All_About_NumPy.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "NumPy is a Python library and is written partially in Python, but most of the parts that require fast computation are written in C or C++."
      ],
      "metadata": {
        "id": "Rl0NVqY954uT"
      }
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3 4 5]\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "arr = np.array([1, 2, 3, 4, 5])\n",
        "\n",
        "print(arr)\n",
        "\n",
        "print(type(arr))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "print(np.__version__)"
      ],
      "metadata": {
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        "id": "a0N-NaDI6UdO",
        "outputId": "b1d84245-456a-4f4c-c5fc-fe94442e5696"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.25.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 0-D Arrays\n",
        "0-D are scalers basically\n"
      ],
      "metadata": {
        "id": "SDswajK553yO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "arr = np.array(42)\n",
        "\n",
        "print(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "26mAnvdc7QyZ",
        "outputId": "ec4102d0-594d-4135-a0f0-d71a13aea87c"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "42\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1-D Arrays"
      ],
      "metadata": {
        "id": "NC9FdeOT7YxT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([1, 2, 3, 4, 5])\n",
        "\n",
        "print(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GkK-bXht7aJD",
        "outputId": "22f1c194-2012-47e6-f0a0-4f981da9dfa0"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2-D Arrays"
      ],
      "metadata": {
        "id": "6HC4ySfD7gq3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "\n",
        "print(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gFxxLA3n7i7G",
        "outputId": "e07438a9-5b3e-4b66-8054-c33db6ac2254"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "3-D arrays"
      ],
      "metadata": {
        "id": "7JkavVbi7uVK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])\n",
        "\n",
        "print(arr)"
      ],
      "metadata": {
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        },
        "id": "k3S6geop7vMl",
        "outputId": "a49be09f-0a5d-4004-cecc-baa11f1c77fa"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[1 2 3]\n",
            "  [4 5 6]]\n",
            "\n",
            " [[1 2 3]\n",
            "  [4 5 6]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Check Number of Dimensions?\n",
        "\n",
        "ChatGPT\n",
        "NumPy Arrays offer the ndim attribute, indicating the array's dimensions."
      ],
      "metadata": {
        "id": "FT8x1Z8371-P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "QkSRHt208F2Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "a = np.array(42)\n",
        "b = np.array([1, 2, 3, 4, 5])\n",
        "c = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "d = np.array([[[1, 2, 3], [4, 5, 6]], [[1, 2, 3], [4, 5, 6]]])\n",
        "\n",
        "print(\"a =\", a.ndim,\"-Dimensional\")\n",
        "print(\"b =\", b.ndim,\"-Dimensional\")\n",
        "print(\"c =\", c.ndim,\"-Dimensional\")\n",
        "print(\"d =\", d.ndim,\"-Dimensional\")\n"
      ],
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        "outputId": "a669f427-6d91-4852-d731-1c6157ef3a60"
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      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "a = 0 -Dimensional\n",
            "b = 1 -Dimensional\n",
            "c = 2 -Dimensional\n",
            "d = 3 -Dimensional\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Higher Dimensional Arrays\n",
        "we can also specify dimentions my ourself"
      ],
      "metadata": {
        "id": "V93cI0UO8-EA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "arr = np.array([1, 2, 3, 4], ndmin=5)\n",
        "\n",
        "print(arr)\n",
        "print('number of dimensions :', arr.ndim)"
      ],
      "metadata": {
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        },
        "id": "UClOPSdJ9ElP",
        "outputId": "3de0db01-1e54-4d9c-b6f0-3955b5a667ef"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[[[1 2 3 4]]]]]\n",
            "number of dimensions : 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "d8OF9a7l9WHM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Access Array Elements"
      ],
      "metadata": {
        "id": "ytetLHtt-i_U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#For 1-D array\n",
        "arr = np.array([1, 2, 3, 4])\n",
        "\n",
        "print(arr[0])\n",
        "#For 2-D array\n",
        "arr = np.array([[1,2,3,4,5], [6,7,8,9,10]])\n",
        "\n",
        "print('2nd element on 1st row: ', arr[0, 1])\n",
        "#For 3-D array\n",
        "arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])\n",
        "\n",
        "print(arr[0, 1, 2])\n"
      ],
      "metadata": {
        "id": "tVyYEJZW-ltl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NumPy Array Slicing"
      ],
      "metadata": {
        "id": "dBI4Od6D_QTG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
        "\n",
        "print(arr[1:5])\n",
        "#print(arr[Start:End])\n",
        "\n",
        "#negative Slicing\n",
        "arr = np.array([1, 2, 3, 4, 5, 6, 7])\n",
        "\n",
        "print(arr[-3:-1])\n",
        "#Slicing 2-D array\n",
        "arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])\n",
        "\n",
        "print(arr[1, 1:4])\n",
        "#exaple 2\n",
        "arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])\n",
        "\n",
        "print(arr[0:2, 2])\n",
        "#example 3\n",
        "arr = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])\n",
        "\n",
        "print(arr[0:2, 1:4])"
      ],
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        "id": "B8mohIlQ_R6_",
        "outputId": "3c200675-bc41-4352-8edd-c4ea639fac4d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 3 4 5]\n",
            "[5 6]\n",
            "[7 8 9]\n",
            "[3 8]\n",
            "[[2 3 4]\n",
            " [7 8 9]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checking the Data Type of an Array\n"
      ],
      "metadata": {
        "id": "z_PrRxOtAE7E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "arr = np.array([1, 2, 3, 4])\n",
        "\n",
        "print(arr.dtype)\n",
        "#examle 2\n",
        "arr = np.array(['apple', 'banana', 'cherry'])\n",
        "\n",
        "print(arr.dtype)\n",
        "#creating arrray with defined datatype\n",
        "arr = np.array([1, 2, 3, 4], dtype='S')\n",
        "\n",
        "print(arr)\n",
        "print(arr.dtype)\n",
        "#Converting Data Type on Existing Arrays\n",
        "arr = np.array([1.1, 2.1, 3.1])\n",
        "\n",
        "newarr = arr.astype('i')\n",
        "\n",
        "print(newarr)\n",
        "print(newarr.dtype)"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "HhMMn3osAIfO",
        "outputId": "75c241af-6da7-4a04-b3e7-2e248d62c8ca"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "int64\n",
            "<U6\n",
            "[b'1' b'2' b'3' b'4']\n",
            "|S1\n",
            "[1 2 3]\n",
            "int32\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# COPY VS View\n",
        "The primary distinction between an array copy and a view lies in their ownership and behavior regarding modifications.\n",
        "\n",
        "A copy creates a new array, retaining ownership of its data. Changes to it won't affect the original array, and vice versa.\n",
        "\n",
        "In contrast, a view is not a new array; it merely provides a perspective on the original array. As such, changes to the view reflect on the original array, and changes to the original array are reflected in the view."
      ],
      "metadata": {
        "id": "kPnTKVhD3CYM"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "YEKFBXZ23Gix"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#copy\n",
        "import numpy as np\n",
        "\n",
        "arr = np.array([1, 2, 3, 4, 5])\n",
        "x = arr.copy()\n",
        "arr[0] = 42\n",
        "\n",
        "print(arr)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XE9D6umi3KrB",
        "outputId": "e82cd68d-1f03-416e-e06e-3145f37bf355"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[42  2  3  4  5]\n",
            "[1 2 3 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#View\n",
        "arr = np.array([1, 2, 3, 4, 5])\n",
        "x = arr.view()\n",
        "arr[0] = 42\n",
        "\n",
        "print(arr)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o0SCbSAy3Pbw",
        "outputId": "7bd75784-7580-4f3e-911c-66ac4ddd31d4"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[42  2  3  4  5]\n",
            "[42  2  3  4  5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#making changes in View\n",
        "\n",
        "arr = np.array([1, 2, 3, 4, 5])\n",
        "x = arr.view()\n",
        "x[0] = 31\n",
        "\n",
        "print(arr)\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7is-TSqg32Y4",
        "outputId": "95d30f0e-cf4a-414e-aa55-b758d697004e"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[31  2  3  4  5]\n",
            "[31  2  3  4  5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#anothere example\n",
        "arr = np.array([1, 2, 3, 4, 5])\n",
        "\n",
        "x = arr.copy()\n",
        "y = arr.view()\n",
        "\n",
        "print(x.base)\n",
        "print(y.base)\n",
        "#The copy will returns None.\n",
        "#The view will returns the original array."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LihEwOTQ4ATe",
        "outputId": "331c4a4b-c44e-4512-8e58-1ac1a80fba76"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "None\n",
            "[1 2 3 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Array Shape"
      ],
      "metadata": {
        "id": "ZMcVMqfP8XHH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "arr = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])\n",
        "\n",
        "print(arr.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0A-B4RNk8bUt",
        "outputId": "3bb02ecd-81ad-4906-f9b0-8b3b8fe83a84"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#another example\n",
        "arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]], [[13, 14, 15], [16, 17, 18]]])\n",
        "\n",
        "print(\"Original array:\")\n",
        "print(arr)\n",
        "print(\"Shape of the array:\", arr.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xVeZkTbB8pLp",
        "outputId": "c3bf3370-ddc1-4eae-c0f2-c8ba105050b0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original array:\n",
            "[[[ 1  2  3]\n",
            "  [ 4  5  6]]\n",
            "\n",
            " [[ 7  8  9]\n",
            "  [10 11 12]]\n",
            "\n",
            " [[13 14 15]\n",
            "  [16 17 18]]]\n",
            "Shape of the array: (3, 2, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Array Reshape\n",
        "Transform the provided 1-D array, comprising 12 elements, into a 2-D array.\n",
        "\n",
        "In this transformation, the outermost dimension will consist of 4 arrays, each containing 3 elements."
      ],
      "metadata": {
        "id": "YKSY8RQ1THRK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
        "\n",
        "newarr = arr.reshape(4, 3)\n",
        "\n",
        "print(newarr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bN_0Ui8mTN77",
        "outputId": "a9057e2b-09f1-43ef-a82f-87a9d62b795f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1  2  3]\n",
            " [ 4  5  6]\n",
            " [ 7  8  9]\n",
            " [10 11 12]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Transform the given 1-D array, which consists of 12 elements, into a 3-D array structure.\n",
        "\n",
        "In this transformation, the outermost dimension will consist of 2 arrays, each containing 3 arrays, and each of these inner arrays will contain 2 elements."
      ],
      "metadata": {
        "id": "je8FYdDKT9SM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n",
        "\n",
        "newarr = arr.reshape(2, 3, 2)\n",
        "\n",
        "print(newarr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9U3tQHp7Twl4",
        "outputId": "f9354092-1934-4660-c0d1-e8686c09a058"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[ 1  2]\n",
            "  [ 3  4]\n",
            "  [ 5  6]]\n",
            "\n",
            " [[ 7  8]\n",
            "  [ 9 10]\n",
            "  [11 12]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NumPy Array Iterating"
      ],
      "metadata": {
        "id": "GwXj6CvuWCjI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Iterate on the elements of the following 1-D array as scaler:\n",
        "arr = np.array([1, 2, 3])\n",
        "\n",
        "for x in arr:\n",
        "  print(x)\n",
        "\n",
        "#Iterate on the elements of the following 2-D array as scaler:\n",
        "arr = np.array([[1, 2, 3], [4, 5, 6]])\n",
        "\n",
        "for x in arr:\n",
        "  for y in x:\n",
        "    print(y)\n",
        "#Iterate on the elements of the following 3-D array as scaler:\n",
        "arr = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])\n",
        "\n",
        "for x in arr:\n",
        "  for y in x:\n",
        "    for z in y:\n",
        "      print(z)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v7Q1qEkaWEz4",
        "outputId": "51f43f2a-c647-4c92-8ff5-c330539e7a9c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n",
            "11\n",
            "12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Iterating Arrays Using nditer()"
      ],
      "metadata": {
        "id": "7AjyRgY9XDJF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])\n",
        "\n",
        "for x in np.nditer(arr):\n",
        "  print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KUt5JHfAXHP2",
        "outputId": "63e8a6db-687b-4063-9cec-770bcdf69997"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([1, 2, 3])\n",
        "\n",
        "for x in np.nditer(arr, flags=['buffered'], op_dtypes=['S']):\n",
        "  print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ixEg-x0LXTGe",
        "outputId": "8f16868e-c234-4175-ac07-6444f2579fa9"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "b'1'\n",
            "b'2'\n",
            "b'3'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([1, 2, 3])\n",
        "\n",
        "for idx, x in np.ndenumerate(arr):\n",
        "  print(idx, x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "roaWPEVmXsEs",
        "outputId": "5e289afe-686c-4fe3-8a6d-7b1f14662ebb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(0,) 1\n",
            "(1,) 2\n",
            "(2,) 3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Joing"
      ],
      "metadata": {
        "id": "lG-2fue2X1jg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr1 = np.array([1, 2, 3])\n",
        "\n",
        "arr2 = np.array([4, 5, 6])\n",
        "\n",
        "arr = np.concatenate((arr1, arr2))\n",
        "\n",
        "print(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o4TiBZDsX07Y",
        "outputId": "2350d2af-4b9e-49a7-c09c-480d3c0e3c36"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 2 3 4 5 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Join two 2-D arrays along rows (axis=1):"
      ],
      "metadata": {
        "id": "AjgsPfFLYKP_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr1 = np.array([[1, 2], [3, 4]])\n",
        "\n",
        "arr2 = np.array([[5, 6], [7, 8]])\n",
        "\n",
        "arr = np.concatenate((arr1, arr2), axis=1)\n",
        "\n",
        "print(arr)"
      ],
      "metadata": {
        "id": "_Z4o9MeTYJI7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using stack Function"
      ],
      "metadata": {
        "id": "BTKRQPtSYWZ3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Stacking Along Columns\n",
        "import numpy as np\n",
        "\n",
        "arr1 = np.array([1, 2, 3])\n",
        "\n",
        "arr2 = np.array([4, 5, 6])\n",
        "\n",
        "arr = np.vstack((arr1, arr2))\n",
        "\n",
        "print(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ei--JRrwKZcD",
        "outputId": "980ce646-7f97-467c-f842-6faa91ab937d"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1 2 3]\n",
            " [4 5 6]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Stacking along depth\n",
        "arr1 = np.array([1, 2, 3])\n",
        "\n",
        "arr2 = np.array([4, 5, 6])\n",
        "\n",
        "arr = np.dstack((arr1, arr2))\n",
        "\n",
        "print(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SXH9icDfKthR",
        "outputId": "edd03f5d-708d-4434-9f81-c4b1af661db8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[[1 4]\n",
            "  [2 5]\n",
            "  [3 6]]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NumPy Splitting Array"
      ],
      "metadata": {
        "id": "wfbtXazBLMIq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([1, 2, 3, 4, 5, 6])\n",
        "\n",
        "newarr = np.array_split(arr, 3)\n",
        "\n",
        "print(newarr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CYWInV89LK3O",
        "outputId": "ef6281d2-e7dc-4e0d-b952-12b4d063e56b"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([1, 2]), array([3, 4]), array([5, 6])]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([1, 2, 3, 4, 5, 6])\n",
        "\n",
        "newarr = np.array_split(arr, 4)\n",
        "\n",
        "print(newarr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L3FguZigLWzm",
        "outputId": "a9e5b6f2-e970-4516-bf54-11ad2fc47667"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([1, 2]), array([3, 4]), array([5]), array([6])]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#spliting 2-D\n",
        "arr = np.array([[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12]])\n",
        "\n",
        "newarr = np.array_split(arr, 3)\n",
        "\n",
        "print(newarr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ab5alQGaLlGQ",
        "outputId": "8a6ddfe7-84d1-4b72-e949-c7515b690036"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([[1, 2],\n",
            "       [3, 4]]), array([[5, 6],\n",
            "       [7, 8]]), array([[ 9, 10],\n",
            "       [11, 12]])]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Similar alternates to vstack() and dstack() are available as vsplit() and dsplit()."
      ],
      "metadata": {
        "id": "-PWXpRrWL4w7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NumPy Searching Arrays"
      ],
      "metadata": {
        "id": "At5sMS1OMDrY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([1, 2, 3, 4, 5, 4, 4])\n",
        "\n",
        "x = np.where(arr == 4)\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DPxDx4bYMGhp",
        "outputId": "5e8131d4-3557-4808-ec10-99a934767bfb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(array([3, 5, 6]),)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " The searchsorted() method is assumed to be used on sorted arrays"
      ],
      "metadata": {
        "id": "XbyJ_HjZMc_j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([6, 7, 8, 9])\n",
        "\n",
        "x = np.searchsorted(arr, 7)\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DHFTGSmFMkye",
        "outputId": "ab2866cf-7320-415f-c339-e8cb60e114b7"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NumPy Sorting Arrays"
      ],
      "metadata": {
        "id": "Kra0VtWuNAwK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([3, 2, 0, 1])\n",
        "\n",
        "print(np.sort(arr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vqFx60n3NEeQ",
        "outputId": "18a19c77-1359-4b35-8e7e-75c1f2d55814"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0 1 2 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This method returns a copy of the array, leaving the original array unchanged."
      ],
      "metadata": {
        "id": "mqsYor1gNPzx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([True, False, True])\n",
        "\n",
        "print(np.sort(arr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X06vMWarNVFT",
        "outputId": "409b23a4-54d9-4d4b-d995-725c3402f142"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[False  True  True]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array(['banana', 'cherry', 'apple'])\n",
        "\n",
        "print(np.sort(arr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZJyziF0DNY6m",
        "outputId": "a3b642c2-0c5e-4fd3-925a-5d1052427579"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['apple' 'banana' 'cherry']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([[3, 2, 4], [5, 0, 1]])\n",
        "\n",
        "print(np.sort(arr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ioNnSry7Ndcx",
        "outputId": "f78f5229-96ed-4526-bd33-b809a883b525"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2 3 4]\n",
            " [0 1 5]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NumPy Filter Array"
      ],
      "metadata": {
        "id": "3gvmMhfHNtR-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([41, 42, 43, 44])\n",
        "\n",
        "x = [True, False, True, False]\n",
        "\n",
        "newarr = arr[x]\n",
        "\n",
        "print(newarr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gUDrvbUsN3SN",
        "outputId": "c2e6c22e-ecff-4077-f074-b4b535d6878c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[41 43]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.array([41, 42, 43, 44])\n",
        "\n",
        "# Create an empty list\n",
        "filter_arr = []\n",
        "\n",
        "# go through each element in arr\n",
        "for element in arr:\n",
        "  # if the element is higher than 42, set the value to True, otherwise False:\n",
        "  if element > 42:\n",
        "    filter_arr.append(True)\n",
        "  else:\n",
        "    filter_arr.append(False)\n",
        "\n",
        "newarr = arr[filter_arr]\n",
        "\n",
        "print(filter_arr)\n",
        "print(newarr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "stlYiXGdN_Vb",
        "outputId": "a11718f2-85f7-4079-ad33-39d4b7d806a2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[False, False, True, True]\n",
            "[43 44]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Random Numbers in NumPy\n",
        "Generate Random Number"
      ],
      "metadata": {
        "id": "dh6L-ds9S3XO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from numpy import random\n",
        "\n",
        "x = random.randint(100)\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H723nD3MTPD6",
        "outputId": "8c5bb869-19b1-4ddb-87e5-7b69d71f9d77"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate random Float Number"
      ],
      "metadata": {
        "id": "qYOnrkdcTc1Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import random\n",
        "\n",
        "x = random.rand()\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yDcvDKAnTcEb",
        "outputId": "5a308f43-8f02-4f3c-b55f-ded0eb473c2e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.03342155723425155\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Genrate Random array"
      ],
      "metadata": {
        "id": "p7xJxBPVTrGR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x=random.randint(100, size=(5))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R-7lmhuPTp0x",
        "outputId": "6403e3f3-56b1-4845-8fae-0dccf0a69c57"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[54  7 10  3 78]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate Random 2D array"
      ],
      "metadata": {
        "id": "mFHXp9NCTzej"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "x = random.randint(100, size=(3, 5))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X8xsKl-GTtSY",
        "outputId": "8abab4be-b7b9-4831-d4f0-c8432d1cb7b7"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[39 28 86  7 68]\n",
            " [50  4 64 83 26]\n",
            " [87 72  3 55 15]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Randaom Data distribution"
      ],
      "metadata": {
        "id": "vjxBGQGlUmm4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Generate a 1-D array containing 100 values, where each value has to be 3, 5, 7 or 9.\n",
        "\n",
        "The probability for the value to be 3 is set to be 0.1\n",
        "\n",
        "The probability for the value to be 5 is set to be 0.3\n",
        "\n",
        "The probability for the value to be 7 is set to be 0.6\n",
        "\n",
        "The probability for the value to be 9 is set to be 0"
      ],
      "metadata": {
        "id": "exm53LpCVBdP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The sum of all probability numbers should be 1."
