02-data-analysis-modules.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Python 金融資料的處理應用\n",
    "\n",
    "> Python 資料分析模組\n",
    "\n",
    "[郭耀仁](https://hahow.in/@tonykuoyj?tr=tonykuoyj) | yaojenkuo@ntu.edu.tw | April 2024"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 什麼是 NumPy\n",
    "\n",
    "> NumPy 是 Numeric Python 的簡稱，是 Python 最重要的資料科學模組之一。NumPy 創造了 `ndarray` 的資料結構類別以及大量的通用函數與聚合函數，讓 Python 使用者能夠對進行快速的數值計算、使用統計函數、進行線性代數運算以及操作隨機的模擬任務等。\n",
    "\n",
    "來源：<https://numpy.org/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## （沒什麼用的冷知識）NumPy 的前身為兩個模組\n",
    "\n",
    "- 1990 年代中期誕生的` Numeric` 與 `Numarray` 模組。\n",
    "- NumPy 於 2005 集兩者之大成問世。\n",
    "\n",
    "來源：<https://www.nature.com/articles/s41586-020-2649-2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 根據說明文件的範例載入\n",
    "\n",
    "來源：<https://numpy.org/doc/stable/user/absolute_beginners.html#how-to-import-numpy>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 如果環境中沒有安裝 `numpy`，載入時會遭遇 `ModuleNotFoundError`\n",
    "\n",
    "```\n",
    "Traceback (most recent call last):\n",
    "  File \"<stdin>\", line 1, in <module>\n",
    "ModuleNotFoundError: No module named 'numpy'\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## NumPy 的核心功能\n",
    "\n",
    "1. 使用 `ndarray` 來進行數值操作。\n",
    "2. 使用模組定義的函數對 `ndarray` 進行數值運算。\n",
    "3. `ndarray` 是其他資料科學模組 Pandas、Matplotlib 與 Scikit-Learn 的基石。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 從 `list` 創造 `ndarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  3  5  7 11]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "prime_list = [2, 3, 5, 7, 11]\n",
    "prime_array = np.array(prime_list)\n",
    "print(prime_array)\n",
    "print(type(prime_array))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "##  利用 NumPy 函數創造內容元素相同的 `ndarray`\n",
    "\n",
    "- `np.zeros()`\n",
    "- `np.ones()`\n",
    "- `np.full()`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 0 0 0]\n",
      "[1. 1. 1. 1. 1.]\n",
      "[6 6 6 6 6]\n"
     ]
    }
   ],
   "source": [
    "print(np.zeros(5, dtype=int))\n",
    "print(np.ones(5, dtype=float))\n",
    "print(np.full(5, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 利用 NumPy 函數創造數列型態的 `ndarray`\n",
    "\n",
    "- `np.arange(start, stop, step)`\n",
    "- `np.linspace(start, stop, num)` 值得注意的是 `stop` 參數預設為包含。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 3 5 7 9]\n",
      "[1 3 5 7 9]\n"
     ]
    }
   ],
   "source": [
    "print(np.arange(1, 11, 2))\n",
    "print(np.linspace(1, 9, 5, dtype=int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 利用 `np.random` 中的函數創造隨機性的 `ndarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.1952533  0.40258042 0.14737346 ... 0.62867906 0.36418401 0.17013949]\n",
      "[ 0.15881748 -1.60656288 -0.43795067 ...  0.11110473  0.49469041\n",
      " -1.52568054]\n",
      "[5 2 1 ... 5 4 3]\n"
     ]
    }
   ],
   "source": [
    "m = 10000\n",
    "uniform_array = np.random.random(m) # module.function()\n",
    "normal_array = np.random.normal(0, 1, m)\n",
    "randint_array = np.random.randint(1, 7, size=m)\n",
    "print(uniform_array)\n",
    "print(normal_array)\n",
    "print(randint_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 常用的 `ndarray` 屬性\n",
    "\n",
    "- `ndarray.ndim` 維度數。\n",
    "- `ndarray.shape` 外型。\n",
    "- `ndarray.size` 元素個數。\n",
    "- `ndarray.dtype` 資料類別。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "(5,)\n",
      "5\n",
      "int64\n"
     ]
    }
   ],
   "source": [
    "prime_list = [2, 3, 5, 7, 11]\n",
    "prime_array = np.array(prime_list)\n",
    "print(prime_array.ndim)\n",
    "print(prime_array.shape)\n",
    "print(prime_array.size)\n",
    "print(prime_array.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `ndarray` 與 `list` 相異的地方\n",
    "\n",
    "- indexing 二維以上的 `ndarray` 可以用更便捷的語法 `[i, j, k, ...]`\n",
    "- 同質性資料結構類別。\n",
    "- 支援元素操作（Elementwise）運算。\n",
    "- 支援特殊的 indexing 語法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## indexing 二維以上的 `ndarray` 可以用 `ndarray` 便捷的語法 `[i, j, k, ...]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5  5]\n",
      " [ 6  6]\n",
      " [55 66]]\n",
      "66\n"
     ]
    }
   ],
   "source": [
    "matrix = np.array([[5, 5],\n",
    "                   [6, 6],\n",
    "                   [55, 66]])\n",
    "print(matrix)\n",
    "print(matrix[2, 1]) # 66 locates at [2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 5  6 66]\n",
      "[[ 5]\n",
      " [ 6]\n",
      " [66]]\n"
     ]
    }
   ],
   "source": [
    "print(matrix[:, 1])\n",
    "print(matrix[:, [1]]) # keep dimension"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 同質性資料結構類別"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'bool'>\n",
      "<class 'bool'>\n",
      "<class 'int'>\n",
      "<class 'float'>\n",
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "heterogeneous_list = [False, True, 5566, 55.66, 'Luke Skywalker']\n",
    "for element in heterogeneous_list:\n",
    "    print(type(element))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.str_'>\n",
      "<class 'numpy.str_'>\n",
      "<class 'numpy.str_'>\n",
      "<class 'numpy.str_'>\n",
      "<class 'numpy.str_'>\n"
     ]
    }
   ],
   "source": [
    "homogeneous_array = np.array(heterogeneous_list)\n",
    "for element in homogeneous_array:\n",
    "    print(type(element))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 支援元素操作（Elementwise）運算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unsupported operand type(s) for ** or pow(): 'list' and 'int'\n"
     ]
    }
   ],
   "source": [
    "# list does not support elementwise\n",
    "primes_list = [2, 3, 5, 7, 11]\n",
    "try:\n",
    "    primes_list**2\n",
    "except TypeError as error_message:\n",
    "    print(error_message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  4,   9,  25,  49, 121])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ndarray supports elementwise\n",
    "primes_array = np.array(primes_list)\n",
    "primes_array**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 支援特殊的 indexing 語法\n",
    "\n",
    "- Fancy indexing\n",
    "- Boolean indexing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 什麼是 Fancy indexing\n",
    "\n",
    "對應 `ndarray` 時中括號允許傳入 `list`，藉此可以更有彈性地取出 `ndarray` 中的元素。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "list indices must be integers or slices, not list\n"
     ]
    }
   ],
   "source": [
    "primes_list = [2, 3, 5, 7, 11]\n",
    "try:\n",
    "    primes_list[[0, 1, 4]]\n",
    "except TypeError as error_message:\n",
    "    print(error_message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  3  5  7 11]\n",
      "[ 2  3 11]\n"
     ]
    }
   ],
   "source": [
    "primes_array = np.array([2, 3, 5, 7, 11])\n",
    "print(primes_array)\n",
    "print(primes_array[[0, 1, 4]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 什麼是 Boolean indexing\n",
    "\n",
    "對應 `ndarray` 時中括號允許傳入由 `bool` 組成的相同長度 `list` 或 `ndarray`，藉此可以更有彈性地取出 `ndarray` 中的元素。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "list indices must be integers or slices, not list\n"
     ]
    }
   ],
   "source": [
    "primes_list = [2, 3, 5, 7, 11]\n",
    "try:\n",
    "    primes_list[[False, True, True, True, True]]\n",
    "except TypeError as error_message:\n",
    "    print(error_message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  3  5  7 11]\n",
      "[ 3  5  7 11]\n",
      "[False  True  True  True  True]\n",
      "[ 3  5  7 11]\n"
     ]
    }
   ],
   "source": [
    "primes_array = np.array([2, 3, 5, 7, 11])\n",
    "print(primes_array)\n",
    "print(primes_array[[False, True, True, True, True]])\n",
    "print(primes_array % 2 == 1)\n",
    "print(primes_array[primes_array % 2 == 1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## NumPy 提供非常豐富的數值運算函數\n",
    "\n",
    "- 通用函數（Universal functions）。\n",
    "- 聚合函數（Aggregate functions）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 什麼是通用函數\n",
    "\n",
    "通用函數是具備向量化（Vectorized）特性的函數，接受固定數量、外型的輸入並對應相同數量、外型的輸出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 2 3 4 5 6 7 8 9]\n",
      "[ 0  1  4  9 16 25 36 49 64 81]\n",
      "[1.00000000e+00 2.71828183e+00 7.38905610e+00 2.00855369e+01\n",
      " 5.45981500e+01 1.48413159e+02 4.03428793e+02 1.09663316e+03\n",
      " 2.98095799e+03 8.10308393e+03]\n"
     ]
    }
   ],
   "source": [
    "array_range = np.arange(10)\n",
    "print(array_range)\n",
    "print(np.power(array_range, 2)) # np.power() is a universal function\n",
    "print(np.exp(array_range))      # np.exp() is a universal function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 轉換純量函數為通用函數\n",
    "\n",
    "使用 `np.vectorize()` 函數將只能作用在單一資料值上的函數轉換為通用函數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "only integer scalar arrays can be converted to a scalar index\n"
     ]
    }
   ],
   "source": [
    "def is_prime(x):\n",
    "    number_of_divisors = 0\n",
    "    for integer in range(1, x + 1):\n",
    "        if x % integer == 0:\n",
    "            number_of_divisors += 1\n",
    "    return number_of_divisors == 2\n",
    "\n",
    "try:\n",
    "    is_prime(np.arange(10))\n",
    "except TypeError as error_message:\n",
    "    print(error_message)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True,  True, False,  True, False,  True, False,\n",
       "       False])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorized_is_prime = np.vectorize(is_prime)\n",
    "vectorized_is_prime(np.arange(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 什麼是聚合函數\n",
