Node 9. Numpy_Pandas_basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "blank-embassy",
   "metadata": {},
   "source": [
    "## 9-2. 배열은 가까이에~ 기본 통계 데이터를 계산해 볼까? (1) 평균 계산하기\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "racial-renewal",
   "metadata": {},
   "source": [
    "#### 평균 계산 직접 하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "generic-fight",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 5  8 9  5  4 54 6 7 68 686 4\n",
      "total: 859\n",
      "average: 71.58333333333333\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "count = 0\n",
    "\n",
    "numbers = list(map(int, input().split()))\n",
    "\n",
    "for num in numbers:\n",
    "    total += num\n",
    "    count += 1\n",
    "    average = total / count\n",
    "\n",
    "\n",
    "print('total:', total)\n",
    "print('average:', average)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "double-duncan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a number : (<Enter key> to quit)4\n",
      "Enter a number : (<Enter key> to quit)3\n",
      "Enter a number : (<Enter key> to quit)5\n",
      "Enter a number : (<Enter key> to quit)7\n",
      "Enter a number : (<Enter key> to quit)5\n",
      "Enter a number : (<Enter key> to quit)\n",
      "\n",
      " average : 4.8\n"
     ]
    }
   ],
   "source": [
    "total = 0\n",
    "count = 0\n",
    "\n",
    "number = input(\"Enter a number : (<Enter key> to quit)\")\n",
    "\n",
    "while number != '':\n",
    "    try:\n",
    "        number = int(number)\n",
    "        total += number\n",
    "        count += 1\n",
    "        average = total / count\n",
    "    except ValueError:\n",
    "        print(\"Not a number!\")\n",
    "    number = input(\"Enter a number : (<Enter key> to quit)\")\n",
    "\n",
    "print(\"\\n average :\", average)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "affected-residence",
   "metadata": {},
   "source": [
    "## 9-3. 배열은 가까이에~ 기본 통계 데이터를 계산해 볼까? (2) 배열을 활용한 평균, 표준편차, 중앙값 계산\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "vietnamese-surname",
   "metadata": {},
   "source": [
    "#### 배열을 지정하는 코딩해보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fixed-premiere",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a number: (<Enter Key> to quit)5\n",
      "Enter a number: (<Enter Key> to quit)4\n",
      "Enter a number: (<Enter Key> to quit)6\n",
      "Enter a number: (<Enter Key> to quit)7\n",
      "Enter a number: (<Enter Key> to quit)3\n",
      "Enter a number: (<Enter Key> to quit)\n",
      "Y: [5.0, 4.0, 6.0, 7.0, 3.0]\n"
     ]
    }
   ],
   "source": [
    "def number():\n",
    "    number = input(\"Enter a number: (<Enter Key> to quit)\")\n",
    "    X = []\n",
    "    while number != '':\n",
    "        try:\n",
    "            number = float(number)\n",
    "            X.append(number)\n",
    "        except ValueError:\n",
    "            print(\"Not a number!\")\n",
    "        number = input(\"Enter a number: (<Enter Key> to quit)\")\n",
    "    return X\n",
    "    \n",
    "Y = number()\n",
    "print(\"Y:\", Y)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "modular-seeker",
   "metadata": {},
   "outputs": [],
   "source": [
    "### 2개 이상의 숫자를 입력받아 리스트에 저장하는 함수\n",
    "def numbers():\n",
    "    X=[]    # X에 빈 리스트를 할당합니다.\n",
    "    while True:\n",
    "        number = input(\"Enter a number (<Enter key> to quit)\") \n",
    "        while number !=\"\":\n",
    "            try:\n",
    "                x = float(number)\n",
    "                X.append(x)    # float형으로 변환한 숫자 입력을 리스트에 추가합니다.\n",
    "            except ValueError:\n",
    "                print('>>> NOT a number! Ignored..')\n",
    "            number = input(\"Enter a number (<Enter key> to quit)\")\n",
    "        if len(X) > 1:  # 저장된 숫자가 2개 이상일 때만 리턴합니다.\n",
    "            return X\n",
    "\n",
    "X=numbers()\n",
    "\n",
    "print('X :', X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "expressed-style",
   "metadata": {},
   "source": [
    "#### python array는 import 해야 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "designed-oliver",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "[1, 2, 3, '4']\n",
      "[1, 5, 2, 3, '4']\n",
      "<class 'array.array'>\n",
      "array('i', [1, 2, 3])\n",
      "array('i', [1, 5, 2, 3])\n"
     ]
    }
   ],
   "source": [
    "import array as arr\n",
    "\n",
    "mylist = [1, 2, 3]   # 이것은 파이썬 built-in list입니다. \n",
    "print(type(mylist))\n",
    "\n",
    "mylist.append('4')  # mylist의 끝에 character '4'를 추가합니다. \n",
    "print(mylist)\n",
    "\n",
    "mylist.insert(1, 5)  # mylist의 두번째 자리에 5를 끼워넣습니다.\n",
    "print(mylist)\n",
    "\n",
    "myarray = arr.array('i', [1, 2, 3])   # 이것은 array입니다. import array를 해야 쓸 수 있습니다.\n",
    "print(type(myarray))\n",
    "\n",
    "# 아래 라인의 주석을 풀고 실행하면 에러가 납니다.\n",
    "#myarray.append('4')    # myarray의 끝에 character '4'를 추가합니다. \n",
    "print(myarray)\n",
    "\n",
    "myarray.insert(1, 5)    # myarray의 두번째 자리에 5를 끼워넣습니다.\n",
    "print(myarray)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "streaming-border",
   "metadata": {},
   "source": [
    "#### 3) 리스트를 활용한 시그마의 표현\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "official-lincoln",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum of X:  17.0\n"
     ]
    }
   ],
   "source": [
    "total = 0.0\n",
    "for i in range(len(X)):\n",
    "    total = total + X[i]\n",
    "mean = total / len(X)\n",
    "\n",
    "print('sum of X: ', total)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bright-syndrome",
   "metadata": {},
   "source": [
    "#### 4) 중앙값\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "traditional-heather",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X : [3.0, 5.0, 2.0, 3.0, 4.0]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def median(nums):  \t\t# nums : 리스트를 지정하는 매개변수\n",
    "    nums.sort()\t\t\t\t\t# sort()로 리스트를 순서대로 정렬\n",
    "    size = len(nums)\n",
    "    p = size // 2\n",
    "    if size % 2 == 0:\t\t   # 리스트의 개수가 짝수일때 \n",
    "        pr = p                         # 4번째 값\n",
    "        pl = p-1                      # 3번째 값\n",
    "        mid= float((nums[pl]+nums[pr])/2)    \n",
    "    else:\t\t\t\t\t\t\t\t# 리스트의 개수가 홀수일때\n",
    "        mid = nums[p]\n",
    "    return mid\n",
    "\n",
    "print('X :', X)\n",
    "median(X)\t\t\t\t\t\t# 매개변수의 값으로 X를 사용함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "right-contamination",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_median(X):\n",
    "    X.sort()\n",
    "    length = len(X)\n",
    "    if length % 2 == 0: # even number\n",
    "        median = X[length/2] + X[legnth/2 + 1]\n",
    "    else:\n",
    "        median = X[round(length/2)]\n",
    "    return median\n",
    "\n",
    "get_median(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collected-conviction",
   "metadata": {},
   "source": [
    "#### 5) 표준편차와 평균\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "crude-intersection",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.4"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def means(nums):\n",
    "    total = 0.0\n",
    "    for i in range(len(nums)):\n",
    "        total = total + nums[i]\n",
    "    return total / len(nums)\n",
    "\n",
    "means(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "olympic-jones",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.019803902718557"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg = means(X)\n",
    "\n",
    "def std_dev(nums, avg):\n",
    "    texp = 0.0\n",
    "    for i in range(len(nums)):\n",
    "        texp = texp + (nums[i] - avg)**2    # 각 숫자와 평균값의 차이의 제곱을 계속 더한 후\n",
    "    return (texp/len(nums)) ** 0.5    # 그 총합을 숫자개수로 나눈 값의 제곱근을 리턴합니다.\n",
    "\n",
    "std_dev(X,avg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "monetary-warehouse",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 표준편차를 구하는 과정(배열과 평균을 받으면,)\n",
    "# 1. 0 숫자 변수 하나 만든다.\n",
    "# 2. 각각의 숫자를 평균에서 뺀다.\n",
    "# 3. 그 값을 제곱하고 0 숫자 변수에 더한다.\n",
    "# 4. 그 값을 for loop으로 다 하고 n개 만큼 나눠준다.\n",
    "# 5. 그리고 **0.5를 해준다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "sought-representation",
   "metadata": {},
   "source": [
    "#### 6) 전체 코드 : main()함수\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "structural-profession",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "당신이 입력한 숫자[2.0, 3.0, 3.0, 4.0, 5.0]의 \n",
      "중앙값은3.0, 평균은3.4, 표준편차는1.019803902718557입니다.\n"
     ]
    }
   ],
   "source": [
    "med = median(X)\n",
    "avg = means(X)\n",
    "std = std_dev(X, avg)\n",
    "print(\"당신이 입력한 숫자{}의 \".format(X))\n",
    "print(\"중앙값은{}, 평균은{}, 표준편차는{}입니다.\".format(med, avg, std))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dedicated-exemption",
   "metadata": {},
   "source": [
    "## 9-5. 끝판왕 등장! numpy로 이 모든걸 한방에! (2) NumPy 주요 기능\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "geological-walter",
   "metadata": {},
   "source": [
    "#### 1) ndarray만들기\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "orange-doubt",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4]\n",
      "<class 'numpy.ndarray'>\n",
      "--------------------------\n",
      "[0 1 2 3 4]\n",
      "<class 'numpy.ndarray'>\n",
      "--------------------------\n",
      "['0' '1' '2' '3' '4']\n",
      "<class 'numpy.ndarray'>\n",
      "--------------------------\n",
      "[0 1 2 3 4]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# 아래 A와 B는 결과적으로 같은 ndarray 객체를 생성합니다. \n",
    "A = np.arange(5)\n",
    "B = np.array([0,1,2,3,4])  # 파이썬 리스트를 numpy ndarray로 변환\n",
    "\n",
    "# 하지만 C는 좀 다를 것입니다. \n",
    "C = np.array([0,1,2,3,'4'])\n",
    "\n",
    "# D도 A, B와 같은 결과를 내겠지만, B의 방법을 권합니다. \n",
    "D = np.ndarray((5,), np.int64, np.array([0,1,2,3,4]))\n",
    "\n",
    "print(A)\n",
    "print(type(A))\n",
    "print(\"--------------------------\")\n",
    "print(B)\n",
    "print(type(B))\n",
    "print(\"--------------------------\")\n",
    "print(C)\n",
    "print(type(C))\n",
    "print(\"--------------------------\")\n",
    "print(D)\n",
    "print(type(D))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moderate-effects",
   "metadata": {},
   "source": [
    "#### 2) 크기 (size, shape, ndim)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "favorite-fever",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "행렬의 모양: (2, 5)\n",
      "행렬의 축 개수: 2\n",
      "행렬 내 원소의 개수: 10\n"
     ]
    }
   ],
   "source": [
    "A = np.arange(10).reshape(2, 5)   # 길이 10의 1차원 행렬을 2X5 2차원 행렬로 바꿔봅니다.\n",
    "\n",
    "print(\"행렬의 모양:\", A.shape)\n",
    "print(\"행렬의 축 개수:\", A.ndim)\n",
    "print(\"행렬 내 원소의 개수:\", A.size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "electronic-glory",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.size"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "arranged-sociology",
   "metadata": {},
   "source": [
    "## 3) type\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "rapid-oklahoma",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 2]\n",
      " [3 4 5]]\n",
      "int64\n",
      "<class 'numpy.ndarray'>\n",
      "-------------------------\n",
      "[0 1 2 3 4 5]\n",
      "int64\n",
      "<class 'numpy.ndarray'>\n",
      "-------------------------\n",
      "['0' '1' '2' '3' '4' '5']\n",
      "<U21\n",
      "<class 'numpy.ndarray'>\n",
      "-------------------------\n",
      "[0 1 2 3 list([4, 5]) 6]\n",
      "object\n",
      "<class 'numpy.ndarray'>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:19: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray\n"
     ]
    }
   ],
   "source": [
    "A= np.arange(6).reshape(2, 3)\n",
    "print(A)\n",
    "print(A.dtype)\n",
    "print(type(A))\n",
    "print(\"-------------------------\")\n",
    "\n",
    "B = np.array([0, 1, 2, 3, 4, 5])  \n",
    "print(B)\n",
    "print(B.dtype)\n",
    "print(type(B))\n",
    "print(\"-------------------------\")\n",
    "\n",
    "C = np.array([0, 1, 2, 3, '4', 5])\n",
    "print(C)\n",
    "print(C.dtype)\n",
    "print(type(C))\n",
    "print(\"-------------------------\")\n",
    "\n",
    "D = np.array([0, 1, 2, 3, [4, 5], 6])  # 이런 ndarray도 만들어질까요?\n",
    "print(D)\n",
    "print(D.dtype)\n",
    "print(type(D))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thrown-classics",
   "metadata": {},
   "source": [
    "#### 위의 deprecate 방지하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "available-consent",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 list([4, 5]) 6]\n",
      "object\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "D = np.array([0, 1, 2, 3, [4, 5], 6], dtype=object)  # warning 메시지를 보고 이렇게 바꿔봅니다.\n",
    "print(D)\n",
    "print(D.dtype)\n",
    "print(type(D))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ranging-opinion",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "annual-orchestra",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(D[4])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distinguished-berry",
   "metadata": {},
   "source": [
    "#### 4) 특수 행렬\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "incorporate-wallet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "vital-irrigation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((5,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "located-philip",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones([3,3])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "occasional-slope",
   "metadata": {},
   "source": [
    "#### 5) 브로드캐스트\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "moved-hepatitis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(9).reshape(3,3)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "equivalent-nancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  2,  4],\n",
       "       [ 6,  8, 10],\n",
       "       [12, 14, 16]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ndarray A에 2를 상수배 했을 때,\n",
    "A * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "logical-clothing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: [[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n",
      "B: [1 2 3]\n",
      "\n",
      "A+B: [[ 1  3  5]\n",
      " [ 4  6  8]\n",
      " [ 7  9 11]]\n"
     ]
    }
   ],
   "source": [
    "# 3 X 3 행렬에 1 X 3 행렬을 더했을 때\n",
    "A = np.arange(9).reshape(3,3)\n",
    "B = np.array([1, 2, 3])\n",
    "print(\"A:\", A)\n",
    "print(\"B:\", B)\n",
    "print(\"\\nA+B:\", A+B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "silver-charleston",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "sealed-edwards",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: [[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n",
      "C: [[1]\n",
      " [2]\n",
      " [3]]\n",
      "\n",
      "A+C: [[ 1  2  3]\n",
      " [ 5  6  7]\n",
      " [ 9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "# 3 X 3 행렬에 3 X 1 행렬을 더했을 때\n",
    "A = np.arange(9).reshape(3,3)\n",
    "C = np.array([[1], [2], [3]])\n",
    "print(\"A:\", A)\n",
    "print(\"C:\", C)\n",
    "print(\"\\nA+C:\", A+C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "western-thermal",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: [[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n",
      "D: [1 2]\n",
      "wrong operation! ValueError\n"
     ]
    }
   ],
   "source": [
    "# 3 X 3 행렬에 1 X 2 행렬을 더하는 것은 허용되지 않습니다. \n",
    "A = np.arange(9).reshape(3,3)\n",
    "D = np.array([1, 2])\n",
    "print(\"A:\", A)\n",
    "print(\"D:\", D)\n",
    "try:\n",
    "    print(\"\\nA+D:\", A+D)\n",
    "except:\n",
    "    print(\"wrong operation! ValueError\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "wrapped-timer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4]\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "can only concatenate list (not \"int\") to list",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-30-1e8b322884f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: can only concatenate list (not \"int\") to list"
     ]
    }
   ],
   "source": [
    "print([1,2]+[3,4])\n",
    "print([1,2]+3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "increased-migration",
   "metadata": {},
   "source": [
    "#### 6) 슬라이스와 인덱싱\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "incorporate-teacher",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: [[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n",
      "B: [0 1 2]\n"
     ]
    }
   ],
   "source": [
    "# 3 X 3 행렬의 첫번째 행을 구해 봅시다. \n",
    "A = np.arange(9).reshape(3,3)\n",
    "print(\"A:\", A)\n",
    "B = A[0]\n",
    "print(\"B:\", B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "monetary-ballet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4, 5])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "possible-nashville",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "# 0, 1을 인덱싱 하면 A의 첫번째 행에서 두번째 값을 참조합니다.\n",
    "# 아래 두 결과는 정확히 같습니다.\n",
    "print(A[0, 1])\n",
    "print(B[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "severe-scratch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 슬라이싱도 비슷합니다.\n",
    "A[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "international-illinois",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 5]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "knowing-demand",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2]\n",
      " [5]\n",
      " [8]]\n",
      "--------------\n",
      "[[1 2]\n",
      " [4 5]\n",
      " [7 8]]\n",
      "--------------\n",
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n",
      "--------------\n",
      "[[2]\n",
      " [5]\n",
      " [8]]\n",
      "--------------\n",
      "[[1 2]\n",
      " [4 5]\n",
      " [7 8]]\n",
      "--------------\n",
      "[[0 1 2]\n",
      " [3 4 5]\n",
      " [6 7 8]]\n"
     ]
    }
   ],
   "source": [
    "# 이 슬라이싱의 결과는 \n",
    "print(A[:,2:])\n",
    "print(\"--------------\")\n",
    "print(A[:,1:])\n",
    "print(\"--------------\")\n",
    "print(A[:,:])\n",
    "print(\"--------------\")\n",
    "# 이 슬라이싱의 결과와 동일합니다.\n",
    "print(A[:,-1:])\n",
    "print(\"--------------\")\n",
    "print(A[:,-2:])\n",
    "print(\"--------------\")\n",
    "print(A[:,-3:])\n",
    "\n",
    "# 위 그림과 비교해서 이해해 보세요."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "relative-surface",
   "metadata": {},
   "source": [
    "#### 7) random\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "buried-framing",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9435646051981472\n",
      "8\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# 의사 난수를 생성하는 예제입니다. 여러번 실행해 보세요.\n",
    "\n",
    "print(np.random.random())   # 0에서 1사이의 실수형 난수 하나를 생성합니다. \n",
    "\n",
    "print(np.random.randint(0,10))   # 0~9 사이 1개 정수형 난수 하나를 생성합니다. \n",
    "\n",
    "print(np.random.choice([0,1,2,3,4,5,6,7,8,9]))   # 리스트에 주어진 값 중 하나를 랜덤하게 골라줍니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "valuable-indiana",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.01737458010370796"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.random()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "corresponding-alignment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(0,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "electoral-acrobat",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.choice([1,3,5,6,4,7,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "decimal-swedish",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 2 8 7 9 0 1 4 3 6]\n",
      "[8 9 4 2 3 6 7 5 0 1]\n"
     ]
    }
   ],
   "source": [
    "# 무작위로 섞인 배열을 만들어 줍니다. \n",
    "# 아래 2가지는 기능면에서 동일합니다. \n",
    "\n",
    "print(np.random.permutation(10))   \n",
    "print(np.random.permutation([0,1,2,3,4,5,6,7,8,9]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "extensive-violation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 0, 8, 5, 6, 1, 9, 2, 4, 7])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.permutation(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "median-luxury",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.42036673  0.61881461  0.79405419  0.55303479 -2.31589202]\n",
      "[ 0.96941091 -0.7038533   0.15617625  0.17416088  0.83835017]\n"
     ]
    }
   ],
   "source": [
    "# 아래 기능들은 어떤 분포를 따르는 변수를 임의로 표본추출해 줍니다. \n",
    "\n",
    "# 이것은 정규분포를 따릅니다.\n",
    "print(np.random.normal(loc=0, scale=1, size=5))    # 평균(loc), 표준편차(scale), 추출개수(size)를 조절해 보세요.\n",
    "\n",
    "# 이것은 균등분포를 따릅니다. \n",
    "print(np.random.uniform(low=-1, high=1, size=5))  # 최소(low), 최대(high), 추출개수(size)를 조절해 보세요."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "contrary-martial",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 9, 6, 4, 2, 1, 0, 8, 5, 3])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.permutation(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "parliamentary-strategy",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: [[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]]\n",
      "\n",
      " [[12 13 14 15]\n",
      "  [16 17 18 19]\n",
      "  [20 21 22 23]]]\n",
      "A의 전치행렬: [[[ 0 12]\n",
      "  [ 4 16]\n",
      "  [ 8 20]]\n",
      "\n",
      " [[ 1 13]\n",
      "  [ 5 17]\n",
      "  [ 9 21]]\n",
      "\n",
      " [[ 2 14]\n",
      "  [ 6 18]\n",
      "  [10 22]]\n",
      "\n",
      " [[ 3 15]\n",
      "  [ 7 19]\n",
      "  [11 23]]]\n",
      "A의 전치행렬의 shape: (4, 3, 2)\n"
     ]
    }
   ],
   "source": [
    "A = np.arange(24).reshape(2,3,4)\n",
    "print(\"A:\", A)               # A는 (2,3,4)의 shape를 가진 행렬입니다. \n",
    "print(\"A의 전치행렬:\", A.T)            \n",
    "print(\"A의 전치행렬의 shape:\", A.T.shape) # A의 전치행렬은 (4,3,2)의 shape를 가진 행렬입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cheap-wages",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "numerical-profession",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "organic-objective",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3, 2)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "chemical-symbol",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A: [[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]]\n",
      "\n",
      " [[12 13 14 15]\n",
      "  [16 17 18 19]\n",
      "  [20 21 22 23]]]\n",
      "B: [[[ 0  4  8]\n",
      "  [12 16 20]]\n",
      "\n",
      " [[ 1  5  9]\n",
      "  [13 17 21]]\n",
      "\n",
      " [[ 2  6 10]\n",
      "  [14 18 22]]\n",
      "\n",
      " [[ 3  7 11]\n",
      "  [15 19 23]]]\n",
      "B.shape: (4, 2, 3)\n"
     ]
    }
   ],
   "source": [
    "# np.transpose는 행렬의 축을 어떻게 변환해 줄지 임의로 지정해 줄 수 있는 일반적인 행렬 전치 함수입니다. \n",
    "# np.transpose(A, (2,1,0)) 은 A.T와 정확히 같습니다.\n",
    "\n",
    "B = np.transpose(A, (2,0,1))\n",
    "print(\"A:\", A)             # A는 (2,3,4)의 shape를 가진 행렬입니다. \n",
    "print(\"B:\", B)             # B는 A의 3, 1, 2번째 축을 자신의 1, 2, 3번째 축으로 가진 행렬입니다.\n",
    "print(\"B.shape:\", B.shape)  # B는 (4,2,3)의 shape를 가진 행렬입니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "korean-unknown",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  4,  8],\n",
       "        [ 1,  5,  9],\n",
       "        [ 2,  6, 10],\n",
       "        [ 3,  7, 11]],\n",
       "\n",
       "       [[12, 16, 20],\n",
       "        [13, 17, 21],\n",
       "        [14, 18, 22],\n",
       "        [15, 19, 23]]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(A, (0,2,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "built-antique",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "treated-taxation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [12, 13, 14, 15]],\n",
       "\n",
       "       [[ 4,  5,  6,  7],\n",
       "        [16, 17, 18, 19]],\n",
       "\n",
       "       [[ 8,  9, 10, 11],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(A, (1,0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "thrown-repository",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2, 4)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(A, (1,0,2)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "early-scope",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[1][0][0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "turkish-tactics",
   "metadata": {},
   "source": [
    "## 9-6. 끝판왕 등장! NumPy로 이 모든 걸 한방에! (3) NumPy로 기본 통계 데이터 계산해 보기\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "regular-sierra",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter a number (<Enter key> to quit)3\n",
      "Enter a number (<Enter key> to quit)5\n",
      "Enter a number (<Enter key> to quit)2\n",
      "Enter a number (<Enter key> to quit)3\n",
      "Enter a number (<Enter key> to quit)6\n",
      "Enter a number (<Enter key> to quit)4\n",
      "Enter a number (<Enter key> to quit)4\n",
      "Enter a number (<Enter key> to quit)3\n",
      "Enter a number (<Enter key> to quit)\n",
      "합 30.0\n",
      "평균값 3.75\n",
      "표준편차 1.1989578808281798\n",
      "중앙값 3.5\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "def numbers():\n",
    "    X = []\n",
    "    number = input(\"Enter a number (<Enter key> to quit)\") \n",
    "    # 하지만 2개 이상의 숫자를 받아야 한다는 제약조건을 제외하였습니다.\n",
    "    while number != \"\":\n",
    "        try:\n",
    "            x = float(number)\n",
    "            X.append(x)\n",
    "        except ValueError:\n",
    "            print('>>> NOT a number! Ignored..')\n",
    "        number = input(\"Enter a number (<Enter key> to quit)\")\n",
    "    return X\n",
    "\n",
    "def main():\n",
    "    nums = numbers()       # 이것은 파이썬 리스트입니다. \n",
    "    num = np.array(nums)   # 리스트를 Numpy ndarray로 변환합니다.\n",
    "    print(\"합\", num.sum())\n",
    "    print(\"평균값\",num.mean())\n",
    "    print(\"표준편차\",num.std())\n",
    "    print(\"중앙값\",np.median(num))   # num.median() 이 아님에 유의해 주세요.\n",
    "\n",
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "competent-jenny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77144516, 0.27517273, 0.50010029, 0.01086635, 0.69960057,\n",
       "       0.18767383, 0.30378409, 0.17027758, 0.22914   , 0.0265343 ])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H = np.random.random(10)\n",
    "H"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "statewide-juice",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.1745948878668644"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "offshore-bachelor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.31745948878668645"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "baking-directive",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.24746951716874396"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "mounted-clone",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.25215636284719106"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(H)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "dependent-flower",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.7636110781140923e-08"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "H.prod()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "accompanied-florist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.arange(4).reshape(2,2)\n",
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "retained-bedroom",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [2, 1]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B = np.array([[1,2],[2,1]])\n",
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "distinguished-immune",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3],\n",
       "       [4, 4]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "minute-shopper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 2],\n",
       "       [4, 3]])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A * B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "lasting-trail",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1],\n",
       "       [8, 7]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.matmul(A,B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "understanding-rally",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2], [3, 4], [5, 6]]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li = [[1,2],[3,4],[5,6]]\n",
    "li"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "hearing-witness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "li[:][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "rapid-margin",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]],\n",
       "\n",
