0rrnmGu6+++6gvtG/8MILWbduHT/96U9ZtGiR3zZr167loou8f/M5OTn06dPHb7sNGzbw3//+lw0bNlBSUkJoaChDhgzh+uuv55e//CUhISFtju9st2zZMt/6x0BJ0tixY+nfvz/Z2dl8+eWXHZJMCSG6FlkzJYQ45xyp3gDgW/d0pjncVgpqd2DURXN+8t2dEoNou/j4eC67zFvQ5L333mu1beP5ptcA7Nq1i1GjRjF//nw2btxIZWUlLpcLi8XC999/z69//WsmTZpEaWlpxz2QVrjdbu655x6mTZvGO++84yvAUF1dzZYtW/jVr37FeeedR3Fx6wVQuqIdO7xbHaSkpNCrV+A1sZMmTQIgIyPjjMQlhDi7yciUEOKsUmUvYP6mFACSTEO5Z9Sqdr2/w91AYV0GA6IvIi1yYrve+1Tl1f6AS7VzYfKvO20ansWWz8KMSZ3Sd1c2b948li1bxrp16yguLqZnz55+273//vuAd8qWTnf8rVZRFC677DLmzJnDiBEjSE5OJiIigqKiIpYtW8azzz7Lnj17uPPOO31Tzs6khx9+mJdffhmdTsf999/PTTfdRN++famrq2P58uU89thj7Nmzh2uuuYYNGzag1WrbdH+r1Yrb7Q46vtDQ0GbPZ3tqXBPUr1+/Vts1lv0+fPgwHo8HjUa+lxbiXCbJlBDinGLQmvjTpNxOjWFA9PQ2TzsVZ4fZs2djMploaGjggw8+4OGHH27RZuvWrRw+fBjwJl9NjRw5khUrVrS4Jj4+npEjR3L11VczcuRIvvjiCw4ePMigQYM65oH48cMPP/Cvf/0LgDfffLNZ7LGxsdx5551ccMEFjB8/nk2bNvHxxx9z/fXXt6mPH/3oR6xbty7oGN944w1uu+22oK9vTWVlJQA9erS+lUHjebvdTl1dHZGRsi5RiHOZJFNCiLPC1JT7mXLClDeNzETuMNEhvfj9hIMtjvsrJS+OCwsL46qrruKDDz7g/fff95tMNY5KpaWlcf7557fp/unp6YwZM4atW7eyevXqM5pMNSZSM2fObJEENho6dCjz5s3j9ddf57333mtzMnU2q6+vBzhphT6j0ej7WZIpIYQkU0KIs4JOY0CHobPDOGcoikKINqyzw+iS5s2bxwcffOAbgRowYIDvnMfj4cMPPwTghhtuQFGUFte73W7ee+89PvzwQ3bu3ElFRYXfjVkPHmyZ7Hak1atXA3DRRRdRV1cXsN3w4cMB2L59e5v7WLt2bVCxCSHE2UqSKSGEEKINZs6cSWxsLGazmffff58//vH4psjr16+nqKgIaDnFD6CmpoYrrriCjRs3nrSfmpqa9gv6JOrq6nxxP/LIIzzyiP/NppsqLy/v6LDOqLAw75cLNput1XZWq9X388n2rRJCdH8yh0YIIYRoA71e79sLqHFKX6PGKn7Dhw9n5MiRLa59+OGH2bhxI4qicNddd7Fy5Upyc3OxWCzU1tZSW1vrmxrocrk6+JEcV11d3eZrHA5Hm6+xWq3U1dUF/a8jn5PGfaXKyspabdd43mAwSDIlhJBkSgghhGirxlGnzMxMX4lsp9PJJ5980ux8U/X19bzzzjsA/P73v+fll1/m0ksvJS0tjejoaMLDwwkPD291il1r/E0pPFGgZKRxVAa8CaGqqqf0r61+9KMfEREREfS/xuevIwwe7N08+8iRI622y8nJAWDgwIFSyU8IIcmUEEII0VZTp06ld+/ewPHRqRUrVmA2m1EUhRtvvLHFNVlZWb7RnMaRrRO5XK6g10o1Fk5oOg3tRIH2h4qOjiY2NhY4eTLRXY0dOxaAwsJCCgsDV9vcvHkzAGPGjDkjcQkhzm6STAkhhBBtpCgKN9xwAwAffPABqqr6kqrJkyfTp0+fFtc0LTIRaK+lzz//vNVkqDVJSUkAHDp0KGCbVasC79t2ySWXALB48eKg+j8Va9euPeVRL3//OqosOsAVV1zhG90L9BxkZGSQnZ0NwKxZszosFiFE1yHJlBBCCBGExql8+fn5rFixwrfJbqCy4k0TrK+++qrFebPZfEqFHwI577zzANi5cyf79u1rcf6HH37ggw8+CHj9gw8+CMCuXbv485//3Gpfdrudo0ePBh3r2ahnz55cffXVAPzjH//AbDY3O6+qKr/73e8ASE5O5qqrrjrjMQohzj6STAkhhBBBGD16NOnp6QDcfffd1NfXo9PpuO666/y279mzJ1OmTAHgiSee4B//+AfZ2dmUlpby6aefMmXKFIqKikhLSwsqnrlz52IymVBVldmzZ/umHebm5vLCCy9w2WWX+aYm+nP++efzwAMPADB//nxmzZrFsmXLKCoqoqqqipycHL766isefPBBUlNTO3QEq7M8+eSTmEwmiouLufDCC1m1ahUVFRXs3LmTa6+91rfh8t/+9je/+1Hl5uaiKAqKorBgwYKgYsjOzmbz5s2+f02T1qbHN2/e7Lfi46JFi3wxBFuKPiMjo1k/FosF8FaYPDEGIc51UhpdCCGECNK8efP44x//6PvAO2PGDBISEgK2f+mll5g6dSo1NTUtSpDr9XoWLVrEf//736BGfRISEnj++ee56667OHz4MDNnzmx2ftKkSTz22GOtTk977rnnMBgMPPvssyxdupSlS5cGbGswdL994dLT03n33Xe56aab2LNnD5dddlmLNo899hi33nprh8Xwl7/8hTfffNPvucmTJzf7fc2aNVx44YXtHsOcOXP8/g1mZGS0iCGYQiRCdCcyMiWEEEIE6cQpfYGm+DUaOXIkW7duZd68efTo0QODwUCvXr244YYb+P777096/cnceeedLFu2jIsvvpioqCiMRiPDhw/nqaeeYt26dSct5a3Vann66afZtWsXv/zlL0lPTyciIgKdTkdcXByTJk3i17/+NRs2bPCNYnU3s2fPZseOHfziF78gLS2NkJAQEhMTueqqq1i1ahV//etfA15bUFDg+3nSpElnItyAMYSHhzNs2LBOiUGIc4ki3yiIrkKvCS1xqfbEzo5DCNGcTgkpdXpsSZ0dR7AURVHlvbD7WrBgAY8//jhpaWnk5uZ2aF+PP/44CxYs4IILLmDDhg0d2lcgF110EWvXruUPf/gDf/nLXzolhka5ubn07dsXCG4UTVEUVFU9ec1/ITqRTPMTXUZX/rAmhBCi+/vmm28A79qrztDQ0MCmTZuIjY3lN7/5TafEIMS5Rqb5CSGEEKLbO3r0qK8ww+jRo9v9/vX19WzZsoWZM2cyderUdr//qfjuu++w2+389re/JSoqqlNiaFqEo3FUSojuTEamhBBCCCFOU1hYmG9T5s5y2WWXSUEIIc4wWTMlhBDinCZrpro3h8PRIsnRarUYjcZOiqh7U1WV+vr6FseNRiNarbZN95I1U6IrkGRKCCHEOU2SKSHOTpJMia5A1kwJIYQQQgghRBAkmRJCCCGEEEKIIEgyJYQQQgghhBBBkGRKCCGEEEIIIYIgyZQQQgghhBBCBEGSKSGEEEIIIYQIgiRTQgghhBBCCBEESaaEEEIIIYQQIgiSTAkhhBBCCCFEEHSdHYAQQgjRmUJDQ0sVRUns7DiEEM2FhoaWdnYMQpyMoqpqZ8cghBBCiGMURdkIPKGq6rITjicC3wBfAH9Q5Q1cnCJFUcYBXwH3q6q6+IRzCpAPXKaq6v7OiE+Irkym+QkhhBBnCUVR0oDBeJOmpseTgbXAYiSREm2kqup24HLgn4qizDvhnAp8CNzYGbEJ0dVJMiWEEEKcPW4APlFV1dF4QFGU3ngTqTdVVf2zJFIiGKqq7gJmAE8rivLTE06/D9x4bJRKCNEGkkwJIYQQZ48b8X6wBUBRlD7AOuBlVVWf6qygRPegquo+4GLgr4qi3NHk1HZABcZ3SmBCdGGSTAkhhBBnAUVR0oEEYMOx3/vjHZF6TlXV5zoxNNGNqKqaBVwE/EFRlHuPHVM5NjrVmbEJ0RVJMiWEEEKcHW4EPlRV1a0oymC8idSTqqq+2Llhie5GVdXDwHTg14qi/OrY4feB6xVF0XZeZEJ0PZJMCSGEEJ3s2FqVG4H3FUUZBqwG/qiq6n9PaKdXFEXfGTGKrkvxMjU9pqpqLt6E6h5FUX6nqmomUA5M7YQQheiyJJkSQgghOt84QAEceCv5/Z+qqosAFEUxKYoyW1GUt4BS4JedFqXoqnoAhYqirFcU5aFjVSNRVTUfb0J1q6Io84H3kKl+QrSJ7DMlhBBCdDJFUZ4FIoFZwP3AKuDHwE/wVmDbBnwKfKaqamFnxSm6LkVRQoFL8P5NXQXk4f2bWgJUAt8Ca/AmU8lNK0oKIQKTZEoIIYToRIqiaIASQId33Up/YAreNVOfAl+qqlrZaQGKbkdRFB1wATAHb3JVDywHfgSEA3eqqvpV50UoRNchyZQQQgjRiRRFmQt8BNQBS/EmUMtVVa3t1MDEOeHYer3xeBOra4GBwA5VVcd1amBCdBGSTAkhhBCd6NimvDOBt1RVtXd2POLcpijKeKCvqqqLOzsWIboCSaaEEEIIIYQQIghSzU8IIYQQQgghgqDr7ACEEKK9aUL1JardldjZcQghmlNCdKUemzOps+MIll4TWuJS7fLaIsRZRqeElDo9tk55bZFpfkKIbkdRFHVy4eOdHYYQ4gSbUuajqqrS2XEES1EU9fHJUpleiLPN/E0pnfbaItP8hBBCCCGEECIIkkwJIYQQQgghRBAkmRJCCCGEEEKIIEgyJYQQQgghhBBBkGRKCCGEEEIIIYIgyZQQQgghhBBCBEGSKSGEEEIIIYQIgiRTQgghhBBCCBEESaaEEEIIIYQQIgi6zg5ACCHEmVP9fQ775y4CYMzmhwjtHdOu9z/80BLKF+8kcnIfhn18e7ve+0yo/HIfpe9uo35fCe4GByE9o4iZMYjku6dgSIo87fu7amwUvfI95q8zsedXoRi0mAbEk3DdaHrcOBZFI99xiq4pp/p7Fu2fC8BDYzYTE9q7Xe+/5PBD7CxfTJ/Iydw+7ON2vfeZsK/yS7aVvktJ/T4c7gaiQnoyKGYGU5LvJtKQdNr3t7lq+L7oFTLNX1Nlz0erGIg3DWB0wnWM7XEjGkVeWzqKJFNCCCHOearHw+EHl1Dx6e5mx205lRS/uonyT3YxZNE8IsYF/wHReqSSzBvfwl5QdfxgA9Ruy6d2Wz6VX+5jyKJ5aEL1QfchhDi7eFQPSw4/yO6KT5sdr7TlsKn4VXaVf8K8IYvoHTEu6D4qrUd4K/NGquwFTY42kF+7jfzabeyr/JJ5Qxah14QG3YcITNJUIYQQ57yChet8iVSPm8Yxau19jN/1fwx69XoMPSNxmRvI+vkHOCvqgrq/x+4i6/b3sBdUoY0Kpf9zsxm34zeM2fggKfdPBUWhesMRjvz+q/Z8WEKITrauYKEvkRrX4ybuG7WW/xu/i+sHvUqkoScNLjMfZP2cOmdFUPd3eey8l3U7VfYCQrVRzO7/HL8Zt4MHx2xkasr9KCgcqd7AV0d+354PSzShqKra2TEIIUS7UhRFnVz4eGeHIboIR2ktGVNewGNz0mPeWPo/fXWz89Yjley+9CU8Nic975hMnwUz29xH8WubyJ2/HBSFYZ/cTuTEtGbnC15YR/4/VoOiMHL5XYQN73laj+lstSllPqqqKp0dR7AURVEfn1zY2WGILqLWUcoLGVNwemyM7TGPq/s/3ex8pfUIL+2+FKfHxuSedzCzz4I297Gp+DWW585HQeH2YZ+QFjmx2fl1BS+wOv8fKCjcNXI5PcOGn85DOmvN35TSaa8tMjIlhBDinFa+eCcemxNFr6X3by9pcd7YL44e88YCUPZhBh6nu819lLy1DYCYGYNaJFIAyfecjy7GBKpK6bvb23x/IcTZZ2f5YpweG1pFzyW9f9vifJyxH2N7zAMgo+xD3B5nm/vYVvIWAINiZrRIpADOT74Hky4GFZXtpe+2+f7i5GTNlBBCdEGWbw9S/Oom6vYUozrchPaJJeHaUfT8+UTKP9lN9q8+A+DEEbrWClDY8i1kTFoIwNDFtxExrjfFr22iYskebLlmFL2WsBE9Sb77fGIuHug3rq5YgMLy7UEAIiemYUgI99smbtYwSv63BXeNjdrNR4ma2u+U72/LNWPLrvDdxx+NQUfszCGUvb8Dy6os+NuVbXwUQrSPg5Zv2VT8KsV1e3CrDmJD+zAq4Vom9vw5u8s/4bPsXwFw4ghdawUoLLZ8FmZMAuC2oYvpHTGOTcWvsadiCWZbLlpFT8+wEZyffDcDYy72G1dXLEBx0PItAGmREwk3JPhtMyxuFltK/ofNXcPR2s30i5p6yvc323KpsGX77uOPTmNgSOxMdpS9T5ZlFVfytzY+CnEykkwJIUQXk/f0agoXrmt2rGF/CUf/XILlm4PEX3360zjcdQ72XfM/6jKafGCyOqnZmEPNxhz6/X0WiTePP+1+OpuqqtTvLQEgfGyvgO3CRyaDVgNuD/X7ituUTNXvKT5+n9b6GNuLsvd34CiuwWmuRx8bdsp9CNEeVuc9zbrChc2OlTTsp+Tonzlo+Ybh8Vf7v7ANHO46/rfvGgrrMnzHnFjJqdlITs1GZvX7O+MTbz7tfjqbqqqU1O8FoFf42IDtksNHokGLBzfF9fvalEwV1+/x/dxaH73Cx7Kj7H1qHMXUO82E6WNPuQ9xcpJMCSFEF1L5daYvkQofk0LqIzMwDUvCZWmg/ONdFP77O+yFVafdT+78r3FZGuizYCYxlw9BGx5C3c5Ccv7wFfajFnIXLCd25hD08f5HcoLlrref1vUakwFFOfVp847iGjwNDgBC0wKXideE6jH0CMdRXIM1u20LxX3tNQohvaIDtms6SmjNrpRkSpxRmZVf+xKplPAxzEh9hCTTMBpcFnaVf8x3hf+myn7668W+zp1Pg8vCzD4LGBJzOSHacArrdvJVzh+w2I+yPHcBQ2JnEq6PP+2+mrK760/reoPG1KbXlhpHMQ5PAwAxoS2n9jbSa0IJN/SgxlFMhTW7TTE1tlfQEB0S+IuapqOEldZsSabamSRTQgjRRaiqSt5fVwJgHNyDoR/dhtZkAEAfayL1kUvQx5m8hQ5Ok6OommGf/qxZKfCYiwdieP1Gds/4Dx6rk8ql+0m6bcJp99XUD4OePK3r27p3lsti9f2sj2s9edHHh+Eorml2zan14f1ApYsyotFrA7bTxR/vv/EaIc4EVVVZmfdXAHoYB3Pb0I8waE0AmPSxXJL6CCZ9HMtz5592X9WOIn427NNmpcAHxlzMjYbX+c/uGTg9VvZXLmVC0m2n3VdTT/4w6LSub+veWVaXxfdzmD6u1bZh+nhqHMXNrjkVDcfaG3VRaDWBt1QI0x1PTBva2Ic4OSlAIYQQXUTd9gJsuWYAev/fxb5Eqqmkn00kpHf0afcVd9Vwv3sqhaUnYhrm3WCyblfXr2rWOCoFoIS0/v1i4/5P7npHq+1O5LZ6F5VrTnr/4+fb2ocQp6OgbjtmWy4AF/f+P18i1dTEpJ8RHXL6G/EOj7vK755KiWHpJJm8awoL63addj+drXFUCkCnhLTatnH/J0cbR8+cbu8XOzpN6/fXNdlfqq19iJOTkSkhhOgiarfnA6DoNMRcNMBvG0WjIfrigZS+ufW0+oq+0P/9AYx942jYV4Kzov3flKWkvRBnXn6tt4KkRtExIOYiv200ioaB0ReztfTN0+prQPSFAc/FGftS0rCP+iD3XGqNlLQXHUVGpoQQootoXAtlSI7yjZL4Y+zX+pSSU2HoERHwnMbo7dtjbXsZ37ONpsnonmp3tdrWY/M+Xm1YyxHB1mgbn6+T3v/4+bb2IcTpaFwLFWVI9o2S+BNnPPXCK4FEGHoEPKfXGAFweto2lfZsZNAcH91zqa2vBXV6bN5rtG1bJ6nXep8vl6f1+7uO3T+YPsTJyciUEEJ0EZ6GY9PFTIETKQBNe3wQ13bOvqpnugCFLsbo+9lZ2fpIW+NIXNNrTq0P74cqV7UV1eVG0flfN+VqMtLXeI0QZ4Lz2JQ0vab1vzuD5vQ/iCsEXjfYkc50AQqj7vjazXpnZattG0fiml5zKkzH2ltd1bhVF1rF/8f6eldFi2tE+5FkSgghuojGJKoxqQrE04XX25zpAhSGnpFoTAY8DQ5sRwMvzPbYXTjK6gAw9m9blTFfe4+KLb8KY1//I4e2fEuTa05/dFGIU9WYRDk9rRc+cXi67nqbM12AItLQE4PGhMPTgMV2NGA7l8dOnaMMgHhj/zbF1NhexUOVLZ84Y1+/7Sy2fN/PcW3sQ5ycTPMTQoguIiQlGvBW2muccuaP9Ujr34KK4xRFIWz4sYIaGQUB29XtKgS3B4CwYT3b1EfYiOPtm+3bdWIfO7z9G3pGSll0cUZFh6QA3kp7ziZTwk5UaT1ypkLq8hRFISnMu+dfQZM9tU5UWLcLD24Aeob539Q7kJ5hI5rcJ3AfBXU7AG+CJ2XR25+MTAkhRBfRWF1PdXmoWnuY2JnpLdqoHg9Vqw+d6dDaTWcUoIi5ZBC1P+RRs/kozoo6v3tnVS7dB4A2MpSISYH3jPEntE8sof3jsWVXULl0Hwk/GdmijcfhwrwyyxvPpYODeBRCBK+xup5HdXG4ai3psTNbtPGoHg5VrT7TobWbzihAMSjmEvJqf+BozWbqnBV+987aV7kUgFBtJGkRk9p0/9jQPsSH9qfCls2+yqWMTPhJizYuj4Mss3dLjcExlwbxKMTJyMiUEEJ0EeHjehHSx/utYv4za3wlt5sqeXMr9vyqMxxZ15YwdzSaUD2q003eP1p+WLTlmil7z/vNbo/rx7S6V1QgSbeOB8CyKouarXktzhe/8j2uynpQFBJvalk2WoiO1Ct8HLEhfQBYk/+Mr+R2U1tL3qTKnt/iuAhsdMJc9JpQ3KqT1Xn/aHHebMtlR9l7AIzpcX2re0UFMj7pVgCyLKvIq2lZxfX74leod1WioDAu8aY231+cnCRTQgjRRSiKQtrvZgDQkFnK/rmLqP7uCE5zA9acSvKeXs3RBcsJSZMFxm1hSIwg+d7zASh7dztHHvkS6+FynBV1VH6dyb65i/BYnegTwkm57wK/99h37RtsSpnPjonP+z2feMt5GAfEg0flwG3vUfbRThxltdiOmsn7+7e+JC7hutGEDW/bNEIhTpeiKMxI+x0ApQ2ZLNo/lyPV39HgNFNpzWF13tMsP7qAmJC2jcqe6yIMiZyffC8A28ve5csjj1BuPUyds4LMyq9ZtG8uTo+VcH0CF6Tc5/ceb+y7lvmbUnh+x0S/589LvIV44wBUPLx34DZ2ln1EraMMs+0o3+b93ZfEjU64jp7Hph2K9iXT/IQQoguJu3IYKfdNpfDFDdRlFLD/+uZ7vkRO7kP81cM58uhS0Mr3Zaeq10PTseWYqfh0N6XvbKP0nW3NzutiTAx+/Qa/UwBPhSZEx+A35pF541vYC6rIfnhJizZRU/vR78kfB3V/IU7XsLgrmZpyHxsKX6SgLoM391/f7HyfyMkMj7+apUceRdNJFfm6oum9HsJsy2F3xadsK32HbaXvNDtv0sVww+DX/U4BPBU6TQjzBr/BW5k3UmUvYEn2wy3a9Iuayo/7nV5xHxGYJFNCCNHFpP5uBhHje1P82mbqdhehOt2E9okl/pqR9PzFZF8ioA2XvYpOlaLRMPBf1xBz6WDK3t1O/b4S3FYHIUmRxMwYRPI952NIijytPoz94hi56h6KXvke87L92POrUAxaTAMTSJg7mh7zxqJoJAEWnWdG6u/oHTGezcWvUVS3G7fqJDa0DyPjr2Fyz1/4EgGDNrgvFc5FGkXDNQP/xeCYS9le9i4l9ftwuK1EhiQxKGYG5yffQ6Qh6bT6iDP2456Rq/i+6BX2m5dRZc9HqxhIMA1kdMJcxvaYh0aR15aOoqiq2tkxCCFEu1IURe2MQgZni9z5X1P82maMg3swevW9nR2OED6bUuajqmrnbGLWDhRFUTujkMHZ4uvc+Wwufo0exsHcO7rrFqMQ3c/8TSmd9toiaaoQQnQjqqpi+dZbzU/W3ggh2ouqqhyyfAsga2+EaEKSKSGE6GKclsAba5a8vhlbjnefqbgr27ZniRDi3NbgDLxx9eaS16m05QDe9VVCCC9ZMyWEEF3M3qteI2paf+KuGIpxUAKKVoMt10z5hxmUvrsdgLDRKcRcMrCTIxVCdCWv7b2K/lHTGBp3BQnGQWgULWZbLhnlH7K99F0AUsJGMzDmkk6OVIizhyRTQgjRxXganJQu+oHSRT/4PR/aN47Br1yHItX8hBBt4PQ08EPpIn4oXeT3fFxoX64b/AoaRar5CdFIkikhhOhi+v19FuaVB6jbUYCzvA5XtQ1tmAHjoARiZ6aTeOt5aI1t3/xRCHFum9Xv7xwwr6Sgbgd1znJsrmoM2jASjINIj53JeYm3otcaOztMIc4qkkwJIUQXEzNjEDEzBnV2GEKIbmZQzAwGxczo7DCE6FJkDogQQgghhBBCBEGSKSGEEEIIIYQIgiRTQgghhBBCCBEEWTMlhBCiU+279g1qNuWSMHc0AxbO6exwzmr24hrqMgqO/SukblcRngYHAGM2P0Ro75hOjlCIs8cb+64lt2YToxPmMmfAws4O56xWYy+moC6DgroMCusyKKrbhcPj3dPwoTGbiQnt3ckRnr0kmRJCCCG6iH2zX8deUNXZYQghupnX982myl7Q2WF0SZJMCSGEEF2MNtpI+KhkNEY9luUHOjscIUQ3YdRGkxw+Cr3GyAHL8s4Op0uQZEoIIYToIvr85QqMA+Ix9osDwLw8U5IpIcRpu6LPX4g3DiDO2A+ATPNySaZOkSRTQgghRBcRe9ngzg5BCNENDY69rLND6LIkmRJCiDOk+rsjlLy1lbodBTgr69EYdOjiTIT2jiFqen/iZ48gJDmq2TX2omosK7OwfHuQ+v0luCob0IToMPSKInr6AHreMZmQnpF++yv7MIPsX30GwOTCx7EXVlH4zw1Y1hzCWVlPSFIkcVcPJ+XeC9CGhQDgNDdQ/Mr3VC7bj72oGl1kKDEzBpH6yCXo48Nb9GHLt5AxaSEAQxffRvjIZAr//R2VX+3HUViNxqQncmIaKfdPJXxUStDPnbvOTsmiHzCvOID1SCWeBgf6+HAiJ6WRfNcUwob3DHht5bL9lH2QQf2eIlwWKxqjHn1cGKF9Y4m+aADxc0aijzEFHZsQne1I9XdsLXmLgrod1Dsr0WkMmHRxxIT2pn/UdEbEzyYqJLnZNdX2IrIsKzlo+ZaS+v00uCrRaUKIMvRiQPR0Jve8g8gQ//9fZZR9yGfZvwLg8cmFVNkL2VD4Tw5Z1lDvrCQyJInhcVdzQcq9hGjDAGhwmvm++BX2Vy6j2l5EqC6SQTEzuCT1EcL18S36sNjyWZgxCYDbhi4mOXwk3xX+m/2VX1HtKESvMZEWOZGpKfeTEj4q6OfO7q7jh5JFHDCvoNJ6BIengXB9PGmRk5iSfBc9w4YHvHZ/5TIyyj6gqH4PVpcFvcZImD6O2NC+DIi+iJHxczDppShMdyfJlBBCnAEFC9eR//TqZsfcDjfuOjv2oxaqvzuCxqin5+0Tm7XZNeM/uKttza9zurEeKMN6oIyyDzNIXzSPiPNSW+2/bk8RmTe+jcvS4DtmyzVT+MJ6an/II/29W3AU17B/3tvYc82+Nk5bHWXv7aBm01FGfHUHuihjwD5cVVb2XPkq1kPlvmMemxPz15mYV2Yx4PnZJFzT9g89dbuLOHDbezhLa5sddxRVU/Hpbio+20OfBTPp+fNJLa7N/s3nlL2/o9kxt9ONu8aGLaeSqtWHCO0dQ8ylMuIjuqZ1BQtZnf90s2NutwO7uw6L/ShHqr9DrzEyseftzdr8Z9cMbO7qE65zUmY9QJn1ABllHzIvfRGpEee12n9R3R7ezryRBpfFd8xsy2V94Qvk1f7ALenvUeMo5u398zDbc31t6pw2dpS9x9GaTdwx4iuMuig/d/eyuqp4dc+VlFsP+Y45PTYyzV+TZV7J7AHPMyrhmlbj9B/7bt47cBu1ztJmx6sdReyu+JQ9FZ8xs88CJvX8eYtrP8/+DTvK3m92zO12YnPXUGnL4VDVamJCezM45tI2xyW6FkmmhBCig1kPl5P/7BoAoi4cQMrd5xPaPw6tyYCzsp76vcWYlx9Ao9e2uNY0uAfRFw0kYlxvDIkR6GJNuMwN1O0spPA/32HNKiPr7o8Ys/5+3+iSPwfv+BB9YjgDnp9N+NheuOvsFL+2mZL/baFmUy6l726nfPFOcHkY+J9riZzSF9XppvyjneQ/swZbTiUF/1xPnz9eHrCPo39egaO8jtTfzyBu1nC0Jj01W/M5+teV2HPNZP/qM0zpiYQNTTrl586WZ2H/9W/irrFhSk8k5f6pRIxPRWPSYztSSdHL32Netp/cP31NaFosMTMG+a6tWnfYl0jFzxlB4k8nEJoag2LQ4iyvoy6jkMql+0CjnHI8jdxWJ3g8bb6ukSZEh6Jr+d9biLYotx5mTf6zAAyIupDzU+4mLrQ/Bq2JemclxfV7OWBejlajb3FtD9NgBkZfRO+IcUQYEjHpYmlwmSms28l3hf+hzJrFR1l3c/+Y9b7RJX8+PHgH4fpEZg94nl7hY7G769hc/BpbSv5Hbs0mtpe+y87yxXhwce3A/9A3cgpu1cnO8o9Yk/8MlbYc1hf8k8v7/DFgHyuO/pk6RzkzUn/P8LhZ6LUm8mu2svLoXzHbc/ks+1ckmtJJCht6ys+dxZbHm/uvx+auIdGUztSU+0mNGI9eY6LSdoTvi15mv3kZX+f+idjQNAbFzPBde7hqnS+RGhE/hwmJPyUmNBWtYqDOWU5hXQb7KpeiBLGdq9NtxUPwry06TQhaRT7en0nybAshRAerWpcNHhV9QjhDFs1rljTpoo0Y+8cTf/UIv9cOX9LyG1F9rAnjgHhif5zO7ktfxpZTScWSPSTePD5wEFoNw5f8HF1kqPcecWH0/csVNGSWUrMpl6MLlqMND2HkqnuaTTXs9dB0bDmVlH+8i4qPd7WaTNnzqxj06vXEXXH8A03cj9KJGJPCrktfwmVuIO/JVaS/c0vgOE+Q89hXvkRq+Jd3oDUe/1CoH2di8KvXc/ihJZQv3snRv64k+pKBKIo3OapacxiAsJHJDHzx2ubPYYwJ06Ae9Lh+zCnH0tSBW96hZlNuUNcC9H9udtB9C9Eou2odKh7C9QnMG7KoWdJk1EUTb+zPiPir/V778+FLWhwz6WOJNw4gPfbHvLz7UiptOeypWML4xJsDxqBBy8+HLyFU551uHKaP44q+f6G0IZPcmk0sP7qAEG0494xc1Wyq4fReD1Fpy2FX+cfsqvi41WSqyp7P9YNeZWjcFb5j6XE/IiViDC/tupQGl5lVeU9yS/o7gZ+sE3yV85gvkbpj+JfotcdH3U36cVw/+FWWHH6IneWLWXn0rwyMvsT32nK4yvvlWHLYSK4d+OIJz2EMPUyDGNPj+lOOpal3DtxCbs2moK4FmN3/uaD7FsFpe8oshBCiTVSX91tGXZzJ7+hTsLRGA7EzhwBQvTGn1ba9HpjmS6SairtyqC/GpF9MarFmCyBulnfNgLOiHnthVcA+Is5LbZZINTIkRZJ89xTAm1g6ympbtPHHemwaHkC/f1zVLJFqqvdvL/a2P1ROw74S33HV7X3eDT1arvUSojvwqC4ATLo4v6NPwTJojQyJnQlATvXGVttO6/WAL5Fqamjclb4YJyX9osWaLYDhcbMAqHdWUGUvDNhHasR5zRKpRpGGJKYk3w14E8taR1mrsTaqtHqn4QFc1e8fzRKppi7u/VsAyq2HKGnY5zvuUd0AhBt6nFJ/onuTkSkhhOhgYcO809qsB8rIe3o1yXdObnXt0YmqN+VS9v4O6nYU4CitxdPgaNHGdqSy1XtETevv93hIauzxNhf0898m7fgCakdZHSEp0X7bxVweeN1R7OXp5D35DXhU6nYUEDszvdV4AWq+9yaI2mgjpkEJuOvtftvpokLRxYXhqqynbneRrxhF4/NuWXOYkkVbSLh+bMCErK2GfXz7yRsJ0cGSwoYBUGY9wOq8p5mcfGera49OlFu9iR1l71NQt4NaRykOT0OLNpW2I63eo3/