    "\n",
    "聚合函數是能夠將多個資料值輸入摘要為單一值輸出的函數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array_range = np.arange(1, 16).reshape(3, 5)\n",
    "np.sum(array_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## NumPy 的聚合函數有兩個值得注意的特性\n",
    "\n",
    "1. 可以沿指定的軸（axis）進行聚合。\n",
    "2. 針對含有 `np.nan`（Not a Number）的 `ndarray` 有相對應名稱的聚合函數可以運算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120.0\n",
      "[18. 21. 24. 27. 30.]\n",
      "[15. 40. 65.]\n"
     ]
    }
   ],
   "source": [
    "# Aggregate along specific axis\n",
    "array_range = np.arange(1, 16, dtype=float).reshape(3, 5)\n",
    "print(np.sum(array_range))\n",
    "print(np.sum(array_range, axis=0))\n",
    "print(np.sum(array_range, axis=1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 針對含有 `np.nan` 的 `ndarray` 有相對應名稱的聚合函數可以運算\n",
    "\n",
    "- `np.sum()` vs. `np.nansum()`\n",
    "- `np.mean()` vs. `np.nanmean()`\n",
    "- ...等。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  2.  3.  4.  5.]\n",
      " [ 6.  7.  8.  9. 10.]\n",
      " [11. 12. 13. 14. nan]]\n",
      "nan\n",
      "105.0\n"
     ]
    }
   ],
   "source": [
    "# Similar function names for array with np.nan\n",
    "array_range = np.arange(1, 16, dtype=float).reshape(3, 5)\n",
    "array_range[2, 4] = np.nan\n",
    "print(array_range)\n",
    "print(np.sum(array_range))\n",
    "print(np.nansum(array_range))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 什麼是 Pandas\n",
    "\n",
    "> Pandas 是 Python 處理表格式資料（Tabular data）的第三方模組，它創造了 `Index`、`Series` 與 `DataFrame` 的資料結構類別，讓 Python 在面對表格式資料時能夠用更直覺的觀念操作。\n",
    "\n",
    "來源：<https://github.com/pandas-dev/pandas>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## （沒什麼用的冷知識）Pandas 跟熊貓「沒有關係」\n",
    "\n",
    "1. **Pan**el(自從版本 0.20.0 之後棄用)\n",
    "2. **Da**taFrame\n",
    "3. **S**eries\n",
    "\n",
    "![](https://media.giphy.com/media/46Zj6ze2Z2t4k/giphy.gif)\n",
    "\n",
    "來源：<https://media.giphy.com/media/46Zj6ze2Z2t4k/giphy.gif>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 根據說明文件的範例載入\n",
    "\n",
    "來源：<https://pandas.pydata.org/docs/user_guide/10min.html>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 如果環境中沒有安裝 Pandas，載入時會遭遇 `ModuleNotFoundError`\n",
    "\n",
    "```\n",
    "Traceback (most recent call last):\n",
    "  File \"<stdin>\", line 1, in <module>\n",
    "ModuleNotFoundError: No module named 'pandas'\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 入門 Pandas 的第一步就是掌握 `Index`、`ndarray`、`Series` 與 `DataFrame` 四個資料結構類別彼此之間的關係\n",
    "\n",
    "- `Series` 由 `Index` 與 `ndarray` 組合而成。\n",
    "- `DataFrame` 由數個共享同一個 `Index` 的 `Series` 組合而成。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Pandas 的 `Index` 類別\n",
    "\n",
    "使用 `pd.Index()` 函數創造 `Index` 類別的實例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([2, 3, 5, 7, 11, 13, 17, 19, 23, 29], dtype='int64')\n",
      "<class 'pandas.core.indexes.base.Index'>\n"
     ]
    }
   ],
   "source": [
    "primes_array = np.array([2, 3, 5, 7, 11, 13, 17, 19, 23, 29])\n",
    "prime_indexes = pd.Index(primes_array)\n",
    "print(prime_indexes)\n",
    "print(type(prime_indexes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `Index` 的基礎屬性\n",
    "\n",
    "- `Index.dtype` 資料類別。\n",
    "- `Index.size` 元素個數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "print(prime_indexes.dtype)\n",
    "print(prime_indexes.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `Index` 類別結合 Python 內建的 `tuple` 與 `set` 兩種資料結構類別的特性\n",
    "\n",
    "- 具有 `tuple` 無法更動的特性。\n",
    "- 具有 `set` 集合運算的特性。 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `Index` 類別具有 `tuple` 無法更動的特性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index does not support mutable operations\n"
     ]
    }
   ],
   "source": [
    "# Index has the characteristics of a tuple\n",
    "primes_array = np.array([2, 3, 5, 7, 11, 13, 17, 19, 23, 29])\n",
    "prime_indexes = pd.Index(primes_array)\n",
    "try:\n",
    "    prime_indexes[-1] = 31\n",
    "except TypeError as error_message:\n",
    "    print(error_message)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `Index` 類別具有 `set` 集合運算的特性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([2, 3, 5, 7, 11, 13, 17, 19, 23, 29], dtype='int64')\n",
      "Index([1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29], dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "# Index has the characteristics of a set\n",
    "primes_array = np.array([2, 3, 5, 7, 11, 13, 17, 19, 23, 29])\n",
    "prime_indexes = pd.Index(primes_array)\n",
    "odd_indexes = pd.Index(np.arange(1, 30, 2))\n",
    "print(prime_indexes)\n",
    "print(odd_indexes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `Index` 具有與 `set` 同樣名稱的集合運算方法\n",
    "\n",
    "- `Index.intersection()` 交集。\n",
    "- `Index.union()` 聯集。\n",
    "- `Index.difference()` 差集。\n",
    "- `Index.symmetric_difference()` 對稱差集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index([3, 5, 7, 11, 13, 17, 19, 23, 29], dtype='int64')\n",
      "Index([1, 2, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29], dtype='int64')\n",
      "Index([2], dtype='int64')\n",
      "Index([1, 9, 15, 21, 25, 27], dtype='int64')\n",
      "Index([1, 2, 9, 15, 21, 25, 27], dtype='int64')\n"
     ]
    }
   ],
   "source": [
    "# Set operations of Index\n",
    "print(prime_indexes.intersection(odd_indexes))\n",
    "print(prime_indexes.union(odd_indexes))\n",
    "print(prime_indexes.difference(odd_indexes))\n",
    "print(odd_indexes.difference(prime_indexes))\n",
    "print(prime_indexes.symmetric_difference(odd_indexes))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Pandas 的 `Series` 類別\n",
    "\n",
    "使用 `pd.Series()` 函數創造 `Series` 類別的實例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      1\n",
      "1      2\n",
      "2      3\n",
      "3      4\n",
      "4      5\n",
      "5      6\n",
      "6      7\n",
      "7      8\n",
      "8      9\n",
      "9     10\n",
      "10    11\n",
      "11    12\n",
      "dtype: int64\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "months_array = np.arange(1, 13)\n",
    "months_series = pd.Series(months_array)\n",
    "print(months_series)\n",
    "print(type(months_series))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `Series` 的基礎屬性與方法\n",
    "\n",
    "- `Series.dtype` 資料類別。\n",
    "- `Series.size` 元素個數。\n",
    "- `Series.index` 取出 `Series` 的 `Index` 部分。  \n",
    "- `Series.values` 取出 `Series` 的 `ndarray` 部分。\n",
    "- `Series.astype()` 轉換 `Series` 的資料類別。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n",
      "12\n"
     ]
    }
   ],
   "source": [
    "print(months_series.dtype)\n",
    "print(months_series.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `Series` 由 `Index` 與 `ndarray` 組合而成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RangeIndex(start=0, stop=12, step=1)\n",
      "<class 'pandas.core.indexes.range.RangeIndex'>\n"
     ]
    }
   ],
   "source": [
    "print(months_series.index)\n",
    "print(type(months_series.index))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  2  3  4  5  6  7  8  9 10 11 12]\n",
      "<class 'numpy.ndarray'>\n"
     ]
    }
   ],
   "source": [
    "print(months_series.values)\n",
    "print(type(months_series.values))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 調整 `Series` 的 `Index`\n",
    "\n",
    "- 在建立的時候指定。\n",
    "- 透過 `Series.index` 更新。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 在建立的時候指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JAN     1\n",
       "FEB     2\n",
       "MAR     3\n",
       "APR     4\n",
       "MAY     5\n",
       "JUN     6\n",
       "JUL     7\n",
       "AUG     8\n",
       "SEP     9\n",
       "OCT    10\n",
       "NOV    11\n",
       "DEC    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_abbreviation = [\"JAN\", \"FEB\", \"MAR\", \"APR\", \"MAY\", \"JUN\", \"JUL\", \"AUG\", \"SEP\", \"OCT\", \"NOV\", \"DEC\"]\n",
    "months_series = pd.Series(months_array, index=months_abbreviation)\n",
    "months_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 透過 `Series.index` 更新"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JAN     1\n",
       "FEB     2\n",
       "MAR     3\n",
       "APR     4\n",
       "MAY     5\n",
       "JUN     6\n",
       "JUL     7\n",
       "AUG     8\n",
       "SEP     9\n",
       "OCT    10\n",
       "NOV    11\n",
       "DEC    12\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_abbreviation = [\"JAN\", \"FEB\", \"MAR\", \"APR\", \"MAY\", \"JUN\", \"JUL\", \"AUG\", \"SEP\", \"OCT\", \"NOV\", \"DEC\"]\n",
    "months_series = pd.Series(months_array)\n",
    "months_series.index = months_abbreviation\n",
    "months_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 如何取出 `Series` 中的元素\n",
    "\n",
    "- 以元素位置 indexing/slicing\n",
    "- 以 `Index` indexing/slicing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 以元素位置 indexing/slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "JAN    1\n",
      "FEB    2\n",
      "MAR    3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(months_series[0])\n",
    "print(months_series[:3])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 以 `Index` indexing/slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "JAN    1\n",
      "FEB    2\n",
      "MAR    3\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(months_series[\"JAN\"])\n",
    "print(months_series[\"JAN\":\"MAR\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 轉換 `Series` 的資料類別\n",
    "\n",
    "- 在建立的時候指定。\n",
