3.2. numpy.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"01Rk2aw2s79W"},"source":["[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pytextbook/pytextbook/blob/main/3.2.%20numpy.ipynb) "]},{"cell_type":"code","execution_count":2,"metadata":{"executionInfo":{"elapsed":298,"status":"ok","timestamp":1676122364051,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"TyFTFTFUs79Z"},"outputs":[],"source":["import numpy as np"]},{"cell_type":"markdown","metadata":{"id":"S9NVGUsls79b"},"source":["## 넘파이 배열<sup>NumPy Arrays</sup>\n","\n","넘파이(numpy)는 Numerical Python의 줄임말로 고성능 수치 계산을 쉽게 할 수 있도록 도와주는 라이브러리다. 자연어를 기계가 이해할 수 있는 벡터 형태로 바꾸려면 다차원 계산이 필수적이기 때문에 넘파이는 텍스트 분석에서 광범위하게 사용되고 있다."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1672230522501,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"t5BygCNLs79b","outputId":"896b0ab6-a36b-4029-bccb-35619db204c3"},"outputs":[{"data":{"text/plain":["array([1., 2., 3.])"]},"execution_count":2,"metadata":{},"output_type":"execute_result"}],"source":["a = np.array([1, 2, 3], dtype = float)\n","a"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":273,"status":"ok","timestamp":1672230569723,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"JcHJdbF9s79d","outputId":"9641a39b-53f3-4b66-cb64-d56773f6c8e5"},"outputs":[{"data":{"text/plain":["1"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["#차원 반환\n","a.ndim"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":299,"status":"ok","timestamp":1672230580905,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"DHWIPyWQs79d","outputId":"031f12da-f613-482b-cdd1-cd90b665f642"},"outputs":[{"data":{"text/plain":["dtype('float64')"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["#데이터 타입 반환\n","a.dtype"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":5,"status":"ok","timestamp":1672230522503,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"CjrgdsRks79d","outputId":"6cfbbe78-2592-413c-c9ec-0443fdc0fd2e"},"outputs":[{"data":{"text/plain":["(3,)"]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["a.shape"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hrXGZT4Vs79e"},"outputs":[],"source":["b = np.array([(1.5,2,3), (4,5,6)], dtype = float)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6,"status":"ok","timestamp":1672230593566,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"-Yv1hG5Os79e","outputId":"60b83cce-cffc-4454-aafb-873048be5540"},"outputs":[{"data":{"text/plain":["(2, 3)"]},"execution_count":7,"metadata":{},"output_type":"execute_result"}],"source":["#object dimension의 구성을 튜플 형태로 반환\n","b.shape"]},{"cell_type":"markdown","metadata":{"id":"tpVO8UBfs79f"},"source":["## 브로드캐스팅<sup>broadcasting</sup>                \n","브로드캐스팅은 산술 연산에서 다른 모양의 배열을 처리하는 방법으로 배열 연산을 빠른 속도로 벡터화한다. 다음 코드는 배열과 스칼라값의 연산이다. 스칼라 값은 배열과 모양이 다르더라도 연산할 수 있다"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":15,"status":"ok","timestamp":1672230522864,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"UL_2lYThs79f","outputId":"152cc9da-65b9-4d91-9291-730970f818fe"},"outputs":[{"data":{"text/plain":["array([1.6, 3.2])"]},"execution_count":8,"metadata":{},"output_type":"execute_result"}],"source":["data = np.array([1.0, 2.0])\n","data * 1.6"]},{"cell_type":"markdown","metadata":{"id":"Y5oT76LtwqT6"},"source":["< 배열과 스칼라값의 연산 > 참고 : https://numpy.org/doc/stable/user/absolute_beginners.html "]},{"cell_type":"markdown","metadata":{"id":"JS-JqwelwyNa"},"source":["다음 코드의 변수 c는 (4, 3)의 배열 형태이고, d는 (3,)으로 1차원 벡터 형태이지만 연산할 수 있다. 이때 1차원 벡터의 크기는 배열의 열의 수인 3과 같아야 한다. "]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":447,"status":"ok","timestamp":1676122369110,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"QKLpm5W4s79g","outputId":"87d077c8-d0cc-4947-c182-5decafb61a64"},"outputs":[{"output_type":"stream","name":"stdout","text":["(4, 3) (3,)\n"]},{"output_type":"execute_result","data":{"text/plain":["array([[ 1,  2,  3],\n","       [11, 12, 13],\n","       [21, 22, 23],\n","       [31, 32, 33]])"]},"metadata":{},"execution_count":3}],"source":["c = np.array([[ 0,  0,  0],\n","           [10, 10, 10],\n","           [20, 20, 20],\n","           [30, 30, 30]])\n","d = np.array([1, 2, 3])\n","\n","print(c.shape, d.shape)\n","c + d"]},{"cell_type":"markdown","metadata":{"id":"4W_CPxD7w5e2"},"source":["다음과 같이 (4, 3) 형태의 배열과 (4,) 형태의 1차원 벡터를 연산하면 열의 크기와 1차원 벡터의 크기가 달라 브로드캐스팅 되지 않고 오류가 발생할 수 있으므로 주의해야 한다."]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":232},"executionInfo":{"elapsed":274,"status":"error","timestamp":1676122379349,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"HgrC0HiDs79g","outputId":"ef2bbf9d-4ba9-419c-d718-92523c95bda5"},"outputs":[{"output_type":"stream","name":"stdout","text":["(4, 3) (4,)\n"]},{"output_type":"error","ename":"ValueError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)","\u001b[0;32m<ipython-input-4-b046b312de3b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\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;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[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mc\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (4,3) (4,) "]}],"source":["e = np.array([1, 2, 3, 4])\n","print(c.shape, e.shape)\n","c + e"]},{"cell_type":"markdown","metadata":{"id":"giHKGYX5xCDG"},"source":["배열의 열 크기와 다른 크기의 1차원 벡터는 더할 수 없다 "]},{"cell_type":"markdown","metadata":{"id":"hIBr68GTs79g"},"source":["## 집계 함수<sup>Aggregate Functions</sup>\n","\n","집계 함수를 통해 기술통계량을 계산핤 수 있다. \n","                 \n","●\tmax() 또는 np.max() : 최댓값을 반환하는 함수                \n","●\tmin() 또는 np.min() : 최솟값을 반환하는 함수    \n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":313,"status":"ok","timestamp":1672231536722,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"8xaUULGIs79h","outputId":"fca1b189-3568-4853-81db-bff419a82f71"},"outputs":[{"data":{"text/plain":["3.0"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["max(a)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":4,"status":"ok","timestamp":1672231537027,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"5voGz-TSs79h","outputId":"3ceb429c-bda7-4935-c5a7-2ad1d0dfc49d","scrolled":true},"outputs":[{"data":{"text/plain":["3.0"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["a.max()"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":302,"status":"ok","timestamp":1672231539536,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"hCUML_CNs79h","outputId":"ee1f85e3-bc9e-4d58-b979-45acf710f4c4"},"outputs":[{"data":{"text/plain":["1.0"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["min(a)"]},{"cell_type":"markdown","metadata":{"id":"GgVC2XLAxlv0"},"source":["● sum() 