UNL/HY0OOr+PsF3WB3zYxIWm+n+scZUSH+C9SMzgm8Ih4euzlfJP3JCoeCup2kH4sCWxNTs33gHdPpQTTIOzuer/tQnVRhOniqHdVUlS321eMovF5P2xZw5aSRYxNuD5gQtZWtw/7uF3uI84cSaaEEKKDRV3Qj+iLB1K1+hCFC9dR9O/vCB/Xi8iJaURN6UvkpLSA62dy/rSMkte3nLQPV53/RKNRoNEZTejxtwFDwsnbeGyugH0Y+7esyNUotF+sd22SR8VeUNVqrI2s2d4E0V1l5YfBT57SNa7K4x+K4q8ZRfHrW2jYX0LOY8s4+tdVRExIJXJCGlEX9CN8XC/ftB0huqJ+URcwMPpiDlWtZl3hQr4r+je9wseRFjmRvlFTSIucFHD9zLKcP7Gl5PWT9mF31bV6PtDojE4T2qRNwknbuDw2v20A4o3+vwwCiA3th4IGFQ9V9oJWY21Uac0GwOqu4skfTq34TL3r+BdWo+KvYUvx65Q07GdZzmOsOvpXUiMmkBY5gX5RF9ArfJy8tpxDJJkSQogzYPBrN1D00kZK396Go6SG2s1Hqd18lMIX1qOPDyPlgWkk/Wxiszfg8o93+RKpqAv6kXjreZiG9EAXY0IT4k2+Cv65nqIXvwNX6wuWFe0pzOrWnsKbv6oGvtxoCNy/RoMmVI+nwYG7vuXImj/u2sAfrgLxONy+nzV6LcM+uZ2CF9ZR/uFOXJYGqtdlU70um/ynVxOSGkPv315MwpyRbe5HClCIs8UNg19jY9FLbCt9mxpHCUdrN3O0djPrC18gTB/PtJQHmJj0s2avLbvKP/YlUv2iLuC8xFvpYRqCSReDVuMtZLO+4J98V/QiHgJ/gQKgUU7+d6xw8jYqgV9bDK2M+mgUDXpNKA5PA44AI0wnsrlPbapxU27P8dctrUbP7cM+YV3BC+ws/5AGl4Xs6nVkV69jdf7TxISkcnHv3zIyYU6b+5ECFF2PPNtCCHEGaEJ09HpoOikPTsOaVUbttnyqN+Zg+fYgzop6cv/0Nc7yOlIfPV4xqvSdbQBETEwj/YNb/X7T6bG2/kHnTHJbAydJqseDx+YEQBsWOOlqSnMsOQvtF8eYDQ8EFZMuMpQ+f7yctMcupX5vCbXb8qjecISqtYex51k4fN8neBocJN7USvEOP6QAhThb6DQhTO/1ENNSHqTMmkV+7TZyqjdy0PIt9c4Kvs79E3XOcmakPuq7Zlupt1BDWsREbk3/wO9ri8tjPWOP4WQc7sCxeFQPzmOjWoZWqg42ZdB4k7O40H48MGZDUDGF6iK5vM8fuTTtMUrq95JXu40j1Rs4XLUWiz2PTw7fh8PTwPjEm9p0XylA0fVIAQohhDiDFEXBNCSRxJvHM+iluYzb+isiJnnXDRS9uqlZQtKQ6d37JO6K9IBTRqwHT23B9Zlgza4IeM52xAwe7zfPIb2iT+l+ocfWajmKavA43Sdp3TpFoyF8ZDI9fzaJIW/MY+ymhzAO8E5LLFi4/rTuLcTZQFEUEk1DGJ94M3MHvcSvxm0lLcK799qmolebJSSlDZkApMddEfC1pcx6sOODPkUVx6bl+WO2HUE9NpITHdLrlO4XE+p9za1xFOH2OE8rNo2iITl8JJN6/ox5Q97gobGbiDcOAGB9wcLTurfoGmRkSgghOpEuykjyHZPJ2nwU1ebCXlCNaaB3fYHH4R11Ut3+p784ymqp2Xz0jMV6MpaVWaTc43+huXmF98MbGoXwsaf2gSdqqrcghsfmxPLNQeJ+dPKiFafKkBRJ4s3jyV2wHEdRNW6rs03FKaQAhTjbGXVRTE6+g6NZm3GpNqrtBSSYBgLgOjZlTVX9f0lR6yjjaM3mMxbryWRZVnJByj1+z2WaVwCgoKFX+NhTul+/qKmAd+Pfg5ZvSI/7UfsEirfC4PjEm1meu4BqRxFOt7VNxSmkAEXXIyNTQgjRwaw5laitrK+x5Zp9P+uij7/pNo7gWL5t+Q2x6vGQ89gy1NMcsWlPtT/kUfl1ZovjjpIail72Vs+Knt4fQ4+IU7qfaVAPXxXC3D99jb2outX21sMVrf5+IttR7/OuCdU3K7IhRFdRac3BowZ+bTHbcn0/G3XRvp8bR3AOWr5tcY1H9bAs5zHc6umN2LSnvNofyKz8usXxGkcJ3xe9DED/6OlEnGKp8h6mQb4qhF/n/olqe1Gr7Sush1v9/URmm/dLLr0mtFmRDdE9ybuHEEJ0sMIX1lOzNY+EOSOInNIXY784NKF6HOV1WFYeIP+5tQBETEprVlEv7sdDKfzXBmo25nD44SX0vHMKhsQIrIfLKfzXBqpWH8I4MAHrofJOemTNhfSO5vB9n2D79YXEzRqG1mSgdmseuX9ZicvcgKLTkPq7S9t0z35/u5LdP/4vjqJqdl/+Msn3nE/MxQMxJEXicbpxFNdQt6OAymX7qd9VxISs3/uuPfLol7iqrMTPGUHkhDRC0mJQdFocxTVUfLGHkje3At69trpK5S17UTWO4hrf79YmJfHr95bgLDteeS20Tyz6uFNbQyK6pvWFL5BXs5URCXPoGzmFOGM/9JpQ6hzlHLCsZG3+cwCkRUxqVlFvaNyP2VD4L3JqNrLk8MNM6XknEYZEyq2H2VD4Lw5VrSbBOJBy66HOemjNRIf05pPD93Gh7dcMi5uFQWsir3YrK3P/QoPLjEbRcWnq79p0zyv7/Y3/7v4x1Y4iXt59Oecn38PAmIuJNCTh9jipcRRTULeD/ZXLKKrfxe8nZPmu/fLIo1hdVYyIn0Na5ARiQtLQKjpqHMXsqfiCrSVvAt69trrKa0u1vYgaR7Hv90rr8ZL4JfV7qXMen1IeG9qHMH3cGY3vbCbJlBBCnAH2XDMFz6+D59f5PR+SFsOA55tXfkq5byqWbw7SkFlK+Uc7Kf9oZ7PzibdNQB9rouBYMtbZ0v54Ofn/+Ja8J1aR98Sq5ie1Gvo/N9u399OpCu0Ty7CPbiPrFx9gz7P4v3djF9Etp9I0ZJaSd2ztmT9hI5NJm3/yfWnOFmXv7wj43/vgLz5o9rsUuTg3mO25rCt4nnU87/d8TEgacwY0Pzc15T4OWr6htCGTneUfsbP8o2bnJyTehkkfy9qC5zos7ra4PO2PfJv/D1blPcGqvCeandOgZXb/53x7P52q2NA+3DbsIz7I+gUWe57fezcyaqNbHCttyKQ0r+VIfKPksJHMTJvfppg6046y9wP+9/7g4C+a/S5FLpqTZEoIITpY6mOXEnV+X6rWZ9OQWYqjtBZ3jQ1teAjGQQnEXj6ExFvPQ2tqXuVOGx7CsE9/RsEL6zAvy8RRXIM2IoSwoUkk3jyeuFnDyH92TSc9qpZ0MUaGL72Dwhc3YP4qE3txNVqTgYgJqfR6YBrho/xvyHkyYcOSGL32Pso/yqBy+QEa9pXgqrKiaDXokyIIG5pI1NT+xF3Z/MPUgIVzqFpziKoNR7AeKsdRVoenzo42ykjY0ETiZg0j4boxaPRSolx0TZemPkbfqPPJrlpPaUMmtY5SbO4aQrThJBgHMST2cs5LvBWD1tTsuhBtOD8b9inrCl4g07yMGkcxIdoIksKGMj7xZobFzWJN/rOd9KhaMupiuGP4UjYUvkim+Suq7cUYtCZSIyYwrdcDpISPCuq+SWHDuG/0WjLKP+JA5XJKGvZhdVWhUbRE6JNIDBtK/6ipDIu7stl1cwYs5FDVGo5UbaDceog6Rxl2Tx1GbRSJYUMZFjeLMQnXodW0zybh4uymqK3sGSKEEF2Roijq5MLHOzuMc4It30LGpIUADF18G1FT+nZuQOKstillPqqqdo15T34oiqI+Prmws8M4J1hs+SzM8FYjvG3oYvpGTenkiMTZbP6mlE57bZECFEIIIYQQQggRBEmmhBBCCCGEECIIkkwJIYQQQgghRBAkmRJCCCGEEEKIIEgyJYQQQgghhBBBkNLoQgghghbaOwapnCiEaG8xob2RyomiK5CRKSGEEEIIIYQIgiRTQgghhBBCCBEESaaEEEIIIYQQIgiyZkoIITrR4YeWUL54J5GT+zDs49s7O5wuIf/ZNRQ8t7bZMdPQJEatuifgNdbDFRT993uq12XjKK9DFxVK+OgUkm6fSPS0/h0ary3XTN3OQmp3FFCXUUD9vhJUuwvgjK43Uz0eyt7fQflHO2k4XIHqcBPSO5rYH6WTfNcUdJGh/uPPt5AxaWGL40MX30bUlL4dHLUI1pLDD7GzfDF9Iidz+7CPOzucLmFN/rOsLXiu2bEk01DuGbUq4DUV1sN8X/RfsqvXUecoJ1QXRUr4aCYm3U7/6GkdHTIA2VXr2VLyBoV1O7G5qgk39KB/1DSmJN9FvLHjXt88qpvShkwK6jIorM2gsG4n5dZDqHhO6e/OYstnYcakFsdvG7qYvlFTOirsdifJlBBCiG7NvOIAh+79GI/V6TvmLKvDsjILy8osUh6YRuojl3RY/xnnv9Bh9z5VHpuTAz99j+rvjjQ7bs0qozCrjIpPdpH+/q0Y+8Z1UoRCdD0HzCv4+NC9OD1W37E6ZxlZlpVkWVYyLeUBLkl9pENj+Cbvb2wofLHZsSp7PtvL3mV3xadcO/A/DIm9rEP6PlqzhUX753bIvbsSSaaEEEJ0SYaUKEavudf7i8b/rPWGQ+W+RMo4pAd9/nQ5YcN7Yi+qpmDhOizLD1D4z/UY+8WRMHd0h8ar7xFO+KgUPFZni6