       "       [[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]],\n",
       "\n",
       "       [[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]],\n",
       "\n",
       "       [[1., 1.],\n",
       "        [1., 1.],\n",
       "        [1., 1.]]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((4,3,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recorded-factor",
   "metadata": {},
   "source": [
    "## 9-8. 데이터의 행렬 변환 (2) 이미지의 행렬 변환\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "intellectual-walter",
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-116-eaaa1d5fd957>, line 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-116-eaaa1d5fd957>\"\u001b[0;36m, line \u001b[0;32m4\u001b[0m\n\u001b[0;31m    list(map(lambda x: str(x) if x % 3 == 0, else x, a))\u001b[0m\n\u001b[0m                                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n",
    "\n",
    "# a에서 3의 배수만 str으로 바꾸기\n",
    "list(map(lambda x: str(x) if x % 3 == 0, else x, a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "pleasant-phone",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(lambda x : x > 5 and x < 10)(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "supported-ownership",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x):\n",
    "    return 0 < x and x < 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "dying-expression",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "equal-sellers",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "related-behavior",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "True\n",
      "True\n",
      "True\n",
      "True\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(f(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "amino-pastor",
   "metadata": {},
   "source": [
    "### reduce()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "looking-charge",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((((((1, 2), 3), 4), 5), 6), 7)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(x,y):\n",
    "    return x, y # 인자로 2개 받고 반환 2개\n",
    "a = [1,2,3,4,5,6,7]\n",
    "from functools import reduce\n",
    "reduce(f,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "explicit-norman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<filter at 0x7f9d96a67990>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter(f, a)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "thrown-superintendent",
   "metadata": {},
   "source": [
    "## 2) 이미지와 관련된 파이썬 라이브러리\n",
    "- matplotlib\n",
    "- PIL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "fiscal-expert",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# matplotlib: 3.3.3\n",
      "# PIL:  8.1.0\n"
     ]
    }
   ],
   "source": [
    "import matplotlib as mpl\n",
    "import PIL\n",
    "\n",
    "print( f'# matplotlib: {mpl.__version__}' )  \n",
    "print(f'# PIL:  {PIL.__version__}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "virtual-equality",
   "metadata": {},
   "source": [
    "#### 3) 간단한 이미지 조작\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "mighty-fields",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/aiffel/aiffel/data_represent/image/newyork.jpg\n",
      "<class 'PIL.JpegImagePlugin.JpegImageFile'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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ABUsQMlJYjDEhCHCKsBGpSwhCLAG7E/HZDkACWFOkUOy2QN8UJhYB/9Dzutd4Ica7z47sviP+GYExxhgwxvT9wIwihJRSeZ5mWYqQNQaUuuOZ7zN4hSlFH9wI7mi3Cq9FyhwhhDG+4y9vM+/DqzD/1m8u9+z5F+RDCBFCHMfhnFNKGWOcuxgRpVSe50k6wMQYo9X72FfAfiDa8r6gDEIIY7BWAwAhOI5jjHHBxm0H/IMv+MGf7dnr/7Ngz568tbaIISOEGGOUUkIIIYQyBwi1CIyVSTpg1GJk4adyu6hg3nYRebGM3l5MixXTIoQAGYQBE9PrddDWdHK8ZfN+2uwV3+680Z698j879uwlKMhXZM8K5hVELCjIOccYBsM2JppQwB/qMiMDsBN52Wbetq2ythDCFIIuu9let9YCArAULN02k+g285C5g3mw/Zp79vr/LNizJ19syArCUUp39uOEckyY67qEoG57XaqUICDkTkGV2Q4vw+2V127bs+3/G2MwRghZa7Wxan19TZuiGATd8XiIvwp7lnw7e77C5yCEbHVGopQQ5nDPgl5fX46TgQWN8Qd3aWbHIS1e7zbzkClE+ABbai5jdKezqZSyW2NXP+yxpbW22+njhwT9W0A+QsgO+TDGlDKMMWbUWtsfdPMkNkbjn45r/G+Hn4wx208yxqg4jrVWu1HX9ACB0V4FECGstZo7NI5TSpjIpTZCEpYRwm1MiYvFxq1rZ5oVH5kYE+MSmkKcm6EbUuJSZHKtNSZW2wgs4ZxhDErnxhjGGKEaIcjyruO4w2QlzTt5LihFSqmClwiBtVZrbYyx1jJHUaYBCUDKWkUpcV2itf7ITvc+w9wFf5PX3LOWr7g0WmulVEEIa60xRkpZfA9gLJg8T7NsiyUAAGCDwHv99TMIYcdxMEbGKMfhSpnhMCIEfN8Bi6WUANhawJhiDFpLraUQOSGFxcUF+TBG2/9FWSoQwlJKhJDjOHku4khJ8bd6wtGeJV9BtR3yFQui1jqXyhhjwCKErJVx0k+GEQAoaSgFhJAQwvO8V199TSlVLodSJbmICUGOy7QGKS1CSGuNgBW6BIwhz1OpkjTrEwqYACZbE1ML/iFsMQHXw4PhRrniSZVYEIDy8xfedr2/1ZUje5Z8BRBCBQURQhjjgpFAqLUIkAakorg/jHoOZZRyITVjRGt55Mjhzc3NF1/8AcI2KJE0Gxibe54jhBJCUEqNAYSw3SZfkg6kSuOkR5ktgjzGgDG3U8bWmjyzlXIrz3StWltaWn7j9bf37duP0Yfn1j6BuB/L/Z4lH0KoSGlgjIutSRFPsQhjRK21CFmEVRp342jAOXddFscxd6jrcWPspz/96XfeefvFF7/HHVsqc0BqOBwAgOM4CGGEEFhsDBCCEIYk7Rubx0kPEw1gjNHGamOsMdZYbYw2RmkbUy69AJZXr7955iXCxKHDNUw/mFn5W4Q9Sz4AIAQVWbWdfgbFvxYhZQzCllBIs8Fw2MWArAHGmLW66GcwNlb57Gc/+4f/6ffePvua6+FKNVRaME4QQJpmnDtFGBFjbIxOksiCStPYgga0tbnc3lkCAFjQYVC1Bnfa/W99608cHnzh859fX1NS7Brv+KHluwcUcqnC8mmtC69Tay2kLP6LkGUUcpFEw4HSQmtwHEfKPM9T30ftdvLZFz49MzP1ve//xfkL7yiVVSoljJEQumDUVmsYbI1VuUgQskJmxqidEA9CBKHC27AYYwtiY3P5m3/8u4zDc88/hQlIlWT54MFepQeLvUw+2BYa6TsghNqOhiCMQSmRZZnMBSEghDDGBEEwHCrXdTc3O//0v/k/LS7d+NFL37+5cN3zHGMsAASBm6YpQgDIIATWmsKH1VpbawspTWEUMN5yeBGyGJNvfOO3by0sfe5znxsZGVtZWTXGNBqNB3yZHijobldMEsjBUjDEIgJIG6SKrAFjTpqqZvUo0teS9AZGriUDSnyEsDRJ6PhGysDBKTWD/nXG2ionPiJIl4ySFg0kIEw9o5z//B/98m/++383/NP2P6z9vbn6C4vX+61mvb0uHE9nWJdzSoi3udinCY8x9GSiYoMx5jR0HZBqmA2j2f1odWn5f/k3/+vm5s2v/Np/N3vw6MoS4u5YDZv+RgcH1Z2AGUIIIQwAxhhKdxxh9L5hrLv9ht2BPWv5YNvsBUEJAcEYM8Zc163Xq7VaLQgCwhnezs4qpQhhsNWxyhLCKOUAWEp94tTpZ595frXT+c6ff//K1Y35I/XFa5uTU0GWao+VvYAbawKvoqTprqwHnk8QDoPQ96HTy5Msa402l1YWv/E7//bmwuoXv/ALjz/+eJ4KQsBY6McRDwNjDMaYc+44nHNGKcEYvz/XbAHd8dhD2AM1HHcFQkhrWy5X7CKyBnGX+4HLOEKK4nw7Om+x0aCUIpQiC7gwMdpYsMZibZDpNL946h9fvLF49tw1RP8VnfrFytjR5ViDj5mNsubm5o1F5SxVDzuOG3Xztemp+aXVxThNSpWwOeJfv3r+L/78D64vLk/PHnzymc9WfLS2FvuOoxGkmcac4JygrUHUtvBRio/E+1J86I5YtN07ocE9bPkMAEihy6UqQiTLMqWU1jrNY6lSqaXWuvAdlNFKKa0twkApEALWWqWK6CBI7Vaqtaef/9Lo9L7Vzc0/+pPfdzEbaTgzzVIvE5s3bmy225Q0D40cmtg3sXjpnfZG2+PB/P5DzdGxy1cX/uIH33n97Bmv4X/1a/+A8/rK0hBpgjBIZUgQKLwTiwZCUKE53E633KHFAgOgth97B3vW8lnQCAhCuFptBn650163kGeZCCsOxphrsAgwphqsUkpbw2iMEEVArTEAAkABYIuoT7SW0ZPHTm1eu3rz+ssqxpff/c3SmP/ej97tRfmP3/g3c9MHZQoX3z174smTZ/70JudPPf2pJ576zCmF8h9854+vXr81PzX32c98bnbmSL/fV5ks1SoygySNwkaokKYgldJKKWMAIUQwI4Rg/GFjQLaUrQ/kct4X7FnyFVEojHG5VGk2R9ZWV9I0HQ6HloSMMZtTY6xFoJTBSmOMKcVaWaWVtRZhQjCyBrQxgyRx3RCs/fRnvjxIX6/XWldvXfjeb72lFxaf+qUvDt9b7tXHx+rTZiMNR2vJxXNr8j3nnH31ze9P759OE12tVyZm5w6dPj7o5IxXuW+NxphAyfO1MXmaEKsxpq5HCeYIEWPAaLB2ewwNKgqTAQDA7rUmCnuWfBiDVhqQJZzVa01KmO/7eZ5rq5ABZTEAstYKKZHRhDnIumDBamy36ngBWcAIhW6orDCdwTOHD6y9+fjbr34XUx6uXlrd6E5iO3fkiQaerNhqubG/JkBGw2Nz+2qYv/jSi7x/uNY6tH//kceOfnUEHVrMU8oIxZBKyV3mc9o3ysUco4HDqesysCTPtZTKGoIQwrcHXqLtHlwPybdLUATetBGSOr4fcs6rVVdKSX2JMeaE6hwpKYrgcxGIRsAIAa2R0kZbgwijBHKVUu5VSuG3vvm9i5fOv/7Dl5oj9c9/9st/8s2/WFi48cix45gw3/We+cxnRsecI0dOOS7vdFdowMZGGi99//vXVlaf+ew/Xu/1KkG1nwLBhhImRQ4Cs4DWmlRp0+8Pr12/cvPGcr8Xl0uNAwcOzUzPqEIWvWP/YA9WG33iyHfvesyiXFf/lGFQEigzFNfAmjCoTYwdZv51D5wcd7njei7rd7O0n49P79uM1i9e/PHRxx3HIZCXAKk8xQghr4x7vV6M1GiDycU3/+ib/2O62p5/7Jmr75xd6cUzX3z86LOff/bTjy0u30qzXljyN1bsrz3zdWn6l985V54Ih+sDYGbE9b717//5P/mn/5e2XK+EyA0koSbPlFRRe92+9uK5haX1mzdvKWlPHH/iM5/57PjYpFaQZfp9mautBlrbnWj2Cj5x5PuoQCiyFhljLEYYUwAshJBSWZcEQWByjTH1A7ffieKsO4zXFxZurKzemh077rjUYRVjzMVrF44cOUKZf2t18Tu/+Zu5iLubG+VK9fhnHn/mhWdOPn96OEijKFlfXVtev6ItSiNvtBuV6/bUk6f+zlc/91v/6rdc7kcZevG73wkq5d/4r//bOIluLSz0h8vra50zZ19duZW5nhOWRz/z6c8+9eSnRlqNPIdoqKSwxlhK77w1RUavCLjsHeX9niVfoSW1WhOgRZC5EJwwXOa0lOQdIRQQlGaDJOuutW8dODXWufrKD//D7/SHm+VgbGS0QVz85nlmLRJard18pdtbHz2wb/aRfS/83ac9Wn578+2FhYX1hXRpcUFlK9WwrmJ66dyZ6jPHbqns7JXep37tC/ni2j/7f/9/vNBZWnrx9/8oVhqu3Li5vLKWpw5j5MjRp08+dnz/3NPVapVgsrGRZql0XT8ImLWQZTsts97fPsayB3VJP3J84sj31xdKoJ1RRAUMQsgCshYhhDEiBiGMUb8fuX7JAlZaZ1mCiZnZNzY+0ZieOtDvxY4dVzpduLG2sHAjrPtKCQ2eMnm9WjarV2dOzR4+csQ69u2z79zqr3S6a3GXl0LnkUce2zd+IB3oa5cWX79+pb22MoZz4uBKJP93/8ff+M7v/uU7Z1/rK6xBKwzNsfrU1JHjj5x49NDTBFEpGkkksiwFAM8LKCFZZvI8dxx3+7y222sAALJgHlq+TzwYY8YYpREl1CEBpTwTKs/zTCMh81oYCplladxoNScOzE1Njr/44veNsSePPxMGde9z49EwPXvhXWvQlYvfuHBh5fLC2uyRzx08+bmR+YN/+cYfbi53ooWFRqN1bOaJI0eOjTSDPE4wS4+8cIjdvHHm7Tf7afLyS+88Wh994rknAb3YHBlP+nJqbn7uwNOPPvbUSOswtiAj2OxEwFNrLaWcMWaNTYVACPm+p/UeCujdBZ848n1Ulk8pCYCMwRiDy33H8WKJtJEjIxPKSIWpsqpar4w2muWRVhJnvh8cO3Zi8ZJYGaweO3rAdYKnnjrdaLR+FLy6eON6xPHI+GxYD195/Ufnrr7DgI5XG8ceeXJ+9qTrVIwiGKy1iVaJSvXjx071bl68eOX8hV4sHfVzX/nS7/zevwfkfPWrX587+ExmNUUw7Amb2WoQxqALibXWhhDsedwYEDKnxNk+MQKomO9gdk5zb2Cvee87KAYMFXpjKVWhevI8jzGW53mRaqvX6+Pj49yhFvTBA0c77cG//lf/9r3zNzB462tD321hgM4F6F/KJmYPjE7UOubqG9e+q41bqkydmv8UDMmPX/3D3/7D/+EnF3/gTbhdJL/xnb+8+b0/Wn3lu2NMn57dh3S2tLCQWFNqtdaX1x23hElpbTVrtw3H3OdM5ukdg7tg6yu+s+UU2orzWbzdC2HvYM+Sz4JmjHFOtIJomAz6kVLKdXmvPySY1et1z/MIQUILADsxMU6Z7XTX5/bPHDi433VRqVQaDAbXrq0xnibRZmJVWBl58aUfDdIBJObkkcMmz9/6ydsv/+gH1y9fxdIbb054hKwtXhn2BgtXb164cGFyfMKvlFZXl//8e98+dHQeDHQ6HWOg0WgwhpUCSjA2OAiKrlmyKDdJ0izLsg/0O0Nbwqq9NaXrvs/bve/AqQWiEbbIINDEAtEO1sSKEKy0tU2p7B99939a3eyenJsdZglVie+WFlcHpepUMDJan5y5cOmVheXV448fKFfJz3/tkXIp/d0/+u/ePPNGtdIaJnFveRFaavaR8XduvDxY7tUrzX1jY92VpZfOvTsyOn5g5Jkbtxb60RLBmeOyNE2ww5773OfzVFy7uWlUoFU5LIWXLi+W593ra2/ul4eVVpRVHM+zAIhqhTrMJw6mGDGwzGistdVavq/d79aCCwAGrHPXS7HbsOvNOEJkZ+jpzgqFEHg+dxwY2PZf/OWfr64sz80fopwpScCyXKiJidbo5ES1WltYuK5l/sgjRyjWP3rx+yu3Nl3Ouu1scXnhxsJKo9HAmNZGx0dHRnq9jucFeZ7funWLus7Mvrl6s1EuV8NKuVarbWxsyFycPH68UR2dGJ24fvVaHMdKKd/3R0fHR8ZH/uQ7L/3B7/3eD3/wHuN8tHVwYmxyanyiHJbCWolz13U8RinGFgHmjBIXoqF9P/8K7KmVateTDwonwxTFLNgaQAhZAA0Dh/HFxXeCsjx8ZPba9St0ZB91mLUyz2StUXVclqTDXn+j2agHvnfmzOtLN9f7bek6eGLsxPjo452ORIR2Vl+uVRugba83YNSplktVJ2iOtAK/RAgrVSuUu8aq1378aqe9zjk9evRotVTmlFUrlc3N9Ws3kl6vV2/Vn3ji5LsXzhujkjS7cfPy5UvvglEyV0HVD4Nytdool+qlsF6vjYyMjNeqjUpp4o5TxB9oFrgXsOvJhwBbQLA1crworACCACNHadTZ2Oz128O0c+3qe3MT+xkhvUQdO358cmb8yrWrvW5nZmZqfKRy4dy73//ud2Uupiamp6enH33kU/XGKKY1ZeF/+u9/Qh036qfdzQ5hzHc8zJnjBTNzs4Qwzw3Onz//4g++t3n+XXDoY888DUgKGYclTmkrCFmnt7G6vtrptDe76fT09Mz0aaFyZEOCMMVGSSmMZdShlINlYImQ2WDQM8Zske+28duD/Nv15CtGjOOtzo3FCA7QoEqN3is/fi2R11O5muW9RtWL41hm/OAjR4499vj1G1cvX3mv1WqUQ+ell1764fe/SyxyuTM/e2DfzEHfHWF4JIm9sFybGJ8ZZlG/O4iGWb3lc+oAJtpAtzfIsowQNoyjkZERiozHyMH5uXLFjQfDtY3VNE6MAcoQJiCE2Dd97NTpJ44eeX5tY81qz+WOyxFnLJccYwqWGANKglaAABACtSVetnekdPfUmgt74Hx2IhVkO1phITco63a7gygrBeVjxx71eLC6vGCRnZ2bnpvfv7B460/+7JuA1ezc5BtvvvKtb35rfHTs9IlnPvPUC+OjUwsLC+feOcs4Nlg6Ppx+4mnPLSX9RCmjtRVCZUrHcbzR6eZKM9cJgmBsbOzoocOHDhxo1RtR3JUqkTKL42G3206SBCxJEzEcDo0xjDq+F5RLdUJIkmRJkmRZ8TVVSmJiuQPMAXI7hbbtDu45SQvsActnDCBkMbaAELagEWgQFrJbK2+M7ysPuu1LV1Zef/016G7u37//ySdOvfzum1eu3GAemtk3cf361Vdfe2V2du7551/4wZ/9+S/8wi+8d3HhwvlLjE89/uRnGG31eyJPxXuXLmeDHi4xRh1krAGI0qxUqydpmiRJr91xCB6t10Hm3W67Ha03qjVjDKUcgaXEczjuddPhQCxcS6dGkFAVglyrXWRLAIQSKASkylhrUNFqw1rLnTtDfXsz27HryVfYPbgd+TcAyoIQIrvw7o3FGy9eu3rZDVtP/vyXU5HeuHHt+vXra2urn/vi6d6w9x/+3TdCl3/p137pW9/8E2bI7L6D3/uLn2htQcrrN64ePjLKHD4+PgmAlTIl5gKAlNo6TErZ7XbL5XKr0fId14i8XA5Bphwj7QDnbnujG0eZVpgSTyt168Zqa2wiyzLOKaY0iQAMOIwoZaQoFNdAMAICxiBUtDr44OCtD3F+dzc+MvLdqw7vbvHFe30dbBUhXpZllDHGEbJaqJWlleVXz/x4cWnh2WefGNs3+eqLbz3yyGfrlcYbr5/rxb2pibELZ6++8eZPauXmFz/7xZ/8+Mz5d6899+yTaxtdYMlzLzytLJ6cxavdM4ThU889/cSbp175y+/G3W7Zd32/hIijlOpnQ+bzJEvK5TKS0nVotdk0WnYGw2gAnU2JLJNSUspFrmv18X/w9//L448d7m1a6oDjIpUDpaAVwrwHgK3FGjAYAoARxsj+VJfoncWX3KsV/KjaVd4tHHyv9/HOv9/1ls9xnDyXGANGyBgNyPSjzuLiQqns/Te//F9vbNz4zp9861Ofet4adObtN/v9rNqobKytbrY3JsbGx0ZGL168uHRr+bHHHuv02levX/nFr3x1du5opzu4srA4jKP98/M3Fq6/d/mSyJKTpx8fnxy/eeMG9xgjTCoRD4Ym16C0ShKHgOtwo0SWWy1snuaBF/Z6g4nJfUePjL740muuyzmnjCFttLVIa2ss/uk7hOxWoQYycJfhMHsJu558UkohVKUS5kplecI97nl8cmb09P4vDnv9M2fePnToyOrKRtRL42RQ8ivXLi1KmZeq5Vq1kudycXExipPJ6cnlJfXuhQuWiG6/r0B994ff/dqv/LrhKzIPnnjy1Gqt3Om22531eDA8vu+ZaDAAbKIoSoaJTDKRJRQZbIyUuTa5VchlvuNYA7FQ/UZjcv7gRJpFeZ5ZKCktCHYIRYQgZMHY8h3ORNHw/va3H8S9ZqQ+qk699/q+P8vff2Q+1N26GN1rd6N7fZ3tVgSAjDZGASjKyfyhec9jSRKFgX/s2PEkitI01Vq2exu1wC/7/nhrpLvZXVq4VWvUp2enSxU/CMNuv/Nvf+sbiOA33njr2LETSRZprajLPv9zX/jCl76wtrayeObNbmfTqCweDgb9fqfdHgwGeZ5TSn3fZw73w0ApAwBxEnU6HSnlG2/85Ec/+uHk5HiaDtNsSCgQUgyTBqWUENn73Vh7uz/BA7ov93q//ibY9Q580egkS0AIEfoB5zyTgrtOUPYuXXoPIdRtt2u12mDYazRrMzNTxBqGUWdjM09z1/UwxvV6NayEaxvrU/smv/b1X+32+6sbq82RkSTtHTy0b727wV02OTV+8NBceWYalHjv3XPtzXWR5VmSGqm01hhjRLBQCgh2Xe663BjV7qwCkknaX9tYKFe5VFmSDggBSinCFiFAyFrQH2LiPmRix97ER7bs3utn426rwb2+DsGEUkYpUMwQwmmaR1HiRYmH03a/zayJ+4NhXx4+eNDx8bsXLtIkq7VG1ja79ZFWrdG6tbTAWk1E7VOf+jQlNggq3/yj3/v5X/xSkg5mDoysb964duv69OykpGhsbHRjaXGwKWWeVSqVTBstpKZKa42sNpYkaUJ4KIQgQAhBmRAIq9m58SCsICy0SYfDXtkHQrDWGpDhDgNrlIWf5t9HGtL7JOtGdv2eTwhBMbKUUsqNFBaQ55ey3ETttud5Iuo/cviQ2c+Xl1d/9MqPK7XWo1OTg2FaCoKRxigw4rpurVbjnJYmmknUX1hY/YVf+JWR1sjy+qaVlWGPamS80Lv46lVOsdYaLOyf3ef7YQK41+sx6gyHQ621UlYZ7biudccZZuBpW5JGC63l1OTB4UAEYdrrd0abkjlUp9JqrLVVSiD+4dIBqz9cvfJRfcjvFXd733s1Inf+/a4nH+c8S4RRGmOMGPa8gGf+zYVFy1cA4MDB/RfPviMScu7c+X1zM9NzB4YLC2+9/OPP/NIvz83NvfnO271eL83icrm8uta3IGdn91drwXf+8jtPP/Op0ana4tLK7PxsnEZvvvnm8YMHhu32+q1bjDFC0EhjhFKKETXGgFGezzzPnZycpK2Sxz2RpUHAbi1evXDxguc5iDir62sG5dPjUZlXAAAhSwi5Y97strXbW4q9vxr0QZnlj+p9cyJI2VrtAwJrLaS2jibqYxO//R//+Py7N281LlLwesMrU/VGFTvp4sabr7/Tmj2078ChmzeXEg1hZUTg4I1z19JBu1wtlw9Ufnz27OShmWCkGmVenHknpkfb6x2DEa/XH//ClxLXHxsf19qur69yQkuB7yCEjUUGRYMhDBDSSxTXlLHdrk0yZQhZ7iw2Riq3esPX3l2b3vdctXmSch8AOlHmeh6S26m09004vyse1JiZe33fn+Xvd73l25JRAVgLyCKwCCGKMHzh87+wuR5dee/PWdDwA+l5zuraUhwlYa164vhJ5vA4jl3XTXUSxUORM45xtVKLokhKiTEeDofM6VBKx8bHX/vJ65PTU/Vmo9kcmZvfv7CwUKs2Xv/Jq/1+HzAqVysuZXkqCCHjkxMNb+z6jeV+L3HDMgBO0zzLlBAaWXTyxOOVSm04jME6lFLPc60WHzydvadeuRt2P/ksAdgaWYosEACMOAKYHn/s1/9O88y7+y5fvLqyeobgwHE0sq7FZHxyYnNzsz8c1GemrDaO55XDcHZqNM/zhaVFqVVR0VP0uL145fL1hZsnTpwIK2Vt7eT09MLiohcGn37uM2traytLyzev3cSAquWaXy5Ja5S2mFJtzXAYi1wjS9NhNuD5xMjM88+/MDs10+8JgqlSwDi8bwbM/4Z6aseS7J11efeTD7A12wMmoRi2zDFCWU4eefSJsJp2N4dJWjIgXY9a0MwvI0a7m5uUUmNMlqWzzYPTk+Ojjfprb7zW6XTDciiE8HzHdd04Sm4tLR45evTRY8estWtrG4No6IdBnCZpHI2Oj09NTZXL1c21dZd7hJBSEF4997Y2KJMqS3JljEhkv5uMjU1EsQAJoAApazCkcWK1Sxl+H8vex7/bP739LTIAe6c55F4gHwACZG/fI4sBAQav38s21rvd/mBscqq9vqFVnmVZa2Y+yYVUKgiCJI0pZzOz04169dqVG5vtLncdZSxgVKlUMAbKSH10dHpqhnM+GAw45wDQaDSiKImiCCHUajTm5+dbrVbUj6LBkDCKCEmzfNAfrq61GWOtVmusNTrsJ/2+vnHl2szIc4wSqbTDCEKIEGp+agDWNv/u2BMXvYK2GxjsIYHLrg8yw1YmRxWDMQAZa8EYMJY7PMwlJHFeFB1qg6WCsanJdq/b7nUNQKlUOn78+MjIyGanffX6NWk0pjRN0/Hx8bGxMWMMIcTzPK31+vr68vLycDiM4ziKIozx7MwMxXjhxs3Lly/Hg2EYho7ndrvd44+fqjcb++ZmZ/ZNZVk2HAys1jJNdJ6tLi9iI32PYrC+63CCZCY+hEwW/3Sob6s56V7bC+4By2cAMCYWW4MsBqOM4Qis0kPquGsbN4bxul7vU8KyaKiNaLRGNjYuZpngnI+Pj0+Oj61trL538WKuZJrmjLFGqzk3N6eUSJLIWjQUqcxlnueDfl8r63IHjC2VQoagXq1pITc22hjjVmNkfHRsc3OTKz0+NjkyMqq1ffnll3/wg++tri0fOnTg1s1lLbNbC9enZ+aNNsIYjLnWllD4EPXUXc90T2EvkA+h4isGawAVzR0N524cx1evXCeYDaM48MppIvbtm4vTNM9z1/Oq1WqpVIrj+MbGUpJGRsPm5ma9WTty5Ei1Wr18+XKcZJy7WuSel+d5HsexNYgQ0mw2GXMCSq21wLgSenFx8fy5C8vLy1EUNYPyqVOnms3m2tra5z//wmOPHT//7ttXr11GoHv9Tre9Mb//EAmd3iClFEqBk+QA8FfyD91h89CeWnbve3rtfselMBAMGiwrbp9FFDGNLeCgv7S8udw5F4mMe1rZKO73xiZH2r2+ZQgpOT0/cfadM/Vma9jrd3tDqWLXd/bPH3S9yjAS1jAjkywbWKoH7cGg1+eUNaoNq0XolTvtzlDJ1177yeXLl0dbI/EwuXnz5pEjR778+c8dO/FYuVxeXl6cbx2qlsM33vjJoRNHR/dNfPe7b964efkn77z66JP7dT5mjDTG0RoTLggh0VAGgStzYA5kqWEcW3vnZrC4uOT2t3sCe8DyfTgwoqurq512L/SqgPHGRhv7YaXSuNbppWl64MCBN954o1yppGm6ub6hrdnsdk6ePGm1YYytrawOen2ZpzLPJNVaW9/1ut1uOShzxrqb7anJqZd/9KMffv8HExMTzXqDYvZrv/wrx44dU8pEUUQIEllWDv2XXnqRMXbk6OFKpTKIzeRMqxbW2t3NktNstMoihU47pSUVBB7nXEphLccY8jxn3Nsb2/G/GnuWfAgR33Gq1SqytB/HQPCjjxw11rbb3VazLoRijBGM3z53ttZodTa71trZ2f2MuxSTpbVFrQQDcBgBo+rVWpIkpx57rFlvqVwQxl595ce//Y1//8gjjz7zzDMAsG8K7d+/3/d9pYx12a31lXfPv+N5ztXrV0+fPl0aqwulnv7M01Ll6SC0OO30b2DWYiSoNiAHlOUJRoG1oLTSmmKMjQa097m3F8iHP3QblCTp2Ng4Ar52/QZr2CCsuIG73t4UQni+v7m5eeTwwfPnz4OxjJCNxaVnX3gOADhlcRxjQA5zlMiKTUMURadPnXK5s7HRrleqVy9f+YM/+INPP/vso48co5SOjIzMzc0XzYfC0FlaW75x+bJIs2o5/PrXvlYuh7V6PU3T2slRznk65OvLm++dWY9uRLXSVKPR4gziOLOIFdMxhVDFkLc9tLreFXv28+WgxkhzvlaeB1323DGHNoWgWjNKmVJmbGR0c3Nzc23t6JEj169eYS4/fPjo6so6Y6y90fYcx6FMiZwC+L7/uc99ziJ07cb1IAheffXVP/rWH//cz/3cV77yFUqp53nT09N5ngutqMPPnT//+9/+1rs3rowfmDn9uedmHz3as+ra5jqulrvJsN0fKBR7ZXBKUVCNvHJHw7X+oF2uhIwRITLXZRjDVpMgiz78sYewByzfBxpnA4CFYtLz1776q41666VXf7fR9Jqj4fr6JmFOqVRqNOrXrrw3Ojq6fOtWf3PzS7/4i0oZq3U5KHU3OwIAG1krlaenJlvTI51et9vtur7/4o9+tHjr1i//2q8eOXjYStUaGw2CIMmzICwPu8l/+uY3z5w5e+j4oRMnThw7eaJWa51592y11RoMh0ksc6uEsRpUrrU0CeehsbrbjQZpOjrawogNBr1SUFKaJpFhfM8ahTuxB8j34eh3iPTRY49+wSEzl69cLJfCNGPdnvIb9Wq1ury8zDkXIr/w5pvzJ08emJ+/urg6NzcnhOCcU4TCsNSqVcuBv7K+NhhE5SA88+Zb7W73V/7OfzY7PdPpdG0uZvfPCSHWNzc6/cF3f/D9M2fOfOlLPzd9cPr4Y49pA29fubg6GM488mgnlz2hlASCkIVEaCmgzW3EfdZs+pvdxsVL783OHPEDN8stgLH2E60A/QixZ8lnrQZgUST3zcz+F7/xGzduXnv5J78/MtLEfqCUXlhYqJS88++eQ75/+vHHV1ZWhJBzs/vfeeedSqlcr1YmRpugxaVLl3SJHT928sL585SzX/3VX52bm9tc39jotI8fOLTZad+8eXN1ff3ChYudXveLX/7SqVNPADdW6STL25ubTzx2KktVe7N/9Oik4ETnBnTKOfM8R6SJFqIxPdkYmfr2t7/dqE2OjTVvXOtzTh0ePOiL9zGB/nQbwm3cc3zuLjUv9/ohLsQBd777nZO9P4jbcsytJmkGADRAdcRdW+uWkA8Ax48/i3F46fqP0yydmWZXL7+DVX7r0oboyv2PPlqq7dtcXjz25BOxlGEYup4zMTGWqmxj0Olz/OTpZ8+eO1uu1r50/LGZsQmZpMPNzlSzkeWDc2+/8drZt27dXAxHWp/94gvN6Ykr6zdPf+qFocwXri/OTM5UPP+9K+9JFAvU5y5Z7aw3wMUa+bQWTu8XgXs+Gc5NXHr0ef7mhW8/Gzw92jyQp6rWhLW1iLgYABsgxfihnc0eN3L7dIsfFfcPozsmU97ZMxybrQtk3/8rW7jTlpidijmkAYCaDyeEvUsj8r+JsnrPWr7hMA+DsuMQY2y3u3b9+k1KaVhqIG0762vZMFO5ZK5TLodxPBxrNTllg0E0tW/W48wtleKe6HT7xx4/ef3q5TzNapNTruuurK4QbR2H9Xq9b3zjj69dudqanpqfnz/+7NMnjh67srR4+Ogj/V5PKME5r1arqUyTJHE5T9NUI6K1DithHmWcc8/zkENq9Wa5RJq1xiCU6xvd0RAQob2uCoMw1QkAYLuV3EDbw9gMbHPu/Xed3M133LnbH2SJxTupFQTWfuxSwo8ww/HRdCD462L7XbZVIZQ4xtgkSVstb6OnL168aKg4dPjAxXNvIqmNEINOuzW5b9++mW5vY2Ri0uOeV6nXqmVC0MrGhgR58NgjncFw0Os3Go1quZLnWXt1Le4NFm/dvHLx0sKtG41688ChQ48eebQ8PrLRaVPG/CBodyOpZblcrpVqt9ZvdXu92sy4kYo4TKTClm0URX7otxq1xajvubyzuZll2chYBUMkoBfwYNjPAEoI4cKsYwCwYND20oI+bA4HMuaOy4w/cMl3DCf+wCyFnZ8U/L5rleRHtA298+Xvu+W755LPe+Qq+uATLAYARkEIlOdKS2CEhoEngCmRbywsEUKMyBGl4xOjfuCuLNyamZ0th+VmqxElw26/3x70KvWqF4TvXrxwdN/s/tm5NIkvnDt/48rVhRvXO+sbWshnPv2ZVr0xNjc7NjaxnkRZJhoT471h5Hmeh70y5gRYr99PkmTcdTnnaZpGUSQqea/XKZUqnDMhMkLQcBj1e3HJbebC9tIsnKxW66zbFZ5DDAK0xb3bfNIf7NYMAAD0Ltftg8GZ4qXM9isD2Dv497F62Xt22U0SHQQEYX9jYxjW8OnTp85dXjrz9hugzfpGR2k7OTUVhGGn0wnD8OD8/rAUDofDOI02up36SE1b9drrr87Pz0+NjkmRv/fu+R/98PuX3ruohDxy6MATj5+anJqVUk7MzMS9QZKmE1PTGQLXC6jFju+6iKSQZllGKSWEuNxZXesQjF3XNcYYo7rdbhwNwei1tbVavdSqu+12O+utdQZ5q76PMw6gdwh3e6N2d/tjAcP7zdP2eN6/6tO80wDrgxbxY8DHYPnu7e/vdZXG9gPSX4sAgGEiMyDEUozKoVuvBsN++8a192q4mqV5pdl0/CDNs1HfP3Xs+GhrpBenw2g4SAaux6UWm5vrge+Ojbb6ne7LL7984fy5zsam7/tHHj/45KknZmZmpMaO4yRpbjAZGZ8ARJQWgEmeZMzlQEhv0Ddga7UaxtgYI/KcE2qMoZRWKhUhBMaQRMM0GcxMz2Pioe6g2Rrvd/uDviUUIb3VE8gig60BwOYDAtMPuXrbv0HvJ+6dC3HxK2yRwUXNsLEIwOKt/32MFHxgReMfNbYbd24vM54HvV7GXRuWXGOjq9cuXr9xSRvR6fdqjebI+OQgy2ZHx06eemJqcjoexsMoylUmZOp4/urqsuuwQ/MHzr35xsaN5TfffJNimNs3Ozs7c/LEiTAMjVSO6xNCri8t7p+eq42MdTbbYakcJbFrUJZlnkfiOEYINZtNQmmeZvFg6HmeEML3/Vartbq6GgZelsaVSmUYddbbK+12+8TheV9rq7W1FBUjf5HdMmBoi0AW6+Kf958+NuhDtnpom4w7v0Xv3xre3u3tPOWuyq4P//G9GouPdc/3oGAs+IFLqDJGra+vXr9+bTDoYWKMVOVqnbu+ztInnnr60MHDa2treS4t0oNe16+G7W7bWnXgwJH11ZXXXvnx0qUbtXrliSeeaNTq9Xq1mBg9MtJcWRtsrK3XJkaZwzudDqVMGhOGZZRmSikAUEpZa0uVsiRoGEVSytHR0UqlgoxlnHS6mxkFAFi4eT3LpV9yuOvnuUzTlDuIYAANcIeTu8MPvHW3d/psFB850DsMs9vTArcGpd5es38aFmHQW6stMkUp1seJu5LvnpfLuznpfy2LeOe7/9VHgpABi7YqOWBbeonsMErL5XIUrzSbo5eun3vv/Fm5tlSZ3OfPz3vVWjtNx/fNT87uv3jlajwYuownOEFaZjrloMrl4PqV869+5y832xunn3hiYmKiXC47jlMqVSxQpdWNmysr3c7YxDj33G6eODg0FBmLc4M8rT1KlTH9fn9qYrpeba61NzhykNGOBcejdVy+cuXixSvvLSwvrG6si/6g0Zp44cuneoNkefPaxHgTCyK1oJhjZJAFbAkgBRZZAIvBGAVgwPgWBLIcU4sBIQJGGGwBAUYAyN62gooCx4AJJJHyPKqlBks8F6QEALCI7FzeIsB31/v4M9ype8WetXwY4yJXRgi6dOVye+WWNz7ildzALWNGK27l61//+ubm5uLyUr1c6Q0HmY1dTk1sZqYn0zj67re+PVhee+rpp2dnj1BKy+VypVIhhORCSKWMtY7jMMY455ZQxpjFWCkjVe4Ywxjr9/sAwBxOgBTgnDebzdXV1WG3lyTxysrSuXPnFi6eB4QPPWK67dnm2OTizR5lI9kwAyMourM/nwVkCpeCYmOt3ekPbixSGoy0DGOEt5qJF+QrFmZCIEpk4FMvoBiBlgohou8Qqj4QV6PAniVftRoaA5mg1hAlHB5OPHL06Fhr4o23r7mUfvHLL0xMz7z9zhnCcJwngEwUDUK/xT33wjtn3/jJa91+59nTp588fTrLaZ5nhBCMcRzHaZpaa5VSYaXkui6l1GCCMFgw1mqjDCOUc96+dctxHNd1LdjC4ZBSKqUqlUoSx1ffuTo/P/+Vr/3Sn/z5t//gN//joBsnsQrcoFKWUbrp8KHnM2gXVnzrBllkwGKDgGtfFy0kUeEcGIvAmDxFFAAQGLxdwlwYP8d3ECaEqUF3YI1q1isMQbfXcVgVAJDF7w8Bfqw973c9+T78U4sgijLXdaXUBvTJxx4bRnEsL3SGGxvdzpeeOn3q9BMXLryLkHUcJ1dimAw5IVEUEZn+5OUfDwa9L3/x82Mjo+vr67XmXMUPfd8XWsVZJpVCCJTR9TCknGmwRmtQCmFCkLUIe66ntR4OhxMzU4SQzAiCUJ6mDLNKpdLdWFdKTs9On3n7TYPkP/h7fxf6yY9+/Mpbb77eHw4ff/z5tc3VZhirGJWgunM2FhmLcDF2F4NRRmlFACOEESAMCDBx0FaMDiMAAhZhhBFCCGXCxHFMSI6JDUuh77vJUBljEOgdxxe2KhDuklm7b9j15LsbpNHEaG14nMD8/JOcTX7rO//67FvXvJL3wuc/2+22V1eXJ6cmuv2OXwpXNwZeECyuLMf9QSkof+rpZ8bHx5dXl8ZGRpMsrzhuLlWaxrlQUglCSFgqAUbGGIQswmCttraoWgeGyUavY60tl8vaWqUUQiiJ4jRNHe6kedYfDvbv3//22Td+67d+U8r8mdOfX1nq3bh5VSk+u//AYLjh0VuNZhXyrc3XTsLaIjAItELYUkM0YGup0FhqyLXOJbMAgIy11iILpOicCWgwjPM0bTTcWrXW2bz82vfOURT+wi/83PqiKVzmwkZ+3LwDgD0gJrU7ssv3fx/4gdGIMS9OMyHNoSOH5+b3JzL91PNPez5dWb5VKwfRoMMYWVlbZA4VWmdCKKHm5w+WS/XNtc1SuT42Pi2UHcZpu9uP0kxZk0ttAEqVilJKKFX0RSWEABgExlqTpmmv0w3LJcp50SnfGJOm6WhrREgxMTFBKe0Pe8+/8NknnnjizJm32uu3Dh6cdhh3neDipQsAVCgTlCp3plktMgYZU5yjQYAxYwwzbjARCqVSxtJ4nHNCMCBQWmYyGaZxPx50Y21wozU6PjGptXnzjbe/+c1v/fl3fnj92ioGBcigO94FWUD36m78zbBnLV8SawuaOw5HTjIUjPFG7ZHD8+1Pffr0+sYixpJj1O0OnVKwvLTYGhtNLLKY75uZq1Wbve5wpFmvT4/fWFyemD4kZZ7nuZTIWOX7fhB4GGOtFAAQQiilyhiwgBDCCIb9QZ7no9OTUkpEMCEkSZIkSeYOHlRCxPEwE2kFgkajtn//HKHI9Gm93EKa97tJotthqaKC4erajRG4c/ZaEQcGALBEIkwNS6XROaznOFO0j5BlzRBrg4VGQoPQWhlkkLW2Xi57XkrcuL9xK9M36iMyi1Z/8uaffv6Z/wJbY7abb+zNDMeDAqOEMiIVcAdyw5Ik2X9gfm5+vyXnoyxWIs0zWfL9xZWVZrOZpmkaRYDtxMRUJnKLcL02kkvNuGusVdoqo6MkFiJrtupe4PeHA68UEkIQwYVtA2MI44SQLBtijGu1Wj+OuOtgguM4zpJUpBnUKoQQIUSSJPtmJ2dmZnrd9pGpIxvdtfHx8etLaw4kt5ZWJsaezKUqAsx3pCxMYdoRZoBBa5srlVqpsaWUM5f1OpvWImsADEEWU0wIdTCgxsjooNvrDaM8k4TQmen9lWDm08++8L4OMA9oykzhnd/Px73iHl8fGYYsRRaIVcQqqoFqSjUjNFMytQbyHDjhjDHHhVz2sjgSQgzTpBv1EiU456s3bplEXH7v7LEDB3Mre4PuzMxMkmcbN5bHSi0jegxlIu0pGbea9Wq1ijAtVxqeF5T8CkdMZ8q1nGtawi5JTD/O5/YfzNPc5TQd9ga9TQyWMuwz6lMed2KVIguOxl44MvHjd9/LG9CG6DNfPinVMkqsaA9xMggBKX7DBrcSWNe8z1mJ5Lxk8poRXRFnRHX00q3+xZRep+VIkTXqRK5fShPl8YpIIc/MRrcDrnaaxJcD3/RWr57R2crCleVutx96BzgfyxHLEdVATOE7G0u0Jkbddz7c8dizls9aC4CttWAQxkQrmxstciNsBgBpmgJAu91O07RUKl+9evXkyZPGmCzLCuOEAfm+zznvDNpK6SzLGGNFYyttDADOVW6VJYQopRjZislFgyjPc2OMQQYDCsNQa6219n2/VCoZY0qlUqvVyrKs3W4XnWLWV9bXV9uPP34wDEuD/oCyje6g65V9rYjnepI6SujU5DrNCScWQRj6GluHeXVcJSXJGcOWA5j1ldU81ww7aRrX6/VyNSAE3bp168y7Vx89dkQItb6+Oj42GcWD0dFxRl0h7tjuIwAAix6qWj4iWE3BImstIMA0FzpLsrVM9tM8tgiyLAPAQsp+v48xsdaeOnUqHiQIoVK97jgOGOu6rpQyjuPhMNLW1BoNjLGUEmFMqcVFX28AY4zFtmhglWWZUirLMmkE5BAEnpRyMOwVO7+11Q0AYIwlIkmStNwoj4+P52upys1jxx9/5MijL7/8cpJEqRiGJWezu+g2pxgOtbBEO4g4lAG1kLibnc6Gpj0v8JlHMII4Emur68lAEkQHBseDiCDrBp4B2Wt3Xn/tjWajPj45du3ajWp1fGJiamZ6jnM/Tm73pdxZ2/+K4riPqiPFna+z673duwMjRLb+IVRJkiY6T3Ex5VEIlaap67ppmq2urp4+fVoIobX2PC8IgsKN0FqvrKykaToYDIQQlFIAKPK2xeCNIsnhOA7n3Pf94g/m5uZKpVIQBJRibaSxSkrJGKGESymNAcYc1/Fd1w2CoNVqba51slgw5B498qhSqj/orK0vCh3J2BpBjLRKWJVZles0tsO+idNkmMQGEA88IFSYXMis3W0LmSstAKlWq96oV1yOtRIuY1mS/6c//OathZVWY2RqfObU408hYP1ehAAjRIpQ0ZZuFZGPuVR9z1o+BMRaQFgRii3q56qbypu5TrNMWARpmlLChJD9fj8ISieOP3Zz8Uaj2nRdV2uNLA79IMsyIUScxlLKoBS6rksI0UoWk2cIIYU7CVtjV22e51rrcjkUIkPUWmWzLCuanBaK2maz6XlBnKXSyGiYxHm62dnodYeeE3Tb/aOHjlar1eXVhfW1pTyLVCA70uTxBiGkFE7xEufEMcpSvlnjOQ+Jwb3l1RubG5046q+trU81J4N6K/TcSinM8yzLMqtk4Dknjx3/vd/9d/9m89Zzn33enTlQKpXXusZxqdUUwBgEAAoAts3Qx8qHvUs+DEYBphZjEDKPhlkcp3km8zwHjKJhXKvVVldX0zR/9tlPE0KCICiXy1LKbq/nco8Rmue5lHIwGHDulMtlz/MQwggha22xk0MGaa2ttUBQnudJkgCAlDJOIzd0hBDaSMZYHA+LFjBJksVxHGepEELkcrO/ubK2PDExXS1X4iwdG5s4NH9gaen6oNsRedKqTMhMxr2B43gm6eapxgZracdPug6lFivCcLXWItTVqj4yMuoa3mw0QJs8TTY2NgBMv9/vDvpPHD8eJ1/54Y++887bZ7vrXrVaHa09xx1IYwCEEdJbGz6wW6qWj9Hv3bPkwwiUBYQ0IJtm64N4dZjczHNJwI2iuFh8O51erVbbv3//YDAIgmA4HHY6nSxJKiUAYweDQb/TT0RSLldKpRLGWAiptWYA1lohBEW02PZp0FLKPM8dx/F9HzNwfEfa3CUMY5xlmee4rdboD37wgxs3bhqAoBJOzk4cOHBwdv++bFVXwlIcJeCZRx999Hvf/06nvbG6tFgamcx0x3oOrWZp0mtHGyU/CFuhBFdhEycpZQwINUZphYKwvn9s1Ci9srBopEmTqFotY2SH/e7K8uLTTz3ph3Dh0sWVlaUf//jlL372ccAOwO1akB2+fagosMDDPd89ABOw1iBsrbVZlsRxnKZ5muQY40F/aK0dDAYAMDU1RSnV2pbL5TiO4zgulUrNZtPzPAAojJzruo7jaK2LhRUAEEJSSq21UkprbYxRSkkpKaVxPNRaCyHSNN3ZOzqO8y/+xb/47d/+7YWFhfn5+SeffPLAgQOlUklK7XoedTgAGGMeP3mqXq8jY3vdrpXC5JISzAiK4kF7s2sQjIyMDaIUU8q4bxCOhtnNW0tLy6tKqTRN4zhut9vXrl959dVXrly6PDo6+stf+/rFC++eP39ubGysVq5opd566621tbUwvLMKaWvalvnYw30YIdjdD1x8Y7cHARZjzYAykCp1HEZY1u2vb3QuD6NOp7fUaQ/jOAXAQiiE7JNPPtHvd12PZLFsb2woIcbHx62Vy6u3pM7ivOf7PsYYIZTnOQAwxrIsk1IOh0MppbU2yzLXda21aZoyxjKkLIEkizmmndWN4Ua37ITvvH7mN//9v50/evD/8E//yYlTJyv1WjxMIDejtaYp84TKamtCADa5/c++/vd7l2/2b230Vm9Ntkas7sbZYGxf5bFPH+YNG7NNxE0mMu7xwTC9fv0Gw+7M1MzM5IyDedYfpoNes+TXfedH3/2LP/2D328vLf38r34mM72/+P6fGaR6nbRaGjkwu3/QVpoojZVF1gIGSwBwocK623W29sMf93q/7nzu3lh27e3xAdu1qFoBIQRhEw16nc76YNhROEeIMMa0ttaaOI5nZqettWEYAqBOZzPLskajkWVZPBwopaJ4CACcc2NMnucWIUIYY0xIaUzRs7vQFqA8z3cCgVJKkaW5SI2SeZoYpZMk6Xa7L7zwwrFjx2q1mpRKiMwYA2CsMuVyOeoPer2eBR0Ewfz8/CNPPPHuu++efv7T68urjVq9MtoM6tWri9epAxv9Nd9WjFYWpMudqakpl3GPOWBMr9M1xnQ6XR0Ezz33HMH41ddef+WVlz1XXbl6LQjdOBm4Xu3YsWOYADYUbfkZ29dv5+turOH4pEEpoJQDpJvtlfWNpW5vnXmCMS6lTNPUWp0kydjYRFFUkeTZ2tqatbbVamVZ0m63jTFRFLnc8Tyv8GQJY5y7hBCkVMG5rcQaQKHz832fMSaMFEJYaykjYBxKCWU4z/PR1pjnBVIqrbU1wBjDGBAFw0MpJVGaUeo77j5n/wuf+9y//F//ZTvuz42OLC6tdSMxi0rDFaU1mpk5OMy7RoMSAltoVGsUk7g/WGp3IMnS4YATevPmzdB1Hn30qDHq5o0rY2NhHMcOqy4trLTKx5984gWwSEoJzgdY9rEvu3uAfB8YXwYAYJWyhGBtk43NxW5vPRcx5ogSEsexEEJKGYZhvV7HmGqtsyyLoqhWqZRKwdJSbzAYME4BIAgCzjlCSCkF25Nhdsrgix0hAERxJISo1+uEEJHFeZ4zjjh3XMZ915udne1uttdXunEcD4dDxphSCmNKiCUMRyJzHBY4rhCZttICnHj81HPXXrj81oVDM4cci9trKwuLC9VmA3F27tKZuJtYjJTRlPJqtcopG3Z60WDQ9IKL771XL5cG3d6ff/s7R48ebtSqKytLcZq5vpOKiLrm2MlD0/umkgEQynRh+X4qrPzxagv2rMNhjMAYpIrandVcxIxhgh1jIM+lVlZrPTc3Xy5VwzDMcxkNE2NVuRwKIYbDQZLGaZpyTl2XY4yLPV/hcBTxl4J/hashpcyyDAAYY4XCz1hVuMNS5saYZrOxb9++RqNRKpWttWCxlFJKmWUiTfIsS4ySQoh+v59mYpBkYa35K3/3H15fW7+1sTF7eP/G5lpvY7VEMRnG8cJSpVxu1RvNeq1eLZU8Hjh0pFndNzOxtro8PjoyNtIyShtjBr3O6vIio3hjvV0uNRgaO33ylz7zzK+o3M8zgxBgC9ggbAFbjC3GFrC1+GNqL7GFPWD5dpTfWykiAABkAOeEuomMu722UtL1OaGuFKoIyHmeNzs7yxgjmEXRZqfTcV2Xc765udHpdJRS1uowDAkhCKGi8DuX0phUW2OsVUpZZAsWFj1Jy+UyxrhwSiilnDOthJBqYHqMOkUckTEmcsUZIEQYIVJlSinfcYuR4wYBcz2kRabM2MzsV770c6+8+KOpyfE8SnrDwcs/esn3Q4eyG2++5fgOIYhyyiimCAe+7zmuFNnU2Gg6jLrtDiNISbO2tqaUrLTGPL+scj43PzMyXuu0I60psS4mdxS/3b6S8HFOONoD5PtwYKIxsSpP+/2uUsqnLkLMWp1lWTwczszMjI1OANh+v58kSRRF4xOj1tpOpzMYDChBlLJiP7eVzEBISgmgASNbDPl1qJSSEFIQLggCa22WZ4kYgrWuywCAUqqUIpgB2DTN8jwvtoYAQAgBxCxinOPEGIQhDH2CKfPCpB+tdQZf/Xt//4dvvXHpxpXPPP/Eyy99X8Sd4/vHsiT16zOEYc6JX/IxGKNl6JccymZb4wvXrq+vro6Pj2+urnQ7HYJoqRJWqiNJLGrluXrlQDoEZELHgfePPbrzQ/vQ8t0z7pxIhopWXwgrjI3WMk1TaSVCCCFirdXKgJTj4+PVanUQ9bvdrjUIYxyGoTE6y7I8z51SUAT2MIEimQZbi6xmDjfWSimpQ7XWhaoFA/E8zxgjhFBKITBKKa0UQVYISTArGhUwxjDGADjPc2MIwsAcKvJYS4W1IczrD2PDKHHcjW6PLy/+V//7//Jf/uv/r8ue2Tc9w7ARQo6PT7poxBhNOFSroTYqjROOiVYKWYjjWKrcc5x2u9tqNVojNSnzOMqHw/ipxx6ZmZnJh0AIaA15Jj1vq+D3NtDPPo7mowG+W/zm7g/zyXogC8gCaIM0gLXIWiBgiU0cajDObYgQFzZbjPtXF8uCD/s9AJjbv88gGcUDBCoatCsOL3ss7XVcY7myDvAwqE3OHkJBnWJgjOZpyiltNBoIiBIaIRLHqeOwLEu63XazVQYksnxAmDTGaAMAREk06OVZomWmpDQS8tX2cqIGqe5j16Q6VkgijvMU8sykCOdGURdTJBzPjI0F5xYvHnrs6MzYxNKVm62wGbUTXKr0jdaij3HO3MpAoSS1sWIqso3S5OaN9c6t/lhrPM1VJmLOjBW45rfWFvBI5eTRg0+4tJFlQ2uVloYzhhAgZBBWCEuENUIIAUGWfpwx2j3rcBDCpNBxlOeZsgZZjLI8abc3AYB4XhiGSZJQSoUQGxsbk5OTMsuTJIuiqNjhYYzzPC00LIV7USy+xhittdWGACrUU6VSiTFmrTVKF/s/KURRKFm41cXTGWMYkDHGGLPjNSMLCCGLkQUo0iRaKqu0MaZWqf7Zd/6k0+lgRjvdLiYkybN+NLQWIUSwZSrVJtcl33cdsr62uLLYnZqcX1lcf/fcBQDsBSXOnJs3FhlzHn/siVKpPBzGAIAQ4hwXO4oH7m7e86TxBzXp+m5ACO90JEHbawYCIEwDzuN0M5M9ILEyubC9dLAJrF7opnKZFYp2rXWtVlteWRwOh/Fw6HkB5QxjnKYpZpQFDjJWSmkAANM828qwFX8QRdFIq1WQDwCMMVIIpVSeZlrINE0LXaAQAgCUUkUxG8dEE11EaiilxGoLgDEiBAABYATIKiH+43/8j1Pl2v75g1k6dElIQ48xpuQQIw0201JSRXxaASFuXl5gNHc9sbZxhdKoWgukirBXZ447te/QyZNPUMqzdOi6JQAgBNDd9nYfb6jlnvd899xv777jw6+jxZg5XpYqrZGxRoiMc95PU4XUyPiYtRZjPBgM+v3+xMREHMfRYJgkidCmUQoZ5xiDzlWRnC2ULEYblQspi+0jchyn3+/mWRYEgTFGCam1llKCsWAsMhZjjAFJrbZ+DqC11lIBAOdcay20StPUZS7I3JjiCZgQDBiB0YHnryyuBOPYK4XMp+32hjG21+u0KhVkkUw1BeYzbvJ80N4Y9tZm6/vOnX336uUbhKMgCKTJ270uInD6iad9rxINBKOu69A0EwgIxni7p8t2ZgiZDzQfuu/YA8suvuOxPRoUGQBLKU6FUNLmuUyzjDFirUzT9NFHH03TtJjlF8dxq9Xa2NiIosQYcF3X8zzqcIwxochzGBgLABThLWWescUKSzHJs0xrjSykcRLHscgymeWgDQEEAJwyz/OKwkqLUBrHaRzHcSyyHFlwXTdwPd9xESXG2iKCWAi0jNIqFy5x9s/PS61SJasjIxJMuVWvNJqEUQtYKeVy16VOZ7Pd77SrpVCotQvvvZwN19KsB5h6brnXtY36wSOHjw8HmRTgOJ6UUBg3hCzY7e42D44Du97y3W0XYJCUUnLONVghBEI2yWKh8qJyot3dTLK40CdrreM4FmlW+LyFUhQRXNRwGCmsVXmex3FiEbEIc881xhRW03PcYjEFY4rpLSLLMcZ5moHjUEqLXhnGmCJkgwGKMhFKKaYEY4woxRgDRtZaY4wtqn+NaTWbv/y1Xzn3xk+E0V4pHGRZIAQQrK22yGAMFGOR5/1O1+bK90tvv/zdfq9TbYwgR4tcLw/WkZn8yte+zlkYDTu+WwFrkkR6PsEYlPpEbJ7uec/3SQO6QyK2tedDFlmwxhGCTk0c9N1WlCz7LmtvLFvgjz/+eKlUWly+1R/0oyhqNZtxHGdZJqWiiHiuL6VyOSMEOw4vpoEbA3mW9Xo9bZEXhL7vE0LiOMrzfHJ8glJK8ZaYWSlJMS56FCGECCGFvEpKOTIy0mw2m80mwbjb7SqlCKOc8+bomIFC6myNMQYBIcRxHJnK0WbrVqW2ur42d/hAUCnHSaKVrPGWUZYiRxkTDzpKx2Cjm7dunX/3Ha0twSFCMOz6lIenT/zykYOf39hIOfMwhjSVhDBCsFbbFmSrf/W2rOBjX3n3wLL74bBWa5NPzUyWSgEgwxgT/S7GcPr06TiOC290MBg0m80iP1YYJ8aYEMIiwBg7jmOtZYQWcpXiZYt+U8Vi6nJndHTU9/1CUmCtVUKWSqUwDB3GaNFXAOPCF3G5U7S6KkRZ/X5/0OsPBoNhFBVlR8XWcMsjoQysrVQqBw8etACZyJnDkzzjriONzKXQVgmRxsmQUqqUeefs+TAMR5utZJgN+pnDSz//c1/9jd/4rzY2oywTnudLAUII38MIgRCK0E+EyflrLLv34zD++sBbSqo7DstiC2CNgwhJh8HpJ750+dq7q6u6PDE/v/8Qq/H19dXhsNtd2xwp14gmST9LIsEc7NcqkciEUVwIznnc7WMLmcsyIbMsq9frFhHuemEYKqU2NjbGRltFlQbFoJRKksQYs9ndnJ+fb7fbyioOlBFEMVAMhkEq0kE8UEppmVstuctrQWAYQGqMFBYTiZASRhvwGI/j2GTC8YNTx05tbGySoORFCBmWaZqAaLm8Ew2G/fWS71y6tdjfXOJ8Ztjpl7zZx4498tyn/s6RYwfTBAkVuBxlCQCA75WUBtDAOb3dFeNjdzLuxN7IcMDtFp47wMiADcuV008+fe3Wp777Z38es/znfuEXi0WwMHXFhi/Pc0IIxohSyjj3EPi+hzHOktQqzS2XektApbQumu2lSQLWuq5bLMFK5kIIMNZxHErZtWvXGCaNRqPb7Ra1bVLKQp3leZ7WWuUCY+wwDgCEEMYYkrqI4BTbRABwGDcW2XxoEapW6khGvTh2KROYhy5t99pA8PjE6PVLVy5cvJrnNu+vT8wd/fILv/70syeq5YNRmiRp5pe5ij9h1uIO7AHybQsL3s+/LclxN240Kk89+emL7y1sdM+GQXUlXyykTYV7oZQaDoeU0mq1VSnXvMDFlDBG8yzL08Qik+e5Uoa7jlIqy2WlUiuKcBljrusyxrSQQoii8pIQbKRyGeec53lOKZ2ZmVlYWDDGcM6LoaYqF0qpQixjjJG5sMYUEemC2QasMsbzfI1zNERSymq9jiVdu3TdAUKAYAomTrzAjzY7b73y+mCpPTd/+MTjf/+xE48cOfgpKUx3nSBXU+6mMXzY1I5PCnY/+ZDZ9njft4IwBgBumuVSwGMnn6pVx94+/2cIaJZlg8EgSZKReqtUKqVpOhwOi66jxhitrUVaKRlHgziOwRjH8wp3Nc9EngsAQBaEEGE55JxbtVXYgSxoraWEaDDct2/fcDjsR/2xsbF2u/0Hf/AHMzMzs9YWNq9Q4RfZDiklyYUW0iiNASFKgGADgCkJPTcxxvfCbtQhhBCJ3jt7jjHWz2QYuqcOHPc5efvSpZs3FoKRqcdOP/f3f/0fKklkgtIs1YhabQBLRA0o5+O/Jz8jdj/5dgZCWQCEd4xflOYYU8r9Ti/zzGDf3MF+dum9y++hmozjGACK/hXdblcIEQQBJdxaZIyxGqQUeZ5rI4sqaoSQ1aYwV8jYLMuM0vVK1aFMa22UtsYghCjC2AJnLEtTKWWjVg+C4KWXXnr9lVcePXEctAFtAIBiwikDAIyQ0ZpjjDGxlBJCqOcU2TdEcJ7nUijX93TfWmUdTFduLHY3NlqtsdjIiNWH4tb5V16HtajSKDX9Rpq4IkMqB4xDxwGJXEBgCRRv+iF4oLu9AnuAfHCHpO82KKUi167HraFZKmIWa6PSLOEKSSk9z/N9v9frdTodz/OazaazBW6R0VpRSpHrEkDW2iKNiwBKQYgQiqKo0OdhjIvkrNbaKk0IoZRYqmSW16rVIAw31tZ7vd7M/Hyr1SKEAIDVW4ldsBYwJoRQTAwyRS9HABBKGqUJwhhRRgixBACklNVyZao1unL1utOwN2/c/N7qcNjuR51VYFVLWG5tFCnf42AhTcCApZ7CjGWZ9T/+u/Ez48HT/2+MO7Mat8EYsdYqpRlDnud1Optr60uuh4uYcCEdHQwGUsparRYEgTFbqf3tfhrCWguwFRxWudBah2EIAFmcbClDhRBCGGNklhd/b62NhxFjzCp9/dq1K1euGGP2HzzQ6/WKYE2xgmulij0ixtiqrYYvOzLpPM/TPMMYVyt1AEQpFVnOKD1y8BBk0kuFXFxdOXdRrfYdWqoSv+z61ECpwtNMR0keVqFSR1muojjD9GNt9niv2P2W74N+LgAApLlgDrfWaAuuw6O1YbffGR0dSfOO1pq4pOj6wzkvl8sA4Ps+cxmmSGphjCGEOJxygrUEjUAIoQGKYJ6UslQqAUCeZoVEpWASQgisyfOcEbq2trawtBiEoeM4yppCiI8BFZliKUTR8wC0sdpQjJEFpRRogjAmjFqMBt1eJSxlWUYIK/54rDUCw7hRqRJEALGxxuiGHEqhxycmJmYm41Q4LgekstQA1UHIgZIkga2BHp9I0LsNjLsrPhGJmdtQ6I6FZasj4NbQMWM049b3ySBt94frtVrZWj28uRloPtWYWLq1mGTp6MRopV7WRDNuMhH71McWkAFsiUi0ZRgbq7TOsjwolywyqUidEqceAgLACMUsixNLaCksF+1vg6nWSta/deO6S/n+sclXry8N87j1aEMQTBlK04hiBNQSZn2fGZMj7UlphMpLQegwrlLpsYBTNgyy1X7Hq5QWV5drrBr30/1HHikfONBLBppiNqmzURmvWNYkYWMaew3i8DhVUgPBiACzCqwyjABSd5HF3yUCc9cBgvfhvu+BZffDQTFhlCoppbDDfi9LUs91h4PBYDAoDFue54yxSqUCABjjohqoyEZs5WExxhjr7Z8AQBGK45yHvm+MkjKXeRonQ8dlgEy7s1EqB0qp1dVVz/OmpqYKzVWxWGOMOedF+LBoZlDYUUKIF/hFYEgIgSgxCLr9nrV2R/xcNNZljE3vm5FaudVKWCrnQoExzOGO4yCyNXgDW8BgtnQWxfCOTzD2LPkQIoSAUkIIEUWR1pJzurGxNhgMHMcBgKLTQOHz7hSB7wBgJ11rhNKcu4XyFCEUeH5RqCbzfDAYYIwZ2ZLUG2OSJLHWNhqNarWaZVmapsVSixDSUikhCgYXras4oTukN8Yoo4vmk8oai4BSygh1OFdK5XmOKZ2bn8+VUylPlivjSnHMq4xXKQspCwt56M6RP4AS8HvHrt/z3U0WqZQqNMlaS0KI67pCZN1uW2tdqVTSNC0ahhZ3vWjIskM7hJA1ppDuSWWEzLnrUsbTOKvUymEYylxkIrfWcs4rpXIcx3mejzSaRSe/qampsqVZlhUMKzIoBOE0TbM0dRiz2gwHA60UJ5hjV1sjjeaOCxjHScIpI/T2fQmCoFA7G7Cj42PvIiAuZ54heVYPm4CtAc0cx9rb5SY7+Lgmbf81sWctn5HKSHAZJ4ACz3U567Y3syRmjAVB0Ol0CCGe5xVi+tvPMkZvz4bavnNYSUMIs9YmSVL0gUzTVIgcY1SrVDBGCEHoeXmepWlSLpfL5TIhRG531QAASqnjOC7nlFJsQQmZJEnRJaNofoAxtggMWGG0UDIV+c4xFJ61tTZXsj7ScoOmAU8Z13Ga09OHHd7Q2nechtWAgWCEilkQGBAG/DG3ub1XfKIP7mcBth/+AIAillEkcHu9ztraSp6nrusCQK/X45xzzot5zEWN4478qbBYxXOBYESZMSCEopT6fkgAGanKYckorbXu9XrVUrlSqVy5ckVJOTo6OhgMiranhUplp+FVGIaVUrnYTZbL5Wq16jqOlBIItgiiNBFCOJ7ruG6upDKmeFZxwLkUQsqRsdHGWIV4SGGBuG5NNINqyBxKHVrMuS/c6jt7Kjyg2/IzYdcvu3fDzr1njG1url+8eHGY95RSxcxSIUSFV+68T8XGq+h6Zq3FCEPRCk0ozhypFCAzMTFRq1SLZuIFp9M0DT1fa33hwoWlxcWZ6WnXddM0xQq5Pt/poZumKer1OcWFnwHWZcgtBWEu3FQjACh4b631MUWMUIcXB6+sLtwUrbVQslyrjk/NdPpxlKg0yT2vVq2LUnmMksDkgLaKM+4MuX+ijcsn+uD+JnA5IAtKSIJRe3PzxvWrIs+1EgUpC6tWLHlKqcL/LTZMW3FgrYtvskwU7gVGdHx8sih7A4Bur42w9Xzn0OEDq2vL/+kPf1+IbGpqQghRbB8LjjLGiq7iRTalWOU5ZYWSACFUtGADgOJ4+tEwzTPOuVJKKKmUKvhaqBAwJfVGqVwLMUNpHmFiS6VSWA4AIWQsub0OmDtXgE8sdr/lQztB1PdNGk9Sa8Ay7mBCLJBKtQkkJdTzfX99fX1L42ls6PmYUWNMkmTW6mLZklJYQBijXr9tsRulGcKo1qgbo3q9DlhjjWbGZklemyidP3vut7/xHzCmp08/FcWpwFZJSYzJtR4kca4kUGIZSeNE5ClNZblW8R2epXHkp5wRw/JWq1UuB/3+kDHOGDfGEAISICOqxv0sz5AWVIrcpcaI0mjFW+RuUqVOz3VKvfag4VcCwobESGMADGDAANYaAEQI+qjisvdDx7n7yXc3WEsxtTq3Gub2zQ56/QtXL6dxUvQkKNTIWwEOITDGxoDWCmOMoNCnKGut0qpwGlzXpRQbYzzPk0r0+/3QdUZbrXffffeP//iP19fXn3zySQCIo8ipljjnjjV5JjDGWxVJlIa+jyhCKAOAIi9nCRiNrYHC9S7KK7U2W4aZc6sNADic+76fx5FQKo5jY1Sep9baybFJmSvXDcfGxuI4BqjAzvi+T7afsYO9S75i3rM0Qui5uXkp9TvvfdsaUqynxeRwpRQIK7TyPE8qI2VekC/Pcy0kIYQySrwgz9NKpTU3N8cZSdPYKO0wGobhiy++uLi8jBA5fPDQ3Oz+UlgOg5KwllJKtLE2N8YUsr9CqOc6XOUajFVWW22KHWcxvTfPsngYCcYwJmmeAUCz2RR5rpnru16tXDEi78vMINg3deC90tVGuC/Lsl5HTk/MlYNgfd0UQpkHfcnvDbuefHeL81mLjQawOM9kuR5OjE9RyoOgVJTQlkolymiWZYCtkKII+BX7Kq2UlNJui5aJwwhBQbkEAL1eDyMThqEQ9E//9E/ffvvtQ4cOzc/PCyEb9brvh8NhzEKutTbGSq2EkkViFwNYbbbD0coAEEIwIQRssdkEbWSWI2Md36eUamtXl1cAQ+6Xq60axiBzIbW0Rh2am9My19rpd4dUhYc/e1RKcDjdjurg9ye6P97mK/eIXU++uwFZ0Nq6jielzNPMYT4nLuFsONxqdgt4S8ayLU6BIuCXG0MIQWhLadze3Ng/Pzc2NqZVJmVerZTXV5e/+93vvvmT1w4feeTw4cODfkQRAYvjYRIEgbJFvwtQ1qjtHqYYY4oJIxQok9YqrY0puqYooIwAophQQlzHKZXLCqzUar0/FFr2cg3IcI9LKYt5SkZ568vRpfM3W43GkycfmZs6GQ+AIG9rIggCZLe3ecgaBEQ/JN99w+1L+34TaAEQQo4D1rI8F9ZgQhylciFEuVymlEojjTEql0UtGWWO77tFcRrnDFswxljQ4+OtarUKYAppydLS0pk3X3/z+9/d9+ixubk5lzu5o2q1pstcY6BcrvazTlH4CwDKmq32fghTjBFAUYJOABmEjLGgTSHKIggzQgvNX+FlV6vVPM8cDUZpKxRF2OGEEJSn2dz+fas3zdOnP/Wp05/VCmSeIYQwcAAwsNU7xG6tCB/r2Pp7xa4n318BhEBrsBYIosxhnDl5JiillUqliLAUqs6i8e2W2NNajDGlBLTRWhNKeegXvc8czqWUZ868ee7cWQjCAwcOlMvlNM1lLsCHIAiY622sb/IyppRShAqzSgjhlG6ZJaWVlNZqAKAIa0IQBiAUjC0kq0bpZBilRlmCsFTWWi11brXWknFiKLUIZIqef/ZLU1X52GMnAlYd9AyyzBqDCVhA2ILZ9vg//vm594o9Sz5kACMQqVVK+QF1HNdlbk5yhGThahRhNoc7Zd9jjFHmGKOKSK8xRkuptcaEFcoXSsnq6so777x99uxZmefHjh9vNRqlSqUY4FEqlYRQhGjHcbTJMcbYIqV1EWQuQsTcYwBgtDZgMFhjDLIWA3IcByGEi3aRAEopA8bxvLQXIwwEYYSAYsw5J56DCAz6g1qtfurxac/l7WVFGfJ9kmUWLEJ/5eS+TyA+uTb5Z4WhYPG2khltzbkHRFwrraQOIpzEsZNl7JFHn+4PQBvkeL61SEloNFpzswfHmhOBG8hcRYO4Vqlzx8uFBM40wUG1Rj1LHdhcXXrnJ69fe/tig5SD3JmrzWLjgmLW0FwY7oYWozzPig4HWZK1hwMEoHKBKQKtlMwjlQgkwbGIm9wmzCfCJkBEiozxWEIsBMxgI0Va5ZzF6bC34RAwjo1EJK3yAnewtFInbnmM9rKFTMuV1Q0ggAiRAJlGuaNTR2dcC64105YYai2zqJjr98l87H7y3QVFftaYQoqHjbGlUsX3/UJGTwgJwzAIgiKrG0VRIVEZDodxHLuu67teEYiulivddvv69es3r18XQuR5Pjk9FZZKURRtyfs8t5ARFGt3ET3ZEioDFKJAANDbs4qKwyu6Y2htCcaMsR2NdAEhxMTEhDGmOBjG2MbGxtmzZ3/nd36n3++PjY1ZpY3R1TpVGoZDCRiBvaNhkr3zm08uPtEH9zdB0f7CGFOIjLS2rVarWqnhOyYaFMNbdjhRJLWQ3crb+q5XqVTyOLl+6cqlixcLIYwBOzM7y1zHIiCcIYzDMGSMFQWUSkizjWI+1p3qQGTx1sPYojlG4YkrIUEbJWURBkIIWYSSJCnaUQ4Gg16vxylDFr79x9/61//yf7l48UKlUqKUDofguEAYpQ4GeF8y7ROeWCuwZ8lXWD5rAeOiwMw26s1KpbbjW6Rp2ul0+v1+IScmgMAY13XL5XKeZUKIWq0WBsHldy9cu3xlc2WtVqmGYVgqlYJqOZWCey7nXFnDHI4J2SnmKEQxhfErVFWF8eOUUUyK8kprEWiLDAJjjVQqF1vBRWsZY5gSwCiKIsZYqVQqKixbrdbp06enpqbfeuWV3/md34niQb1Za7c3EQYN2m63xbwzn4vsJ/3ufsIP76+PO2RFgIAYY4KgFASlIvZR2L8dm1c0xhNCOIxRSvM8L5dK5XL55s2bC1ev9zc7OhejrRHGWL3VtACpzAEjpfXOE3dCeoUTDQBqu0QNABBCDNMd4QIYW8RWHOaW/cBjnGHCMOGcc9cpXhAA1tbWut1usSKvLC75jvv8c8+dfuG5qzeu/sX3/oJxUq6WhARjNcKFr7H1QNsb30+4A7J3vV1krbWEYK0BIQQGI0QIZkVWt9A5ux4vBEvGGAxbYpYi8zs6OhrH8Rs/eV1HKTVQCkJOaGRttVbT1gDBFiENFhEsldJRJITIsszlrsXb/XGlLLzdopOzUdoqY5QFi7AFgjAnzOOOpgwD2qokx6hoqQEYlUqlohCOOxRbUFI6nJ88eXL6yOFGs3Lx0qXVjdVKaSqOROA6WS4I5nfW8X2iSbeNPWv5iswpIaCUKbTxSppihS3YwDkvFOpFKwJCiOe4RUivUa1prS9evNjtdvM4QcbOTEwWWqmwXAJGmOdyzi0CylhhQQvZVREmLFyHImpTLPHF+mu1QcYia5HF1iJjrFIqjZMkiuV2Iw4oYo2MJklSNCKKoggAGGNKSIpJfbTx1V/9+ujUeBRFSllkgSBQWYq352js5Bux/aRTcC+Tr/A2dhY+pZS1qCjMRgh5nlcMtS+KLSilnudRTIrA3ubm5sWLF6vVar/bk0KMjY2BtaVSyXGcIguCKdkR3DOHV6vVohB9J1xXdNoruK6UwhbvVCfZ7b4tIs+zJMnTdEs6qlSe55kseg6RonqNIhx4flFu5zJOHd4aHZmYmHA9jxDEODHalsPSneeOd+qHPtluB/2Yh878b+JubXfvqggnCizeGgKzU7QLCAAol8Ywz2PGgDHa5fXAr0owuVbVatVqs7GxwQkNgiC3BmMomu2N1psiztdvLHOFk82hysWJZ08s3LqlkN03PmYpJoSkuSCBbwl2KeOEuoQRQODwROSpSQBpihDWNoniYH+JMz/Nda4kx04mpbXW8UugTZSmXJmMKbfo82yM67oiT5MochzHYnBdLkVOKc1EThDW2igDjJZ6/bxZGxumokoBcYIECKkwMz9VoFt8MpD55O6s9qzlAzDWFgWRAFCMr7HFnKA4jos6yDiOiyTYjqrZd1yEUKfTabfbcRwPBoPZ2dmiuxljDAgGgDzPjTGcMk5o8RUAiqkKcRzvzOcoxmIV0RlrrQGrtbXWamPSNB0Mh7kUFrAXBkWSw2yPKwdtGCYjrRaj1FqbZVmn0xFCMIcLIdIsc1232WxWKhWLIE2VEAq2F9wPPj7JoJ+0Bt93w70eJ8a4mDBWDLsxxliLi1BLmqYFBYvhacaYQgdQ9NUbDoc3rl2/detWUVU+PT0tpbQYOZ7DGEMEJ1nGuQvGWDBFVbbVRmllEDDKAbQWsiBfsZQX3XY555hSxjmyRkqprHEC3y+XSOBjRnWSSCkLKmPH9V2vaFBOANVqNSHE9evXz5w5c/369f/z/+P/Wa2VKWeUUpMbazWhlLu0oODuwifXJv8NgbAFvSVnK3hrDFSr1aIeoiBHGIbeVvs9VaiOlVJrK6uLi4vdbrcUhuPj48x12p0uItj1fUyJAVBK+T5F2iJjDNZaKWuQgf9/e3/Wa1mWpIlhZramPZ35Tu4ePsWQkZmRQ3RlDaouVLPZLVHqhkiBFAQIhABBL3rQi/QgCPoP/R8ISg9iq0GKLVIEWiCbXeyq6qysITMrMiJjHjx8uvM90x7XWmZ6WNdvRmZllCqrKjKvB9rgcFw/7n7uvnvbsWXDZ98n2lmXZ2R9pxQApCrEWpsKiG3TMKGEyBL6oQ/MOssjQlNvi6LY2dmJYWy1iX3X1rUmAsHC5WcX5++9996bb7755o/fyrLsO9/5zmQ2BcTeD8roDJEIRCIzXvNhxs+1L63zAQBAor9AxEsis1E1TtDilNFPJpO0SdT3l0tiR08PHzx40Lats7aqqoODg6Zptm0zmk3yskieF72HyJk2GrVCoihpyTENNoxSCokEUlnDAG3XgSJUGlBpazyTAnR5lu/M2YfOt23fdYG7ts60GeVZVZQo0LTdW3/+xps/fuv9Dz9crlf37t37n/7jf/QP/uE/PBUJPPR9m2VWGxSx0vsQAn7Oo7zOB++X2PmSvgCwMCER6Rj6VJAys3Mu5HmiE5hMJiEMmXXb9eaTTz558uSJs3ZnZ2c6nZZZ/tHTRzGyzTLtbBROI2CtFAYmgwYVISERIYJSMcauvtSW6boOEVOL2FiTaWsyZ7U1EnyMRZbNb9/YrtfO977r++CNMYXLNdHp06OPP/740w8/+P0/+INhvdKL+e7+3m/93d/+yquvvvn2j83+AlHaweSxDMhIBKgQf7K/8bN2jbOqa6fD8XlF7edd51/yyUa6HHORJqUwxqiNKctyuVy++OKL0YfVamlIzWYzEbm4uHj8+PHh4SGIVFW1u7tbZvl2u+37vppO04JjCEGTSsNcjpGUoEiMUZEyWYZWR4Bu2LRtmxSORqNRURSiSClVd20Xfc5ERjOwKKrGIyCsQPwwlDZTMR49fvLeO++++edvvPHnf16SUoA3X3xptrfjhVMAzop8M2xHRZGXORH0fY/KoBdk0PTLE2n+27IvbeRLs12QK+IpYJG0J/vgwSdJSGO9Xo/LKp3IDx48ePToUd/382STKTMfHR1lWTadTvOyiAg+BkR0SqdqN2FYOEZUWimljEERh06TkmdKk8aYNsYhhhDZw6DRZMoqQmvteDTLy8KRuVidr07Pv/9Hf/TP//P/4ujjj2/cuHlz/6BdrUajkTDnef7KvbuT2Wy9Xt+4dXNwsZpUWT2BSDKIIq0NCH4ukuD6svNdQ+f7RSPxT+hI5LNi9wBigoci14rAh0FY7d2Wd95+5+mjD/YWFfvt0K+qwhgLh0cPz87O3n7vrRDCwcHBYrHQWq/7zWq1WtfrnYOD5XaTl8V0PEGNUbjrO1bK7Ez6GEIIgYPiABFdcN3Qz0w2smU5moO7wHJcR76kHT359N6dO5PSae4lxHhaf/zfniyX63c++vEHH3/09p/+8KJroBvAmKoqyvk4VqYdNvfu3ZndOODc5DvTWNoVhhlPR36xwdPOt9Z9Q1Pft5pAgQP4aQxz8sbrzE167Zzvb8uYWSQyAz9Ty91u148efXp+fr67u5sU51PDZbVaHR0dpaCYjte2bdN8zDlXFJVCyrOSlAmD74aBEYqqINLEgCAgLIIJP4eojDGCyGGIYYgx+r4jovG40uNx4PjkyeFH77377tvvffDu++uLNQwBDIOigxu3fuvvfOvxh588+vjjYdvkqKioXrn3onEWEff398fVKPpoBZfbze7ezXuv3F2enC0fMwMWhYr+56QfeL2rDfjyON9PGg2Xy4IiUUSYE2IlGger9dmDTz8ajUZnZ2fn5+ez2axpmkePHh0eHq7X693d3aIoZrNZ2t/uus45N51OgaUYFVmWEVHg2A69tTYN2Yj5Cq4HcMlQ1vZNiD4xPTqtqqoSiUiy3Gx/8IMfvPXGG0cffUzacjcAI1VjLthW5eL+HTUfyWG++9Wv3H3h9qOPH9x44dbE5vlkfOf+/f2DG1FYa5eDujmeLR882kBNRJneExE/QIygP1NvkFwGv2uWz/+sPffOhz8ZrP3065i4REVESIEx0Cw3m+1ZWZbJsZxz77///vHx8XQ6vXnzZlKgTFyi6/U6oQ0AYL1eT6dTMtqH4DkKAqpLUbUrepcE4EvOF2KwViNK9IMiIOCj48Ozs7Pv/cF/v1wuDcL87p37L9x58uDx0dFZkeV2kU0ms9nevhCWefHavXu37OgP/sV/tzm72A6dz/Sdo8ff/o3v/O7v/m693vyTf/JPNttuu6qL7MZrX/n6v/8//j/dvX3bMygFP8NB+lyASZ975/s8IwVIwsxIQoSkoOvXSvOjw8OdnR2t9cXFRYzxhRdeUEp1XVdVVVLg7fs+tUiUUsMwxCEWrkBUTVuHKNpmNiuUcQgKUYg0UWQQABJBEFIqDcq8QvH98OTRw3ffe+fho0cBsBiNF4vZznSyN9t9/8MHLFKMJ22GPtPTuzfn83kcfMyNZ2GJZz94N2baZ/qdk/P3/uzPzz749Ndef33fVk/f+KGZ7ILevLt987e++uT2jZu9hxjYZRY+f4X+etpz73w/p0BBSa8rpeRS6BZFYL25YLkUqX/06NFoNPrKV76itX7nnXeWy+Xe3l7qEid2FUgophByl5VlGWKs61o7W2R54l6BZ+dsOndT8GNmVrHp6uB7Z00YuqdPHp+dnYHE3f0D7/vReHxw54UbOwejH72duepgdx/mNnfZYrE7n87ms5nSOu8VGRuMF6MgM9P93Q7iH/7p91rf333pxSenpyfHF4BYVeV4OjFGWYuUQQh/8R4AXO99tufe+X7Wnt31GKPWKngUiYgYuT89O1wuT/f29hJFwWg0Sq3guq5Ho9HR0VECsj8buDEAeO9n4xIRm6Zt+m5WFS7PSF9u+MIzGme48jzmAAMAJ7B037UAUmSuKGwANZ9O9vd377/4ktVm0zUObQQMhOOylMxFFFOU+bRUS8+Z4kzfePGOt9SjEBKHuDq7uDHbee3mnX/11scvf+vbf/8f/nvf+trXEh/BEPmzu2A/gfT921bLr8QSSU8Mif4RE4Xo+cXpvZ2b5+fnSff26dOnCbHy8OFDYwwAOOe01mmr6BkUj/veN00XAhvttLLC6AMrrVKRi6iAQRiFUQQvVquEIxzaTkTGk8l8Ph9CPwCNxlVeFllZQOTgeWdnPF8sNs7vzua3Dm6MnDtyn6zX64lkt+/fv8iOpzf2zKjogl+uV0PXj/Li5t5+rsytl178X/xH/+Fv/86/067h8PBwOj2Qn6ccfv1nvc+98/HP/OkSzsdZlns/dJ23pigqfvp0eXz8dDItTpcX1XSy2NtbN7Uo8sIRwZXFZrMJIby4vyciy9PNfD5Xzm5XqyKfrFYXTRy0VdliRKjQGVE4RM8hkoBBEO/77QaCV0qhHW3PG1YoOqw3m4Wf7u3tKeOenJ+U5ejGwW1tCtTQc1AZ2oqMK6XKcm1jjGZSjVoqD27EP8uKfK+8s+Nc5vrQDNEPuGzDRRcNyuvf/sr9W98eGlwtz2eT+XJ1Mp3uen95C9LeEF5xA1/jwcf1/3j8Na3vvAhkWQEAqfHRD20IQ6LYTuGNma9UqYqiSLpCSfFCa+29H4/HIsgMiIpQp1CntTXGZa5wLjfGKTJKGa2sMc653HdD3/cg5JwLMZ6enp6dnXnvp5PJYjavqsoo7bQZjUprrUBUgFZZa0zuitw5QRDCcjqe7+1OFnMGqKrx3bt3s7IIzB8/+KQaj2eL/f/L//X//N/+f//1bD4W1nv7c99f59P1c+1L63whAAg5qxBJRLquOT8/937Y2dnJsixBqi450WJM5W1VVSKy3W6dc0l6ZT6fJ+90zhVFYa01ztrM2SxzzqWGS0r7UuclYehRsCiKnZ0959zTp0/feOvNH//4xxDZKp1rW+WFCPd93zRbbQwK5tYVJs+tSy1u0TBZzMWqfDwyLsur8satm1meT2bTYjx5cnxiXbk6Oi2qqbF6GIIEuJQglJ+qMxivO13Lc3/sfp4pMojCnLiqXNu2p6fHd+/euX379uPHjzebTcIPd12XWnrDMBRFkbbOptMpAHRdNx6PZaiJyBWFM7Yaj5SgthaRog+fFS260lDgICBibTbbWdRd2w5+uVwK47apd3f3u7Z9/fXXR1VV5K7zQ5CAUWfGaVJEUGR5cuiiKuPgszyXsVjtdnb3Zjs7eVnuHOyffvDRdGH+4//9//F3/t7fPTlus8wtl+sin/JfGKNd5zo32ZfX+RQhQt8zIillum5AVLdv32bm1WqVztYrz0ubO0k6oSzLhCpNOORaDRzFuWw0GuVZAZEDgO8HM8ToIwoQKcIEMo0hRIqIoIiU0W4+n6fejSKzXa8ffPDR8uTMN93tO7eqqnDOKE3QoUbDABxFaWut1UiOtJ5OCZQxhpS5efvOa93w+OGTLMsmO3svffUbI/0dIHOxWr94N/e9C/2gtb362a/xOPen7EvrfCKAkPYzrPfeGHP3zv1bt15479OPEsY4CSI456y1ibEqSQUltHOSQk0ge++91jrLMiGMPnqRfugpIjNrIqWUaB1BIJE598GkoAvismy+WKTX5+NZXdfn5+c//MEPHj18UK834zs3UAEAAakoCCRktCZlBDNQIqIZWNuImI+r+y+/dHp6fnBw88F7D+7c/2p9mp2fDbPZZL2C8TjfLP3l2uVnot31j3xf2pwPMBEzKqWg7/3uzt5rr30zeOi6bjqd5nme+ikpadNaJ12htm3LskxbjPP5PITAISYxD2NMjHHwPjAD4ZVgWhqsXU14h8EDI4JCVEVRLBaL8WTinNNIu/PF7YOb47LabFYhDpF92zdKqcScgcagVsiiGSZkC7TTvDRKC2EfQzWaWJMd7O0fnZ39+Rtv2awUUEVp2jaAQBI2op/O+eS6ExZ8eZ0PURDFGGUMMPPe3sGdO3dPTs5Go1EqI5JLpTM3yzLnXNLpSxse3vuyLPu+F0FEZbQz2jFDjEKknc3T1xwhfSGCqdU3Lqs8z5Oz5mWV8KRa6+l4kqYjVZlbfUlCCgCEShCYlMqsUirGqKLkSueoC237rmNm0rocj/Isy4xd7Bz85//lf4Fk2269WcNopPtOou9/tXf7r2eUlrue318agNKQjYAVAREipZdjjNpyiJJn47N199HRp6GIu5XldhO7mlC29bppt1XpysJy7BWEvcVECW83mxdu3Y+QrWo5wg3t5n3OHfUZxJFVk3VzwFiEfoIM1CsjbLmDLreoVFzxuVddh16RJVOElhXae7fuliUZ42eLrBs2feiHHgxOoR+vMXiUkSbqujGL1lqV2drQKUmfFaxct40GckNu7+aNmv13vnb3oz/8vQ/f+ePdxRixJQtd6MEZr8FriDqyiqAiUqRnd+ba/vrSRr4rnqgY4+Bj37dEMJ1OEy3aJX8FY0rmsixLnZdLljSgRLMigruLnclkkmWZMhq1UkazwjYMokg0BZAIUZBJgTEqy2z0AVg0Ka11Xder7QYIh+CLogCi1Wp1enq6Xq8RMYm/JeKElFzGGIP3wXMqg7RSlwxaz/g9uq574dZ9KGfe82iUG+PW6yYNY37V9/uvY1/aguNqdY0IIvdd1wJyUZTbYWjbdggegABYK5sVZYwRUY3H48wVSVqybVvbNCxqujMdFSNExcxgDChiTU0YbG61MU6M1UYPQ9N1IhJ9P0XrfWz6zuYEisqsBEP1avng6aeD923bDjEIUDqOjTGdCDMPfWj7tu+GkGrmGJ3JjDFp9Th9ioqi6uqmVDv/zr/7P7+xf6/ecgyY54VSMAwB8Pl7lM/fFf/VTSmltUJQonzkPsbgfe+977ouBiGlEQS1stb6LFdKTcaTGGNdt5PJxAep63Y6W6QmXIyxF0DFpGCIYdvWQ9eWed5TzLSJPgxD73QWY+zauirHNrN7s50W+ezw8Pj85MHHHz56+PFsNktUQzEg6cxai6hAKOnAhP5S7UhEkMUVzhiDIhzED4FkyPN8dX5x0Q7/6H/2Hx3sHxweXuRulmUwDM8Vjuoz9qV1PkRRShMSIoAGpMAc+r5ve9/2QxAus8wAWGu1ss4FpZRzLqk17+zsnV9sRGQ+37FGO2294OWxyBw4DjGQU0FCMwx55nbnuwicDbJer79975U8K5+sLo4/fvje4wfvv/32yeqs36xuvHxvNJ6SAgbabpvMWkAFhGVR5C6n1FshY7XTqJnBKouSquYILN57Q6rrOhv0fP9G14JW1jnYbmPf11U1Ds/hgO1L63wJYxxCJAUQex/ayB4CpjYKgCrLCrXSWgdhrXVZjrS2fe/zPC/Lsm4GzyrPc4CgyTAQIRCAgKqK0jkXZNhdLLCwlcuHpn708MHR4dn5+emfvvdwW9cPz09Pzi5a8TZ3Oy/s7+99M89dXhYXFxccQojROEtEhFpQM3PXdV3T2WfilyiQuK20Un2IKTSS1mHwRJn3TBGqqgweACCJuCrtfsV3/Be3L7PzJVZuQyqEbrvd1PXGZZVShrQyaIpRpbTuui6RUxVFkf7X7u4eIjqXGUxyZgpBEYAiRAGlaD4ZZWVBRoosP1qdffD+u++8+aMf/vD79ZOTpt3ap9vm9Ax25tlkVs1HL96/O72xMy6LgUUbesbSrKxxAJTYiWKMCbtfEaXGoQimhA8RfTd4H5GFAY2xXd1rZRUBAAwD54UyBvreP48Vx5fW+ZAkodyIkCNvt+vNZsOibOZcmzNSnuekTNM0SQM3zXmJaDabxSjOOZNViGi0tcawYgKUyAzRWTsdjbfd+l/9d//yn/7X/692uXz55RdfefEle/vu8fFxrR4/YHnh1Vcns/lZ7KrppJqMjTPtegtRfAha28Rb5b0nlFlVZSZDFu+9KI2MwsjMzmUAkFiqhrZTSlmjsixrzzoA6LqQ5WQMdd1Q176qyn74Fd/wv4ZdO+f7ObB4APh8JgNGEUAhzwjABgQAIwr1LSlNIW4q456cf9QOR1pl6/V6vxwpovFkmhm7aepEQ2ZI9QBny7P9vZtCXNdtO7S3ZnvdMGhCMtZC2/XeqmF/tIvd5vv/6vv/yT/9p13Tts36f/Lv/wcvvXxPWXzv+386XlTNTmni7ODVu1FZu1k1hm4u5iEMhvjpk0c6dxKZmYuq7Do/mpTbem2yu23blmVRWvfk/EgglmVe19tbd26/+/4HztnVdvXCzZsSeTqd+GPfdGezapclxIgAbJ0C5CSNDqB+VgbsF7yfv+j9/7x//1exa+d8fxvGAOS9zwsjYNu232xWAIyI6/VyZtze3h5Zt2nq1XIT2VdVmdqBbdteLWfM5/OiKALz3fv3Hj58iIbv37mnpfvBd//sX/43//yTH/149/Vv/dq3f20tviiKYRjm1Xg8nvYnbWCuxmMg6n2HilIPD9E0zRaFtFEiQSkVQgiCWmtjc0js4b5X+aXU1na7DRG3q7WIEKkkjToMgzEa6VKcDTA11DWLb9tOq+JXfdt/YfsyOR8DXO1QsohoDT7QdrtZb84BAyKuVqt+PLt15+6maU8PT1abNQCMRiPPsW3b3g8Jz9L3cbyYdF0XRT744IPd3d0bN+fvv/P+n/7hv3zn+2+dvv0mmPy1r752Y//gbKhns9ne3q6IB5aqqgaJ870d0qqtt8ZlRZUrhah0GpRlxvUSlVI+SpoRJ/h+3/dt17iDA2H03td1a03ZNF3SuFdkFBnvtxJZa5fkloiQAZTSRqlERHlp139Z/Jl9aSYcPwsjstbGCMEzMw++DbFPy5QAQESbbb1cLi+1+bRGpLrrsizLy6rp2qZpOULTdeWoun//rjHqzR/9+T/7z/4f3/3uH4JCGJe2Kl9+8aUUJq2108k8xc6qGA3sx7NxAGmHXllVFEWMMfqhb1r2IWWWigwAhBD6EBUiMsYhDsOQmYwAUEhEqqoCAKNsZp21Ns8ypQwAFKUlhURJ7QgSlzP/TJ/lShXhetuXIPJdqXrKsw89A4AxavAeAKy1g6+39bkxZn9/3xi3Xm9PT0+brp3NFsPQGeO6ULdtuzPfdc6dnwdBIiJj85s3b52dnvzRH/3Rhx+9ua43r//W67mqvu83VV71zKYodidlRKq7tg/RuazvakHMinzb9cxBa20U+X7oh3bofPQMEbbbJisqVDrw0PU9gkpObMhcaRVpJE2XeFVjnFZKaz0qqxg9DxRCNwxdlmVK6xiBI8jnZXZ4rSVPr++V/XXt8shhhhgvoVBNu9zWqyzLbt48iMwn52ddN4ig0tplRZZlPoTtphnPpiCUpr2kFRFdXFx877t/ZK3+xjdf+7XfeH1nf3fZbSSjvfu3TZn3Eqv5NB9XolSMkQAvTi7KsjRGD8OglDIKCSAMXbveQmT2gUPsus4Yi4iBYxRARKsdsDjnog/AGIMAUL1t67ptNltERFDCWBSFIZPl2mVGIIYQEEFrrbW21v70TeBnv661fQmcL6V6P0v9HyMQkVLU9/1muxTxVVWMRqPz8+VyubRZlqDzeZ5X4xEzRGHn3LZtGBFRiUDg+N3vfW/vYP83f/M3X375xaoqjFFFke3s7Tnnpns7m75lo/LxxOSuH0LCSM/mk4RLddqgAPgIPoau1WQkph6eOOeCcGRWxnCMzDwMXmvtfSAi4CTcJcMwbDZbhWStBRGIcHJyUtcbrVWeO1LAnOD78tMYen72+3X3vy+B86XD5WdNRBLNd9u2bVtbZ4qiEOHT09OmbpP8S99759xoNEqEzMbYxAbZ+wERjXYffPDRN7/52v7+PrBMRuOd2RwiZ1rvzBdeGLQZz6eo1Wqz6fzgnPPDkN6NgzdKk4BEBhGJrNTlnm9yplSw2swlBY6u6xTqhN0nIk2qKC61N4gocy459OPHj3/4wx8eHx8mtRlEQHyOn6BW8vOHgmk89Vc3/osbLH/p+3zuv/+8u/k5WQ0nNIdkz8i/BUADMXAXOz2YlfC2mOyWZXnRNhfLfmcx2ay268Ojmc2UsfNq/M6P3+1juPnCWEztiBu/KjJrIPzwrR9+81tfd9X8Yl17ltFodH5xzBTrgW7ee3Fz1u6MD2wdp+NJh+3TVdP7vpxMMjeKAZGygdHkI9a26To2+fnxw/Fo3Kw3hSlUwDD4ibETZcTIgMMgg81UjAFiQInig1htiryPwbNsts3NGy9wH9dnm//3f/af4H/8v37t5X9wcXGiZF8ESIkiBOou9UiEflJuIH9eg04+pyTGz7vRX0D58hx/bv5yS9D2lA8pMswcgwDA3t5eOapQkYgwwhBDZAaAxWJnu2kAwBgzmc2UMUB47949Ilqv10Q0moyXy6W11tksy/PtdptlGTNvNhsiisJd15VlmTQmr7T/EjQ6NfacNkkTCxG990MIUZiIJF6CWa5g/TbLmDkMPr1bYm2LMc5ms9F87523Pzg+OqmqSikgAqWU/FxWjJ93IFwr+9I6X2pMKHVJXps4WYY+eOF+GLZNzSCj0SihOLMsm0xmzCyImSsWi93VatW27d27d0W4ruuEjF+v19ZlyujEpDGZTJxzAJCE19q+M84CQNrtsNYmzQ8ASHqCCV4KAAkun1ztEjoPmIa8CRaaRKmTEIhCSktM3dDv7O9lbgziiEgpJwKI4EMfY3wW8JI9H42+nwiC/Yz9om/0i77P39b3/Ty7AvcywzAM9bbdbDZ935NWpsiiCFo93VkEjut66/IMEIvRiEhXVRVC+PGPf2yMKqpyuVwKxKIojo6e2szlZaGsCSJVWSbF8jzPA8fee1QqMqehbaoqEs8VMzdNA5GJqG+7pM+WeD3IaKMvU72kMI1a9cHbzMkz/csraeqmaUajkcTs117/nb293c26EQFjgYjy/PMgLZ8b/L7o+/9XsS9t5CNMDTBh5hCYmUVQKeXKYra7k41Km2WT2TTE2PthtrOIUQi1UqYcT9774P2PH3zyjW99k5nPz0+dc0rjhx9+eOPGjSzLjM18DJPJ5Pj4uGka4+xqtWr7rhxVQThVBqkgAICEhqrruiwKAmTmy4qEmUEQ0ZCKg0/yWinypRhJgKEfrDYJ+UJarTYbbc1v/ebf+x/91t+3FoIHUuA9pMoDAH9K1x5TqXutQ+CXNvJBYjETguRzzjnnsqyoh85mbjyblqNKGxNBnHOTyQRRtf2gtOn7/t13351MZr/+67/OEIloNCr7vl+uV3v7+50flFK9j7u7u6lQVUptmnoYBptnAJCoJtNhmmTrUzE7mUySLMx0PAkhXO6XMCulkgpXyguJyHtvM5eoKRExbZMQ0Waz0db+L//D/81ieme59FlWKgVd1yBK27a/aBz5t5HvC7RU5KX8nYiMuRTXA0KbZ/s3b4ynk23bdEOfV2UUZiBrMq31O+++P/Tht3/7t5MDzaZjRDw9PZ1Op0VRNG1vstzlmdY6d1lKKImIQbz3gpeulnLB5DdpO73M8nqzSXOzdC6nPXMCDN6nZ+69B8IonGWZ08YPw6UcOkiMMaWeuzt7bcNt06cg55wzRiv9Waf5TOZ3vWuOL7fzEWLi5wMR8T50XZeXZVGWi8XC5fl6vU4lpLU2xjidTrXWb7/99ng6+c53vvPJp596751zdV0fHx/v7u6CImYej8fz2c7FxUXy5mEYjLVE1LRtFEmulmVZQgwgYuKiRMTNZiMhXiaCCFeszonzhZmTzgcAJMLxyxU7kXR2937I8/z4aEVoR6Oq7733MS9UuELQP2/ya8+N6uTnmchVH5+ACQABBYDZ+zzDEDkrpnXfGWU4Ut/XAhPl8uV6Zcj2fT8M4eb+TQlqUs4Z4F/8i/+qLObf+c3fWLVtPq3QoNqG9ebCFLYLDDpvujDTapxVT49XpJUMQwa4PT6qrGX2Ozs7H378iXLWlrnRbhg8sEjvM9IiaBDbPrTbFSnHw/F4/wVJi+cxGmeUUkFhrJuu63gIbRjarpvNdiypKitQIHdZCKFUkxjAD6y1RoShBxESSeM1eRZNEAQBCQToF3y+n6tr/Ldkn+37fjmABambSgA/OXOYWYkR4RijCCBiURRaUzmqRKSua40DIo6rUYIthRD++M/+1BhXTSeLxSKzrmcWkb7rhrYBlt35QkRCP5S7BSKSgO+HYjFNbZcYo1GXLGmJ+SCbFlpL27Zd16VezHa7TbWw9zEBakIIVzO3GONPuHU5aq3TmZ7+KvX5fvanfs4Pruf76i/tKrP5yYf8sm2bVs7S88vzfDwep6LSONd13cnJyWg0Stprb/3ox6vz1a0bN8uiqPJiPB47bdiHZrsZ+h4Rb968KSEmDhdCFQZfbzbOOYjsvU8VbiojhmFg5tRBbJomTZCBZblcAsAwDN3QJ+e7pHoRSCf4ZXcaEQA4xCRwlEJRjBEIXZ4BClJMTDR/4dR6zp7mcx/5MDEvAPykrYACgMY4kZj6FFrroR8qPVbKdX7ww5A5t+HNdrs9ODiw1r713e+99aO3fvt3f0f3MWjIrIuDBw1KKd/3CskZN5tO33n3fWCcTucS49B12/Um+lCHbde2XdNqIyHE9XqdeAhS5ZE6yUlPtWmabLTw3nsfUvdEaR1DYGYik2hSPZFSCtXlJhGwXHIUCYuIMUYgAgiiADAgAKiU135myHvJDAyIAL/wXtEvMw17zj4rP8cwdbOuUC2XLmjS6plgluWj0YRIW5ul6oGZl8ul9/7WrVuz2eydH7/9r3/vf5hUk1df+erx8fH9O/eKPF8vV8DSdw1xrIqsyF0c/MMHnwKAiFycr8LgUcC33Xa1Dv2QdtGbpkldlbIs67oOIRRF4ZxjZhTgEHLnUiQWBOecsqbv+zh4DjGhkaNwohM1pKzSxpg8zxOJW9O1Q/BIETACyGcbyH/B856D2Rp8SSLf5VdyuS2DqeYAEUFCl2Xz+bxvuyzLvPd10xzsHRwfHmlRL9zcP3ry9A/+4A+k6775679TZGUY4u5ip2v7osyNcx++/+4BmXFeRtWeHB1/+vEnRVFslpsYPUZZzGZD23nvOUSFGAaPShVFMZ1OU7suyzLn7LJr05A3EeQnREWMUVkDAKEfUoQehgFSY4V5iOHSR0USQXTdNom+EoABIiCAaABGImGEnyzy4DO3S+J/v3Bw+beR729oaagPAKmdgWkyCwCr1QoAEiAgz/MY4wcffLA8O7/zyitf/+prR0+evnDzdpVXHIK19vT4qGtaYLZGWWPOTk7Pz8+n01ld18MQOMTdxc4wDH3bJY9JPeTd3d30ztba6XSKiGk1c3V+AT4gIoeYsHpp4qyU0qTS5SVIMxACQPQhPMv50lZ53/eBGTAiAQAjyf8fR7n2nZfrfn1/BUsHzc+eMuGZNEVavQagofdnZ2dFUaQeb1VVQ9v1fb9YLPZ3d2/evPn++x/u7e1NJpPJZLJer994440bN26kQKWUapqGY5zNZgCQyKwm1ejKRWKMqaU8nU5TqZHo7buuS/iXzWYDz4IZAKSipO27JCOY5ECS5lZq/qXfr5p8VwLSCSKKiH/p9Ow5OHPhGjof48//FbHz0gB6UgwSQUQJQJAmclAatRPQEIEiGG90n3exFktk+w1sytGt6a27F8MJF8M6NIer43w+uehar3VU7nTVV4sbH14cxbF193b9xJ6H7UePPnrt3t2/+43XvC3XHrTbe/DwLM/nhLnNdTHKzFxWeJbvVVvYNmFQFsdVdWt3xyxm7x09erw6sYvq3Ucf1qHtCS/qum+DyiY3FjuEmvqa/cD1eYlyvDnLFuMGo1TVUBQbY7bW2pv73dj6kWn18Oj84bI+abuV7zZPPvrguD30ei0iGtiCwcCIPuLAyJE4EkcEBgIAJazlWnvhtXO+zzNETWSY0fsIAFojGVBKlGZEjyRKiTZAdPkzZUXVDwyKnLPD0Dz69ONPP/kg+CZTmd8OlS7no3lbd3GIe3t7+7sHx0+OXrr70ot3Xjw7OT9+cvLqy1+7e+el3/u938cYX7xzp2ub5dnZjf39Oy+8kGm9PD2t8vz+7duL6SR0bdfWBjEM/fvvvXf06OFrr37llbt3jcjNvd1pOfLb7TjL1udnozyTIfR1QywWVaYMD32mKFPUbzahaaTvuetGzo2c2xmPZ6OqyvLcWEOKmCHE0LXPEIHqJ91gQXX9N9V+nj03BQeCRgQR5hhRp9aXRGETc2AJLElwVpEQIBlsuWcOGnB5sTx9etauhwzLl2+/Erf14bIu5vnuyJFsjabRZDQmtvPRzs58PHLWlGEY98Nmue49D+PRTjGtPv7hnwwQvv7t1xY3d/1hBEc7N2ae/CdPP310/OTo4lQRzCfTXvwI4ezosK5ra+1kNH353u3V8eHjB59EHya7Y9+1y/WKSfm21QzIQuyHZvP04ac8dFVVDU0znlQkwZHOlPEhGASDoAE1gITYd54jCGGMoAGIlAePqK41fOVz7LmJfH2fuLgZEQl1ZGBBJI0URHyUMHAcWNoIDUPDsKx7nZe6yD769ONPHn7I0qHmpl2en9XCVqvC2aocTZXL+sibthuNJi/cvn++3Hz6+Bh0VntYNsN4cWM8r85WJ8cnT77+2lf+zq9/S2kZz6qvfePVb7/+zdXq4sGDj7PcOudWqxUZOrh5I8YBUL7xzde+/trXmnZ7fnE6nY61Ad836+2q950gMERSEDgwx6LIm3az3lxcLM/qZgUYuq7ZbFYMDCiRPQATgVKotW7bxg9dZI+IgsIIQiCSGGbpZ58mXvcFoucm8uXOJKQxAEQRjoyIxpAdEEAhMlIE8oABgJGgrLZVQc326OP3vrc6/PiFOy/qfGEGvAjN3o0d4+B0/XTbbuuwFh3274zLWY5Z/+TjB6vN6t7i3qSckICIuI6Pj4+mLn/9G9+cZsWHj58WRT4y9kd/8qff+zffffzk4d3bd0bGXfhoAiot1WzSNM3b779TVeO9mwc707lSKivy/ny7Wq2yvuqjj8JR4bavx7FHS23fuzJDQ6DVaD723oNBg5ac4gGVsybPymlVzUadDIA+xkFUFBJJe8o/VdXSZ7bXrrs9N85nDHp/eZ9D8KjQGCMiziqlgEH1PvoYlVKkNRM6Q23nj87O+iHM5/P5dEwZBc8vf+W1MndPHz85OT4UgKLMZ6Pq7t27Ww4fP37Yc7j3ystFUXStd8ZkzmVmKIfia9/66u6NxdnqhCm4yi7rizd//FYkeOmVV+/evb2zt3v/lZf39/ebbT29f0MZHaMgYpEVm9Xae//SSy/NsvK9994riiqEIEqDVuPFrJqNhuCj8Gwxz/NcEJTWg/eD90oZ0AoV9OyX2+W2qwPE5frixTvopREaFGGMkURdFtDJEX8q97vuRzH+3/7wel3i58qFRQhh0E4jSeAhLxwRnF2c9XGb504orJqLpl+DYlYxchAVjc6NbettqCbBmirKOgzx6KLNXWY1FZkTiDGGo5PD6WK+3TS9H8iY0WjU1J0CJFAEUObZer1WSpFWm81GKZVXZdd14GVcjbIsUxpDCApQa6rrera32NRbjiIihBiGiIHLLM+J6rrOjAWlR+OxBx5YuqHvQkyd56LIttstM4cwWGvLcnR6fCJBtFKZzpptc3ZyPpvN9qZ3EHFWvGyUlWEsrCVEIiUEAsn5GAUIWImgQEDzS3x6v5g9N5GPELS2xkBkjuwjy7pef/Txe48ff7SzM6/GRc9dG1ugOMjQtvV8f7fMSJGUeZWpenW+RtoocpPRtNmsM8qUwPHxiTbKKt3X26qoMs7qtqnXG2uyzLlu0+ZlaZ1GEus0Ek2nY61154eqKhy4pCRTr9rxeGwU9n0/my3aurXagoam7oBQQuSBB/D90MzG0/V6neZ7EYGMXq9XojKjLIfoTNZRH3gosrLrupP6UBhnswkwDnWvlbp/9/b9+/dPDjfDMAh4Ihsuszr6gmFQX5Thf/oHX2xy8IuOazxdKjmhUGLNAQAS2AxNWZasNsMA+bRzbvTk9L0PPvjo5PijCHHvxsFkPunioJwip5bL5Xg8BgAU1iSKgAiEA3BswXDwGiT2Q5G7tm20JtJKMtX7EIMopZ0uDBkeJAxDzKKIKCJDipk5RE3GGBPikJKApBKDiISKiLSRtO/IcjWiiIho46XCG0NErbz3WqvxdNLVTEREQFoJYpTAzFEYIvshArMiY7Wz1uZ5kef5sDlZLs+67Whnd0LxTlWO2c8UGQiCAooBAUguD5C/xCc/77n8oni+v8n7PDeRbz4v2waAVJ5nh4efvPfeH627R+PpbD6fNk2jCKxWyjpySgBQWNgTkSbQChWBAgGtUFSMHBEMYjTKae0JLpkVBYwIgpAIASsQpYC07jSlNoaPTIDGOKUUEhGYS+AJIjxTNGEGABLBBJeKMeHzRIQdk2KyqCIQMqJEiCBd8J1XSilrBCMDiIigoIBCLSQhgvAzSCILRMhcBbBs27ZtMwOdd06CBw1aND5v8e/aOd8VSC1BgtICPQK0nRcEpYfed+v6uJeLrIrjKWxqaXFQocu4R0INBCTkTAQDqIRUJBDAmFYmmH0fYgBSJGIE8sCDADKRskUkDhwBFZMeSBMiEOY6pC0ejoEQnXFaEzD7yAwYIyACEQqiCIpwBC2X8NC0SqsBWZjX0SMha2KIRMjKIEmNEY1Fa5RRqAhEWAKgVkTRewHFLD4IKzEIQakemJtzUrGcKZ33m+XTTXdUqNu7u/vIk0RToOTZvQOALx6Z/Dex5wZGT4JAGkT13htj9vZ2eohNvxmiRwXakXE6Suj7FglIBMVLDEHQoBYiBGAOzEwKWASNUig6N8prIFTGsAgRaCIklchAEURIQJvILAisEJEYYeCQyJmjCCMjKoWACpmZIyAIC4sAKVTKIV6Ck6P0iAiMIsIiqAiAY4zKZmQ0GcUIIQYvTEhIAoSgFUcOHAlBjBKjGIHQEQ5EQVujHbMXwKg0YA8kP1XsXsL6fsHn+7flD3+V97l2ke/zLM/12UUHprGuQK1Wm3UrF0WRr9uViBBNtCZgYo4CqJWqyoI5iAgRakRAZs+RvYmkIluMAKCGqD0LIUnASApAK1IGVAocwhyjqUrvPaA2xigEZgaOQIDMSKyQgJAIhQSJSEmGilEEAAWVJF0/YmR0l4LQIQhDIERELcJGQAEYAAFQgAaJkIhUG3qJokMEZidgGZT3gGhtcXq+Pl+doAGXLUzhQhPablXJJGV7CVlGAvHaRxX9RQe+v62ojwh57lRWdSE2zbYb6mxspouRHechhNF0nI9zREwLGcwcUr4lAQAQRURU1DZaR5lEtqT6vndZlssIiYQQtUokiwmFBSyMIizOGaVQmJRSyDGGAaImImAEVIgISIwEAAIEAIW2zM/SwUs0PyCAF/I+gkFDDsAphUZpAOhWPSpGpVCRMWLRkSalFKLEGKM3ImJtlrssyTmbQW7ceGG+Z11VrZd9vdnGlqrCXHkefAbZyPi5UOa/LYLvv8n7XLvI9xdyvvQ1XJzHvFRWl+ttA1zuLu4N6vDxk4vF2TK27Qpxq1SMUWvtrA2e6+6CVAr+zByQABQohTCekYAGbLZ1keXeR20NAAx1670f4kBEVpNCSizdfrwrIigRWIADgBAKEWmIRBqVEkEfOAozXC4IP9v68TFGAU4/VNc1vuu11kWZKSRENJoQkSBXSpEmZTQoIKLES+mc897HIJo0GzcQkZCIeLOXjyozUjmUSoHSBJQbyhX/NMPUM2XH69xpvnbO93mWZSoE4H4gwqLKV6erN370wz/6k3/zuzdfGIYAABA5sfIAwND3QFEpAuR2aJtm60OnlLJWSzFVSBpwu96MywoA0oK378MwDEPojaaiKAprNIBEXupSaQSWMHQSo7HKaoWIvt6Q0YQqCnTD4CMLkFJK02UanfZ/RIQUKKW2Xd00jTFmXBUAwCGmMMUNIiIqQK1Io1IqgUkvAaeMVjutTYyRg4gIjO5uu47dk7/z67/1ne/8xv1bL7cX48G3lyujzxMfOOD//bvXawi4ChuQOMFsZPMWoG9Ba4/G5OFYuXJUrh99/PC/+hf/6ff/zfcmdLg729V2kud5UqL33ltrm6ZJLAXDMPR9f6Wm57333lNRCjMiWqUBAAXSSY0SErlsGLyI5FmmtRaRECERNiKK1poI07pGvamnswkzG1JEIMxFUSDKqvPDMCRYaAIqO+eGYUj75wlTLc/g8iKyWq3UM0sY0oSbX6/X3vvEWgkAaRdJaw3MzRAOZnKxVeDm08XBa7/7v/rWt75+HJWXnmQyhAi9M8qUGYRBJPxqn+dfZtfO+SBDDmD6oFjXslVUjCsfkUYiHz549Maf/Td/8sd/fLh898ZkMlKHh48P79/9elrOSDuRWZa1bZuSv/SA4Sc6bCHGmE2mbdvGwRtjnHMKL9HCvm+dc0SUnM9qk95htdkmXilEQUQivNx6DFyNSu+9RlIK/TAopYah82QSR8fV902EfIkEKL1+uZwbLonk0xLkpeoaYvo9Xe3VD5IWOIjo5OJYabc/J2V353svnVxszv1iPK/+0f/2/1CM7KS6EWLstsp33ikW9rkuf6WP8y+za+d8rQ+A3gXIyHjtnS2Qzz/45OP3/+yfvvGjd86O36/sVBwrrydm7WzZ+ZDY7FKdked5opL9GfKbBHOPMZq8TE5JkLRVRCMZY5ar8zzPtdZp08wYkzygabpEdiaSgPKXmxMSJS+ytm01krU6hgAAbVuPdva11l3XJaS+iCTWqavLuKLlS0tDaf0ndeOu0PMAkEiDEpI+XXly4lk5Wi7X47JbboZmkOlsVh18ZbO9eOfTB996/Zu/8Vv/wcEL9/Pila4VTn00ur6H8LVzPiJkZBchM9RAHwf++P3v/j//2T9rT34kYHLdaTBeRUtKxbO6bgvlxuOxiGitE0lKCCHtWCQXvCQ66fu+70MI4/ni8kkLhBASkVQiJktnYnrMid4veB9CyLLMWisSh2EQ4bRsEYbgMlvXdXI+ENFae9+LzkIITdMQUdpRTysjwzBccYymVeK0cySCV+QEV/EPALquS9u+yfPSzRGRetPcvn3Lma4fZDHfb70/OeuVlaeDYvRk79y9c+8f/Hv/u3u373qGpu4TX+X1tGtXcPQ1gAJmr8iBiUVJxnVPD9/eh0fBozNUZbOBLPto0GZlSYonk0nKmVJG/zPxL2V7afU6hLC/2Ems39qa8XgcQthut03T7O7up2M3ybmkkNMH74J3zmVZBpDESkM6r8MQyqoYjUYkkJowzjnmsG5927aTycQYk4rWJEk6Ho/TclqavOEzyQ0/SNrMBYCr11NHMDnfFVFG+hQVd4tPPv5Qmf5gd3+1PDfW3rsxq/utay587M9r609Fuq0Mg1hblM6H6xv5rp3zWQesCAc1MK+3m0lZFUWFiH3n9/duVkavztd1GybzmUY4W60nxgKj0lqhQiGJwFEkQvoaEIiUM0qTcSaLMRqlc5dpUpfstgCJ/TONLEIIfT9EEWutUtoalRofIgIgKVYlQozNajOPMxExpBAlxUiRqMhZbZxziFjXdRi8tbbv+65pP3ukXkKyibyX4D0iptooUZQCQIwxuW+K04lPw3u/vLi4c/PGRX1Sb7bT8VgZtTz+FLXsHdzYbJfHLUY/FEVRWnvWwdB0prq+OrzXzvkG3yplUIsg9iGCdrPZ/nx2MLs4rY+3keN4PB6X837oSOPtGzcXo9FisUjRKCn3XbHJpnKSiFLKlcIJsiAiKEpHYe8HADDG+ACJnrFuWxHJyyLLMkRU7FPVgighhO7ZCs+9O/fmixkAWKVFoh8GrTVzQJ1fztOekUOm757O30TIEkJIb6KUUsoBQN/3V7K/6ehPjJHJF5MXpiShjs20KqbdwfLo4qMPH+3s3TjYf2VVb08/PS0qNSlmRZaDNm3oe+3y3Sy2/zby/dVNMkAkBWSAvHa5rlQ5nuXvf++dF196dWc0a7Z1xH7n1q7L6KLZprZFerrp8AWA9MrVLm3qlaQCc5QXPoTovRCm/wWQui0ZAHBsmSWEoAavlCYiRZenoVLknEsFxyWvwDB471uBGH3XtkqpGL1nlSjqY4yTySTLsuRSMcamaVI+mjwpJXnhkr2UCXXmsswVMca+6wCAY+QICEoRcOS+98Mw7N7YcQRNjC/dv+eUPTo+f/zgdLq7uHn7FlB/dqYNUVVV48Kta/C1J7p+j/iZXb8rM5GV8gG6VlwxOz8fbu/sTib3RpNbkbO2j9PZgsZz7TLnoFREzIn0eLPZiMhkMkmekULgFUmoc+78/NxaO67ydrsVAeecMGbGGOP6vhflm6bxPkbfnZ6cMXNVjrTW2shkMtFabzaNiGhFSiltLRF1XQcAbds556pyfHFx0XWdqwqXWZYinbyD76fTKRKcnByLyP7+PiJut1vrTKq126621hZlnihd+qFFxGpUwGXNYYhAaTTG9ENLCpYnR3W9wao43p7dvvPy/a+PTh88PDk9PF0u183qrMad1+8sdqeHp20k5cheDT3kGUlm+uMXDTh4LvF8qIgISYEICgYiIsWjSYUU88JMJlWRuUEn5QmlkELojBk7VxFR27bMAYC1Nt73RJBlFkAScXGW2cVi0XctM4vAdrvdrOukg4CoevGJ14yIdnbmaZDaNE0iLU1/pbXmGFIbJdGYKqWs0lVVjcpqPp+LyCAxVbLp6GRm51yMcbFYDMOQtIdS8EtN46rSaRbXdfEyMUAE4M1mk2gPACBGn8gFrc0eP3wym44P7t5br9rMWQI52N+5dXvn6PDx4yM569E5lwS08DND3s/63PUBWV0750vc6kRABBFZSARl72C37VtBdrkFlL5vwaMSA0oppay1qS2XcvaUqqfMKWVXKZ1KX/RtKyJK6RBC29Vt02dZprV2VWaNSZ22PB8XeeW9X2mlDKWidTwqnXP1drNarWKMo9EIkkhzHNq2Tc1qRCRFiJhYqrquK4oiVbJFUXRdl2XZldhGCKGqqhig67qu655hEQAAElmviGRZlno06QdUSimBcVHuTCcYoGv7utsWzGVR3blzs+6XeAKj0UhEFJIVVADhLwSh6+N/vzLn+8vg188EPJmZQA1x2L+5bwrryrwYFcjStExEzloy1pGxWoEwAhpFSpFEAI6j0ahtW4nBKBJrnNGb1fKwbUbTCRKS1o5oNJnYrE9UKalZmPh4+raRGEREEc/nO6nT1jRNXdcpcKaOYBrftU3TNE29ucw+F7s7IpIZC5F91w9IoR8uhZAAk/YkxMvxhqrQhz4MXfQ9AGil8Jl8SPS9iASCvm0RkYBTCbIzXxilm23tu9YaR4qw7dbr490bt4zWAcJiZ5cBUZGOoAH8z/OzawLivHaRD