      ],
      "metadata": {
        "id": "VBJ7SqFeVHjU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = random.choice([3, 5, 7, 9], p=[0.1, 0.3, 0.6, 0.0], size=(100))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SPkJ4681Ul5R",
        "outputId": "e372dc6c-6ebb-447e-8afd-bf39f3ff485b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[7 3 7 5 7 7 5 7 7 7 7 7 7 7 7 5 7 3 3 7 7 7 7 5 7 7 7 5 5 3 7 5 7 5 5 5 7\n",
            " 5 3 7 5 5 3 7 5 7 7 7 7 7 7 7 7 5 7 7 7 5 5 7 7 7 7 5 7 7 7 7 7 3 7 5 5 7\n",
            " 5 7 7 7 7 5 7 5 5 5 3 7 5 7 7 7 7 3 3 7 7 7 5 5 7 3]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Random Permutations**\n",
        " shuffle() and permutation()"
      ],
      "metadata": {
        "id": "H_7LgoNXVUyP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Shuffling Arrays"
      ],
      "metadata": {
        "id": "XmPys857VeCu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import random\n",
        "import numpy as np\n",
        "\n",
        "arr = np.array([1, 2, 3, 4, 5])\n",
        "\n",
        "random.shuffle(arr)\n",
        "\n",
        "print(arr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BnaYDIY1VhwB",
        "outputId": "1c762752-d105-4702-e6c5-314a4302be7d"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2 1 3 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generating Permutation of Arrays"
      ],
      "metadata": {
        "id": "RT2-z5MpVni0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import random\n",
        "import numpy as np\n",
        "\n",
        "arr = np.array([1, 2, 3, 4, 5])\n",
        "\n",
        "print(random.permutation(arr))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jcSbSUHGVpJv",
        "outputId": "8604a966-769c-4c68-eae1-9a8f379193a2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[3 5 2 1 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Normal Distribution\n",
        "Use the random.normal() method to get a Normal Data Distribution.\n",
        "\n",
        "It has three parameters:\n",
        "\n",
        "loc - (Mean) where the peak of the bell exists.\n",
        "\n",
        "scale - (Standard Deviation) how flat the graph distribution should be.\n",
        "\n",
        "size - The shape of the returned array."
      ],
      "metadata": {
        "id": "mrm8rOtKYtZX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = random.normal(size=(2, 3))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mMI8ZkU1Y5Nb",
        "outputId": "f421352a-de09-44d1-d426-0895608e913b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.36191184  2.02477212  1.07333234]\n",
            " [ 0.01062074 -1.57469718  1.53860779]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x = random.normal(loc=1, scale=2, size=(2, 3))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkNSaMsFY_MF",
        "outputId": "ce115077-306f-4e12-b9e9-3dccf0abd867"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1.80736749  1.7119536   1.41792882]\n",
            " [ 0.80643114 -0.09706229 -0.81855078]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.distplot(random.normal(size=1000), hist=False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "7jzUC_muZGYQ",
        "outputId": "db095676-2575-4cab-fd3f-f32c848f7bf0"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-26-2f9e2247caac>:5: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.normal(size=1000), hist=False)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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AiKjPmc2CdfuI1EmRULvaf+tPq1BvV/wyeSAAYO2u9tcaIyLbwwBERH1u+8lynNbUwUMpx68dqPWn1eKpUZBJJfgpvxqnNdxLkMgeMAARUZ8ShKutP4smRsDbTSFyRb0vzNsVc0ZYVq/f+FOhuMUQUacwABFRn/rhdAVOlungppDh4cmRYpfTZ349OQIAsPVwCarruTo0ka1jACKiPiMIAt76wTIu5lcTBsHH3fFaf1qNGeiD+AFqGJvN+CinSOxyiOgGGICIqM/8mF+FvOJaqFyk+M3kKLHL6VMSiQS/bmnhen9fEUxmToknsmUMQETUZ1pbfx4YNxABno6/Fc2ckcHwdnOBRteIn/KrxC6HiK6DAYiI+sT+C9XIKaiBQibFI1Mdu/WnlVIuw53xoQCATw9dErkaIroeBiAi6hNv77S0/tybNAAhaleRq+k/9yYOAAB8d0IDXWOTyNUQUUcYgIio1+UV12LPuSrIpBIsmRYtdjn9alSYGrGBHjA0m/H10TKxyyGiDjAAEVGve7tlz6+7Roch3NdN5Gr6l0QisbYCsRuMyHYxABFRrzpRqsX3pyogkQCPTneu1p9Wd40Og1QCHLp4GUXVV8Quh4jawQBERL1qbdZ5AMDtcaGICvAQuRpxBHqpMD7KDwDw7XF2gxHZIgYgIuo1JbUN+Pa4BgCcbuzP/7plpGVrjNb/H0RkWxiAiKjXbNpbCJNZwMRoPwwP9RK7HFHNHhEMicQyILxM2yB2OUT0PxiAiKhX6A3N1i0gHHnPr84K9FIhcaAPACCDrUBENocBiIh6xScHi1HX2IxIf3fMGBIodjk2YQ67wYhsFgMQEfWY2Sxg495CAEDqpAhIpRJxC7IRrQHoQGENKuu4QzyRLWEAIqIe23u+GoXVV+CplOOeMQPELsdmDPBxQ9wANQQB+P5UudjlENHPMAARUY99mHMRADBvdBjclXKRq7EtKcOCAABZZypEroSIfo4BiIh6pKKuEdtPWFo3fpk8UORqbE/reKgfz1XB2GwWuRoiasUAREQ98snBS2g2Cxg90BvDQpx76nt7RoR6wd9DAb3RhIMXa8Quh4haMAARUbeZzQI+PmCZ+v7LcWz9aY9UKsG0wZZWoKwzlSJXQ0StGICIqNuyL1SjuKYBnio5bo8LFbscmzV9SAAAjgMisiUMQETUbVtzSwAAd8SHwlUhE7ka2zU1NgBSCXC2vB4ltVwVmsgWMAARUbdcMTYjo2Wjz7tHh4lcjW1Tu7lgTMuq0GwFIrINDEBE1C3bT5RDbzRhoK8bEgf5iF2OzWvtBtvFcUBENoEBiIi6ZethS/fXvNFhkEi48vONTI61BKB9F6phMgsiV0NEDEBE1GUVukb8eM7SksHur84ZFaaGp0oOXWMzjpdoxS6HyOkxABFRl/33SCnMAjBmoDci/N3FLscuyKQSjI/yA2DZOoSIxMUARERd9s0xy+DnO+M59b0rJka3BqAqkSshIgYgIuqS0toG5BbVQiIBbhkVInY5dmVSjD8Ay+7whmaTyNUQOTebCEBr1qxBREQEVCoVkpOTkZOT0+G1W7duRVJSEry9veHu7o6EhAS89957ba4RBAHLli1DSEgIXF1dkZKSgnPnzvX1yyByCt8e1wAAkgb5IMhLJXI19iU20AP+Hko0NpmRe7FW7HKInJroAWjLli1IS0vD8uXLkZubi/j4eMyePRsVFe2vleHr64vnn38e2dnZOHr0KFJTU5GamorvvvvOes2rr76KN998E+vWrcP+/fvh7u6O2bNno7Gxsb9eFpHDau3+upWtP10mkUjYDUZkI0QPQKtWrcLixYuRmpqK4cOHY926dXBzc8OGDRvavX769Om46667MGzYMERHR+OJJ55AXFwcfvzxRwCW1p/Vq1fjz3/+M+bOnYu4uDhs3rwZpaWl2LZtWz++MiLHU6ZtwKGLlwEAt4xkAOqOSTGWAPRTPgMQkZhEDUBGoxGHDh1CSkqK9ZxUKkVKSgqys7Nv+HxBEJCZmYkzZ85g6tSpAICCggJoNJo291Sr1UhOTu7wngaDATqdrs1BRNf69tjV7q9gNbu/umNitGUc0NFLWlwxNotcDZHzEjUAVVVVwWQyISgoqM35oKAgaDSaDp+n1Wrh4eEBhUKB2267DW+99RZuvvlmALA+ryv3TE9Ph1qtth7h4eE9eVlEDiujZfwPu7+6b4CPK4K9VGg2C8grqhW7HCKnJXoXWHd4enoiLy8PBw4cwN/+9jekpaUhKyur2/dbunQptFqt9SguLu69YokcRI3eiIMXawAAs0YE3eBq6ohEIsHYSF8AQE5hjcjVEDkvuZjf3N/fHzKZDOXl5W3Ol5eXIzg4uMPnSaVSxMTEAAASEhJw6tQppKenY/r06dbnlZeXIyTk6qfU8vJyJCQktHs/pVIJpVLZw1dD5Nh+OF0BswAMD/HCAB83scuxa+MifPDlkVIcYAAiEo2oLUAKhQKJiYnIzMy0njObzcjMzMSECRM6fR+z2QyDwQAAiIyMRHBwcJt76nQ67N+/v0v3JKK2vj9p+aCSMpytPz3V2gJ0uKgWTSazyNUQOSdRW4AAIC0tDQsXLkRSUhLGjRuH1atXQ6/XIzU1FQCwYMEChIWFIT09HYBlvE5SUhKio6NhMBjwzTff4L333sPatWsBWJqXn3zySbz88suIjY1FZGQkXnjhBYSGhmLevHlivUwiu9bYZMLulr2/ZjEA9djgQE+oXV2gbWjCyVId4sO9xS6JyOmIHoDmz5+PyspKLFu2DBqNBgkJCcjIyLAOYi4qKoJUerWhSq/X49FHH8WlS5fg6uqKoUOH4v3338f8+fOt1zzzzDPQ6/V45JFHUFtbi8mTJyMjIwMqFWetEHVH9vlqXDGaEKJWYUSol9jl2D2pVIKkQT7IPF2BA4U1DEBEIpAIgiCIXYSt0el0UKvV0Gq18PLiL3uipVuP4aOcIvxq/CC8NG+k2OU4hHW7zuOVb09j1vAg/GtBktjlEDmErrx/2+UsMCLqP4IgIPMUx//0trERlnFABy9eBj+HEvU/BiAiuq4TpTpU1BngppBhfJSv2OU4jFFhaqhcpKjRG3G+sl7scoicDgMQEV3XrrOWwc8To/2hlMtErsZxKORSJLSM/ckpuCxuMUROiAGIiK6rNQBNGxIgciWOx9oNxvWAiPodAxARdUjX2GTd/HT6YAag3tYagLgiNFH/YwAiog7tza+CySwgKsAd4b5c/bm3jRnkA6kEuHS5AWXaBrHLIXIqDEBE1KGsM5bur+mDA0WuxDF5KOUYEaoGAOQUsBWIqD8xABFRuwRB4PifftDaDcZ9wYj6FwMQEbXrXEU9yrSNUMqlSI7k9Pe+Mi7SBwBwgDPBiPoVAxARtSvrTAUAYEK0H1QunP7eV5JaWoDOlNeh9opR5GqInAcDEBG1y9r9xdlffcrfQ4moAHcAsM64I6K+xwBERNfQG5qtXTLTh3AAdF8bx+nwRP2OAYiIrpF9vhpGkxkDfd0Q4cfp730tcZBlHNChQrYAEfUXBiAiukbWWcv4n+lDAiCRSESuxvG1jgM6WqKFodkkcjVEzoEBiIjaEATBuv4Px//0jwg/N/i5K2BsNuN4iVbscoicAgMQEbVRUKXHpcsNUMikmBDtJ3Y5TkEikVi7wQ6yG4yoXzAAEVEbra0/4yJ94aaQi1yN80iKaAlAnAlG1C8YgIiojd3nLAFo6mB/kStxLomDLOOADl28DEEQRK6GyPExABGRlaHZhH0XqgEAUzn+p1+NDPOCUi5Fjd6IC1V6scshcngMQERkdajwMhqbzAj0VGJIkKfY5TgVpVyG+AHeADgdnqg/MAARkdWulu6vKbGc/i6GROs4IC6ISNTXGICIyGrP2SoAHP8jlqRBHAhN1F8YgIgIAFBZZ8DJMh0AYHIMA5AYWqfCX6jUo0bPjVGJ+hIDEBEBAH7Mt3R/jQzzgp+HUuRqnJO3mwIxgR4AuDEqUV9jACIiAMDulu6vKbGc/SWmq91gHAdE1JcYgIgIZrOAPedaxv8wAImKK0IT9Q8GICLCaU0dquoNcFPIrG/AJI6xLRujHrukRWMTN0Yl6isMQERkXf15QpQfFHL+WhDTID83+HsoYDRxY1SivsTfdESEPdb1fzj7S2xtNkblQGiiPsMAROTkrhibcaDA8kY7hdtf2ISkln3BOA6IqO8wABE5uf0FNTCazAjzdkWUv7vY5RCurgidW8SNUYn6CgMQkZPbffbq7u/c/sI2jAxVc2NUoj7GAETk5Dj93fYo5FLrxqgHC7keEFFfYAAicmKltQ3Ir6iHVAJMjOYAaFti3RiV44CI+gQDEJETa539FR/uDbWbi8jV0M+NbQlA3BKDqG/YRABas2YNIiIioFKpkJycjJycnA6vXb9+PaZMmQIfHx/4+PggJSXlmusXLVoEiUTS5pgzZ05fvwwiu7Ob3V82a8zAlo1Rq/SorjeIXA2R4xE9AG3ZsgVpaWlYvnw5cnNzER8fj9mzZ6OioqLd67OysvDAAw9g586dyM7ORnh4OGbNmoWSkpI2182ZMwdlZWXW46OPPuqPl0NkN0xmAT+2BqDB7P6yNd5uCsRyY1SiPiN6AFq1ahUWL16M1NRUDB8+HOvWrYObmxs2bNjQ7vUffPABHn30USQkJGDo0KF45513YDabkZmZ2eY6pVKJ4OBg6+Hj0/Hy/gaDATqdrs1B5OiOlWihbWiCp0puHXBLtiWJ3WBEfUbUAGQ0GnHo0CGkpKRYz0mlUqSkpCA7O7tT97hy5Qqamprg6+vb5nxWVhYCAwMxZMgQLFmyBNXV1R3eIz09HWq12nqEh4d37wUR2ZE9LdPfJ0X7Qy4T/bMQtSOxdUFEBiCiXifqb72qqiqYTCYEBQW1OR8UFASNRtOpezz77LMIDQ1tE6LmzJmDzZs3IzMzEytWrMCuXbtwyy23wGRqf2PBpUuXQqvVWo/i4uLuvygiO9G6/9cUdn/ZrKSWLTG4MSpR75OLXUBPvPLKK/j444+RlZUFlUplPX///fdb/zxq1CjExcUhOjoaWVlZmDlz5jX3USqVUCqV/VIzkS2oa2xCblEtAA6AtmWWjVGVqKo34OglLcZF+t74SUTUKaK2APn7+0Mmk6G8vLzN+fLycgQHB1/3uStXrsQrr7yC7du3Iy4u7rrXRkVFwd/fH/n5+T2umcgR/JRfDZNZQISfG8J93cQuhzogkUgwLtLSCpRT0HE3PhF1nagBSKFQIDExsc0A5tYBzRMmTOjwea+++ipeeuklZGRkICkp6Ybf59KlS6iurkZISEiv1E1k73aetsyynD4kUORK6EaSI/0AWPZsI6LeI/rIx7S0NKxfvx6bNm3CqVOnsGTJEuj1eqSmpgIAFixYgKVLl1qvX7FiBV544QVs2LABERER0Gg00Gg0qK+vBwDU19fj6aefxr59+1BYWIjMzEzMnTsXMTExmD17tiivkciWmM0CfjhjCUAzhzEA2brkqKs7wzeZzCJXQ+Q4RB8DNH/+fFRWVmLZsmXQaDRISEhARkaGdWB0UVERpNKrOW3t2rUwGo24995729xn+fLlePHFFyGTyXD06FFs2rQJtbW1CA0NxaxZs/DSSy9xnA8RgOOlWlTWGeCukHFMiR0YHOgJbzcX1F5pwtFLWiQO6nhJDyLqPNEDEAA8/vjjePzxx9t9LCsrq83XhYWF172Xq6srvvvuu16qjMjx/NDS/TUlNgBKuUzkauhGpFIJkiN98d2JcuwvqGYAIuoloneBEVH/ag1AN7H7y25YxwFd4Dggot7CAETkRCp0jTh6SQsAmD6E09/txdVxQDVo5jggol7BAETkRHa2DH6OH6BGoKfqBleTrRga7AUvlRx6owknSrlVD1FvYAAiciKZp1q6v4YG3eBKsiUyqcQ6YH3fBa4HRNQbGICInERjkwk/5lt2f+f0d/szPsoyDmjveQYgot7AAETkJPYX1OCK0YQgLyVGhHqJXQ510aQYy55tOQU1MDZzHBBRTzEAETmJ1tWfbxoaCIlEInI11FVDgjzh76FAQ5MJh4u4OzxRTzEAETkBQRCQedqy5x7H/9gnqVSCCdGWVqCf2A1G1GMMQEROIL+iHsU1DVDIpZgU4yd2OdRNk6ItP7ufWsZyEVH3MQAROYHMlu6vCVF+cFPYxALw1A2t44COFNei3tAscjVE9o0BiMgJ/HCKm586gnBfNwz0dUOzWUBOAbvBiHqCAYjIwVXVG3DwomULhZuGMgDZu9YuzB/PMQAR9QQDEJGD+/5kOcwCMCpMjQE+bmKXQz3U2g32Y36lyJUQ2TcGICIHl3FCAwCYMzJY5EqoN0yK9odUApwtr0dpbYPY5RDZLQYgIgema2yyzhiaPYIByBH4uCsQH+4NANh9lq1ARN3VrQB04cKF3q6DiPrAztMVaDIJiA5wR0ygh9jlUC+ZPtgylivrDAMQUXd1KwDFxMRgxowZeP/999HY2NjbNRFRL/mO3V8OadqQAACW9YCaTNwWg6g7uhWAcnNzERcXh7S0NAQHB+O3v/0tcnJyers2IuqBxiYTdp62tBDMGREicjXUm+LC1PB1V6DO0Izci9wWg6g7uhWAEhIS8Pe//x2lpaXYsGEDysrKMHnyZIwcORKrVq1CZSWbZYnEtvN0BRqaTAjzdsXIMG5+6kikUgmmxlpmg+3iOCCibunRIGi5XI67774bn3zyCVasWIH8/Hw89dRTCA8Px4IFC1BWVtZbdRJRF/33SCkA4I74UG5+6oBau8EYgIi6p0cB6ODBg3j00UcREhKCVatW4amnnsL58+exY8cOlJaWYu7cub1VJxF1QV1jE35o2f7ijnh2fzmiqbEBkEiAE6U6aLQci0nUVd0KQKtWrcKoUaMwceJElJaWYvPmzbh48SJefvllREZGYsqUKdi4cSNyc3N7u14i6oTvT5XD0GxGVIA7hoew+8sR+XkokdAyHf77U+XiFkNkh7oVgNauXYtf/vKXuHjxIrZt24bbb78dUmnbWwUGBuLf//53rxRJRF3z5RFL9/Mdcez+cmQ3Dw8CwABE1B3dCkA7duzAs88+i5CQtk3rgiCgqKgIAKBQKLBw4cKeV0hEXXJZb7QukHdHfKjI1VBfmtUSgPbmV3N3eKIu6lYAio6ORlVV1TXna2pqEBkZ2eOiiKj7Mk5o0GwWMDzEi4sfOrjoAA9E+LnBaDJzVWiiLupWABIEod3z9fX1UKlUPSqIiHrmy5/N/iLHJpFIrnaDnWQ3GFFXyLtycVpaGgDLP7ply5bBze3qztImkwn79+9HQkJCrxZIRJ1XoWtE9oVqAMDtcZz95QxuHh6M9XsK8MOZCjSbzJDLuMUjUWd0KQAdPnwYgKUF6NixY1AoFNbHFAoF4uPj8dRTT/VuhUTUaV8fK4MgAGMGeiPc1+3GTyC7N2agN3zcXHD5ShNyCmswMdpf7JKI7EKXAtDOnTsBAKmpqfj73/8OLy9OryWyJez+cj5ymRQpw4LwyaFLyDiuYQAi6qRutZW+++67DD9ENqa45gpyi2ohlQC3jWL3lzO5teXn/e1xDUzm9sdoElFbnW4Buvvuu7Fx40Z4eXnh7rvvvu61W7du7XFhRNQ1Xx21rP0zPsoPgV6cjOBMJsX4w0slR2WdAQcLa5Ac5Sd2SUQ2r9MBSK1WWxdUU6vVfVYQEXWdIAjYdrgEALu/nJFCLsXNw4PxWe4lfHOsjAGIqBM6HYDefffddv9MROI7UarDmfI6KORSa3cIOZfb4iwB6NvjGiy/YwSkUq4ATnQ93RoD1NDQgCtXrli/vnjxIlavXo3t27f3WmFE1HmfHroEwLI1gtrVReRqSAyTYwLgqZKjos6Agxcvi10Okc3rVgCaO3cuNm/eDACora3FuHHj8Prrr2Pu3LlYu3ZtrxZIRNfXZDLjvy2zv+4dM0Dkakgslm4wy6KIXx8tFbkaItvXrQCUm5uLKVOmAAA+/fRTBAcH4+LFi9i8eTPefPPNLt9vzZo1iIiIgEqlQnJyMnJycjq8dv369ZgyZQp8fHzg4+ODlJSUa64XBAHLli1DSEgIXF1dkZKSgnPnznW5LiJ7kHWmEjV6IwI8lZgSyynQzqx1/NdXR8vQZDKLXA2RbetWALpy5Qo8PT0BANu3b8fdd98NqVSK8ePH4+LFi12615YtW5CWlobly5cjNzcX8fHxmD17NioqKtq9PisrCw888AB27tyJ7OxshIeHY9asWSgpKbFe8+qrr+LNN9/EunXrsH//fri7u2P27NlobGzszsslsmmftXR/zUsI5SrATm5KjD/8PRSo1hux5xz3BiO6nm79toyJicG2bdtQXFyM7777DrNmzQIAVFRUdHl9oFWrVmHx4sVITU3F8OHDsW7dOri5uWHDhg3tXv/BBx/g0UcfRUJCAoYOHYp33nkHZrMZmZmZACytP6tXr8af//xnzJ07F3Fxcdi8eTNKS0uxbdu2du9pMBig0+naHET24LLeiMzTlj2g7mb3l9OTy6TWVqCtuSU3uJrIuXUrAC1btgxPPfUUIiIikJycjAkTJgCwtAaNHj260/cxGo04dOgQUlJSrhYklSIlJQXZ2dmduseVK1fQ1NQEX19fAEBBQQE0Gk2be6rVaiQnJ3d4z/T0dKjVausRHh7e6ddAJKavjpaiyWTZ+X1YCBcnJeDu0ZYgvONkOeoam0Suhsh2dSsA3XvvvSgqKsLBgweRkZFhPT9z5ky88cYbnb5PVVUVTCYTgoKC2pwPCgqCRqPp1D2effZZhIaGWgNP6/O6cs+lS5dCq9Vaj+Li4k6/BiIxfdryKf+eRLb+kMXIMC9EB7jD0GzGt8c793uUyBl1aS+wnwsODkZwcHCbc+PGjetxQV3xyiuv4OOPP0ZWVhZUqu6vfKtUKqFUKnuxMqK+l19RjyPFtZBJJZibwMUPyUIikeDuMQPw2ndnsO1wCX6RxBZtovZ0qwVIr9fjhRdewMSJExETE4OoqKg2R2f5+/tDJpOhvLy8zfny8vJrwtX/WrlyJV555RVs374dcXFx1vOtz+vOPYnsyWe5lsHP0wcHwN+DAZ6uag3E2ReqUaZtELkaItvUrRag3/zmN9i1axd+9atfISQkxLpFRlcpFAokJiYiMzMT8+bNAwDrgObHH3+8w+e9+uqr+Nvf/obvvvsOSUlJbR6LjIxEcHAwMjMzkZCQAADQ6XTYv38/lixZ0q06iWyNySzgc3Z/UQcG+LhhXKQvcgpqsO1wKZZMjxa7JCKb060A9O233+Lrr7/GpEmTelxAWloaFi5ciKSkJIwbNw6rV6+GXq9HamoqAGDBggUICwtDeno6AGDFihVYtmwZPvzwQ0RERFjH9Xh4eMDDwwMSiQRPPvkkXn75ZcTGxiIyMhIvvPACQkNDrSGLyN5ln6+GRtcItasLZg4LFLscskF3jw5DTkENPj98Cb+bFtXtD6pEjqpbAcjHx8c666qn5s+fj8rKSixbtgwajQYJCQnIyMiwDmIuKiqCVHq1p27t2rUwGo24995729xn+fLlePHFFwEAzzzzDPR6PR555BHU1tZi8uTJyMjI6NE4ISJb0tr9dUd8CJRymcjVkC26ZVQIlv33BM6W1+NkmQ4jQrmJNdHPSQRBELr6pPfffx9ffPEFNm3aBDc3t76oS1Q6nQ5qtRparbbL6xoR9bV6QzPGvvw9GppM2ProRIwZ6CN2SWSjHv3gEL45psHiKZF4/rbhYpdD1Oe68v7drRag119/HefPn0dQUBAiIiLg4tJ288Xc3Nzu3JaIOuGbY2VoaDIhyt8do8O9xS6HbNi8hDB8c0yDL/JK8dwtwyDjDvFEVt0KQBxLQySerS3dX/ckDuC4Drqu6UMC4ePmgoo6A37Mr8K0wQFil0RkM7oVgJYvX97bdRBRJxTXXMG+CzWQSIB5o8PELodsnEIuxZ3xodiUfRGfHrrEAET0M93eObG2thbvvPMOli5dipqaGgCWrq+fb0pKRL1r22HLv68JUX4I83YVuRqyB/cmWhZC3H5CA20Dt8YgatWtAHT06FEMHjwYK1aswMqVK1FbWwsA2Lp1K5YuXdqb9RFRC0EQ8HlLAOLGp9RZI8O8MDjIA4ZmM74+WiZ2OUQ2o1sBKC0tDYsWLcK5c+faTC2/9dZbsXv37l4rjoiuOnpJiwtVeqhcpJgzkquaU+dIJBLc27JYZuvyCUTUzQB04MAB/Pa3v73mfFhYWKc3MSWirmlt/Zk1PBgeym5v40dOaF5CGGRSCQ5dvIwLlfVil0NkE7oVgJRKJXQ63TXnz549i4AADrIj6m1NJjO+PFIKALiLg5+piwK9VJga6w+ArUBErboVgO6880789a9/RVOTZUCdRCJBUVERnn32Wdxzzz29WiARAXvOVaJab4SfuwJTWt7IiLqidTD01twSmMxdXv+WyOF0KwC9/vrrqK+vR0BAABoaGjBt2jTExMTA09MTf/vb33q7RiKn9/lhS+vPHfGhkMu6PXmTnNjMYYHwUslRpm1E9vlqscshEl23BhKo1Wrs2LEDP/30E44cOYL6+nqMGTMGKSkpvV0fkdOra2zC9hOWsXV3j2H3F3WPykWGOxNC8f6+InyWewmT2ZJITq7LAchsNmPjxo3YunUrCgsLIZFIEBkZieDgYAiCwJVpiXpZxnENDM1mRAW4Y1QYN7Sk7rtnzAC8v68I3x4vw1/njoCnyuXGTyJyUF1qSxcEAXfeeSd+85vfoKSkBKNGjcKIESNw8eJFLFq0CHfddVdf1UnktLbltaz9MzqMHzCoRxLCvREd4I7GJjO+OcY1gci5dSkAbdy4Ebt370ZmZiYOHz6Mjz76CB9//DGOHDmC77//Hj/88AM2b97cV7USOZ0ybQP2tozXmJvA7i/qGYlEgnusawJx1X5ybl0KQB999BH+9Kc/YcaMGdc8dtNNN+G5557DBx980GvFETm7/+aVQhCAsRE+CPd1E7sccgDzWoJ0TkENSmobRK6GSDxdCkBHjx7FnDlzOnz8lltuwZEjR3pcFBFZtC5+eNdobn1BvSPU2xXjo3wBAF/ksRWInFeXAlBNTQ2CgoI6fDwoKAiXL1/ucVFEBJwtr8NpTR1cZBLcOopbX1DvaW0F2na4BILANYHIOXUpAJlMJsjlHU8ck8lkaG5u7nFRRATrxpVTYwPg7aYQuRpyJLeMCoFCJsXZ8nqcKqsTuxwiUXRpGrwgCFi0aBGUSmW7jxsMhl4pisjZCYKAr1tm6dwWFyJyNeRo1K4umDksEN8e12BbXgmGh3qJXRJRv+tSAFq4cOENr1mwYEG3iyEii7Pl9civqIdCJkXK8I67nYm6a25CGL49rsF/80rx3JyhkEq5xAI5ly4FoHfffbev6iCin/n6qGXri6mDA+DFxeqoD8wYGgBPpRwaXSMOFV3G2AhfsUsi6lfcVIjIxgiCgK9aur9uZ/cX9RGlXIabR1haF786UipyNUT9jwGIyMacKa/DhUo9FHIpZg4LFLsccmB3xIUCAL45ruEO8eR0GICIbEzr7K/pgwO4VxP1qUkx/lC7uqCyzoCcghqxyyHqVwxARDZEEARrAOLsL+prCrkUc0ZY1pj66ii7wci5MAAR2ZBTZXW4UKWHUi7FzGGc/UV97/Z4S9DOOK5Bs8kscjVE/YcBiMiGfH3M8il8xpBAeCi7NEmTqFsmRPnB112Bar0R+9kNRk6EAYjIRrD7i8Qgl0lxc0tr43cnNCJXQ9R/GICIbMSJUh0Kq69A5SLFTUM5+4v6z5yRlnFA353QwMzZYOQkGICIbMQ3LWv/zBgSCHd2f1E/mhjjBw+lHOU6A45cqhW7HKJ+wQBEZCO2nywHcPXTOFF/UcplmNHS6pjBbjByEgxARDbgQqVl7y8XmcT6RkTUn1qnw393XANBYDcYOT4GICIbsKOl9Wd8lB/3/iJRTB8SAIVcisLqKzhbXi92OUR9jgGIyAa0BqBZ3PmdROKulGNqrD8AzgYj58AARCSyyjoDDhVdBgCkMACRiFJapsNnnioXuRKivid6AFqzZg0iIiKgUqmQnJyMnJycDq89ceIE7rnnHkREREAikWD16tXXXPPiiy9CIpG0OYYOHdqHr4CoZ344XQ5BAOIGqBGidhW7HHJircsvHLmkRYWuUeRqiPqWqAFoy5YtSEtLw/Lly5Gbm4v4+HjMnj0bFRUV7V5/5coVREVF4ZVXXkFwcMczZUaMGIGysjLr8eOPP/bVSyDqse0nLJ+2b+bWFySyQC8V4geoAQA/nG7/9zCRoxA1AK1atQqLFy9Gamoqhg8fjnXr1sHNzQ0bNmxo9/qxY8fitddew/333w+lUtnhfeVyOYKDg62Hv7//deswGAzQ6XRtDqL+oDc0Y09+FQBg1ghOfyfxte5B9/0pBiBybKIFIKPRiEOHDiElJeVqMVIpUlJSkJ2d3aN7nzt3DqGhoYiKisKDDz6IoqKi616fnp4OtVptPcLDw3v0/Yk6a8+5ShibzRjo64bBQR5il0OEmcMs3WA/5leisckkcjVEfUe0AFRVVQWTyYSgoLbN/kFBQdBouj8DITk5GRs3bkRGRgbWrl2LgoICTJkyBXV1dR0+Z+nSpdBqtdajuLi429+fqCus3V/DgyCRSESuhggYHuKFULUKjU1m7D1fJXY5RH3G4dbbv+WWW6x/jouLQ3JyMgYNGoT//Oc/ePjhh9t9jlKpvG6XGlFfaDaZkdkyzoLT38lWSCQS3DQsEO/vK8L3pypw01D+3STHJFoLkL+/P2QyGcrL2063LC8vv+4A567y9vbG4MGDkZ+f32v3JOoNOYU10DY0wcfNBYmDfMQuh8hqZkvo2XWmkqtCk8MSLQApFAokJiYiMzPTes5sNiMzMxMTJkzote9TX1+P8+fPIyQkpNfuSdQbWhc/nDksCHKZ6CtSEFklR/lCIZeipLYB5yv1YpdD1CdE/a2blpaG9evXY9OmTTh16hSWLFkCvV6P1NRUAMCCBQuwdOlS6/VGoxF5eXnIy8uD0WhESUkJ8vLy2rTuPPXUU9i1axcKCwuxd+9e3HXXXZDJZHjggQf6/fURdUQQhDbjf4hsiZtCjuRIXwDArrOVIldD1DdEHQM0f/58VFZWYtmyZdBoNEhISEBGRoZ1YHRRURGk0qsZrbS0FKNHj7Z+vXLlSqxcuRLTpk1DVlYWAODSpUt44IEHUF1djYCAAEyePBn79u1DQEBAv742ous5U16HktoGKOVSTIm9/jINRGKYGhuAPeeqsPtsJR6eHCl2OUS9TiKwg/caOp0OarUaWq0WXl5eYpdDDugfWfl4NeMMZgwJwLup48Quh+gaZ8vrMOuN3VDKpTiyfBZULjKxSyK6oa68f3PgAZEIfmhZZO4mrv5MNio20AMhahUMzWbsL6gRuxyiXscARNTPLuuNyG3Z/LR17yUiWyORSDBtsGXowK4zHAdEjocBiKif7T5XCbMADAnyRJg3Nz8l22UNQGe5LQY5HgYgon6Wae3+YusP2baJMf6QSSU4X6nHpctXxC6HqFcxABH1o2aT2TqtmN1fZOvUri4YHe4NANh9lttikGNhACLqR4eLa6FtaIK329U3FiJbxm4wclQMQET9qLX7a9rgAK7+THZh2hBLAPopvxpNJrPI1RD1Hv4GJupHO1s2P2X3F9mLkaFq+LorUG9oxuGiWrHLIeo1DEBE/eTS5Ss4U14HqeRqtwKRrZNKJdbVytkNRo6EAYion7S2/iQO8oG3m0Lkaog67+o4IK4HRI6DAYion/xg7f7i6s9kX6bEWgLQ8RIdKusMIldD1DsYgIj6QYPRhL3nqwFw/A/ZnwBPJUaEWvZV+imf0+HJMTAAEfWDveerYGg2I8zbFYODPMQuh6jLJreMA9p9jt1g5BgYgIj6QWv314yhAZBIJCJXQ9R1U1u6wX48VwVBEESuhqjnGICI+pggCNYANJPjf8hOJQ7ygVIuRUWdAecq6sUuh6jHGICI+thpTR3KtI1QuUgxIdpP7HKIukXlIsO4SF8AwJ5zHAdE9o8BiKiPtbb+TIz2h8pFJnI1RN3Xuh7QHo4DIgfAAETUx3Zax/9w9hfZt9bp8Psv1MDQbBK5GqKeYQAi6kOX9UbkFl0GwOnvZP+GBnvC30OJhiYTci/Wil0OUY8wABH1od3nKmEWgCFBngjzdhW7HKIekUgkmBxjGcf2Yz67wci+MQAR9aEf2P1FDmbyz6bDE9kzBiCiPmIyC9a9k9j9RY6idSD00RItLuuNIldD1H0MQER95HDRZdReaYLa1QVjBnqLXQ5RrwjyUmFwkAcEAdbtXYjsEQMQUR9p7f6aOjgAchn/qZHjmBzT0g3GcUBkx/hbmaiPXN39PUDkSoh6V2s32O6z3BaD7BcDEFEfKK1twGlNHSQSYNpgjv8hx5Ic5QsXmQQltQ0orL4idjlE3cIARNQHdp6xtP6MDveGr7tC5GqIepebQo7EQT4AgB+5KjTZKQYgoj6w09r9xdYfckytq0JzXzCyVwxARL2sscmEn/Its2Nu4u7v5KAmx1jGAWWfr0azySxyNURdxwBE1Mv2XahGQ5MJIWoVhoV4il0OUZ8YGaaGt5sL6gzNOHKpVuxyiLqMAYiol7V2f00fEgiJRCJyNUR9QyaVYFJ06+7w7AYj+8MARNSLBEHAD2c4/oecw+SW6fDcFoPsEQMQUS86X1mP4poGKORSTGrZNJLIUbWOAzpcXIu6xiaRqyHqGgYgol7Uuvjh+Cg/uCnkIldD1LfCfd0Q6e8Ok1nAvgs1YpdD1CUMQES9yLr68xCu/kzOobUVaA/XAyI7I3oAWrNmDSIiIqBSqZCcnIycnJwOrz1x4gTuueceREREQCKRYPXq1T2+J1Fv0TU24WDhZQCc/k7Og+OAyF6JGoC2bNmCtLQ0LF++HLm5uYiPj8fs2bNRUVHR7vVXrlxBVFQUXnnlFQQHB/fKPYl6y+6zlWg2C4gOcMdAPzexyyHqFxOi/SCTSnChSo/iGm6LQfZD1AC0atUqLF68GKmpqRg+fDjWrVsHNzc3bNiwod3rx44di9deew33338/lEplr9wTAAwGA3Q6XZuDqKt2nCwHAKQMY+sPOQ8vlYt1W4ysM/ygSfZDtABkNBpx6NAhpKSkXC1GKkVKSgqys7P79Z7p6elQq9XWIzw8vFvfn5yXsdlsHf8za0T7rZNEjmrGEMuSDzvPcBwQ2Q/RAlBVVRVMJhOCgtp+Wg4KCoJGo+nXey5duhRardZ6FBcXd+v7k/PaX1CNusZm+HsoMTrcW+xyiPrVjKGWQf97z1ehsckkcjVEnSP6IGhboFQq4eXl1eYg6orvTlgC9s3DgyCVcvVnci5DgjwRqlahscmM7AvVYpdD1CmiBSB/f3/IZDKUl5e3OV9eXt7hAGcx7kl0I2azYB3/M2sEx/+Q85FIJJjesvJ51mmOAyL7IFoAUigUSExMRGZmpvWc2WxGZmYmJkyYYDP3JLqRoyValOsMcFfIMDGaqz+Tc/r5OCBBEESuhujGRF2qNi0tDQsXLkRSUhLGjRuH1atXQ6/XIzU1FQCwYMEChIWFIT09HYBlkPPJkyetfy4pKUFeXh48PDwQExPTqXsS9bbtLd1f04cGQimXiVwNkTgmRvtBIZOiqOYKLlTpER3gIXZJRNclagCaP38+KisrsWzZMmg0GiQkJCAjI8M6iLmoqAhS6dVGqtLSUowePdr69cqVK7Fy5UpMmzYNWVlZnbonUW9rHf