Smox353VJvn4pCyn0X0OOGsWiMeqrWHiZ3wXLs+VVk3f4+I1fcjSak+UeDkF7RTDj4ewDshdXsuujfZzR2Ic60KEMK945eA4AmwIqY8oZDvkSqh3EIl/f5Ez3DhlNtL2JdwUIOWJazvvCfxBn7MTqhYxKOnWUf+RKpITEzmd7rIaJCkimu38uK3D9TZj3Ax4d+yV0jvibBNLBDYmgUG9qHXuFjKarbRYUt+5SuiQ7pxe8nHASg2l7Iv3dd1JEhdhhJpoQQQnRJiqKgDQtptU3eU9/gsTrRJ4QzbPHt6GNNAOjjwhj82g1kznub6vXZHH3qG+JmDUMTqm/3OAe9fB3hY1II6RUNQPGrm85oMlW/t5jyxbsA6P1/F9Hrwem+cz2uH0Non1j2XfMG1kPllL6zjZ4/bz7tpunzrDG2//MjxNlGURRCtGGttvkm7ymcHivh+gRuH7YYkz4WgDB9HDcMfo23M+eRXb2eb44+xbC4Weg1/qfRBsvptvJN3lMA9I+azg2DX0NRFAAGRE8nedhi/r3rYuqc5Xyb/3duGPxau/YPEGfsxy3p75ISPhqjLhqA9w/87JSTqabPs15jbPf4zhQpQCGEEKJbcpTWYlmRBUDyPef7EqlGiqKQ+vsZADhLajGvzOqQOOJmDfMlUp2h9O1toKroYk0k33N+i/ORE9OImTHoeFshRKtqHaVkWVYAcH7yPb5EqpGiKMxI9Y7m1jpLyDKvbPcYDlhWUussBWBG6u98iVQjkz6W85O960gPmJdT6yhr9xgiDUkMiL7Ql0idq2RkSghxznKa69k+5hlUl4d+T19F4rxxAdtaD5ezc/qx6RRv3UTMJYN85+r3l2BecYDqddlYD1fgrrWhCQ/BNCCe2CuGknjreLRGQ5vj23ftG9RsyiVh7mgGLJzjt0319znsn7sIgDGbHyK0d4zfdjVbjlL6zjZqf8jDUV6HJkSHsX88cVcNI+mnE1pM7eoOLKsPgqoC3oTGn/ARyYT0icWea8ayKov4q4afyRDPCMu33mk0sZcPQWPw/985btYwLKuysB4qx5ZrJrRPrN924tTUO808s30MHtXFVf2eZlzivIBty62HeXGnd7TwpiFvMSjm+Pq9kvr9HDCvILt6HRXWw9jctYRowok3DWBo7BWMT7wVg7bt3+i/se9acms2MTphLnMGLPTbJqf6e996mIfGbCYmtLffdkdrtrCt9B3yan+gzlGOThNCvLE/w+KuYkLST9FpWh897ooOWlaj4n1tGRY3y2+b5PARxIb0wWzPJcuyiuHxV7VzDN8AEBvSh+TwEX7bDIubxYqjf0ZF5aDlW8Yl3tiuMQiv7vfuKYQQp0gfG0bUtP5UrT5ExZI9rSZT5Uv2AKCLNRE9/Xh1pPp9Jey+7KUW7d1VVmq35VO7LZ/yxTtJf/9WDAnh7f8gTkJ1e8h57KsWIw5uu4u6DG91ufKPdpL+7i0YEiPOeHwdqX5PMQCGpEhCkqMCtosY2wt7rpn6fSVnKrQzxllZj6O4BoDwsb0Ctmt6rn5vsSRTpylMH0v/qGkcqlrNnoolrSZTe8qXAGDSxdI/+vgUzJL6fby0u2XhAKu7ivzabeTXbmNn+WJuTX+fcENC+z+Ik/Cobr7KeYxtpW83O+5y2ymoy6CgLoOd5R9xS/q7RBgSz3h8Ham43vt+EGlIIiokOWC7XhFjMdtzKanf1wEx7PX1EUhUSDKRhiRqHCWUHGsv2p8kU0KIc1r8nJFUrT5EzeZcHKW1AROKys+8b55xVw5D0WmPn1Aganp/YmemY0pPxJAYgTbcgKOklqrVhyh65XsaMks58tsvGPJG4A9UHSV3wXJK396GotOQdPtE4n8ykpDe0XgaHFStOUze37+lIbOUrDs+ZPiSn6Fo2zb72211gscTdHyaEF3z57Md2bIrAQhJ8z9a1yikd7S3fa4Z1eNBCVDMoiuyZlf4fg5NDfw8hPSKBo0CHrXZNSJ4I+PncKhqNbk1m6l1lAZMKPZUfgbAsLgr0SpNP5Yp9I+aTnrsTBJN6UQYEjFow6l1lHCoajXfF71CaUMmXxz5LfOGvNHxD+gEy3MXsK30bTSKjolJtzMy/idEh/TG4WngcNUavs37O6UNmXyYdQc/G74EjdK2/8+dbisegn9t0WlCTng+20/lsTVBMSFprbaLDvGO5pltuXhUDxqlfV5bPKoHsy33WAypJ4khlRpHySmvYxJtJ8mUEOKcFjtzCBqjHo/VScXne0i+s+XeFnU7C7HlmgGIn9N8OkXY0CSGvndri2v0sWGEDU0i5vIh7J7xHywrs7BmV2DsH98xD8SP2owCSv63BYD+C+eQMGfk8ZMxJhJvHk/EhFT2/Oi/1G3Pp3LpfuKvbts0twO3vEPNptygY+z/3Gx6XD8m6Otb47Q0AN5iE63Rx3vPq3YX7noHuoj2XSjemVyW4yWbGx+nPxq9Fl1kKK4qa7NrRPCGxM5ErzHi9FjZU/E5U5LvbNGmsG6n70PxiPjmU3mTwoZy69D3WlwTpo8lKWwoQ2Iu5z+7Z5BlWUmFNbtD9xM6UUFtBltK/gfAnP4LGZlwPHYTMYxPvJnUiAn8d8+PyK/bzv7KpQyPv7pNfbxz4BZyazYFHePs/s8xpsf1QV/fmganBfAWm2hNmN77eu9S7Tjc9YTq2mf03+Guw606mvVxshisLku79C1a6j5fvwkhRBC0JgMxlw0GoOJz/9MgKo6NSoX0iibivNa/BTyRaWACpuE9AajemHMakbZdYyIVfdGA5olUE6ZBPXwJYsVnu89YbGeCp8G7r5QmtPXvDZtW8PPUOzo0pjPN3XD88SgnWRfX+Dw0vUYEz6A1MTjGO01vb8XnftvsqfgM8JaITo04r033TzANpKfJ++VHTvXG4AMNQmMiNSD6omaJVFM9TIN8CeLuY4+zu3B6vF/U6E5Soa9pBT+Hp77d+ncc698bQ+tr0hpjcLjbr3/RnIxMCSHOefGzR1L5+V7qdxZizalstnGp6vFQ8aU3yYq7eniLikngXZdUsWQPFV/spWFfCU5LA6rd1aKd7Uhlxz0IP6q/9yZvkVP64q63B2xnGtwDgPrdxW3uY9jHtwcXnBDngJHxs9lb+TmF9TuptOYQZ+zrO+dRPeyt+BKA4XFX+31t8ahu9lQsYW/FF5Q07KPBacGltvx/udJ2Zvcty6n+HoC+kVOwt/IhvYfJ+0VVcX3bv6i5fdjHwQUnxBkmyZQQ4pwXfdEAdNFGXFVWKj/bQ6+HL/Sdq9l8FGdJLQDxs1tWTHLV2jhwy7vUbs07aT/uusAJTXtz19t9cec9sYq8J1ad9BqnuXt9c6kxeUdaPLaWiW1THpvz+DVhba+6eDbTmo4/Hn8JflONz0PTa8TpGRB9EUZdNFZXFXsqP+PCXg/7zh2t2Uyt01v0ZET87BbX2ly1vHvgFvJqt560H7u7rt1iPnlf9b64V+U9waq8J056Tb3T3NFhnVF6jXebBZfH1mo7Z5PzBk3r043bwqA5vs2Dy9P6+0pjDIaT7JslgifJlBDinKfRa4m7chil72yj4vO9zZKpxil+xiE9CBua1OLa3AXLvYmUopB40zhifzwUY784tBEhKDrvTOrMm96hdmseqiv4xdRt5appe+KmOtxtvuZsLkChj/F+4HBWtp4kOiu85xWDFm03S6Z0McfLZjc+Tn88TjeuGluLa8Tp0Wr0DIu7km2l77C34vNmyVTjFL8exiEkhQ1tce3y3AXk1W5FQWFc4k0Mjf0xccZ+hGgj0BwrrPBO5k3k1W7Fo7aeKLcnu6umzdc0ru9pi7O5AIVJ7y3mUu9sfbZBvdNbzEWrGNo1mTFow9EqBtyqw9fHyWIw6lovxCOCJ8mUEELgLSxR+s42rIfKqd9bTNjwnnicbsxf7Qcgwc+olLvBQcWn3ukrKfdPJfWRS1q0AXB34DqcQAma1nR8HdDAf1/rd1StPZzNBShC+8dR/d0R7EdbX3htz6/ytu8b160q+QHNCp7Y8iwEKhBvL6gCj9riGnH6RsTPYVvpO5RbD1Fcv5eeYcNxe5zsN3/lPZ8wu8U1DncDuys+BWBqyv1ckvqI33t35DqYQAmaXnt8VOTagf/2O6rWHs7mAhRxof05Uv0dFvvRVttV2fOPte/bbpX8ADSKhtjQPpRbD2Kxtz4rourY+fjQM1eg5FzTvd41hBAiSBET0zAc24uocTSqeu1hXFVWUBTi/CQj1uwK32hO3JX+N4VVXe6g10o1Fk5oOg3tRM4y/9N7dFFGdNHeEQbb0e41xeZUhY3wFv5wlNRgLw78bXrtjgJv+2EtRx67On1cGIaekQDUZRQEbFe34/i5sGMFU0T7SIuYSJTBuxdR42jU4eq1WF1VKCiMiJvd4poKa7ZvNGdY3JV+7+tWXUGvlWosnOBsZZpanbPM73GjLgqjLhoAs631ZKK76hnmfT+ocZRQYw+81rSgdgcASWH+3x9OLwZv8ZGCuh0B21Tbi6hxlByLofttSH62kJEpIYQAFEUh/urhFL20kYrP95L62KVUfO5NqiLG9SK0d8spEqr9+LQ4NcBUN/OKrFaTodboj23ya8sJnAxVrQ+8d0jUBf2oXLqPyqX76fXg9IDtTsfZXIAi5uJBoCigqpiX7qPnHZNbtKnfW4z9WNn7mEsHn+kQz4iYSwZR+s42zCuy6PuEC42h5Vt/5VLvpqLGgQmyYW87UxSF4fFXs7HoJfZWfM6lqY+x51h1v14R44gJ7d3iGneTIhMe1f9rS5Z5RavJUGvC9d5Nfs22wBVGs6vWBzzXL+oC9lUuZX/lUqb3ejCoGE7mbC5AMSjmYhQUVFT2mZcyuecdLdoU1+/FbM8FYHDMpR0Qwwx2V3yK2ZbrG/E80b7KpQAoKAyK8T9zQpw+GZkSQohjGqfCOYqqqVp7GPOKLO/xOf6nyDVu9gpQ9c3BFuedlgaOPnnywg+BhI9KAaB+XwkNWS2/Ja7NKKDyi8C72if9YhIADftLyH9+bat9eewu71SvbsSQGEHM5d4EqfCljb59pxqpqsrRv30DgD4pgtjLumcylXjLeFAUXJX1FL/yfYvzNVvzsBz7+028ZfyZDu+c0DgVrtpRxOGqtWSZVxw77r+seONmrwAHq75pcb7BaWHV0SeDjiclfBQAJfX7KGvIanG+oDaDvZVfBLx+UtIvvNc37Gdt/vOt9uXy2KmyBx4V7YoiDIkMjrkcgI2FL/n2nWqkqirfHP2bt60+icGxl7V7DENiLiNC790I+pu8p1BVtdn5BqeFjUUvedvGziTC0KPdYxBekkwJIcQxYcN7Yhzo/cY259GleBocKDoNcbP8T48wJEYQMd77oafgXxso/M932HLNOMrrqFy2n71Xv46zpJaQXtFBxRM3axgaox5UlayfvU/V2sM4LQ3Y8i0Uv7aJzHlvY0iODHh95HmpJP18oje+Z9Zw4KfvYvn2II6SGlzVVmx5FizfHCTnT8vYMeE53+hEd5L66Aw0Rj3O0lr2z11E1fpsnOZ66vcWc/DOD6leexiAtEdnNNtvqpEt38KmlPlsSplP/rNrgorBlmumdnu+71/TpLXp8drt+bhqW440lH2Y4Yuhsdx9W4QN70nCXO+H57x/rCbvH99iO2rGUVZL2Uc7OXDbe+BRMQ5MIPFmSaY6Qs+w4SQYBwKwNOdRHJ4GNIqO4XGz/LaPMCTSO8L732JDwb/4rvA/mG251DnK2V+5jNf3Xk2ts4TokF5BxTMsbhZ6jREVlfezfsbhqrU0OC1YbPlsKn6NtzPnEXlsaqI/qZHnMTHp5wCsKXiGdw/8lIOWb6lxlGB1VWOx5XHQ8g3Lcv7Eczsm+EZIupMZqY+i1xipdZayaP9csqvWU+80U1y/lw8P3snh6rXedmmPNttvqpHFls/8TSnM35TCmvxn29y/XmtkRuqjAByuWsOHB++kuH4v9U4z2VXrWbR/LnXOMvQaI5f09r/mLqPsQ18MjeXu26q4fi/5tdt9/6yuagDs7tpmx/Nrtwd1/65ApvkJIUQT8bNHkP/0at8H3qip/dDHBa7C1PdvV7LvJ//DXWtvUYJc0Wvp//xsyt7dHtSojz4ujD4LZnLkkS+x5ZrJvOntZufDx/ai1wPTvB+GA+gzfyaKXkvxK5uwfHPQNwLhj6LvmKp6nck0MIGBL17Dofs+oSGzlMwb32rRJuWBaSTMHd1hMRQsXEf54p1+z+296rVmvw9dfBtRU/r6bXs6+j55JY6iGqq/O0LhC+spfKH5FK6Q3tEMfuNGNCfZ2FcEb0T8bFbnP+0bpekXNZUwfVzA9lf2/Rv/2/cT7O7aFiXItYqe2f2fZ3vZu0GN+oTp45jZZwFfHnkEsy2XtzNvana+V/hYpvV6gPcO3BbwHjP7zEer6NlU/AoHLd9w0NJyBK1pvN1Ngmkg1wx8kU8O3UdpQyZvZd7Yos20lAcYnTC3w2IY3eM6KmzZbCh8kUzzMjLNy5qd12uMXDvw3ySYBnZYDB9k/dzv32Bx/V5e23tVs2OPTy7ssDg6k7xqCiFEE/FzvMmU7/fZI1ttHzY0iRFf3UnB82up3nAEV40NfVwYERNSSb5rCuGjUih7N/hv5BJvHo8hOYqilzdSv6cY1eEmpE8MCT8ZSc87JlO7Lb/V6xWthj5/vJyEa0dT+vZWar7PxVFcg8fmRBsRSmjfWCLOSyV25hAiJ6QFHefZLHZmOiOX303RKxupXn8ER3kdushQwsekkHT7RKKnBa5y5WhSuCJ8bHCjAKerMQZNmMG3wXJbaY160t+/hbL3dlC+eCcNh8q9f0u9o4m9YijJd01BF9ny23PRfkbEz2F1/tO+30eepApeUthQ7hzxFWsLnudI9QZsrhrC9HGkRkxgSvJdpISPYnvZu0HHMz7xZqIMyWwsepni+j24VQcxIX0YmfATJve8g/zaba1er1G0XN7nj4xOuJatpW+TW/M9NY5inB4bodoIYkP7khpxHkNiZ5IWOSHoOM9m6bEzuXvkcjYWvcKR6vXUOcoJ1UWSEj6GiUm30z96WsBraxzHC1f0Ch8bdAwzUn9H38jz2VLyBoV1GdhcNYQbEugXNY3zk+8m3hj49a0xBoMmzLfBsmg75cQ5lkII0dUpiqJOLny8s8MQHST/2TUUPLeWkF7RjN3y8MkvOJ2+nltDwbNriZiQyvAlP+/QvgLZd+0b1GzKJeXBaaT+tnMXkdvyLWRMWggEN4q2KWU+qqoqHRDaGaEoitpdv10XsCb/WdYWPEd0SC8eHrulg/t6jrUFz5IaMYGfD1/SoX0F8sa+a8mt2cS0lAe5JPW3nRJDI4stn4UZ3nW+tw1dTN+oKW26fv6mlE57bZE1U0IIIUQA1Ru8padTH53RKf27rQ5qdxSgizaSfPf5nRKDEKL9HaneAOBb93SmOdxWCmp3YNRFc37y3Z0SQ3ch0/yEEEJ0SfaCKjalzAfANDSJUavuadf7uxsc1GUUEn3RACInds4UyNof8lDtLpJ/fWGnTcNrOholxLmgyl7A/E3eaqpJpqHcMyr4qqz+ONwNFNZlMCD6ItIiJ7brvU9VXu0PuFQ7Fyb/mlBd4EJGHanpaFRXJsmUEEII4YfWZGBS7p86NYbo6QOQKatCdC8GrYk/Tcrt1BgGRE/vtgUhzjRZMyWE6HZkzVT35nG4UJ3u5gc1GrTG7lcx7GygqiqeBkeL45pQPYq2basFZM2UOJu5PA7cavNN1jVo0GuNnRRR96aqKg5PQ4vjek0oGqVt1WU7c82UjEwJIYToUjQGHRjk7etMURQFbVhIZ4chRIfTaQzoMHR2GOcMRVEI0QbeeqSrkAIUQgghhBBCCBEESaaEEEIIIYQQIgiSTAkhhBBCCCFEECSZEkIIIYQQQoggSDIlhBBCCCGEEEGQZEoIIYQQQgghgiDJlBBCCCGEEEIEQZIpIYQQQgghhAiCJFNCCCGEEEIIEQRFVdXOjkEIIdqVJlRfotpdiZ0dhxCiOSVEV+qxOZM6O45g6TWhJS7VLq8tQpxldEpIqdNj65TXFkmmhBBCCCGEECIIMs1PCCGEEEIIIYIgyZQQQgghhBBCBEGSKSGEEEIIIYQIgiRTQgghhBBCCBEESaaEEEIIIYQQIgiSTAkhhBBCCCFEECSZEkIIIYQQQoggSDIlhBBCCCGEEEGQZEoIIYQQQgghgiDJlBBCCCGEEEIEQZIpIYQQQgghhAiCJFNCCCGEEEIIEQRJpoQQQgghhBAiCJJMCSGEEEIIIUQQJJkSQgghhBBCiCBIMiWEEEIIIYQQQZBkSgghhBBCCCGCIMmUEEIIIYQQQgRBkikhhBBCCCGECIIkU0IIIYQQQggRhP8HektFgp3M7zEAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import export_text\n",
        "iris = load_iris()\n",
        "r = export_text(clf_tree, feature_names=iris['feature_names'])\n",
        "r"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        },
        "id": "-SFU2GltBe-Y",
        "outputId": "585b2ea3-cabf-4b22-99e4-32632a41b0c3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'|--- petal length (cm) <= 2.60\\n|   |--- class: 0\\n|--- petal length (cm) >  2.60\\n|   |--- petal width (cm) <= 1.65\\n|   |   |--- petal length (cm) <= 4.95\\n|   |   |   |--- class: 1\\n|   |   |--- petal length (cm) >  4.95\\n|   |   |   |--- petal width (cm) <= 1.55\\n|   |   |   |   |--- class: 2\\n|   |   |   |--- petal width (cm) >  1.55\\n|   |   |   |   |--- petal length (cm) <= 5.45\\n|   |   |   |   |   |--- class: 1\\n|   |   |   |   |--- petal length (cm) >  5.45\\n|   |   |   |   |   |--- class: 2\\n|   |--- petal width (cm) >  1.65\\n|   |   |--- class: 2\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 157