4ElAorCyOlsHYawt3dgXS5aMSkCIQJEDP0AnvOxTvgRY4xSqDXFiESw3a4BIMssohsGk+d529bDMAy9v5piWWeUpkTVY6yOMVpnLlXlgQHBWF3X9bP5hGitdVkm4ZfUDkTEoigQEQVSC+bhw4fpmC6KIhUcieYsRd+rvmPiDa+q6vj4KFW+WZZprZS67K2k6JhlDhEQMc8za40xxo7GwJd19KgqESUQt/16s9n03RBALfZvBsaIpPDzVHivi/0KI9/Pf50YYmAEpIgKNZEKncxneyYfxagHLxpEUEEMQ98J4oo5hgEA0oQ3aj0MA4jtui7Pc2d1CGG7WXH0MQyEgooAkUUQQGkdJfS+83HwXY8o1tpxWSCiD30ihhq4S2M3RQnuJW3b1nWdsjGttbEqhNC33TAMSd2qaZqjp4e7u7tZlrV1E8IlEB8AkhcmAY8YQt92wkERECmjiVCEEwAR+66PMXK87HgjcIwRIe88G4UmKmaGyIBSGOfM6Om644BA2XzvZs/gCSlKJBD+OUxq1+TkvXaRz5LqQyQgENCgLeg4wHS62N+/XRSFzgoLUDmtQQx3xpjv/NY3rvjwrrAtCQmSGmPpuNzb2+u6LoSw6ULXdd4PzrmizNKMlTmEZki5mnMmhDAM3Wq1Ojs7e3K6TaM5a5QxpijyJJeakjARUYB1XXOIVzRnwzBorReLxWg0SlXFJUAmhFSDA0ASuyrLsutvpVbOVScyZXjj8fj8/Dy1GNNwOWFkjs7OM6NtVaBQjLHvW/YDUlRknS2UK6vJ7oWHkOD2HAESae5nkH7X48yFa+h8CkEBqqRfRUYhSKTc2awYA1HwABBFGWN1pdRoNProow+IqK7rS+0ea5PzrVaro6Ojs7MzEZnP56+//nqi5358vDw+Pl4uL4qiuHP3hYODgyyzRDRsO0Rs2xoAuq4BgMH35xdniFmCORlNInJ+fr7ZbJJ7pfGDAmzbFgVms1k6K/M8B4B0Pev1OsuydFKn1kw6f68O7sh96rlcUYRba4uiePvttx4+fOi9n0wmd+7cWSwWCYe1d+PAEKoy63Sd6bzbblW7Db7L7SjPa21dlhd+ACGICB5YgYJnyBq4Tp4HAJq+YHYtZojIGAOpyFnOHlTcOFvN+0ePjx9vl2c37u7j+Ns9NEM3EivYkyjTGRCAEDgKj/OZRKgmebNcz3dGpaW25uX2/PufvMVI42iGYUiT0yuSxvF4fLZZAYCQFZHzuv/9P/5+YuhmYxFR5YUXOTw6PTu9QEStFHNIIi07i8VkMtksV2Hwb7311u3X7uRZaW3OkbTK//l/+V8jGt8HANDGsIRE0ugy84//8T/uukb3dVEUm83mu7//r1988cU8z1OVrfJcRIhUvW2ZgUiHECbj6VbW64ulUqqqqnTHEHEYhpOjY+89xzifTB89PhpXI6NU0zRKxyEEVjiZzu/efvH+C3cniylG/9F6c746vLn3HWnqMWZDBN50RVWweEQtGW6brtLKFmahlp988igvrTbYmVeGtmObFYr9ZpsbO6js5z/IX/CU/lzd3s/YFx75BECeXcilJJUASvzBmz98/PRhvTl7/2F2+yvZi199UVvceugj4DOSGyVkFULgbsm//fd++4Mfv1M3Jw8+/vTiZEuGqry4e/8V04XRaLS/v5/QSkqp7Xbrvf9GWVwN6VNoSR0NlRfz+Xw6HimlOISuaYTZWrtaXSyXy0k1GlVV3/cK0Bm73W5ffPmlg/2bzNi1ochHf/LH3z88PM7LLHk5cxKxgrIsDw72mLmkyMzler27v/e1r30tHevOuU8efrrdNMaY+XQGQMEzAO3v77MLp6enafKRhm9aaw7xKy+/Yq111ubWtW1LgJm1Wuu+2wb2pqwi09HR6dHh+d58Mq7yV7/+bV42j/E+954jM5EtNFqIEvs29nWj9SgrzaefXrz16e8/engyHZez+Wh2/+VR5byBAATWkrPgv2iP+Il94c7HidKBFICQgAAQisSw/OT7ONQ7ut+enofJvLjnC31j7mJTAZAwhxgFBSwy9F3frT997523fvgnzfmRI9hf3JzNJpt27c/X5XSKiJvNZr1eJwhd27be+7pr08AN0hJHCKnAhCGg8NDUiMghxBCUUlVeJKRnCGG1Wq1Wq9y63cVOWZYXZ0unc2YIHnwfh77puwFxMFpzjGnNRARQuGvrEMKmXqeRXZIa7Pu+aZrUoxaIIpo5DINPFJfLpYk61JstwCUIXogkcmpP9n1PiLPxxDk3rkZN0/R9f3dnfHh8GoemHM93pxUPsl2ev/3GJy2HwmWvvnhrZ5apATZto6EXbkucsIYBmkLHsnv41pv/w/rD/8+U7PqpqMV8d3Z7Nr2zwnE/hMiqib+gvvzfzL5w50O6pGZFRBYgAMLIcVgPWzKSlXbbqadPn27++z8c21tMalMMDDIkHK9EjMF39dBs4/nJi7cO9r7xaqYVCgFhCPvj6ez0/DiEYbv1fd9qrUMYRGKWWe+9IbTmEirHzJxnzCzKKKWARSksylJCbNt6s1nN5hNnVNvVKKA1Nc22G5Wvfu0r0YBSKnOFc7kI/rt//++JSNcNQx8AOaGwAFhrffNgv65rGlWj0ajrmgTc6vs+Rh+jXixmZZlbmzVN07WDcw4AlVLbZj3KM2NMVVVkNFxtbzyDlyJL13VIEiGixrPzlc2qclRERAxc5G4xe+Fgd/qDH705KsfZZrY6OTHzl0EbhTLEoHCkkUTXVpPaPvrkySdjrff3D/qHD5vNWddsCIVBAiAjNkEq+uUlhV+88yEiCwECEAooBAIJsTe8Dc0wxHpiApmTB+/86ThbhBiNCYGZgYhIkIU9cyCA6WK/a3DTt+s2uFw7na/69snDC+1DnudVVaUuWlKZAoBRVQlzDEESXUU60ZQCpVEkoUiRRTgE72OMbduuVitgqaoqlcypPzd0bdPXg+uzrGfmFw72ptPpZrPR2iR4vYjked62DRFBCEEp0jowDDF4jp6955gRDm0XhkEjsR+AA4EREQ5hWo0G64TQaI1IiBhBnvFWFYbU2dnZarVKcwtEDNmOD935eUNEVV4NTPX5Job+O9/6egzhvH/y+Edvs/095F5jR0o0W89xQM2KoaWz0yezhVkvn0S7qJt23WIIFpkyBoMKAH6ZKx9fuPORCIOklazISY9YvAx5WWy3YYiMgbzv3nnz/Vm5DMKo6hBB0ChDoCJzJAJjXHzvE5MRgx6GjkzQxnVt7Ps+du2rr746m09YaBiGtm0fPnqwXC5n1RgAUjP5qm1rjFltWudcii4+9Km6LMu8beuTk5OvfvWrBwd7IhKja4f2jTfeYA/P0FAhxae0VNG2PQAkZPJ4PL5C0KCyiNg0dZbbvb29NASpquLtd97abrfpW6c69xI7jcbHwMxIBIoQMfAlMCIhcTabjTHm4MZe09Xb7bbI5Pzi8OTkEBGLrOyaBjju7S5++MMnpat6yoem75AUMqo4GVe6bQOKp6KBtTR5sIO6fbtXwLz1vhbuECJKwCiKCAW/VM6nQfgzhZIgIEiUcLpuQoAb83Ffd+Nx+fLXXrz/wi3jCNqlsALQQggQ5DIBJo0zk0HfSe87nXtDxvfUd93Do6eLxSLGmHK+siy/8pWvOOdimyRTICVzCTyntV6tmzRvjdFf1r8cjDGucB988EHCNqdTL8/zxWJR2mp3d7dpmn5onTPGmPV6led5UYy22616Jgye+ilKqT5g27aPHj0cfHfV4IgxvvDCC8PQJ4hNitBN0xRFEWqPiEJIWiFREPbe+xhS2wgRT09PvffJ6YuiKABowEyV09G0Kqd9F9q2VZomOxmwWtedmU4wU4uiXLarzBD4AzKqZbWsT+vQt8HnEaxxq4AcTQTD2nSkPA0deWYu+HOq3S/EN75gw0v99VRvAAAIski01obAiHh+ttFuZzbbyfN8CK02CsEKa4iMJIQpunANrUHsOg7sneojePCOYhSR9Xqdok6e5+m5WmvDEFEgjdgBEqtfAJa9nV3vfdu0AFwURVUVqZGrnd7Z2Ul7jVmWXVxcrFar6XTqqJhOp1VVhTggChFGHkajkSJb11JVRZbZtm2VUklbpsyL6XwWJZ6eHiutgWTY+pOzUyOQuywt11ljnHOaFJKMJlNU6fYgKorM7dCHEIqqfJZQwuHhYV3XydedkrJyo8keA3Y+ZOV4vHMwDIONjaKsNBsFrsMwnUy8hXFRTNRUUJ33tSszX/aH56eBWaKQoETmEJHFEiEZEvbxl7rQ84U7X0QSBRwAkbWmth12J+VFv9bAmcahbueTqXPFyelREgD1fQc81Ns+hDCZls7pPvRD8J51CKA1KS2howDC3PadH7q+yPLtelNVFQEqpDD45fmFDGytRaRt3YrIaDRCxNVqdbGuEwqGCIbgDapUCEut1uttURR57kOoy7JUSq03K0ZZrVbb7Xa73SbvJMg5mNX2DEA2m3UaS2ittMa+7y+OzqfT6dD1wXuOkRDLvCiKotmumq4VBCAMzOC9MqYqx77uRUQQAdD3YQheWVNV46Oz4yLLUr86/VDRh0wZGlHc1E1TT6qZAWxW5xSC0uqsWSm9JCq89JZc28HEFcByvjqJUVqjraGz7capyzkkUJmNdDd0qFXbbYzLDRrU9KVqtcBnID2pjrjCPiFi5KBJh+DzPOeIAkSkAG2WmxB8ZOiGgUWUtkU+6fpm8D1zIIXOOWEIDEkaLwWeBKFLb74zWTRNAwApzboCqUe5vKQYGRETc6NSyhVlOp2LoojRJNDeeDymoOu6Xq1Wfd9vt9uyLEWkbVsRMMYCQMJuxRjbtmvbdjJZpOMSALz3iUXeObdZSbpIxJSIUt/5pLfmvR9CQEWoKCWUdV2nBYA0x0vDkjR2azsfRSNZVBpEI0kU7novghxFADhKjIHZE3ci4rQLYVjXW22w9x5JlDEuzy9YN5s1x+i0ySwKi0SgXy4U4ZdR7XKMSXvzyvnquj4+PAwhyHBo0WG2Lst8HQktQcgAlNG5cTj4BmJMDbOA4oVbP7AEDQrZgFAA6ft+tVqlYzfB4FKbrSiK1F5OadbVjLUbLncyjFFaayJIueCmaUWkaZrj42PmkAav1pnJZNL3fVpCK4oiKZduNpv9/RtEtFwuE8tA13VlWZblKMKlilpa32yazvs+XWSSQiVSSoExhigyszMmoRejsDZGWZOKobIs08cmYWrSYNqQ6mKmsBS2IY4ItHFosxy9J0tEwuCCZ2ENpJGscFDWOkVFtFluApuLi5PHx0+ZZRvmR6dHtHMydL1GGwKLZ6Pxlxj4fumRTysCgKZpnjx5pJQqdW0K4tiLFKeHF130xvngxbkSAE7On7Zdk+cuy4rIEmNgGWL0UdgYg6iGPkyyLAUeABiGIZ2h6/X64ScPr7gyACDBS5m59zE5Yl6kRck+PWBjXNvWSVFDJMYYN5uND4P4jzebDTxLKOHZCqMIFkVxfn5eVZUx5uLi4jKqKQ0Aw9CHODBz29bD0BljgKMxZrPZJuILa7IEIFWijDEM0ve9IGhnSakQAiMbpRJ0anl+kWgYNlnehAIp9N1WIUQvwlgVWdc1SAENiGQhBB8UiULVCgdDGAIvQ5jNp36A5fK8GXtFOqA/PDp0e6dt3UiuKQpFRBBQv7zg9/8DTqCuHaVpNqgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=212x300 at 0x7F9D94F49AD0>"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from PIL import Image, ImageColor\n",
    "import os\n",
    "img_path = os.getenv(\"HOME\") + \"/aiffel/data_represent/image/newyork.jpg\"\n",
    "img = Image.open(img_path)\n",
    "print(img_path)\n",
    "print(type(img))\n",
    "img"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "acceptable-training",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PIL.JpegImagePlugin.JpegImageFile"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "caroline-canon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ABUsQMlJYjDEhCHCKsBGpSwhCLAG7E/HZDkACWFOkUOy2QN8UJhYB/9Dzutd4Ica7z47sviP+GYExxhgwxvT9wIwihJRSeZ5mWYqQNQaUuuOZ7zN4hSlFH9wI7mi3Cq9FyhwhhDG+4y9vM+/DqzD/1m8u9+z5F+RDCBFCHMfhnFNKGWOcuxgRpVSe50k6wMQYo9X72FfAfiDa8r6gDEIIY7BWAwAhOI5jjHHBxm0H/IMv+MGf7dnr/7Ngz568tbaIISOEGGOUUkIIIYQyBwi1CIyVSTpg1GJk4adyu6hg3nYRebGM3l5MixXTIoQAGYQBE9PrddDWdHK8ZfN+2uwV3+680Z698j879uwlKMhXZM8K5hVELCjIOccYBsM2JppQwB/qMiMDsBN52Wbetq2ythDCFIIuu9let9YCArAULN02k+g285C5g3mw/Zp79vr/LNizJ19syArCUUp39uOEckyY67qEoG57XaqUICDkTkGV2Q4vw+2V127bs+3/G2MwRghZa7Wxan19TZuiGATd8XiIvwp7lnw7e77C5yCEbHVGopQQ5nDPgl5fX46TgQWN8Qd3aWbHIS1e7zbzkClE+ABbai5jdKezqZSyW2NXP+yxpbW22+njhwT9W0A+QsgO+TDGlDKMMWbUWtsfdPMkNkbjn45r/G+Hn4wx208yxqg4jrVWu1HX9ACB0V4FECGstZo7NI5TSpjIpTZCEpYRwm1MiYvFxq1rZ5oVH5kYE+MSmkKcm6EbUuJSZHKtNSZW2wgs4ZxhDErnxhjGGKEaIcjyruO4w2QlzTt5LihFSqmClwiBtVZrbYyx1jJHUaYBCUDKWkUpcV2itf7ITvc+w9wFf5PX3LOWr7g0WmulVEEIa60xRkpZfA9gLJg8T7NsiyUAAGCDwHv99TMIYcdxMEbGKMfhSpnhMCIEfN8Bi6WUANhawJhiDFpLraUQOSGFxcUF+TBG2/9FWSoQwlJKhJDjOHku4khJ8bd6wtGeJV9BtR3yFQui1jqXyhhjwCKErJVx0k+GEQAoaSgFhJAQwvO8V199TSlVLodSJbmICUGOy7QGKS1CSGuNgBW6BIwhz1OpkjTrEwqYACZbE1ML/iFsMQHXw4PhRrniSZVYEIDy8xfedr2/1ZUje5Z8BRBCBQURQhjjgpFAqLUIkAakorg/jHoOZZRyITVjRGt55Mjhzc3NF1/8AcI2KJE0Gxibe54jhBJCUEqNAYSw3SZfkg6kSuOkR5ktgjzGgDG3U8bWmjyzlXIrz3StWltaWn7j9bf37duP0Yfn1j6BuB/L/Z4lH0KoSGlgjIutSRFPsQhjRK21CFmEVRp342jAOXddFscxd6jrcWPspz/96XfeefvFF7/HHVsqc0BqOBwAgOM4CGGEEFhsDBCCEIYk7Rubx0kPEw1gjNHGamOsMdZYbYw2RmkbUy69AJZXr7955iXCxKHDNUw/mFn5W4Q9Sz4AIAQVWbWdfgbFvxYhZQzCllBIs8Fw2MWArAHGmLW66GcwNlb57Gc/+4f/6ffePvua6+FKNVRaME4QQJpmnDtFGBFjbIxOksiCStPYgga0tbnc3lkCAFjQYVC1Bnfa/W99608cHnzh859fX1NS7Brv+KHluwcUcqnC8mmtC69Tay2kLP6LkGUUcpFEw4HSQmtwHEfKPM9T30ftdvLZFz49MzP1ve//xfkL7yiVVSoljJEQumDUVmsYbI1VuUgQskJmxqidEA9CBKHC27AYYwtiY3P5m3/8u4zDc88/hQlIlWT54MFepQeLvUw+2BYa6TsghNqOhiCMQSmRZZnMBSEghDDGBEEwHCrXdTc3O//0v/k/LS7d+NFL37+5cN3zHGMsAASBm6YpQgDIIATWmsKH1VpbawspTWEUMN5yeBGyGJNvfOO3by0sfe5znxsZGVtZWTXGNBqNB3yZHijobldMEsjBUjDEIgJIG6SKrAFjTpqqZvUo0teS9AZGriUDSnyEsDRJ6PhGysDBKTWD/nXG2ionPiJIl4ySFg0kIEw9o5z//B/98m/++383/NP2P6z9vbn6C4vX+61mvb0uHE9nWJdzSoi3udinCY8x9GSiYoMx5jR0HZBqmA2j2f1odWn5f/k3/+vm5s2v/Np/N3vw6MoS4u5YDZv+RgcH1Z2AGUIIIQwAxhhKdxxh9L5hrLv9ht2BPWv5YNvsBUEJAcEYM8Zc163Xq7VaLQgCwhnezs4qpQhhsNWxyhLCKOUAWEp94tTpZ595frXT+c6ff//K1Y35I/XFa5uTU0GWao+VvYAbawKvoqTprqwHnk8QDoPQ96HTy5Msa402l1YWv/E7//bmwuoXv/ALjz/+eJ4KQsBY6McRDwNjDMaYc+44nHNGKcEYvz/XbAHd8dhD2AM1HHcFQkhrWy5X7CKyBnGX+4HLOEKK4nw7Om+x0aCUIpQiC7gwMdpYsMZibZDpNL946h9fvLF49tw1RP8VnfrFytjR5ViDj5mNsubm5o1F5SxVDzuOG3Xztemp+aXVxThNSpWwOeJfv3r+L/78D64vLk/PHnzymc9WfLS2FvuOoxGkmcac4JygrUHUtvBRio/E+1J86I5YtN07ocE9bPkMAEihy6UqQiTLMqWU1jrNY6lSqaXWuvAdlNFKKa0twkApEALWWqWK6CBI7Vaqtaef/9Lo9L7Vzc0/+pPfdzEbaTgzzVIvE5s3bmy225Q0D40cmtg3sXjpnfZG2+PB/P5DzdGxy1cX/uIH33n97Bmv4X/1a/+A8/rK0hBpgjBIZUgQKLwTiwZCUKE53E633KHFAgOgth97B3vW8lnQCAhCuFptBn650163kGeZCCsOxphrsAgwphqsUkpbw2iMEEVArTEAAkABYIuoT7SW0ZPHTm1eu3rz+ssqxpff/c3SmP/ej97tRfmP3/g3c9MHZQoX3z174smTZ/70JudPPf2pJ576zCmF8h9854+vXr81PzX32c98bnbmSL/fV5ks1SoygySNwkaokKYgldJKKWMAIUQwI4Rg/GFjQLaUrQ/kct4X7FnyFVEojHG5VGk2R9ZWV9I0HQ6HloSMMZtTY6xFoJTBSmOMKcVaWaWVtRZhQjCyBrQxgyRx3RCs/fRnvjxIX6/XWldvXfjeb72lFxaf+qUvDt9b7tXHx+rTZiMNR2vJxXNr8j3nnH31ze9P759OE12tVyZm5w6dPj7o5IxXuW+NxphAyfO1MXmaEKsxpq5HCeYIEWPAaLB2ewwNKgqTAQDA7rUmCnuWfBiDVhqQJZzVa01KmO/7eZ5rq5ABZTEAstYKKZHRhDnIumDBamy36ngBWcAIhW6orDCdwTOHD6y9+fjbr34XUx6uXlrd6E5iO3fkiQaerNhqubG/JkBGw2Nz+2qYv/jSi7x/uNY6tH//kceOfnUEHVrMU8oIxZBKyV3mc9o3ysUco4HDqesysCTPtZTKGoIQwrcHXqLtHlwPybdLUATetBGSOr4fcs6rVVdKSX2JMeaE6hwpKYrgcxGIRsAIAa2R0kZbgwijBHKVUu5VSuG3vvm9i5fOv/7Dl5oj9c9/9st/8s2/WFi48cix45gw3/We+cxnRsecI0dOOS7vdFdowMZGGi99//vXVlaf+ew/Xu/1KkG1nwLBhhImRQ4Cs4DWmlRp0+8Pr12/cvPGcr8Xl0uNAwcOzUzPqEIWvWP/YA9WG33iyHfvesyiXFf/lGFQEigzFNfAmjCoTYwdZv51D5wcd7njei7rd7O0n49P79uM1i9e/PHRxx3HIZCXAKk8xQghr4x7vV6M1GiDycU3/+ib/2O62p5/7Jmr75xd6cUzX3z86LOff/bTjy0u30qzXljyN1bsrz3zdWn6l985V54Ih+sDYGbE9b717//5P/mn/5e2XK+EyA0koSbPlFRRe92+9uK5haX1mzdvKWlPHH/iM5/57PjYpFaQZfp9mautBlrbnWj2Cj5x5PuoQCiyFhljLEYYUwAshJBSWZcEQWByjTH1A7ffieKsO4zXFxZurKzemh077rjUYRVjzMVrF44cOUKZf2t18Tu/+Zu5iLubG+VK9fhnHn/mhWdOPn96OEijKFlfXVtev6ItSiNvtBuV6/bUk6f+zlc/91v/6rdc7kcZevG73wkq5d/4r//bOIluLSz0h8vra50zZ19duZW5nhOWRz/z6c8+9eSnRlqNPIdoqKSwxlhK77w1RUavCLjsHeX9niVfoSW1WhOgRZC5EJwwXOa0lOQdIRQQlGaDJOuutW8dODXWufrKD//D7/SHm+VgbGS0QVz85nlmLRJard18pdtbHz2wb/aRfS/83ac9Wn578+2FhYX1hXRpcUFlK9WwrmJ66dyZ6jPHbqns7JXep37tC/ni2j/7f/9/vNBZWnrx9/8oVhqu3Li5vLKWpw5j5MjRp08+dnz/3NPVapVgsrGRZql0XT8ImLWQZTsts97fPsayB3VJP3J84sj31xdKoJ1RRAUMQsgCshYhhDEiBiGMUb8fuX7JAlZaZ1mCiZnZNzY+0ZieOtDvxY4dVzpduLG2sHAjrPtKCQ2eMnm9WjarV2dOzR4+csQ69u2z79zqr3S6a3GXl0LnkUce2zd+IB3oa5cWX79+pb22MoZz4uBKJP93/8ff+M7v/uU7Z1/rK6xBKwzNsfrU1JHjj5x49NDTBFEpGkkksiwFAM8LKCFZZvI8dxx3+7y222sAALJgHlq+TzwYY8YYpREl1CEBpTwTKs/zTCMh81oYCplladxoNScOzE1Njr/44veNsSePPxMGde9z49EwPXvhXWvQlYvfuHBh5fLC2uyRzx08+bmR+YN/+cYfbi53ooWFRqN1bOaJI0eOjTSDPE4wS4+8cIjdvHHm7Tf7afLyS+88Wh994rknAb3YHBlP+nJqbn7uwNOPPvbUSOswtiAj2OxEwFNrLaWcMWaNTYVACPm+p/UeCujdBZ848n1Ulk8pCYCMwRiDy33H8WKJtJEjIxPKSIWpsqpar4w2muWRVhJnvh8cO3Zi8ZJYGaweO3rAdYKnnjrdaLR+FLy6eON6xPHI+GxYD195/Ufnrr7DgI5XG8ceeXJ+9qTrVIwiGKy1iVaJSvXjx071bl68eOX8hV4sHfVzX/nS7/zevwfkfPWrX587+ExmNUUw7Amb2WoQxqALibXWhhDsedwYEDKnxNk+MQKomO9gdk5zb2Cvee87KAYMFXpjKVWhevI8jzGW53mRaqvX6+Pj49yhFvTBA0c77cG//lf/9r3zNzB462tD321hgM4F6F/KJmYPjE7UOubqG9e+q41bqkydmv8UDMmPX/3D3/7D/+EnF3/gTbhdJL/xnb+8+b0/Wn3lu2NMn57dh3S2tLCQWFNqtdaX1x23hElpbTVrtw3H3OdM5ukdg7tg6yu+s+UU2orzWbzdC2HvYM+Sz4JmjHFOtIJomAz6kVLKdXmvPySY1et1z/MIQUILADsxMU6Z7XTX5/bPHDi433VRqVQaDAbXrq0xnibRZmJVWBl58aUfDdIBJObkkcMmz9/6ydsv/+gH1y9fxdIbb054hKwtXhn2BgtXb164cGFyfMKvlFZXl//8e98+dHQeDHQ6HWOg0WgwhpUCSjA2OAiKrlmyKDdJ0izLsg/0O0Nbwqq9NaXrvs/bve/AqQWiEbbIINDEAtEO1sSKEKy0tU2p7B99939a3eyenJsdZglVie+WFlcHpepUMDJan5y5cOmVheXV448fKFfJz3/tkXIp/d0/+u/ePPNGtdIaJnFveRFaavaR8XduvDxY7tUrzX1jY92VpZfOvTsyOn5g5Jkbtxb60RLBmeOyNE2ww5773OfzVFy7uWlUoFU5LIWXLi+W593ra2/ul4eVVpRVHM+zAIhqhTrMJw6mGDGwzGistdVavq/d79aCCwAGrHPXS7HbsOvNOEJkZ+jpzgqFEHg+dxwY2PZf/OWfr64sz80fopwpScCyXKiJidbo5ES1WltYuK5l/sgjRyjWP3rx+yu3Nl3Ouu1scXnhxsJKo9HAmNZGx0dHRnq9jucFeZ7funWLus7Mvrl6s1EuV8NKuVarbWxsyFycPH68UR2dGJ24fvVaHMdKKd/3R0fHR8ZH/uQ7L/3B7/3eD3/wHuN8tHVwYmxyanyiHJbCWolz13U8RinGFgHmjBIXoqF9P/8K7KmVateTDwonwxTFLNgaQAhZAA0Dh/HFxXeCsjx8ZPba9St0ZB91mLUyz2StUXVclqTDXn+j2agHvnfmzOtLN9f7bek6eGLsxPjo452ORIR2Vl+uVRugba83YNSplktVJ2iOtAK/RAgrVSuUu8aq1378aqe9zjk9evRotVTmlFUrlc3N9Ws3kl6vV2/Vn3ji5LsXzhujkjS7cfPy5UvvglEyV0HVD4Nytdool+qlsF6vjYyMjNeqjUpp4o5TxB9oFrgXsOvJhwBbQLA1crworACCACNHadTZ2Oz128O0c+3qe3MT+xkhvUQdO358cmb8yrWrvW5nZmZqfKRy4dy73//ud2Uupiamp6enH33kU/XGKKY1ZeF/+u9/Qh036qfdzQ5hzHc8zJnjBTNzs4Qwzw3Onz//4g++t3n+XXDoY888DUgKGYclTmkrCFmnt7G6vtrptDe76fT09Mz0aaFyZEOCMMVGSSmMZdShlINlYImQ2WDQM8Zske+28duD/Nv15CtGjOOtzo3FCA7QoEqN3is/fi2R11O5muW9RtWL41hm/OAjR4499vj1G1cvX3mv1WqUQ+ell1764fe/SyxyuTM/e2DfzEHfHWF4JIm9sFybGJ8ZZlG/O4iGWb3lc+oAJtpAtzfIsowQNoyjkZERiozHyMH5uXLFjQfDtY3VNE6MAcoQJiCE2Dd97NTpJ44eeX5tY81qz+WOyxFnLJccYwqWGANKglaAABACtSVetnekdPfUmgt74Hx2IhVkO1phITco63a7gygrBeVjxx71eLC6vGCRnZ2bnpvfv7B460/+7JuA1ezc5BtvvvKtb35rfHTs9IlnPvPUC+OjUwsLC+feOcs4Nlg6Ppx+4mnPLSX9RCmjtRVCZUrHcbzR6eZKM9cJgmBsbOzoocOHDhxo1RtR3JUqkTKL42G3206SBCxJEzEcDo0xjDq+F5RLdUJIkmRJkmRZ8TVVSmJiuQPMAXI7hbbtDu45SQvsActnDCBkMbaAELagEWgQFrJbK2+M7ysPuu1LV1Zef/016G7u37//ySdOvfzum1eu3GAemtk3cf361Vdfe2V2du7551/4wZ/9+S/8wi+8d3HhwvlLjE89/uRnGG31eyJPxXuXLmeDHi4xRh1krAGI0qxUqydpmiRJr91xCB6t10Hm3W67Ha03qjVjDKUcgaXEczjuddPhQCxcS6dGkFAVglyrXWRLAIQSKASkylhrUNFqw1rLnTtDfXsz27HryVfYPbgd+TcAyoIQIrvw7o3FGy9eu3rZDVtP/vyXU5HeuHHt+vXra2urn/vi6d6w9x/+3TdCl3/p137pW9/8E2bI7L6D3/uLn2htQcrrN64ePjLKHD4+PgmAlTIl5gKAlNo6TErZ7XbL5XKr0fId14i8XA5Bphwj7QDnbnujG0eZVpgSTyt168Zqa2wiyzLOKaY0iQAMOIwoZaQoFNdAMAICxiBUtDr44OCtD3F+dzc+MvLdqw7vbvHFe30dbBUhXpZllDHGEbJaqJWlleVXz/x4cWnh2WefGNs3+eqLbz3yyGfrlcYbr5/rxb2pibELZ6++8eZPauXmFz/7xZ/8+Mz5d6899+yTaxtdYMlzLzytLJ6cxavdM4ThU889/cSbp175y+/G3W7Zd32/hIijlOpnQ+bzJEvK5TKS0nVotdk0WnYGw2gAnU2JLJNSUspFrmv18X/w9//L448d7m1a6oDjIpUDpaAVwrwHgK3FGjAYAoARxsj+VJfoncWX3KsV/KjaVd4tHHyv9/HOv9/1ls9xnDyXGANGyBgNyPSjzuLiQqns/Te//F9vbNz4zp9861Ofet4adObtN/v9rNqobKytbrY3JsbGx0ZGL168uHRr+bHHHuv02levX/nFr3x1du5opzu4srA4jKP98/M3Fq6/d/mSyJKTpx8fnxy/eeMG9xgjTCoRD4Ym16C0ShKHgOtwo0SWWy1snuaBF/Z6g4nJfUePjL740muuyzmnjCFttLVIa2ss/uk7hOxWoQYycJfhMHsJu558UkohVKUS5kplecI97nl8cmb09P4vDnv9M2fePnToyOrKRtRL42RQ8ivXLi1KmZeq5Vq1kudycXExipPJ6cnlJfXuhQuWiG6/r0B994ff/dqv/LrhKzIPnnjy1Gqt3Om22531eDA8vu+ZaDAAbKIoSoaJTDKRJRQZbIyUuTa5VchlvuNYA7FQ/UZjcv7gRJpFeZ5ZKCktCHYIRYQgZMHY8h3ORNHw/va3H8S9ZqQ+qk699/q+P8vff2Q+1N26GN1rd6N7fZ3tVgSAjDZGASjKyfyhec9jSRKFgX/s2PEkitI01Vq2exu1wC/7/nhrpLvZXVq4VWvUp2enSxU/CMNuv/Nvf+sbiOA33njr2LETSRZprajLPv9zX/jCl76wtrayeObNbmfTqCweDgb9fqfdHgwGeZ5TSn3fZw73w0ApAwBxEnU6HSnlG2/85Ec/+uHk5HiaDtNsSCgQUgyTBqWUENn73Vh7uz/BA7ov93q//ibY9Q580egkS0AIEfoB5zyTgrtOUPYuXXoPIdRtt2u12mDYazRrMzNTxBqGUWdjM09z1/UwxvV6NayEaxvrU/smv/b1X+32+6sbq82RkSTtHTy0b727wV02OTV+8NBceWYalHjv3XPtzXWR5VmSGqm01hhjRLBQCgh2Xe663BjV7qwCkknaX9tYKFe5VFmSDggBSinCFiFAyFrQH2LiPmRix97ER7bs3utn426rwb2+DsGEUkYpUMwQwmmaR1HiRYmH03a/zayJ+4NhXx4+eNDx8bsXLtIkq7VG1ja79ZFWrdG6tbTAWk1E7VOf+jQlNggq3/yj3/v5X/xSkg5mDoysb964duv69OykpGhsbHRjaXGwKWWeVSqVTBstpKZKa42sNpYkaUJ4KIQgQAhBmRAIq9m58SCsICy0SYfDXtkHQrDWGpDhDgNrlIWf5t9HGtL7JOtGdv2eTwhBMbKUUsqNFBaQ55ey3ETttud5Iuo/cviQ2c+Xl1d/9MqPK7XWo1OTg2FaCoKRxigw4rpurVbjnJYmmknUX1hY/YVf+JWR1sjy+qaVlWGPamS80Lv46lVOsdYaLOyf3ef7YQK41+sx6gyHQ621UlYZ7biudccZZuBpW5JGC63l1OTB4UAEYdrrd0abkjlUp9JqrLVVSiD+4dIBqz9cvfJRfcjvFXd733s1Inf+/a4nH+c8S4RRGmOMGPa8gGf+zYVFy1cA4MDB/RfPviMScu7c+X1zM9NzB4YLC2+9/OPP/NIvz83NvfnO271eL83icrm8uta3IGdn91drwXf+8jtPP/Op0ana4tLK7PxsnEZvvvnm8YMHhu32+q1bjDFC0EhjhFKKETXGgFGezzzPnZycpK2Sxz2RpUHAbi1evXDxguc5iDir62sG5dPjUZlXAAAhSwi5Y97strXbW4q9vxr0QZnlj+p9cyJI2VrtAwJrLaS2jibqYxO//R//+Py7N281LlLwesMrU/VGFTvp4sabr7/Tmj2078ChmzeXEg1hZUTg4I1z19JBu1wtlw9Ufnz27OShmWCkGmVenHknpkfb6x2DEa/XH//ClxLXHxsf19qur69yQkuB7yCEjUUGRYMhDBDSSxTXlLHdrk0yZQhZ7iw2Riq3esPX3l2b3vdctXmSch8AOlHmeh6S26m09004vyse1JiZe33fn+Xvd73l25JRAVgLyCKwCCGKMHzh87+wuR5dee/PWdDwA+l5zuraUhwlYa164vhJ5vA4jl3XTXUSxUORM45xtVKLokhKiTEeDofM6VBKx8bHX/vJ65PTU/Vmo9kcmZvfv7CwUKs2Xv/Jq/1+HzAqVysuZXkqCCHjkxMNb+z6jeV+L3HDMgBO0zzLlBAaWXTyxOOVSm04jME6lFLPc60WHzydvadeuRt2P/ksAdgaWYosEACMOAKYHn/s1/9O88y7+y5fvLqyeobgwHE0sq7FZHxyYnNzsz8c1GemrDaO55XDcHZqNM/zhaVFqVVR0VP0uL145fL1hZsnTpwIK2Vt7eT09MLiohcGn37uM2traytLyzev3cSAquWaXy5Ja5S2mFJtzXAYi1wjS9NhNuD5xMjM88+/MDs10+8JgqlSwDi8bwbM/4Z6aseS7J11efeTD7A12wMmoRi2zDFCWU4eefSJsJp2N4dJWjIgXY9a0MwvI0a7m5uUUmNMlqWzzYPTk+Ojjfprb7zW6XTDciiE8HzHdd04Sm4tLR45evTRY8estWtrG4No6IdBnCZpHI2Oj09NTZXL1c21dZd7hJBSEF4997Y2KJMqS3JljEhkv5uMjU1EsQAJoAApazCkcWK1Sxl+H8vex7/bP739LTIAe6c55F4gHwACZG/fI4sBAQav38s21rvd/mBscqq9vqFVnmVZa2Y+yYVUKgiCJI0pZzOz04169dqVG5vtLncdZSxgVKlUMAbKSH10dHpqhnM+GAw45wDQaDSiKImiCCHUajTm5+dbrVbUj6LBkDCKCEmzfNAfrq61GWOtVmusNTrsJ/2+vnHl2szIc4wSqbTDCEKIEGp+agDWNv/u2BMXvYK2GxjsIYHLrg8yw1YmRxWDMQAZa8EYMJY7PMwlJHFeFB1qg6WCsanJdq/b7nUNQKlUOn78+MjIyGanffX6NWk0pjRN0/Hx8bGxMWMMIcTzPK31+vr68vLycDiM4ziKIozx7MwMxXjhxs3Lly/Hg2EYho7ndrvd44+fqjcb++ZmZ/ZNZVk2HAys1jJNdJ6tLi9iI32PYrC+63CCZCY+hEwW/3Sob6s56V7bC+4By2cAMCYWW4MsBqOM4Qis0kPquGsbN4bxul7vU8KyaKiNaLRGNjYuZpngnI+Pj0+Oj61trL538WKuZJrmjLFGqzk3N6eUSJLIWjQUqcxlnueDfl8r63IHjC2VQoagXq1pITc22hjjVmNkfHRsc3OTKz0+NjkyMqq1ffnll3/wg++tri0fOnTg1s1lLbNbC9enZ+aNNsIYjLnWllD4EPXUXc90T2EvkA+h4isGawAVzR0N524cx1evXCeYDaM48MppIvbtm4vTNM9z1/Oq1WqpVIrj+MbGUpJGRsPm5ma9WTty5Ei1Wr18+XKcZJy7WuSel+d5HsexNYgQ0mw2GXMCSq21wLgSenFx8fy5C8vLy1EUNYPyqVOnms3m2tra5z//wmOPHT//7ttXr11GoHv9Tre9Mb//EAmd3iClFEqBk+QA8FfyD91h89CeWnbve3rtfselMBAMGiwrbp9FFDGNLeCgv7S8udw5F4mMe1rZKO73xiZH2r2+ZQgpOT0/cfadM/Vma9jrd3tDqWLXd/bPH3S9yjAS1jAjkywbWKoH7cGg1+eUNaoNq0XolTvtzlDJ1177yeXLl0dbI/EwuXnz5pEjR778+c8dO/FYuVxeXl6cbx2qlsM33vjJoRNHR/dNfPe7b964efkn77z66JP7dT5mjDTG0RoTLggh0VAGgStzYA5kqWEcW3vnZrC4uOT2t3sCe8DyfTgwoqurq512L/SqgPHGRhv7YaXSuNbppWl64MCBN954o1yppGm6ub6hrdnsdk6ePGm1YYytrawOen2ZpzLPJNVaW9/1ut1uOShzxrqb7anJqZd/9KMffv8HExMTzXqDYvZrv/wrx44dU8pEUUQIEllWDv2XXnqRMXbk6OFKpTKIzeRMqxbW2t3NktNstMoihU47pSUVBB7nXEphLccY8jxn3Nsb2/G/GnuWfAgR33Gq1SqytB/HQPCjjxw11rbb3VazLoRijBGM3z53ttZodTa71trZ2f2MuxSTpbVFrQQDcBgBo+rVWpIkpx57rFlvqVwQxl595ce//Y1//8gjjz7zzDMAsG8K7d+/3/d9pYx12a31lXfPv+N5ztXrV0+fPl0aqwulnv7M01Ll6SC0OO30b2DWYiSoNiAHlOUJRoG1oLTSmmKMjQa097m3F8iHP3QblCTp2Ng4Ar52/QZr2CCsuIG73t4UQni+v7m5eeTwwfPnz4OxjJCNxaVnX3gOADhlcRxjQA5zlMiKTUMURadPnXK5s7HRrleqVy9f+YM/+INPP/vso48co5SOjIzMzc0XzYfC0FlaW75x+bJIs2o5/PrXvlYuh7V6PU3T2slRznk65OvLm++dWY9uRLXSVKPR4gziOLOIFdMxhVDFkLc9tLreFXv28+WgxkhzvlaeB1323DGHNoWgWjNKmVJmbGR0c3Nzc23t6JEj169eYS4/fPjo6so6Y6y90fYcx6FMiZwC+L7/uc99ziJ07cb1IAheffXVP/rWH//cz/3cV77yFUqp53nT09N5ngutqMPPnT//+9/+1rs3rowfmDn9uedmHz3as+ra5jqulrvJsN0fKBR7ZXBKUVCNvHJHw7X+oF2uhIwRITLXZRjDVpMgiz78sYewByzfBxpnA4CFYtLz1776q41666VXf7fR9Jqj4fr6JmFOqVRqNOrXrrw3Ojq6fOtWf3PzS7/4i0oZq3U5KHU3OwIAG1krlaenJlvTI51et9vtur7/4o9+tHjr1i//2q8eOXjYStUaGw2CIMmzICwPu8l/+uY3z5w5e+j4oRMnThw7eaJWa51592y11RoMh0ksc6uEsRpUrrU0CeehsbrbjQZpOjrawogNBr1SUFKaJpFhfM8ahTuxB8j34eh3iPTRY49+wSEzl69cLJfCNGPdnvIb9Wq1ury8zDkXIr/w5pvzJ08emJ+/urg6NzcnhOCcU4TCsNSqVcuBv7K+NhhE5SA88+Zb7W73V/7OfzY7PdPpdG0uZvfPCSHWNzc6/cF3f/D9M2fOfOlLPzd9cPr4Y49pA29fubg6GM488mgnlz2hlASCkIVEaCmgzW3EfdZs+pvdxsVL783OHPEDN8stgLH2E60A/QixZ8lnrQZgUST3zcz+F7/xGzduXnv5J78/MtLEfqCUXlhYqJS88++eQ75/+vHHV1ZWhJBzs/vfeeedSqlcr1YmRpugxaVLl3SJHT928sL585SzX/3VX52bm9tc39jotI8fOLTZad+8eXN1ff3ChYudXveLX/7SqVNPADdW6STL25ubTzx2KktVe7N/9Oik4ETnBnTKOfM8R6SJFqIxPdkYmfr2t7/dqE2OjTVvXOtzTh0ePOiL9zGB/nQbwm3cc3zuLjUv9/ohLsQBd777nZO9P4jbcsytJmkGADRAdcRdW+uWkA8Ax48/i3F46fqP0yydmWZXL7+DVX7r0oboyv2PPlqq7dtcXjz25BOxlGEYup4zMTGWqmxj0Olz/OTpZ8+eO1uu1r50/LGZsQmZpMPNzlSzkeWDc2+/8drZt27dXAxHWp/94gvN6Ykr6zdPf+qFocwXri/OTM5UPP+9K+9JFAvU5y5Z7aw3wMUa+bQWTu8XgXs+Gc5NXHr0ef7mhW8/Gzw92jyQp6rWhLW1iLgYABsgxfihnc0eN3L7dIsfFfcPozsmU97ZMxybrQtk3/8rW7jTlpidijmkAYCaDyeEvUsj8r+JsnrPWr7hMA+DsuMQY2y3u3b9+k1KaVhqIG0762vZMFO5ZK5TLodxPBxrNTllg0E0tW/W48wtleKe6HT7xx4/ef3q5TzNapNTruuurK4QbR2H9Xq9b3zjj69dudqanpqfnz/+7NMnjh67srR4+Ogj/V5PKME5r1arqUyTJHE5T9NUI6K1DithHmWcc8/zkENq9Wa5RJq1xiCU6xvd0RAQob2uCoMw1QkAYLuV3EDbw9gMbHPu/Xed3M133LnbH2SJxTupFQTWfuxSwo8ww/HRdCD462L7XbZVIZQ4xtgkSVstb6OnL168aKg4dPjAxXNvIqmNEINOuzW5b9++mW5vY2Ri0uOeV6nXqmVC0MrGhgR58NgjncFw0Os3Go1quZLnWXt1Le4NFm/dvHLx0sKtG41688ChQ48eebQ8PrLRaVPG/CBodyOpZblcrpVqt9ZvdXu92sy4kYo4TKTClm0URX7otxq1xajvubyzuZll2chYBUMkoBfwYNjPAEoI4cKsYwCwYND20oI+bA4HMuaOy4w/cMl3DCf+wCyFnZ8U/L5rleRHtA298+Xvu+W755LPe+Qq+uATLAYARkEIlOdKS2CEhoEngCmRbywsEUKMyBGl4xOjfuCuLNyamZ0th+VmqxElw26/3x70KvWqF4TvXrxwdN/s/tm5NIkvnDt/48rVhRvXO+sbWshnPv2ZVr0xNjc7NjaxnkRZJhoT471h5Hmeh70y5gRYr99PkmTcdTnnaZpGUSQqea/XKZUqnDMhMkLQcBj1e3HJbebC9tIsnKxW66zbFZ5DDAK0xb3bfNIf7NYMAAD0Ltftg8GZ4qXM9isD2Dv497F62Xt22U0SHQQEYX9jYxjW8OnTp85dXjrz9hugzfpGR2k7OTUVhGGn0wnD8OD8/rAUDofDOI02up36SE1b9drrr87Pz0+NjkmRv/fu+R/98PuX3ruohDxy6MATj5+anJqVUk7MzMS9QZKmE1PTGQLXC6jFju+6iKSQZllGKSWEuNxZXesQjF3XNcYYo7rdbhwNwei1tbVavdSqu+12O+utdQZ5q76PMw6gdwh3e6N2d/tjAcP7zdP2eN6/6tO80wDrgxbxY8DHYPnu7e/vdZXG9gPSX4sAgGEiMyDEUozKoVuvBsN++8a192q4mqV5pdl0/CDNs1HfP3Xs+GhrpBenw2g4SAaux6UWm5vrge+Ojbb6ne7LL7984fy5zsam7/tHHj/45KknZmZmpMaO4yRpbjAZGZ8ARJQWgEmeZMzlQEhv0Ddga7UaxtgYI/KcE2qMoZRWKhUhBMaQRMM0GcxMz2Pioe6g2Rrvd/uDviUUIb3VE8gig60BwOYDAtMPuXrbv0HvJ+6dC3HxK2yRwUXNsLEIwOKt/32MFHxgReMfNbYbd24vM54HvV7GXRuWXGOjq9cuXr9xSRvR6fdqjebI+OQgy2ZHx06eemJqcjoexsMoylUmZOp4/urqsuuwQ/MHzr35xsaN5TfffJNimNs3Ozs7c/LEiTAMjVSO6xNCri8t7p+eq42MdTbbYakcJbFrUJZlnkfiOEYINZtNQmmeZvFg6HmeEML3/Vartbq6GgZelsaVSmUYddbbK+12+8TheV9rq7W1FBUjf5HdMmBoi0AW6+Kf958+NuhDtnpom4w7v0Xv3xre3u3tPOWuyq4P//G9GouPdc/3oGAs+IFLqDJGra+vXr9+bTDoYWKMVOVqnbu+ztInnnr60MHDa2treS4t0oNe16+G7W7bWnXgwJH11ZXXXvnx0qUbtXrliSeeaNTq9Xq1mBg9MtJcWRtsrK3XJkaZwzudDqVMGhOGZZRmSikAUEpZa0uVsiRoGEVSytHR0UqlgoxlnHS6mxkFAFi4eT3LpV9yuOvnuUzTlDuIYAANcIeTu8MPvHW3d/psFB850DsMs9vTArcGpd5es38aFmHQW6stMkUp1seJu5LvnpfLuznpfy2LeOe7/9VHgpABi7YqOWBbeonsMErL5XIUrzSbo5eun3vv/Fm5tlSZ3OfPz3vVWjtNx/fNT87uv3jlajwYuownOEFaZjrloMrl4PqV869+5y832xunn3hiYmKiXC47jlMqVSxQpdWNmysr3c7YxDj33G6eODg0FBmLc4M8rT1KlTH9fn9qYrpeba61NzhykNGOBcejdVy+cuXixSvvLSwvrG6si/6g0Zp44cuneoNkefPaxHgTCyK1oJhjZJAFbAkgBRZZAIvBGAVgwPgWBLIcU4sBIQJGGGwBAUYAyN62gooCx4AJJJHyPKqlBks8F6QEALCI7FzeIsB31/v4M9ype8WetXwY4yJXRgi6dOVye+WWNz7ildzALWNGK27l61//+ubm5uLyUr1c6Q0HmY1dTk1sZqYn0zj67re+PVhee+rpp2dnj1BKy+VypVIhhORCSKWMtY7jMMY455ZQxpjFWCkjVe4Ywxjr9/sAwBxOgBTgnDebzdXV1WG3lyTxysrSuXPnFi6eB4QPPWK67dnm2OTizR5lI9kwAyMourM/nwVkCpeCYmOt3ekPbixSGoy0DGOEt5qJF+QrFmZCIEpk4FMvoBiBlgohou8Qqj4QV6PAniVftRoaA5mg1hAlHB5OPHL06Fhr4o23r7mUfvHLL0xMz7z9zhnCcJwngEwUDUK/xT33wjtn3/jJa91+59nTp588fTrLaZ5nhBCMcRzHaZpaa5VSYaXkui6l1GCCMFgw1mqjDCOUc96+dctxHNd1LdjC4ZBSKqUqlUoSx1ffuTo/P/+Vr/3Sn/z5t//gN//joBsnsQrcoFKWUbrp8KHnM2gXVnzrBllkwGKDgGtfFy0kUeEcGIvAmDxFFAAQGLxdwlwYP8d3ECaEqUF3YI1q1isMQbfXcVgVAJDF7w8Bfqw973c9+T78U4sgijLXdaXUBvTJxx4bRnEsL3SGGxvdzpeeOn3q9BMXLryLkHUcJ1dimAw5IVEUEZn+5OUfDwa9L3/x82Mjo+vr67XmXMUPfd8XWsVZJpVCCJTR9TCknGmwRmtQCmFCkLUIe66ntR4OhxMzU4SQzAiCUJ6mDLNKpdLdWFdKTs9On3n7TYPkP/h7fxf6yY9+/Mpbb77eHw4ff/z5tc3VZhirGJWgunM2FhmLcDF2F4NRRmlFACOEESAMCDBx0FaMDiMAAhZhhBFCCGXCxHFMSI6JDUuh77vJUBljEOgdxxe2KhDuklm7b9j15LsbpNHEaG14nMD8/JOcTX7rO//67FvXvJL3wuc/2+22V1eXJ6cmuv2OXwpXNwZeECyuLMf9QSkof+rpZ8bHx5dXl8ZGRpMsrzhuLlWaxrlQUglCSFgqAUbGGIQswmCttraoWgeGyUavY60tl8vaWqUUQiiJ4jRNHe6kedYfDvbv3//22Td+67d+U8r8mdOfX1nq3bh5VSk+u//AYLjh0VuNZhXyrc3XTsLaIjAItELYUkM0YGup0FhqyLXOJbMAgIy11iILpOicCWgwjPM0bTTcWrXW2bz82vfOURT+wi/83PqiKVzmwkZ+3LwDgD0gJrU7ssv3fx/4gdGIMS9OMyHNoSOH5+b3JzL91PNPez5dWb5VKwfRoMMYWVlbZA4VWmdCKKHm5w+WS/XNtc1SuT42Pi2UHcZpu9uP0kxZk0ttAEqVilJKKFX0RSWEABgExlqTpmmv0w3LJcp50SnfGJOm6WhrREgxMTFBKe0Pe8+/8NknnnjizJm32uu3Dh6cdhh3neDipQsAVCgTlCp3plktMgYZU5yjQYAxYwwzbjARCqVSxtJ4nHNCMCBQWmYyGaZxPx50Y21wozU6PjGptXnzjbe/+c1v/fl3fnj92ioGBcigO94FWUD36m78zbBnLV8SawuaOw5HTjIUjPFG7ZHD8+1Pffr0+sYixpJj1O0OnVKwvLTYGhtNLLKY75uZq1Wbve5wpFmvT4/fWFyemD4kZZ7nuZTIWOX7fhB4GGOtFAAQQiilyhiwgBDCCIb9QZ7no9OTUkpEMCEkSZIkSeYOHlRCxPEwE2kFgkajtn//HKHI9Gm93EKa97tJotthqaKC4erajRG4c/ZaEQcGALBEIkwNS6XROaznOFO0j5BlzRBrg4VGQoPQWhlkkLW2Xi57XkrcuL9xK9M36iMyi1Z/8uaffv6Z/wJbY7abb+zNDMeDAqOEMiIVcAdyw5Ik2X9gfm5+vyXnoyxWIs0zWfL9xZWVZrOZpmkaRYDtxMRUJnKLcL02kkvNuGusVdoqo6MkFiJrtupe4PeHA68UEkIQwYVtA2MI44SQLBtijGu1Wj+OuOtgguM4zpJUpBnUKoQQIUSSJPtmJ2dmZnrd9pGpIxvdtfHx8etLaw4kt5ZWJsaezKUqAsx3pCxMYdoRZoBBa5srlVqpsaWUM5f1OpvWImsADEEWU0wIdTCgxsjooNvrDaM8k4TQmen9lWDm08++8L4OMA9oykzhnd/Px73iHl8fGYYsRRaIVcQqqoFqSjUjNFMytQbyHDjhjDHHhVz2sjgSQgzTpBv1EiU456s3bplEXH7v7LEDB3Mre4PuzMxMkmcbN5bHSi0jegxlIu0pGbea9Wq1ijAtVxqeF5T8CkdMZ8q1nGtawi5JTD/O5/YfzNPc5TQd9ga9TQyWMuwz6lMed2KVIguOxl44MvHjd9/LG9CG6DNfPinVMkqsaA9xMggBKX7DBrcSWNe8z1mJ5Lxk8poRXRFnRHX00q3+xZRep+VIkTXqRK5fShPl8YpIIc/MRrcDrnaaxJcD3/RWr57R2crCleVutx96BzgfyxHLEdVATOE7G0u0Jkbddz7c8dizls9aC4CttWAQxkQrmxstciNsBgBpmgJAu91O07RUKl+9evXkyZPGmCzLCuOEAfm+zznvDNpK6SzLGGNFYyttDADOVW6VJYQopRjZislFgyjPc2OMQQYDCsNQa6219n2/VCoZY0qlUqvVyrKs3W4XnWLWV9bXV9uPP34wDEuD/oCyje6g65V9rYjnepI6SujU5DrNCScWQRj6GluHeXVcJSXJGcOWA5j1ldU81ww7aRrX6/VyNSAE3bp168y7Vx89dkQItb6+Oj42GcWD0dFxRl0h7tjuIwAAix6qWj4iWE3BImstIMA0FzpLsrVM9tM8tgiyLAPAQsp+v48xsdaeOnUqHiQIoVK97jgOGOu6rpQyjuPhMNLW1BoNjLGUEmFMqcVFX28AY4zFtmhglWWZUirLMmkE5BAEnpRyMOwVO7+11Q0AYIwlIkmStNwoj4+P52upys1jxx9/5MijL7/8cpJEqRiGJWezu+g2pxgOtbBEO4g4lAG1kLibnc6Gpj0v8JlHMII4Emur68lAEkQHBseDiCDrBp4B2Wt3Xn/tjWajPj45du3ajWp1fGJiamZ6jnM/Tm73pdxZ2/+K4riPqiPFna+z673duwMjRLb+IVRJkiY6T3Ex5VEIlaap67ppmq2urp4+fVoIobX2PC8IgsKN0FqvrKykaToYDIQQlFIAKPK2xeCNIsnhOA7n3Pf94g/m5uZKpVIQBJRibaSxSkrJGKGESymNAcYc1/Fd1w2CoNVqba51slgw5B498qhSqj/orK0vCh3J2BpBjLRKWJVZles0tsO+idNkmMQGEA88IFSYXMis3W0LmSstAKlWq96oV1yOtRIuY1mS/6c//OathZVWY2RqfObU408hYP1ehAAjRIpQ0ZZuFZGPuVR9z1o+BMRaQFgRii3q56qbypu5TrNMWARpmlLChJD9fj8ISieOP3Zz8Uaj2nRdV2uNLA79IMsyIUScxlLKoBS6rksI0UoWk2cIIYU7CVtjV22e51rrcjkUIkPUWmWzLCuanBaK2maz6XlBnKXSyGiYxHm62dnodYeeE3Tb/aOHjlar1eXVhfW1pTyLVCA70uTxBiGkFE7xEufEMcpSvlnjOQ+Jwb3l1RubG5046q+trU81J4N6K/TcSinM8yzLMqtk4Dknjx3/vd/9d/9m89Zzn33enTlQKpXXusZxqdUUwBgEAAoAts3Qx8qHvUs+DEYBphZjEDKPhlkcp3km8zwHjKJhXKvVVldX0zR/9tlPE0KCICiXy1LKbq/nco8Rmue5lHIwGHDulMtlz/MQwggha22xk0MGaa2ttUBQnudJkgCAlDJOIzd0hBDaSMZYHA+LFjBJksVxHGepEELkcrO/ubK2PDExXS1X4iwdG5s4NH9gaen6oNsRedKqTMhMxr2B43gm6eapxgZracdPug6lFivCcLXWItTVqj4yMuoa3mw0QJs8TTY2NgBMv9/vDvpPHD8eJ1/54Y++887bZ7vrXrVaHa09xx1IYwCEEdJbGz6wW6qWj9Hv3bPkwwiUBYQ0IJtm64N4dZjczHNJwI2iuFh8O51erVbbv3//YDAIgmA4HHY6nSxJKiUAYweDQb/TT0RSLldKpRLGWAiptWYA1lohBEW02PZp0FLKPM8dx/F9HzNwfEfa3CUMY5xlmee4rdboD37wgxs3bhqAoBJOzk4cOHBwdv++bFVXwlIcJeCZRx999Hvf/06nvbG6tFgamcx0x3oOrWZp0mtHGyU/CFuhBFdhEycpZQwINUZphYKwvn9s1Ci9srBopEmTqFotY2SH/e7K8uLTTz3ph3Dh0sWVlaUf//jlL372ccAOwO1akB2+fagosMDDPd89ABOw1iBsrbVZlsRxnKZ5muQY40F/aK0dDAYAMDU1RSnV2pbL5TiO4zgulUrNZtPzPAAojJzruo7jaK2LhRUAEEJSSq21UkprbYxRSkkpKaVxPNRaCyHSNN3ZOzqO8y/+xb/47d/+7YWFhfn5+SeffPLAgQOlUklK7XoedTgAGGMeP3mqXq8jY3vdrpXC5JISzAiK4kF7s2sQjIyMDaIUU8q4bxCOhtnNW0tLy6tKqTRN4zhut9vXrl959dVXrly6PDo6+stf+/rFC++eP39ubGysVq5opd566621tbUwvLMKaWvalvnYw30YIdjdD1x8Y7cHARZjzYAykCp1HEZY1u2vb3QuD6NOp7fUaQ/jOAXAQiiE7JNPPtHvd12PZLFsb2woIcbHx62Vy6u3pM7ivOf7PsYYIZTnOQAwxrIsk1IOh0MppbU2yzLXda21aZoyxjKkLIEkizmmndWN4Ua37ITvvH7mN//9v50/evD/8E//yYlTJyv1WjxMIDejtaYp84TKamtCADa5/c++/vd7l2/2b230Vm9Ntkas7sbZYGxf5bFPH+YNG7NNxE0mMu7xwTC9fv0Gw+7M1MzM5IyDedYfpoNes+TXfedH3/2LP/2D328vLf38r34mM72/+P6fGaR6nbRaGjkwu3/QVpoojZVF1gIGSwBwocK623W29sMf93q/7nzu3lh27e3xAdu1qFoBIQRhEw16nc76YNhROEeIMMa0ttaaOI5nZqettWEYAqBOZzPLskajkWVZPBwopaJ4CACcc2NMnucWIUIYY0xIaUzRs7vQFqA8z3cCgVJKkaW5SI2SeZoYpZMk6Xa7L7zwwrFjx2q1mpRKiMwYA2CsMuVyOeoPer2eBR0Ewfz8/CNPPPHuu++efv7T68urjVq9MtoM6tWri9epAxv9Nd9WjFYWpMudqakpl3GPOWBMr9M1xnQ6XR0Ezz33HMH41ddef+WVlz1XXbl6LQjdOBm4Xu3YsWOYADYUbfkZ29dv5+turOH4pEEpoJQDpJvtlfWNpW5vnXmCMS6lTNPUWp0kydjYRFFUkeTZ2tqatbbVamVZ0m63jTFRFLnc8Tyv8GQJY5y7hBCkVMG5rcQaQKHz832fMSaMFEJYaykjYBxKCWU4z/PR1pjnBVIqrbU1wBjDGBAFw0MpJVGaUeo77j5n/wuf+9y//F//ZTvuz42OLC6tdSMxi0rDFaU1mpk5OMy7RoMSAltoVGsUk7g/WGp3IMnS4YATevPmzdB1Hn30qDHq5o0rY2NhHMcOqy4trLTKx5984gWwSEoJzgdY9rEvu3uAfB8YXwYAYJWyhGBtk43NxW5vPRcx5ogSEsexEEJKGYZhvV7HmGqtsyyLoqhWqZRKwdJSbzAYME4BIAgCzjlCSCkF25Nhdsrgix0hAERxJISo1+uEEJHFeZ4zjjh3XMZ915udne1uttdXunEcD4dDxphSCmNKiCUMRyJzHBY4rhCZttICnHj81HPXXrj81oVDM4cci9trKwuLC9VmA3F27tKZuJtYjJTRlPJqtcopG3Z60WDQ9IKL771XL5cG3d6ff/s7R48ebtSqKytLcZq5vpOKiLrm2MlD0/umkgEQynRh+X4qrPzxagv2rMNhjMAYpIrandVcxIxhgh1jIM+lVlZrPTc3Xy5VwzDMcxkNE2NVuRwKIYbDQZLGaZpyTl2XY4yLPV/hcBTxl4J/hashpcyyDAAYY4XCz1hVuMNS5saYZrOxb9++RqNRKpWttWCxlFJKmWUiTfIsS4ySQoh+v59mYpBkYa35K3/3H15fW7+1sTF7eP/G5lpvY7VEMRnG8cJSpVxu1RvNeq1eLZU8Hjh0pFndNzOxtro8PjoyNtIyShtjBr3O6vIio3hjvV0uNRgaO33ylz7zzK+o3M8zgxBgC9ggbAFbjC3GFrC1+GNqL7GFPWD5dpTfWykiAABkAOeEuomMu722UtL1OaGuFKoIyHmeNzs7yxgjmEXRZqfTcV2Xc765udHpdJRS1uowDAkhCKGi8DuX0phUW2OsVUpZZAsWFj1Jy+UyxrhwSiilnDOthJBqYHqMOkUckTEmcsUZIEQYIVJlSinfcYuR4wYBcz2kRabM2MzsV770c6+8+KOpyfE8SnrDwcs/esn3Q4eyG2++5fgOIYhyyiimCAe+7zmuFNnU2Gg6jLrtDiNISbO2tqaUrLTGPL+scj43PzMyXuu0I60psS4mdxS/3b6S8HFOONoD5PtwYKIxsSpP+/2uUsqnLkLMWp1lWTwczszMjI1OANh+v58kSRRF4xOj1tpOpzMYDChBlLJiP7eVzEBISgmgASNbDPl1qJSSEFIQLggCa22WZ4kYgrWuywCAUqqUIpgB2DTN8jwvtoYAQAgBxCxinOPEGIQhDH2CKfPCpB+tdQZf/Xt//4dvvXHpxpXPPP/Eyy99X8Sd4/vHsiT16zOEYc6JX/IxGKNl6JccymZb4wvXrq+vro6Pj2+urnQ7HYJoqRJWqiNJLGrluXrlQDoEZELHgfePPbrzQ/vQ8t0z7pxIhopWXwgrjI3WMk1TaSVCCCFirdXKgJTj4+PVanUQ9bvdrjUIYxyGoTE6y7I8z51SUAT2MIEimQZbi6xmDjfWSimpQ7XWhaoFA/E8zxgjhFBKITBKKa0UQVYISTArGhUwxjDGADjPc2MIwsAcKvJYS4W1IczrD2PDKHHcjW6PLy/+V//7//Jf/uv/r8ue2Tc9w7ARQo6PT7poxBhNOFSroTYqjROOiVYKWYjjWKrcc5x2u9tqNVojNSnzOMqHw/ipxx6ZmZnJh0AIaA15Jj1vq+D3NtDPPo7mowG+W/zm7g/zyXogC8gCaIM0gLXIWiBgiU0cajDObYgQFzZbjPtXF8uCD/s9AJjbv88gGcUDBCoatCsOL3ss7XVcY7myDvAwqE3OHkJBnWJgjOZpyiltNBoIiBIaIRLHqeOwLEu63XazVQYksnxAmDTGaAMAREk06OVZomWmpDQS8tX2cqIGqe5j16Q6VkgijvMU8sykCOdGURdTJBzPjI0F5xYvHnrs6MzYxNKVm62wGbUTXKr0jdaij3HO3MpAoSS1sWIqso3S5OaN9c6t/lhrPM1VJmLOjBW45rfWFvBI5eTRg0+4tJFlQ2uVloYzhhAgZBBWCEuENUIIAUGWfpwx2j3rcBDCpNBxlOeZsgZZjLI8abc3AYB4XhiGSZJQSoUQGxsbk5OTMsuTJIuiqNjhYYzzPC00LIV7USy+xhittdWGACrUU6VSiTFmrTVKF/s/KURRKFm41cXTGWMYkDHGGLPjNSMLCCGLkQUo0iRaKqu0MaZWqf7Zd/6k0+lgRjvdLiYkybN+NLQWIUSwZSrVJtcl33cdsr62uLLYnZqcX1lcf/fcBQDsBSXOnJs3FhlzHn/siVKpPBzGAIAQ4hwXO4oH7m7e86TxBzXp+m5ACO90JEHbawYCIEwDzuN0M5M9ILEyubC9dLAJrF7opnKZFYp2rXWtVlteWRwOh/Fw6HkB5QxjnKYpZpQFDjJWSmkAANM828qwFX8QRdFIq1WQDwCMMVIIpVSeZlrINE0LXaAQAgCUUkUxG8dEE11EaiilxGoLgDEiBAABYATIKiH+43/8j1Pl2v75g1k6dElIQ48xpuQQIw0201JSRXxaASFuXl5gNHc9sbZxhdKoWgukirBXZ447te/QyZNPUMqzdOi6JQAgBNDd9nYfb6jlnvd899xv777jw6+jxZg5XpYqrZGxRoiMc95PU4XUyPiYtRZjPBgM+v3+xMREHMfRYJgkidCmUQoZ5xiDzlWRnC2ULEYblQspi+0jchyn3+/mWRYEgTFGCam1llKCsWAsMhZjjAFJrbZ+DqC11lIBAOdcay20StPUZS7I3JjiCZgQDBiB0YHnryyuBOPYK4XMp+32hjG21+u0KhVkkUw1BeYzbvJ80N4Y9tZm6/vOnX336uUbhKMgCKTJ270uInD6iad9rxINBKOu69A0EwgIxni7p8t2ZgiZDzQfuu/YA8suvuOxPRoUGQBLKU6FUNLmuUyzjDFirUzT9NFHH03TtJjlF8dxq9Xa2NiIosQYcF3X8zzqcIwxochzGBgLABThLWWescUKSzHJs0xrjSykcRLHscgymeWgDQEEAJwyz/OKwkqLUBrHaRzHcSyyHFlwXTdwPd9xESXG2iKCWAi0jNIqFy5x9s/PS61SJasjIxJMuVWvNJqEUQtYKeVy16VOZ7Pd77SrpVCotQvvvZwN19KsB5h6brnXtY36wSOHjw8HmRTgOJ6UUBg3hCzY7e42D44Du97y3W0XYJCUUnLONVghBEI2yWKh8qJyot3dTLK40CdrreM4FmlW+LyFUhQRXNRwGCmsVXmex3FiEbEIc881xhRW03PcYjEFY4rpLSLLMcZ5moHjUEqLXhnGmCJkgwGKMhFKKaYEY4woxRgDRtZaY4wtqn+NaTWbv/y1Xzn3xk+E0V4pHGRZIAQQrK22yGAMFGOR5/1O1+bK90tvv/zdfq9TbYwgR4tcLw/WkZn8yte+zlkYDTu+WwFrkkR6PsEYlPpEbJ7uec/3SQO6QyK2tedDFlmwxhGCTk0c9N1WlCz7LmtvLFvgjz/+eKlUWly+1R/0oyhqNZtxHGdZJqWiiHiuL6VyOSMEOw4vpoEbA3mW9Xo9bZEXhL7vE0LiOMrzfHJ8glJK8ZaYWSlJMS56FCGECCGFvEpKOTIy0mw2m80mwbjb7SqlCKOc8+bomIFC6myNMQYBIcRxHJnK0WbrVqW2ur42d/hAUCnHSaKVrPGWUZYiRxkTDzpKx2Cjm7dunX/3Ha0twSFCMOz6lIenT/zykYOf39hIOfMwhjSVhDBCsFbbFmSrf/W2rOBjX3n3wLL74bBWa5NPzUyWSgEgwxgT/S7GcPr06TiOC290MBg0m80iP1YYJ8aYEMIiwBg7jmOtZYQWcpXiZYt+U8Vi6nJndHTU9/1CUmCtVUKWSqUwDB3GaNFXAOPCF3G5U7S6KkRZ/X5/0OsPBoNhFBVlR8XWcMsjoQysrVQqBw8etACZyJnDkzzjriONzKXQVgmRxsmQUqqUeefs+TAMR5utZJgN+pnDSz//c1/9jd/4rzY2oywTnudLAUII38MIgRCK0E+EyflrLLv34zD++sBbSqo7DstiC2CNgwhJh8HpJ750+dq7q6u6PDE/v/8Qq/H19dXhsNtd2xwp14gmST9LIsEc7NcqkciEUVwIznnc7WMLmcsyIbMsq9frFhHuemEYKqU2NjbGRltFlQbFoJRKksQYs9ndnJ+fb7fbyioOlBFEMVAMhkEq0kE8UEppmVstuctrQWAYQGqMFBYTiZASRhvwGI/j2GTC8YNTx05tbGySoORFCBmWaZqAaLm8Ew2G/fWS71y6tdjfXOJ8Ztjpl7zZx4498tyn/s6RYwfTBAkVuBxlCQCA75WUBtDAOb3dFeNjdzLuxN7IcMDtFp47wMiADcuV008+fe3Wp777Z38es/znfuEXi0WwMHXFhi/Pc0IIxohSyjj3EPi+hzHOktQqzS2XektApbQumu2lSQLWuq5bLMFK5kIIMNZxHErZtWvXGCaNRqPb7Ra1bVLKQp3leZ7WWuUCY+wwDgCEEMYYkrqI4BTbRABwGDcW2XxoEapW6khGvTh2KROYhy5t99pA8PjE6PVLVy5cvJrnNu+vT8wd/fILv/70syeq5YNRmiRp5pe5ij9h1uIO7AHybQsL3s+/LclxN240Kk89+emL7y1sdM+GQXUlXyykTYV7oZQaDoeU0mq1VSnXvMDFlDBG8yzL08Qik+e5Uoa7jlIqy2WlUiuKcBljrusyxrSQQoii8pIQbKRyGeec53lOKZ2ZmVlYWDDGcM6LoaYqF0qpQixjjJG5sMYUEemC2QasMsbzfI1zNERSymq9jiVdu3TdAUKAYAomTrzAjzY7b73y+mCpPTd/+MTjf/+xE48cOfgpKUx3nSBXU+6mMXzY1I5PCnY/+ZDZ9njft4IwBgBumuVSwGMnn6pVx94+/2cIaJZlg8EgSZKReqtUKqVpOhwOi66jxhitrUVaKRlHgziOwRjH8wp3Nc9EngsAQBaEEGE55JxbtVXYgSxoraWEaDDct2/fcDjsR/2xsbF2u/0Hf/AHMzMzs9YWNq9Q4RfZDiklyYUW0iiNASFKgGADgCkJPTcxxvfCbtQhhBCJ3jt7jjHWz2QYuqcOHPc5efvSpZs3FoKRqcdOP/f3f/0fKklkgtIs1YhabQBLRA0o5+O/Jz8jdj/5dgZCWQCEd4xflOYYU8r9Ti/zzGDf3MF+dum9y++hmozjGACK/hXdblcIEQQBJdxaZIyxGqQUeZ5rI4sqaoSQ1aYwV8jYLMuM0vVK1aFMa22UtsYghCjC2AJnLEtTKWWjVg+C4KWXXnr9lVcePXEctAFtAIBiwikDAIyQ0ZpjjDGxlBJCqOcU2TdEcJ7nUijX93TfWmUdTFduLHY3NlqtsdjIiNWH4tb5V16HtajSKDX9Rpq4IkMqB4xDxwGJXEBgCRRv+iF4oLu9AnuAfHCHpO82KKUi167HraFZKmIWa6PSLOEKSSk9z/N9v9frdTodz/OazaazBW6R0VpRSpHrEkDW2iKNiwBKQYgQiqKo0OdhjIvkrNbaKk0IoZRYqmSW16rVIAw31tZ7vd7M/Hyr1SKEAIDVW4ldsBYwJoRQTAwyRS9HABBKGqUJwhhRRgixBACklNVyZao1unL1utOwN2/c/N7qcNjuR51VYFVLWG5tFCnf42AhTcCApZ7CjGWZ9T/+u/Ez48HT/2+MO7Mat8EYsdYqpRlDnud1Optr60uuh4uYcCEdHQwGUsparRYEgTFbqf3tfhrCWguwFRxWudBah2EIAFmcbClDhRBCGGNklhd/b62NhxFjzCp9/dq1K1euGGP2HzzQ6/WKYE2xgmulij0ixtiqrYYvOzLpPM/TPMMYVyt1AEQpFVnOKD1y8BBk0kuFXFxdOXdRrfYdWqoSv+z61ECpwtNMR0keVqFSR1muojjD9GNt9niv2P2W74N+LgAApLlgDrfWaAuuw6O1YbffGR0dSfOO1pq4pOj6wzkvl8sA4Ps+cxmmSGphjCGEOJxygrUEjUAIoQGKYJ6UslQqAUCeZoVEpWASQgisyfOcEbq2trawtBiEoeM4yppCiI8BFZliKUTR8wC0sdpQjJEFpRRogjAmjFqMBt1eJSxlWUYIK/54rDUCw7hRqRJEALGxxuiGHEqhxycmJmYm41Q4LgekstQA1UHIgZIkga2BHp9I0LsNjLsrPhGJmdtQ6I6FZasj4NbQMWM049b3ySBt94frtVrZWj28uRloPtWYWLq1mGTp6MRopV7WRDNuMhH71McWkAFsiUi0ZRgbq7TOsjwolywyqUidEqceAgLACMUsixNLaCksF+1vg6nWSta/deO6S/n+sclXry8N87j1aEMQTBlK04hiBNQSZn2fGZMj7UlphMpLQegwrlLpsYBTNgyy1X7Hq5QWV5drrBr30/1HHikfONBLBppiNqmzURmvWNYkYWMaew3i8DhVUgPBiACzCqwyjABSd5HF3yUCc9cBgvfhvu+BZffDQTFhlCoppbDDfi9LUs91h4PBYDAoDFue54yxSqUCABjjohqoyEZs5WExxhjr7Z8AQBGK45yHvm+MkjKXeRonQ8dlgEy7s1EqB0qp1dVVz/OmpqYKzVWxWGOMOedF+LBoZlDYUUKIF/hFYEgIgSgxCLr9nrV2R/xcNNZljE3vm5FaudVKWCrnQoExzOGO4yCyNXgDW8BgtnQWxfCOTzD2LPkQIoSAUkIIEUWR1pJzurGxNhgMHMcBgKLTQOHz7hSB7wBgJ11rhNKcu4XyFCEUeH5RqCbzfDAYYIwZ2ZLUG2OSJLHWNhqNarWaZVmapsVSixDSUikhCgYXras4oTukN8Yoo4vmk8oai4BSygh1OFdK5XmOKZ2bn8+VUylPlivjSnHMq4xXKQspCwt56M6RP4AS8HvHrt/z3U0WqZQqNMlaS0KI67pCZN1uW2tdqVTSNC0ahhZ3vWjIskM7hJA1ppDuSWWEzLnrUsbTOKvUymEYylxkIrfWcs4rpXIcx3mejzSaRSe/qampsqVZlhUMKzIoBOE0TbM0dRiz2gwHA60UJ5hjV1sjjeaOCxjHScIpI/T2fQmCoFA7G7Cj42PvIiAuZ54heVYPm4CtAc0cx9rb5SY7+Lgmbf81sWctn5HKSHAZJ4ACz3U567Y3syRmjAVB0Ol0CCGe5xVi+tvPMkZvz4bavnNYSUMIs9YmSVL0gUzTVIgcY1SrVDBGCEHoeXmepWlSLpfL5TIhRG531QAASqnjOC7nlFJsQQmZJEnRJaNofoAxtggMWGG0UDIV+c4xFJ61tTZXsj7ScoOmAU8Z13Ga09OHHd7Q2nechtWAgWCEilkQGBAG/DG3ub1XfKIP7mcBth/+AIAillEkcHu9ztraSp6nrusCQK/X45xzzot5zEWN4478qbBYxXOBYESZMSCEopT6fkgAGanKYckorbXu9XrVUrlSqVy5ckVJOTo6OhgMiranhUplp+FVGIaVUrnYTZbL5Wq16jqOlBIItgiiNBFCOJ7ruG6upDKmeFZxwLkUQsqRsdHGWIV4SGGBuG5NNINqyBxKHVrMuS/c6jt7Kjyg2/IzYdcvu3fDzr1njG1url+8eHGY95RSxcxSIUSFV+68T8XGq+h6Zq3FCEPRCk0ozhypFCAzMTFRq1SLZuIFp9M0DT1fa33hwoWlxcWZ6WnXddM0xQq5Pt/poZumKer1OcWFnwHWZcgtBWEu3FQjACh4b631MUWMUIcXB6+sLtwUrbVQslyrjk/NdPpxlKg0yT2vVq2LUnmMksDkgLaKM+4MuX+ijcsn+uD+JnA5IAtKSIJRe3PzxvWrIs+1EgUpC6tWLHlKqcL/LTZMW3FgrYtvskwU7gVGdHx8sih7A4Bur42w9Xzn0OEDq2vL/+kPf1+IbGpqQghRbB8LjjLGiq7iRTalWOU5ZYWSACFUtGADgOJ4+tEwzTPOuVJKKKmUKvhaqBAwJfVGqVwLMUNpHmFiS6VSWA4AIWQsub0OmDtXgE8sdr/lQztB1PdNGk9Sa8Ay7mBCLJBKtQkkJdTzfX99fX1L42ls6PmYUWNMkmTW6mLZklJYQBijXr9tsRulGcKo1qgbo3q9DlhjjWbGZklemyidP3vut7/xHzCmp08/FcWpwFZJSYzJtR4kca4kUGIZSeNE5ClNZblW8R2epXHkp5wRw/JWq1UuB/3+kDHOGDfGEAISICOqxv0sz5AWVIrcpcaI0mjFW+RuUqVOz3VKvfag4VcCwobESGMADGDAANYaAEQI+qjisvdDx7n7yXc3WEsxtTq3Gub2zQ56/QtXL6dxUvQkKNTIWwEOITDGxoDWCmOMoNCnKGut0qpwGlzXpRQbYzzPk0r0+/3QdUZbrXffffeP//iP19fXn3zySQCIo8ipljjnjjV5JjDGWxVJlIa+jyhCKAOAIi9nCRiNrYHC9S7KK7U2W4aZc6sNADic+76fx5FQKo5jY1Sep9baybFJmSvXDcfGxuI4BqjAzvi+T7afsYO9S75i3rM0Qui5uXkp9TvvfdsaUqynxeRwpRQIK7TyPE8qI2VekC/Pcy0kIYQySrwgz9NKpTU3N8cZSdPYKO0wGobhiy++uLi8jBA5fPDQ3Oz+UlgOg5KwllJKtLE2N8YUsr9CqOc6XOUajFVWW22KHWcxvTfPsngYCcYwJmmeAUCz2RR5rpnru16tXDEi78vMINg3deC90tVGuC/Lsl5HTk/MlYNgfd0UQpkHfcnvDbuefHeL81mLjQawOM9kuR5OjE9RyoOgVJTQlkolymiWZYCtkKII+BX7Kq2UlNJui5aJwwhBQbkEAL1eDyMThqEQ9E//9E/ffvvtQ4cOzc/PCyEb9brvh8NhzEKutTbGSq2EkkViFwNYbbbD0coAEEIwIQRssdkEbWSWI2Md36eUamtXl1cAQ+6Xq60axiBzIbW0Rh2am9My19rpd4dUhYc/e1RKcDjdjurg9ye6P97mK/eIXU++uwFZ0Nq6jielzNPMYT4nLuFsONxqdgt4S8ayLU6BIuCXG0MIQWhLadze3Ng/Pzc2NqZVJmVerZTXV5e/+93vvvmT1w4feeTw4cODfkQRAYvjYRIEgbJFvwtQ1qjtHqYYY4oJIxQok9YqrY0puqYooIwAophQQlzHKZXLCqzUar0/FFr2cg3IcI9LKYt5SkZ568vRpfM3W43GkycfmZs6GQ+AIG9rIggCZLe3ecgaBEQ/JN99w+1L+34TaAEQQo4D1rI8F9ZgQhylciFEuVymlEojjTEql0UtGWWO77tFcRrnDFswxljQ4+OtarUKYAppydLS0pk3X3/z+9/d9+ixubk5lzu5o2q1pstcY6BcrvazTlH4CwDKmq32fghTjBFAUYJOABmEjLGgTSHKIggzQgvNX+FlV6vVPM8cDUZpKxRF2OGEEJSn2dz+fas3zdOnP/Wp05/VCmSeIYQwcAAwsNU7xG6tCB/r2Pp7xa4n318BhEBrsBYIosxhnDl5JiillUqliLAUqs6i8e2W2NNajDGlBLTRWhNKeegXvc8czqWUZ868ee7cWQjCAwcOlMvlNM1lLsCHIAiY622sb/IyppRShAqzSgjhlG6ZJaWVlNZqAKAIa0IQBiAUjC0kq0bpZBilRlmCsFTWWi11brXWknFiKLUIZIqef/ZLU1X52GMnAlYd9AyyzBqDCVhA2ILZ9vg//vm594o9Sz5kACMQqVVK+QF1HNdlbk5yhGThahRhNoc7Zd9jjFHmGKOKSK8xRkuptcaEFcoXSsnq6so777x99uxZmefHjh9vNRqlSqUY4FEqlYRQhGjHcbTJMcbYIqV1EWQuQsTcYwBgtDZgMFhjDLIWA3IcByGEi3aRAEopA8bxvLQXIwwEYYSAYsw5J56DCAz6g1qtfurxac/l7WVFGfJ9kmUWLEJ/5eS+TyA+uTb5Z4WhYPG2khltzbkHRFwrraQOIpzEsZNl7JFHn+4PQBvkeL61SEloNFpzswfHmhOBG8hcRYO4Vqlzx8uFBM40wUG1Rj1LHdhcXXrnJ69fe/tig5SD3JmrzWLjgmLW0FwY7oYWozzPig4HWZK1hwMEoHKBKQKtlMwjlQgkwbGIm9wmzCfCJkBEiozxWEIsBMxgI0Va5ZzF6bC34RAwjo1EJK3yAnewtFInbnmM9rKFTMuV1Q0ggAiRAJlGuaNTR2dcC64105YYai2zqJjr98l87H7y3QVFftaYQoqHjbGlUsX3/UJGTwgJwzAIgiKrG0VRIVEZDodxHLuu67teEYiulivddvv69es3r18XQuR5Pjk9FZZKURRtyfs8t5ARFGt3ET3ZEioDFKJAANDbs4qKwyu6Y2htCcaMsR2NdAEhxMTEhDGmOBjG2MbGxtmzZ3/nd36n3++PjY1ZpY3R1TpVGoZDCRiBvaNhkr3zm08uPtEH9zdB0f7CGFOIjLS2rVarWqnhOyYaFMNbdjhRJLWQ3crb+q5XqVTyOLl+6cqlixcLIYwBOzM7y1zHIiCcIYzDMGSMFQWUSkizjWI+1p3qQGTx1sPYojlG4YkrIUEbJWURBkIIWYSSJCnaUQ4Gg16vxylDFr79x9/61//yf7l48UKlUqKUDofguEAYpQ4GeF8y7ROeWCuwZ8lXWD5rAeOiwMw26s1KpbbjW6Rp2ul0+v1+IScmgMAY13XL5XKeZUKIWq0WBsHldy9cu3xlc2WtVqmGYVgqlYJqOZWCey7nXFnDHI4J2SnmKEQxhfErVFWF8eOUUUyK8kprEWiLDAJjjVQqF1vBRWsZY5gSwCiKIsZYqVQqKixbrdbp06enpqbfeuWV3/md34niQb1Za7c3EQYN2m63xbwzn4vsJ/3ufsIP76+PO2RFgIAYY4KgFASlIvZR2L8dm1c0xhNCOIxRSvM8L5dK5XL55s2bC1ev9zc7OhejrRHGWL3VtACpzAEjpfXOE3dCeoUTDQBqu0QNABBCDNMd4QIYW8RWHOaW/cBjnGHCMOGcc9cpXhAA1tbWut1usSKvLC75jvv8c8+dfuG5qzeu/sX3/oJxUq6WhARjNcKFr7H1QNsb30+4A7J3vV1krbWEYK0BIQQGI0QIZkVWt9A5ux4vBEvGGAxbYpYi8zs6OhrH8Rs/eV1HKTVQCkJOaGRttVbT1gDBFiENFhEsldJRJITIsszlrsXb/XGlLLzdopOzUdoqY5QFi7AFgjAnzOOOpgwD2qokx6hoqQEYlUqlohCOOxRbUFI6nJ88eXL6yOFGs3Lx0qXVjdVKaSqOROA6WS4I5nfW8X2iSbeNPWv5iswpIaCUKbTxSppihS3YwDkvFOpFKwJCiOe4RUivUa1prS9evNjtdvM4QcbOTEwWWqmwXAJGmOdyzi0CylhhQQvZVREmLFyHImpTLPHF+mu1QcYia5HF1iJjrFIqjZMkiuV2Iw4oYo2MJklSNCKKoggAGGNKSIpJfbTx1V/9+ujUeBRFSllkgSBQWYq352js5Bux/aRTcC+Tr/A2dhY+pZS1qCjMRgh5nlcMtS+KLSilnudRTIrA3ubm5sWLF6vVar/bk0KMjY2BtaVSyXGcIguCKdkR3DOHV6vVohB9J1xXdNoruK6UwhbvVCfZ7b4tIs+zJMnTdEs6qlSe55kseg6RonqNIhx4flFu5zJOHd4aHZmYmHA9jxDEODHalsPSneeOd+qHPtluB/2Yh878b+JubXfvqggnCizeGgKzU7QLCAAol8Ywz2PGgDHa5fXAr0owuVbVatVqs7GxwQkNgiC3BmMomu2N1psiztdvLHOFk82hysWJZ08s3LqlkN03PmYpJoSkuSCBbwl2KeOEuoQRQODwROSpSQBpihDWNoniYH+JMz/Nda4kx04mpbXW8UugTZSmXJmMKbfo82yM67oiT5MochzHYnBdLkVOKc1EThDW2igDjJZ6/bxZGxumokoBcYIECKkwMz9VoFt8MpD55O6s9qzlAzDWFgWRAFCMr7HFnKA4jos6yDiOiyTYjqrZd1yEUKfTabfbcRwPBoPZ2dmiuxljDAgGgDzPjTGcMk5o8RUAiqkKcRzvzOcoxmIV0RlrrQGrtbXWamPSNB0Mh7kUFrAXBkWSw2yPKwdtGCYjrRaj1FqbZVmn0xFCMIcLIdIsc1232WxWKhWLIE2VEAq2F9wPPj7JoJ+0Bt93w70eJ8a4mDBWDLsxxliLi1BLmqYFBYvhacaYQgdQ9NUbDoc3rl2/detWUVU+PT0tpbQYOZ7DGEMEJ1nGuQvGWDBFVbbVRmllEDDKAbQWsiBfsZQX3XY555hSxjmyRkqprHEC3y+XSOBjRnWSSCkLKmPH9V2vaFBOANVqNSHE9evXz5w5c/369f/z/+P/Wa2VKWeUUpMbazWhlLu0oODuwifXJv8NgbAFvSVnK3hrDFSr1aIeoiBHGIbeVvs9VaiOlVJrK6uLi4vdbrcUhuPj48x12p0uItj1fUyJAVBK+T5F2iJjDNZaKWuQgf9/e3/Wa1mWpIlhZramPZ35Tu4ePsWQkZmRQ3RlDaouVLPZLVHqhkiBFAQIhABBL3rQi/QgCPoP/R8ISg9iq0GKLVIEWiCbXeyq6qysITMrMiJjHjx8uvM90x7XWmZ6WNdvRmZllCqrKjKvB9rgcFw/7n7uvnvbsWXDZ98n2lmXZ2R9pxQApCrEWpsKiG3TMKGEyBL6oQ/MOssjQlNvi6LY2dmJYWy1iX3X1rUmAsHC5WcX5++9996bb7755o/fyrLsO9/5zmQ2BcTeD8roDJEIRCIzXvNhxs+1L63zAQBAor9AxEsis1E1TtDilNFPJpO0SdT3l0tiR08PHzx40Lats7aqqoODg6Zptm0zmk3yskieF72HyJk2GrVCoihpyTENNoxSCokEUlnDAG3XgSJUGlBpazyTAnR5lu/M2YfOt23fdYG7ts60GeVZVZQo0LTdW3/+xps/fuv9Dz9crlf37t37n/7jf/QP/uE/PBUJPPR9m2VWGxSx0vsQAn7Oo7zOB++X2PmSvgCwMCER6Rj6VJAys3Mu5HmiE5hMJiEMmXXb9eaTTz558uSJs3ZnZ2c6nZZZ/tHTRzGyzTLtbBROI2CtFAYmgwYVISERIYJSMcauvtSW6boOEVOL2FiTaWsyZ7U1EnyMRZbNb9/YrtfO977r++CNMYXLNdHp06OPP/740w8/+P0/+INhvdKL+e7+3m/93d/+yquvvvn2j83+AlHaweSxDMhIBKgQf7K/8bN2jbOqa6fD8XlF7edd51/yyUa6HHORJqUwxqiNKctyuVy++OKL0YfVamlIzWYzEbm4uHj8+PHh4SGIVFW1u7tbZvl2u+37vppO04JjCEGTSsNcjpGUoEiMUZEyWYZWR4Bu2LRtmxSORqNRURSiSClVd20Xfc5ERjOwKKrGIyCsQPwwlDZTMR49fvLeO++++edvvPHnf16SUoA3X3xptrfjhVMAzop8M2xHRZGXORH0fY/KoBdk0PTLE2n+27IvbeRLs12QK+IpYJG0J/vgwSdJSGO9Xo/LKp3IDx48ePToUd/382STKTMfHR1lWTadTvOyiAg+BkR0SqdqN2FYOEZUWimljEERh06TkmdKk8aYNsYhhhDZw6DRZMoqQmvteDTLy8KRuVidr07Pv/9Hf/TP//P/4ujjj2/cuHlz/6BdrUajkTDnef7KvbuT2Wy9Xt+4dXNwsZpUWT2BSDKIIq0NCH4ukuD6svNdQ+f7RSPxT+hI5LNi9wBigoci14rAh0FY7d2Wd95+5+mjD/YWFfvt0K+qwhgLh0cPz87O3n7vrRDCwcHBYrHQWq/7zWq1WtfrnYOD5XaTl8V0PEGNUbjrO1bK7Ez6GEIIgYPiABFdcN3Qz0w2smU5moO7wHJcR76kHT359N6dO5PSae4lxHhaf/zfniyX63c++vEHH3/09p/+8KJroBvAmKoqyvk4VqYdNvfu3ZndOODc5DvTWNoVhhlPR36xwdPOt9Z9Q1Pft5pAgQP4aQxz8sbrzE167Zzvb8uYWSQyAz9Ty91u148efXp+fr67u5sU51PDZbVaHR0dpaCYjte2bdN8zDlXFJVCyrOSlAmD74aBEYqqINLEgCAgLIIJP4eojDGCyGGIYYgx+r4jovG40uNx4PjkyeFH77377tvvffDu++uLNQwBDIOigxu3fuvvfOvxh588+vjjYdvkqKioXrn3onEWEff398fVKPpoBZfbze7ezXuv3F2enC0fMwMWhYr+56QfeL2rDfjyON9PGg2Xy4IiUUSYE2IlGger9dmDTz8ajUZnZ2fn5+ez2axpmkePHh0eHq7X693d3aIoZrNZ2t/uus45N51OgaUYFVmWEVHg2A69tTYN2Yj5Cq4HcMlQ1vZNiD4xPTqtqqoSiUiy3Gx/8IMfvPXGG0cffUzacjcAI1VjLthW5eL+HTUfyWG++9Wv3H3h9qOPH9x44dbE5vlkfOf+/f2DG1FYa5eDujmeLR882kBNRJneExE/QIygP1NvkFwGv2uWz/+sPffOhz8ZrP3065i4REVESIEx0Cw3m+1ZWZbJsZxz77///vHx8XQ6vXnzZlKgTFyi6/U6oQ0AYL1eT6dTMtqH4DkKAqpLUbUrepcE4EvOF2KwViNK9IMiIOCj48Ozs7Pv/cF/v1wuDcL87p37L9x58uDx0dFZkeV2kU0ms9nevhCWefHavXu37OgP/sV/tzm72A6dz/Sdo8ff/o3v/O7v/m693vyTf/JPNttuu6qL7MZrX/n6v/8//j/dvX3bMygFP8NB+lyASZ975/s8IwVIwsxIQoSkoOvXSvOjw8OdnR2t9cXFRYzxhRdeUEp1XVdVVVLg7fs+tUiUUsMwxCEWrkBUTVuHKNpmNiuUcQgKUYg0UWQQABJBEFIqDcq8QvH98OTRw3ffe+fho0cBsBiNF4vZznSyN9t9/8MHLFKMJ22GPtPTuzfn83kcfMyNZ2GJZz94N2baZ/qdk/P3/uzPzz749Ndef33fVk/f+KGZ7ILevLt987e++uT2jZu9hxjYZRY+f4X+etpz73w/p0BBSa8rpeRS6BZFYL25YLkUqX/06NFoNPrKV76itX7nnXeWy+Xe3l7qEid2FUgophByl5VlGWKs61o7W2R54l6BZ+dsOndT8GNmVrHp6uB7Z00YuqdPHp+dnYHE3f0D7/vReHxw54UbOwejH72duepgdx/mNnfZYrE7n87ms5nSOu8VGRuMF6MgM9P93Q7iH/7p91rf333pxSenpyfHF4BYVeV4OjFGWYuUQQh/8R4AXO99tufe+X7Wnt31GKPWKngUiYgYuT89O1wuT/f29hJFwWg0Sq3guq5Ho9HR0VECsj8buDEAeO9n4xIRm6Zt+m5WFS7PSF9u+MIzGme48jzmAAMAJ7B037UAUmSuKGwANZ9O9vd377/4ktVm0zUObQQMhOOylMxFFFOU+bRUS8+Z4kzfePGOt9SjEBKHuDq7uDHbee3mnX/11scvf+vbf/8f/nvf+trXEh/BEPmzu2A/gfT921bLr8QSSU8Mif4RE4Xo+cXpvZ2b5+fnSff26dOnCbHy8OFDYwwAOOe01mmr6BkUj/veN00XAhvttLLC6AMrrVKRi6iAQRiFUQQvVquEIxzaTkTGk8l8Ph9CPwCNxlVeFllZQOTgeWdnPF8sNs7vzua3Dm6MnDtyn6zX64lkt+/fv8iOpzf2zKjogl+uV0PXj/Li5t5+rsytl178X/xH/+Fv/86/067h8PBwOj2Qn6ccfv1nvc+98/HP/OkSzsdZlns/dJ23pigqfvp0eXz8dDItTpcX1XSy2NtbN7Uo8sIRwZXFZrMJIby4vyciy9PNfD5Xzm5XqyKfrFYXTRy0VdliRKjQGVE4RM8hkoBBEO/77QaCV0qhHW3PG1YoOqw3m4Wf7u3tKeOenJ+U5ejGwW1tCtTQc1AZ2oqMK6XKcm1jjGZSjVoqD27EP8uKfK+8s+Nc5vrQDNEPuGzDRRcNyuvf/sr9W98eGlwtz2eT+XJ1Mp3uen95C9LeEF5xA1/jwcf1/3j8Na3vvAhkWQEAqfHRD20IQ6LYTuGNma9UqYqiSLpCSfFCa+29H4/HIsgMiIpQp1CntTXGZa5wLjfGKTJKGa2sMc653HdD3/cg5JwLMZ6enp6dnXnvp5PJYjavqsoo7bQZjUprrUBUgFZZa0zuitw5QRDCcjqe7+1OFnMGqKrx3bt3s7IIzB8/+KQaj2eL/f/L//X//N/+f//1bD4W1nv7c99f59P1c+1L63whAAg5qxBJRLquOT8/937Y2dnJsixBqi450WJM5W1VVSKy3W6dc0l6ZT6fJ+90zhVFYa01ztrM2SxzzqWGS0r7UuclYehRsCiKnZ0959zTp0/feOvNH//4xxDZKp1rW+WFCPd93zRbbQwK5tYVJs+tSy1u0TBZzMWqfDwyLsur8satm1meT2bTYjx5cnxiXbk6Oi2qqbF6GIIEuJQglJ+qMxivO13Lc3/sfp4pMojCnLiqXNu2p6fHd+/euX379uPHjzebTcIPd12XWnrDMBRFkbbOptMpAHRdNx6PZaiJyBWFM7Yaj5SgthaRog+fFS260lDgICBibTbbWdRd2w5+uVwK47apd3f3u7Z9/fXXR1VV5K7zQ5CAUWfGaVJEUGR5cuiiKuPgszyXsVjtdnb3Zjs7eVnuHOyffvDRdGH+4//9//F3/t7fPTlus8wtl+sin/JfGKNd5zo32ZfX+RQhQt8zIillum5AVLdv32bm1WqVztYrz0ubO0k6oSzLhCpNOORaDRzFuWw0GuVZAZEDgO8HM8ToIwoQKcIEMo0hRIqIoIiU0W4+n6fejSKzXa8ffPDR8uTMN93tO7eqqnDOKE3QoUbDABxFaWut1UiOtJ5OCZQxhpS5efvOa93w+OGTLMsmO3svffUbI/0dIHOxWr94N/e9C/2gtb362a/xOPen7EvrfCKAkPYzrPfeGHP3zv1bt15479OPEsY4CSI456y1ibEqSQUltHOSQk0ge++91jrLMiGMPnqRfugpIjNrIqWUaB1BIJE598GkoAvismy+WKTX5+NZXdfn5+c//MEPHj18UK834zs3UAEAAakoCCRktCZlBDNQIqIZWNuImI+r+y+/dHp6fnBw88F7D+7c/2p9mp2fDbPZZL2C8TjfLP3l2uVnot31j3xf2pwPMBEzKqWg7/3uzt5rr30zeOi6bjqd5nme+ikpadNaJ12htm3LskxbjPP5PITAISYxD2NMjHHwPjAD4ZVgWhqsXU14h8EDI4JCVEVRLBaL8WTinNNIu/PF7YOb47LabFYhDpF92zdKqcScgcagVsiiGSZkC7TTvDRKC2EfQzWaWJMd7O0fnZ39+Rtv2awUUEVp2jaAQBI2op/O+eS6ExZ8eZ0PURDFGGUMMPPe3sGdO3dPTs5Go1EqI5JLpTM3yzLnXNLpSxse3vuyLPu+F0FEZbQz2jFDjEKknc3T1xwhfSGCqdU3Lqs8z5Oz5mWV8KRa6+l4kqYjVZlbfUlCCgCEShCYlMqsUirGqKLkSueoC237rmNm0rocj/Isy4xd7Bz85//lf4Fk2269WcNopPtOou9/tXf7r2eUlrue318agNKQjYAVAREipZdjjNpyiJJn47N199HRp6GIu5XldhO7mlC29bppt1XpysJy7BWEvcVECW83mxdu3Y+QrWo5wg3t5n3OHfUZxJFVk3VzwFiEfoIM1CsjbLmDLreoVFzxuVddh16RJVOElhXae7fuliUZ42eLrBs2feiHHgxOoR+vMXiUkSbqujGL1lqV2drQKUmfFaxct40GckNu7+aNmv13vnb3oz/8vQ/f+ePdxRixJQtd6MEZr8FriDqyiqAiUqRnd+ba/vrSRr4rnqgY4+Bj37dEMJ1OEy3aJX8FY0rmsixLnZdLljSgRLMigruLnclkkmWZMhq1UkazwjYMokg0BZAIUZBJgTEqy2z0AVg0Ka11Xder7QYIh+CLogCi1Wp1enq6Xq8RMYm/JeKElFzGGIP3wXMqg7RSlwxaz/g9uq574dZ9KGfe82iUG+PW6yYNY37V9/uvY1/aguNqdY0IIvdd1wJyUZTbYWjbdggegABYK5sVZYwRUY3H48wVSVqybVvbNCxqujMdFSNExcxgDChiTU0YbG61MU6M1UYPQ9N1IhJ9P0XrfWz6zuYEisqsBEP1avng6aeD923bDjEIUDqOjTGdCDMPfWj7tu+GkGrmGJ3JjDFp9Th9ioqi6uqmVDv/zr/7P7+xf6/ecgyY54VSMAwB8Pl7lM/fFf/VTSmltUJQonzkPsbgfe+977ouBiGlEQS1stb6LFdKTcaTGGNdt5PJxAep63Y6W6QmXIyxF0DFpGCIYdvWQ9eWed5TzLSJPgxD73QWY+zauirHNrN7s50W+ezw8Pj85MHHHz56+PFsNktUQzEg6cxai6hAKOnAhP5S7UhEkMUVzhiDIhzED4FkyPN8dX5x0Q7/6H/2Hx3sHxweXuRulmUwDM8Vjuoz9qV1PkRRShMSIoAGpMAc+r5ve9/2QxAus8wAWGu1ss4FpZRzLqk17+zsnV9sRGQ+37FGO2294OWxyBw4DjGQU0FCMwx55nbnuwicDbJer79975U8K5+sLo4/fvje4wfvv/32yeqs36xuvHxvNJ6SAgbabpvMWkAFhGVR5C6n1FshY7XTqJnBKouSquYILN57Q6rrOhv0fP9G14JW1jnYbmPf11U1Ds/hgO1L63wJYxxCJAUQex/ayB4CpjYKgCrLCrXSWgdhrXVZjrS2fe/zPC/Lsm4GzyrPc4CgyTAQIRCAgKqK0jkXZNhdLLCwlcuHpn708MHR4dn5+emfvvdwW9cPz09Pzi5a8TZ3Oy/s7+99M89dXhYXFxccQojROEtEhFpQM3PXdV3T2WfilyiQuK20Un2IKTSS1mHwRJn3TBGqqgweACCJuCrtfsV3/Be3L7PzJVZuQyqEbrvd1PXGZZVShrQyaIpRpbTuui6RUxVFkf7X7u4eIjqXGUxyZgpBEYAiRAGlaD4ZZWVBRoosP1qdffD+u++8+aMf/vD79ZOTpt3ap9vm9Ax25tlkVs1HL96/O72xMy6LgUUbesbSrKxxAJTYiWKMCbtfEaXGoQimhA8RfTd4H5GFAY2xXd1rZRUBAAwD54UyBvreP48Vx5fW+ZAkodyIkCNvt+vNZsOibOZcmzNSnuekTNM0SQM3zXmJaDabxSjOOZNViGi0tcawYgKUyAzRWTsdjbfd+l/9d//yn/7X/692uXz55RdfefEle/vu8fFxrR4/YHnh1Vcns/lZ7KrppJqMjTPtegtRfAha28Rb5b0nlFlVZSZDFu+9KI2MwsjMzmUAkFiqhrZTSlmjsixrzzoA6LqQ5WQMdd1Q176qyn74Fd/wv4ZdO+f7ObB4APh8JgNGEUAhzwjABgQAIwr1LSlNIW4q456cf9QOR1pl6/V6vxwpovFkmhm7aepEQ2ZI9QBny7P9vZtCXNdtO7S3ZnvdMGhCMtZC2/XeqmF/tIvd5vv/6vv/yT/9p13Tts36f/Lv/wcvvXxPWXzv+386XlTNTmni7ODVu1FZu1k1hm4u5iEMhvjpk0c6dxKZmYuq7Do/mpTbem2yu23blmVRWvfk/EgglmVe19tbd26/+/4HztnVdvXCzZsSeTqd+GPfdGezapclxIgAbJ0C5CSNDqB+VgbsF7yfv+j9/7x//1exa+d8fxvGAOS9zwsjYNu232xWAIyI6/VyZtze3h5Zt2nq1XIT2VdVmdqBbdteLWfM5/OiKALz3fv3Hj58iIbv37mnpfvBd//sX/43//yTH/149/Vv/dq3f20tviiKYRjm1Xg8nvYnbWCuxmMg6n2HilIPD9E0zRaFtFEiQSkVQgiCWmtjc0js4b5X+aXU1na7DRG3q7WIEKkkjToMgzEa6VKcDTA11DWLb9tOq+JXfdt/YfsyOR8DXO1QsohoDT7QdrtZb84BAyKuVqt+PLt15+6maU8PT1abNQCMRiPPsW3b3g8Jz9L3cbyYdF0XRT744IPd3d0bN+fvv/P+n/7hv3zn+2+dvv0mmPy1r752Y//gbKhns9ne3q6IB5aqqgaJ870d0qqtt8ZlRZUrhah0GpRlxvUSlVI+SpoRJ/h+3/dt17iDA2H03td1a03ZNF3SuFdkFBnvtxJZa5fkloiQAZTSRqlERHlp139Z/Jl9aSYcPwsjstbGCMEzMw++DbFPy5QAQESbbb1cLi+1+bRGpLrrsizLy6rp2qZpOULTdeWoun//rjHqzR/9+T/7z/4f3/3uH4JCGJe2Kl9+8aUUJq2108k8xc6qGA3sx7NxAGmHXllVFEWMMfqhb1r2IWWWigwAhBD6EBUiMsYhDsOQmYwAUEhEqqoCAKNsZp21Ns8ypQwAFKUlhURJ7QgSlzP/TJ/lShXhetuXIPJdqXrKsw89A4AxavAeAKy1g6+39bkxZn9/3xi3Xm9PT0+brp3NFsPQGeO6ULdtuzPfdc6dnwdBIiJj85s3b52dnvzRH/3Rhx+9ua43r//W67mqvu83VV71zKYodidlRKq7tg/RuazvakHMinzb9cxBa20U+X7oh3bofPQMEbbbJisqVDrw0PU9gkpObMhcaRVpJE2XeFVjnFZKaz0qqxg9DxRCNwxdlmVK6xiBI8jnZXZ4rSVPr++V/XXt8shhhhgvoVBNu9zWqyzLbt48iMwn52ddN4ig0tplRZZlPoTtphnPpiCUpr2kFRFdXFx877t/ZK3+xjdf+7XfeH1nf3fZbSSjvfu3TZn3Eqv5NB9XolSMkQAvTi7KsjRGD8OglDIKCSAMXbveQmT2gUPsus4Yi4iBYxRARKsdsDjnog/AGIMAUL1t67ptNltERFDCWBSFIZPl2mVGIIYQEEFrrbW21v70TeBnv661fQmcL6V6P0v9HyMQkVLU9/1muxTxVVWMRqPz8+VyubRZlqDzeZ5X4xEzRGHn3LZtGBFRiUDg+N3vfW/vYP83f/M3X375xaoqjFFFke3s7Tnnpns7m75lo/LxxOSuH0LCSM/mk4RLddqgAPgIPoau1WQkph6eOOeCcGRWxnCMzDwMXmvtfSAi4CTcJcMwbDZbhWStBRGIcHJyUtcbrVWeO1LAnOD78tMYen72+3X3vy+B86XD5WdNRBLNd9u2bVtbZ4qiEOHT09OmbpP8S99759xoNEqEzMbYxAbZ+wERjXYffPDRN7/52v7+PrBMRuOd2RwiZ1rvzBdeGLQZz6eo1Wqz6fzgnPPDkN6NgzdKk4BEBhGJrNTlnm9yplSw2swlBY6u6xTqhN0nIk2qKC61N4gocy459OPHj3/4wx8eHx8mtRlEQHyOn6BW8vOHgmk89Vc3/osbLH/p+3zuv/+8u/k5WQ0nNIdkz8i/BUADMXAXOz2YlfC2mOyWZXnRNhfLfmcx2ay268Ojmc2UsfNq/M6P3+1juPnCWEztiBu/KjJrIPzwrR9+81tfd9X8Yl17ltFodH5xzBTrgW7ee3Fz1u6MD2wdp+NJh+3TVdP7vpxMMjeKAZGygdHkI9a26To2+fnxw/Fo3Kw3hSlUwDD4ibETZcTIgMMgg81UjAFiQInig1htiryPwbNsts3NGy9wH9dnm//3f/af4H/8v37t5X9wcXGiZF8ESIkiBOou9UiEflJuIH9eg04+pyTGz7vRX0D58hx/bv5yS9D2lA8pMswcgwDA3t5eOapQkYgwwhBDZAaAxWJnu2kAwBgzmc2UMUB47949Ilqv10Q0moyXy6W11tksy/PtdptlGTNvNhsiisJd15VlmTQmr7T/EjQ6NfacNkkTCxG990MIUZiIJF6CWa5g/TbLmDkMPr1bYm2LMc5ms9F87523Pzg+OqmqSikgAqWU/FxWjJ93IFwr+9I6X2pMKHVJXps4WYY+eOF+GLZNzSCj0SihOLMsm0xmzCyImSsWi93VatW27d27d0W4ruuEjF+v19ZlyujEpDGZTJxzAJCE19q+M84CQNrtsNYmzQ8ASHqCCV4KAAkun1ztEjoPmIa8CRaaRKmTEIhCSktM3dDv7O9lbgziiEgpJwKI4EMfY3wW8JI9H42+nwiC/Yz9om/0i77P39b3/Ty7AvcywzAM9bbdbDZ935NWpsiiCFo93VkEjut66/IMEIvRiEhXVRVC+PGPf2yMKqpyuVwKxKIojo6e2szlZaGsCSJVWSbF8jzPA8fee1QqMqehbaoqEs8VMzdNA5GJqG+7pM+WeD3IaKMvU72kMI1a9cHbzMkz/csraeqmaUajkcTs117/nb293c26EQFjgYjy/PMgLZ8b/L7o+/9XsS9t5CNMDTBh5hCYmUVQKeXKYra7k41Km2WT2TTE2PthtrOIUQi1UqYcT9774P2PH3zyjW99k5nPz0+dc0rjhx9+eOPGjSzLjM18DJPJ5Pj4uGka4+xqtWr7rhxVQThVBqkgAICEhqrruiwKAmTmy4qEmUEQ0ZCKg0/yWinypRhJgKEfrDYJ+UJarTYbbc1v/ebf+x/91t+3FoIHUuA9pMoDAH9K1x5TqXutQ+CXNvJBYjETguRzzjnnsqyoh85mbjyblqNKGxNBnHOTyQRRtf2gtOn7/t13351MZr/+67/OEIloNCr7vl+uV3v7+50flFK9j7u7u6lQVUptmnoYBptnAJCoJtNhmmTrUzE7mUySLMx0PAkhXO6XMCulkgpXyguJyHtvM5eoKRExbZMQ0Waz0db+L//D/81ieme59FlWKgVd1yBK27a/aBz5t5HvC7RU5KX8nYiMuRTXA0KbZ/s3b4ynk23bdEOfV2UUZiBrMq31O+++P/Tht3/7t5MDzaZjRDw9PZ1Op0VRNG1vstzlmdY6d1lKKImIQbz3gpeulnLB5DdpO73M8nqzSXOzdC6nPXMCDN6nZ+69B8IonGWZ08YPw6UcOkiMMaWeuzt7bcNt06cg55wzRiv9Waf5TOZ3vWuOL7fzEWLi5wMR8T50XZeXZVGWi8XC5fl6vU4lpLU2xjidTrXWb7/99ng6+c53vvPJp596751zdV0fHx/v7u6CImYej8fz2c7FxUXy5mEYjLVE1LRtFEmulmVZQgwgYuKiRMTNZiMhXiaCCFeszonzhZmTzgcAJMLxyxU7kXR2937I8/z4aEVoR6Oq7733MS9UuELQP2/ya8+N6uTnmchVH5+ACQABBYDZ+zzDEDkrpnXfGWU4Ut/XAhPl8uV6Zcj2fT8M4eb+TQlqUs4Z4F/8i/+qLObf+c3fWLVtPq3QoNqG9ebCFLYLDDpvujDTapxVT49XpJUMQwa4PT6qrGX2Ozs7H378iXLWlrnRbhg8sEjvM9IiaBDbPrTbFSnHw/F4/wVJi+cxGmeUUkFhrJuu63gIbRjarpvNdiypKitQIHdZCKFUkxjAD6y1RoShBxESSeM1eRZNEAQBCQToF3y+n6tr/Ldkn+37fjmABambSgA/OXOYWYkR4RijCCBiURRaUzmqRKSua40DIo6rUYIthRD++M/+1BhXTSeLxSKzrmcWkb7rhrYBlt35QkRCP5S7BSKSgO+HYjFNbZcYo1GXLGmJ+SCbFlpL27Zd16VezHa7TbWw9zEBakIIVzO3GONPuHU5aq3TmZ7+KvX5fvanfs4Pruf76i/tKrP5yYf8sm2bVs7S88vzfDwep6LSONd13cnJyWg0Stprb/3ox6vz1a0bN8uiqPJiPB47bdiHZrsZ+h4Rb968KSEmDhdCFQZfbzbOOYjsvU8VbiojhmFg5tRBbJomTZCBZblcAsAwDN3QJ+e7pHoRSCf4ZXcaEQA4xCRwlEJRjBEIXZ4BClJMTDR/4dR6zp7mcx/5MDEvAPykrYACgMY4kZj6FFrroR8qPVbKdX7ww5A5t+HNdrs9ODiw1r713e+99aO3fvt3f0f3MWjIrIuDBw1KKd/3CskZN5tO33n3fWCcTucS49B12/Um+lCHbde2XdNqIyHE9XqdeAhS5ZE6yUlPtWmabLTw3nsfUvdEaR1DYGYik2hSPZFSCtXlJhGwXHIUCYuIMUYgAgiiADAgAKiU135myHvJDAyIAL/wXtEvMw17zj4rP8cwdbOuUC2XLmjS6plgluWj0YRIW5ul6oGZl8ul9/7WrVuz2eydH7/9r3/vf5hUk1df+erx8fH9O/eKPF8vV8DSdw1xrIqsyF0c/MMHnwKAiFycr8LgUcC33Xa1Dv2QdtGbpkldlbIs67oOIRRF4ZxjZhTgEHLnUiQWBOecsqbv+zh4DjGhkaNwohM1pKzSxpg8zxOJW9O1Q/BIETACyGcbyH/B856D2Rp8SSLf5VdyuS2DqeYAEUFCl2Xz+bxvuyzLvPd10xzsHRwfHmlRL9zcP3ry9A/+4A+k6775679TZGUY4u5ip2v7osyNcx++/+4BmXFeRtWeHB1/+vEnRVFslpsYPUZZzGZD23nvOUSFGAaPShVFMZ1OU7suyzLn7LJr05A3EeQnREWMUVkDAKEfUoQehgFSY4V5iOHSR0USQXTdNom+EoABIiCAaABGImGEnyzy4DO3S+J/v3Bw+beR729oaagPAKmdgWkyCwCr1QoAEiAgz/MY4wcffLA8O7/zyitf/+prR0+evnDzdpVXHIK19vT4qGtaYLZGWWPOTk7Pz8+n01ld18MQOMTdxc4wDH3bJY9JPeTd3d30ztba6XSKiGk1c3V+AT4gIoeYsHpp4qyU0qTS5SVIMxACQPQhPMv50lZ53/eBGTAiAQAjyf8fR7n2nZfrfn1/BUsHzc+eMuGZNEVavQagofdnZ2dFUaQeb1VVQ9v1fb9YLPZ3d2/evPn++x/u7e1NJpPJZLJer994440bN26kQKWUapqGY5zNZgCQyKwm1ejKRWKMqaU8nU5TqZHo7buuS/iXzWYDz4IZAKSipO27JCOY5ECS5lZq/qXfr5p8VwLSCSKKiH/p9Ow5OHPhGjof48//FbHz0gB6UgwSQUQJQJAmclAatRPQEIEiGG90n3exFktk+w1sytGt6a27F8MJF8M6NIer43w+uehar3VU7nTVV4sbH14cxbF193b9xJ6H7UePPnrt3t2/+43XvC3XHrTbe/DwLM/nhLnNdTHKzFxWeJbvVVvYNmFQFsdVdWt3xyxm7x09erw6sYvq3Ucf1qHtCS/qum+DyiY3FjuEmvqa/cD1eYlyvDnLFuMGo1TVUBQbY7bW2pv73dj6kWn18Oj84bI+abuV7zZPPvrguD30ei0iGtiCwcCIPuLAyJE4EkcEBgIAJazlWnvhtXO+zzNETWSY0fsIAFojGVBKlGZEjyRKiTZAdPkzZUXVDwyKnLPD0Dz69ONPP/kg+CZTmd8OlS7no3lbd3GIe3t7+7sHx0+OXrr70ot3Xjw7OT9+cvLqy1+7e+el3/u938cYX7xzp2ub5dnZjf39Oy+8kGm9PD2t8vz+7duL6SR0bdfWBjEM/fvvvXf06OFrr37llbt3jcjNvd1pOfLb7TjL1udnozyTIfR1QywWVaYMD32mKFPUbzahaaTvuetGzo2c2xmPZ6OqyvLcWEOKmCHE0LXPEIHqJ91gQXX9N9V+nj03BQeCRgQR5hhRp9aXRGETc2AJLElwVpEQIBlsuWcOGnB5sTx9etauhwzLl2+/Erf14bIu5vnuyJFsjabRZDQmtvPRzs58PHLWlGEY98Nmue49D+PRTjGtPv7hnwwQvv7t1xY3d/1hBEc7N2ae/CdPP310/OTo4lQRzCfTXvwI4ezosK5ra+1kNH353u3V8eHjB59EHya7Y9+1y/WKSfm21QzIQuyHZvP04ac8dFVVDU0znlQkwZHOlPEhGASDoAE1gITYd54jCGGMoAGIlAePqK41fOVz7LmJfH2fuLgZEQl1ZGBBJI0URHyUMHAcWNoIDUPDsKx7nZe6yD769ONPHn7I0qHmpl2en9XCVqvC2aocTZXL+sibthuNJi/cvn++3Hz6+Bh0VntYNsN4cWM8r85WJ8cnT77+2lf+zq9/S2kZz6qvfePVb7/+zdXq4sGDj7PcOudWqxUZOrh5I8YBUL7xzde+/trXmnZ7fnE6nY61Ad836+2q950gMERSEDgwx6LIm3az3lxcLM/qZgUYuq7ZbFYMDCiRPQATgVKotW7bxg9dZI+IgsIIQiCSGGbpZ58mXvcFoucm8uXOJKQxAEQRjoyIxpAdEEAhMlIE8oABgJGgrLZVQc326OP3vrc6/PiFOy/qfGEGvAjN3o0d4+B0/XTbbuuwFh3274zLWY5Z/+TjB6vN6t7i3qSckICIuI6Pj4+mLn/9G9+cZsWHj58WRT4y9kd/8qff+zffffzk4d3bd0bGXfhoAiot1WzSNM3b779TVeO9mwc707lSKivy/ny7Wq2yvuqjj8JR4bavx7FHS23fuzJDQ6DVaD723oNBg5ac4gGVsybPymlVzUadDIA+xkFUFBJJe8o/VdXSZ7bXrrs9N85nDHp/eZ9D8KjQGCMiziqlgEH1PvoYlVKkNRM6Q23nj87O+iHM5/P5dEwZBc8vf+W1MndPHz85OT4UgKLMZ6Pq7t27Ww4fP37Yc7j3ystFUXStd8ZkzmVmKIfia9/66u6NxdnqhCm4yi7rizd//FYkeOmVV+/evb2zt3v/lZf39/ebbT29f0MZHaMgYpEVm9Xae//SSy/NsvK9994riiqEIEqDVuPFrJqNhuCj8Gwxz/NcEJTWg/eD90oZ0AoV9OyX2+W2qwPE5frixTvopREaFGGMkURdFtDJEX8q97vuRzH+3/7wel3i58qFRQhh0E4jSeAhLxwRnF2c9XGb504orJqLpl+DYlYxchAVjc6NbettqCbBmirKOgzx6KLNXWY1FZkTiDGGo5PD6WK+3TS9H8iY0WjU1J0CJFAEUObZer1WSpFWm81GKZVXZdd14GVcjbIsUxpDCApQa6rrera32NRbjiIihBiGiIHLLM+J6rrOjAWlR+OxBx5YuqHvQkyd56LIttstM4cwWGvLcnR6fCJBtFKZzpptc3ZyPpvN9qZ3EHFWvGyUlWEsrCVEIiUEAsn5GAUIWImgQEDzS3x6v5g9N5GPELS2xkBkjuwjy7pef/Txe48ff7SzM6/GRc9dG1ugOMjQtvV8f7fMSJGUeZWpenW+RtoocpPRtNmsM8qUwPHxiTbKKt3X26qoMs7qtqnXG2uyzLlu0+ZlaZ1GEus0Ek2nY61154eqKhy4pCRTr9rxeGwU9n0/my3aurXagoam7oBQQuSBB/D90MzG0/V6neZ7EYGMXq9XojKjLIfoTNZRH3gosrLrupP6UBhnswkwDnWvlbp/9/b9+/dPDjfDMAh4Ihsuszr6gmFQX5Thf/oHX2xy8IuOazxdKjmhUGLNAQAS2AxNWZasNsMA+bRzbvTk9L0PPvjo5PijCHHvxsFkPunioJwip5bL5Xg8BgAU1iSKgAiEA3BswXDwGiT2Q5G7tm20JtJKMtX7EIMopZ0uDBkeJAxDzKKIKCJDipk5RE3GGBPikJKApBKDiISKiLSRtO/IcjWiiIho46XCG0NErbz3WqvxdNLVTEREQFoJYpTAzFEYIvshArMiY7Wz1uZ5kef5sDlZLs+67Whnd0LxTlWO2c8UGQiCAooBAUguD5C/xCc/77n8oni+v8n7PDeRbz4v2waAVJ5nh4efvPfeH627R+PpbD6fNk2jCKxWyjpySgBQWNgTkSbQChWBAgGtUFSMHBEMYjTKae0JLpkVBYwIgpAIASsQpYC07jSlNoaPTIDGOKUUEhGYS+AJIjxTNGEGABLBBJeKMeHzRIQdk2KyqCIQMqJEiCBd8J1XSilrBCMDiIigoIBCLSQhgvAzSCILRMhcBbBs27ZtMwOdd06CBw1aND5v8e/aOd8VSC1BgtICPQK0nRcEpYfed+v6uJeLrIrjKWxqaXFQocu4R0INBCTkTAQDqIRUJBDAmFYmmH0fYgBSJGIE8sCDADKRskUkDhwBFZMeSBMiEOY6pC0ejoEQnXFaEzD7yAwYIyACEQqiCIpwBC2X8NC0SqsBWZjX0SMha2KIRMjKIEmNEY1Fa5RRqAhEWAKgVkTRewHFLD4IKzEIQakemJtzUrGcKZ33m+XTTXdUqNu7u/vIk0RToOTZvQOALx6Z/Dex5wZGT4JAGkT13htj9vZ2eohNvxmiRwXakXE6Suj7FglIBMVLDEHQoBYiBGAOzEwKWASNUig6N8prIFTGsAgRaCIklchAEURIQJvILAisEJEYYeCQyJmjCCMjKoWACpmZIyAIC4sAKVTKIV6Ck6P0iAiMIsIiqAiAY4zKZmQ0GcUIIQYvTEhIAoSgFUcOHAlBjBKjGIHQEQ5EQVujHbMXwKg0YA8kP1XsXsL6fsHn+7flD3+V97l2ke/zLM/12UUHprGuQK1Wm3UrF0WRr9uViBBNtCZgYo4CqJWqyoI5iAgRakRAZs+RvYmkIluMAKCGqD0LIUnASApAK1IGVAocwhyjqUrvPaA2xigEZgaOQIDMSKyQgJAIhQSJSEmGilEEAAWVJF0/YmR0l4LQIQhDIERELcJGQAEYAAFQgAaJkIhUG3qJokMEZidgGZT3gGhtcXq+Pl+doAGXLUzhQhPablXJJGV7CVlGAvHaRxX9RQe+v62ojwh57lRWdSE2zbYb6mxspouRHechhNF0nI9zREwLGcwcUr4lAQAQRURU1DZaR5lEtqT6vndZlssIiYQQtUokiwmFBSyMIizOGaVQmJRSyDGGAaImImAEVIgISIwEAAIEAIW2zM/SwUs0PyCAF/I+gkFDDsAphUZpAOhWPSpGpVCRMWLRkSalFKLEGKM3ImJtlrssyTmbQW7ceGG+Z11VrZd9vdnGlqrCXHkefAbZyPi5UOa/LYLvv8n7XLvI9xdyvvQ1XJzHvFRWl+ttA1zuLu4N6vDxk4vF2TK27Qpxq1SMUWvtrA2e6+6CVAr+zByQABQohTCekYAGbLZ1keXeR20NAAx1670f4kBEVpNCSizdfrwrIigRWIADgBAKEWmIRBqVEkEfOAozXC4IP9v68TFGAU4/VNc1vuu11kWZKSRENJoQkSBXSpEmZTQoIKLES+mc897HIJo0GzcQkZCIeLOXjyozUjmUSoHSBJQbyhX/NMPUM2XH69xpvnbO93mWZSoE4H4gwqLKV6erN370wz/6k3/zuzdfGIYAABA5sfIAwND3QFEpAuR2aJtm60OnlLJWSzFVSBpwu96MywoA0oK378MwDEPojaaiKAprNIBEXupSaQSWMHQSo7HKaoWIvt6Q0YQqCnTD4CMLkFJK02UanfZ/RIQUKKW2Xd00jTFmXBUAwCGmMMUNIiIqQK1Io1IqgUkvAaeMVjutTYyRg4gIjO5uu47dk7/z67/1ne/8xv1bL7cX48G3lyujzxMfOOD//bvXawi4ChuQOMFsZPMWoG9Ba4/G5OFYuXJUrh99/PC/+hf/6ff/zfcmdLg729V2kud5UqL33ltrm6ZJLAXDMPR9f6Wm57333lNRCjMiWqUBAAXSSY0SErlsGLyI5FmmtRaRECERNiKK1poI07pGvamnswkzG1JEIMxFUSDKqvPDMCRYaAIqO+eGYUj75wlTLc/g8iKyWq3UM0sY0oSbX6/X3vvEWgkAaRdJaw3MzRAOZnKxVeDm08XBa7/7v/rWt75+HJWXnmQyhAi9M8qUGYRBJPxqn+dfZtfO+SBDDmD6oFjXslVUjCsfkUYiHz549Maf/Td/8sd/fLh898ZkMlKHh48P79/9elrOSDuRWZa1bZuSv/SA4Sc6bCHGmE2mbdvGwRtjnHMKL9HCvm+dc0SUnM9qk95htdkmXilEQUQivNx6DFyNSu+9RlIK/TAopYah82QSR8fV902EfIkEKL1+uZwbLonk0xLkpeoaYvo9Xe3VD5IWOIjo5OJYabc/J2V353svnVxszv1iPK/+0f/2/1CM7KS6EWLstsp33ikW9rkuf6WP8y+za+d8rQ+A3gXIyHjtnS2Qzz/45OP3/+yfvvGjd86O36/sVBwrrydm7WzZ+ZDY7FKdked5opL9GfKbBHOPMZq8TE5JkLRVRCMZY5ar8zzPtdZp08wYkzygabpEdiaSgPKXmxMSJS+ytm01krU6hgAAbVuPdva11l3XJaS+iCTWqavLuKLlS0tDaf0ndeOu0PMAkEiDEpI+XXly4lk5Wi7X47JbboZmkOlsVh18ZbO9eOfTB996/Zu/8Vv/wcEL9/Pila4VTn00ur6H8LVzPiJkZBchM9RAHwf++P3v/j//2T9rT34kYHLdaTBeRUtKxbO6bgvlxuOxiGitE0lKCCHtWCQXvCQ66fu+70MI4/ni8kkLhBASkVQiJktnYnrMid4veB9CyLLMWisSh2EQ4bRsEYbgMlvXdXI+ENFae9+LzkIITdMQUdpRTysjwzBccYymVeK0cySCV+QEV/EPALquS9u+yfPSzRGRetPcvn3Lma4fZDHfb70/OeuVlaeDYvRk79y9c+8f/Hv/u3u373qGpu4TX+X1tGtXcPQ1gAJmr8iBiUVJxnVPD9/eh0fBozNUZbOBLPto0GZlSYonk0nKmVJG/zPxL2V7afU6hLC/2Ems39qa8XgcQthut03T7O7up2M3ybmkkNMH74J3zmVZBpDESkM6r8MQyqoYjUYkkJowzjnmsG5927aTycQYk4rWJEk6Ho/TclqavOEzyQ0/SNrMBYCr11NHMDnfFVFG+hQVd4tPPv5Qmf5gd3+1PDfW3rsxq/utay587M9r609Fuq0Mg1hblM6H6xv5rp3zWQesCAc1MK+3m0lZFUWFiH3n9/duVkavztd1GybzmUY4W60nxgKj0lqhQiGJwFEkQvoaEIiUM0qTcSaLMRqlc5dpUpfstgCJ/TONLEIIfT9EEWutUtoalRofIgIgKVYlQozNajOPMxExpBAlxUiRqMhZbZxziFjXdRi8tbbv+65pP3ukXkKyibyX4D0iptooUZQCQIwxuW+K04lPw3u/vLi4c/PGRX1Sb7bT8VgZtTz+FLXsHdzYbJfHLUY/FEVRWnvWwdB0prq+OrzXzvkG3yplUIsg9iGCdrPZ/nx2MLs4rY+3keN4PB6X837oSOPtGzcXo9FisUjRKCn3XbHJpnKSiFLKlcIJsiAiKEpHYe8HADDG+ACJnrFuWxHJyyLLMkRU7FPVgighhO7ZCs+9O/fmixkAWKVFoh8GrTVzQJ1fztOekUOm757O30TIEkJIb6KUUsoBQN/3V7K/6ehPjJHJF5MXpiShjs20KqbdwfLo4qMPH+3s3TjYf2VVb08/PS0qNSlmRZaDNm3oe+3y3Sy2/zby/dVNMkAkBWSAvHa5rlQ5nuXvf++dF196dWc0a7Z1xH7n1q7L6KLZprZFerrp8AWA9MrVLm3qlaQCc5QXPoTovRCm/wWQui0ZAHBsmSWEoAavlCYiRZenoVLknEsFxyWvwDB471uBGH3XtkqpGL1nlSjqY4yTySTLsuRSMcamaVI+mjwpJXnhkr2UCXXmsswVMca+6wCAY+QICEoRcOS+98Mw7N7YcQRNjC/dv+eUPTo+f/zgdLq7uHn7FlB/dqYNUVVV48Kta/C1J7p+j/iZXb8rM5GV8gG6VlwxOz8fbu/sTib3RpNbkbO2j9PZgsZz7TLnoFREzIn0eLPZiMhkMkmekULgFUmoc+78/NxaO67ydrsVAeecMGbGGOP6vhflm6bxPkbfnZ6cMXNVjrTW2shkMtFabzaNiGhFSiltLRF1XQcAbds556pyfHFx0XWdqwqXWZYinbyD76fTKRKcnByLyP7+PiJut1vrTKq126621hZlnihd+qFFxGpUwGXNYYhAaTTG9ENLCpYnR3W9wao43p7dvvPy/a+PTh88PDk9PF0u183qrMad1+8sdqeHp20k5cheDT3kGUlm+uMXDTh4LvF8qIgISYEICgYiIsWjSYUU88JMJlWRuUEn5QmlkELojBk7VxFR27bMAYC1Nt73RJBlFkAScXGW2cVi0XctM4vAdrvdrOukg4CoevGJ14yIdnbmaZDaNE0iLU1/pbXmGFIbJdGYKqWs0lVVjcpqPp+LyCAxVbLp6GRm51yMcbFYDMOQtIdS8EtN46rSaRbXdfEyMUAE4M1mk2gPACBGn8gFrc0eP3wym44P7t5br9rMWQI52N+5dXvn6PDx4yM569E5lwS08DND3s/63PUBWV0750vc6kRABBFZSARl72C37VtBdrkFlL5vwaMSA0oppay1qS2XcvaUqqfMKWVXKZ1KX/RtKyJK6RBC29Vt02dZprV2VWaNSZ22PB8XeeW9X2mlDKWidTwqnXP1drNarWKMo9EIkkhzHNq2Tc1qRCRFiJhYqrquK4oiVbJFUXRdl2XZldhGCKGqqhig67qu655hEQAAElmviGRZlno06QdUSimBcVHuTCcYoGv7utsWzGVR3blzs+6XeAKj0UhEFJIVVADhLwSh6+N/vzLn+8vg188EPJmZQA1x2L+5bwrryrwYFcjStExEzloy1pGxWoEwAhpFSpFEAI6j0ahtW4nBKBJrnNGb1fKwbUbTCRKS1o5oNJnYrE9UKalZmPh4+raRGEREEc/nO6nT1jRNXdcpcKaOYBrftU3TNE29ucw+F7s7IpIZC5F91w9IoR8uhZAAk/YkxMvxhqrQhz4MXfQ9AGil8Jl8SPS9iASCvm0RkYBTCbIzXxilm23tu9YaR4qw7dbr490bt4zWAcJiZ5cBUZGOoAH8z/OzawLivHaRD4ElAorCyOlsHYawt3dgXS5aMSkCIQJEDP0AnvOxTvgRY4xSqDXFiESw3a4BIMssohsGk+d529bDMAy9v5piWWeUpkTVY6yOMVpnLlXlgQHBWF3X9bP5hGitdVkm4ZfUDkTEoigQEQVSC+bhw4fpmC6KIhUcieYsRd+rvmPiDa+q6vj4KFW+WZZprZS67K2k6JhlDhEQMc8za40xxo7GwJd19KgqESUQt/16s9n03RBALfZvBsaIpPDzVHivi/0KI9/Pf50YYmAEpIgKNZEKncxneyYfxagHLxpEUEEMQ98J4oo5hgEA0oQ3aj0MA4jtui7Pc2d1CGG7WXH0MQyEgooAkUUQQGkdJfS+83HwXY8o1tpxWSCiD30ihhq4S2M3RQnuJW3b1nWdsjGttbEqhNC33TAMSd2qaZqjp4e7u7tZlrV1E8IlEB8AkhcmAY8YQt92wkERECmjiVCEEwAR+66PMXK87HgjcIwRIe88G4UmKmaGyIBSGOfM6Om644BA2XzvZs/gCSlKJBD+OUxq1+TkvXaRz5LqQyQgENCgLeg4wHS62N+/XRSFzgoLUDmtQQx3xpjv/NY3rvjwrrAtCQmSGmPpuNzb2+u6LoSw6ULXdd4PzrmizNKMlTmEZki5mnMmhDAM3Wq1Ojs7e3K6TaM5a5QxpijyJJeakjARUYB1XXOIVzRnwzBorReLxWg0SlXFJUAmhFSDA0ASuyrLsutvpVbOVScyZXjj8fj8/Dy1GNNwOWFkjs7OM6NtVaBQjLHvW/YDUlRknS2UK6vJ7oWHkOD2HAESae5nkH7X48yFa+h8CkEBqqRfRUYhSKTc2awYA1HwABBFGWN1pdRoNProow+IqK7rS+0ea5PzrVaro6Ojs7MzEZnP56+//nqi5358vDw+Pl4uL4qiuHP3hYODgyyzRDRsO0Rs2xoAuq4BgMH35xdniFmCORlNInJ+fr7ZbJJ7pfGDAmzbFgVms1k6K/M8B4B0Pev1OsuydFKn1kw6f68O7sh96rlcUYRba4uiePvttx4+fOi9n0wmd+7cWSwWCYe1d+PAEKoy63Sd6bzbblW7Db7L7SjPa21dlhd+ACGICB5YgYJnyBq4Tp4HAJq+YHYtZojIGAOpyFnOHlTcOFvN+0ePjx9vl2c37u7j+Ns9NEM3EivYkyjTGRCAEDgKj/OZRKgmebNcz3dGpaW25uX2/PufvMVI42iGYUiT0yuSxvF4fLZZAYCQFZHzuv/9P/5+YuhmYxFR5YUXOTw6PTu9QEStFHNIIi07i8VkMtksV2Hwb7311u3X7uRZaW3OkbTK//l/+V8jGt8HANDGsIRE0ugy84//8T/uukb3dVEUm83mu7//r1988cU8z1OVrfJcRIhUvW2ZgUiHECbj6VbW64ulUqqqqnTHEHEYhpOjY+89xzifTB89PhpXI6NU0zRKxyEEVjiZzu/efvH+C3cniylG/9F6c746vLn3HWnqMWZDBN50RVWweEQtGW6brtLKFmahlp988igvrTbYmVeGtmObFYr9ZpsbO6js5z/IX/CU/lzd3s/YFx75BECeXcilJJUASvzBmz98/PRhvTl7/2F2+yvZi199UVvceugj4DOSGyVkFULgbsm//fd++4Mfv1M3Jw8+/vTiZEuGqry4e/8V04XRaLS/v5/QSkqp7Xbrvf9GWVwN6VNoSR0NlRfz+Xw6HimlOISuaYTZWrtaXSyXy0k1GlVV3/cK0Bm73W5ffPmlg/2bzNi1ochHf/LH3z88PM7LLHk5cxKxgrIsDw72mLmkyMzler27v/e1r30tHevOuU8efrrdNMaY+XQGQMEzAO3v77MLp6enafKRhm9aaw7xKy+/Yq111ubWtW1LgJm1Wuu+2wb2pqwi09HR6dHh+d58Mq7yV7/+bV42j/E+954jM5EtNFqIEvs29nWj9SgrzaefXrz16e8/engyHZez+Wh2/+VR5byBAATWkrPgv2iP+Il94c7HidKBFICQgAAQisSw/OT7ONQ7ut+enofJvLjnC31j7mJTAZAwhxgFBSwy9F3frT997523fvgnzfmRI9hf3JzNJpt27c/X5XSKiJvNZr1eJwhd27be+7pr08AN0hJHCKnAhCGg8NDUiMghxBCUUlVeJKRnCGG1Wq1Wq9y63cVOWZYXZ0unc2YIHnwfh77puwFxMFpzjGnNRARQuGvrEMKmXqeRXZIa7Pu+aZrUoxaIIpo5DINPFJfLpYk61JstwCUIXogkcmpP9n1PiLPxxDk3rkZN0/R9f3dnfHh8GoemHM93pxUPsl2ev/3GJy2HwmWvvnhrZ5apATZto6EXbkucsIYBmkLHsnv41pv/w/rD/8+U7PqpqMV8d3Z7Nr2zwnE/hMiqib+gvvzfzL5w50O6pGZFRBYgAMLIcVgPWzKSlXbbqadPn27++z8c21tMalMMDDIkHK9EjMF39dBs4/nJi7cO9r7xaqYVCgFhCPvj6ez0/DiEYbv1fd9qrUMYRGKWWe+9IbTmEirHzJxnzCzKKKWARSksylJCbNt6s1nN5hNnVNvVKKA1Nc22G5Wvfu0r0YBSKnOFc7kI/rt//++JSNcNQx8AOaGwAFhrffNgv65rGlWj0ajrmgTc6vs+Rh+jXixmZZlbmzVN07WDcw4AlVLbZj3KM2NMVVVkNFxtbzyDlyJL13VIEiGixrPzlc2qclRERAxc5G4xe+Fgd/qDH705KsfZZrY6OTHzl0EbhTLEoHCkkUTXVpPaPvrkySdjrff3D/qHD5vNWddsCIVBAiAjNkEq+uUlhV+88yEiCwECEAooBAIJsTe8Dc0wxHpiApmTB+/86ThbhBiNCYGZgYhIkIU9cyCA6WK/a3DTt+s2uFw7na/69snDC+1DnudVVaUuWlKZAoBRVQlzDEESXUU60ZQCpVEkoUiRRTgE72OMbduuVitgqaoqlcypPzd0bdPXg+uzrGfmFw72ptPpZrPR2iR4vYjked62DRFBCEEp0jowDDF4jp6955gRDm0XhkEjsR+AA4EREQ5hWo0G64TQaI1IiBhBnvFWFYbU2dnZarVKcwtEDNmOD935eUNEVV4NTPX5Job+O9/6egzhvH/y+Edvs/095F5jR0o0W89xQM2KoaWz0yezhVkvn0S7qJt23WIIFpkyBoMKAH6ZKx9fuPORCIOklazISY9YvAx5WWy3YYiMgbzv3nnz/Vm5DMKo6hBB0ChDoCJzJAJjXHzvE5MRgx6GjkzQxnVt7Ps+du2rr746m09YaBiGtm0fPnqwXC5n1RgAUjP5qm1rjFltWudcii4+9Km6LMu8beuTk5OvfvWrBwd7IhKja4f2jTfeYA/P0FAhxae0VNG2PQAkZPJ4PL5C0KCyiNg0dZbbvb29NASpquLtd97abrfpW6c69xI7jcbHwMxIBIoQMfAlMCIhcTabjTHm4MZe09Xb7bbI5Pzi8OTkEBGLrOyaBjju7S5++MMnpat6yoem75AUMqo4GVe6bQOKp6KBtTR5sIO6fbtXwLz1vhbuECJKwCiKCAW/VM6nQfgzhZIgIEiUcLpuQoAb83Ffd+Nx+fLXXrz/wi3jCNqlsALQQggQ5DIBJo0zk0HfSe87nXtDxvfUd93Do6eLxSLGmHK+siy/8pWvOOdimyRTICVzCTyntV6tmzRvjdFf1r8cjDGucB988EHCNqdTL8/zxWJR2mp3d7dpmn5onTPGmPV6led5UYy22616Jgye+ilKqT5g27aPHj0cfHfV4IgxvvDCC8PQJ4hNitBN0xRFEWqPiEJIWiFREPbe+xhS2wgRT09PvffJ6YuiKABowEyV09G0Kqd9F9q2VZomOxmwWtedmU4wU4uiXLarzBD4AzKqZbWsT+vQt8HnEaxxq4AcTQTD2nSkPA0deWYu+HOq3S/EN75gw0v99VRvAAAIski01obAiHh+ttFuZzbbyfN8CK02CsEKa4iMJIQpunANrUHsOg7sneojePCOYhSR9Xqdok6e5+m5WmvDEFEgjdgBEqtfAJa9nV3vfdu0AFwURVUVqZGrnd7Z2Ul7jVmWXVxcrFar6XTqqJhOp1VVhTggChFGHkajkSJb11JVRZbZtm2VUklbpsyL6XwWJZ6eHiutgWTY+pOzUyOQuywt11ljnHOaFJKMJlNU6fYgKorM7dCHEIqqfJZQwuHhYV3XydedkrJyo8keA3Y+ZOV4vHMwDIONjaKsNBsFrsMwnUy8hXFRTNRUUJ33tSszX/aH56eBWaKQoETmEJHFEiEZEvbxl7rQ84U7X0QSBRwAkbWmth12J+VFv9bAmcahbueTqXPFyelREgD1fQc81Ns+hDCZls7pPvRD8J51CKA1KS2howDC3PadH7q+yPLtelNVFQEqpDD45fmFDGytRaRt3YrIaDRCxNVqdbGuEwqGCIbgDapUCEut1uttURR57kOoy7JUSq03K0ZZrVbb7Xa73SbvJMg5mNX2DEA2m3UaS2ittMa+7y+OzqfT6dD1wXuOkRDLvCiKotmumq4VBCAMzOC9MqYqx77uRUQQAdD3YQheWVNV46Oz4yLLUr86/VDRh0wZGlHc1E1TT6qZAWxW5xSC0uqsWSm9JCq89JZc28HEFcByvjqJUVqjraGz7capyzkkUJmNdDd0qFXbbYzLDRrU9KVqtcBnID2pjrjCPiFi5KBJh+DzPOeIAkSkAG2WmxB8ZOiGgUWUtkU+6fpm8D1zIIXOOWEIDEkaLwWeBKFLb74zWTRNAwApzboCqUe5vKQYGRETc6NSyhVlOp2LoojRJNDeeDymoOu6Xq1Wfd9vt9uyLEWkbVsRMMYCQMJuxRjbtmvbdjJZpOMSALz3iUXeObdZSbpIxJSIUt/5pLfmvR9CQEWoKCWUdV2nBYA0x0vDkjR2azsfRSNZVBpEI0kU7novghxFADhKjIHZE3ci4rQLYVjXW22w9x5JlDEuzy9YN5s1x+i0ySwKi0SgXy4U4ZdR7XKMSXvzyvnquj4+PAwhyHBo0WG2Lst8HQktQcgAlNG5cTj4BmJMDbOA4oVbP7AEDQrZgFAA6ft+tVqlYzfB4FKbrSiK1F5OadbVjLUbLncyjFFaayJIueCmaUWkaZrj42PmkAav1pnJZNL3fVpCK4oiKZduNpv9/RtEtFwuE8tA13VlWZblKMKlilpa32yazvs+XWSSQiVSSoExhigyszMmoRejsDZGWZOKobIs08cmYWrSYNqQ6mKmsBS2IY4ItHFosxy9J0tEwuCCZ2ENpJGscFDWOkVFtFluApuLi5PHx0+ZZRvmR6dHtHMydL1GGwKLZ6Pxlxj4fumRTysCgKZpnjx5pJQqdW0K4tiLFKeHF130xvngxbkSAE7On7Zdk+cuy4rIEmNgGWL0UdgYg6iGPkyyLAUeABiGIZ2h6/X64ScPr7gyACDBS5m59zE5Yl6kRck+PWBjXNvWSVFDJMYYN5uND4P4jzebDTxLKOHZCqMIFkVxfn5eVZUx5uLi4jKqKQ0Aw9CHODBz29bD0BljgKMxZrPZJuILa7IEIFWijDEM0ve9IGhnSakQAiMbpRJ0anl+kWgYNlnehAIp9N1WIUQvwlgVWdc1SAENiGQhBB8UiULVCgdDGAIvQ5jNp36A5fK8GXtFOqA/PDp0e6dt3UiuKQpFRBBQv7zg9/8DTqCuHaVpNqgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=212x300 at 0x7F9D94DBCD50>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image.open(img_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "comprehensive-writer",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(212, 300)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "velvet-telescope",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(212, 300)\n"
     ]
    }
   ],
   "source": [
    "W, H = img.size\n",
    "print((W, H))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "serious-public",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'JPEG'"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "sustainable-support",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(212, 300)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "worldwide-insight",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'RGB'"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "moving-feeling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=170x70 at 0x7F9D94DC9550>"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.crop((30,30,200,100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "outstanding-township",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "저장 완료!\n"
     ]
    }
   ],
   "source": [
    "# 새로운 이미지 파일명\n",
    "cropped_img_path = os.getenv(\"HOME\") + \"/aiffel/data_represent/image/cropped_img.jpg\"\n",
    "img.crop((30,30,100,100)).save(cropped_img_path)\n",
    "print(\"저장 완료!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "historic-teach",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "img.crop((30, 30, 200, 100)).save(os.getenv(\"HOME\")+\"/aiffel/data_represent/image/dj_test_cropped_image.jpg\")\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "necessary-devil",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cropped_img.jpg  dj_test_cropped_image.jpg  newyork.jpeg  newyork.jpg\r\n"
     ]
    }
   ],
   "source": [
    "!ls ~/aiffel/data_represent/image"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coated-logan",
   "metadata": {},
   "source": [
    "#### 5. 행렬로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "special-reynolds",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'PIL.JpegImagePlugin.JpegImageFile'>\n",
      "<class 'numpy.ndarray'>\n",
      "(300, 212, 3)\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "img_arr = np.array(img)\n",
    "print(type(img))\n",
    "print(type(img_arr))\n",
    "print(img_arr.shape)\n",
    "print(img_arr.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "experienced-backup",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[133, 180, 250],\n",
       "        [133, 180, 250],\n",
       "        [133, 180, 250],\n",
       "        ...,\n",
       "        [133, 180, 250],\n",
       "        [133, 180, 250],\n",
       "        [133, 180, 250]],\n",
       "\n",
       "       [[133, 180, 250],\n",
       "        [133, 180, 250],\n",
       "        [133, 180, 250],\n",
       "        ...,\n",
       "        [133, 180, 250],\n",
       "        [133, 180, 250],\n",
       "        [133, 180, 250]],\n",
       "\n",
       "       [[133, 180, 250],\n",
       "        [133, 180, 250],\n",
       "        [133, 180, 250],\n",
       "        ...,\n",
       "        [133, 180, 250],\n",
       "        [133, 180, 250],\n",
       "        [133, 180, 250]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[164, 199, 255],\n",
       "        [164, 199, 255],\n",
       "        [164, 199, 255],\n",
       "        ...,\n",
       "        [164, 199, 255],\n",
       "        [164, 199, 255],\n",
       "        [164, 199, 255]],\n",
       "\n",
       "       [[164, 199, 255],\n",
       "        [164, 199, 255],\n",
       "        [164, 199, 255],\n",
       "        ...,\n",
       "        [164, 199, 255],\n",
       "        [164, 199, 255],\n",
       "        [164, 199, 255]],\n",
       "\n",
       "       [[164, 199, 255],\n",
       "        [164, 199, 255],\n",
       "        [164, 199, 255],\n",
       "        ...,\n",
       "        [164, 199, 255],\n",
       "        [164, 199, 255],\n",
       "        [164, 199, 255]]], dtype=uint8)"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mexican-milton",
   "metadata": {},
   "source": [
    "#### 흑백모드로 열기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "selective-elizabeth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=212x300 at 0x7F9D94DC9950>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_g = Image.open(img_path).convert('L')\n",
    "img_g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "elect-numbers",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=L size=212x300 at 0x7F9D94F29750>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img.convert(\"L\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "increased-settlement",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'>\n",
      "(300, 212)\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "img_g_arr = np.array(img_g)\n",
    "print(type(img_g_arr))\n",
    "print(img_g_arr.shape)\n",
    "print(img_g_arr.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "romantic-matthew",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[174, 174, 174, ..., 174, 174, 174],\n",
       "       [174, 174, 174, ..., 174, 174, 174],\n",
       "       [174, 174, 174, ..., 174, 174, 174],\n",
       "       ...,\n",
       "       [195, 195, 195, ..., 195, 195, 195],\n",
       "       [195, 195, 195, ..., 195, 195, 195],\n",
       "       [195, 195, 195, ..., 195, 195, 195]], dtype=uint8)"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_g_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "complex-italian",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300, 212, 3)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_arr.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "professional-looking",
   "metadata": {},
   "source": [
    "#### 7. get color\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "victorian-shelf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(255, 0, 0)\n",
      "(255, 0, 0, 255)\n",
      "(255, 255, 0)\n"
     ]
    }
   ],
   "source": [
    "red = ImageColor.getcolor('RED','RGB')\n",
    "reda = ImageColor.getcolor('red','RGBA')\n",
    "yellow = ImageColor.getcolor('yellow','RGB')\n",
    "print(red)\n",
    "print(reda)\n",
    "print(yellow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "brief-merit",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(128, 0, 128)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ImageColor.getcolor('purple', 'RGB')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "japanese-humor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 300, 212)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(img_arr,(2,0,1)).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "common-sailing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2, 3)"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def test():\n",
    "    return 1, 2, 3\n",
    "test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "raising-jamaica",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(test())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "desirable-channels",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "pressing-person",
   "metadata": {},
   "outputs": [],
   "source": [
    "def test_2():\n",
    "    return"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "electronic-strain",
   "metadata": {},
   "outputs": [],
   "source": [
    "def personal_info(**kwargs):\n",
    "    for kw, arg in kwargs.items():\n",
    "        print(\"key :\", kw, \"|\", \"value :\", arg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "united-syria",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "key : name | value : DJ\n",
      "key : age | value : 18\n",
      "key : sex | value : male\n"
     ]
    }
   ],
   "source": [
    "personal_info(name='DJ', age=18, sex='male')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "practical-brush",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_info = {'city':'Yongin', 'street':'Moonjeong_ro', 'house_num':'101-104'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "dimensional-meditation",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "personal_info() takes 0 positional arguments but 1 was given",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-186-6d209ce71157>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpersonal_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_info\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: personal_info() takes 0 positional arguments but 1 was given"
     ]
    }
   ],
   "source": [
    "personal_info(my_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "listed-spanish",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PIL.JpegImagePlugin.JpegImageFile"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "driving-brooklyn",
   "metadata": {},
   "source": [
    "## 9-9. 구조화된 데이터란?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "foreign-pattern",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 파이썬 dict 로 표현한 전화번호부입니다. \n",
    "Country_PhoneNumber = {'Korea': 82, 'America': 1, 'Swiss': 41, 'Italy': 39, 'Japan': 81, 'China': 86, 'Rusia': 7}\n",
    "Country_PhoneNumber['Korea']  # 키를 가지고 값을 조회할 수 있습니다."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "exterior-separate",
   "metadata": {},
   "source": [
    "## 9-10. 딕셔너리(dictionary)를 활용한 간단한 판타지 게임 logic 설계"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optical-vatican",
   "metadata": {},
   "source": [
    "#### 물품을 보여주는 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "annoying-uncle",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Congraturation!! you got a treasure box\n",
      "you have rope 2pcs\n",
      "you have apple 10pcs\n",
      "you have torch 6pcs\n",
      "you have gold coin 50pcs\n",
      "you have knife 1pcs\n",
      "you have arrow 30pcs\n"
     ]
    }
   ],
   "source": [
    "treasure_box = {'rope':2, \n",
    "                'apple':10, \n",
    "                'torch': 6, \n",
    "                'gold coin': 50, \n",
    "                'knife': 1, \n",
    "                'arrow': 30}\n",
    "\n",
    "def display_stuff(treasure_box):\n",
    "    print(\"Congraturation!! you got a treasure box\")\n",
    "    for k, v in treasure_box.items():\n",
    "        print(f\"you have {k} {v}pcs\")\n",
    "display_stuff(treasure_box)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lightweight-chemistry",
   "metadata": {},
   "source": [
    "#### 물품을 통해 얻을 은화를 보여주는 함수\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "domestic-summer",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rope : 1coins/pcs * 2pcs = 2 coins\n",
      "apple : 2coins/pcs * 10pcs = 20 coins\n",
      "torch : 2coins/pcs * 6pcs = 12 coins\n",
      "gold coin : 5coins/pcs * 50pcs = 250 coins\n",
      "knife : 30coins/pcs * 1pcs = 30 coins\n",
      "arrow : 1coins/pcs * 30pcs = 30 coins\n",
      "total_coin :  344\n"
     ]
    }
   ],
   "source": [
    "coin_per_treasure = {'rope':1,\n",
    "        'apple':2,\n",
    "        'torch': 2,\n",
    "        'gold coin': 5, \n",
    "        'knife': 30,\n",
    "        'arrow': 1}\n",
    "\n",
    "def total_silver(treasure_box, coin_per_treasure):\n",
    "    total_coin = 0\n",
    "    for treasure in treasure_box:\n",
    "        coin = coin_per_treasure[treasure] * treasure_box[treasure]\n",
    "        print(\"{} : {}coins/pcs * {}pcs = {} coins\".format(\n",
    "          treasure, coin_per_treasure[treasure], treasure_box[treasure], coin))\n",
    "        total_coin += coin\n",
    "    print('total_coin : ', total_coin)\n",
    "total_silver(treasure_box, coin_per_treasure)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decreased-animation",
   "metadata": {},
   "source": [
    "#### 딕셔너리 안의 딕셔너리로 저장하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "federal-introduction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'coin': 1, 'pcs': 2}"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "treasure_box = {'rope': {'coin': 1, 'pcs': 2},\n",
    "                'apple': {'coin': 2, 'pcs': 10},\n",
    "                'torch': {'coin': 2, 'pcs': 6},\n",
    "                'gold coin': {'coin': 5, 'pcs': 50},\n",
    "                'knife': {'coin': 30, 'pcs': 1},\n",
    "               \t'arrow': {'coin': 1, 'pcs': 30}\n",
    "               }\n",
    "treasure_box['rope']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "saving-worthy",
   "metadata": {},
   "source": [
    "## 9-11. 구조화된 데이터와 pandas (1) Series\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "traditional-topic",
   "metadata": {},
   "source": [
    "#### (1) Series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "placed-batman",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "3    3\n",
       "dtype: object"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# series에는 배열 형태인 리스트나 튜플, 딕셔너리를 넣어서 만들 수 있음\n",
    "\n",
    "import pandas as pd\n",
    "ser = pd.Series(['a','b','c',3])\n",
    "ser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "seeing-liberty",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['a', 'b', 'c', 3], dtype=object)"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "least-bracket",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=4, step=1)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "shared-thesaurus",
   "metadata": {},
   "source": [
    "#### 인덱스 설정 : Series의 인자로 넣어주는 방법\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "mediterranean-instrument",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "i    a\n",
       "j    b\n",
       "k    c\n",
       "h    3\n",
       "dtype: object"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2 = pd.Series(['a', 'b', 'c', 3], index=['i', 'j', 'k', 'h'])\n",
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "nervous-vacation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5    a\n",
       "6    b\n",
       "7    c\n",
       "8    3\n",
       "dtype: object"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2.index = [5,6,7,8]\n",
    "ser2"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "desperate-discretion",
   "metadata": {},
   "source": [
    "#### 인덱스 설정: 할당 연산자\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "white-portuguese",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jhon     a\n",
       "Steve    b\n",
       "Jack     c\n",
       "Bob      3\n",
       "dtype: object"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2.index = ['Jhon', 'Steve', 'Jack', 'Bob']\n",
    "ser2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "palestinian-landscape",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Jhon', 'Steve', 'Jack', 'Bob'], dtype='object')"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser2.index"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "colored-rescue",
   "metadata": {},
   "source": [
    "### 딕셔너리를 Series로 넣을 경우"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "id": "shared-croatia",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Korea      82\n",
       "America     1\n",
       "Swiss      41\n",
       "Italy      39\n",
       "Japan      81\n",
       "China      86\n",
       "Rusia       7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Country_PhoneNumber = {'Korea': 82, 'America': 1, 'Swiss': 41, 'Italy': 39, 'Japan': 81, 'China': 86, 'Rusia': 7}\n",
    "ser3 = pd.Series(Country_PhoneNumber)\n",
    "ser3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "visible-stable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser3['Korea']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "id": "christian-newcastle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Italy    39\n",
       "Japan    81\n",
       "China    86\n",
       "Rusia     7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser3['Italy':]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "metric-aside",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Country_Name\n",
       "Korea      82\n",
       "America     1\n",
       "Swiss      41\n",
       "Italy      39\n",
       "Japan      81\n",
       "China      86\n",
       "Rusia       7\n",
       "Name: Country_PhoneNumber, dtype: int64"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser3.name = 'Country_PhoneNumber'\n",
    "ser3.index.name = 'Country_Name'\n",
    "ser3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "multiple-symbol",
   "metadata": {},
   "source": [
    "## 9-12. 구조화된 데이터와 pandas (2) DataFrame\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "established-principle",
   "metadata": {},
   "source": [
    "#### (2) DataFrame"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "disciplinary-mileage",
   "metadata": {},
   "source": [
    "#### Series로 변환\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "limited-wildlife",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region      [Korea, America, Chaina, Canada, Italy]\n",
       "Sales                      [300, 200, 500, 150, 50]\n",
       "Amount                        [90, 80, 100, 30, 10]\n",
       "Employee                         [20, 10, 30, 5, 3]\n",
       "dtype: object"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Region' : ['Korea', 'America', 'Chaina', 'Canada', 'Italy'],\n",
    "        'Sales' : [300, 200, 500, 150, 50],\n",
    "        'Amount' : [90, 80, 100, 30, 10],\n",
    "        'Employee' : [20, 10, 30, 5, 3]\n",
    "        }\n",
    "s = pd.Series(data)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "lesbian-residence",
   "metadata": {},
   "source": [
    "### DataFrame으로 변환"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "decreased-demand",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Employee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Korea</td>\n",
       "      <td>300</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>America</td>\n",
       "      <td>200</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Chaina</td>\n",
       "      <td>500</td>\n",
       "      <td>100</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Canada</td>\n",
       "      <td>150</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Italy</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Region  Sales  Amount  Employee\n",
       "0    Korea    300      90        20\n",
       "1  America    200      80        10\n",
       "2   Chaina    500     100        30\n",
       "3   Canada    150      30         5\n",
       "4    Italy     50      10         3"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = pd.DataFrame(data)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "featured-cisco",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Region', 'Sales', 'Amount', 'Employee'], dtype='object')"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "relative-person",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=5, step=1)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "wireless-quilt",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>one</th>\n",
       "      <td>Korea</td>\n",
       "      <td>300</td>\n",
       "      <td>90</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>America</td>\n",
       "      <td>200</td>\n",
       "      <td>80</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>Chaina</td>\n",
       "      <td>500</td>\n",
       "      <td>100</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>Canada</td>\n",
       "      <td>150</td>\n",
       "      <td>30</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>five</th>\n",
       "      <td>Italy</td>\n",
       "      <td>50</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             a    b    c   d\n",
       "one      Korea  300   90  20\n",
       "two    America  200   80  10\n",