8zazj/jpHzclfKkRzliz3nqrDzdAUDENk8icC2ymvodDqo1WpotVoOiKbryq+oR8qqXXCRSXDohZvhpXIRuyQi0fz7xwK89NVJTIrxwwe/GS92OeSEuvL+zVlgRD3QOvh5QrQ/ww85vRkte+DlFNSg3tAscjVE18cARNQD20+y+4uoVaS/Owb5uaHJJOCnfO4NRraNAYiomyp0jThcVAvAsv4PkbOTSCTW2WDcFoNsHQMQUTdtb+n+Sgj3RpCXSuRqiGzD9JZusJ2nOR2ebBsDEFE3befih0TXGB/lB5WLFBpdI06V1YldDlGHGICIuqH2ihF7W8Y4zBrOVcaJWqlcZJgU7Q8A2MluMLJhDEBE3bD9RDmazQKGBnsiJpDrnRD93IyWbTFaW0mJbBEDEFE3fHWsDABw26gQkSshsj2zhgdBIgGOFNeiTNsgdjlE7WIAIuqiGr3ROsX3tjgGIKL/FeilwpiBPgAsraVEtogBiKiLvjuhgcksYHiIF6K43D9Ru+aMsIyNa90qhsjWMAARddHXR1u6v9j6Q9Sh2S0BaH9BDS7rjSJXQ3QtBiCiLqiqN2DveUv31+0MQEQdGujnhmEhXjCZBXx/it1gZHsYgIi64OujZTALQNwANQb5uYtdDpFNm92yRha7wcgWMQARdcHnh0sAAPMSwkSuhMj23TLS0kq6+2wVdI1NIldD1BYDEFEnFVTpkVdcC5lUgjviQ8Uuh8jmDQ7yQGygB4wmM747zlYgsi0MQESdtK2l9WdyjD8CPJUiV0Nk+yQSCe5s+bDwZcvkASJbwQBE1AmCIGBbniUA3TWa3V9EnXV7SwD6Kb8K1fUGkashuooBiKgTDhfX4mL1FbgpZNz8lKgLIv3dMSpMDZNZwDfsBiMbwgBE1AmfHroEwLK2iZtCLnI1RPbF2g12pFTkSoiuYgAiuoEGowlf5ll+cd+XOEDkaojsT+uioTkFNSip5d5gZBsYgIhu4NvjZagzNCPc1xXjo/zELofI7oR6u2J8lC8AYGtLayqR2BiAiG5gy4FiAMAvEsMhlUpErobIPt2XGA4A+DT3EgRBELkaIgYgousqrNJjf0ENJBLg3iR2fxF11y2jguGhlONi9RXkFNSIXQ4RAxDR9fznoKX1Z2psAELUriJXQ2S/3BRy3DbKMhboE3aDkQ1gACLqgLHZjP8ctPyinj82XORqiOzffS2tqN8cK4Pe0CxyNeTsGICIOpBxQoOqegOCvJS4eTjX/iHqqcRBPojyd8cVo4lT4kl0DEBEHXgvuxAA8MC4gXCR8Z8KUU9JJBLcP87Smro5+yIHQ5Oo+FudqB2nynQ4UHgZcqkED4wbKHY5RA7jF0nhUMqlOFmmQ27RZbHLISfGAETUjvf2XQRgWfk5yEslcjVEjsPbTYG5CZaVoTftvShyNeTMGICI/of2SpN15/eHxg8SuRoix7NgQgQAy2DoCl2juMWQ02IAIvofH+RcxBWjCUODPa2r1xJR7xkZpsaYgd5oNgv4MKdI7HLISTEAEf2ModmEjT8VAgAemRoFiYQrPxP1hUWTIgFYBkM3GE0iV0POiAGI6Gf+m1eKijrL1Pfb40LFLofIYd06Mhjhvq6o0Rux5QBbgaj/MQARtRAEAe/sKQAApE6KhELOfx5EfUUuk+KRqdEAgPV7CtBkMotcETkb/oYnarHzTAXOlNfBXSHj1HeifnBf4gD4eyhQUtuA/+ZxYUTqXzYRgNasWYOIiAioVCokJycjJyfnutd/8sknGDp0KFQqFUaNGoVvvvmmzeOLFi2CRCJpc8yZM6cvXwLZOUEQ8GZmPgDgwfGDoHZ1EbkiIsencpHh15MtY4H+kZUPk5kLI1L/ET0AbdmyBWlpaVi+fDlyc3MRHx+P2bNno6Kiot3r9+7diwceeAAPP/wwDh8+jHnz5mHevHk4fvx4m+vmzJmDsrIy6/HRRx/1x8shO7XnXBXyimuhcpFi8ZQoscshchoPtXzgOF+px+cty08Q9QfRA9CqVauwePFipKamYvjw4Vi3bh3c3NywYcOGdq//+9//jjlz5uDpp5/GsGHD8NJLL2HMmDF4++2321ynVCoRHBxsPXx8fDqswWAwQKfTtTnIeQiCgL9nngMAPJg8CAGeSpErInIeXioXLJluGQv0xo6zMDRzRhj1D1EDkNFoxKFDh5CSkmI9J5VKkZKSguzs7Hafk52d3eZ6AJg9e/Y112dlZSEwMBBDhgzBkiVLUF1d3WEd6enpUKvV1iM8nDt/O5O956tx6OJlKORS/HYqW3+I+tvCCREI8lKipLYBH+7njDDqH6IGoKqqKphMJgQFtd1pOygoCBqNpt3naDSaG14/Z84cbN68GZmZmVixYgV27dqFW265BSZT+58sli5dCq1Waz2Ki4t7+MrIXgiCgFe/OwMAeGBsOAK57QVRv3NVyPDEzMEAgLd/yEddY5PIFZEzkItdQF+4//77rX8eNWoU4uLiEB0djaysLMycOfOa65VKJZRKdns4o4zjGhwproWriwyP3RQjdjlETuu+pAF4Z88FXKjS483Mc3j+tuFil0QOTtQWIH9/f8hkMpSXl7c5X15ejuDg4HafExwc3KXrASAqKgr+/v7Iz8/vedHkMJpNZrzW0vqzeEokAj3Z+kMkFheZFMvusISed38qxNnyOpErIkcnagBSKBRITExEZmam9ZzZbEZmZiYmTJjQ7nMmTJjQ5noA2LFjR4fXA8ClS5dQXV2NkJCQ3imcHMJ/Dl7ChSo9fN0VWMyxP0Simz4kELOGB6HZLGDZF8chCJwWT31H9FlgaWlpWL9+PTZt2oRTp05hyZIl0Ov1SE1NBQAsWLAAS5cutV7/xBNPICMjA6+//jpOnz6NF198EQcPHsTjjz8OAKivr8fTTz+Nffv2obCwEJmZmZg7dy5iYmIwe/ZsUV4j2R5dYxNW7TgLAHhsRgw8VVz3h8gWvHD7cCjlUuy7UINteZwWT31H9AA0f/58rFy5EsuWLUNCQgLy8vKQkZFhHehcVFSEsrIy6/UTJ07Ehx9+iH/961+Ij4/Hp59+im3btmHkyJEAAJlMhqNHj+LOO+/E4MGD8fDDDyMxMRF79uzhOB+yeivzHKrqDYjyd8evxg8SuxwiahHu64Y/zIwFACz/4gTKdY0iV0SOSiKwjfEaOp0OarUaWq0WXl5eYpdDvSy/oh5zVu9Gs1nAu6ljMWNIoNglEdHPNJnMuPsfe3GsRIsZQwKwYdFYSCQSscsiO9CV92/RW4CI+pMgCPjLlyfQbBYwc2ggww+RDXKRSfH6L+KhkEmx80wl/nOQS5NQ72MAIqfy3yOl2HOuCgqZFC/czmm2RLZqcJAn0mZZ1gZa/t8TOKPhrDDqXQxA5DRq9Eb85cuTAIDHb4pBhL+7yBUR0fUsnhKFKbH+aGwyY8kHh1BvaBa7JHIgDEDkNF7++iRq9EYMDvLA76ZFi10OEd2ATCrB6vkJCPZS4UKlHs9+dpRT46nXMACRU9h5ugJbc0sgkQCv3BMHhZx/9YnsgZ+HEmseHA25VIKvj5Zh9ffnxC6JHATfBcjhVdcb8PSnRwEAv54UiTEDfUSuiIi6InGQL16eZ1nq5O+Z5/D54UsiV0SOgAGIHJogCHhu6zFU1RswOMgDT88eInZJRNQN948biN9Os6zY/uynx7DnXKXIFZG9YwAih/ZhThF2nCyHQibF6vmjoXKRiV0SEXXTs7OH4rZRITCazFi8+SByCmrELonsGAMQOazjJVrrrK+nZg/G8FAuaklkz6RSCd6Yn4DpQwLQ2GTGrzcewOGiy2KXRXaKAYgckvZKE5Z8cAjGZjNShgXiN5O52SmRI1DIpVj3UCLGR/mi3tCMh97Zj30XqsUui+wQAxA5HJNZwP/9Jw/FNQ0I93XF6/clQCrlMvpEjkLlIsO/F47FxGg/6I0mLNyQg52nK8Qui+wMAxA5nFe+PYUfTldAIZfiH79MhNqNO70TORp3pRwbFo3FTUMDYWg24+FNB7A5u1DsssiOMACRQ/k4pwjr9xQAAF6/Lx6jBqhFroiI+orKRYZ1DyXinjEDYBaAZV+cwIv/PYFmk1ns0sgOMACRw/jhdDn+vO04AODJlFjcER8qckVE1NcUcilW3heHZ+ZYlrjYuLcQv9l8EHWNTSJXRraOAYgcQk5BDZa8n4tms4C7RofhiZmxYpdERP1EIpHg0ekxWPvgGKhcpMg6U4l712ajsEovdmlkwxiAyO4dvVSLhzcegKHZjJlDA/HqvXGQSDjomcjZ3DIqBFsemYAATyXOlNfhjrd+xLfHysQui2wUAxDZtcNFl/HgO/tRZ2jGuAhfrHlwDFxk/GtN5Kziw73x5eOTMTbCB3WGZiz5IBcv/vcEjM0cF0Rt8Z2C7FZOQQ1+9e8c1DVaws+G1LFc6ZmIEKxW4cPF461bZ2zcW4j71u1Fcc0VkSsjW8IARHbp22NleOjf+1FvaMaEKD9s/PVYeCjlYpdFRDbCRSbF0luG4d8Lk6B2dcGRS1rc+vc92Ha4BIIgiF0e2QAGILIrgiDgnT0X8OiHuTA2m3Hz8CBsWDQWbgqGHyK61sxhQfj6D5MxeqA36gzNeHJLHv7wcR60VzhLzNkxAJHdaDCa8H9b8vDy16cgCMCvxg/CuocS4apgtxcRdWyAjxs++e0EpN08GDKpBF8eKcWcv+/G3vwqsUsjEUkEtgVeQ6fTQa1WQ6vVwsuLG2jagvyKOjz+4WGc1tRBJpXg+VuHIXVSBGd7EVGXHC66jP/bkofCast4oIUTBuGZOUPhzi50h9CV928GoHYwANkOQRDw/r6LePnrUzA0m+HvocDbvxyD8VF+YpdGRHZKb2jGy1+fwkc5RQCAAT6ueOXuOEyO9Re5MuopBqAeYgCyDecr6/H858ew70INAGBKrD9evy8egV4qkSsjIkew51wlnvvsGEpqGwAAD4wLx9Jbh8FLxf0D7RUDUA8xAIlLb2jGul3n8c9dF2A0meHqIsMzc4Zg4YQI7upORL2q3tCMVzNOY3P2RQBAgKcSz986DHMTQtnFbocYgHqIAUgchmYTPjtUgje+P4vKOgMAYNrgALw8byTCfd1Ero6IHNn+C9V4busxFLRsnzEu0hd/nTsCQ4P5HmBPGIB6iAGof10xNuOjnGKs330BGl0jAGCgrxuW3jIUc0YG81MYEfULQ7MJ7+wpwFs/nENjkxkyqQS/Gj8IT8yMhY+7QuzyqBMYgHqIAah/VNcb8FFOETb8VIgavREAEOSlxCNTo/HQ+IFQyjm9nYj6X0ltA17+6iS+Pa4BAHgq5Vg8NQq/nhzJBVdtHANQDzEA9R2zWcBP56vwcU4xtp/UoMlk+es30NcNS6ZH4+4xYQw+RGQT9pyrxP/75jROlekAAL7uCjw6PRoPjR/EbXdsFANQDzEA9b78ijp8dbQMn+VeQnFNg/V83AA1Hp4cidtGhUDOTUyJyMaYzQK+OV6GVdvP4kLL+CB/DwV+mTwID40fiEBPzkq1JQxAPcQA1HOCICC/oh5fHyvDN8fKcLa83vqYp0qOu0aHYf7YcIwIVYtYJRFR5zSbzNiaW4K/Z56zTpt3kUlwe1woUidFIG6At7gFEgAGoB5jAOoevaEZ2eersetsJbLOVrRp6XGRSTA5xh93xIfilpEh3L6CiOxSs8mM706UY8NPBTh08bL1/JAgT9wRH4Lb40IR4e8uYoXOjQGohxiAOqeusQm5RbU4WFiDnIIa5BZdto7pASyhZ0psAG4dFYKbhwVB7cbFxYjIcRy9VIt3fyrE10fLYDSZrefjBqgxe0QwJsf4Y2SYGjKuX9ZvGIB6iAHoWnpDM06V6XCyTIeTpTocvaTFaY0O5v/52xPu64rpgwMxbXAAJkT7cX8dInJ42oYmfHdCgy+PlGLv+WqYfvaL0VMlx/goP0yM9kPcAG8MDfbk78U+xADUQ84agExmASWXG3Chqh6FVXoUVl9BQZUeBVV6FF++gvb+pgz0dUNShA/GRvgiOdIXkf7uXLeHiJxWVb0B3x7XYPfZSuy7UI26xuY2j0skQISfO4aFeCIm0BMDvF0R5uOKUG9XhHqrOAu2h+wuAK1ZswavvfYaNBoN4uPj8dZbb2HcuHEdXv/JJ5/ghRdeQGFhIWJjY7FixQrceuut1scFQcDy5cuxfv161NbWYtKkSVi7di1iY2M7VY8jBiCTWUDtFSMq6gwo0zagtLYRZdoGlNU2olTbgDJtI0prG9p0Yf2vYC8Vhod6YXiIF0aEemHMIB8EcV8uIqJ2NZvMOFGqw0/nq5BTUIOTpTpUtKxy3xF3hQxqVxd4ubpA7eoCD6UcCrnUcsgs/3WRSaGUX/2z9b8ySZuvXWRSuCpk8FTJ4aWSw0PpAk+VHG4KmcN+ULWrALRlyxYsWLAA69atQ3JyMlavXo1PPvkEZ86cQWBg4DXX7927F1OnTkV6ejpuv/12fPjhh1ixYgVyc3MxcuRIAMCKFSuQnp6OTZs2ITIyEi+88AKOHTuGkydPQqW68Ru2LQYgQRBgNJlhaDajsckEQ5MZdY3N0DU2Wf7b0GT9s7ahCdX1BlTVG1HV8t8aveGa7qr2KORSRPi5IcLPHZEB7oj0c0eEvztiAz3g56Hs+xdKROTAqusNOFVWh5NlWhRUXUFJbQNKaxtQcrkBDU2mfqlBKgE8lHJ4qiyByPJnObxcLV+3nvdUucBLJW9zzkMph1Iug0LeEsJkUpvao9GuAlBycjLGjh2Lt99+GwBgNpsRHh6O3//+93juueeuuX7+/PnQ6/X46quvrOfGjx+PhIQErFu3DoIgIDQ0FH/84x/x1FNPAQC0Wi2CgoKwceNG3H///dfc02AwwGC4msq1Wi0GDhyI4uLiXg1A2w6X4OMDxRAEASZBgNkswCwIMAuWtSZMggCTWYAgWFpszIKAJpMZjc1mGJvN7XZBdZWPmwuC1CoEe6kQolYh0EuJEC9Xy9feKoSoXW3qLzMRkTMQBAHahibroWv5YHvF0Iwms+U9wGgyo6lZsP652WSG0WR5n2hqNqPJbEZT67mW943GZjPqGpugNzSj3mBqMz6pt7jIJNYWqtYWKJlUAokEkEokkEksf5ZIJJBK0PK1BHNHh+L+sQN7tRadTofw8HDU1tZCrb7BMiuCiAwGgyCTyYTPP/+8zfkFCxYId955Z7vPCQ8PF954440255YtWybExcUJgiAI58+fFwAIhw8fbnPN1KlThT/84Q/t3nP58uUCAB48ePDgwYOHAxzFxcU3zCCiDkWvqqqCyWRCUFBQm/NBQUE4ffp0u8/RaDTtXq/RaKyPt57r6Jr/tXTpUqSlpVm/NpvNqKmpgZ+fX4/7SVvTaG+3JlHf4s/NPvHnZp/4c7NPtvhzEwQBdXV1CA0NveG1nIsHQKlUQqlsO77F29u7V7+Hl5eXzfwFoc7jz80+8edmn/hzs0+29nO7YddXC1E3X/L394dMJkN5eXmb8+Xl5QgODm73OcHBwde9vvW/XbknERERORdRA5BCoUBiYiIyMzOt58xmMzIzMzFhwoR2nzNhwoQ21wPAjh07rNdHRkYiODi4zTU6nQ779+/v8J5ERETkXETvAktLS8PChQuRlJSEcePGYfXq1dDr9UhNTQUALFiwAGFhYUhPTwcAPPHEE5g2bRpef/113Hbbbfj4449x8OBB/Otf/wJgGWX+5JNP4uWXX0ZsbKx1GnxoaCjmzZvX769PqVRi+fLl13SxkW3jz80+8edmn/hzs0/2/nMTfRo8ALz99tvWhRATEhLw5ptvIjk5GQAwffp0REREYOPGjdbrP/nkE/z5z3+2LoT46quvtrsQ4r/+9S/U1tZi8uTJ+Mc//oHBgwf390sjIiIiG2QTAYiIiIioP4k6BoiIiIhIDAxARERE5HQYgIiIiMjpMAARERGR02EAEonBYEBCQgIkEgny8vLELoc6UFhYiIcffhiRkZFwdXVFdHQ0li9fDqPRKHZp1I41a9YgIiICKpUKycnJyMnJEbskuo709HSMHTsWnp6eCAwMxLx583DmzBmxy6IueOWVV6zLz9gbBiCRPPPMM53aq4TEdfr0aZjNZvzzn//EiRMn8MYbb2DdunX405/+JHZp9D+2bNmCtLQ0LF++HLm5uYiPj8fs2bNRUVEhdmnUgV27duGxxx7Dvn37sGPHDjQ1NWHWrFnQ6/Vil0adcODAAfzzn/9EXFyc2KV0C6fBi+Dbb79FWloaPvvsM4wYMQKHDx9GQkKC2GVRJ7322mtYu3YtLly4IHYp9DPJyckYO3Ys3n77bQCWVeXDw8Px+9//Hs8995zI1VFnVFZWIjAwELt27cLUqVPFLoeuo76+HmPGjME//vEPvPzyy0hISMDq1avFLqtL2ALUz8rLy7F48WK89957cHNzE7sc6gatVgtfX1+xy6CfMRqNOHToEFJSUqznpFIpUlJSkJ2dLWJl1BVarRYA+O/LDjz22GO47bbb2vybszeib4XhTARBwKJFi/C73/0OSUlJKCwsFLsk6qL8/Hy89dZbWLlypdil0M9UVVXBZDIhKCiozfmgoCCcPn1apKqoK8xmM5588klMmjQJI0eOFLscuo6PP/4Yubm5OHDggNil9AhbgHrBc889B4lEct3j9OnTeOutt1BXV4elS5eKXbLT6+zP7OdKSkowZ84c3HfffVi8eLFIlRM5psceewzHjx/Hxx9/LHYpdB3FxcV44okn8MEHH0ClUoldTo9wDFAvqKysRHV19XWviYqKwi9+8Qt8+eWXkEgk1vMmkwkymQwPPvggNm3a1NelUovO/swUCgUAoLS0FNOnT8f48eOxceNGSKX87GBLjEYj3Nzc8Omnn7bZ9HjhwoWora3FF198IV5xdEOPP/44vvjiC+zevRuRkZFil0PXsW3bNtx1112QyWTWcyaTCRKJBFKpFAaDoc1jtowBqB8VFRVBp9NZvy4tLcXs2bPx6aefIjk5GQMGDBCxOupISUkJZsyYgcTERLz//vt284/b2SQnJ2PcuHF46623AFi6VAYOHIjHH3+cg6BtlCAI+P3vf4/PP/8cWVlZiI2NFbskuoG6ujpcvHixzbnU1FQMHToUzz77rF11X3IMUD8aOHBgm689PDwAANHR0Qw/NqqkpATTp0/HoEGDsHLlSlRWVlofCw4OFrEy+l9paWlYuHAhkpKSMG7cOKxevRp6vR6pqalil0YdeOyxx/Dhhx/iiy++gKenJzQaDQBArVbD1dVV5OqoPZ6enteEHHd3d/j5+dlV+AEYgIiua8eOHcjPz0d+fv41IZWNp7Zl/vz5qKysxLJly6DRaJCQkICMjIxrBkaT7Vi7di0AYPr06W3Ov/vuu1i0aFH/F0ROhV1gRERE5HQ4kpOIiIicDgMQEREROR0GICIiInI6DEBERETkdBiAiIiIyOkwABEREZHTYQAiIiIip8MARERERE6HAYiIiIicDgMQEREROR0GICIiInI6/x/hWy11aPHU8QAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The curve of a Normal Distribution is also known as the Bell Curve because of the bell-shaped curve."