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf_tree.predict(X_test)\n",
        "print(\"Accuracy of Decision Tree model is:\",\n",
        "metrics.accuracy_score(y_test, y_pred)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZg1vQ9ECdHf",
        "outputId": "a3885d8e-12a6-47b1-c3ca-c262efd547db"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Decision Tree model is: 93.33333333333333\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,15))\n",
        "cm = confusion_matrix(y_test, y_pred, labels = [0,1,2])\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=[0,1,2])\n",
        "disp.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "x5N7huA0C4la",
        "outputId": "4da2b64e-bc78-4443-c4e4-74782e499816"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "clf_RF = RandomForestClassifier(n_estimators=50)\n",
        "clf_RF.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "-wFzPQPeCJCQ",
        "outputId": "7978efae-487f-4e15-e261-56cc58374c12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=50)"
            ],
            "text/html": [
              "<style>#sk-container-id-22 {color: black;background-color: white;}#sk-container-id-22 pre{padding: 0;}#sk-container-id-22 div.sk-toggleable {background-color: white;}#sk-container-id-22 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-22 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-22 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-22 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-22 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-22 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-22 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-22 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-22 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-22 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-22 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-22 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-22 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-22 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-22 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-22 div.sk-item {position: relative;z-index: 1;}#sk-container-id-22 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-22 div.sk-item::before, #sk-container-id-22 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-22 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-22 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-22 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-22 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-22 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-22 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-22 div.sk-label-container {text-align: center;}#sk-container-id-22 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-22 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-22\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" checked><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=50)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 175
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_RF.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NY0EKnr1CPvS",
        "outputId": "490aa311-9067-4adc-c460-54a94d9e6e3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 176
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf_RF.predict(X_test)\n",
        "print(\"Accuracy of Random Forest model is:\",\n",
        "metrics.accuracy_score(y_test, y_pred)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fR1itLMBCUlN",
        "outputId": "7218850c-e564-41b5-afbb-5751b8196619"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Random Forest model is: 96.66666666666667\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,15))\n",
        "cm = confusion_matrix(y_test, y_pred, labels = [0,1,2])\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=[0,1,2])\n",
        "disp.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "waB9VJR3D006",
        "outputId": "aba760e7-4827-4dcb-b5cf-801734a4d67a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
        "clf_AB = AdaBoostClassifier(n_estimators=50)\n",
        "clf_AB.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "oJb-UOUWFz0Y",