       "three   Chaina  500  100  30\n",
       "four    Canada  150   30   5\n",
       "five     Italy   50   10   3"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.index=['one','two','three','four','five']\n",
    "d.columns = ['a','b','c','d']\n",
    "d"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "proved-highland",
   "metadata": {},
   "source": [
    "## 9-13. pandas와 함께 EDA 시작하기\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "modified-effectiveness",
   "metadata": {},
   "source": [
    "#### CSV 파일 읽기\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "id": "standard-sustainability",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "csv_path = os.getenv(\"HOME\") + \"/aiffel/data_represent/data/covid19_italy_region.csv\"\n",
    "data = pd.read_csv(csv_path)\n",
    "type(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "separate-opposition",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>SNo</th>\n",
       "      <th>Date</th>\n",
       "      <th>Country</th>\n",
       "      <th>RegionCode</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>HospitalizedPatients</th>\n",
       "      <th>IntensiveCarePatients</th>\n",
       "      <th>TotalHospitalizedPatients</th>\n",
       "      <th>HomeConfinement</th>\n",
       "      <th>CurrentPositiveCases</th>\n",
       "      <th>NewPositiveCases</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>TotalPositiveCases</th>\n",
       "      <th>TestsPerformed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>13</td>\n",
       "      <td>Abruzzo</td>\n",
       "      <td>42.351222</td>\n",
       "      <td>13.398438</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>17</td>\n",
       "      <td>Basilicata</td>\n",
       "      <td>40.639471</td>\n",
       "      <td>15.805148</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>4</td>\n",
       "      <td>P.A. Bolzano</td>\n",
       "      <td>46.499335</td>\n",
       "      <td>11.356624</td>\n",
       "      <td>0</td>\n",
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       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>18</td>\n",
       "      <td>Calabria</td>\n",
       "      <td>38.905976</td>\n",
       "      <td>16.594402</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>15</td>\n",
       "      <td>Campania</td>\n",
       "      <td>40.839566</td>\n",
       "      <td>14.250850</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>10</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SNo                 Date Country  RegionCode    RegionName   Latitude  \\\n",
       "0    0  2020-02-24T18:00:00     ITA          13       Abruzzo  42.351222   \n",
       "1    1  2020-02-24T18:00:00     ITA          17    Basilicata  40.639471   \n",
       "2    2  2020-02-24T18:00:00     ITA           4  P.A. Bolzano  46.499335   \n",
       "3    3  2020-02-24T18:00:00     ITA          18      Calabria  38.905976   \n",
       "4    4  2020-02-24T18:00:00     ITA          15      Campania  40.839566   \n",
       "\n",
       "   Longitude  HospitalizedPatients  IntensiveCarePatients  \\\n",
       "0  13.398438                     0                      0   \n",
       "1  15.805148                     0                      0   \n",
       "2  11.356624                     0                      0   \n",
       "3  16.594402                     0                      0   \n",
       "4  14.250850                     0                      0   \n",
       "\n",
       "   TotalHospitalizedPatients  HomeConfinement  CurrentPositiveCases  \\\n",
       "0                          0                0                     0   \n",
       "1                          0                0                     0   \n",
       "2                          0                0                     0   \n",
       "3                          0                0                     0   \n",
       "4                          0                0                     0   \n",
       "\n",
       "   NewPositiveCases  Recovered  Deaths  TotalPositiveCases  TestsPerformed  \n",
       "0                 0          0       0                   0               5  \n",
       "1                 0          0       0                   0               0  \n",
       "2                 0          0       0                   0               1  \n",
       "3                 0          0       0                   0               1  \n",
       "4                 0          0       0                   0              10  "
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "id": "increased-berry",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Recovered</th>\n",
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       "      <td>793</td>\n",
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       "      <td>ITA</td>\n",
       "      <td>9</td>\n",
       "      <td>Toscana</td>\n",
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       "      <td>11.255889</td>\n",
       "      <td>1120</td>\n",
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       "      <td>P.A. Trento</td>\n",
       "      <td>46.068935</td>\n",
       "      <td>11.121231</td>\n",
       "      <td>345</td>\n",
       "      <td>76</td>\n",
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       "      <td>1483</td>\n",
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       "      <th>795</th>\n",
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       "      <td>2020-04-01T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>10</td>\n",
       "      <td>Umbria</td>\n",
       "      <td>43.106758</td>\n",
       "      <td>12.388247</td>\n",
       "      <td>173</td>\n",
       "      <td>45</td>\n",
       "      <td>218</td>\n",
       "      <td>646</td>\n",
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       "      <td>17</td>\n",
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       "      <td>2020-04-01T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>2</td>\n",
       "      <td>Valle d'Aosta</td>\n",
       "      <td>45.737503</td>\n",
       "      <td>7.320149</td>\n",
       "      <td>85</td>\n",
       "      <td>27</td>\n",
       "      <td>112</td>\n",
       "      <td>428</td>\n",
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       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>59</td>\n",
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       "      <th>797</th>\n",
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       "      <td>2020-04-01T17:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>5</td>\n",
       "      <td>Veneto</td>\n",
       "      <td>45.434905</td>\n",
       "      <td>12.338452</td>\n",
       "      <td>1718</td>\n",
       "      <td>350</td>\n",
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      ],
      "text/plain": [
       "     SNo                 Date Country  RegionCode     RegionName   Latitude  \\\n",
       "793  793  2020-04-01T17:00:00     ITA           9        Toscana  43.769231   \n",
       "794  794  2020-04-01T17:00:00     ITA           4    P.A. Trento  46.068935   \n",
       "795  795  2020-04-01T17:00:00     ITA          10         Umbria  43.106758   \n",
       "796  796  2020-04-01T17:00:00     ITA           2  Valle d'Aosta  45.737503   \n",
       "797  797  2020-04-01T17:00:00     ITA           5         Veneto  45.434905   \n",
       "\n",
       "     Longitude  HospitalizedPatients  IntensiveCarePatients  \\\n",
       "793  11.255889                  1120                    297   \n",
       "794  11.121231                   345                     76   \n",
       "795  12.388247                   173                     45   \n",
       "796   7.320149                    85                     27   \n",
       "797  12.338452                  1718                    350   \n",
       "\n",
       "     TotalHospitalizedPatients  HomeConfinement  CurrentPositiveCases  \\\n",
       "793                       1417             3015                  4432   \n",
       "794                        421             1062                  1483   \n",
       "795                        218              646                   864   \n",
       "796                        112              428                   540   \n",
       "797                       2068             6156                  8224   \n",
       "\n",
       "     NewPositiveCases  Recovered  Deaths  TotalPositiveCases  TestsPerformed  \n",
       "793               259        182     253                4867           36575  \n",
       "794               124        214     173                1870            7675  \n",
       "795                17        194      37                1095            9080  \n",
       "796                 3         32      59                 631            1717  \n",
       "797               470        902     499                9625          112746  "
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "id": "existing-gravity",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>SNo</th>\n",
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       "      <th>NewPositiveCases</th>\n",
       "      <th>Recovered</th>\n",
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       "      <td>ITA</td>\n",
       "      <td>13</td>\n",
       "      <td>Abruzzo</td>\n",
       "      <td>42.351222</td>\n",
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       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>17</td>\n",
       "      <td>Basilicata</td>\n",
       "      <td>40.639471</td>\n",
       "      <td>15.805148</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2020-02-24T18:00:00</td>\n",
       "      <td>ITA</td>\n",
       "      <td>4</td>\n",
       "      <td>P.A. Bolzano</td>\n",
       "      <td>46.499335</td>\n",
       "      <td>11.356624</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   SNo                 Date Country  RegionCode    RegionName   Latitude  \\\n",
       "0    0  2020-02-24T18:00:00     ITA          13       Abruzzo  42.351222   \n",
       "1    1  2020-02-24T18:00:00     ITA          17    Basilicata  40.639471   \n",
       "2    2  2020-02-24T18:00:00     ITA           4  P.A. Bolzano  46.499335   \n",
       "\n",
       "   Longitude  HospitalizedPatients  IntensiveCarePatients  \\\n",
       "0  13.398438                     0                      0   \n",
       "1  15.805148                     0                      0   \n",
       "2  11.356624                     0                      0   \n",
       "\n",
       "   TotalHospitalizedPatients  HomeConfinement  CurrentPositiveCases  \\\n",
       "0                          0                0                     0   \n",
       "1                          0                0                     0   \n",
       "2                          0                0                     0   \n",
       "\n",
       "   NewPositiveCases  Recovered  Deaths  TotalPositiveCases  TestsPerformed  \n",
       "0                 0          0       0                   0               5  \n",
       "1                 0          0       0                   0               0  \n",
       "2                 0          0       0                   0               1  "
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "amber-leather",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['SNo', 'Date', 'Country', 'RegionCode', 'RegionName', 'Latitude',\n",
       "       'Longitude', 'HospitalizedPatients', 'IntensiveCarePatients',\n",
       "       'TotalHospitalizedPatients', 'HomeConfinement', 'CurrentPositiveCases',\n",
       "       'NewPositiveCases', 'Recovered', 'Deaths', 'TotalPositiveCases',\n",
       "       'TestsPerformed'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "continuous-microwave",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 798 entries, 0 to 797\n",
      "Data columns (total 17 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   SNo                        798 non-null    int64  \n",
      " 1   Date                       798 non-null    object \n",
      " 2   Country                    798 non-null    object \n",
      " 3   RegionCode                 798 non-null    int64  \n",
      " 4   RegionName                 798 non-null    object \n",
      " 5   Latitude                   798 non-null    float64\n",
      " 6   Longitude                  798 non-null    float64\n",
      " 7   HospitalizedPatients       798 non-null    int64  \n",
      " 8   IntensiveCarePatients      798 non-null    int64  \n",
      " 9   TotalHospitalizedPatients  798 non-null    int64  \n",
      " 10  HomeConfinement            798 non-null    int64  \n",
      " 11  CurrentPositiveCases       798 non-null    int64  \n",
      " 12  NewPositiveCases           798 non-null    int64  \n",
      " 13  Recovered                  798 non-null    int64  \n",
      " 14  Deaths                     798 non-null    int64  \n",
      " 15  TotalPositiveCases         798 non-null    int64  \n",
      " 16  TestsPerformed             798 non-null    int64  \n",
      "dtypes: float64(2), int64(12), object(3)\n",
      "memory usage: 106.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "federal-newspaper",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>RegionCode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>HospitalizedPatients</th>\n",
       "      <th>IntensiveCarePatients</th>\n",
       "      <th>TotalHospitalizedPatients</th>\n",
       "      <th>HomeConfinement</th>\n",
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       "      <th>NewPositiveCases</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>TotalPositiveCases</th>\n",
       "      <th>TestsPerformed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>798.00000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "      <td>798.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>398.50000</td>\n",
       "      <td>10.190476</td>\n",
       "      <td>43.046293</td>\n",
       "      <td>12.225955</td>\n",
       "      <td>533.058897</td>\n",
       "      <td>82.596491</td>\n",
       "      <td>615.655388</td>\n",
       "      <td>674.998747</td>\n",
       "      <td>1290.654135</td>\n",
       "      <td>138.553885</td>\n",
       "      <td>205.706767</td>\n",
       "      <td>162.677945</td>\n",
       "      <td>1659.038847</td>\n",
       "      <td>7932.164160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>230.50705</td>\n",
       "      <td>5.798698</td>\n",
       "      <td>2.490342</td>\n",
       "      <td>2.660615</td>\n",
       "      <td>1532.090841</td>\n",
       "      <td>190.735985</td>\n",
       "      <td>1719.508711</td>\n",
       "      <td>1627.086600</td>\n",
       "      <td>3282.834715</td>\n",
       "      <td>327.616052</td>\n",
       "      <td>1060.433732</td>\n",
       "      <td>700.264327</td>\n",
       "      <td>4945.629053</td>\n",
       "      <td>17067.087279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>38.115697</td>\n",
       "      <td>7.320149</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>199.25000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>41.125596</td>\n",
       "      <td>11.121231</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>230.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>398.50000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>43.616760</td>\n",
       "      <td>12.388247</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>80.500000</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>205.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>210.500000</td>\n",
       "      <td>1610.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>597.75000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>45.434905</td>\n",
       "      <td>13.768136</td>\n",
       "      <td>325.750000</td>\n",
       "      <td>71.750000</td>\n",
       "      <td>396.250000</td>\n",
       "      <td>552.750000</td>\n",
       "      <td>998.750000</td>\n",
       "      <td>117.750000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>55.750000</td>\n",
       "      <td>1110.750000</td>\n",
       "      <td>7493.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>797.00000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>46.499335</td>\n",
       "      <td>16.867367</td>\n",
       "      <td>11927.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>13269.000000</td>\n",
       "      <td>12496.000000</td>\n",
       "      <td>25765.000000</td>\n",
       "      <td>3251.000000</td>\n",
       "      <td>11415.000000</td>\n",
       "      <td>7593.000000</td>\n",
       "      <td>44773.000000</td>\n",
       "      <td>121449.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             SNo  RegionCode    Latitude   Longitude  HospitalizedPatients  \\\n",
       "count  798.00000  798.000000  798.000000  798.000000            798.000000   \n",
       "mean   398.50000   10.190476   43.046293   12.225955            533.058897   \n",
       "std    230.50705    5.798698    2.490342    2.660615           1532.090841   \n",
       "min      0.00000    1.000000   38.115697    7.320149              0.000000   \n",
       "25%    199.25000    5.000000   41.125596   11.121231              5.000000   \n",
       "50%    398.50000   10.000000   43.616760   12.388247             64.000000   \n",
       "75%    597.75000   15.000000   45.434905   13.768136            325.750000   \n",
       "max    797.00000   20.000000   46.499335   16.867367          11927.000000   \n",
       "\n",
       "       IntensiveCarePatients  TotalHospitalizedPatients  HomeConfinement  \\\n",
       "count             798.000000                 798.000000       798.000000   \n",
       "mean               82.596491                 615.655388       674.998747   \n",
       "std               190.735985                1719.508711      1627.086600   \n",
       "min                 0.000000                   0.000000         0.000000   \n",
       "25%                 0.000000                   6.000000         8.000000   \n",
       "50%                16.000000                  80.500000       109.000000   \n",
       "75%                71.750000                 396.250000       552.750000   \n",
       "max              1342.000000               13269.000000     12496.000000   \n",
       "\n",
       "       CurrentPositiveCases  NewPositiveCases     Recovered       Deaths  \\\n",
       "count            798.000000        798.000000    798.000000   798.000000   \n",
       "mean            1290.654135        138.553885    205.706767   162.677945   \n",
       "std             3282.834715        327.616052   1060.433732   700.264327   \n",
       "min                0.000000        -17.000000      0.000000     0.000000   \n",
       "25%               14.250000          2.000000      0.000000     0.000000   \n",
       "50%              205.000000         32.000000      3.000000     6.000000   \n",
       "75%              998.750000        117.750000     34.000000    55.750000   \n",
       "max            25765.000000       3251.000000  11415.000000  7593.000000   \n",
       "\n",
       "       TotalPositiveCases  TestsPerformed  \n",
       "count          798.000000      798.000000  \n",
       "mean          1659.038847     7932.164160  \n",
       "std           4945.629053    17067.087279  \n",
       "min              0.000000        0.000000  \n",
       "25%             14.250000      230.000000  \n",
       "50%            210.500000     1610.000000  \n",
       "75%           1110.750000     7493.000000  \n",
       "max          44773.000000   121449.000000  "
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe() # 컬럼별 기본적 통계값을 반환해줌"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "coral-candy",
   "metadata": {},
   "source": [
    "#### .isnull().sum() 으로 컬럼별 null 값 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "acoustic-improvement",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNo                          0\n",
       "Date                         0\n",
       "Country                      0\n",
       "RegionCode                   0\n",
       "RegionName                   0\n",
       "Latitude                     0\n",
       "Longitude                    0\n",
       "HospitalizedPatients         0\n",
       "IntensiveCarePatients        0\n",
       "TotalHospitalizedPatients    0\n",
       "HomeConfinement              0\n",
       "CurrentPositiveCases         0\n",
       "NewPositiveCases             0\n",
       "Recovered                    0\n",
       "Deaths                       0\n",
       "TotalPositiveCases           0\n",
       "TestsPerformed               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rotary-grace",
   "metadata": {},
   "source": [
    "#### EDA - 통계\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "seventh-reward",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Friuli Venezia Giulia    38\n",
       "Abruzzo                  38\n",
       "P.A. Bolzano             38\n",
       "Lazio                    38\n",
       "Emilia-Romagna           38\n",
       "Veneto                   38\n",
       "Lombardia                38\n",
       "Calabria                 38\n",
       "Molise                   38\n",
       "Umbria                   38\n",
       "Toscana                  38\n",
       "Liguria                  38\n",
       "Piemonte                 38\n",
       "Sicilia                  38\n",
       "Campania                 38\n",
       "Basilicata               38\n",
       "Valle d'Aosta            38\n",
       "Sardegna                 38\n",
       "Puglia                   38\n",
       "P.A. Trento              38\n",
       "Marche                   38\n",
       "Name: RegionName, dtype: int64"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['RegionName'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "widespread-viking",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ITA    798\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Country'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "rough-newman",
   "metadata": {},
   "source": [
    "#### .value_counts().sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "operational-price",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "798"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['RegionName'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "continued-greek",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "798"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Country'].value_counts().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "moving-polyester",
   "metadata": {},
   "source": [
    "#### .sum()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "significant-pollution",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "총 감염자 1323913\n",
      "전체 검사자 수 6329867\n",
      "사망자 수 129817\n",
      "회복자 수 164154\n"
     ]
    }
   ],
   "source": [
    "print(\"총 감염자\", data['TotalPositiveCases'].sum())\n",
    "print(\"전체 검사자 수\", data['TestsPerformed'].sum())\n",
    "print(\"사망자 수\", data['Deaths'].sum())\n",
    "print(\"회복자 수\", data['Recovered'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "extreme-scheduling",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SNo                                                                     318003\n",
       "Date                         2020-02-24T18:00:002020-02-24T18:00:002020-02-...\n",
       "Country                      ITAITAITAITAITAITAITAITAITAITAITAITAITAITAITAI...\n",
       "RegionCode                                                                8132\n",
       "RegionName                   AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...\n",
       "Latitude                                                          34350.941591\n",
       "Longitude                                                          9756.312476\n",
       "HospitalizedPatients                                                    425381\n",
       "IntensiveCarePatients                                                    65912\n",
       "TotalHospitalizedPatients                                               491293\n",
       "HomeConfinement                                                         538649\n",
       "CurrentPositiveCases                                                   1029942\n",
       "NewPositiveCases                                                        110566\n",
       "Recovered                                                               164154\n",
       "Deaths                                                                  129817\n",
       "TotalPositiveCases                                                     1323913\n",
       "TestsPerformed                                                         6329867\n",
       "dtype: object"
      ]
     },
     "execution_count": 247,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 컬럼별 sum()\n",
    "\n",
    "data.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advised-history",
   "metadata": {},
   "source": [
    "#### .corr()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "printable-product",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8555291657820235\n",
      "0.7612209669865387\n",
      "0.975268668316802\n"
     ]
    },
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>RegionCode</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SNo</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.006385</td>\n",
       "      <td>0.004550</td>\n",
       "      <td>-0.009905</td>\n",
       "      <td>0.304934</td>\n",
       "      <td>0.356195</td>\n",
       "      <td>0.311208</td>\n",
       "      <td>0.424331</td>\n",
       "      <td>0.373320</td>\n",
       "      <td>0.290469</td>\n",
       "      <td>0.206334</td>\n",
       "      <td>0.251302</td>\n",
       "      <td>0.327629</td>\n",
       "      <td>0.427816</td>\n",
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       "    <tr>\n",
       "      <th>RegionCode</th>\n",
       "      <td>-0.006385</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.959568</td>\n",
       "      <td>0.652820</td>\n",
       "      <td>-0.288057</td>\n",
       "      <td>-0.321805</td>\n",
       "      <td>-0.292356</td>\n",
       "      <td>-0.275990</td>\n",
       "      <td>-0.289923</td>\n",
       "      <td>-0.316658</td>\n",
       "      <td>-0.205056</td>\n",
       "      <td>-0.218793</td>\n",
       "      <td>-0.267394</td>\n",
       "      <td>-0.271954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Latitude</th>\n",
       "      <td>0.004550</td>\n",
       "      <td>-0.959568</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.547164</td>\n",
       "      <td>0.232802</td>\n",
       "      <td>0.262618</td>\n",
       "      <td>0.236559</td>\n",
       "      <td>0.246010</td>\n",
       "      <td>0.245838</td>\n",
       "      <td>0.264146</td>\n",
       "      <td>0.168196</td>\n",
       "      <td>0.181795</td>\n",
       "      <td>0.224989</td>\n",
       "      <td>0.244442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Longitude</th>\n",
       "      <td>-0.009905</td>\n",
       "      <td>0.652820</td>\n",
       "      <td>-0.547164</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.259902</td>\n",
       "      <td>-0.279147</td>\n",
       "      <td>-0.262538</td>\n",
       "      <td>-0.208338</td>\n",
       "      <td>-0.240774</td>\n",
       "      <td>-0.270345</td>\n",
       "      <td>-0.180529</td>\n",
       "      <td>-0.197115</td>\n",
       "      <td>-0.226441</td>\n",
       "      <td>-0.158840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HospitalizedPatients</th>\n",
       "      <td>0.304934</td>\n",
       "      <td>-0.288057</td>\n",
       "      <td>0.232802</td>\n",
       "      <td>-0.259902</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980457</td>\n",
       "      <td>0.999762</td>\n",
       "      <td>0.923389</td>\n",
       "      <td>0.981327</td>\n",
       "      <td>0.912586</td>\n",
       "      <td>0.923919</td>\n",
       "      <td>0.944874</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.809607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>IntensiveCarePatients</th>\n",
       "      <td>0.356195</td>\n",
       "      <td>-0.321805</td>\n",
       "      <td>0.262618</td>\n",
