10-Coding_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://nbviewer.jupyter.org/github/twMr7/Python-Machine-Learning/blob/master/10-Coding_Project.ipynb\">\n",
    "        Open In Jupyter nbviewer\n",
    "        <img style=\"float: center;\" src=\"https://nbviewer.jupyter.org/static/img/nav_logo.svg\" width=\"120\" />\n",
    "    </a>\n",
    "</p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/twMr7/Python-Machine-Learning/blob/master/10-Coding_Project.ipynb)\n",
    "\n",
    "# 10. 程式開發專案 Coding Project\n",
    "\n",
    "使用互動式的 Jupyter QtConsole 或 Notebook 介面，只適合用來作先期的實驗測試，離開這些介面後原有的函式、變數都不會被保留，當然也沒辦法被其他程式重複使用。 因此，有需要重複執行的功能就通常會把測試過有用的程式碼集合起來，寫成一個副檔名是 **“.py”** 的 **script** 檔案。\n",
    "\n",
    "當 script 越寫越長，會開始需要將程式碼有結構化的組織起來。 除了常用的功能可以定義成函式以外，不同性質的函式可以分類拆成多個不同的 script 檔案。 而根據經驗，將函式的功能定義得越簡單，程式碼越不容易出錯，所以時常可以看到稍大型一點的專案會把一個檔案就只放一個函式。\n",
    "\n",
    "+ [**10.1 模組（Modules）**](#modules)\n",
    "+ [**10.2 套件（Packages）**](#packages)\n",
    "+ [**10.3 迷你專案（An Example Mini-Project）**](#mini-project)\n",
    "+ [**10.4 擴充專案功能練習（Expanding Project Features）**](#expanding-project)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"modules\"></a>\n",
    "\n",
    "## 10.1 模組 Modules\n",
    "\n",
    "在 Python 環境裡，一個 script 檔案就是一個模組，模組的名稱就是檔案的名稱（不包括 .py 的副檔名），模組裡面所有定義的函式及變數都需要透過 `import` 才能被另外一個 script 檔案使用。 Python 要 import 一個模組時，會先從內建模組開始搜尋起，找不到的話則依序尋找以下位置：\n",
    "\n",
    "1. 叫用 `import` 的 script 檔案的相同目錄。\n",
    "2. 系統環境變數 `PYTHONPATH` 的搜尋路徑清單。\n",
    "3. 標準函式庫的目錄。\n",
    "4. 副檔名為 `.pth` 的路徑設定檔。\n",
    "5. 其他函式庫套件 `Lib/site-packages` 路徑。\n",
    "\n",
    "並不是只有副檔名是 .py 的 script 檔才能 `import`，由名稱選取的套件（package）或模組（module），可以是以下幾種格式：\n",
    "+ 副檔名是 **.py** 的程式碼文字檔。\n",
    "+ 副檔名是 **.pyc** 的編譯過的 byte code 檔案。\n",
    "+ 目錄名稱符合，而且存在套件必要的 `__init__.py` 檔。\n",
    "+ 由 C++ 程式編譯來的，副檔名是 **.pyd** 的動態連結延伸模組\n",
    "+ 副檔名是 **.zip** 的壓縮檔，**import** 時會自動解壓縮。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "兩種輸入模組的語法可以使用 `import ...` 以及 `from ... import ...`，差別在於是否需要透過模組名稱來存取模組內的函式或變數名字。\n",
    "\n",
    "### § `import` 陳述\n",
    "```\n",
    "import module1\n",
    "import module2, module3\n",
    "import module4 as m4\n",
    "```\n",
    "使用 `import` 的陳述，因為是整個模組物件直接輸入，必需透過模組名稱來存取模組內的函式或變數名字。 如：`module1.func1()`，`m4.func4()`，`module2.var2`。\n",
    "\n",
    "### § `from` 陳述\n",
    "```\n",
    "from module1 import func1\n",
    "from module2 import func2, var2\n",
    "from module3 import *\n",
    "from package import module4 as m4\n",
    "```\n",
    "使用 `from` 的陳述，因為指定了要從模組輸入的函式或變數名字，因此不需要再透過模組名稱來存取。 如： `func1()`，`func2()`，`var2`。\n",