      ],
      "metadata": {
        "id": "JXdEML8pZOrO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Binomial Distribution\n",
        "Binomial Distribution is a Discrete Distribution.\n",
        "\n",
        "It describes the outcome of binary scenarios, e.g. toss of a coin, it will either be head or tails.\n",
        "\n",
        "It has three parameters:\n",
        "\n",
        "n - number of trials.\n",
        "\n",
        "p - probability of occurence of each trial (e.g. for toss of a coin 0.5 each).\n",
        "\n",
        "size - The shape of the returned array."
      ],
      "metadata": {
        "id": "rDJjLF6eZn9D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = random.binomial(n=10, p=0.5, size=10)\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7c8UYaBmZsrn",
        "outputId": "341d8333-d267-4c73-f457-dd3e7f5424f1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6 7 6 3 7 6 6 6 3 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualization of binomial"
      ],
      "metadata": {
        "id": "6P2XoGf0Z8FV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.distplot(random.binomial(n=10, p=0.5, size=1000), hist=True, kde=False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "6tXQkkXXZ7SE",
        "outputId": "21ed2fda-39c0-495a-fe90-b2f21955be69"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-28-c6d35445dc7e>:5: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.binomial(n=10, p=0.5, size=1000), hist=True, kde=False)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Normal VS Binomial Visualization"
      ],
      "metadata": {
        "id": "3h26tns_aMFF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.distplot(random.normal(loc=50, scale=5, size=1000), hist=False, label='normal')\n",
        "sns.distplot(random.binomial(n=100, p=0.5, size=1000), hist=False, label='binomial')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "BSnkjk06aRSv",
        "outputId": "0c613603-302c-4780-90eb-0b5a3dc900ea"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-29-a77a88c19db8>:5: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.normal(loc=50, scale=5, size=1000), hist=False, label='normal')\n",
            "<ipython-input-29-a77a88c19db8>:6: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.binomial(n=100, p=0.5, size=1000), hist=False, label='binomial')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Poisson Distribution\n",
        "Poisson Distribution is a Discrete Distribution.\n",
        "\n",
        "It estimates how many times an event can happen in a specified time. e.g. If someone eats twice a day what is the probability he will eat thrice?\n",
        "\n",
        "It has two parameters:\n",
        "\n",
        "lam - rate or known number of occurrences e.g. 2 for above problem.\n",
        "\n",
        "size - The shape of the returned array."
      ],
      "metadata": {
        "id": "iyNppdWAAFC5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Generate a random 1x10 distribution for occurrence 2\n",
        "from numpy import random\n",
        "\n",
        "x = random.poisson(lam=2, size=10)\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZXnf-NSAOUM",
        "outputId": "67eeb473-6dea-427b-b925-d4773137e158"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 1 3 2 2 3 1 3 0 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Difference Between Normal and Poisson Distribution"
      ],
      "metadata": {
        "id": "RzArMewrAjsh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.distplot(random.normal(loc=50, scale=7, size=1000), hist=False, label='normal')\n",
        "sns.distplot(random.poisson(lam=50, size=1000), hist=False, label='poisson')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "VbL6lEvaAjG2",
        "outputId": "ab0d01c2-5241-4500-c080-dcdaae258e09"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-2-4de7a42d9cd4>:5: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.normal(loc=50, scale=7, size=1000), hist=False, label='normal')\n",
            "<ipython-input-2-4de7a42d9cd4>:6: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.poisson(lam=50, size=1000), hist=False, label='poisson')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Difference Between Binomial and Poisson Distribution"
      ],
      "metadata": {
        "id": "bxrkDRZCAwP8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.distplot(random.binomial(n=1000, p=0.01, size=1000), hist=False, label='binomial')\n",
        "sns.distplot(random.poisson(lam=10, size=1000), hist=False, label='poisson')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 804
        },
        "id": "BWaXHU6jAvcE",
        "outputId": "15fd1c7e-eefd-44f3-8fae-47312ffeb16f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-3-a53ab341603a>:5: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.binomial(n=1000, p=0.01, size=1000), hist=False, label='binomial')\n",
            "<ipython-input-3-a53ab341603a>:6: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.poisson(lam=10, size=1000), hist=False, label='poisson')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#uniform\n",
        "\n",
        "sns.distplot(random.uniform(size=1000), hist=False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "dcPe_AKvBCqE",
        "outputId": "7a8dabcd-457e-4dc4-abda-1d7546d79dc4"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-c79f9d63cd9a>:3: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.uniform(size=1000), hist=False)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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nJ2PBggUIDg7G5MmTbZCYqHs+OZSHvLI6+Hup8bsb+0sdx6IGBnph3ew4ZBZX48P9ufgurQgFlQ04lF2GQ9llbb5mdIQv7hrZG1OHB7PUWEFUkBeenzoIS788g2Vfn0NCv16IDuI/6sj52c20lEwmu+bIzbPPPovt27fj9OnT5sfuvfdeVFRUYOfOnR16H47ckFSqG5ow8c3dKKvV4dU7h+KB+D5SR7IqIQTSCqqRUVSN/LI6VNY3wUOthFajxKBgLYb19mahsQEhBB768Ci+P1eMgYGe+HLhdVedKiSyZw4zctNZBw4cQGJiYqvHJk+ejD/84Q9XfU1jYyMaG39eTFdVxY2tSBrv/pSNslodIv164J5RYVLHsTqZTIbBIVoMDuE/IqQkk8nwxt3DMWX1T8goqsGyHWl46Y6hUscisiqHuluqsLAQgYGt7ywJDAxEVVUV6uvr23zNsmXL4O3tbf4KC3P+DxWyP8XVDXjvpywAwJ8mR8GNtzaTDfl5qrHiN6bTwz88kIvvz9nu+AwiKTj9T9jFixejsrLS/JWfny91JHJBbyVnok5nQEyYDw98JEncEBWA+eMjAAB/+vwkiqsbpA1EZEUOVW6CgoJQVNT6XxxFRUXQarVwd297W3a1Wg2tVtvqi8iWckpr8enhPADAoinRvB2XJPPslGhEB3nhSq0Oz3x+EkYrHJ1BZA8cqtwkJCQgOTm51WO7du1CQkKCRImIrm3Ft+nQGwVuiPJHQr9eUschF6ZxU+Af942AWinHnowSvPV9ptSRiKxC0nJTU1OD48eP4/jx4wBMt3ofP34ceXmmf+UuXrwYc+bMMT//scceQ1ZWFv785z/j3LlzePvtt/HZZ5/h6aefliI+0TWdvFiBr04WQCYD/jzZefa0Icc1MNALr945DACwOjkDP6TzeAZyPpKWm6NHj2LEiBEYMWIEACApKQkjRozAkiVLAAAFBQXmogMAffv2xfbt27Fr1y7ExMRg5cqVeO+997jHDdklIYT54MI7Y0N51xDZjbvjeuOB+HAIAfxh03Hkl9VJHYnIouxmnxtb4T43ZCt7MkowZ/1hqBRyJP9xIsJ6ekgdicisUW/ArH8dxPH8CgwJ0eLfj4/j/jdk1zrz+e1Qa26IHIXRKPD6TtOozW/H9mGxIbujViqw9rcj0auHCmcuV+GFL05361R3InvCckNkBf89eRlnLlfBU63EQic7ZoGcR7C3O966bwTkMmBLykX838FcqSMRWQTLDZGF6fRGrPg2HQDw2MRI9OyhkjgR0dWN6++HZ6eYFrv/5cszXGBMToHlhsjCPjmUi/yyevh7qfHgdTyFmezfI9dH4jdxvWEUwMKNqUgr4DE15NhYbogsqKZRb9475KmbBsBD5VDHt5GLkslkePXOYUiI7IVanQHzPjjMO6jIobHcEFnQu3uycKVWh75+PTBrNM8xI8ehUsqx7rdxGBjoiaKqRsxZfxgl1Y3XfiGRHWK5IbKQkupGvMvDMcmBeXu44aMH4xHq447s0lrMXX8Y5bU6qWMRdRp/+hJZyFvfnzcfjnkrD8ckBxXkrcHHC+Lh56nC2YIq3P/eIZSx4JCDYbkhsoCc0lp8csi0m/azU6J4OCY5tL5+PfDpw2Ph56lGWkEV7nvnIIqreIo4OQ6WGyILWLkrA3qjwMSB/hjXz0/qOETdNiDQC5seGYsALzXSi6px59v7kVFULXUsog5huSHqplMXK/HfE5chk8G8XwiRM+gf4Iktj41DpF8PXKqox11r92NPRonUsYiuieWGqJtajlmYwcMxyQmF9/LAvx8fh1F9fFHdoMfcDw7jb7syYDDyqAayXyw3RN3w0/kS7M0shZtChqSbB0odh8gqfHuo8PGCeNw3xnSS+N+Tz+P+dw8ip7RW6mhEbWK5Ieoio1Fg+dc8HJNcg8ZNgWUzh2HVPTFwd1PgUHYZJq/egzU/ZKKhySB1PKJWWG6IuuirUwU/H445iYdjkmuYObI3vvnD9biuvx8a9Ua8+U06Jr75A/7vYC7qdSw5ZB9kwsXOuK+qqoK3tzcqKyuh1XJ9BHVNo96AxFU/Ir+sHkk3D8TvbxogdSQimxJCYNuxS1j5bQYuVdQDALw0SswcEYrbYkIwIswHSm5kSRbUmc9vlhuiLnh/bzZe+eosArzU2P2nG3iGFLmsRr0Bmw7n4729Wcgvqzc/rtUoEdfHF4OCtYj094Sfpwq+Hioo5DLIZTLU6fSoamhCdYMeVQ16VDc0obZRj5oGPWoaDaht1EOhkMHDTQF/LzX6B3hiULAW0UFe3EfKRbHctIPlhrqrsr4JE9/8ARV1TVg+cxjuHRMudSQiyRmNAnszS/Hv1Iv4MaMEFXVNVnmfUB93TB4ShLnj+qBPrx5WeQ+yTyw37WC5oe5a9nUa/vVjFgYEeOLrpyZw6J3ofxiMAicvVuD0pUqcLajGxfI6XKnRoaJOB4MQMAqgh0oBL40btO5KeKnd4KlRwlOthJdGiR5qJXqoFNAbBep0BlyuqEdmcQ1OXqxEffPiZaVchlmjw/BU4gAEeGkkvmKyhc58fnMsnagTLlXU44N9OQCARbdGs9gQtUEhl2FEuC9GhPta9Ps2NBmwJ6MEHx/Kw56MEmw8lIevTxfirftGYHx/7gxOP+NPZqJOWPltOnR6I+L79sSN0QFSxyFyKRo3BW4ZEoSPHhyDzY+MxaBgLcpqdZj9/iGs3X0BLjYRQe1guSHqoDOXK7Ht2CUAwHNTB3FRI5GE4iN7YdsT4/CbuN4wCtNO4Wt+yJQ6FtkJlhuiDlr+9TkIAUyPCUFMmI/UcYhcnsZNgTd/E4PnpprOdFvxbQY27MuWOBXZA5Ybog7Yk1GCn86bjln40y1RUschol945Pp+eKp5r6m//Pcsdp0tkjgRSY3lhugaDEaBZc3HLMweG4HwXjxmgcje/CFxAOYk9AEA/HnLCRRVNUiciKTEckN0DV8cu4S0gip4aZT43Y08ZoHIHslkMjw/bRCGhGhRXteEpM+Ow8iTy10Wyw1ROxqaDFj5bToA4MlJ/eHbQyVxIiK6GrVSgX/cNwLubgrsy7yC9Vx/47JYboja8cG+HFyubECItwbzxkVIHYeIrqGfvydeuG0QAODv351HaU2jxIlICiw3RFdRVqvD2823lj4zOQoaN4XEiYioI+4bHY5hod6obtRj5bcZUschCbDcEF3FP7/PRHWjHoOCtZgRGyp1HCLqILlchiXTBwMANh/Jw9nLVRInIltjuSFqQ+6VWvzfwRwAwHNToyGXc8M+IkcyOqInpg0PhlEAr+1IkzoO2RjLDVEb3vwmHU0GgQkD/DBhgL/UcYioCxZNiYZSLsPezFIcyyuXOg7ZEMsN0f84kV+Br04WQCYDFt86SOo4RNRFYT09MGOEaUp53Y8XJE5DtsRyQ/QLQgjzEPbMEb0xOEQrcSIi6o7HJkYCAL49W4TM4hqJ05CtsNwQ/cIP6cU4lF0GlVKOpFsGSh2HiLqpf4AXbh4cCCGAd/Zw9MZVsNwQNdMbjFi2w3TMwvzxEQj1cZc4ERFZwuM39AMAbDt2CcU8lsElsNwQNft36kWcL66Bj4cbnriBxywQOYuR4b6I6+OLJoPAltSLUschG2C5IQJQp9Nj1S7TZl8LJ/WHt7ubxImIyJLuHR0GANh8JJ9nTrkAlhsiAOv3ZqOoqhG9fd0xu/lkYSJyHtOGB8NLrUTulToczLoidRyyMpYbcnmlNY1Y92MWAOBPk6OgVvKYBSJn46FS4vbYEADApiP5Eqcha2O5IZf3VvJ51DTqMSzUG9OHh0gdh4is5N7R4QCAnacLUV6rkzgNWRPLDbm07NJabDyUBwBYzGMWiJza0FAtBgdroTMY8eWJy1LHIStiuSGXtuKbdOiNApOi/DGun5/UcYjIimQyGWaONO1YvP1kgcRpyJpYbshlHcsrx/ZTBZDLgEU8ZoHIJUwdFgwAOJJbhsJK7nnjrFhuyGWt+DYdAHDXyN6ICvKSOA0R2UKIjzvi+vhCCGDHKY7eOCuWG3JJ+y+UYl/mFbgpZHgqcYDUcYjIhm4bbhq92c5y47RYbsjlCCGw6lvThn33jg5Hb18PiRMRkS1NHRYMmQxIyS3H5Yp6qeOQFbDckMvZc74UR3PLoVbKsfBGHrNA5GoCtRqMjugJgFNTzorlhlyKEAIrm9fazB7bB4FajcSJiEgK05oXFn9zplDiJGQNLDfkUnadLcLJi5XwUCnwWPNJwUTkehIHBwIwTU1V1HFDP2fDckMuw2gU5sMx542LgJ+nWuJERCSVUB93RAd5wSiAHzNKpI5DFsZyQy5j+6kCnCushpdaiUeuj5Q6DhFJbFJ0AAAgOa1Y4iRkaZKXmzVr1iAiIgIajQbx8fE4fPhwu89fvXo1oqKi4O7ujrCwMDz99NNoaOBGTNQ+g1Fg9XemUZsFEyLh46GSOBERSe2m5nLzY0YJ9AajxGnIkiQtN5s3b0ZSUhKWLl2K1NRUxMTEYPLkySgubrtFf/LJJ1i0aBGWLl2KtLQ0vP/++9i8eTOee+45GycnR/PtmUJcKKmFVqPEg9dFSB2HiOzAiHBf+Hi4obK+Cal5FVLHIQuStNysWrUKDz/8MObPn4/Bgwdj3bp18PDwwPr169t8/v79+zF+/Hjcf//9iIiIwC233IL77rvvmqM95NqEEFj74wUAwNxxEfDSuEmciIjsgUIuww0D/QEAyeeKJE5DliRZudHpdEhJSUFiYuLPYeRyJCYm4sCBA22+Zty4cUhJSTGXmaysLOzYsQNTp0696vs0Njaiqqqq1Re5ln2ZV3DyYiU0bnLMGxchdRwisiM3DjLdNfU91904FaVUb1xaWgqDwYDAwMBWjwcGBuLcuXNtvub+++9HaWkprrvuOgghoNfr8dhjj7U7LbVs2TK89NJLFs1OjmXtj5kATLsR9+IdUkT0C9cP8INMBpwvrkFhZQOCvLn3lTOQfEFxZ+zevRuvvfYa3n77baSmpmLr1q3Yvn07Xnnllau+ZvHixaisrDR/5efn2zAxSe1EfgX2ZV6BQi7Dggl9pY5DRHbGx0OFYaHeAExnzpFzkGzkxs/PDwqFAkVFrec5i4qKEBQU1OZrXnzxRcyePRsLFiwAAAwbNgy1tbV45JFH8Pzzz0Mu/3VXU6vVUKv5r3VXta55rc0dMSE8Q4qI2jSunx9OXqzE3sxSzBzZW+o4ZAGSjdyoVCrExcUhOTnZ/JjRaERycjISEhLafE1dXd2vCoxCoQBgWjRK9EsXSmqws3lrde5GTERXc11/PwDA/swr/CxxEpKN3ABAUlIS5s6di1GjRmHMmDFYvXo1amtrMX/+fADAnDlzEBoaimXLlgEApk+fjlWrVmHEiBGIj49HZmYmXnzxRUyfPt1ccohavPNjFoQAEgcFYmCgl9RxiMhOjYrwhUopR2FVAy6U1KJ/gKfUkaibJC03s2bNQklJCZYsWYLCwkLExsZi586d5kXGeXl5rUZqXnjhBchkMrzwwgu4dOkS/P39MX36dLz66qtSXQLZqcLKBmw9dhEA8DhHbYioHRo3BUb18cX+C1ew/0Ipy40TkAkXG4OrqqqCt7c3KisrodVqpY5DVrJsRxr+tScLY/r2xGePtj3NSUTUYs0PmXjzm3RMHhKIf80eJXUcakNnPr8d6m4poo6o0+nx6eE8AMDDE3iGFBFd2/jmdTcHLlyBwehS/+Z3Siw35HS+OHYZVQ16hPf0wI3NZ8cQEbVnWKg3vDRKVDXoceZypdRxqJtYbsipCCGwYX82AGBOQh8o5DKJExGRI1DIZRgT0RMAcDi7TOI01F0sN+RU9l+4goyiGnioFLhndJjUcYjIgYzuy3LjLFhuyKls2J8DALg7rje0PCCTiDphdPPIzdHccu534+BYbshpXK6oR3KaacfrOQl9JE5DRI5mWKg31Eo5ymp1uFBSI3Uc6gaWG3Ianx3Nh1EAY/r2RP8AbtpHRJ2jUsoRG+YDADicXS5tGOoWlhtyCnqDEZuPmA5FfSA+XOI0ROSoxjSvuzmSw3U3jozlhpzC7vQSFFQ2wNfDDVOGtn3wKhHRtYzmHVNOgeWGnMInzZv23R3XG2olzxkjoq4Z2ccXchlwqaIelyvqpY5DXcRyQw6voLIeu9OLAQD3jeGUFBF1nadaiSEh3gA4NeXIWG7I4W1NvWReSBzpzwPviKh7RkX4AgBSc7mo2FGx3JBDE0Lg3ymm07/vjustcRoicgYjw03l5lh+hbRBqMtYbsihpeZVIKu0Fu5uCkwdFix1HCJyAiPCfQAAZy9XoaHJIG0Y6hKWG3JoW5pHbaYOC4anWilxGiJyBqE+7vD3UkNvFDh1iYdoOiKWG3JYDU0GfHXiMgBOSRGR5chkMoxsHr05lsd1N46I5YYc1rdni1DdqEdvX3fEN2+8RURkCSNa1t3kVUgbhLqE5YYc1pfHLwEA7hwRCrlcJnEaInImI5qPYWC5cUwsN+SQKup0+DGjBABwe0yIxGmIyNkM6+0NhVyGwqoGFFRyMz9Hw3JDDunr04VoMggMCtZiQCAPySQiy/JQKREdZPrZkppbIW0Y6jSWG3JI/2mekrojlqM2RGQdI7io2GGx3JDDKaxswKHmQ+2mc0qKiKxkRJhpUfFxbubncFhuyOF8dfIyhABGR/gi1Mdd6jhE5KRiwkxnTJ25XAW9wShxGuoMlhtyONtPFQDgqA0RWVeknyd6qBSobzIgs6RG6jjUCSw35FAuV9TjWF4FZDJgypAgqeMQkROTy2UYGmoavTl5kTsVOxKWG3IoO08XAgBG9fFFgFYjcRoicnYxzfvdnGK5cSgsN+RQvj5tmpK6dSgPySQi6xtmHrmpkDYIdQrLDTmM4qoGHM013ZI5ZSinpIjI+ob3NpWbtIJq6PRcVOwoWG7IYXxzphBCALFhPgjhXVJEZAPhPT3g7e4GncGI9MJqqeNQB7HckMPYccq03mbqMI7aEJFtyGQy8+jNyUsV0oahDmO5IYdQUafD4RzTxn1ThnC9DRHZTku54aJix8FyQw5hd3oJDEaBqEAvhPfykDoOEbmQYaE+AIATLDcOg+WGHMKus0UAgMTBARInISJX07JTcUZRNRqaDBKnoY5guSG716g34MeMEgDAzYO53oaIbCtIq0HPHioYjAIZRVxU7AhYbsjuHcwqQ02jHv5eagxv3nOCiMhWZDIZBgdrAQBnL1dJnIY6guWG7N53LVNSgwIgl8skTkNErmhIiKncnGG5cQhdKjdZWVmWzkHUJiEEvkszlZubBwdKnIaIXNXg5nJztoDlxhF0qdz0798fkyZNwscff4yGhgZLZyIyO1tQhYLKBri7KTCun5/UcYjIRbWM3KQVVMFgFBKnoWvpUrlJTU3F8OHDkZSUhKCgIDz66KM4fPiwpbMRYXe6aSHxuH69oHFTSJyGiFxVXz9PaNzkqNMZkHulVuo4dA1dKjexsbH4+9//jsuXL2P9+vUoKCjAddddh6FDh2LVqlUoKSmxdE5yUbvTiwEAN0TzFnAiko5CLkNUENfdOIpuLShWKpWYOXMmPv/8c7z++uvIzMzEM888g7CwMMyZMwcFBQWWykkuqLKuCal5FQCAGwb6SxuGiFzeEK67cRjdKjdHjx7FE088geDgYKxatQrPPPMMLly4gF27duHy5cu44447LJWTXNBPmaZdifsHeCKsJ3clJiJptdwOzpEb+6fsyotWrVqFDz74AOnp6Zg6dSo++ugjTJ06FXK5qSv17dsXGzZsQEREhCWzkotpWW8zKYqjNkQkPfPIDcuN3etSuVm7di0efPBBzJs3D8HBbR9iGBAQgPfff79b4ch1GY3CXG5uiOJ6GyKSXnSQFnIZUFrTiOLqBgR4aaSORFfRpXKza9cuhIeHm0dqWgghkJ+fj/DwcKhUKsydO9ciIcn1nC2oQmlNI3qoFBgV4St1HCIiuKsUiPT3RGZxDc5crkJAFMuNverSmpt+/fqhtLT0V4+XlZWhb9++3Q5F1HKWVEI/P6iVvAWciOwDj2FwDF0qN0K0vYFRTU0NNBo2Weq+/RdM5XnCAG7cR0T2YzDX3TiETk1LJSUlATAdIrZkyRJ4ePx8B4vBYMChQ4cQGxtr0YDkehqaDDiSUw4AGN+f5YaI7AdvB3cMnSo3x44dA2AauTl16hRUKpX511QqFWJiYvDMM89YNiG5nJTccuj0RgRq1ejn30PqOEREZi3TUtmltahp1MNT3aWlq2RlnfpT+eGHHwAA8+fPx9///ndotVqrhCLXtjfTNCU1vr8fZDKeAk5E9qOXpxpBWg0KqxpwrqAKoyJ6Sh2J2tClNTcffPABiw1Zzb6WcsODMonIDrWsu+FmfvarwyM3M2fOxIYNG6DVajFz5sx2n7t169ZuByPXVFnXhFOXKgFwvQ0R2achIVp8f66Yi4rtWIdHbry9vc1TBN7e3u1+dcaaNWsQEREBjUaD+Pj4a54uXlFRgSeffBLBwcFQq9UYOHAgduzY0an3JPt1IKsUQgD9/HsgyJt33hGR/TEfw1BQKXESupoOj9x88MEHbf7/3bF582YkJSVh3bp1iI+Px+rVqzF58mSkp6cjIODXu9LqdDrcfPPNCAgIwJYtWxAaGorc3Fz4+PhYJA9Jb1/mFQDAdRy1ISI71TItlVFYgyaDEW6Kbh3TSFbQpWXe9fX1EEKYbwXPzc3Ftm3bMHjwYNxyyy0d/j6rVq3Cww8/jPnz5wMA1q1bh+3bt2P9+vVYtGjRr56/fv16lJWVYf/+/XBzcwMAnl/lZFrW24xjuSEiOxXm6wEvtRLVjXpcKKlBdBDXoNqbLtXNO+64Ax999BEA0zTRmDFjsHLlStxxxx1Yu3Zth76HTqdDSkoKEhMTfw4jlyMxMREHDhxo8zVffvklEhIS8OSTTyIwMBBDhw7Fa6+9BoPBcNX3aWxsRFVVVasvsk+XK+qRVVoLuQwYG9lL6jhERG2Sy2UY1DI1dYmfKfaoS+UmNTUVEyZMAABs2bIFQUFByM3NxUcffYR//OMfHfoepaWlMBgMCAwMbPV4YGAgCgsL23xNVlYWtmzZAoPBgB07duDFF1/EypUr8de//vWq77Ns2bJW64HCwsI6eJVkay2jNsN7+8Db3U3iNEREVzeYm/nZtS6Vm7q6Onh5eQEAvv32W8ycORNyuRxjx45Fbm6uRQP+ktFoREBAAN555x3ExcVh1qxZeP7557Fu3bqrvmbx4sWorKw0f+Xn51stH3WP+Rbw/hy1ISL7Fh1k+gxML6yWOAm1pUvlpn///vjiiy+Qn5+Pb775xrzOpri4uMP73/j5+UGhUKCoqKjV40VFRQgKCmrzNcHBwRg4cCAUip8PUhw0aBAKCwuh0+nafI1arYZWq231RfZHCIF9F0yLiXkLOBHZu+jmaalzLDd2qUvlZsmSJXjmmWcQERGB+Ph4JCQkADCN4owYMaJD30OlUiEuLg7Jycnmx4xGI5KTk83f73+NHz8emZmZMBqN5scyMjIQHBzc6igIcjzni2tQUt0ItVKOkeG+UschImrXwEBPyGRAaU0jSmsapY5D/6NL5ebuu+9GXl4ejh49ip07d5ofv+mmm/C3v/2tw98nKSkJ7777Lj788EOkpaXh8ccfR21trfnuqTlz5mDx4sXm5z/++OMoKyvDU089hYyMDGzfvh2vvfYannzyya5cBtmRvedNU1Jj+vaExk1xjWcTEUnLQ6VEeE/THcOcmrI/XT7xKygo6FfTR2PGjOnU95g1axZKSkqwZMkSFBYWIjY2Fjt37jQvMs7Ly4Nc/nP/CgsLwzfffIOnn34aw4cPR2hoKJ566ik8++yzXb0MshP7LzTfAs4jF4jIQUQHeSH3Sh3OFVZzOt3OdKnc1NbWYvny5UhOTkZxcXGraSLAdFdTRy1cuBALFy5s89d27979q8cSEhJw8ODBTuUl+2YwChzKKgPAxcRE5DiigrT45kwRzvGOKbvTpXKzYMEC/Pjjj5g9ezaCg4N5cjN1S1pBFaob9fBUKzEkpHPHdxARScV8x1QRp6XsTZfKzddff43t27dj/Pjxls5DLuhQtmnUZlSELxRyFmUicgwt5SajqBoGo+DPLzvSpQXFvr6+6Nmzp6WzkIs60lxuxvTl3ykichx9evWAxk2OhiYj8srqpI5Dv9ClcvPKK69gyZIlqKvjHyZ1jxACh3Oay00Eyw0ROQ6FXIYBAabRG667sS9dmpZauXIlLly4gMDAQERERJgPsWyRmppqkXDk/C6U1KCsVge1Uo5hvbnehogcS1SQF05dqsS5wmrcOixY6jjUrEvlZsaMGRaOQa6qZb3NiHAfqJXc34aIHAuPYbBPXSo3S5cutXQOclE/r7fhLeBE5Hiig1qOYeC0lD3p0pobAKioqMB7772HxYsXo6zM9AGVmpqKS5cuWSwcOTchhHnkhuttiMgRRTWP3OSW1aFOp5c4DbXo0sjNyZMnkZiYCG9vb+Tk5ODhhx9Gz549sXXrVuTl5eGjjz6ydE5yQhfL61FQ2QClXIaRfXykjkNE1Gn+Xmr4eapQWqPD+aIaxIT5SB2J0MWRm6SkJMybNw/nz5+HRqMxPz516lTs2bPHYuHIuR1uHrUZGuoND1WXTwIhIpJUFNfd2J0ulZsjR47g0Ucf/dXjoaGhKCws7HYocg1Hmm8Bj+f+NkTkwKICTetu0rjuxm50qdyo1WpUVf36DzEjIwP+/v7dDkWu4TA37yMiJxAdzJEbe9OlcnP77bfj5ZdfRlNTEwBAJpMhLy8Pzz77LO666y6LBiTnVFzdgKzSWshkwKg+LDdE5Lhabgc/V1gNIYTEaQjoYrlZuXIlampq4O/vj/r6ekycOBH9+/eHl5cXXn31VUtnJCd0JLscABAV6AVvD7drPJuIyH4NCPCCTAaU1epQUtModRxCF++W8vb2xq5du7Bv3z6cOHECNTU1GDlyJBITEy2dj5wU19sQkbNwVynQt1cPZJXWIr2wGgFemmu/iKyq0+XGaDRiw4YN2Lp1K3JyciCTydC3b18EBQVBCAGZjKei0rUd4uZ9ROREooK8zOVmwgCuPZVap6alhBC4/fbbsWDBAly6dAnDhg3DkCFDkJubi3nz5uHOO++0Vk5yIpV1TebdPEf39ZU4DRFR97XcDp5WwEXF9qBTIzcbNmzAnj17kJycjEmTJrX6te+//x4zZszARx99hDlz5lg0JDmXo7llEALo69eDw7dE5BRajmFIL+Lt4PagUyM3n376KZ577rlfFRsAuPHGG7Fo0SJs3LjRYuHIOR3JMS0mHh3BURsicg4td0ydL6qBwcg7pqTWqXJz8uRJTJky5aq/fuutt+LEiRPdDkXOLTXPVG7i+rDcEJFzCO/pAXc3BRr1RuRcqZU6jsvrVLkpKytDYGDgVX89MDAQ5eXl3Q5FzqvJYMTJixUAWG6IyHnI5TIMDPQEAJzjuhvJdarcGAwGKJVXX6ajUCig1/NUVLq6tIIqNDQZodUoEennKXUcIiKLMa+74TEMkuvUgmIhBObNmwe1Wt3mrzc2cvMial9qrmlkb2QfX8jl3DaAiJxH1C92KiZpdarczJ0795rP4Z1S1J6UvAoAwMhwTkkRkXOJZrmxG50qNx988IG1cpCLaBm54XobInI2LSM3eWV1qG3Uo4e6S4cAkAV06Wwpoq4oqmrApYp6yGVATJiP1HGIiCyql6ca/l6mZRsZRRy9kRLLDdlMy6hNVJAWnvwXDRE5IU5N2QeWG7KZlJbFxOE+0gYhIrKSlnKTznIjKZYbshlu3kdEzi6q+Xbwc7wdXFIsN2QTjXoDTl8y/Y+dd0oRkbP65ciNEDyGQSosN2QTpy9VQWcwomcPFfr08pA6DhGRVfQP8IRcBpTXNaG4mnu/SYXlhmzCvHlfuC9kMm7eR0TOSeOmQF+/HgC4qFhKLDdkEy3rbUb28ZE2CBGRlfEYBumx3JDVCSHMd0rFcb0NETk58+3gPEBTMiw3ZHWXKupRXN0IpVyG4b19pI5DRGRVPGNKeiw3ZHUtozaDQ7RwVykkTkNEZF0t01KZxTVoMhglTuOaWG7I6o7xsEwiciG9fd3RQ6WAzmBETmmt1HFcEssNWd3Pi4lZbojI+cnlMgzk1JSkWG7Iqup1Bpy93LJ5n4+0YYiIbCSaOxVLiuWGrOrkxQrojQKBWjVCfdyljkNEZBM8Y0paLDdkVSl53LyPiFxPyx1TabwdXBIsN2RVqbkVAHhYJhG5lpaRm0sV9ahuaJI4jethuSGrEUKYFxOP4J1SRORCfDxUCNJqAAAZRRy9sTWWG7Ka3Ct1KKvVQaWQY2ioVuo4REQ2xakp6bDckNW0bN43NFQLtZKb9xGRa4kO5qJiqbDckNWk/mIxMRGRqzGfMcXbwW2O5YasxnxYJhcTE5EL+nmvm2oIISRO41pYbsgqqhuazIvouDMxEbmifv6eUMplqG7Qo6CyQeo4LoXlhqziRH4ljAII9XFHYPMdA0RErkSllCPSvwcATk3ZGssNWQXPkyIiaj01RbbDckNWYV5vw/OkiMiFtdwOfo63g9uUXZSbNWvWICIiAhqNBvHx8Th8+HCHXrdp0ybIZDLMmDHDugGpU4xGgWMcuSEiwiDeDi4JycvN5s2bkZSUhKVLlyI1NRUxMTGYPHkyiouL231dTk4OnnnmGUyYMMFGSamjLpTUoKpBD42bHIOCuXkfEbmuqOZpqQslNdDpjRKncR2Sl5tVq1bh4Ycfxvz58zF48GCsW7cOHh4eWL9+/VVfYzAY8MADD+Cll15CZGSkDdNSR7Sstxne2wduCsn/ihERSSbEWwMvjRJ6o8CFkhqp47gMST95dDodUlJSkJiYaH5MLpcjMTERBw4cuOrrXn75ZQQEBOChhx6yRUzqJB6WSURkIpPJzJv5cWrKdpRSvnlpaSkMBgMCAwNbPR4YGIhz5861+Zq9e/fi/fffx/Hjxzv0Ho2NjWhsbDT/d1UVb8ezthTuTExEZBYV5IUjOeVIK6zCDIRKHcclONScQXV1NWbPno13330Xfn5+HXrNsmXL4O3tbf4KCwuzckrXVlnXhMxi09DrCN4pRURkXnfDkRvbkXTkxs/PDwqFAkVFRa0eLyoqQlBQ0K+ef+HCBeTk5GD69Onmx4xG0wItpVKJ9PR09OvXr9VrFi9ejKSkJPN/V1VVseBYUWq+adQmopcH/DzVEqchIpLeIPPp4Jw5sBVJR25UKhXi4uKQnJxsfsxoNCI5ORkJCQm/en50dDROnTqF48ePm79uv/12TJo0CcePH2+ztKjVami12lZfZD3HcjklRUT0S9HNd40WVTXiSk3jNZ5NliDpyA0AJCUlYe7cuRg1ahTGjBmD1atXo7a2FvPnzwcAzJkzB6GhoVi2bBk0Gg2GDh3a6vU+Pj4A8KvHSRop3N+GiKgVT7USEb08kHOlDmkF1bhuAEe1rU3ycjNr1iyUlJRgyZIlKCwsRGxsLHbu3GleZJyXlwe53KGWBrksg1HgeF4FAI7cEBH90uAQLXKu1OFsQSWuG9CxNaPUdZKXGwBYuHAhFi5c2Oav7d69u93XbtiwwfKBqEvSC6tRqzOgh0ph3nKciIiAwcFa7DhViLOXue7GFjgkQhbTsnlfbLgPFHKZxGmIiOzH4BDTups0njFlEyw3ZDGp5sMyOSVFRPRLg4O9AQCZJTVoaDJInMb5sdyQxbSM3IzgYmIiolYCtWr4erjBYBQ4X8RjGKyN5YYsorSmETlX6gAAI8NYboiIfkkmk5mnps4WVEqcxvmx3JBFHGu+S6p/gCe8PdykDUNEZIcGN+93w0XF1sdyQxaRwvU2RETt+nnkhuXG2lhuyCJSzZv3+UgbhIjITrUsKk4rqIbRKCRO49xYbqjbdHojTuRXAADiuJiYiKhNkf49oFLKUdOoR355ndRxnBrLDXXbmcuVaNQb4ePhhkg/T6njEBHZJTeFHFGBpg1Oue7GulhuqNt+ud5Gzs37iIiuyryomOturIrlhrqtpdzwsEwiovaZFxVz5MaqWG6oW4QQONpcbkax3BARtYt3TNkGyw11S35ZPUqqG+GmkCEmzEfqOEREdi26+VDhgsoGlNXqJE7jvFhuqFtS8soAAENCvKFxU0ichojIvnlp3NCnlwcAII2jN1bDckPdcjSneTExp6SIiDqEOxVbH8sNdUsK19sQEXUK75iyPpYb6rLK+iakF1UDAOIiWG6IiDqCd0xZH8sNddnx/AoIAYT39ECAl0bqOEREDqGl3GSW1KChySBxGufEckNdlpJjWkzMKSkioo4L0mrg6+EGg1HgfFGN1HGcEssNddlRbt5HRNRpMpnMPHpz5nKlxGmcE8sNdYneYMTx5sMyR3G9DRFRpwwJMZ0QfprlxipYbqhLzhVWo05ngJdGiYEBXlLHISJyKMNCTeXm1EWWG2tguaEuOdq83mYkD8skIuq0lnKTVliNJoNR4jTOh+WGuqRlvQ037yMi6rw+vTzgpVFCpzcio3lLDbIclhvqklRu3kdE1GUymYxTU1bEckOddrmiHpcrG6CQyxAb7iN1HCIih2QuN5dYbiyN5YY67UjzepvBwVp4qJQSpyEickzDejffMcVyY3EsN9Rph7JN5WZM354SJyEiclzmRcUF1dDpuajYklhuqNMOZV0BAMSz3BARdVl4Tw9oNUroDFxUbGksN9QpJdWNuFBSC5mMIzdERN0hk8k4NWUlLDfUKYebp6SiAr3g46GSOA0RkWMb2jw1dZLlxqJYbqhTDmWbpqTGRvaSOAkRkeOL6e0DADh5sULSHM6G5YY65VCWaeSG622IiLovJswHAHCuoBoNTQZpwzgRlhvqsLJaHdKbF71xvQ0RUfeFeGvg76WG3ih4QrgFsdxQh7WstxkQ4IlenmqJ0xAROT6ZTGaemjqWVyFpFmfCckMd1rLeJj6SozZERJYyonmn9xM8hsFiWG6ow35eb8PFxEREltIycnM8v1zaIE6E5YY6pLKuCWmFVQA4ckNEZEnDw7whkwH5ZfW4UtModRynwHJDHXIkpwxCAJF+PRDgpZE6DhGR09Bq3NDP3xMAcIK3hFsEyw11CNfbEBFZz89TU1x3YwksN9QhLYdlcr0NEZHlxTYvKj6eXyFpDmfBckPXVN3QZD73hCM3RESWF9sycpNXDqNRSBvGCbDc0DUdzS2HUZhOsA32dpc6DhGR04kO9oK7mwJVDXpcKKmROo7DY7mha+KRC0RE1uWmkCMmzHSIZkoubwnvLpYbuqafFxNzvQ0RkbWM6mP6B+RRlptuY7mhdlU1NOFk866ZY7nehojIauL6+ALgyI0lsNxQuw5euAKDUaCvXw/09vWQOg4RkdMaGW4qN9mltdzMr5tYbqhd+zJLAQDj+3NKiojImrw93DAgwLSZH0dvuoflhtq1t7ncXNffX+IkRETOb1RE89RUHstNd7Dc0FUVVNbjQkkt5DIggYuJiYisLq55UXFKDstNd7Dc0FXtyzTdJTWstw+8PdwkTkNE5PxaFhWfvFSJRr1B4jSOi+WGrmqfeUqKozZERLYQ0csDfp4q6PRGnLrIc6a6yi7KzZo1axAREQGNRoP4+HgcPnz4qs999913MWHCBPj6+sLX1xeJiYntPp+6RghhXm8zvr+fxGmIiFyDTCbD6AjT1FTLmX7UeZKXm82bNyMpKQlLly5FamoqYmJiMHnyZBQXF7f5/N27d+O+++7DDz/8gAMHDiAsLAy33HILLl26ZOPkzi29qBol1Y3QuMnNtycSEZH1jW1e43gw64rESRyX5OVm1apVePjhhzF//nwMHjwY69atg4eHB9avX9/m8zdu3IgnnngCsbGxiI6OxnvvvQej0Yjk5GQbJ3duu9NLAJgWEmvcFBKnISJyHS0HFB/NKUeTwShxGsckabnR6XRISUlBYmKi+TG5XI7ExEQcOHCgQ9+jrq4OTU1N6Nmz7d1zGxsbUVVV1eqLrm13umnk7IaoAImTEBG5loEBXvD1cEN9k8G8Qzx1jqTlprS0FAaDAYGBga0eDwwMRGFhYYe+x7PPPouQkJBWBemXli1bBm9vb/NXWFhYt3M7u5pGPY4234Y4cSD3tyEisiW5XIYxfVvW3XBqqiskn5bqjuXLl2PTpk3Ytm0bNBpNm89ZvHgxKisrzV/5+fk2Tul49mWWQm8UiOjlgQi/HlLHISJyOS3rbg5lcVFxVyilfHM/Pz8oFAoUFRW1eryoqAhBQUHtvnbFihVYvnw5vvvuOwwfPvyqz1Or1VCr1RbJ6ypa1ttw1IaISBrxfU3l5mhOGfQGI5QKhx6LsDlJf7dUKhXi4uJaLQZuWRyckJBw1de98cYbeOWVV7Bz506MGjXKFlFdhhACezJM5YbrbYiIpBEd5AVvdzfU6gw4fZlrRTtL8iqYlJSEd999Fx9++CHS0tLw+OOPo7a2FvPnzwcAzJkzB4sXLzY///XXX8eLL76I9evXIyIiAoWFhSgsLERNTY1Ul+BUMotrcKmiHiql3DwsSkREtvXLdTf7L5RKnMbxSF5uZs2ahRUrVmDJkiWIjY3F8ePHsXPnTvMi47y8PBQUFJifv3btWuh0Otx9990IDg42f61YsUKqS3Aq358z3SUV37cn3FW8BZyISCrXNW+guvc8y01nSbrmpsXChQuxcOHCNn9t9+7drf47JyfH+oFc2HdppvVPNw8OvMYziYjImq4bYCo3R3PKUa8z8B+cnSD5yA3Zjys1jUjJNd0CftMglhsiIilF+vVAqI87dAYjbwnvJJYbMvshvQRGAQwO1iLUx13qOERELk0mk2FC8+jNT5ya6hSWGzLbdda0cWIip6SIiOzChAGmLTl+Ol8icRLHwnJDAICGJgP2ZJj+ZXALyw0RkV0Y168XZDIgo6gGRVUNUsdxGCw3BAA4cOEK6psMCNJqMCREK3UcIiIC4NtDheGh3gA4NdUZLDcEAPj2rOkuqcTBAZDJZBKnISKiFi1TUy0brNK1sdwQ9AYjvjljWm8zeUj7x14QEZFtTYwylZsfM0qgNxglTuMYWG4Ih7LLUFarg6+HGxK4KzERkV0ZEeYDXw83VNY34Wjzdh3UPpYbwvZTph2gJw8J4uFsRER2RqmQY1LzWX/JaUXXeDYBLDcuT28w4pvTpimpqcOCJU5DRERtadlYNTmtWOIkjoHlxsUdyi7DlZYpqX6ckiIiskfXD/SDm0KGrNJaXCjhQdHXwnLj4n45JeXGKSkiIrvkpXHD2OY1kZyaujZ+mrmwJoMROzklRUTkEG6KNq27+Y5TU9fEcuPCfkwvQVmtDn6eKozjlBQRkV1rWXdzNKcMpTWNEqexbyw3LmzbsUsAgDtiQ3mXFBGRnQvr6YFhod4wCphH3alt/ERzUZX1TdjVPG9754hQidMQEVFH3DbctITgq5OXJU5i31huXNSOUwXQ6Y2ICvTiWVJERA5iWnO5OZRdhuJqHqR5NSw3Lmpr6kUAwJ0jQ3mWFBGRg+jt64HYMB8IAXx9ilNTV8Ny44Jyr9TiSE45ZDJgRiynpIiIHAmnpq6N5cYFfXI4DwBw/QB/BHlrJE5DRESd0bJ1x5GcchRU1kucxj6x3LiYRr0Bnx81TUk9EB8ucRoiIuqsEB93jInoCeDnu16pNZYbF7PzdCHKanUI0mpwY/OGUERE5FjuHtUbAPD50YsQQkicxv6w3LiYjYdMU1L3jgnj3jZERA5q2rBgeKgUyC6txdHccqnj2B1+urmQ80XVOJxdBoVchntHc0qKiMhR9VArzQuLPzuSL3Ea+8Ny40I+2J8DwHQ+CRcSExE5tntGhQEwHYBc06iXOI19YblxEaU1jdiSYlpI/NB1fSVOQ0RE3RXXxxeR/j1QpzPgqxO8LfyXWG5cxEf7c6DTGxET5oMxfXtKHYeIiLpJJpPh3tGm0ZsN+3O4sPgXWG5cQL3OgI8O5gIAHpkQyR2JiYicxKxR4XB3U+BcYTUOZF2ROo7dYLlxAZ+n5KOirgnhPT0wZWiQ1HGIiMhCvD3ccHec6bbw9XuzJU5jP1hunFyj3oB1uy8AABZM6AuFnKM2RETOZN74CABA8rliZJfWShvGTrDcOLlPD+XhcmUDgrQa88p6IiJyHv38PTEpyh9CABv2cfQGYLlxanU6Pf75g2nU5nc39YfGTSFxIiIisoaHrosEAGw6ko/iqgaJ00iP5caJfXQgF6U1jQjr6Y7fxHHUhojIWY3v3wtxfXzRqDfi7ealCK6M5cZJldfqsLb5L/hTNw2ESsk/aiIiZyWTyZB080AAwCeH81z+tHB+4jmpFd+mo7K+CVGBXpgRGyJ1HCIisrJx/XphTN+e0OmNePsH1x69YblxQqcvVeKTw6YDMl+6YwgPyCQicgG/HL3ZdCTPpe+c4qeekxFC4C9fnoEQwPSYEIyN7CV1JCIispGxkb1wQ5Q/mgwCL/33jMvuWsxy42Q2HsrD0dxyuLsp8NzUaKnjEBGRjS25bTDcFDLsTi9Bclqx1HEkwXLjRHKv1OK1HWkAgGcmRyHY213iREREZGuR/p5YMMF0a/hLX51BQ5NB4kS2x3LjJAxGgT9+dgJ1OgPGRvbE/HERUkciIiKJLJzUH0FaDfLL6rHim3Sp49gcy42TWPNDJo7mlsNTrcSbd8dAzmMWiIhcVg+1Eq/eORQA8N7ebOzLLJU4kW2x3DiB788V4W/fZQAA/nL7EIT19JA4ERERSe2mQYG4Pz4cAPDHz06gsq5J4kS2w3Lj4LJKavDUp8chBPDbseHm02GJiIhemDYIff16oLCqAU9/dhwGo2vcPcVy48CKqxvw4IYjqG7UY1QfXyy5bYjUkYiIyI54qJT4x70joFbK8f25Yry+85zUkWyC5cZBVdTpMPu9w8i5Uoewnu54+7cjecQCERH9yrDe3njzNzEAgHf2ZGHzkTyJE1kfPw0dUFmtDnPXH0Z6UTUCvNTY+NBYBHhppI5FRER26vaYEPz+pgEAgMVbT2HbsYsSJ7IulhsHk3elDnet3Y8TFyvh6+GGjxfEI7wXFxATEVH7/nDTANw3JgxGASR9dgL/TnHegsNy40AOXLiCmWv3Ibu0FqE+7vj8sQQMDPSSOhYRETkAuVyGV2cMwwPx4RAC+OPnJ7D6uwwYnXCRsVLqAHRtBqPAW9+fxz+Sz8MogEHBWmyYPxqBWk5FERFRx8nlMvx1xlC4uynw3t5srP7uPM4VVOP1u4fD291N6ngWw5EbO3csrxx3rNmL1d+Zis09o3rj348nsNgQEVGXyGQyvHDbYLxx93CoFHLsPFOIyX/bg+/PFUkdzWJkwsWODK2qqoK3tzcqKyuh1WqljnNVOaW1eOv7TGw9dhFCAFqNEi/dMQR3juA+NkREZBnH8sqR9NkJZJfWAgBuGRyIP0+JQv8A+1vy0JnPb5YbOyKEwJGccnx8MBdfnbyMlmnQu0b2xuKp0fDzVEsbkIiInE69zoBVu9Lx/t5sGAUglwHTY0Iwb1wEYsN8IJPZx3E+nfn8totpqTVr1iAiIgIajQbx8fE4fPhwu8///PPPER0dDY1Gg2HDhmHHjh02Smp5QgicvlSJld+m46ZVP+Kefx3AlydMxebG6AB88eR4rLwnhsWGiIiswl2lwPPTBuObP1yPyUMCYRTAf45fxp1v78dtb+3Fuh8vIPdKrdQxO0XykZvNmzdjzpw5WLduHeLj47F69Wp8/vnnSE9PR0BAwK+ev3//flx//fVYtmwZbrvtNnzyySd4/fXXkZqaiqFDh17z/aQeuamsa0JaYRVOX6pEal45DmWV4UqtzvzrHioFpg8PweyEPhga6m3zfERE5NpOXazEhv05+O/Jy9DpjebHI3p5IKFfL8T09kFUkBcGBnqhh9p29yU51LRUfHw8Ro8ejX/+858AAKPRiLCwMPzud7/DokWLfvX8WbNmoba2Fl999ZX5sbFjxyI2Nhbr1q275vtZq9w06g0oqmxEWZ0O5bU6lNfpUFarQ0lNIy6W1SOvrA55ZXWorP/1wWXubgpMHOiPyUMDkTgoEF4a51mxTkREjqmsVoevTxdgx6kCHMwqa/NcqvCeHugf4IlArQaBWjUCvEz/N9TXHdFBlh1A6Mznt6S3gut0OqSkpGDx4sXmx+RyORITE3HgwIE2X3PgwAEkJSW1emzy5Mn44osv2nx+Y2MjGhsbzf9dWVkJwPSbZEmHsq7goQ+Pdui5wd4aDA7xwpAQb4zq44uhoT7moxOErh5VunqLZiMiIuosJYDpg3wxfZAvqhqakJpbjqO55UgvrMb5omqU1uiQU1CHnILSX702KsgL/358nEXztHxud2RMRtJyU1paCoPBgMDAwFaPBwYG4ty5tg/3KiwsbPP5hYWFbT5/2bJleOmll371eFhYWBdTd18+gPZXFRERETmufADev558sYjq6mp4e7e/bMPpN/FbvHhxq5Eeo9GIsrIy9OrVy6orwKuqqhAWFob8/Hy7uyvLFlz5+l352gHXvn5eu2teO+Da12+raxdCoLq6GiEhIdd8rqTlxs/PDwqFAkVFrTcOKioqQlBQUJuvCQoK6tTz1Wo11OrWdxr5+Ph0PXQnabVal/uL/kuufP2ufO2Aa18/r901rx1w7eu3xbVfa8SmhaS3gqtUKsTFxSE5Odn8mNFoRHJyMhISEtp8TUJCQqvnA8CuXbuu+nwiIiJyLZJPSyUlJWHu3LkYNWoUxowZg9WrV6O2thbz588HAMyZMwehoaFYtmwZAOCpp57CxIkTsXLlSkybNg2bNm3C0aNH8c4770h5GURERGQnJC83s2bNQklJCZYsWYLCwkLExsZi586d5kXDeXl5kMt/HmAaN24cPvnkE7zwwgt47rnnMGDAAHzxxRcd2uPGltRqNZYuXfqrKTFX4crX78rXDrj29fPaXfPaAde+fnu8dsn3uSEiIiKyJLs4foGIiIjIUlhuiIiIyKmw3BAREZFTYbkhIiIip8JyY0FlZWV44IEHoNVq4ePjg4ceegg1NTXtPv93v/sdoqKi4O7ujvDwcPz+9783n39l79asWYOIiAhoNBrEx8fj8OH2D5X4/PPPER0dDY1Gg2HDhmHHjh02Smp5nbn2d999FxMmTICvry98fX2RmJh4zd8re9bZP/cWmzZtgkwmw4wZM6wb0Mo6e/0VFRV48sknERwcDLVajYEDBzrs3/3OXvvq1avNP9/CwsLw9NNPo6GhwUZpLWfPnj2YPn06QkJCIJPJrnqW4S/t3r0bI0eOhFqtRv/+/bFhwwar57SWzl7/1q1bcfPNN8Pf3x9arRYJCQn45ptvbBO2hSCLmTJlioiJiREHDx4UP/30k+jfv7+47777rvr8U6dOiZkzZ4ovv/xSZGZmiuTkZDFgwABx11132TB112zatEmoVCqxfv16cebMGfHwww8LHx8fUVRU1Obz9+3bJxQKhXjjjTfE2bNnxQsvvCDc3NzEqVOnbJy8+zp77ffff79Ys2aNOHbsmEhLSxPz5s0T3t7e4uLFizZO3n2dvfYW2dnZIjQ0VEyYMEHccccdtglrBZ29/sbGRjFq1CgxdepUsXfvXpGdnS12794tjh8/buPk3dfZa9+4caNQq9Vi48aNIjs7W3zzzTciODhYPP300zZO3n07duwQzz//vNi6dasAILZt29bu87OysoSHh4dISkoSZ8+eFW+99ZZQKBRi586dtglsYZ29/qeeekq8/vrr4vDhwyIjI0MsXrxYuLm5idTUVNsEFkKw3FjI2bNnBQBx5MgR82Nff/21kMlk4tKlSx3+Pp999plQqVSiqanJGjEtZsyYMeLJJ580/7fBYBAhISFi2bJlbT7/nnvuEdOmTWv1WHx8vHj00UetmtMaOnvt/0uv1wsvLy/x4YcfWiui1XTl2vV6vRg3bpx47733xNy5cx263HT2+teuXSsiIyOFTqezVUSr6ey1P/nkk+LGG29s9VhSUpIYP368VXNaW0c+3P/85z+LIUOGtHps1qxZYvLkyVZMZhsduf62DB48WLz00kuWD3QVnJaykAMHDsDHxwejRo0yP5aYmAi5XI5Dhw51+PtUVlZCq9VCqZR8f8Wr0ul0SElJQWJiovkxuVyOxMREHDhwoM3XHDhwoNXzAWDy5MlXfb696sq1/6+6ujo0NTWhZ8+e1oppFV299pdffhkBAQF46KGHbBHTarpy/V9++SUSEhLw5JNPIjAwEEOHDsVrr70Gg8Fgq9gW0ZVrHzduHFJSUsxTV1lZWdixYwemTp1qk8xScpafd5ZiNBpRXV1t05959vsJ6mAKCwsREBDQ6jGlUomePXuisLCwQ9+jtLQUr7zyCh555BFrRLSY0tJSGAwG8y7SLQIDA3Hu3Lk2X1NYWNjm8zv6e2MvunLt/+vZZ59FSEjIr3742buuXPvevXvx/vvv4/jx4zZIaF1duf6srCx8//33eOCBB7Bjxw5kZmbiiSeeQFNTE5YuXWqL2BbRlWu///77UVpaiuuuuw5CCOj1ejz22GN47rnnbBFZUlf7eVdVVYX6+nq4u7tLlEwaK1asQE1NDe655x6bvSdHbq5h0aJFkMlk7X519EOtPVVVVZg2bRoGDx6Mv/zlL90PTnZp+fLl2LRpE7Zt2waNRiN1HKuqrq7G7Nmz8e6778LPz0/qOJIwGo0ICAjAO++8g7i4OMyaNQvPP/881q1bJ3U0q9u9ezdee+01vP3220hNTcXWrVuxfft2vPLKK1JHIxv65JNP8NJLL+Gzzz771QCANXHk5hr++Mc/Yt68ee0+JzIyEkFBQSguLm71uF6vR1lZGYKCgtp9fXV1NaZMmQIvLy9s27YNbm5u3Y1tVX5+flAoFCgqKmr1eFFR0VWvNSgoqFPPt1ddufYWK1aswPLly/Hdd99h+PDh1oxpFZ299gsXLiAnJwfTp083P2Y0GgGYRjXT09PRr18/64a2oK782QcHB8PNzQ0KhcL82KBBg1BYWAidTgeVSmXVzJbSlWt/8cUXMXv2bCxYsAAAMGzYMNTW1uKRRx7B888/3+rMQGdztZ93Wq3WpUZtNm3ahAULFuDzzz+3+Ui18/7tshB/f39ER0e3+6VSqZCQkICKigqkpKSYX/v999/DaDQiPj7+qt+/qqoKt9xyC1QqFb788kuH+Ne8SqVCXFwckpOTzY8ZjUYkJycjISGhzdckJCS0ej4A7Nq166rPt1dduXYAeOONN/DKK69g586drdZlOZLOXnt0dDROnTqF48ePm79uv/12TJo0CcePH0dYWJgt43dbV/7sx48fj8zMTHOpA4CMjAwEBwc7TLEBunbtdXV1vyowLSVPOPmRhs7y8647Pv30U8yfPx+ffvoppk2bZvsANlu67AKmTJkiRowYIQ4dOiT27t0rBgwY0OpW8IsXL4qoqChx6NAhIYQQlZWVIj4+XgwbNkxkZmaKgoIC85der5fqMjpk06ZNQq1Wiw0bNoizZ8+KRx55RPj4+IjCwkIhhBCzZ88WixYtMj9/3759QqlUihUrVoi0tDSxdOlSh74VvDPXvnz5cqFSqcSWLVta/RlXV1dLdQld1tlr/1+OfrdUZ68/Ly9PeHl5iYULF4r09HTx1VdfiYCAAPHXv/5Vqkvoss5e+9KlS4WXl5f49NNPRVZWlvj2229Fv379xD333CPVJXRZdXW1OHbsmDh27JgAIFatWiWOHTsmcnNzhRBCLFq0SMyePdv8/JZbwf/0pz+JtLQ0sWbNGoe+Fbyz179x40ahVCrFmjVrWv3Mq6iosFlmlhsLunLlirjvvvuEp6en0Gq1Yv78+a0+wLKzswUA8cMPPwghhPjhhx8EgDa/srOzpbmITnjrrbdEeHi4UKlUYsyYMeLgwYPmX5s4caKYO3duq+d/9tlnYuDAgUKlUokhQ4aI7du32zix5XTm2vv06dPmn/HSpUttH9wCOvvn/kuOXm6E6Pz179+/X8THxwu1Wi0iIyPFq6++avf/eLmazlx7U1OT+Mtf/iL69esnNBqNCAsLE0888YQoLy+3ffBuutrP6pbrnTt3rpg4ceKvXhMbGytUKpWIjIwUH3zwgc1zW0pnr3/ixIntPt8WZEI4+fggERERuRSuuSEiIiKnwnJDREREToXlhoiIiJwKyw0RERE5FZYbIiIiciosN0RERORUWG6IiIjIqbDcEBERkVNhuSEiIiKnwnJDREREToXlhoiIiJwKyw0RERE5lf8HQPejTTr0DQ8AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#logistic\n",
        "x = random.logistic(loc=1, scale=2, size=(2, 3))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVBnb8qDBPqn",
        "outputId": "4cee8e9f-f068-4973-8727-ccf41b9a3ce0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0.13219776 -1.11141194  3.33468591]\n",
            " [-2.10473309  3.10490293 -4.94286176]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Multinomial Distribution\n",
        "Multinomial distribution is a generalization of binomial distribution.\n",
        "\n",
        "It describes outcomes of multi-nomial scenarios unlike binomial where scenarios must be only one of two. e.g. Blood type of a population, dice roll outcome.\n",
        "\n",
        "It has three parameters:\n",
        "\n",
        "n - number of possible outcomes (e.g. 6 for dice roll).\n",
        "\n",
        "pvals - list of probabilties of outcomes (e.g. [1/6, 1/6, 1/6, 1/6, 1/6, 1/6] for dice roll).\n",
        "\n",
        "size - The shape of the returned array."
      ],
      "metadata": {
        "id": "NXkiUCiQBp-e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import random\n",
        "\n",
        "x = random.multinomial(n=6, pvals=[1/6, 1/6, 1/6, 1/6, 1/6, 1/6])\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pYr9HlAkBq15",
        "outputId": "389fe754-a683-4923-98c4-d0cd28eb8dbe"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 1 1 1 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Multinomial samples will NOT produce a single value! They will produce one value for each pval."
      ],
      "metadata": {
        "id": "Epq0WT-NB6Nu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "# Exponential Distribution\n",
        "Exponential distribution is used for describing time till next event e.g. failure/success etc.\n",
        "\n",
        "It has two parameters:\n",
        "\n",
        "scale - inverse of rate ( see lam in poisson distribution ) defaults to 1.0.\n",
        "\n",
        "size - The shape of the returned array."
      ],
      "metadata": {
        "id": "PCDXUDbsCEVI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = random.exponential(scale=2, size=(2, 3))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vFzUsSWXCKNA",
        "outputId": "bce4faf5-4e7b-4709-9248-7bb28f31aaa6"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2.96395390e-01 5.26634858e+00 1.00083170e-02]\n",
            " [6.45163575e-01 1.33309719e+00 1.40825736e+01]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualization of Exponential Distribution"
      ],
      "metadata": {
        "id": "SwENgyy3CWL9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from numpy import random\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "sns.distplot(random.exponential(size=1000), hist=False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "4mZQWlFKCUoK",
        "outputId": "fc7b0cd1-610b-4555-82fd-72a20d1929ed"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-9-7836324b8821>:5: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.exponential(size=1000), hist=False)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Poisson distribution deals with number of occurences of an event in a time period whereas exponential distribution deals with the time between these events**\n",
        "\n"
      ],
      "metadata": {
        "id": "FdpuSqzACj8U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Chi Square Distribution\n",
        "Chi Square distribution is used as a basis to verify the hypothesis.\n",
        "\n",
        "It has two parameters:\n",
        "\n",
        "df - (degree of freedom).\n",
        "\n",
        "size - The shape of the returned array.\n"
      ],
      "metadata": {