    "- 透過 `Series.astype()` 轉換 `Series` 的資料類別。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 在建立的時候指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.0\n",
       "1      2.0\n",
       "2      3.0\n",
       "3      4.0\n",
       "4      5.0\n",
       "5      6.0\n",
       "6      7.0\n",
       "7      8.0\n",
       "8      9.0\n",
       "9     10.0\n",
       "10    11.0\n",
       "11    12.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "months_series = pd.Series(months_array, dtype=float)\n",
    "months_series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `Series.astype()` 轉換 `Series` 的資料類別"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\n",
      "0      1.0\n",
      "1      2.0\n",
      "2      3.0\n",
      "3      4.0\n",
      "4      5.0\n",
      "5      6.0\n",
      "6      7.0\n",
      "7      8.0\n",
      "8      9.0\n",
      "9     10.0\n",
      "10    11.0\n",
      "11    12.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "months_series = pd.Series(months_array)\n",
    "print(months_series.dtype)\n",
    "print(months_series.astype(float))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Pandas 的 `DataFrame` 類別\n",
    "\n",
    "使用 `pd.DataFrame()` 函數創造 `DataFrame` 類別的實例。\n",
    "\n",
    "- 輸入以欄為基準（Column-based）的資料內容。\n",
    "- 輸入以列為基準（Row-based）的資料內容。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 輸入以欄為基準（Column-based）的資料內容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      title  imdb_rating  release_year\n",
      "0  The Shawshank Redemption          9.3          1994\n",
      "1           The Dark Knight          9.0          2008\n",
      "2          Schindler's List          8.9          1993\n",
      "3              Forrest Gump          8.8          1994\n",
      "4                 Inception          8.7          2010\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "movie_df = pd.DataFrame()\n",
    "movie_df[\"title\"] = [\"The Shawshank Redemption\", \"The Dark Knight\", \"Schindler's List\", \"Forrest Gump\", \"Inception\"]\n",
    "movie_df[\"imdb_rating\"] = [9.3, 9.0, 8.9, 8.8, 8.7]\n",
    "movie_df[\"release_year\"] = [1994, 2008, 1993, 1994, 2010]\n",
    "print(movie_df)\n",
    "print(type(movie_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 輸入以列為基準（Row-based）的資料內容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      title  imdb_rating  release_year\n",
      "0  The Shawshank Redemption          9.3          1994\n",
      "1           The Dark Knight          9.0          2008\n",
      "2          Schindler's List          8.9          1993\n",
      "3              Forrest Gump          8.8          1994\n",
      "4                 Inception          8.7          2010\n",
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "movies = [\n",
    "    {\"title\": \"The Shawshank Redemption\", \"imdb_rating\": 9.3, \"release_year\": 1994},\n",
    "    {\"title\": \"The Dark Knight\", \"imdb_rating\": 9.0, \"release_year\": 2008},\n",
    "    {\"title\": \"Schindler's List\", \"imdb_rating\": 8.9, \"release_year\": 1993},\n",
    "    {\"title\": \"Forrest Gump\", \"imdb_rating\": 8.8, \"release_year\": 1994},\n",
    "    {\"title\": \"Inception\", \"imdb_rating\": 8.7, \"release_year\": 2010},\n",
    "]\n",
    "movie_df = pd.DataFrame(movies)\n",
    "print(movie_df)\n",
    "print(type(movie_df))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Jupyter Notebook 針對 `DataFrame` 類別有特別的顯示外觀"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>title</th>\n",
       "      <th>imdb_rating</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inception</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      title  imdb_rating  release_year\n",
       "0  The Shawshank Redemption          9.3          1994\n",
       "1           The Dark Knight          9.0          2008\n",
       "2          Schindler's List          8.9          1993\n",
       "3              Forrest Gump          8.8          1994\n",
       "4                 Inception          8.7          2010"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `DataFrame` 的基礎屬性\n",
    "\n",
    "- `DataFrame.dtypes` 資料類別。\n",
    "- `DataFrame.shape` 外型。\n",
    "- `DataFrame.index` 取出列標籤（row labels）部分。  \n",
    "- `DataFrame.columns` 取出欄標籤（column labels）的部分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "title            object\n",
      "imdb_rating     float64\n",
      "release_year      int64\n",
      "dtype: object\n",
      "(5, 3)\n",
      "RangeIndex(start=0, stop=5, step=1)\n",
      "Index(['title', 'imdb_rating', 'release_year'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(movie_df.dtypes)\n",
    "print(movie_df.shape)\n",
    "print(movie_df.index)\n",
    "print(movie_df.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `DataFrame` 由數個 `Series` 共享同一個 `Index` 組成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.indexes.range.RangeIndex'>\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n",
      "<class 'pandas.core.series.Series'>\n"
     ]
    }
   ],
   "source": [
    "print(type(movie_df.index))\n",
    "print(type(movie_df[\"title\"]))\n",
    "print(type(movie_df[\"imdb_rating\"]))\n",
    "print(type(movie_df[\"release_year\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `DataFrame` 的基礎方法\n",
    "\n",
    "- `DataFrame.head(n)` 檢視前 `n` 列。\n",
    "- `DataFrame.tail(n)` 檢視後 `n` 列。\n",
    "- `DataFrame.describe()`  檢視數值欄位的描述性統計。\n",
    "- `DataFrame.info()` 檢視詳細資訊。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 檢視前 `n` 列、後 `n` 列\n",
    "\n",
    "- `DataFrame.head(n)` 檢視前 `n` 列。\n",
    "- `DataFrame.tail(n)` 檢視後 `n` 列。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>title</th>\n",
       "      <th>imdb_rating</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The Shawshank Redemption</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>The Dark Knight</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Schindler's List</td>\n",
       "      <td>8.9</td>\n",
       "      <td>1993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      title  imdb_rating  release_year\n",
       "0  The Shawshank Redemption          9.3          1994\n",
       "1           The Dark Knight          9.0          2008\n",
       "2          Schindler's List          8.9          1993"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>title</th>\n",
       "      <th>imdb_rating</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Forrest Gump</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inception</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2010</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          title  imdb_rating  release_year\n",
       "3  Forrest Gump          8.8          1994\n",
       "4     Inception          8.7          2010"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `DataFrame.describe()` 檢視數值欄位的描述性統計"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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>imdb_rating</th>\n",
       "      <th>release_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.940000</td>\n",
       "      <td>1999.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.230217</td>\n",
       "      <td>8.438009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.700000</td>\n",
       "      <td>1993.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8.800000</td>\n",
       "      <td>1994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.900000</td>\n",
       "      <td>1994.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>9.300000</td>\n",
       "      <td>2010.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       imdb_rating  release_year\n",
       "count     5.000000      5.000000\n",
       "mean      8.940000   1999.800000\n",
       "std       0.230217      8.438009\n",
       "min       8.700000   1993.000000\n",
       "25%       8.800000   1994.000000\n",
       "50%       8.900000   1994.000000\n",
       "75%       9.000000   2008.000000\n",
       "max       9.300000   2010.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movie_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## `DataFrame.info()` 檢視詳細資訊"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 3 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   title         5 non-null      object \n",
      " 1   imdb_rating   5 non-null      float64\n",
      " 2   release_year  5 non-null      int64  \n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 252.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "movie_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 什麼是 Matplotlib\n",
    "\n",
    "> Matplotlib 是 Python 資料視覺化的第三方模組。\n",
    "\n",
    "來源：<https://matplotlib.org>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## （沒什麼用的冷知識）Matplotlib 與 Matlab 有一點關係\n",
    "\n",
    "- Matrix plotting library.\n",
    "- 模仿 Matlab 作圖的指令與功能。\n",
    "\n",
    "來源：<https://matplotlib.org/stable/users/project/history.html>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 根據說明文件的範例載入\n",
    "\n",
    "實際上主要在使用的是 `matplotlib.pyplot`\n",
    "\n",
    "來源：<https://matplotlib.org/stable/tutorials/introductory/usage.html#sphx-glr-tutorials-introductory-usage-py>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 如果環境中沒有安裝 Matplotlib，載入時會遭遇 `ModuleNotFoundError`\n",
    "\n",
    "```\n",
    "Traceback (most recent call last):\n",
    "  File \"<stdin>\", line 1, in <module>\n",
    "ModuleNotFoundError: No module named 'matplotlib'\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 新手學習 Matplotlib 的最大挑戰：兩種不同的作圖語法\n",
    "\n",
    "1. Matlab 風格。\n",