       "      <td>-0.279147</td>\n",
       "      <td>0.980457</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.984517</td>\n",
       "      <td>0.916971</td>\n",
       "      <td>0.970160</td>\n",
       "      <td>0.930570</td>\n",
       "      <td>0.876121</td>\n",
       "      <td>0.892060</td>\n",
       "      <td>0.958143</td>\n",
       "      <td>0.840098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalHospitalizedPatients</th>\n",
       "      <td>0.311208</td>\n",
       "      <td>-0.292356</td>\n",
       "      <td>0.236559</td>\n",
       "      <td>-0.262538</td>\n",
       "      <td>0.999762</td>\n",
       "      <td>0.984517</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.924459</td>\n",
       "      <td>0.981982</td>\n",
       "      <td>0.916342</td>\n",
       "      <td>0.920400</td>\n",
       "      <td>0.940839</td>\n",
       "      <td>0.982391</td>\n",
       "      <td>0.814551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HomeConfinement</th>\n",
       "      <td>0.424331</td>\n",
       "      <td>-0.275990</td>\n",
       "      <td>0.246010</td>\n",
       "      <td>-0.208338</td>\n",
       "      <td>0.923389</td>\n",
       "      <td>0.916971</td>\n",
       "      <td>0.924459</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.979855</td>\n",
       "      <td>0.831487</td>\n",
       "      <td>0.850907</td>\n",
       "      <td>0.896904</td>\n",
       "      <td>0.959858</td>\n",
       "      <td>0.928523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CurrentPositiveCases</th>\n",
       "      <td>0.373320</td>\n",
       "      <td>-0.289923</td>\n",
       "      <td>0.245838</td>\n",
       "      <td>-0.240774</td>\n",
       "      <td>0.981327</td>\n",
       "      <td>0.970160</td>\n",
       "      <td>0.981982</td>\n",
       "      <td>0.979855</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.892083</td>\n",
       "      <td>0.903833</td>\n",
       "      <td>0.937337</td>\n",
       "      <td>0.990303</td>\n",
       "      <td>0.886860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewPositiveCases</th>\n",
       "      <td>0.290469</td>\n",
       "      <td>-0.316658</td>\n",
       "      <td>0.264146</td>\n",
       "      <td>-0.270345</td>\n",
       "      <td>0.912586</td>\n",
       "      <td>0.930570</td>\n",
       "      <td>0.916342</td>\n",
       "      <td>0.831487</td>\n",
       "      <td>0.892083</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.760710</td>\n",
       "      <td>0.775184</td>\n",
       "      <td>0.865022</td>\n",
       "      <td>0.755386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recovered</th>\n",
       "      <td>0.206334</td>\n",
       "      <td>-0.205056</td>\n",
       "      <td>0.168196</td>\n",
       "      <td>-0.180529</td>\n",
       "      <td>0.923919</td>\n",
       "      <td>0.876121</td>\n",
       "      <td>0.920400</td>\n",
       "      <td>0.850907</td>\n",
       "      <td>0.903833</td>\n",
       "      <td>0.760710</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986324</td>\n",
       "      <td>0.954026</td>\n",
       "      <td>0.741827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths</th>\n",
       "      <td>0.251302</td>\n",
       "      <td>-0.218793</td>\n",
       "      <td>0.181795</td>\n",
       "      <td>-0.197115</td>\n",
       "      <td>0.944874</td>\n",
       "      <td>0.892060</td>\n",
       "      <td>0.940839</td>\n",
       "      <td>0.896904</td>\n",
       "      <td>0.937337</td>\n",
       "      <td>0.775184</td>\n",
       "      <td>0.986324</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.975269</td>\n",
       "      <td>0.761221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalPositiveCases</th>\n",
       "      <td>0.327629</td>\n",
       "      <td>-0.267394</td>\n",
       "      <td>0.224989</td>\n",
       "      <td>-0.226441</td>\n",
       "      <td>0.983282</td>\n",
       "      <td>0.958143</td>\n",
       "      <td>0.982391</td>\n",
       "      <td>0.959858</td>\n",
       "      <td>0.990303</td>\n",
       "      <td>0.865022</td>\n",
       "      <td>0.954026</td>\n",
       "      <td>0.975269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.855529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestsPerformed</th>\n",
       "      <td>0.427816</td>\n",
       "      <td>-0.271954</td>\n",
       "      <td>0.244442</td>\n",
       "      <td>-0.158840</td>\n",
       "      <td>0.809607</td>\n",
       "      <td>0.840098</td>\n",
       "      <td>0.814551</td>\n",
       "      <td>0.928523</td>\n",
       "      <td>0.886860</td>\n",
       "      <td>0.755386</td>\n",
       "      <td>0.741827</td>\n",
       "      <td>0.761221</td>\n",
       "      <td>0.855529</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                SNo  RegionCode  Latitude  Longitude  \\\n",
       "SNo                        1.000000   -0.006385  0.004550  -0.009905   \n",
       "RegionCode                -0.006385    1.000000 -0.959568   0.652820   \n",
       "Latitude                   0.004550   -0.959568  1.000000  -0.547164   \n",
       "Longitude                 -0.009905    0.652820 -0.547164   1.000000   \n",
       "HospitalizedPatients       0.304934   -0.288057  0.232802  -0.259902   \n",
       "IntensiveCarePatients      0.356195   -0.321805  0.262618  -0.279147   \n",
       "TotalHospitalizedPatients  0.311208   -0.292356  0.236559  -0.262538   \n",
       "HomeConfinement            0.424331   -0.275990  0.246010  -0.208338   \n",
       "CurrentPositiveCases       0.373320   -0.289923  0.245838  -0.240774   \n",
       "NewPositiveCases           0.290469   -0.316658  0.264146  -0.270345   \n",
       "Recovered                  0.206334   -0.205056  0.168196  -0.180529   \n",
       "Deaths                     0.251302   -0.218793  0.181795  -0.197115   \n",
       "TotalPositiveCases         0.327629   -0.267394  0.224989  -0.226441   \n",
       "TestsPerformed             0.427816   -0.271954  0.244442  -0.158840   \n",
       "\n",
       "                           HospitalizedPatients  IntensiveCarePatients  \\\n",
       "SNo                                    0.304934               0.356195   \n",
       "RegionCode                            -0.288057              -0.321805   \n",
       "Latitude                               0.232802               0.262618   \n",
       "Longitude                             -0.259902              -0.279147   \n",
       "HospitalizedPatients                   1.000000               0.980457   \n",
       "IntensiveCarePatients                  0.980457               1.000000   \n",
       "TotalHospitalizedPatients              0.999762               0.984517   \n",
       "HomeConfinement                        0.923389               0.916971   \n",
       "CurrentPositiveCases                   0.981327               0.970160   \n",
       "NewPositiveCases                       0.912586               0.930570   \n",
       "Recovered                              0.923919               0.876121   \n",
       "Deaths                                 0.944874               0.892060   \n",
       "TotalPositiveCases                     0.983282               0.958143   \n",
       "TestsPerformed                         0.809607               0.840098   \n",
       "\n",
       "                           TotalHospitalizedPatients  HomeConfinement  \\\n",
       "SNo                                         0.311208         0.424331   \n",
       "RegionCode                                 -0.292356        -0.275990   \n",
       "Latitude                                    0.236559         0.246010   \n",
       "Longitude                                  -0.262538        -0.208338   \n",
       "HospitalizedPatients                        0.999762         0.923389   \n",
       "IntensiveCarePatients                       0.984517         0.916971   \n",
       "TotalHospitalizedPatients                   1.000000         0.924459   \n",
       "HomeConfinement                             0.924459         1.000000   \n",
       "CurrentPositiveCases                        0.981982         0.979855   \n",
       "NewPositiveCases                            0.916342         0.831487   \n",
       "Recovered                                   0.920400         0.850907   \n",
       "Deaths                                      0.940839         0.896904   \n",
       "TotalPositiveCases                          0.982391         0.959858   \n",
       "TestsPerformed                              0.814551         0.928523   \n",
       "\n",
       "                           CurrentPositiveCases  NewPositiveCases  Recovered  \\\n",
       "SNo                                    0.373320          0.290469   0.206334   \n",
       "RegionCode                            -0.289923         -0.316658  -0.205056   \n",
       "Latitude                               0.245838          0.264146   0.168196   \n",
       "Longitude                             -0.240774         -0.270345  -0.180529   \n",
       "HospitalizedPatients                   0.981327          0.912586   0.923919   \n",
       "IntensiveCarePatients                  0.970160          0.930570   0.876121   \n",
       "TotalHospitalizedPatients              0.981982          0.916342   0.920400   \n",
       "HomeConfinement                        0.979855          0.831487   0.850907   \n",
       "CurrentPositiveCases                   1.000000          0.892083   0.903833   \n",
       "NewPositiveCases                       0.892083          1.000000   0.760710   \n",
       "Recovered                              0.903833          0.760710   1.000000   \n",
       "Deaths                                 0.937337          0.775184   0.986324   \n",
       "TotalPositiveCases                     0.990303          0.865022   0.954026   \n",
       "TestsPerformed                         0.886860          0.755386   0.741827   \n",
       "\n",
       "                             Deaths  TotalPositiveCases  TestsPerformed  \n",
       "SNo                        0.251302            0.327629        0.427816  \n",
       "RegionCode                -0.218793           -0.267394       -0.271954  \n",
       "Latitude                   0.181795            0.224989        0.244442  \n",
       "Longitude                 -0.197115           -0.226441       -0.158840  \n",
       "HospitalizedPatients       0.944874            0.983282        0.809607  \n",
       "IntensiveCarePatients      0.892060            0.958143        0.840098  \n",
       "TotalHospitalizedPatients  0.940839            0.982391        0.814551  \n",
       "HomeConfinement            0.896904            0.959858        0.928523  \n",
       "CurrentPositiveCases       0.937337            0.990303        0.886860  \n",
       "NewPositiveCases           0.775184            0.865022        0.755386  \n",
       "Recovered                  0.986324            0.954026        0.741827  \n",
       "Deaths                     1.000000            0.975269        0.761221  \n",
       "TotalPositiveCases         0.975269            1.000000        0.855529  \n",
       "TestsPerformed             0.761221            0.855529        1.000000  "
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data['TestsPerformed'].corr(data['TotalPositiveCases']))\n",
    "print(data['TestsPerformed'].corr(data['Deaths']))\n",
    "print(data['TotalPositiveCases'].corr(data['Deaths']))\n",
    "\n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "applied-wisdom",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CurrentPositiveCases</th>\n",
       "      <th>NewPositiveCases</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>TotalPositiveCases</th>\n",
       "      <th>TestsPerformed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CurrentPositiveCases</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.892083</td>\n",
       "      <td>0.903833</td>\n",
       "      <td>0.937337</td>\n",
       "      <td>0.990303</td>\n",
       "      <td>0.886860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NewPositiveCases</th>\n",
       "      <td>0.892083</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.760710</td>\n",
       "      <td>0.775184</td>\n",
       "      <td>0.865022</td>\n",
       "      <td>0.755386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recovered</th>\n",
       "      <td>0.903833</td>\n",
       "      <td>0.760710</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986324</td>\n",
       "      <td>0.954026</td>\n",
       "      <td>0.741827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Deaths</th>\n",
       "      <td>0.937337</td>\n",
       "      <td>0.775184</td>\n",
       "      <td>0.986324</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.975269</td>\n",
       "      <td>0.761221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalPositiveCases</th>\n",
       "      <td>0.990303</td>\n",
       "      <td>0.865022</td>\n",
       "      <td>0.954026</td>\n",
       "      <td>0.975269</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.855529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TestsPerformed</th>\n",
       "      <td>0.886860</td>\n",
       "      <td>0.755386</td>\n",
       "      <td>0.741827</td>\n",
       "      <td>0.761221</td>\n",
       "      <td>0.855529</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      CurrentPositiveCases  NewPositiveCases  Recovered  \\\n",
       "CurrentPositiveCases              1.000000          0.892083   0.903833   \n",
       "NewPositiveCases                  0.892083          1.000000   0.760710   \n",
       "Recovered                         0.903833          0.760710   1.000000   \n",
       "Deaths                            0.937337          0.775184   0.986324   \n",
       "TotalPositiveCases                0.990303          0.865022   0.954026   \n",
       "TestsPerformed                    0.886860          0.755386   0.741827   \n",
       "\n",
       "                        Deaths  TotalPositiveCases  TestsPerformed  \n",
       "CurrentPositiveCases  0.937337            0.990303        0.886860  \n",
       "NewPositiveCases      0.775184            0.865022        0.755386  \n",
       "Recovered             0.986324            0.954026        0.741827  \n",
       "Deaths                1.000000            0.975269        0.761221  \n",
       "TotalPositiveCases    0.975269            1.000000        0.855529  \n",
       "TestsPerformed        0.761221            0.855529        1.000000  "
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 불필요한 컬럼 삭제\n",
    "\n",
    "data.drop(['Latitude','Longitude','Country','Date','HospitalizedPatients',  'IntensiveCarePatients', 'TotalHospitalizedPatients','HomeConfinement','RegionCode','SNo'], axis=1, inplace=True)\n",
    "\n",
    "data.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "nasty-response",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RegionName              798\n",
       "CurrentPositiveCases    798\n",
       "NewPositiveCases        798\n",
       "Recovered               798\n",
       "Deaths                  798\n",
       "TotalPositiveCases      798\n",
       "TestsPerformed          798\n",
       "dtype: int64"
      ]
     },
     "execution_count": 254,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "deluxe-europe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "798"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['RegionName'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "continent-religious",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RegionName              Abruzzo\n",
       "CurrentPositiveCases          0\n",
       "NewPositiveCases            -17\n",
       "Recovered                     0\n",
       "Deaths                        0\n",
       "TotalPositiveCases            0\n",
       "TestsPerformed                0\n",
       "dtype: object"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "animal-enterprise",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RegionName              Veneto\n",
       "CurrentPositiveCases     25765\n",
       "NewPositiveCases          3251\n",
       "Recovered                11415\n",
       "Deaths                    7593\n",
       "TotalPositiveCases       44773\n",
       "TestsPerformed          121449\n",
       "dtype: object"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "fresh-athens",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>CurrentPositiveCases</th>\n",
       "      <th>NewPositiveCases</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>TotalPositiveCases</th>\n",
       "      <th>TestsPerformed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>Liguria</td>\n",
       "      <td>21</td>\n",
       "      <td>-17</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    RegionName  CurrentPositiveCases  NewPositiveCases  Recovered  Deaths  \\\n",
       "134    Liguria                    21               -17          4       0   \n",
       "\n",
       "     TotalPositiveCases  TestsPerformed  \n",
       "134                  25             121  "
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['NewPositiveCases'] == -17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "id": "affected-rehabilitation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CurrentPositiveCases    1290.654135\n",
       "NewPositiveCases         138.553885\n",
       "Recovered                205.706767\n",
       "Deaths                   162.677945\n",
       "TotalPositiveCases      1659.038847\n",
       "TestsPerformed          7932.164160\n",
       "dtype: float64"
      ]
     },
     "execution_count": 261,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "radio-gather",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CurrentPositiveCases     205.0\n",
       "NewPositiveCases          32.0\n",
       "Recovered                  3.0\n",
       "Deaths                     6.0\n",
       "TotalPositiveCases       210.5\n",
       "TestsPerformed          1610.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "suspended-friendly",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5"
      ]
     },
     "execution_count": 265,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "whole-gallery",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "490370.12827080244"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Deaths'].var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "willing-pearl",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CurrentPositiveCases     3282.834715\n",
       "NewPositiveCases          327.616052\n",
       "Recovered                1060.433732\n",
       "Deaths                    700.264327\n",
       "TotalPositiveCases       4945.629053\n",
       "TestsPerformed          17067.087279\n",
       "dtype: float64"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "electric-peeing",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CurrentPositiveCases    1.077700e+07\n",
       "NewPositiveCases        1.073323e+05\n",
       "Recovered               1.124520e+06\n",
       "Deaths                  4.903701e+05\n",
       "TotalPositiveCases      2.445925e+07\n",
       "TestsPerformed          2.912855e+08\n",
       "dtype: float64"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "threatened-jacksonville",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame._add_numeric_operations.<locals>.var of         RegionName  CurrentPositiveCases  NewPositiveCases  Recovered  Deaths  \\\n",
       "0          Abruzzo                     0                 0          0       0   \n",
       "1       Basilicata                     0                 0          0       0   \n",
       "2     P.A. Bolzano                     0                 0          0       0   \n",
       "3         Calabria                     0                 0          0       0   \n",
       "4         Campania                     0                 0          0       0   \n",
       "..             ...                   ...               ...        ...     ...   \n",
       "793        Toscana                  4432               259        182     253   \n",
       "794    P.A. Trento                  1483               124        214     173   \n",
       "795         Umbria                   864                17        194      37   \n",
       "796  Valle d'Aosta                   540                 3         32      59   \n",
       "797         Veneto                  8224               470        902     499   \n",
       "\n",
       "     TotalPositiveCases  TestsPerformed  \n",
       "0                     0               5  \n",
       "1                     0               0  \n",
       "2                     0               1  \n",
       "3                     0               1  \n",
       "4                     0              10  \n",
       "..                  ...             ...  \n",
       "793                4867           36575  \n",
       "794                1870            7675  \n",
       "795                1095            9080  \n",
       "796                 631            1717  \n",
       "797                9625          112746  \n",
       "\n",
       "[798 rows x 7 columns]>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "national-allowance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Deaths'].argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "id": "preceding-nepal",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'argmin'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-279-2db88e9e0f66>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/opt/conda/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5458\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5459\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5460\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5461\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5462\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'argmin'"
     ]
    }
   ],
   "source": [
    "data.argmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "alternative-wrapping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "786"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Deaths'].argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "id": "atlantic-keeping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "786"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Deaths'].idxmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "automated-armor",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Deaths'].idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "id": "central-penny",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionName</th>\n",
       "      <th>CurrentPositiveCases</th>\n",
       "      <th>NewPositiveCases</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>TotalPositiveCases</th>\n",
       "      <th>TestsPerformed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abruzzo</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AbruzzoBasilicata</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AbruzzoBasilicataP.A. Bolzano</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AbruzzoBasilicataP.A. BolzanoCalabria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AbruzzoBasilicataP.A. BolzanoCalabriaCampania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>793</th>\n",
       "      <td>AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...</td>\n",
       "      <td>1018831</td>\n",
       "      <td>109952</td>\n",
       "      <td>162812</td>\n",
       "      <td>129049</td>\n",
       "      <td>1310692</td>\n",
       "      <td>6198649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>794</th>\n",
       "      <td>AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...</td>\n",
       "      <td>1020314</td>\n",
       "      <td>110076</td>\n",
       "      <td>163026</td>\n",
       "      <td>129222</td>\n",
       "      <td>1312562</td>\n",
       "      <td>6206324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...</td>\n",
       "      <td>1021178</td>\n",
       "      <td>110093</td>\n",
       "      <td>163220</td>\n",
       "      <td>129259</td>\n",
       "      <td>1313657</td>\n",
       "      <td>6215404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...</td>\n",
       "      <td>1021718</td>\n",
       "      <td>110096</td>\n",
       "      <td>163252</td>\n",
       "      <td>129318</td>\n",
       "      <td>1314288</td>\n",
       "      <td>6217121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...</td>\n",
       "      <td>1029942</td>\n",
       "      <td>110566</td>\n",
       "      <td>164154</td>\n",
       "      <td>129817</td>\n",
       "      <td>1323913</td>\n",
       "      <td>6329867</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>798 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            RegionName  CurrentPositiveCases  \\\n",
       "0                                              Abruzzo                     0   \n",
       "1                                    AbruzzoBasilicata                     0   \n",
       "2                        AbruzzoBasilicataP.A. Bolzano                     0   \n",
       "3                AbruzzoBasilicataP.A. BolzanoCalabria                     0   \n",
       "4        AbruzzoBasilicataP.A. BolzanoCalabriaCampania                     0   \n",
       "..                                                 ...                   ...   \n",
       "793  AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...               1018831   \n",
       "794  AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...               1020314   \n",
       "795  AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...               1021178   \n",
       "796  AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...               1021718   \n",
       "797  AbruzzoBasilicataP.A. BolzanoCalabriaCampaniaE...               1029942   \n",
       "\n",
       "     NewPositiveCases  Recovered  Deaths  TotalPositiveCases  TestsPerformed  \n",
       "0                   0          0       0                   0               5  \n",
       "1                   0          0       0                   0               5  \n",
       "2                   0          0       0                   0               6  \n",
       "3                   0          0       0                   0               7  \n",
       "4                   0          0       0                   0              17  \n",
       "..                ...        ...     ...                 ...             ...  \n",
       "793            109952     162812  129049             1310692         6198649  \n",
       "794            110076     163026  129222             1312562         6206324  \n",
       "795            110093     163220  129259             1313657         6215404  \n",
       "796            110096     163252  129318             1314288         6217121  \n",
       "797            110566     164154  129817             1323913         6329867  \n",
       "\n",
       "[798 rows x 7 columns]"
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "answering-insider",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           0\n",
       "1           0\n",
       "2           0\n",
       "3           0\n",
       "4           0\n",
       "        ...  \n",
       "793    129049\n",
       "794    129222\n",
       "795    129259\n",
       "796    129318\n",
       "797    129817\n",
       "Name: Deaths, Length: 798, dtype: int64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Deaths'].cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "id": "documentary-checklist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1           NaN\n",
       "2           NaN\n",
       "3           NaN\n",
       "4           NaN\n",
       "         ...   \n",
       "793    1.875000\n",
       "794   -0.316206\n",
       "795   -0.786127\n",
       "796    0.594595\n",
       "797    7.457627\n",
       "Name: Deaths, Length: 798, dtype: float64"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Deaths'].pct_change()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "objective-zoning",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
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