또는 np.sum() : 합계를 반환하는 함수  "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":337,"status":"ok","timestamp":1672231542218,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"6W2aNIJTs79i","outputId":"7ebd5477-a9a0-4708-a533-92506a2f8d9c"},"outputs":[{"data":{"text/plain":["6.0"]},"execution_count":14,"metadata":{},"output_type":"execute_result"}],"source":["a.sum()"]},{"cell_type":"markdown","metadata":{"id":"omnZ21OFxq05"},"source":["●\tnp.mean(s, axis = 0) : 열을 따라 산술 평균을 반환하는 함수:             \n","●\tnp.mean(s, axis = 1) : 행을 따라 산술 평균을 반환하는 함수   "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1672231542523,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"K7AUMCWCs79i","outputId":"d3c78626-bc6c-4a01-c6cc-49ba0c2ce4e4"},"outputs":[{"name":"stdout","output_type":"stream","text":["2차원 배열 : [[10, 20, 30], [30, 40, 50], [50, 60, 70], [70, 80, 90]]\n","열에 따른 산술 평균 : [40. 50. 60.]\n","행에 따른 산술 평균 : [20. 40. 60. 80.]\n"]}],"source":["s = [[10, 20, 30],\n","       [30, 40, 50],\n","       [50, 60, 70],\n","       [70, 80, 90]]\n","\n","print(\"2차원 배열 :\", s)\n","print(\"열에 따른 산술 평균 :\", np.mean(s, axis=0))\n","print(\"행에 따른 산술 평균 :\", np.mean(s, axis=1))"]},{"cell_type":"markdown","metadata":{"id":"WGGUVjL_ypme"},"source":["np.where() : 괄호 안의 조건에 맞는 값을 찾아서 그 원소의 인덱스를 배열로 반환하는 함수: "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":451,"status":"ok","timestamp":1672231544440,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"apOAy0hys79i","outputId":"95e4e4bd-f97a-4f15-b98d-07f2a935cacd"},"outputs":[{"data":{"text/plain":["(array([0, 1]),)"]},"execution_count":16,"metadata":{},"output_type":"execute_result"}],"source":["np.where(a < 3)"]},{"cell_type":"markdown","metadata":{"id":"86_IHx6wy8-0"},"source":["## 배열 생성 \n","   \n","●\tnp.zeros((2, 3)): 원소가 모두 0으로 이루어진 2열 3행의 배열 생성         \n","●\tnp.ones((2, 3)): 원소가 모두 1로 이루어진 2열 3행의 배열 생성               \n","●\tnp.linspace(start, stop,step): 시작과 끝 사이에 균일하게  숫자를 생성            \n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2,"status":"ok","timestamp":1672231544726,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"2TXsY6Y-s79j","outputId":"4626c433-9a36-4406-d02a-3beaadf61413"},"outputs":[{"data":{"text/plain":["array([0.  , 0.25, 0.5 , 0.75, 1.  , 1.25, 1.5 , 1.75, 2.  ])"]},"execution_count":17,"metadata":{},"output_type":"execute_result"}],"source":["np.linspace(0, 2, 9) # 0에서 2까지 균일하게 9개의 숫자 생성"]},{"cell_type":"markdown","metadata":{"id":"uNIgKj5h0EfP"},"source":["## 난수 생성      \n","● random.random()은 균등 분포로 표본을 추출     \n","● random.rand()는 균등 비율로 표본을 추출           \n","● random.randn()은 정규 분포로 표본을 추출          "]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":277,"status":"ok","timestamp":1672231547723,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"oPk6jj7Qs79j","outputId":"25544a0c-ad38-42f0-cabf-28f5f9629ad5"},"outputs":[{"data":{"text/plain":["array([[0.08446562, 0.50397425],\n","       [0.94712967, 0.07868268]])"]},"execution_count":18,"metadata":{},"output_type":"execute_result"}],"source":["d = np.random.random((2, 2))\n","d"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":424,"status":"ok","timestamp":1672231548481,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"9oQaQhNus79j","outputId":"331a8795-5593-4e3a-8f9d-6e18bc4b8fa2"},"outputs":[{"name":"stdout","output_type":"stream","text":["[[ 0.43247626 -3.10471758  2.19432572]\n"," [-3.138515   -0.96466087 -1.79560433]]\n"]}],"source":["e = np.random.normal(0, 2, (2, 3))\n","print(e)"]},{"cell_type":"markdown","metadata":{"id":"6EwZCeDD0lj5"},"source":["## 맷플롯립을 통한 넘파이 배열의 시각화"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":283},"executionInfo":{"elapsed":339,"status":"ok","timestamp":1672231553048,"user":{"displayName":"송영숙","userId":"01138230862948206591"},"user_tz":-540},"id":"eKxK7DO2s79j","outputId":"69974ca7-2f33-4f13-b924-7f7c8e417bca"},"outputs":[{"data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjAAAAGdCAYAAAAMm0nCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAApA0lEQVR4nO3df1RU953/8RegjKLOWFQYOKIh2qrEXxENzkni0UoZDcnWDdmNG6sm8cfRA55VUkV2PUZNt1hNVq0m2p7sFnerqyYbk0bWH4irbioaQ8v6q3Kq1cVEB2ysM0oVFOb7R77cZhQTQcjMB56Pc+45zr2fufO+meq8+rmfz+eG+f1+vwAAAAwSHuwCAAAAGosAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwTrtgF9BS6urqdPHiRXXp0kVhYWHBLgcAANwHv9+va9euKT4+XuHh9+5nabUB5uLFi0pISAh2GQAAoAkuXLignj173vN4qw0wXbp0kfTFfwC73R7kagAAwP3w+XxKSEiwfsfvpdUGmPrbRna7nQADAIBhvm74B4N4AQCAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIzTLtgFAMDXeWhhwV37zi9PD0IlAEIFPTAAAMA4BBgAAGAcAgwAADAOAQYAABinUQFm/fr1Gjx4sOx2u+x2u1wul3bu3GkdHz16tMLCwgK2WbNmBZyjvLxc6enpioqKUkxMjObPn6/bt28HtNm/f7+GDRsmm82mvn37Kj8/v+lXCAAAWp1GzULq2bOnli9frm9/+9vy+/3auHGjvv/97+u3v/2tHnnkEUnSjBkztGzZMus9UVFR1p9ra2uVnp4up9OpQ4cO6dKlS5oyZYrat2+vH//4x5Kkc+fOKT09XbNmzdKmTZtUVFSk6dOnKy4uTm63uzmuGUArxEwloG1pVIB55plnAl7/0z/9k9avX6/Dhw9bASYqKkpOp7PB9+/Zs0enTp3S3r17FRsbq6FDh+q1115TTk6OlixZosjISG3YsEGJiYl64403JEkDBgzQRx99pFWrVhFgAACApAcYA1NbW6stW7aoqqpKLpfL2r9p0yZ1795dAwcOVG5urv785z9bx4qLizVo0CDFxsZa+9xut3w+n06ePGm1SU1NDfgst9ut4uLir6ynurpaPp8vYAMQ+h5aWHDXBgBfp9EL2R0/flwul0s3b95U586dtX37diUlJUmSXnjhBfXu3Vvx8fE6duyYcnJyVFZWpvfee0+S5PF4AsKLJOu1x+P5yjY+n083btxQx44dG6wrLy9PS5cubezlAAAAAzU6wPTr10+lpaXyer169913NXXqVB04cEBJSUmaOXOm1W7QoEGKi4vT2LFjdfbsWfXp06dZC79Tbm6usrOzrdc+n08JCQkt+pkAACA4Gn0LKTIyUn379lVycrLy8vI0ZMgQrVmzpsG2KSkpkqQzZ85IkpxOpyoqKgLa1L+uHzdzrzZ2u/2evS+SZLPZrNlR9RsAAGidHngdmLq6OlVXVzd4rLS0VJIUFxcnSXK5XDp+/LgqKyutNoWFhbLb7dZtKJfLpaKiooDzFBYWBoyzAQAAbVujbiHl5uZq/Pjx6tWrl65du6bNmzdr//792r17t86ePavNmzfrqaeeUrdu3XTs2DHNmzdPo0aN0uDBgyVJaWlpSkpK0uTJk7VixQp5PB4tWrRImZmZstlskqRZs2Zp3bp1WrBggV5++WXt27dP27ZtU0EBA/sA/AWDfYG2rVEBprKyUlOmTNGlS5fkcDg0ePBg7d69W9/73vd04cIF7d27V6tXr1ZVVZUSEhKUkZGhRYsWWe+PiIjQjh07NHv2bLlcLnXq1ElTp04NWDcmMTFRBQUFmjdvntasWaOePXvq7bffZgo10IYQTgB8nTC/3+8PdhEtwefzyeFwyOv1Mh4GCGEtGVZYyA4wz/3+fvMsJAAAYBwCDAAAMA4BBgAAGKfRC9kBwINggC6A5kAPDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4zALCUCLYcYRgJZCDwwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMwCwlAm9HQrKjzy9ODUAmAB0UPDAAAMA4BBgAAGIdbSABaLRbSA1ovemAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHWUgAmg2zfgB8U+iBAQAAxiHAAAAA4xBgAACAcQgwAADAOAziBdCm3TnwmKdTA2agBwYAABiHAAMAAIxDgAEAAMYhwAAAAOMwiBdAk7SlVXcZ6AuEHnpgAACAcQgwAADAOAQYAABgnEYFmPXr12vw4MGy2+2y2+1yuVzauXOndfzmzZvKzMxUt27d1LlzZ2VkZKiioiLgHOXl5UpPT1dUVJRiYmI0f/583b59O6DN/v37NWzYMNlsNvXt21f5+flNv0IAaISHFhbctQEIPY0KMD179tTy5ctVUlKiTz75RN/97nf1/e9/XydPnpQkzZs3Tx9++KHeeecdHThwQBcvXtSzzz5rvb+2tlbp6emqqanRoUOHtHHjRuXn52vx4sVWm3Pnzik9PV1jxoxRaWmp5s6dq+nTp2v37t3NdMkAAMB0YX6/3/8gJ4iOjtbKlSv13HPPqUePHtq8ebOee+45SdLp06c1YMAAFRcXa+TIkdq5c6eefvppXbx4UbGxsZKkDRs2KCcnR5cvX1ZkZKRycnJUUFCgEydOWJ8xceJEXb16Vbt27brvunw+nxwOh7xer+x2+4NcIoAGtOWeCWYhAS3nfn+/mzwGpra2Vlu2bFFVVZVcLpdKSkp069YtpaamWm369++vXr16qbi4WJJUXFysQYMGWeFFktxut3w+n9WLU1xcHHCO+jb157iX6upq+Xy+gA0AALROjV4H5vjx43K5XLp586Y6d+6s7du3KykpSaWlpYqMjFTXrl0D2sfGxsrj8UiSPB5PQHipP15/7Kva+Hw+3bhxQx07dmywrry8PC1durSxlwPgPrXlHhcAoafRPTD9+vVTaWmpjhw5otmzZ2vq1Kk6depUS9TWKLm5ufJ6vdZ24cKFYJcEAABaSKN7YCIjI9W3b19JUnJyso4ePao1a9bo+eefV01Nja5evRrQC1NRUSGn0ylJcjqd+vjjjwPOVz9L6ctt7py5VFFRIbvdfs/eF0my2Wyy2WyNvRwAAGCgB14Hpq6uTtXV1UpOTlb79u1VVFRkHSsrK1N5eblcLpckyeVy6fjx46qsrLTaFBYWym63KykpyWrz5XPUt6k/BwAAQKN6YHJzczV+/Hj16tVL165d0+bNm7V//37t3r1bDodD06ZNU3Z2tqKjo2W32zVnzhy5XC6NHDlSkpSWlqakpCRNnjxZK1askMfj0aJFi5SZmWn1nsyaNUvr1q3TggUL9PLLL2vfvn3atm2bCgq4/w4AAL7QqABTWVmpKVOm6NKlS3I4HBo8eLB2796t733ve5KkVatWKTw8XBkZGaqurpbb7dZbb71lvT8iIkI7duzQ7Nmz5XK51KlTJ02dOlXLli2z2iQmJqqgoEDz5s3TmjVr1LNnT7399ttyu93NdMkAAMB0D7wOTKhiHRigeTEL6S9YBwZoOS2+DgwAAECwEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcdoFuwAAoeehhQXBLgEAvhI9MAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcVjIDgAL1zVSQ/+9zi9PD0IlQNtFDwwAADAOPTAA0Azu7JWhRwZoWfTAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGaVSAycvL04gRI9SlSxfFxMRowoQJKisrC2gzevRohYWFBWyzZs0KaFNeXq709HRFRUUpJiZG8+fP1+3btwPa7N+/X8OGDZPNZlPfvn2Vn5/ftCsEAACtTqMCzIEDB5SZmanDhw+rsLBQt27dUlpamqqqqgLazZgxQ5cuXbK2FStWWMdqa2uVnp6umpoaHTp0SBs3blR+fr4WL15stTl37pzS09M1ZswYlZaWau7cuZo+fbp27979gJcLAABag0Y9SmDXrl0Br/Pz8xUTE6OSkhKNGjXK2h8VFSWn09ngOfbs2aNTp05p7969io2N1dChQ/Xaa68pJydHS5YsUWRkpDZs2KDExES98cYbkqQBAwboo48+0qpVq+R2uxt7jQAAoJV5oDEwXq9XkhQdHR2wf9OmTerevbsGDhyo3Nxc/fnPf7aOFRcXa9CgQYqNjbX2ud1u+Xw+nTx50mqTmpoacE63263i4uJ71lJdXS2fzxewAQCA1qnJD3Osq6vT3Llz9fjjj2vgwIHW/hdeeEG9e/dWfHy8jh07ppycHJWVlem9996TJHk8noDwIsl67fF4vrKNz+fTjRs31LFjx7vqycvL09KlS5t6OQAAwCBNDjCZmZk6ceKEPvroo4D9M2fOtP48aNAgxcXFaezYsTp79qz69OnT9Eq/Rm5urrKzs63XPp9PCQkJLfZ5AAAgeJp0CykrK0s7duzQf//3f6tnz55f2TYlJUWSdObMGUmS0+lURUVFQJv61/XjZu7Vxm63N9j7Ikk2m012uz1gAwAArVOjAozf71dWVpa2b9+uffv2KTEx8WvfU1paKkmKi4uTJLlcLh0/flyVlZVWm8LCQtntdiUlJVltioqKAs5TWFgol8vVmHIBAEAr1agAk5mZqV/+8pfavHmzunTpIo/HI4/Hoxs3bkiSzp49q9dee00lJSU6f/68fvWrX2nKlCkaNWqUBg8eLElKS0tTUlKSJk+erP/93//V7t27tWjRImVmZspms0mSZs2apT/84Q9asGCBTp8+rbfeekvbtm3TvHnzmvnyAQCAiRoVYNavXy+v16vRo0crLi7O2rZu3SpJioyM1N69e5WWlqb+/fvrlVdeUUZGhj788EPrHBEREdqxY4ciIiLkcrn0gx/8QFOmTNGyZcusNomJiSooKFBhYaGGDBmiN954Q2+//TZTqAEAgCQpzO/3+4NdREvw+XxyOBzyer2MhwG+5KGFBcEuoU04vzw92CUARrrf32+ehQQAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYJwmPwsJAHBvDU1XZ2o10HzogQEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHJ5GDbRyDT0VGQBMRw8MAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjMAsJAL4hd84IO788PUiVAOajBwYAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxmlUgMnLy9OIESPUpUsXxcTEaMKECSorKwtoc/PmTWVmZqpbt27q3LmzMjIyVFFREdCmvLxc6enpioqKUkxMjObPn6/bt28HtNm/f7+GDRsmm82mvn37Kj8/v2lXCAAAWp1GBZgDBw4oMzNThw8fVmFhoW7duqW0tDRVVVVZbebNm6cPP/xQ77zzjg4cOKCLFy/q2WeftY7X1tYqPT1dNTU1OnTokDZu3Kj8/HwtXrzYanPu3Dmlp6drzJgxKi0t1dy5czV9+nTt3r27GS4ZAACYLszv9/ub+ubLly8rJiZGBw4c0KhRo+T1etWjRw9t3rxZzz33nCTp9OnTGjBggIqLizVy5Ejt3LlTTz/9tC5evKjY2FhJ0oYNG5STk6PLly8rMjJSOTk5Kigo0IkTJ6zPmjhxoq5evapdu3bdV20+n08Oh0Ner1d2u72plwgY784nICN08DRq4G73+/vd7kE+xOv1SpKio6MlSSUlJbp165ZSU1OtNv3791evXr2sAFNcXKxBgwZZ4UWS3G63Zs+erZMnT+rRRx9VcXFxwDnq28ydO/eetVRXV6u6utp67fP5HuTSACMRVgC0FU0exFtXV6e5c+fq8ccf18CBAyVJHo9HkZGR6tq1a0Db2NhYeTweq82Xw0v98fpjX9XG5/Ppxo0bDdaTl5cnh8NhbQkJCU29NAAAEOKaHGAyMzN14sQJbdmypTnrabLc3Fx5vV5ru3DhQrBLAgAALaRJt5CysrK0Y8cOHTx4UD179rT2O51O1dTU6OrVqwG9MBUVFXI6nVabjz/+OOB89bOUvtzmzplLFRUVstvt6tixY4M12Ww22Wy2plwOAAAwTKN6YPx+v7KysrR9+3bt27dPiYmJAceTk5PVvn17FRUVWfvKyspUXl4ul8slSXK5XDp+/LgqKyutNoWFhbLb7UpKSrLafPkc9W3qzwEAANq2RvXAZGZmavPmzfrggw/UpUsXa8yKw+FQx44d5XA4NG3aNGVnZys6Olp2u11z5syRy+XSyJEjJUlpaWlKSkrS5MmTtWLFCnk8Hi1atEiZmZlWD8qsWbO0bt06LViwQC+//LL27dunbdu2qaCAAYoAAKCRPTDr16+X1+vV6NGjFRcXZ21bt2612qxatUpPP/20MjIyNGrUKDmdTr333nvW8YiICO3YsUMRERFyuVz6wQ9+oClTpmjZsmVWm8TERBUUFKiwsFBDhgzRG2+8obfffltut7sZLhkAAJjugdaBCWWsA4O2iGnUZmEdGOBu9/v7zbOQAACAcQgwAADAOAQYAABgnAd6lAAAoOkaGrPEuBjg/tADAwAAjEOAAQAAxiHAAAAA4zAGBgBCyJ3jYhgTAzSMHhgAAGAcAgwAADAOt5AAg/HoAABtFT0wAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOO0C3YBAO7PQwsLgl0CAIQMemAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOOwkB0AhLCGFjA8vzw9CJUAoYUeGAAAYBwCDAAAME6jA8zBgwf1zDPPKD4+XmFhYXr//fcDjr/44osKCwsL2MaNGxfQ5sqVK5o0aZLsdru6du2qadOm6fr16wFtjh07pieffFIdOnRQQkKCVqxY0firAwAArVKjA0xVVZWGDBmiN998855txo0bp0uXLlnbf/zHfwQcnzRpkk6ePKnCwkLt2LFDBw8e1MyZM63jPp9PaWlp6t27t0pKSrRy5UotWbJEP//5zxtbLgAAaIUaPYh3/PjxGj9+/Fe2sdlscjqdDR773e9+p127duno0aMaPny4JGnt2rV66qmn9Prrrys+Pl6bNm1STU2N/vVf/1WRkZF65JFHVFpaqn/+538OCDoAAKBtapExMPv371dMTIz69eun2bNn6/PPP7eOFRcXq2vXrlZ4kaTU1FSFh4fryJEjVptRo0YpMjLSauN2u1VWVqY//elPDX5mdXW1fD5fwAYAAFqnZg8w48aN07/927+pqKhIP/nJT3TgwAGNHz9etbW1kiSPx6OYmJiA97Rr107R0dHyeDxWm9jY2IA29a/r29wpLy9PDofD2hISEpr70gAAQIho9nVgJk6caP150KBBGjx4sPr06aP9+/dr7Nixzf1xltzcXGVnZ1uvfT4fIQYAgFaqxadRP/zww+revbvOnDkjSXI6naqsrAxoc/v2bV25csUaN+N0OlVRURHQpv71vcbW2Gw22e32gA0AALROLR5gPv30U33++eeKi4uTJLlcLl29elUlJSVWm3379qmurk4pKSlWm4MHD+rWrVtWm8LCQvXr10/f+ta3WrpkAAAQ4hp9C+n69etWb4oknTt3TqWlpYqOjlZ0dLSWLl2qjIwMOZ1OnT17VgsWLFDfvn3ldrslSQMGDNC4ceM0Y8YMbdiwQbdu3VJWVpYmTpyo+Ph4SdILL7ygpUuXatq0acrJydGJEye0Zs0arVq1qpkuGwDMdefjBXi0ANqiRvfAfPLJJ3r00Uf16KOPSpKys7P16KOPavHixYqIiNCxY8f0V3/1V/rOd76jadOmKTk5Wf/zP/8jm81mnWPTpk3q37+/xo4dq6eeekpPPPFEwBovDodDe/bs0blz55ScnKxXXnlFixcvZgo1AACQJIX5/X5/sItoCT6fTw6HQ16vl/EwMFJDD/EDGkIPDFqT+/395llIAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxmv1p1AAaj0XrAKBx6IEBAADGIcAAAADjcAsJAAzX0C1Ino+E1o4eGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOAQYAABgHAIMAAAwDgEGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABinXbALAAA0v4cWFty17/zy9CBUArQMemAAAIBxCDAAAMA4BBgAAGAcxsAAQdDQ+AQAwP2jBwYAABiHAAMAAIzT6ABz8OBBPfPMM4qPj1dYWJjef//9gON+v1+LFy9WXFycOnbsqNTUVP3+978PaHPlyhVNmjRJdrtdXbt21bRp03T9+vWANseOHdOTTz6pDh06KCEhQStWrGj81QEAgFap0QGmqqpKQ4YM0Ztvvtng8RUrVuinP/2pNmzYoCNHjqhTp05yu926efOm1WbSpEk6efKkCgsLtWPHDh08eFAzZ860jvt8PqWlpal3794qKSnRypUrtWTJEv385z9vwiUCAIDWJszv9/ub/OawMG3fvl0TJkyQ9EXvS3x8vF555RX98Ic/lCR5vV7FxsYqPz9fEydO1O9+9zslJSXp6NGjGj58uCRp165deuqpp/Tpp58qPj5e69ev1z/+4z/K4/EoMjJSkrRw4UK9//77On369H3V5vP55HA45PV6Zbfbm3qJQItgEC+CgYXsYIL7/f1u1jEw586dk8fjUWpqqrXP4XAoJSVFxcXFkqTi4mJ17drVCi+SlJqaqvDwcB05csRqM2rUKCu8SJLb7VZZWZn+9Kc/NfjZ1dXV8vl8ARsAAGidmnUatcfjkSTFxsYG7I+NjbWOeTwexcTEBBbRrp2io6MD2iQmJt51jvpj3/rWt+767Ly8PC1durR5LgRoRvS2IFTc+b9FemRgslYzCyk3N1der9faLly4EOySAABAC2nWAON0OiVJFRUVAfsrKiqsY06nU5WVlQHHb9++rStXrgS0aegcX/6MO9lsNtnt9oANAAC0Ts0aYBITE+V0OlVUVGTt8/l8OnLkiFwulyTJ5XLp6tWrKikpsdrs27dPdXV1SklJsdocPHhQt27dstoUFhaqX79+Dd4+AgAAbUujA8z169dVWlqq0tJSSV8M3C0tLVV5ebnCwsI0d+5c/ehHP9KvfvUrHT9+XFOmTFF8fLw1U2nAgAEaN26cZsyYoY8//li//vWvlZWVpYkTJyo+Pl6S9MILLygyMlLTpk3TyZMntXXrVq1Zs0bZ2dnNduEAAMBcjR7E+8knn2jMmDHW6/pQMXXqVOXn52vBggWqqqrSzJkzdfXqVT3xxBPatWuXOnToYL1n06ZNysrK0tixYxUeHq6MjAz99Kc/tY47HA7t2bNHmZmZSk5OVvfu3bV48eKAtWIAAEDb9UDrwIQy1oFBqGAWEkIVs5AQioKyDgwAAMA3gQADAACMQ4ABAADGIcAAAADjNOujBAAwaBcAvgn0wAAAAOMQYAAAgHG4hQQAbVRDtztZGwamoAcGAAAYhwADAACMQ4ABAADGIcAAAADjEGAAAIBxCDAAAMA4BBgAAGAcAgwAADAOAQYAABiHAAMAAIxDgAEAAMYhwAAAAOPwMEcAgOXOBzzycEeEKnpgAACAcQgwAADAONxCAh7And3tAIBvBj0wAADAOPTAAI1AjwsAhAZ6YAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIdZSACAe2po5h2PF0AooAcGAAAYhwADAACMQ4ABAADGIcAAAADjMIgXuAceGwAAoYseGAAAYJxmDzBLlixRWFhYwNa/f3/r+M2bN5WZmalu3bqpc+fOysjIUEVFRcA5ysvLlZ6erqioKMXExGj+/Pm6fft2c5cKAGiChxYWBGxAMLTILaRHHnlEe/fu/cuHtPvLx8ybN08FBQV655135HA4lJWVpWeffVa//vWvJUm1tbVKT0+X0+nUoUOHdOnSJU2ZMkXt27fXj3/845YoFwAAGKZFAky7du3kdDrv2u/1evUv//Iv2rx5s7773e9Kkn7xi19owIABOnz4sEaOHKk9e/bo1KlT2rt3r2JjYzV06FC99tprysnJ0ZIlSxQZGdkSJQMAAIO0yBiY3//+94qPj9fDDz+sSZMmqby8XJJUUlKiW7duKTU11Wrbv39/9erVS8XFxZKk4uJiDRo0SLGxsVYbt9stn8+nkydP3vMzq6ur5fP5AjYAANA6NXuASUlJUX5+vnbt2qX169fr3LlzevLJJ3Xt2jV5PB5FRkaqa9euAe+JjY2Vx+ORJHk8noDwUn+8/ti95OXlyeFwWFtCQkLzXhgAAAgZzX4Lafz48dafBw8erJSUFPXu3Vvbtm1Tx44dm/vjLLm5ucrOzrZe+3w+QgwAAK1Ui0+j7tq1q77zne/ozJkzcjqdqqmp0dWrVwPaVFRUWGNmnE7nXbOS6l83NK6mns1mk91uD9gAAEDr1OIB5vr16zp79qzi4uKUnJys9u3bq6ioyDpeVlam8vJyuVwuSZLL5dLx48dVWVlptSksLJTdbldSUlJLlwsAAAwQ5vf7/c15wh/+8Id65pln1Lt3b128eFGvvvqqSktLderUKfXo0UOzZ8/Wf/3Xfyk/P192u11z5syRJB06dEjSF9Oohw4dqvj4eK1YsUIej0eTJ0/W9OnTGzWN2ufzyeFwyOv10huD+8J6FkDzOb88PdglwFD3+/vd7GNgPv30U/3d3/2dPv/8c/Xo0UNPPPGEDh8+rB49ekiSVq1apfDwcGVkZKi6ulput1tvvfWW9f6IiAjt2LFDs2fPlsvlUqdOnTR16lQtW7asuUsFAACGavYemFBBDwwaix4YoPnQA4Omut/fb56FBAAAjEOAAQAAxiHAAAAA4xBgAACAcQgwAADAOC3yNGog1DHjCGhZd/4dY1YSmhs9MAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4xBgAACAcVjIDm0CC9cBwdXQ30EWt8ODIMCg1SGsAEDrxy0kAABgHAIMAAAwDreQAABBwQMf8SDogQEAAMahBwbGY9AuALQ99MAAAADjEGAAAIBxuIUEAAgJLHaHxqAHBgAAGIceGBiFAbsAAIkAAwAIYawVg3vhFhIAADAOAQYAABiHW0gIaYx5AQA0hAADADAGU61Rj1tIAADAOAQYAABgHG4hIWQw3gVAUzDVum2iBwYAABiHHhgEDT0uAICmIsAAAFqV+/k/R9xmMh8BBt8IelsAhBKmY5svpAPMm2++qZUrV8rj8WjIkCFau3atHnvssWCXhTsQTgAA37SQDTBbt25Vdna2NmzYoJSUFK1evVput1tlZWWKiYkJdnltGoEFQGvErSezhPn9fn+wi2hISkqKRowYoXXr1kmS6urqlJCQoDlz5mjhwoVf+36fzyeHwyGv1yu73d7S5bZahBUA+Is7Awy3oprf/f5+h2QPTE1NjUpKSpSbm2vtCw8PV2pqqoqLixt8T3V1taqrq63XXq9X0hf/IVq7ga/uvmvfiaXuJr0PAHBvvea90+g2Df17fD///t7Pv+OtUf3v9tf1r4RkgPnjH/+o2tpaxcbGBuyPjY3V6dOnG3xPXl6eli5detf+hISEFqkx1DlWB7sCAIDU9H+P2/q/49euXZPD4bjn8ZAMME2Rm5ur7Oxs63VdXZ2uXLmibt26KSwsLIiV3ZvP51NCQoIuXLjAba4QwvcSmvheQhffTWgy9Xvx+/26du2a4uPjv7JdSAaY7t27KyIiQhUVFQH7Kyoq5HQ6G3yPzWaTzWYL2Ne1a9eWKrFZ2e12o/7H1VbwvYQmvpfQxXcTmkz8Xr6q56VeSD5KIDIyUsnJySoqKrL21dXVqaioSC6XK4iVAQCAUBCSPTCSlJ2dralTp2r48OF67LHHtHr1alVVVemll14KdmkAACDIQjbAPP/887p8+bIWL14sj8ejoUOHateuXXcN7DWZzWbTq6++etetLwQX30to4nsJXXw3oam1fy8huw4MAADAvYTkGBgAAICvQoABAADGIcAAAADjEGAAAIBxCDAhprq6WkOHDlVYWJhKS0uDXU6bdv78eU2bNk2JiYnq2LGj+vTpo1dffVU1NTXBLq1NevPNN/XQQw+pQ4cOSklJ0ccffxzsktq0vLw8jRgxQl26dFFMTIwmTJigsrKyYJeFOyxfvlxhYWGaO3dusEtpdgSYELNgwYKvXT4Z34zTp0+rrq5OP/vZz3Ty5EmtWrVKGzZs0D/8wz8Eu7Q2Z+vWrcrOztarr76q3/zmNxoyZIjcbrcqKyuDXVqbdeDAAWVmZurw4cMqLCzUrVu3lJaWpqqqqmCXhv/v6NGj+tnPfqbBgwcHu5QWwTTqELJz505lZ2frP//zP/XII4/ot7/9rYYOHRrssvAlK1eu1Pr16/WHP/wh2KW0KSkpKRoxYoTWrVsn6YuVuRMSEjRnzhwtXLgwyNVBki5fvqyYmBgdOHBAo0aNCnY5bd7169c1bNgwvfXWW/rRj36koUOHavXq1cEuq1nRAxMiKioqNGPGDP37v/+7oqKigl0O7sHr9So6OjrYZbQpNTU1KikpUWpqqrUvPDxcqampKi4uDmJl+DKv1ytJ/P0IEZmZmUpPTw/4e9PahOxKvG2J3+/Xiy++qFmzZmn48OE6f/58sEtCA86cOaO1a9fq9ddfD3Ypbcof//hH1dbW3rUKd2xsrE6fPh2kqvBldXV1mjt3rh5//HENHDgw2OW0eVu2bNFvfvMbHT16NNiltCh6YFrQwoULFRYW9pXb6dOntXbtWl27dk25ubnBLrlNuN/v5cs+++wzjRs3Tn/zN3+jGTNmBKlyIDRlZmbqxIkT2rJlS7BLafMuXLigv//7v9emTZvUoUOHYJfTohgD04IuX76szz///CvbPPzww/rbv/1bffjhhwoLC7P219bWKiIiQpMmTdLGjRtbutQ25X6/l8jISEnSxYsXNXr0aI0cOVL5+fkKDyf3f5NqamoUFRWld999VxMmTLD2T506VVevXtUHH3wQvOKgrKwsffDBBzp48KASExODXU6b9/777+uv//qvFRERYe2rra1VWFiYwsPDVV1dHXDMZASYEFBeXi6fz2e9vnjxotxut959912lpKSoZ8+eQayubfvss880ZswYJScn65e//GWr+YtvmpSUFD322GNau3atpC9uWfTq1UtZWVkM4g0Sv9+vOXPmaPv27dq/f7++/e1vB7skSLp27Zr+7//+L2DfSy+9pP79+ysnJ6dV3eJjDEwI6NWrV8Drzp07S5L69OlDeAmizz77TKNHj1bv3r31+uuv6/Lly9Yxp9MZxMranuzsbE2dOlXDhw/XY489ptWrV6uqqkovvfRSsEtrszIzM7V582Z98MEH6tKlizwejyTJ4XCoY8eOQa6u7erSpctdIaVTp07q1q1bqwovEgEGuKfCwkKdOXNGZ86cuStI0nH5zXr++ed1+fJlLV68WB6PR0OHDtWuXbvuGtiLb8769eslSaNHjw7Y/4tf/EIvvvjiN18Q2hxuIQEAAOMwGhEAABiHAAMAAIxDgAEAAMYhwAAAAOMQYAAAgHEIMAAAwDgEGAAAYBwCDAAAMA4BBgAAGIcAAwAAjEOAAQAAxiHAAAAA4/w/2OAoDqD4yzEAAAAASUVORK5CYII=\n","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["import matplotlib.pyplot as plt\n","\n","plt.hist(np.random.normal(loc=0.0, scale=1.0, size=100000), bins=100)\n","plt.show()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ECabJEkErL6A"},"outputs":[],"source":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"putb_-vUrL6B"},"outputs":[],"source":[]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3 (ipykernel)","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.7"},"toc":{"base_numbering":1,"nav_menu":{},"number_sections":true,"sideBar":true,"skip_h1_title":false,"title_cell":"Table of 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