    "2. 物件導向風格。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Matlab 風格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(0, np.pi*4, 100)\n",
    "plt.figure()\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(x, np.sin(x))\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, np.cos(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## Matlab 風格的優缺點\n",
    "\n",
    "- 優點：指令式語法，比較直觀。\n",
    "- 缺點：狀態型介面，需要依程式碼順序閱讀才能理解。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 物件導向風格"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAi8AAAGdCAYAAADaPpOnAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8pXeV/AAAACXBIWXMAAA9hAAAPYQGoP6dpAACCWklEQVR4nO3dd1zV9fcH8NcdcC/zIiB7KwgKLnCAe0SpmZWVZjkyTStzNUztW2al2fBnZWpqZkuzcmQ5ElNBczMUFRmy976Xebnj8/vjwi0SEZTL+47zfDzuH9zulRc3/XA+5714HMdxIIQQQggxEHzWAQghhBBC2oOKF0IIIYQYFCpeCCGEEGJQqHghhBBCiEGh4oUQQgghBoWKF0IIIYQYFCpeCCGEEGJQqHghhBBCiEERsg7Q0dRqNfLz82FjYwMej8c6DiGEEELagOM4VFVVwc3NDXx+670Voyte8vPz4enpyToGIYQQQu5BTk4OPDw8Wn2N0RUvNjY2ADQ/vK2tLeM0hBBCCGkLmUwGT09P7e/x1hhd8dI0VGRra0vFCyGEEGJg2jLlQ6cTdmNiYjBx4kS4ubmBx+PhwIEDd31PdHQ0QkNDIRaL4efnhy1btugyIiGEEEIMjE6Ll5qaGvTp0wcbN25s0+szMjIwfvx4DBs2DPHx8VixYgUWLlyIvXv36jImIYQQQgyIToeNxo0bh3HjxrX59Vu2bIGXlxc2bNgAAAgKCsLly5fxySefYPLkyTpKSQghhBBDoldzXs6dO4fIyMhmzz344IP4+uuvoVAoYGZmdtt75HI55HK59muZTKbznMZCreaQVlKNS5nluJJTCZUaEJvxIRIKIDbjw97KHCMCuqK7kzUtOydEBziOw40CGdJLalAkq0dxlRxFsnpU1Svh7WCJAGcbBDjbwN/ZGrbi269/hJgqvSpeCgsL4ezs3Ow5Z2dnKJVKlJaWwtXV9bb3rF27Fu+++25nRTR4CpUa++Py8Of1QlzOqoC0TtHq698/lARvB0uMCXTG2J5OGOhjD6GA9jYk5H7kV9Zhf3we9sXl4lZJTZveE+Rqi1kR3pjU1x1iM4GOExKi3/SqeAFun2XMcVyLzzdZvnw5li5dqv26aakVaU6hUmNvbC42nkxDbkWd9nkLMwH6e9sh1KsLLEVC1CtUkCvVqFeocKukBudvlSGrrBY7/s7Ajr8z4OtohRXjgzA2yIm6MYS00+nUEmyJvoWzt8rQeGmD2IyP3u52cJaI4WwjgrOtGBbmAmSU1iClqAqpRdUolNUjqUCGZXsT8eGRm3hmkDemh3vD2VbM9gcihBG9Kl5cXFxQWFjY7Lni4mIIhUI4ODi0+B6RSASRSNQZ8QySSs3h19gcfHHin6LF0VqE54b4YGh3R/R0s4VZK52UarkSZ1JLEHWjGH/dLEJGaQ3mfncZEd0c8NaEnujpRsvRCbmb0mo53vvjBn5LyNc+N9DXHk/098C4EBfY3GVIqLymAXtjc7HzbCbyKuuw8WQatkTfwrwRflg0JgDmQuqGEtOiV8VLeHg4fv/992bPHTt2DGFhYS3OdyGtK66qx8Ld8TifXg5AU7S8OLIbpg30goV529rO1iIhHgp2xUPBrqiqV2DTqVv4+kwGzt4qw4QvTmPqAE+sGB9014svIaaI4zj8cjkXHxxOgrROAT4PmBHug+eH+sLT3rLNf469lTnmDvfDc0N8EHWjCDv+zsClzAp8efIWTiWXYMOUvvB3vvvGXoQYCx7XNC6jA9XV1UhLSwMA9OvXD+vXr8eoUaNgb28PLy8vLF++HHl5efjuu+8AaJZKBwcHY968eZg7dy7OnTuH+fPnY/fu3W1ebSSTySCRSCCVSk16k7qzt0qxcHcCSqvlsDQXYOkDAXhmkHebi5bW5JTX4sOjN3HoagEAoLuTNbbPCIOPo9V9/9mEGIviqnos2p2Ac+llAICerrb4cHIIenvYdciffzixACv2J6KyVgGRkI83xwViZrgP+HwaziWGqT2/v3VavJw6dQqjRo267fmZM2di586dmDVrFjIzM3Hq1Cntf4uOjsaSJUtw/fp1uLm5YdmyZZg/f36bv6epFy9qNYdNp9KwPioFag7o4WyDL5/pj+5O1h3+vS6kl2HRTwkolNVDYmGGTc/0x5Dujh3+fQgxNLdKqjFzx0XkVtTBwkyAJQ/4Y/YQ3w6f7F4sq8frv15FdEoJAGCYvyM2PdOfOqHEIOlN8cKCKRcv9QoVXv4xDn/dLAYAPBHqgfcmBXdIt+VOimX1eOH7WCTkVELA5+F/E4IwM8KHJvMSk3U5sxxzvruMyloFfBws8c1zA+Grw64kx3H44XwWPjichHqFGr09JPj2uYHoYmWus+9JiC5Q8WKCxUu9QoW5313G6dRSiIR8vPdoMJ4K65xVV/UKFVbsS8S++DwAwLODvbD6kWBqXxOTcySxAIv2JKBBqUZfTzt8PTMMDtads6DgWp4UM3ZcRHlNAwKcrfHD84PgRKuRiAFpz+9vmqJuBP5duFiaC/D984M6rXABALGZAJ8+1QcrxgeCxwN+OJ+N9w8lwcjqYkJa9f35LLy0Kw4NSjXGBjlj99zBnVa4AECwuwQ/zxsMZ1sRUoqq8eRX55BTXttp35+QzkTFi4GrV6jwwvex2sLlm1kDMNDXvtNz8Hg8vDC8Gz5+og8AYMffGdhwPLXTcxDCwsEr+fjfgWvgOE3n8avpoTodrr2T7k42+HV+BDztLZBVVosnt5zDrZLqTs9BiK5R8WLAmgqXmJQSWJgJsGPWAAzya3k/nM7yRKgHVk3sCQD47K9UbD+dzjQPIbp2Nq0Ur/6cAACYFeGD9yYFQ8BwyNTT3hK/zIuAv5M1CmX1mLnjIkqr5Xd/IyEGhIoXA8VxHF795UqzwmUw48Klyawhvnj1gQAAmuMF9lzKZpyIEN24kS/DvO9joVBxmBDiircf7qkXk9VdJGL89MJg+DhYIreiDnO/u4x6hYp1LEI6DBUvBmrTqVs4dLUAQj4PX88MQ3g3/ShcmiwY3R0vDPcDACzfl4ioG0WMExHSsXIrajHrm4uokisxyNcenz7VR68mqTtYi7Bj1gBILMwQn12J13+9SvPQiNGg4sUAnbhZhE+OJQMA3p3UCxF6uLcKj8fD8nGBmDrAE2oOWLonARmlbTuAjhB9J61VYNY3l1BcJUeAszW2zgjTy8MS/bpaY/Oz/SHk8/D7lXz8H81DI0aCihcDk1ZcjUW7E8BxwLRBXnhmkDfrSHfE4/Hw3qPBCPPugiq5Ei/+EIvaBiXrWITcl6Yh27TiarhKxPh29kBILPR3U7iIbo5Y81gIAODzv1JxoHFLA0IMGRUvBkRWr8AL319GlVyJAT5dsGpiL9aR7spMwMeXz/SHo7UINwursHL/NWpdE4P29ZkMHE8qgrmAj20zwuAqsWAd6a6eGuCJ+SO6AQDe+PUqruVJGSci5P5Q8WIg1GoOi39KQHpJDVwlYmx6JtRgTpJ1thVj47R+EPB52B+fhx/OZ7GORMg9ScipxLqjNwEAbz0chGB3CeNEbffGgz0wNsgZDSo1Fu6Opy4oMWiG8duP4JuzmThxsxgiIR9bp4ehq03nbX7VEQb7OWDZQz0AAKv/uIH47ArGiQhpH2mdAgt2xUGh4jA+xAXTB+vvkG1L+HwePn6iN1xsxUgvrcHq32+wjkTIPaPixQCkFlVp7/b+93BPhHgYzt3ev80d5oeHerlAoeLw8o9xkNYpWEcipE04jsOyX68it6IOnvYW+HByb71YEt1eXazMsX5KH/B4wE+XcnA4sYB1JELuCRUveq5BqcbixrNSRvboimcGebGOdM94PB4+frI3vB0skS+tx/t/0J0fMQzfncvC0euFMBPw8OW0/rA14FObI7o54sXG+S9v7r2K/Mo6xokIaT8qXvTc53+l4nq+DHaWZvjIQO/2/s1GbIZPntTc+f0Sm4sTN2n/F6LfbpVU44PDSQCA5eOC0NvDjm2gDrDkgQD08ZBAVq/E4j0JUKlpEj0xLFS86LHYrHJsOpUGAFjzWIjRnBA7wMces4f4AgDe3JsIaS0NHxH9pFZzeHPvVTQo1Rge0BXPDfFhHalDmAn4+GxqP1iZC3Axoxxbom+xjkRIu1Dxoqdq5Eos/fkK1BzweD93jA9xZR2pQ70W2QN+jlYorpLj3T+us45DSIt+uJCFS5kVsDIXYM1jwQbf+fw3H0crrHpEs93CZ8dTkVZMBzgSw0HFi5768MhNZJXVwk0ixqpJ+r+fS3tZmAvw8ZN9wOcB++LycJyODyB6JreiFuuOaCbKLxsXCI8ulowTdbwnQj0wskdXNKjUWLE/EWoaPiIGgooXPZSQU4kfLmj2Qvn4yT4GPTmwNaHeXTBnWOP5R/sTUVnbwDgRIRocx2Hl/muoaVAhzLsLntXjnazvB4/Hw3uTgmFhphk++iU2h3UkQtqEihc9o1SpsXJ/IrjG4aIhenhuUUda+kAAunW1QkmVHO8fSmIdhxAAmm5gdEoJzIV8rHuit14duNjRPO0tsbTxFPg1h2+ipErOOBEhd0fFi575/nwWrufLYCsWYsWEINZxdE5sJsBHT/QBAPwam4vYLNq8jrBVUiXH6sZl/IvH+qNbV2vGiXTvuSE+6OVmC2mdAu/RFgbEAFDxokeKZPX49FgKAOCNhwLhaG1Yu+jeq1DvLngy1AMA8PZv12jZJmFqzeEkSOsU6OVmi7mNw5rGTijgY+3jIeDzgINX8nEquZh1JEJaRcWLHln9xw1Uy5Xo62mHaQMNdzO6e7FsXCBsxUJcz5dh1wU6+4iwEZtVjv3xeeDxNNsTmAlM5xLZ28MOsyI0Wxi8deAanX1E9Jrp/MvUczEpJTh0tQB8HvD+o8FGPcbeEkdrEV6N1Jx99PGfySirpnF30rnUag6rDmqGTJ4K9UQfTzu2gRh4NTIA7nYWyK2ow5bodNZxCLkjKl70QL1Chbd/uwYAmBnhY1An1XakZwZ5IcjVFrJ6JT46msw6DjExv8TmIDFPChuREK83HiJqaqxEQrzVONfuq+hbyKOjA4ieouJFD3zzdyYyy2rhZCPSzvo3RUIBH+817mmz53IOnTxNOo20TqEtmBeN9TeZ+WYteSjYBQN97SFXqvFR44GwhOgbKl4YK6uWY9NJzREAyx4KhI2R7unSVmE+9pjcv2ny7nWavEs6xed/paKspgHdulphZoQP6zhM8Xg8vP1wT/B4wG8J+bQCkOglKl4Y23A8FVVyJYLdbfFYP3fWcfTCm+MCYSMWIjFPiv3xeazjECOXVlyFb89mAgDemdjLpCbp3kmwu0S7AvC9P27QzrtE73TKv9JNmzbB19cXYrEYoaGhOH369B1fe+rUKfB4vNseN28aX/syrbgKuy5mAwBWju9pcpN076SrjQgvj+oOAPj0WDLqFSrGiYix4jgO7/5+A0o1h7FBzhge0JV1JL3x2oM9YGUuQEJOJQ5eyWcdh5BmdF687NmzB4sXL8bKlSsRHx+PYcOGYdy4ccjOzm71fcnJySgoKNA+/P39dR210609fBOqxotmeDcH1nH0yqwIH7jbWaBAWo9v/s5kHYcYqZjUUpxOLYW5gI//PWz8m0K2h5ONGC+P1txEfHjkJi2dJnpF58XL+vXr8fzzz2POnDkICgrChg0b4Onpic2bN7f6PicnJ7i4uGgfAoFA11E71dm0Uvx1sxhCPg/LxweyjqN3xGYCvBqpmby86WQaymvo3CPSsdRqDh82Hrw4I9wb3g5WjBPpn9lDfOHRxQKFsnp8RUuniR7RafHS0NCA2NhYREZGNns+MjISZ8+ebfW9/fr1g6urK8aMGYOTJ0/e8XVyuRwymazZQ9+p1Jz2HJ9nB3ubxPbj9+LRvu7o6WqLKrkSX5xIZR2HGJmDV/KRVCCDjUioHaYkzYnNBFgxXtOR2hqTjuKqesaJCNHQafFSWloKlUoFZ2fnZs87OzujsLCwxfe4urpi69at2Lt3L/bt24cePXpgzJgxiImJafH1a9euhUQi0T48PT07/OfoaPvicnGjQAYbsRALxxjfcFhH4fN52gvnD+ezkFVWwzgRMRZypQqfHNMsjZ4/shu6WJkzTqS/xgW7oK+nHeoUKnx5Io11HEIAdNKEXR6v+URUjuNue65Jjx49MHfuXPTv3x/h4eHYtGkTJkyYgE8++aTF1y9fvhxSqVT7yMnR7yPd6xUqrI/SnF/0yujusKeLZquG+jtieEBXKFQcPv6TNq4jHWPXhWzkVtTByUaE2UN8WcfRazweD280btq362I2csprGSciRMfFi6OjIwQCwW1dluLi4tu6Ma0ZPHgwUlNbHjYQiUSwtbVt9tBnP17IRoG0Hm4SMWaE+7COYxCWjwsEjwf8cbUACTmVrOMQA1dVr8AXjR2ExWMDYGFuXPPpdCGimyOG+TtCoeLwf403X4SwpNPixdzcHKGhoYiKimr2fFRUFCIiItr858THx8PV1bWj43W6GrlSuyHdwjH+EJvRRbMtglxttRvXfXgkiXEaYui2xaSjvKYBfo5WeCrMg3Ucg/H6g5ruy/6EPNws1P+5hcS46XzYaOnSpdi+fTt27NiBpKQkLFmyBNnZ2Zg/fz4AzbDPjBkztK/fsGEDDhw4gNTUVFy/fh3Lly/H3r17sWDBAl1H1blv/s5AWU0DfBwsMTmULprtseSBAJgL+DifXo6zaaWs4xADVVxVj22nMwAAbzzUA0LakK7NenvYYXyICzgO+ORP6r4QtoS6/gZTpkxBWVkZVq9ejYKCAgQHB+Pw4cPw9vYGABQUFDTb86WhoQGvvfYa8vLyYGFhgV69euHQoUMYP368rqPqlLRWga9iNEsNlzwQQLt4tpO7nQWeHuiJb89l4dOoFIR3c7jjvClC7mTjiTTUKVTo62mHB3u5sI5jcJY+0ANHrxXieFIRYrPKEeptzzoSMVE8juOMat9nmUwGiUQCqVSqV/NfPv7zJr48eQuBLjY4vHAY7aZ7D4pl9Rj20UnIlWrsfG4ARvZwYh2JGJC8yjqM+vgUGlRq7Jo7CBHdHFlHMkjLfr2KPZdzMNDXHnteGEw3EaTDtOf3N93+d4KSKjl2nMkEACx9IIAKl3vkZCvGjHBNx259VAqMrO4mOvblyTQ0qNQY7GdPhct9WDTWH+ZCPi5mlCM6pYR1HGKiqHjpBJtOaVrVfTzt8EDPtq+yIrebP6IbLM0FuJorRdSNItZxiIHIKa/FL5c12ygsGRvAOI1hc7OzwPTBmpuIDcdT6SaCMEHFi47lV9bhx/OaOT2vRQZQi/U+OViLMCvCB4Cm+0Kn3ZK2+PJkGhQqDkO6O2CQH50jdr/mjfCD2IyPhJxK6r4QJqh40bFNpzSt6kG+9hjanVrVHeGF4X6wEQlxs7AKh68VsI5D9Fx2WS1+jc0FQF2XjuJkI8azg6j7Qtih4kWHCqR1+PlS40XzAeq6dBQ7S3M8P0yzK+r/RaVARd0X0oovTqRCqeYwPKArwnxodUxHeYG6L4QhKl50aMupW9quy2BqVXeo2UN9IbEww62SGvx+JZ91HKKnMktrsC8+DwCwZCydI9aRqPtCWKLiRUeKZPXYfUkzQXARXTQ7nK3YDHMbuy8bT6bR3BfSos9PpEKl5jCqR1f08+rCOo7RmTeiG3VfCBNUvOjI5lO30KBUY4BPF4RT10UnZkT4wFYsRFpxNY5ca/mUcmK6MkprcKCp6/IAzXXRha42Ilp5RJig4kUHimX12H1Rs8Jo0Ria66IrtmIzPNd4IvAXJ1Kp+0Ka+fJkGtQcMDrQCb097FjHMVovDKfuC+l8VLzowFcx6ZAr1Qj17oIh3anrokuzh/jCunHlUVQS7ftCNHLKa7G/sevyyujujNMYt393X/6Pui+kk1Dx0sFKquT48UIWAM3J0dR10S2JpRlmRmgunF+coAsn0dgSfQsqNYdh/o4016UTNHVfruRU4gwdnEo6ARUvHWxrzC3UK9To62mH4f60r0tneH6oHyzNBbiWJ8OpZGpbm7pCaT1+uazZomDBKOq6dIauNiI8PdALgObwS0J0jYqXDlRe04AfzjfNdaGuS2extzLHs41t68/+ou6LqfsqRrNFwUBfe9pNtxO9MNwPZgIeLmSU41JmOes4xMhR8dKBvvk7A3UKFULcJRjZoyvrOCZl7jA/iISaSYPUtjZdJVVy7LqguYGguS6dy1VigSdCPQFQ94XoHhUvHaSqXoGdZzMBAC+P6kZdl07W1UaEaYM0besv/qILp6nafkYzWb6Ppx0dx8HAiyO6QcDnITqlBFdzK1nHIUaMipcO8v35LFTVK9HdyRqRPV1YxzFJ84Z3g7mAj4uZ5biYQW1rU1NR04AfzjVOlh/dnW4gGPBysMQjfdwAaJaqE6IrVLx0gLoGFb4+nQEAeGlkN/D5dNFkwUUixuRQDwCaAzGJafnm7wzUNKgQ5GqL0YFOrOOYrJdGdgOPB/x5vQgpRVWs4xAjRcVLB9hzKRtlNQ3w6GKhvesgbMwf4Qc+DziVXIJreVLWcUgn+few7SvUdWHK39kGD/XSdJ+p+0J0hYqX+9SgVGNrTDoAYP6IbhAK6CNlydvBChMbC8jN0bcYpyGdZdeFbMjqlfDraqX9xUnYeblxifrvV/KRWVrDOA0xRvSb9j4diM9DvrQeTjYiPNE4ZEHYenFkNwDA4cQCpJdUM05DdK1eocL2M5ph2xdH0LCtPgh2l2BUj65Qc5oNAwnpaFS83AeVmtPe3c8d5gexmYBxIgIAgS62GBvkDI4unCbh19hclFTJ4SYRY1Jfd9ZxSKMFjUvV98blolBazzgNMTZUvNyHw4kFyCitgZ2lmXaZLtEPL43SdF/2xeUhr7KOcRqiK0qVGl/FNN5ADPeDuZAuafoi1NseA33soVBx2H46nXUcYmToX/o94jgOm05pLprPRfjCSiRknIj8W3+vLgj3c4BSzWFbDF04jdWhxALklNfB3socUwfQDYS+ebHxJmLXxWxU1DQwTkOMCRUv9yg6pQRJBTJYmQu0BwMS/dI0afCnS9korZYzTkM6mlrNYdNJzQ3E7CE+sDCnYVt9MzKgK3q62qK2QYVvz2WyjkOMCBUv96ip6zJtkBfsLM0ZpyEtGdLdAX08JKhXqLHz70zWcUgHO3GzGMlFVbAWCTE93Id1HNICHo+nnUC/82wmauRKxomIsaDi5R7EZml2cDUT8PD8UD/WccgdaC6cmu7Lt+cyUVWvYJyIdBTNsK1mD5FnB3tDYmHGOBG5k/EhrvB2sERlrQK7L2azjkOMBBUv92BzY9dlcn8PuEjEjNOQ1kT2dEa3rlaoqldqD+wjhu9CRjnisithLuRj9lAf1nFIKwR8HuYN13Rftp/OgFypYpyIGAMqXtopubAKx5OKweNpjoAn+o3P52H+iMYL55kM1CvowmkMmm4gngz1gJMN3UDou8mh7nCyEaFQVo8D8Xms4xAj0CnFy6ZNm+Dr6wuxWIzQ0FCcPn261ddHR0cjNDQUYrEYfn5+2LJlS2fEbJOmfUPGB7vCr6s14zSkLSb1dYerRIySKjn2xdGF09Bdy5MiOqUEfB60d/REv4mEAswZ5gsA2BKdDpWaY5yIGDqdFy979uzB4sWLsXLlSsTHx2PYsGEYN24csrNbbuFnZGRg/PjxGDZsGOLj47FixQosXLgQe/fu1XXUu8opr8XBK/kA/tnFleg/cyEfc4dpumRfxdyiC6eBa7qBmNjHDV4OlozTkLaaNkgzNymjtAZ/Xi9kHYcYOJ0XL+vXr8fzzz+POXPmICgoCBs2bICnpyc2b97c4uu3bNkCLy8vbNiwAUFBQZgzZw5mz56NTz75RNdR72prjOaOYZi/I4LdJazjkHaYOtATdpZmyCqrxeHEAtZxyD3KLK3R/v9rGg4khsFaJMTMcM22EptP3QLH0U2EodoWk44rOZVMM+i0eGloaEBsbCwiIyObPR8ZGYmzZ8+2+J5z587d9voHH3wQly9fhkJx+2oRuVwOmUzW7KELJVVy/Hw5BwDwUuMKFmI4LM2FmBXhA4AunIbsq5h0qDlgdKATglxtWcch7TQzwgdiMz4S86Q4k1bKOg65B2nF1VhzJAmTvvwbOeW1zHLotHgpLS2FSqWCs7Nzs+ednZ1RWNhy27CwsLDF1yuVSpSW3v6Xfe3atZBIJNqHp6dnx/0A//FUmCcG+9ljsJ+9zr4H0Z2Z4T6wNBfgRoEMMal04TQ0xbJ67I3NBUDDtobKwVqk3Qm5adI1MSxbY26B44CxQc7wtGc3bNspE3Z5vOanvHIcd9tzd3t9S88DwPLlyyGVSrWPnJycDkh8u642Irz3aDB2zRncanaiv7pYmePpgZoL56aTaYzTkPb6+u8MNKjUCPPuggE+dANhqOYO94OQz8PZW2VIYDz0QNqnQFqH/Y2rxZrOj2NFp8WLo6MjBALBbV2W4uLi27orTVxcXFp8vVAohIODw22vF4lEsLW1bfbQJT6fChdDNmeYL8wEPFzIKEdsVgXrOKSNpHUK/HheM8mf9UWT3B93Owvt6d+bT9FNhCHZFpMBhYrDYD979PfqwjSLTosXc3NzhIaGIioqqtnzUVFRiIiIaPE94eHht73+2LFjCAsLg5kZ7aJJ7o+rxAKP9aMLp6H54XwWquVK9HC2wageTqzjkPs0f4Rm9d+f14uQVlzFOA1pi4qaBu0OyS/qwbxPnQ8bLV26FNu3b8eOHTuQlJSEJUuWIDs7G/PnzwegGfaZMWOG9vXz589HVlYWli5diqSkJOzYsQNff/01XnvtNV1HJSZi3ohu4PGA40nFSC6kC6e+q2tQYceZDACauS40bGv4/J1tENlT033fEk2nvhuCnWczUadQoZebLYb7O7KOo/viZcqUKdiwYQNWr16Nvn37IiYmBocPH4a3t2bJXEFBQbM9X3x9fXH48GGcOnUKffv2xXvvvYfPP/8ckydP1nVUYiK6dbXGuGAXANR9MQQ/X85BWU0DPO0t8HBvV9ZxSAdpmnR9ID4P+ZV1jNOQ1tTIldh5NhOA/txA8DgjWzMqk8kgkUgglUp1Pv+FGK5reVI8/MUZCPg8nHptJNNZ8+TOFCo1Rn58CnmVdXjv0WBMH+zNOhLpQE9vPY9z6WV4bogP3pnYi3UccgfbT6fj/UNJ8HW0wvGlIyDQ0dzP9vz+prONiEkKdpdgmL8jVGoOX8XQkk199VtCPvIq6+BoLcKToR6s45AO1jT5+qeLOSirljNOQ1oiV6qw/bRm2HbecD+dFS7tRcULMVlNmw3+fDkXxVX1jNOQ/1KrOe2w3pxhvhCbCRgnIh1taHdHhLhLUKdQaYcliH45EJ+HQlk9nG1FeKy/O+s4WlS8EJM12M8e/bzs0KBUY8eZTNZxyH8cu1GIWyU1sBUL8cwgL9ZxiA7weDy83Nh92Xk2E1X1t++iTthRqTnthOo5Q/0gEurPDQQVL8Rk8Xg8bfflh/NZkNbRhVNfcByHL09qhvNmRvjARkzbJBiryJ4u6NbVClX1SvxwvuUDewkbhxMLkFFaA4mFGabp2Q0EFS/EpI0JdEKAszWq5Ur8cD6LdRzS6ExaKRLzpBCb8bVnUhHjxOf/cxPx9Zl01CtUjBMRoOkGQjNs+9wQH1iJhIwTNUfFCzFpzS+cGahtUDJORABoL5pPD/SCg7WIcRqia4/0dYO7nQVKqxu0B+AStk7cLMbNwipYmQv08gaCihdi8h7u7Qove0uU1zRg1wVqW7MWl12B8+nlMBPwMHeYH+s4pBOYCfiY17jr7lfR6VCo1IwTmTaO47Cx8Qbi2cHesLM0Z5zodlS8EJMnFPDxUuOGWdtOU9uatY0nNBfNR/u6w83OgnEa0lmeCvOEo7U58irr8FtCPus4Ju1cehnisythLuTj+WG+rOO0iIoXQgA83t8DrhIximRy/BqbyzqOybqWJ8WJm8Xg84CXRrE/P4V0HrGZAM8P1XRfNp9Kg1ptVPunGpRNjZPlpw7whJONmHGallHxQggAcyEfLwzXXDi3RN+itjUjTXNdJvZxg6+jFeM0pLM9O9gLNmIhbpXU4Mi1QtZxTFJCTiXOpJVCyOdpr4n6iIoXQhpNHeAFR2tz5FZQ25qFlKIq7S+sl6nrYpJsxGZ4rnFy6BcnUqn7wkDTDcSj/dzh0UV/j02h4oWQRhbm/7StN51Kg4ounJ2q6aL5UC8XBDjbME5DWJk91BdW5gLcLKzC8aQi1nFMys1CGaJuFIHH++fgTH1FxQsh//LsYC9ILMyQXlKDo9S27jQZpTX4/Yqm27VgNHVdTJmdpTlmaLsvaTCys4P12hd/aW4gxge7oltXa8ZpWkfFCyH/YiM20+5p8MWJVLpwdpLNp9Kg5oDRgU4IdpewjkMYmzPUFxZmAiTmSXEquYR1HJOQUlSFw9cKAACvjNH/GwgqXgj5j+eG+Gjb1sduUNta13IrarEvLg8AzXUhGg7WIjw7WLMd/Wd/0U1EZ9B0uYBxwS4IdLFlHeeuqHgh5D/sLM0xa4gPAOCz43Th1LUt0begVHMY0t0Bod5dWMchemLucD+IhHzt6heiO2nFVfjjqmbY9pXR/ozTtA0VL4S0YM5QP1iZC3CjQEbdFx0qkNbh50uafXUWjDKMiybpHE42Yjw9UNN9aZqLQXSjqesS2dMZPd30v+sCUPFCSIu6WFH3pTN8eTINDSo1BvnaY7CfPes4RM/MH9EN5gI+LmaW43x6Ges4RulWSbV2svzCMYZzA0HFCyF3QN0X3cqtqMWeS5pD+JY8EAAej8c4EdE3LhIxnhrgAQDYcDyFcRrj9OUJzWT5sUHOBjVZnooXQu6Aui+69eXJW1CoOER0c8BgPwfWcYieenFkd5gL+DifXo6zNPelQ2WU1uBAgmay/CID6roAVLwQ0irqvuhGTnktfrn8T9eFkDtxt7PA1IGeAIBPo1LoJqIDbTzxzxYFIR6G03UBqHghpFXUfdGNL06kQqnmMMzfEQN8aK4Lad3Lo7pDJOQjNqsC0Sm070tHSCuuxv54zWR5Q5rr0oSKF0Lu4t/dlz+vU/flfmWV1WBv474u1HUhbeFsK8b0wd4AgPXUfekQ/3c8BWoOeKCnM/p62rGO025UvBByF//uvvxfVAqdeXSfPv9Lc27UyB5d0d+L9nUhbTN/ZDdYmgtwNVeKKBrCvS/X8qQ4dLUAPB7waqRh3kBQ8UJIG7wwrBtsxUIkF1Xh4JU81nEMVkZpjbZVvWSsYV40CRuO1iLt0R3ro1LoxOn7sD5Ks3JrYm83g9hNtyVUvBDSBhJLM8xvPGV1fVQKGpRqxokM0/ooTat6TKAT+hhgq5qw9cJwP9iIhLhZWIUjdHDqPYnNKseJm8UQ8HkGPWxLxQshbfRchC+62oiQU16Hny5ls45jcBJzpfj9Sn5jq7oH6zjEANlZmuP5Yb4ANHM2aAi3fTiOw8d/JgMAngz1gK+jFeNE906nxUtFRQWmT58OiUQCiUSC6dOno7KystX3zJo1Czwer9lj8ODBuoxJSJtYmAu0s/I//ysNtQ1KxokMy0d/3gQAPNrX3WC2ICf6Z/ZQX0gszBpXy9AQbnv8nVaG8+nlMBfw8YoBrjD6N50WL9OmTUNCQgKOHj2Ko0ePIiEhAdOnT7/r+x566CEUFBRoH4cPH9ZlTELabEqYJ7zsLVFaLcc3f2eyjmMwTqeW4HRqKcwFfCw14FY1Yc9WbIaXGodwPz2WjHqFinEiw8BxHD4+pum6PDPYC+52FowT3R+dFS9JSUk4evQotm/fjvDwcISHh2Pbtm34448/kJyc3Op7RSIRXFxctA97e9oHgugHc+E/v3y3RN9CZW0D40T6T63msO6opuvyzGAveNpbMk5EDN3MCB+421mgQFpPNxFtdOxGEa7kVMLCTICXRnZnHee+6ax4OXfuHCQSCQYNGqR9bvDgwZBIJDh79myr7z116hScnJwQEBCAuXPnori4+I6vlcvlkMlkzR6E6NIjfdwQ6GKDqnolNkffYh1H7/2RWIBreTJYi4RYMMrwL5qEPbGZQLvEd9PJNJTX0E1EaxqUanx4RHMD8fxQzdw9Q6ez4qWwsBBOTk63Pe/k5ITCwjvPEh83bhx+/PFHnDhxAp9++ikuXbqE0aNHQy6Xt/j6tWvXaufUSCQSeHp6dtjPQEhL+HweXn9QM+F059+ZKJDWMU6kvxqUanzSOEFw3nA/OFgb/kWT6IdH+7qjp6stquRKfHEilXUcvfbD+SxklNbA0VqkXTVp6NpdvKxateq2CbX/fVy+fBkAWjwlluO4Vk+PnTJlCiZMmIDg4GBMnDgRR44cQUpKCg4dOtTi65cvXw6pVKp95OTktPdHIqTdRgc6YaCPPeRKNT462vowqCnbfTEb2eW1cLQWaVeJENIR+HweVowPAqD55ZxVVsM4kX6S1irweWNxt/SBAFiLhIwTdYx2/xQLFizA1KlTW32Nj48Prl69iqKi23dBLCkpgbOzc5u/n6urK7y9vZGa2nJlLRKJIBLR3RzpXDweD/97uCce+fIM9sfnYXq4N+0W+x+yegU+/0vz73bRWH9YmhvHRZPoj6H+jhge0BUxKSX4+M9kbJzWn3UkvfPFiVRU1ioQ4GyNp8I8WMfpMO2+mjg6OsLR0fGurwsPD4dUKsXFixcxcOBAAMCFCxcglUoRERHR5u9XVlaGnJwcuLq6tjcqIToV4iHB5P4e+DU2F6t/v4H9L0W02lU0NV/8lYqymgb4Olph6gAaziW6sXxcIE6nluCPqwWYM6zSIM/p0ZWsshp8ey4TALBifBCEAuPZ2k1nP0lQUBAeeughzJ07F+fPn8f58+cxd+5cPPzww+jR458NqgIDA7F//34AQHV1NV577TWcO3cOmZmZOHXqFCZOnAhHR0c89thjuopKyD17/cEesDQXICGnEgev5LOOozdulVRrV4G8/XBPmBnRRZPolyBXW0zur+kofHDoBh3a+C/rjt6EQqU5vX1kj9vnoBoynV5RfvzxR4SEhCAyMhKRkZHo3bs3vv/++2avSU5OhlQqBQAIBAIkJiZi0qRJCAgIwMyZMxEQEIBz587BxsZGl1EJuSfOtmLtnhMfHrmJugbac4LjOKz+/QaUag6jA50wKtC4LppE/yx9IABiMz4uZVbgtwS6iQCAy5nlOJxYCD4PWDkhiHWcDqfTQWh7e3v88MMPrb7m31WyhYUF/vzzT11GIqTDzRnmh90Xc5BXWYetMelYNNawd668XyduFiM6pQRmAs28IEJ0zc3OAq+M9sfHfybjg8NJGBPkBBuxGetYzKjVHN774wYAYMoAT4M9fLE11Msl5D6JzQR4c1wgAM3Gdaa8dFquVGkvmrOH+hr02SnEsMwZpvn7VlIlx2fHTXvp9O5L2biSK4W1SGjQhy+2hooXQjrAw71dEerdBXUKFdYevsk6DjPf/J2JzLJadLUR4ZXRpt2BIp1LJBTgnYmaTt83ZzORUlTFOBEbJVVyrGvckO7VyAA42YgZJ9INKl4I6QA8Hg+rJvYCnwccvJKP6JQS1pE6XbGsHl80Lo1+86FAo9lPghiOkT2cENnTGSo1h7d/u2aSk3fXHk6CrF6JXm62mD7Ym3UcnaHihZAOEuIhwcwIHwDAWwcSTW7y7vuHklDToEJfTzs81s+ddRxiov73cE+IhHycTy83uRWA526VYV98Hng84IPHQoxqafR/Ge9PRggDr0b2gKtEjJzyOmz4K4V1nE5z/EYRDl7JB58HrJ7UC3w+7XdD2PC0t8TLjWdorTmchGq5knGiztGgVON/v10DAEwb6GX0+91Q8UJIB7IWCfHepGAAwPbTGbiRb/wHhcrqFXjrgOaiOXeYH3p72LENREzeC8P94O1giSKZHB8dNY05aNtOpyOtuBqO1uZ448FA1nF0jooXQjrY2J7OGB/iApWaw/J9V6FSG/e4+9rDN1Eoq4ePg6XRrmwghkVsJsD7j2puIr47l4Wzt0oZJ9KtnPJa7eGUK8YHQWJp/MvEqXghRAfemdgLNiIhruRK8X3j9tzG6OytUuy+mA0A+HByb4jNBIwTEaIxzL8rnh7oBQB449erqDHS4SO1msPrv15BvUKNwX72JjPfjIoXQnTA2VaMZY17v3z8ZzJyK2oZJ+p4dQ0qLN+XCAB4ZpAXBvs5ME5ESHMrJwTB3c4CuRV1WHskiXUcndjxdwbOp5fD0lyAdZN7m8z5alS8EKIj0wZ6Icy7C2oaVFiyJ8Hoho/WRyUjq6wWrhKxdpM+QvSJtUiIj57oDQD44Xw2/k4zruGj5MIqfHQ0GQDw1oSe8HYwnU0hqXghREf4fB7WP9UX1iIhLmVW4MuTaawjdZgL6WX4+kwGAOCDx4JNeit2ot+GdHfEs4P/GT6qqlcwTtQxGpRqLN6TgAaVGqMDnfD0QNM6uZ2KF0J0yMvBUjtx8LO/UhGbVc440f0rq5Zj4U/xUHPA5P4eGB3ozDoSIa1aPi4IHl0skFdZhzWHjWP4aMPxFCQVyNDF0gwfTg4xmeGiJlS8EKJjj/Zzx2P93KFSc1j0UwJkBnznp1ZzWPrzFRTJ5OjW1QqrJ/ViHYmQu7ISCfHxE30AALsv5uC3hDzGie7P5cxybIm+BQBY+3iI0R4B0BoqXgjpBKsn9YKnvWbi4NuNe6IYoq9i0hGdUgKRkI8vn+kPKzoCgBiI8G4OeGlkNwDAm3sTDfbso8raBiz5OUHb+Xwo2JV1JCaoeCGkE9iIzfDZ1H4Q8Hk4kJCPfXG5rCO12+XMcnxyTDM58N1HeiHQxZZxIkLa59XIHhjS3QF1ChXm/xBrcPNflCo1FuyKR055HTztLfDOIz1ZR2KGihdCOkl/ry5YPEZz0vLyfYlIyKlkG6gdKmoa8MrueKjUHB7p44YpA0xrciAxDgI+D59P7QdXiRjpJTV449erBnV444dHbuJMWikszATYOj0MtiY8UZ6KF0I60UujumN0oBPkSjXmfHsZeZV1rCPdlUKlxsKf4lEgrYevoxXWPG56kwOJ8XCwFuHLZ/rDTMDDkWuF2lVz+m5vbC62N2Zd/1QfBLmadueTihdCOpGAz8PnT/dDoIsNSqvleH7nJb1uXXMch2V7r+J0quZub+O0frCmeS7EwPX36oL/PawZcll75KbeHx9wJacSy/drNoR8ZXR3jAsxzXku/0bFCyGdzFokxI5ZA9DVRoSbhVVYuDseSpWadawWfXIsGfvi8iDg87Dpmf7o5SZhHYmQDjF9sLd2FeAL38Xiam4l60gtKpbVY973sWhQqjE2yAlLxtL5YQAVL4Qw4WZnge0zwiA24+NkcgneP6R/e098fy4TX55sXI75WAhGBToxTkRIx+HxeFj7eAjC/RxQLVdixo6LercCqbRajme/voBCWT26O1nj/6b0BZ9PQ7YAFS+EMNPH0w7/91RfAMDOs5lYH5WiN5MHj14rxNsHrwMAlj4QgKdogi4xQmIzAbbNDENfTztU1irw7PYLyC7Tj3PIyqrleGbbBaQUVcPZVoTtM8JoJ+t/oeKFEIbGhbhi5fggAMDnf6Xi/UNJzAuYv5KKsOineHAcMG2QF14Z3Z1pHkJ0yVokxM7nBqCHsw2Kq+SYtv08CqX1TDOVVcsxbdsFJBdVwdlWhJ9eCIePo+mcW9QWVLwQwtjc4X5YNVEzefDrMxl4c28is0Mcf7yQhbnfXYZcqUZkT2esfqQXrSwiRs/O0hzfPz8Q3g6WyK2ow7Tt55l1YMqq5Xhmu6ZwcbIRYffcwfClwuU2VLwQogdmDfHFx0/0Bp8H7Lmcg4U/xaNB2XmTeDmOwyd/JmPl/mtQc8BTYR748pn+EAroEkFMg5OtGD88P0i7B8ykL8/g3K2yTs2QVVaDadsu4GZhY+HywmD4dbXu1AyGgq5MhOiJJ8M8sXGaZv+JQ1cL8NzOiyiS6b593aBU49VfrmBj46nXi8f6Y93k3jCjwoWYGE97S+x/aQh6e0hQUavA9K8v4PvzWZ3yvY9eK8TDX5xBclEVujYWLt2ocLkjHsd6gL2DyWQySCQSSKVS2Nqa9iY+xDCdSi7G/B9iUa9Qw87SDGsfC9HZvg6pRVV4c18iYrMqIODzsPaxEJqcS0xevUKFN369ioNX8gEAzwzywqpHeumkoG9QqrHu6E3tZnmh3l3wxdP94GZn0eHfS9+15/c3FS+E6KHUoios3pOA6/kyAMDj/d2x6pFeHbYdeL1ChU0n07A5+hYUKg5W5gJsfKY/RvWg5dCEAJqh1M3Rt/Dxn8ngOCDA2RorJ/TEiICuHfY9MktrsPTnBMRlVwIA5g7zxRsPBZps17M9v791+gl98MEHiIiIgKWlJezs7Nr0Ho7jsGrVKri5ucHCwgIjR47E9evXdRmTEL3j72yD/S8NwcujuoHPA/bF5WHchtP4+VIO6hWq+/qzz90qw/jPTuPzE2lQqDiMDXLCsaUjqHAh5F94PB5eGtkd26aHwc7SDClF1Zi54yJmfXMRacX3tx9MVlkNXv/lCsasj0ZcdiVsxEJsnR6KlRN6mmzh0l467by88847sLOzQ25uLr7++mtUVlbe9T3r1q3DBx98gJ07dyIgIADvv/8+YmJikJycDBsbm7u+nzovxNhczizH0p+vILtcs/rBwcoc0wZ5YfpgbzjZitv0Z5TXNOBgQh72xefhaq4UAOBkI8K7j/TCQ8EutKKIkFZIaxX4/EQqvj2bCaWag4DPw1NhHpjYxw0DfOzbXHBkltZg06k07I3L064oHNmjK1Y/EgwvB0td/ggGQe+GjXbu3InFixfftXjhOA5ubm5YvHgxli1bBgCQy+VwdnbGunXrMG/evLt+LypeiDGqkSvx/fksfHc2E/mNe1CYCXgY2t0RPVxs0cPFGv5ONvB1tEJVvRLFVfUokslRKKvH6ZQSnEwuhkKl+acu5PMwZYAn3ngoEBIL2vSKkLZKL6nGmsM3cTypSPucrViIkT2cMCbICd4OVhCb8SEWCiAy46OqXonLmRW4nFmOS1nlyCn/5yDWkT26YtEYf/Tz6sLiR9FLBlu8pKeno1u3boiLi0O/fv20z0+aNAl2dnb49ttvb3uPXC6HXC7Xfi2TyeDp6UnFCzFKSpUax24UYceZDFzOqmjXe4PdbTG5v+Zu0dFapKOEhBi/c7fKsDcuFyduFqO8pqHN7+PzgOEBXbFwjD/6U9Fym/YUL3p1PGxhYSEAwNnZudnzzs7OyMpqebna2rVr8e677+o8GyH6QCjgY3yIK8aHuOJ6vhSxWRVIKapCSlE1UoqqUFmrgIDPg5ONSPOwFaO7kzUe7euOHi53H3YlhNxdeDcHhHdzgErNISGnAlE3inE6tQSVtQrIlSrUK9SQK1UQ8vno62mHAT5dEOZjj/7eXehU9g7S7k9x1apVdy0WLl26hLCwsHsO9d/xd47j7jgmv3z5cixdulT7dVPnhRBj18tN0uyUZ47jUCVXwspcCAEd3kaIzgn4PIR62yPU2x5vjgu87b+39ruL3J92Fy8LFizA1KlTW32Nj4/PPYVxcXEBoOnAuLr+s69FcXHxbd2YJiKRCCIRtcAJ4fF4HbaUmhBy/6hw0Z12Fy+Ojo5wdHTURRb4+vrCxcUFUVFR2jkvDQ0NiI6Oxrp163TyPQkhhBBiWHS6oDw7OxsJCQnIzs6GSqVCQkICEhISUF1drX1NYGAg9u/fD0BTpS5evBhr1qzB/v37ce3aNcyaNQuWlpaYNm2aLqMSQgghxEDodObQ22+/3WyFUFM35eTJkxg5ciQAIDk5GVKpVPuaN954A3V1dXjppZdQUVGBQYMG4dixY23a44UQQgghxo+OByCEEEIIcwa7VLojNNViMpmMcRJCCCGEtFXT7+229FSMrnipqtKcOUHLpQkhhBDDU1VVBYlE0uprjG7YSK1WIz8/HzY2Nh2+TK1pD5mcnBwakmoBfT6to8/nzuizaR19Pq2jz6d1hvL5cByHqqoquLm5gc9vfT2R0XVe+Hw+PDw8dPo9bG1t9fovAGv0+bSOPp87o8+mdfT5tI4+n9YZwudzt45LEzp7mxBCCCEGhYoXQgghhBgUKl7aQSQS4Z133qHjCO6APp/W0edzZ/TZtI4+n9bR59M6Y/x8jG7CLiGEEEKMG3VeCCGEEGJQqHghhBBCiEGh4oUQQgghBoWKF0IIIYQYFCpe2mjTpk3w9fWFWCxGaGgoTp8+zTqSXli7di0GDBgAGxsbODk54dFHH0VycjLrWHpr7dq14PF4WLx4MesoeiMvLw/PPvssHBwcYGlpib59+yI2NpZ1LL2gVCrx1ltvwdfXFxYWFvDz88Pq1auhVqtZR2MiJiYGEydOhJubG3g8Hg4cONDsv3Mch1WrVsHNzQ0WFhYYOXIkrl+/ziZsJ2vts1EoFFi2bBlCQkJgZWUFNzc3zJgxA/n5+ewC3ycqXtpgz549WLx4MVauXIn4+HgMGzYM48aNQ3Z2NutozEVHR+Pll1/G+fPnERUVBaVSicjISNTU1LCOpncuXbqErVu3onfv3qyj6I2KigoMGTIEZmZmOHLkCG7cuIFPP/0UdnZ2rKPphXXr1mHLli3YuHEjkpKS8NFHH+Hjjz/GF198wToaEzU1NejTpw82btzY4n//6KOPsH79emzcuBGXLl2Ci4sLHnjgAe2Zd8astc+mtrYWcXFx+N///oe4uDjs27cPKSkpeOSRRxgk7SAcuauBAwdy8+fPb/ZcYGAg9+abbzJKpL+Ki4s5AFx0dDTrKHqlqqqK8/f356KiorgRI0ZwixYtYh1JLyxbtowbOnQo6xh6a8KECdzs2bObPff4449zzz77LKNE+gMAt3//fu3XarWac3Fx4T788EPtc/X19ZxEIuG2bNnCICE7//1sWnLx4kUOAJeVldU5oToYdV7uoqGhAbGxsYiMjGz2fGRkJM6ePcsolf6SSqUAAHt7e8ZJ9MvLL7+MCRMmYOzYsayj6JWDBw8iLCwMTz75JJycnNCvXz9s27aNdSy9MXToUPz1119ISUkBAFy5cgVnzpzB+PHjGSfTPxkZGSgsLGx2rRaJRBgxYgRdq1sglUrB4/EMtstpdAczdrTS0lKoVCo4Ozs3e97Z2RmFhYWMUuknjuOwdOlSDB06FMHBwazj6I2ffvoJcXFxuHTpEusoeic9PR2bN2/G0qVLsWLFCly8eBELFy6ESCTCjBkzWMdjbtmyZZBKpQgMDIRAIIBKpcIHH3yAp59+mnU0vdN0PW7pWp2VlcUikt6qr6/Hm2++iWnTpun9QY13QsVLG/F4vGZfcxx323OmbsGCBbh69SrOnDnDOoreyMnJwaJFi3Ds2DGIxWLWcfSOWq1GWFgY1qxZAwDo168frl+/js2bN1PxAs18ux9++AG7du1Cr169kJCQgMWLF8PNzQ0zZ85kHU8v0bW6dQqFAlOnToVarcamTZtYx7lnVLzchaOjIwQCwW1dluLi4tsqfFP2yiuv4ODBg4iJiYGHhwfrOHojNjYWxcXFCA0N1T6nUqkQExODjRs3Qi6XQyAQMEzIlqurK3r27NnsuaCgIOzdu5dRIv3y+uuv480338TUqVMBACEhIcjKysLatWupePkPFxcXAJoOjKurq/Z5ulb/Q6FQ4KmnnkJGRgZOnDhhsF0XgFYb3ZW5uTlCQ0MRFRXV7PmoqChEREQwSqU/OI7DggULsG/fPpw4cQK+vr6sI+mVMWPGIDExEQkJCdpHWFgYnnnmGSQkJJh04QIAQ4YMuW1pfUpKCry9vRkl0i+1tbXg85tfpgUCgckulW6Nr68vXFxcml2rGxoaEB0dTddq/FO4pKam4vjx43BwcGAd6b5Q56UNli5diunTpyMsLAzh4eHYunUrsrOzMX/+fNbRmHv55Zexa9cu/Pbbb7CxsdF2qCQSCSwsLBinY8/Gxua2+T9WVlZwcHCgeUEAlixZgoiICKxZswZPPfUULl68iK1bt2Lr1q2so+mFiRMn4oMPPoCXlxd69eqF+Ph4rF+/HrNnz2YdjYnq6mqkpaVpv87IyEBCQgLs7e3h5eWFxYsXY82aNfD394e/vz/WrFkDS0tLTJs2jWHqztHaZ+Pm5oYnnngCcXFx+OOPP6BSqbTXant7e5ibm7OKfe/YLnYyHF9++SXn7e3NmZubc/3796elwI0AtPj45ptvWEfTW7RUurnff/+dCw4O5kQiERcYGMht3bqVdSS9IZPJuEWLFnFeXl6cWCzm/Pz8uJUrV3JyuZx1NCZOnjzZ4vVm5syZHMdplku/8847nIuLCycSibjhw4dziYmJbEN3ktY+m4yMjDteq0+ePMk6+j3hcRzHdWaxRAghhBByP2jOCyGEEEIMChUvhBBCCDEoVLwQQgghxKBQ8UIIIYQQg0LFCyGEEEIMChUvhBBCCDEoVLwQQgghxKBQ8UIIIYQQg0LFCyGEEEIMChUvhBBCCDEoRncwo1qtRn5+PmxsbMDj8VjHIYQQQkgbcByHqqoquLm53Xaa+n8ZXfGSn58PT09P1jEIIYQQcg9ycnLg4eHR6muMrnixsbEBoPnhbW1tGachhBBCSFvIZDJ4enpqf4+3xuiKl6ahIltbWypeCCGEEAPTlikfNGGXEEIIIQZFp8VLTEwMJk6cCDc3N/B4PBw4cOCu74mOjkZoaCjEYjH8/PywZcsWXUYkhBBCiIHRafFSU1ODPn36YOPGjW16fUZGBsaPH49hw4YhPj4eK1aswMKFC7F3715dxiSEEEKIAdHpnJdx48Zh3LhxbX79li1b4OXlhQ0bNgAAgoKCcPnyZXzyySeYPHmyjlK2XVKBDC62YthZmtEybEIIISaH4ziU1zSgtLoBPVzuPrFWV/Rqwu65c+cQGRnZ7LkHH3wQX3/9NRQKBczMzG57j1wuh1wu134tk8l0kk2l5jBp499oUKlhIxLCy8ESXvaW8HawwgM9ndHfy44KGkI6UG2DEhfSy3E6tRR/p5Uio7Sm+Qt4QLeu1hjm74ih3R0x0NceYjMBm7CEGCGO43AhoxwnbxYjs6wG2eV1yCmvRbVciS6WZoh/O/Luf4iO6FXxUlhYCGdn52bPOTs7Q6lUorS0FK6urre9Z+3atXj33Xd1nq2ytgFdrMxQJJOjSq7E9XwZrudrCqUt0bcQ6GKDaYO88Gg/d9iKby+yCCF3x3EcolNKsDUmHZcyy6FQca2+PqlAhqQCGbbGpMNcyMdgPwfMH+GHiG6OnZSYEONTWduAX2NzsftiNm6V1LT4GrGZAHUNKliYs7lh0KviBbh9iRTHcS0+32T58uVYunSp9uumdeIdzcFahAsrxqKuQYXcilpkldUiu7wWiXlSHLlWgJuFVXj7t+tYe/gmHuvvjtcje6CLlXmH5yDEWMVlV2DdkZu4kFGufc6ji0VjZ6UrentIIBT8cx1QKDnE51TgTGopzqSVokBaj5iUEsSklGB4QFe88WAPBLtLWPwohBikkio51h29iYNX8tGgVAMALM0FmBDiil5uto0jDlbw6GLBvMupV8WLi4sLCgsLmz1XXFwMoVAIBweHFt8jEokgEok6Ix4AwMJcAH9nG/g7/zPWt2piL+yPz8WPF7KRWlyNXReycex6ET54LBgP9nLptGyEGKJbJdX46OhN/Hm9CABgLuRjxmBvPDvYG94Olq0Ox3o5WGJSX3dwHIdbJdX47lwWdl3I1hYxj/Rxw+sP9oCnvWVn/TiEGByO43DwSj7eOXgdlbUKAEAvN1tMG+SFSX3dYS3Sq1IBgJ4VL+Hh4fj999+bPXfs2DGEhYW1ON9FX0gszTBriC9mRvjgfHo53v7tGlKLqzHv+1g80scNqx7pBXvqwhBym98S8rBs71XUK9Tg84DJ/T2w+IEAuNtZtOvP4fF46O5kg9WTgvH8UF98eiwFB6/k4+CVfJy4WYz1T/VBJN1IEHKbYlk9Vh64hqgbmpuHXm62WD0pWO/ncfK4pnEZHaiurkZaWhoAoF+/fli/fj1GjRoFe3t7eHl5Yfny5cjLy8N3330HQLNUOjg4GPPmzcPcuXNx7tw5zJ8/H7t3727zaiOZTAaJRAKpVMpsh916hQqf/5WKr2LSoVJzcLQ2xydP9sHIHk5M8hCibxQqNdYevokdf2cAAIZ0d8A7E3shwLnjVi9cy5Ni1cHruJxVAQB4ZXR3LB4bAAFffy/IhHSmo9cKsGxvIqR1CpgJeFg42h/zR3aDmYDN/rXt+f2t0+Ll1KlTGDVq1G3Pz5w5Ezt37sSsWbOQmZmJU6dOaf9bdHQ0lixZguvXr8PNzQ3Lli3D/Pnz2/w99aF4aXI1txKv/3IVyUVVEPB5+Ghyb0wObf2wKUKMXUmVHAt2xWnntiwY1R1LHtBNUaFQqbHmcBK++TsTADAioCs+m9oXdpbUCSWm7duzmVj1+3VwHBDiLsHHT/ZGoAvb35l6U7ywoE/FCwDIlSos35eIfXF5AICV44Mwd7gf41SEsHEjX4bZOy+hUFYPa5EQnz7Vp1Pmhe2Pz8XyfYmoV6jhZW+Jb54bgG5drXX+fQnRNxzH4f+Op+Lzv1IBANMHe+OdiT0hZNRt+bf2/P5mn9bIiYQCfPJEH8wd5gsA+OBwEtYeSYKR1YyE3FVyYRWe2X4ehbJ6dHeyxm8LhnTahPbH+nlg74sR8LS3QHZ5LaZtO4+sspaXgBJirFRqDm8duKYtXJaMDcDqSb30onBpL8NLbID4fB5WTuiJ5eMCAQBfRafjjV+vQqWmAoaYhvSSajyz/QIqahXo4yHBvpciOr3z0ctNggMvDUGAszWKZHJM23YBuRW1nZqBEFYalGq8sjsOP17IBo8HvPdoMBaN9dfrSbmtoeKlE80b0Q0fPdEbfB7wS2wuVv9+nTowxOhll9Vi2rYLKK2WI8jVFt/OHshsI0cHaxF+mDMIfo5WyKuswzPbL6BQWs8kCyGdheM4vLn3Kg4nFsJMwMPGp/tj+mBv1rHuCxUvneypME98/nQ/AMC357Lw9ZkMxokI0Z28yjo8vU0zVOTvZI0fnh/IfLKsk40YP84dBE97C2SV1WLa9vMoqZLf/Y2EGKgNx1OxLz4PAj4PW6eHYULv23erNzRUvDDwcG83rBivGUL64HASjiQWME5ESMerrG3As9svIK+yDr6OVvhxziA4WHfehpKtcZVYYNecwXCTiJFeUoPpX19AjVzJOhYhHe6Xyzn4rHGOy/uPBmNUoHFs2UHFCyNzh/lh+mBvcByweE8C4rIrWEcipMOo1BwW/ZSAjNIauNtZ4Mc5g+BkK2YdqxlPe0vsmjsYjtYi3Cyswht7r9IwLjEqf6eVYvm+RADASyO74emBXowTdRwqXhjh8Xh4Z2JPjAl0glypxpxvL9PqB2I0/i8qBdEpJRCb8bF1Rijc2rljbmfxcbTClmf7Q8jn4dDVAmyNSWcdiZAOkVxYhfnfx0Kp5vBIHze8FtmDdaQORcULQ0IBH58/3Q/B7rYor2nAnG8vo65BxToWIfflz+uF2HhSs7P2h4/3Ri83/T4cMczHHu9M7AkAWHf0Js6kljJORMj9kdYp8Py3l1AlV2Kgjz0+frI3+Ea2szQVL4xZiYTYMXMAnGxESC2uxuo/rrOORMg9Syuuxqs/XwEAPDfEB4/2c2ecqG2eHeyNJ0I9oOaAV3bHIaecllATw8RxHFbsS0RuRR087S2wdUYoREK2J0DrAhUvesDJVowNU/qCxwN2X8zB71fyWUcipN2q6hWY9/1lVMuVGORrjxXjg1hHajMej4f3Hw1GiLsEFbUKvPhjLOoV1AUlhuenSzk4lFgAIZ+HL57uz3x1n65Q8aInIro74qWR3QAAK/Yl0p0fMThvHbiGWyU1cLEVY+O0/swOd7tXYjMBtkwPhb2VOa7lyfDeHzdYRyKkXVKLqvDu75ru/WsP9kBfTzu2gXTIsK4uRm7x2AD097JDlVyJV3bHQ6FSs45ESJsculqA3xLyIeDzsOnZ/uhqox9LotvL3c4Cn0/V7MP044VsRKeUME5ESNvUK1RYsCse9Qo1hvk74oVhxn2GHhUvesRMwMdnU/vBRixEQk4l1kelsI5EyF2VVMnx1oF/lmP29+rCONH9GerviFkRPgCAZb9ehbROwTYQIW3w/qEbSC6qgqO1COuf6mt0E3T/i4oXPeNpb4l1k3sDADafuoWzt2jlA9FfHMdh+b5EVNQq0NPVFq+M9mcdqUMseygQvo5WKJTV492DNIme6LeoG0X44Xw2AGD9U30MtvPZHlS86KHxIa54eqAnAGD5vkSaOEj01t64PBxPKoKZgIf1U/rAXGgclxQLcwE+ebIP+DxgX3wejl4rZB2JkBbJ6hVYuV/T+XxhuB+GB3RlnKhzGMeVxggtHx8EF1sxsspqseF4Kus4hNwmv7JO25VY8kAAAl1sGSfqWKHeXTBvhGYS/cr9iSirpvOPiP5Zd+Qmiqvk8HW0wtIHAljH6TRUvOgpW7EZ3ns0GACw7XQ6ruVJGSci5B8cx+GNX6+iSq5EPy87o50cuHisP3o426CspgEr91+j4wOIXrmYUY4fL2iGi9Y+HgKxmfHt53InVLzosQd6OmNCiCtUas28AiWtPiJ6Ym9cHs6klUJsxsenT/aB0MCWRbeVSCjAp0/1gZDPw9HrhTh2o4h1JEIAaFYXvbnvKgBg6gBPDPZzYJyocxnnFceIvPNIT9iKhUjMk+KbvzNZxyEE0loF1h5OAqBZ3u/X1ZpxIt0KdpfgheGaztLq32+gtoFOnybsfXkyDeklNehqI8LycYazIWRHoeJFzznZiPHWBM25K59GJSO7jDavI2x9ciwZZTUN6O5kjdlDfFnH6RQLRneHu50F8irrsPFEGus4xMTdLJRh86lbAIDVj/SCxNKMcaLOR8WLAXgyzAMR3RxQr1Bj5YFEGncnzCTmSvHDhSwAwHuTgo1mddHdWJoLtYc3bjudjrTiasaJiKlSqzm8uTcRSjWHyJ7OeCjYhXUkJkzjymPgeDwe1jwWAnMhH6dTS2ncnTChUnN460AiOA6Y1NcN4d1Ma4z9gZ7OGB3oBIWKwzsHafIuYWNvXC4SciphLRJi9aRg8HjGvRndnVDxYiB8HK20Kzo+OJQEuZL2fiGd66dL2biSK4WNSIiVBnToYkfh8XhYNbEXREI+/k4rw+9XC1hHIiamWq7ER38mAwBeGd0dLhIx40TsUPFiQF4c2Q3OtiJkl9dix5lM1nGICSmrluOjo5qL5pIHAuBka5oXTS8HS7w0sjsA4P0/bqCqno4OIJ3ny5NpKKmSw8fBErOG+LCOwxQVLwbESiTEsocCAQAbT6SiWFbPOBExFR//mQxpnQJBrraYEe7NOg5T80b4wdvBEsVVcnz+F20gSTpHVlkNvj6dAQBYOaEnRELT2dOlJVS8GJhH+7qjr6cdahpU2vYhIbp0s1CGPZdzAADvTepltHu6tJXYTIBVj/QCAOw8m0krAEmn+OBQEhpUmhOjxwY5sY7DnGlfhQwQn8/Trnr4NTYXV3Iq2QYiRm/t4ZvgOGB8iAvCfOxZx9ELo3o4YZi/IxQqDh/9eZN1HGLk/k7TLNQQ8Hn438M9TXaS7r91SvGyadMm+Pr6QiwWIzQ0FKdPn77ja0+dOgUej3fb4+ZNukA06efVBY/3cwcArPr9Oq16IDoTk1KC6JQSmAl4eOPBQNZx9MrycUHg8YA/rhYgLruCdRxipJQqNVb/fgMA8OwgLwQ42zBOpB90Xrzs2bMHixcvxsqVKxEfH49hw4Zh3LhxyM7ObvV9ycnJKCgo0D78/f11HdWgvPFQICzNBYjPrsTBK/ms4xAjpFJzWNO4k+70wT7wcbRinEi/9HSzxRP9PQAAaw4l0U0E0Yndl3KQXFQFO0szLDGhgxfvRufFy/r16/H8889jzpw5CAoKwoYNG+Dp6YnNmze3+j4nJye4uLhoHwKBaU9O+i8XiRgvjdScePvJsWQ0KOncI9Kx9sXl4mZhFWzEQrwyujvrOHrp1cgeEJvxcTmrAn9ep/2XSMeqkSvx2XHNpPDFY/xhZ2nOOJH+0Gnx0tDQgNjYWERGRjZ7PjIyEmfPnm31vf369YOrqyvGjBmDkydP3vF1crkcMpms2cNUzB7qi642IuSU12H3xdY7WYS0R12DCp8c+2c/iS5WdNFsiYtEjLmN+y+tO3oTCjo8lXSgb/7OQGm1HF72lpg2yLRX+f2XTouX0tJSqFQqODs7N3ve2dkZhYWFLb7H1dUVW7duxd69e7Fv3z706NEDY8aMQUxMTIuvX7t2LSQSifbh6enZ4T+HvrI0F2LhGM1w2hcnUlEjpwPjSMf4+kw6imRyeHSxwIxwH9Zx9Nq8Ed3gaG2OjNIa7LpANxGkY1TUNOCr6HQAwKuRASZzFEdbdcqn8d+Z0RzH3XG2dI8ePTB37lz0798f4eHh2LRpEyZMmIBPPvmkxdcvX74cUqlU+8jJyenw/Pps6gBPeDtYorS6AV+fyWAdhxiB0mq59tC31x/sAbEZDdm2xlokxOKxmrkIG46nQEYb15EOsOlUGqrkSgS52mJibzfWcfSOTosXR0dHCASC27osxcXFt3VjWjN48GCkpra8GZRIJIKtrW2zhykxE/DxamQPAMDWmHSU1zQwTkQM3aaTt1DToEJvDwldNNto6gBPdOtqhYpahXYjMULuVX5lHb49pzkA9Y2HeoDPp6XR/6XT4sXc3ByhoaGIiopq9nxUVBQiIiLa/OfEx8fD1dW1o+MZjYdDXNHLzRbVciW+PJnGOg4xYAXSOu2p0a8/SBfNthL+6ybi6zMZqKCbCHIfNhxPQYNSjUG+9hgZ0JV1HL2k82GjpUuXYvv27dixYweSkpKwZMkSZGdnY/78+QA0wz4zZszQvn7Dhg04cOAAUlNTcf36dSxfvhx79+7FggULdB3VYPH5PLzReGzA9+eykFdZxzgRMVRfnEhDg1KNgb72GNrdkXUcg/JQLxf0dNXcRGyJucU6DjFQacVV+DU2FwCwbFwgbUh3BzovXqZMmYINGzZg9erV6Nu3L2JiYnD48GF4e2tmThcUFDTb86WhoQGvvfYaevfujWHDhuHMmTM4dOgQHn/8cV1HNWjD/R0R7ueABpUa/xeVwjoOMUDZZbX4+ZJmzthrkT3ootlOfD4Prz2omfvy7dlMFFfR2WOk/T7+MxlqDojs6Yz+Xl1Yx9FbPM7IdlaSyWSQSCSQSqUmN/8lIacSj375N/g84NiSEejuZM06EjEgS39OwL64PAwP6IrvZg9kHccgcRyHxzefRXx2JWZF+GjPQCKkLa7mVuKRjZpr+J+Lh8PfxHbTbc/vb1p7ZUT6etphbJAz1Jxm6TQhbZVWXIUD8XkAgFdpF897xuPx8Frj3JddF7JpCJe0y4bGDeke7etucoVLe1HxYmQWj9Xs+3LwSj7SiqsYpyGG4v+Op2pb1X087VjHMWhDuv8zhLuRbiJIGyXkVOLEzWII+Dy8MoaOw7kbKl6MTLC7BJE9ncFxwGd/0cojcnfX86U4dLUAPB6wNJK6Lh3h1cbP8efLucgsrWGchhiCDcc1cxUf7esOXzpH7K6oeDFCTRtm/XE1HylF1H0hrfu/KE134OHebgh0Ma15YroS5mOPkT26QqXm8Nlf1H0hrYvLrsCp5BII+DwsHEPniLUFFS9GqKebLR7q5dLYfaELJ7mza3lSHE8qAp/3z5Aj6RivPqCZ+/JbQh4yqPtCWtE01+Xxfu7wdqCuS1tQ8WKkFjX+IjqcWIDkQuq+kJY1Tex+pI8bunWl1WkdKcRDgjGBTlBzoM0jyR3FZlUgJkXTdVlAp7e3GRUvRirI1RbjgjXdl8+p+0JakFQgw5/Xi8DjgS6aOtI08XJ/fB6yy2oZpyH6qGmuy+T+1HVpDypejFhT9+VQYgFuFsoYpyH6ZuMJTTdgQogrujvRskxd6OtphxEBmrkvm05R94U0F5tVjtOppRDyeXhlNA3btgcVL0Ys0MUWE0I0Z0JR94X8W0pRFQ5fKwAAumjq2MLG7suvsbnIraDuC/lH01yXJ0I94GlvyTiNYaHixci90jhz/ci1Qtr3hWhtPJEGjgPGBbughwt1XXQp1LsLhnZ3hFLNYfMpOvOIaCTkVOJ0aikEfB5eHkXDtu1FxYuRC3Sx1e77sukkXTgJkFZcjd+v5gOguS6dpan78vPlHOTTrrsE/wzbPtrXnbou94CKFxPQ9Avqtyv5NGmQYNNJTdflgZ7O6OUmYR3HJAz0tcdgP3soVBy+iqabCFOXVCDD8STNZPmXRnVjHccgUfFiAnp72GF446TBzXThNGmZpTU4kKA5w2ghzXXpVE3dl92XclAkoxOnTVnT0vnxIa60RcE9ouLFRLzS2H35NTYHBVJqW5uqzaduQc0Bo3p0RYgHdV06U7ifAwb4dEGDUo1tMems4xBGbpVU41CiZrL8Aprrcs+oeDERA3zsMci3qW1NF05TlF9Zh33xuQCABdR16XQ83j8TM3+8kI2KmgbGiQgLm07eAscBY4OcEeRKx3HcKypeTEjTktjdF7NRUiVnnIZ0tm2n06FQcRjsZ49Q7y6s45ikEQFd0cvNFnUKFb45m8k6DulkOeW12mFbmix/f6h4MSFDujugr6cd5Eo1tp+h7ospKa2WY/fFbACgZZkM/bv7svPvDFTVKxgnIp1pc/QtqNQchvk7oq+nHes4Bo2KFxPC4/G0c19+OJeFylpqW5uKb/7OQL1CjT4eEgzt7sg6jkl7qJcLunW1gqxeiR8vZLOOQzpJobQev17WDNvSxpD3j4oXEzM60AlBrraoaVDh27NZrOOQTiCrV+C7xv/XL43qDh6PxziRaePzeXhxpOYmYvvpDNQrVIwTkc6w/XQ6GlRqDPDpgoG+9qzjGDwqXkwMj8fDiyM1+wrsPJuB2gYl40RE174/l4UquRL+TtZ4IMiZdRwCYFJfN7jbWaC0Wo5fLuewjkN0rKKmAbsah21fomHbDkHFiwkaH+wCbwdLVNQqsPsiXTiNWV2DCjvOZADQbIbF51PXRR+YCfiYN8IPALAlOh0KlZpxIqJL357LRG2DCj1dbTEyoCvrOEaBihcTJBTwMW+4pvuyLSYdDUq6cBqrny5lo6ymAR5dLDCxtxvrOORfngrzhKO1OfIq6/BbQj7rOERHauRK7GxcWfbiyG40bNtBqHgxUZND3eFkI0KhrB77G/f+IMalQanG1sbN0OaP6AahgP656xOxmQDPD9V0XzadSoNazTFORHRh98VsVNYq4ONgifEhrqzjGA26mpkokVCAOcN8AWja1iq6cBqd3xLyUCCtR1cbEZ4I9WAdh7Tg2cFesBULkV5Sg2M3iljHIR1MrlRh22nNDcS8Ed0goGHbDkPFiwmbNsgbEgszZJTW4Oi1QtZxSAdSqzlsaTzHas5QX4jNBIwTkZbYiM0wI9wHALD5VBo4jm4ijMn+uDwUyeRwthXh8f7urOMYFSpeTJi1SIiZET4ANG1runAaj2M3inCrpAa2YiGmDfJiHYe0YtYQH4iEfFzJleLcrTLWcUgHUak5fNU4bDt3mB9EQrqB6EhUvJi45yJ8YGEmwPV8GWJSS1nHIR2A4/45PXxGuA9sxGaME5HWOFqLMGWAJwDQqe9G5Mi1AmSU1sDO0gxPD6QbiI7WKcXLpk2b4OvrC7FYjNDQUJw+fbrV10dHRyM0NBRisRh+fn7YsmVLZ8Q0SV2szLX/sDY1HtNODNu5W2W4klMJkZCPWUN8WMchbTB3mB8EfB5Op5YiMVfKOg65TxzHYfMpTSE6M9wHViIh40TGR+fFy549e7B48WKsXLkS8fHxGDZsGMaNG4fs7Ja3xc7IyMD48eMxbNgwxMfHY8WKFVi4cCH27t2r66gma+5wX5gJeLiQUY647ArWcch9arp7nzrAE47WIsZpSFt42lvikT6apeybo+kmwtDFpJbier4MFmYCzGocmicdS+fFy/r16/H8889jzpw5CAoKwoYNG+Dp6YnNmze3+PotW7bAy8sLGzZsQFBQEObMmYPZs2fjk08+0XVUk+UqscCjfTWTyZruFohhSsyV4nRqKQR8HuYM82Mdh7TD/BGavZeOXCvErZJqxmnI/dh8SlOAPj3QC12szBmnMU46LV4aGhoQGxuLyMjIZs9HRkbi7NmzLb7n3Llzt73+wQcfxOXLl6FQ3H4Cq1wuh0wma/Yg7TdvRDfweEDUjSKkFlWxjkPuUdNd+6Q+bvC0t2SchrRHDxcbjA1yAscBW6Pp1HdDFZddgfPp5TAT8LTbUZCOp9PipbS0FCqVCs7Ozc9TcXZ2RmFhy0tzCwsLW3y9UqlEaentE0rXrl0LiUSifXh6enbcD2BCujtZ48GeLgA0+74Qw3OrpBpHGpe8z288v4oYlqZzx/bF56JAWsc4DbkXTd3rR/u6w83OgnEa49UpE3b/ux0yx3GtbpHc0utbeh4Ali9fDqlUqn3k5NBZPfeq6Rfebwl5yKukC6eh2RqdDo4DxgY5IcDZhnUccg9Cve0x0NceChWH7aczWMch7ZRaVIWoG0Xg8aA9u4rohk6LF0dHRwgEgtu6LMXFxbd1V5q4uLi0+HqhUAgHB4fbXi8SiWBra9vsQe5NX087RHRzgFLNYVsMdV8MSYG0Dvsaj3l4cSSdWmvImrovuy9mo6KmgXEa0h5Nk+UjezqjuxPdQOiSTosXc3NzhIaGIioqqtnzUVFRiIiIaPE94eHht73+2LFjCAsLg5kZ7Vehay81/uL76VI2yunCaTC2n86AQsVhkK89Qr27sI5D7sPIgK7o6WqL2gYVvj2XyToOaaPcilocbDxgk24gdE/nw0ZLly7F9u3bsWPHDiQlJWHJkiXIzs7G/PnzAWiGfWbMmKF9/fz585GVlYWlS5ciKSkJO3bswNdff43XXntN11EJgCHdHRDiLkG9Qo2df1Pb2hBU1DRg90XN1gMvjaKLpqHj8Xja7svOs5mokSsZJyJtsf10BpRqDhHdHNDX0451HKOn8+JlypQp2LBhA1avXo2+ffsiJiYGhw8fhre3NwCgoKCg2Z4vvr6+OHz4ME6dOoW+ffvivffew+eff47JkyfrOipB8wvnt+eyUE0XTr337blM1Dao0MvNFsP9HVnHIR1gfIgrvB0sUVmrwE+XaB6fviurluOnS5rfYy/SZPlOweOM7EAbmUwGiUQCqVRK81/ukUrN4YH10UgvrcHK8UGYO5wmnumrGrkSQ9adQGWtAhun9cPDvd1YRyIdZNeFbKzYnwhXiRjRr4+CuZBOc9FXnx5Lxhcn0hDsbovfFwxtdUEKubP2/P6mfw3kNgI+T7th1rbT6ZArVYwTkTvZfTEblbUK+DhYYlywK+s4pANNDnWHk40IBdJ6HIjPYx2H3EFVvQLfns0EALw8sjsVLp2EihfSokf7ucNVIkZxlRx7Y+nCqY/kSpV2Oe28Ed0g4NNF05iIhALtJmdbom9BpTaqJrnR+PFCNmT1Svh1tcKDvVxYxzEZVLyQFpkL+ZjbuL38luhbUKrUjBOR/zoQn4dCWT2cbUV4vL876zhEB6YN8obEwgzppTX483rLG3sSduoV/9xAvDiiG/h0A9FpqHghdzR1oCfsrcyRXV6LQ4kFrOOQf1GpOXzVuBPynKF+EAkFjBMRXbAWCTEzXLO4YdOpNBjZFEWD90tsLkqr5XC3s8Cj/egGojNR8ULuyNJciOcaT0TddPIW1NS21htHrhUgvbQGEgszPD3Ii3UcokOzhvjCwkyAa3kyxKTefkQKYUOpUuOrxk3p5g7zhZmAfp12Jvq0SatmhPvAWiREclEVTtwsZh2HQHNcxpcnNRfNWRGa/z/EeNlbmePpgZoC9csTaYzTkCa/X81HbkUdHKzMMWUA3UB0NipeSKsklmZ4drCmbf0lta31wombxUgqkMHKXIDnhviwjkM6wQvD/WAu4ONiZjkuZpSzjmPy1GoOmxpvIGYP9YWFOQ3bdjYqXshdzR7qA3MhH/HZlTifThdOljiOw8aTmrvvZwd7w87SnHEi0hlcJGJMDvUAAHx5krovrEUlFSG1uBo2IiGmN85JIp2LihdyV042YkwJ8wRAF07Wzt0qQ3x2JcyFfDzfuIyWmIYXG5fDR6eUIDFXyjqOyeI4DptOabou08O9YSumM/dYoOKFtMkLw/0g5PNwJq0U8dkVrOOYrKauy9QBnnCyETNOQzqTl4MlHumj2UGZbiLYOZ1aiis5lRCb8TF7KN1AsELFC2kTT3tLPNa4FPALmjTIRFx2Bc7eKoOQz8MLdGSDSWo6N+fo9UKkFlUxTmN6OI7DFydSAQDTBnrD0VrEOJHpouKFtNlLo7qDz9NMGL2WR23rzta00uTRfu7w6GLJOA1hIcDZBg/2cgYA7dAF6Tzn08txKbMC5gI+3UAwRsULaTNfRytMbGxbb6TuS6e6kS/DXzeLwePRqbWmbsEofwDAwSv5yC6rZZzGtGw8qem6PDXAAy4SGrZliYoX0i4LRnUHj6dpWycXUtu6szTNcRgf4opuXa0ZpyEshXhIMDygK1RqDpuj6Sais8RmVeDvNM2wbdPBtYQdKl5Iu/g722BcsObwsY00abBTpBRV4fA1zfEMC0Z1Z5yG6IOFozV/D365nIvcCuq+dIamuS6T+3vQsK0eoOKFtFtT2/qPq/m4VVLNOI3x+/yvVHAc8FAvFwS52rKOQ/RAmI89hnR3gFLN0dyXTnA1txKnkksg4PPw0ijquugDKl5Iu/V0s8XYIGdwHC3Z1LXUoirtoZgLx/gzTkP0yaIxAQCAXy7nIK+yjnEa49a0wnJSHzd4O1gxTkMAKl7IPXqlsW39WwJNGtSlz0+kgeOAB3s5o6cbdV3IPwb62iOimwMUKg6b6CZCZ5IKZIi6UQQeT7PikugHKl7IPenjaYcRjZMGm8aCScdKK67CH1fzAVDXhbRsUePfi5+p+6Iznx3XXN/Gh7iiuxNNltcXVLyQe7Z4rObCuS8+DxmlNYzTGJ8vGrsukT2d0ctNwjoO0UOD/BwQ7qfpvmw+Rd2XjnYtT4qj1wvB4wGL6QZCr1DxQu5ZP68uGB3oBJWaw+d/UfelI6UVV+PgFeq6kLtb1HgT8fOlXORT96VDbTieAgB4pI8b/J1tGKch/0bFC7kvSx/QTBr8LSEPacW070tH2XhCs8JobJAzgt2p60LubLCfAwb52qNBpcZmWnnUYRJyKnE8qRh83j/Dc0R/UPFC7kuwuwSRPZ2h5oANx6n70hH+3XVpGpojpDWLx2puIvZcorkvHeX/ojRdl8f6ecCPNobUO1S8kPu2pLH7ciixADcLZYzTGL7/i0qBmroupB3CuzlgsJ+m+/IFDeHet9isckSnaPZ1oa6LfqLihdy3IFdbTAhxBccBG6Lownk/ruVJcSixADwe8NqDAazjEAPy+oM9AAC/xOYinTaPvC+fHtN0XZ4M9YCXA+2mq4+oeCEdYtFYf+2ZR3Ti9L37+M9kAJrNsAJdaF8X0nah3vYY0ziBfn3jkAdpv3O3ynD2VhnMBDwsGE37uugrKl5IhwhwtsEjjSdON83QJ+1zIb0M0SklEPJ52qE4Qtrjtcbuyx9XC3A9n24i2ovjOO1cl6kDvOgMIz2m0+KloqIC06dPh0QigUQiwfTp01FZWdnqe2bNmgUej9fsMXjwYF3GJB1k0Rh/8HnA8aRiXM4sZx3HoHAcp+26TBngSVuQk3sS5GqrvYn4pPHvE2m7k8nFuJhZDnMhn84w0nM6LV6mTZuGhIQEHD16FEePHkVCQgKmT59+1/c99NBDKCgo0D4OHz6sy5ikg/h1tcaUAZ4AgDWHk8BxHONEhuNUcgkuZ1VAJOTTvi7kvix5IAACPg8nk0twiW4i2kyl5rDuiKbgey7CB64SC8aJSGt0VrwkJSXh6NGj2L59O8LDwxEeHo5t27bhjz/+QHJy63cEIpEILi4u2oe9vb2uYpIOtnhsAMRmfMRlV+LP60Ws4xgEtfqfrsusCB8424oZJyKGzNfRCk+FeQAAPj6aTDcRbbQ3LhfJRVWQWJjhpZE010Xf6ax4OXfuHCQSCQYNGqR9bvDgwZBIJDh79myr7z116hScnJwQEBCAuXPnori4+I6vlcvlkMlkzR6EHWdbMeYM9QMAfPTnTShVasaJ9N+hxALcKJDBRiTE/BHUqib3b+EYf5gL+biYqVnyS1pXr1Bp57q8PKobJJZmjBORu9FZ8VJYWAgnJ6fbnndyckJhYeEd3zdu3Dj8+OOPOHHiBD799FNcunQJo0ePhlwub/H1a9eu1c6pkUgk8PT07LCfgdybeSP8YG9ljvSSGuy5nMM6jl6TK1X45Jim6zJnmB+6WJkzTkSMgavEAjMGewMAPjxyEyo1dV9a883fmSiQ1sNNIsaMcB/WcUgbtLt4WbVq1W0Tav/7uHz5MgCAx+Pd9n6O41p8vsmUKVMwYcIEBAcHY+LEiThy5AhSUlJw6NChFl+/fPlySKVS7SMnh35ZsmYjNsMrjUsMNxxPRW2DknEi/fXd2SxkldXCyUaEOcN8WcchRuTlUd1hKxbiZmEVfqGbiDuqqGnApsZDLZdG9oDYTMA4EWkLYXvfsGDBAkydOrXV1/j4+ODq1asoKrp9zkNJSQmcnZ3b/P1cXV3h7e2N1NSWNz8TiUQQiURt/vNI53hmkDe++TsT2eW12H46gyahtqC8pgGfn9D8vX7twR6wErX7nyMhd9TFyhwLx/jj/UNJ+ORYCh7u4wZr+jt2my9PpqGqXolAFxs81s+ddRzSRu3uvDg6OiIwMLDVh1gsRnh4OKRSKS5evKh974ULFyCVShEREdHm71dWVoacnBy4urq2NyphyFzI1+458VX0LZRVtzzsZ8o2HE9BVb0SPV1tMbm/B+s4xAjNCPeBj4MlSqvl2NzYXSD/yK2oxXfnsgAAy8YFQsC/86gA0S86m/MSFBSEhx56CHPnzsX58+dx/vx5zJ07Fw8//DB69OihfV1gYCD2798PAKiursZrr72Gc+fOITMzE6dOncLEiRPh6OiIxx57TFdRiY48HOKKEHcJahpU+JR2/GwmrbgKP17IBgC89XAQXTSJTpgL+Vg+PggAsO10BnIrahkn0i/rjiajQaVGuJ8DRgZ0ZR2HtINO93n58ccfERISgsjISERGRqJ37974/vvvm70mOTkZUqlmJ0iBQIDExERMmjQJAQEBmDlzJgICAnDu3DnY2NjoMirRAT6fh7cmaC6cuy9m07EB//LBoSSo1Bwe6OmMiG6OrOMQIxbZ01lzaKNSjY+O0sZ1Tc7dKsPvV/LB4wErJwS1OheT6B8eZ2SbAMhkMkgkEkilUtja0tkw+mDh7ngcvJKPUO8u+HV+uMlfJGJSSjBjx0UI+TxELR0BX0faTZfo1rU8KSZuPAOOA/a9FIH+Xl1YR2JKqVLj4S/O4GZhFZ4Z5IUPHgthHYmgfb+/6WwjonMrxgfB0lyA2KwK7I/PYx2HKaVKjfcP3QCgmY9AhQvpDMHuEjzROK/qvT9umPzGdT+cz8LNwirYWZrhtcged38D0TtUvBCdc5GItaezrjl8E1X1CsaJ2Pn+fBZSiqphZ2mGRbQCi3Si1x7sAUtzAeKzK7E3znRvIsqq5dpTt1+N7EF7KxkoKl5Ip3h+qC98Ha1QWi3H53+1vOzd2BVK6/HpMc1F87XIHrSLJ+lUzrZivDJaUzCvOZyEipoGxonY+PjPZMgaV/lNG+jFOg65R1S8kE4hEgrw9sSeADS7WaYVVzFO1Pne/f06quVK9POyo4smYWLOMF/0cLZBeU0D1h5JYh2n013JqdTu+r16Ui9a5WfAqHghnWZUDyeMDXKCUs1h1UHTGnf/K6kIR64VQsDnYc1jIeDTRZMwYCbgY83jwQCAny/n4kJ6GeNEnUet5vDOwevgOOCxfu4I86EDfw0ZFS+kU/3v4Z4wF/JxJq3UZMbdaxuUePu36wCAOUN9EeRKq+AIO6He9ni