        "outputId": "4c615261-12bc-40c6-fa30-f6e4ac5b7dab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "AdaBoostClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-23 {color: black;background-color: white;}#sk-container-id-23 pre{padding: 0;}#sk-container-id-23 div.sk-toggleable {background-color: white;}#sk-container-id-23 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-23 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-23 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-23 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-23 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-23 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-23 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-23 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-23 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-23 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-23 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-23 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-23 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-23 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-23 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-23 div.sk-item {position: relative;z-index: 1;}#sk-container-id-23 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-23 div.sk-item::before, #sk-container-id-23 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-23 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-23 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-23 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-23 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-23 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-23 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-23 div.sk-label-container {text-align: center;}#sk-container-id-23 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-23 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-23\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>AdaBoostClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" checked><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">AdaBoostClassifier</label><div class=\"sk-toggleable__content\"><pre>AdaBoostClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "clf_AB.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Vf40tLLF9jw",
        "outputId": "3a4c7850-0409-4766-d0db-e4ea03fdecc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9666666666666667"
            ]
          },
          "metadata": {},
          "execution_count": 180
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = clf_RF.predict(X_test)\n",
        "print(\"Accuracy of Random Forest model is:\",\n",
        "metrics.accuracy_score(y_test, y_pred)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrQxUkbaGCdl",
        "outputId": "7ec2fe88-40b4-4569-9933-beef19de52a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy of Random Forest model is: 100.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure(figsize=(15,15))\n",
        "cm = confusion_matrix(y_test, y_pred, labels = [0,1,2])\n",
        "disp = ConfusionMatrixDisplay(confusion_matrix=cm,display_labels=[0,1,2])\n",
        "disp.plot()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "CTuJzWQ5GGRv",
        "outputId": "3a105540-973f-4e57-e44c-827f82d7ac50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x1080 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exercise\n",
        "\n"
      ],
      "metadata": {
        "id": "-8qdD4CJtTls"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "H-_mjBLQtbaa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Generating the data\n",
        "\n",
        "a=[]\n",
        "b=[]\n",
        "c=[]\n",
        "d=[]\n",
        "e=[]\n",
        "for x in range (50000):\n",
        "    a+=[ <choice> * np.random.rand() ]\n",
        "    b+=[ <choice> * np.random.rand() ]\n",
        "    c+=[ <choice> * np.random.rand() ]\n",
        "    d+=[ <choice> * np.random.rand() ]\n",
        "    if <choice> *a[x]+<choice> *b[x]+<choice> *c[x]+<choice> *d[x]><choice>:\n",
        "        e+=[0]\n",
        "    else:\n",
        "        e+=[1]\n",
        "\n",
        "data = pd.DataFrame({'A':a,'B':b,'C':c,'D':d,'E':e})"
      ],
      "metadata": {
        "id": "m8xLcnc6t387"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data, test_data = <fill>(data)\n",
        "x_train=train_data[['A','B','C','D']]\n",
        "y_train=train_data['E']"
      ],
      "metadata": {
        "id": "TVwSSMBduOsQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression(random_state=0,max_iter=<choice>)\n",
        "model.fit(x_train,y_train)\n",
        "x_test=test_data[['A','B','C','D']]\n",
        "y_test=test_data['E']\n",
        "print(accuracy_score(y_test, model.<fill>(x_test)))"
      ],
      "metadata": {
        "id": "SOhiWR4nugLE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "coefficients = pd.concat([pd.DataFrame(data[['A','B','C','D']].columns),pd.DataFrame(np.transpose(model.coef_))], axis = 1)\n",
        "print(coefficients)"
      ],
      "metadata": {
        "id": "aE3g4bDpusFo"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}