        "id": "W6INv6RhCu9E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Draw out a sample for chi squared distribution with degree of freedom 2 with size 2x3:\n",
        "\n",
        "from numpy import random\n",
        "\n",
        "x = random.chisquare(df=2, size=(2, 3))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DmGkbJb6Ct_2",
        "outputId": "905cefa3-988b-4e94-a816-bb361db66fac"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[6.28915993 0.93370888 5.17074556]\n",
            " [2.39907236 0.24531987 0.44330155]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Rayleigh Distribution\n",
        "Rayleigh distribution is used in signal processing.\n",
        "\n",
        "It has two parameters:\n",
        "\n",
        "scale - (standard deviation) decides how flat the distribution will be default 1.0).\n",
        "\n",
        "size - The shape of the returned array."
      ],
      "metadata": {
        "id": "-Zj14wyWDE_p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "#Draw out a sample for rayleigh distribution with scale of 2 with size 2x3:\n",
        "\n",
        "from numpy import random\n",
        "\n",
        "x = random.rayleigh(scale=2, size=(2, 3))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jO9ok8_ZDERa",
        "outputId": "08603dd7-326e-463e-b549-93d6575b3ac9"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[3.28164587 2.26125321 1.52258954]\n",
            " [2.19492465 3.40824286 4.3239312 ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualization\n",
        "sns.distplot(random.rayleigh(size=1000), hist=False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "ToaaDLHYDS0O",
        "outputId": "b4e34979-6aa7-4180-8e11-b5e321c54d47"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-12-60253e3bb494>:2: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.rayleigh(size=1000), hist=False)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**At unit stddev and 2 degrees of freedom rayleigh and chi square represent the same distributions**"
      ],
      "metadata": {
        "id": "jy29IGg2DY6J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Pareto Distribution\n",
        "A distribution following Pareto's law i.e. 80-20 distribution (20% factors cause 80% outcome).\n",
        "\n",
        "It has two parameter:\n",
        "\n",
        "a - shape parameter.\n",
        "\n",
        "size - The shape of the returned array.\n"
      ],
      "metadata": {
        "id": "WyhXF23IDjmt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Draw out a sample for pareto distribution with shape of 2 with size 2x3:\n",
        "\n",
        "from numpy import random\n",
        "\n",
        "x = random.pareto(a=2, size=(2, 3))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAH3sK_uDize",
        "outputId": "c691a833-5484-4885-f4cf-c8c7d571a092"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.09390682 1.16005489 0.06018042]\n",
            " [0.03285279 0.39242003 0.08161308]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualization\n",
        "sns.distplot(random.pareto(a=2, size=1000), kde=False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "6KpjcsbkDroo",
        "outputId": "d2bd08f9-81b5-4209-faf8-8e63b5912ba2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-85f60403aaef>:2: UserWarning: \n",
            "\n",
            "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
            "\n",
            "Please adapt your code to use either `displot` (a figure-level function with\n",
            "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "\n",
            "For a guide to updating your code to use the new functions, please see\n",
            "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
            "\n",
            "  sns.distplot(random.pareto(a=2, size=1000), kde=False)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Zipf Distribution\n",
        "Zipf distributions are used to sample data based on zipf's law.\n",
        "\n",
        "Zipf's Law: In a collection, the nth common term is 1/n times of the most common term. E.g. the 5th most common word in English occurs nearly 1/5 times as often as the most common word.\n",
        "\n",
        "It has two parameters:\n",
        "\n",
        "a - distribution parameter.\n",
        "\n",
        "size - The shape of the returned array."
      ],
      "metadata": {
        "id": "ErZvo96FD1Hx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy import random\n",
        "\n",
        "x = random.zipf(a=2, size=(2, 3))\n",
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BuWWkvWgD6wV",
        "outputId": "c3d78c7a-a4a6-4598-9f12-9fa00b127559"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[2 3 1]\n",
            " [2 3 1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualization\n",
        "x = random.zipf(a=2, size=1000)\n",
        "sns.distplot(x[x<10], kde=False)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "I5VxWw-HD-dr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "sGcz0PtnEOxR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NumPy ufuncs\n",
        "ufuncs stands for \"Universal Functions\" and they are NumPy functions that operate on the ndarray object.\n",
        "\n"
      ],
      "metadata": {
        "id": "yqUOJcqSGQ0S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Vactorization** instead of itration over whole Array"
      ],
      "metadata": {
        "id": "JY6RPClQGnWD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "x = [1, 2, 3, 4]\n",
        "y = [4, 5, 6, 7]\n",
        "z = np.add(x, y)\n",
        "\n",
        "print(z)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-0c0c6VTGmFb",
        "outputId": "abb1cf36-56d0-4e5c-93d3-7cf3587d0863"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 5  7  9 11]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# we can create our own universal function with the help of frompyfunc()\n",
        "To create your own ufunc, you have to define a function, like you do with normal functions in Python, then you add it to your NumPy ufunc library with the frompyfunc() method.\n",
        "\n",
        "The frompyfunc() method takes the following arguments:\n",
        "\n",
        "function - the name of the function.\n",
        "inputs - the number of input arguments (arrays).\n",
        "outputs - the number of output arrays."
      ],
      "metadata": {
        "id": "_oCKKgw1HJ6b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#EXample\n",
        "import numpy as np\n",
        "\n",
        "def myadd(x, y):\n",
        "  return x+y\n",
        "\n",
        "myadd = np.frompyfunc(myadd, 2, 1)\n",
        "\n",
        "print(myadd([1, 2, 3, 4], [5, 6, 7, 8]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GZadRpwVHaxY",
        "outputId": "7eac895d-be11-46fc-d67f-3fe637b5e6ec"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6 8 10 12]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# List of basic arithmetic functions in NumPy:\n",
        "    \"add\": np.add,\n",
        "    \"subtract\": np.subtract,\n",
        "    \"multiply\": np.multiply,\n",
        "    \"divide\": np.divide,\n",
        "    \"power\": np.power,\n",
        "    \"sqrt\": np.sqrt,\n",
        "    \"sin\": np.sin,\n",
        "    \"cos\": np.cos,\n",
        "    \"tan\": np.tan,\n",
        "    \"arcsin\": np.arcsin,\n",
        "    \"arccos\": np.arccos,\n",
        "    \"arctan\": np.arctan,\n",
        "    \"log\": np.log,\n",
        "    \"log10\": np.log10\n",
        "    \"exp\": np.exp\n",
        "    \"absolute value\": np.absolute(arr)\n",
        "    \"LCM\":np.lcm(num1, num2)\n",
        "    \"LCM in Array\":np.lcm.reduce(arr)\n",
        "    \"GCD\":np.gcd(num1, num2)\n",
        "    \"GCD in Array\":np.gcd.reduce(arr)\n",
        "     \"radian to degree\":np.rad2deg(arr)\n",
        "     \"unique array\":np.unique(arr)\n",
        "     \"union\":np.union1d(arr1, arr2)\n",
        "     \"intersection\":np.intersect1d(arr1, arr2, assume_unique=True)\n",
        "     \"difference\":np.setdiff1d(set1, set2, assume_unique=True)\n"
      ],
      "metadata": {
        "id": "K_V720tQIO3g"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Rounding Decimals\n",
        "There are primarily five ways of rounding off decimals in NumPy:\n",
        "\n",
        "truncation\n",
        "fix\n",
        "rounding\n",
        "floor\n",
        "ceil"
      ],
      "metadata": {
        "id": "nkszuMQ-ImIK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "arr = np.trunc([-3.1666, 3.6667])\n",
        "arr = np.fix([-3.1666, 3.6667])\n",
        "arr = np.around(3.1666, 2)\n",
        "arr = np.floor([-3.1666, 3.6667])\n",
        "arr = np.ceil([-3.1666, 3.6667])"
      ],
      "metadata": {
        "id": "l7gOwQDwIx4e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NumPy Logs"
      ],
      "metadata": {
        "id": "GEvoPnIxJIwV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Log at Base 2\n",
        "arr = np.arange(1, 10)\n",
        "\n",
        "print(np.log2(arr))\n",
        "#Log at Base 10\n",
        "arr = np.arange(1, 10)\n",
        "\n",
        "print(np.log10(arr))\n",
        "#Natural Log, or Log at Base e\n",
        "arr = np.arange(1, 10)\n",
        "\n",
        "print(np.log(arr))\n",
        "#Log at Any Base\n",
        "from math import log\n",
        "import numpy as np\n",
        "\n",
        "nplog = np.frompyfunc(log, 2, 1)\n",
        "\n",
        "print(nplog(100, 15))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rYPOzfH7JM3w",
        "outputId": "b2e04ac0-a885-44cf-ed16-99fac2c741f7"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.         1.         1.5849625  2.         2.32192809 2.5849625\n",
            " 2.80735492 3.         3.169925  ]\n",
            "[0.         0.30103    0.47712125 0.60205999 0.69897    0.77815125\n",
            " 0.84509804 0.90308999 0.95424251]\n",
            "[0.         0.69314718 1.09861229 1.38629436 1.60943791 1.79175947\n",
            " 1.94591015 2.07944154 2.19722458]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#summation and addition\n",
        "What is the difference between summation and addition?\n",
        "\n",
        "Addition is done between two arguments whereas summation happens over n elements."
      ],
      "metadata": {
        "id": "MahqE7YeJuOr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "arr1 = np.array([1, 2, 3])\n",
        "arr2 = np.array([1, 2, 3])\n",
        "\n",
        "newarr = np.sum([arr1, arr2], axis=1)\n",
        "\n",
        "print(newarr)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DQCaR5XnJ2g3",
        "outputId": "5c9f26ae-22ec-461e-b4dd-034fe4a99b5f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6 6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "arr = np.array([1, 2, 3])\n",
        "\n",
        "newarr = np.cumsum(arr)\n",
        "\n",
        "print(newarr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7fl49LlJ51S",
        "outputId": "74ba1fc0-3d9f-43da-eeaa-8920edb1de0c"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 3 6]\n"
          ]
        }
      ]
    }
  ]
}