6sfO38sA1NChN4/DUb89lIiGnElbmArw5LpB1HHKfqHghncrbwUo7UXX179dRJKtnnEj3PjueirzKOrjbWWDRWJqkS9h786FAOFqbI624GltjbrGOo3NZZTXaPW7eHBcIZ1sx40TkflHxQjrdvOF+CHGXQFavxMr914x6+CipQIbtZzIAaMbYLc3pbBnCnsTSDG9N0MxB++JEGjJLaxgn0h21msObexNRp1BhsJ89nhnkzToS6QBUvJBOJxTw8fGTvWEm4OF4UhEOXslnHUknFCo13tx7FSo1h4d6uWBMUNsPJCVE1yb1dcPQ7o6QK9VYvi8RarVx3kTsupiNc+llsDATYN3k3jTfzEhQ8UKYCHSxxYJRmiGUVQevo6TK+A5u/OJEGq7kSmEjFuKdR3qyjkNIMzweD+8/GgwLMwHOpZdhx98ZrCN1uNyKWqw9rFlV9fqDPeDtQJtCGgsqXggzL43qhiBXW1TUKvDOwWus43So2KxybDyh2c/mg8dC4CqxYJyIkNv5OFrhrYc15499dDQZN/JljBN1HI7jsHxfImoaVAjz7oJZET6sI5EORMULYcZMwMfHT/SGkM/D4cRC/HHVOIaPquVKLN6TAHXjksxH+rixjkTIHU0b6IWxQU5oUKmxeE886hUq1pE6xJ5LOTidWgqRkI+PnqDhImNDxQthKthdgpdGdgMALN+biKwyw584uOrgdeSUa1YXvTupF+s4hLSKx+Phw8m94WhtjpSiaqw7epN1pPt2s1CGVb9rtid4NTIAfl2tGSciHY2KF8LcK2P8EebdBVVyJV76Mc6g7/wOJxbg19hc8HjA/03pC1sxHQFA9J+jtQgfP9EHgGYH7JiUEsaJ7l219jqixvCArpgz1I91JKIDVLwQ5swEfHwxrR/srcxxPV+G1X/cYB3pnhRI67B8XyIA4MUR3TDQl3bwJIZjVKATpg/WLCN+9ZcrKKs2vEn0TfNc0ktq4CoRY8OUvjRcZKSoeCF6wVVigQ1T+oLHA3ZdyMaBeMPafbeuQYUXvouFtE6BEHcJFo8NYB2JkHZbMT4I3bpaoaRKjhd/jDO43Xd/OJ+F36/kQ8jnYWPjDRExTlS8EL0xPKCr9tTb5fsSkVpkGIc3chyH1365gsQ8KeytzLHpmf4wF9I/LWJ4LMwF2PJsKKxFQlzMKMf/DhjOJpJXcyvx3h+aZdFvjgtEqDd1Po0ZXWGJXlk0xh9DujugTqHCiz/GQVqnYB3prj7/Kw2HEgtgJuBh8zP94WlvyToSIffM39kGXzzdD3wesOdyDr75O5N1pLsqq5bjpR/j0KBSI7KnM54f6ss6EtExKl6IXhHwefhsaj8424qQVlyNud9d1usJvIcTC/B/x1MAAO8/GoxBfg6MExFy/0YFOmHFeM3+L+8fuoFoPZ7AWy1XYtY3l5BbUQcve0t8/GQf8Hg0z8XYUfFC9I6jtQjfzBoIm8bW9Su746FU6d/Y+7U8KZb+nAAAmD3EF1MGeLENREgHen6oL54M9YCaAxbsikNacTXrSLeRK1WY9/1l7ZDtzucGQGJBK/xMARUvRC/1dLPF9plhMBfyEXWjCCv2J+rV2HtacTVm77yEeoUaIwK6YsX4QNaRCOlQPB4P7z8WrNnGoF6JWd9cRE55LetYWio1hyV7EvB3WhmszAXY+dwA2s/FhFDxQvTWID8HbGwce//5ci7WNR5pz1pyYRWmbj2H4io5Al1s8MW0fhAK6J8SMT4ioQBbpofCx8ESuRV1mPLVOb04gZrjOPzvt2s4nFgIcwEfW2eEobeHHetYpBPRFZfotcheLvhwcm8AwJboW/j8r1SmHZjr+VJM3XoOpdUN6Olqi11zB9NGdMSoOVqLsGdeOPy6WiFfWo8pW88xHUJSqzmsPXITuy5kg8cDNkztiyHdHZnlIWxQ8UL03lNhnnhznGZYZn1UCpbtvcpk/4mruZWYtu0CKmoV6OMhwe65g2kfCWISnG3F2PNCOAKcrVEkk2Pq1vNIYbCVQb1ChVd2x2NrTDoA4L1JwRgf4trpOQh7VLwQgzB/RDe8+0gv7RDSzB0XIa3tvGXUZ1JL8cy2C5DWKdDfyw7fzxkEiSV1XIjp6Gojwu65gxHkaovSak0BczmzvNO+f0mV5ns2bUvwyZN98GzjjsDE9FDxQgzGzAgffD1zAKzMBTiXXobHNv2t8/F3hUqND4/cxPQdF1AlV2Kgjz2+e34QDRURk+RgLcLuuYMQ4i5BeU0DnvrqHD7/KxUqtW6HcpMLq/Dol38jIacSEgszfDd7EJ4I9dDp9yT6jcfp0xKODiCTySCRSCCVSmFra8s6DtGBpAIZnt95CfnSethZmmHF+CA80d+jw88wyS6rxSs/xeNKTiUAYNogL7z9cE+IzQQd+n0IMTTVciX+d+Aa9jce4zHI1x4bpvaFq8SiQ7+PSs1h14UsrDuajGq5Ej4Oltgxi1YVGav2/P6m4oUYpGJZPeZ8dxlXc6UAgP5edlg9KRjB7pL7/rNVag774nLx7u83UC1XwlYsxLrJvTGOxtYJaWZfXC7+d+AaahpUsLM0w+pJwXg4xLVDbiTisivw9m/XcC1PBgAY6GOPLdNDaZ6ZEdOb4uWDDz7AoUOHkJCQAHNzc1RWVt71PRzH4d1338XWrVtRUVGBQYMG4csvv0SvXr3a9D2peDEdDUo1vvk7A5/9lYraBhX4PODZwd5YNMYfDtaidv959QoV9sblYmtMOrLKNPtZhHl3wWdP94O7XcfeURJiLDJKa7BwdzwS8zQ3Et2drDFvuB8m9XW/pzO+imX1+ORYMn6+nAsAsBEL8VpkDzwzyIu2JDByelO8vPPOO7Czs0Nubi6+/vrrNhUv69atwwcffICdO3ciICAA77//PmJiYpCcnAwbG5u7vp+KF9NTIK3DB4eS8MfVAgCAkM9DRHdHPBziishezrCzvPOdmlrNIb20BlE3ivD1mQyUVssBAHaWZnhhuB9eGOZHF0xC7qJBqcbGk2n45kwGquRKAICrRIznh/piTJAzvO0tW+3GlFXLcfR6IQ5dLcD59DI0TaF5ItQDb44LhOM93IwQw6M3xUuTnTt3YvHixXctXjiOg5ubGxYvXoxly5YBAORyOZydnbFu3TrMmzfvrt+LihfTdTatFB8evakdSgI0hcxAX3u42Ipha2EGiYUZbC3MUCitw9VcKa7ny1DdeLEFADeJGHOG+WHqQE9YmgtZ/BiEGCxZvQK7LmTj6zMZKKmSa5+3EQsR4i5BiIcETjZiSOsUkNUpIK1TIL+yDpezKppN+g317oIV4+lkaFNjsMVLeno6unXrhri4OPTr10/7/KRJk2BnZ4dvv/32tvfI5XLI5f/8I5HJZPD09KTixYSll1TjcGIB/rhagJuFd9+LQmzGR4i7BFMGeOGRPm731OomhPyjXqHCvrg8/BKbg+v5sjbtyxTiLsGE3q6YEOJKJ7ObqPYUL3p1a1lYWAgAcHZ2bva8s7MzsrKyWnzP2rVr8e677+o8GzEcfl2tsWC0PxaM9kdacTVis8pRWau5y5PWKVBZp4C9pTl6e2juBLt3taahIUI6kNhMgGmDvDBtkBcUKjVSiqpwLU+Kq7lSSOsUkFiYwc5S0wm1szDHID97eDtYsY5NDEi7i5dVq1bdtVi4dOkSwsLC7jnUf48z5zjujkecL1++HEuXLtV+3dR5IQTQTB7s7kTLKglhxUzARy83CXq5STBlAOs0xFi0u3hZsGABpk6d2uprfHx87imMi4sLAE0HxtX1n2WpxcXFt3VjmohEIohENJmLEEIIMRXtLl4cHR3h6KibQ7B8fX3h4uKCqKgo7ZyXhoYGREdHY926dTr5noQQQggxLDod6M/OzkZCQgKys7OhUqmQkJCAhIQEVFf/cyJpYGAg9u/fD0AzXLR48WKsWbMG+/fvx7Vr1zBr1ixYWlpi2rRpuoxKCCGEEAOh0wm7b7/9drMVQk3dlJMnT2LkyJEAgOTkZEil/yxtfeONN1BXV4eXXnpJu0ndsWPH2rTHCyGEEEKMn9EdDyCVSmFnZ4ecnBxaKk0IIYQYiKYFN5WVlZBIWj/qRa+WSneEqirNvh604ogQQggxPFVVVXctXoyu86JWq5Gfnw8bG5s7Lq++V01VIXV1WkafT+vo87kz+mxaR59P6+jzaZ2hfD4cx6Gqqgpubm7g81ufkmt0nRc+nw8PDw+dfg9bW1u9/gvAGn0+raPP587os2kdfT6to8+ndYbw+dyt49KEthUlhBBCiEGh4oUQQgghBoWKl3YQiUR45513aEffO6DPp3X0+dwZfTato8+ndfT5tM4YPx+jm7BLCCGEEONGnRdCCCGEGBQqXgghhBBiUKh4IYQQQohBoeKFEEIIIQaFipc22rRpE3x9fSEWixEaGorTp0+zjqQX1q5diwEDBsDGxgZOTk549NFHkZyczDqW3lq7dq329HSikZeXh2effRYODg6wtLRE3759ERsbyzqWXlAqlXjrrbfg6+sLCwsL+Pn5YfXq1VCr1ayjMRETE4OJEyfCzc0NPB4PBw4caPbfOY7DqlWr4ObmBgsLC4wcORLXr19nE7aTtfbZKBQKLFu2DCEhIbCysoKbmxtmzJiB/Px8doHvExUvbbBnzx4sXrwYK1euRHx8PIYNG4Zx48YhOzubdTTmoqOj8fLLL+P8+fOIioqCUqlEZGQkampqWEfTO5cuXcLWrVvRu3dv1lH0RkVFBYYMGQIzMzMcOXIEN27cwKeffgo7OzvW0fTCunXrsGXLFmzcuBFJSUn46KOP8PHHH+OLL75gHY2Jmpoa9OnTBxs3bmzxv3/00UdYv349Nm7ciEuXLsHFxQUPPPCA9sw7Y9baZ1NbW4u4uDj873//Q1xcHPbt24eUlBQ88sgjDJJ2EI7c1cCBA7n58+c3ey4wMJB78803GSXSX8XFxRwALjo6mnUUvVJVVcX5+/tzUVFR3IgRI7hFixaxjqQXli1bxg0dOpR1DL01YcIEbvbs2c2ee/zxx7lnn32WUSL9AYDbv3+/9mu1Ws25uLhwH374ofa5+vp6TiKRcFu2bGGQkJ3/fjYtuXjxIgeAy8rK6pxQHYw6L3fR0NCA2NhYREZGNns+MjISZ8+eZZRKf0mlUgCAvb094yT65eWXX8aECRMwduxY1lH0ysGDBxEWFoYnn3wSTk5O6NevH7Zt28Y6lt4YOnQo/vrrL6SkpAAArly5gjNnzmD8+PGMk+mfjIwMFBYWNrtWi0QijBgxgq7VLZBKpeDxeAbb5TS6gxk7WmlpKVQqFZydnZs97+zsjMLCQkap9BPHcVi6dCmGDh2K4OBg1nH0xk8//YS4uDhcunSJdRS9k56ejs2bN2Pp0qVYsWIFLl68iIULF0IkEmHGjBms4zG3bNkySKVSBAYGQiAQQKVS4YMPPsDTTz/NOpreaboet3StzsrKYhFJb9XX1+PNN9/EtGnT9P6gxjuh4qWNeDxes685jrvtOVO3YMECXL16FWfOnGEdRW/k5ORg0aJFOHbsGMRiMes4eketViMsLAxr1qwBAPTr1w/Xr1/H5s2bqXiBZr7dDz/8gF27dqFXr15ISEjA4sWL4ebmhpkzZ7KOp5foWt06hUKBqVOnQq1WY9OmTazj3DMqXu7C0dERAoHgti5LcXHxbRW+KXvllVdw8OBBxMTEwMPDg3UcvREbG4vi4mKEhoZqn1OpVIiJicHGjRshl8shEAgYJmTL1dUVPXv2bPZcUFAQ9u7dyyiRfnn99dfx5ptvYurUqQCAkJAQZGVlYe3atVS8/IeLiwsATQfG1dVV+zxdq/+hUCjw1FNPISMjAydOnDDYrgtAq43uytzcHKGhoYiKimr2fFRUFCIiIhil0h8cx2HBggXYt28fTpw4AV9fX9aR9MqYMWOQmJiIhIQE7SMsLAzPPPMMEhISTLpwAYAhQ4bctrQ+JSUF3t7ejBLpl9raWvD5zS/TAoHAZJdKt8bX1xcuLi7NrtUNDQ2Ijo6mazX+KVxSU1Nx/PhxODg4sI50X6jz0gZLly7F9OnTERYWhvDwcGzduhXZ2dmYP38+62jMvfzyy9i1axd+++032NjYaDtUEokEFhYWjNOxZ2Njc9v8HysrKzg4ONC8IABLlixBREQE1qxZg6eeegoXL17E1q1bsXXrVtbR9MLEiRPxwQcfwMvLC7169UJ8fDzWr1+P2bNns47GRHV1NdLS0rRfZ2RkICEhAfb29vDy8sLixYuxZs0a+Pv7w9/fH2vWrIGlpSWmTZvGMHXnaO2zcXNzwxNPPIG4uDj88ccfUKlU2mu1vb09zM3NWcW+d2wXOxmOL7/8kvP29ubMzc25/v3701LgRgBafHzzzTeso+ktWird3O+//84FBwdzIpGICwwM5LZu3co6kt6QyWTcokWLOC8vL04sFnN+fn7cypUrOblczjoaEydPnmzxejNz5kyO4zTLpd955x3OxcWFE4lE3PDhw7nExES2oTtJa59NRkbGHa/VJ0+eZB39nvA4juM6s1gihBBCCLkfNOeFEEIIIQaFihdCCCGEGBQqXgghhBBiUKh4IYQQQohBoeKFEEIIIQaFihdCCCGEGBQqXgghhBBiUKh4IYQQQohBoeKFEEIIIQaFihdCCCGEGBQqXgghhBBiUKh4IYQQQohB+X9XbG4uMrDqUwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2, 1)\n",
    "axes[0].plot(x, np.sin(x))\n",
    "axes[1].plot(x, np.cos(x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 物件導向風格的優缺點\n",
    "\n",
    "- 優點：物件型介面，不需要依程式碼順序閱讀也能理解。\n",
    "- 缺點：對不熟悉物件導向概念的人不友善。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 視覺化的標準五步驟\n",
    "\n",
    "1. 建立 `ndarray`\n",
    "2. 建立「畫布物件」與「軸物件」。\n",
    "3. 使用「軸物件」的作圖方法建立主要圖形。\n",
    "4. 使用「軸物件」的作圖方法添加圖形元素。\n",
    "5. 顯示或者儲存圖形。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 建立 `ndarray`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, np.pi*2, 100)\n",
    "sinx = np.sin(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 建立「畫布物件」與「軸物件」\n",
    "\n",
    "使用 `plt.subplots()` 函數。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'matplotlib.figure.Figure'>\n",
      "<class 'matplotlib.axes._axes.Axes'>\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "print(type(fig))\n",
    "print(type(ax))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 使用「軸物件」的作圖方法建立主要圖形\n",
    "\n",
    "`AxesSubplot.plot()` 能夠建立線圖。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11184fe10>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, sinx)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 使用「軸物件」的作圖方法添加圖形元素"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.0, 5.0)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, sinx)\n",
    "ax.set_title('A simple plot')\n",
    "ax.set_xlabel('x')\n",
    "ax.set_ylim(-5, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 顯示圖形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plt.axes()\n",
    "ax.plot(x, sinx)\n",
    "ax.set_title('A simple plot')\n",
    "ax.set_xlabel('x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "## 儲存圖形：可支援的圖片格式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'eps': 'Encapsulated Postscript',\n",
       " 'jpg': 'Joint Photographic Experts Group',\n",
       " 'jpeg': 'Joint Photographic Experts Group',\n",
       " 'pdf': 'Portable Document Format',\n",
       " 'pgf': 'PGF code for LaTeX',\n",
       " 'png': 'Portable Network Graphics',\n",
       " 'ps': 'Postscript',\n",
       " 'raw': 'Raw RGBA bitmap',\n",
       " 'rgba': 'Raw RGBA bitmap',\n",
       " 'svg': 'Scalable Vector Graphics',\n",
       " 'svgz': 'Scalable Vector Graphics',\n",
       " 'tif': 'Tagged Image File Format',\n",
       " 'tiff': 'Tagged Image File Format',\n",
       " 'webp': 'WebP Image Format'}"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "fig.canvas.get_supported_filetypes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "axes = plt.axes()\n",
    "axes.plot(x, sinx)\n",
    "axes.set_title('A simple plot')\n",
    "axes.set_xlabel('x')\n",
    "fig.savefig('a_simple_plot.png')"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "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": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}