    "\n",
    "### § 模組 `import` 只會發生一次\n",
    "由於一個模組的 `import` 是相當昂貴的操作，因此 `import` 只會在程式的執行生命週期內發生一次，輸入的模組只會有一份。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § 模組的使用模式： `__name__` 以及 `__main__`\n",
    "\n",
    "所有的模組都有一個內建的 `__name__` 屬性，在 Python 建立該模組時就會自動指定。\n",
    "+ 如果 script 檔是被執行成最上層的程式，`__name__` 屬性會被指定為 `'__main__'`，例如：\n",
    "    1. 在命令列下指令 `python somescript.py`，或\n",
    "    2. 在命令列下指令 `python -m somescript.py`，或\n",
    "    3. 在 *ipython* 或 *jupyter notebook* 執行 `%run somescript.py`。\n",
    "+ 如果 script 檔是被 `import` 方式載入的，`__name__` 屬性會被指定為模組的名字。\n",
    "\n",
    "除了原本就是設計為程式進入點的 script 以外，也時常可以看到純函式的模組包含這樣的檢查：\n",
    "```\n",
    "if __name__ == '__main__':\n",
    "    main()\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"packages\"></a>\n",
    "\n",
    "## 10.2 套件 Packages\n",
    "\n",
    "套件是利用檔案系統來結構化組織模組的方式。 以設計一個音訊處理的 `sound` 套件為例（以下範例來自 [Python官方文件](https://docs.python.org/3/tutorial/modules.html#packages) ），不同聲音檔案格式的讀寫，適合有各自獨立的模組來處理，然後集合成一個 `formats` 的子套件。 然後音訊資料又可以套用許多不同的特效及濾波，以下是一個可能的套件結構。\n",
    "\n",
    "```\n",
    "sound/                          套件最上層目錄\n",
    "      __init__.py               初始化 sound 套件\n",
    "      formats/                  子套件： 檔案格式轉換\n",
    "              __init__.py\n",
    "              wavread.py\n",
    "              wavwrite.py\n",
    "              mp3read.py\n",
    "              mp3write.py\n",
    "              ...\n",
    "      effects/                  子套件： 特效\n",
    "              __init__.py\n",
    "              echo.py\n",
    "              surround.py\n",
    "              reverse.py\n",
    "              ...\n",
    "      filters/                  子套件： 濾波\n",
    "              __init__.py\n",
    "              equalizer.py\n",
    "              vocoder.py\n",
    "              karaoke.py\n",
    "              ...\n",
    "\n",
    "```\n",
    "\n",
    "+ 屬於 Python 套件的目錄下，必須要存在一個 `__init__.py` 的檔案，內容可以是空的。\n",
    "+ `import` 的階層按照目錄的階層安排，如 `from sound.effects import echo`。\n",
    "+ 如果要提供 `from sound.effects import *` 的功能，在 \"sound/effects/__init__.py\" 檔案裡要有一行 `__all__ = ['echo', 'surround', 'reverse']`，否則只會有 `sound.effects` 被 import。\n",
    "+ 子套件之間需要交互 import 時，可以使用絕對或相對路徑兩種方式：\n",
    "    - **絕對方式** - 例如 `sound.filters.karaoke` 需要使用 `echo` 模組的功能，可以用 `from sound.effects import echo`。\n",
    "    - **相對路徑** - 例如在 `surround` 模組裡，可以這樣用 `from . import echo`，`from .. import formats`，或 `from ..filters import equalizer`。\n",
    "+ 套件如果要執行成上層應用程式，必須透過 `python -m somepackage` 的方式，且套件裡要存在 `__main__.py` 的模組作為程式的主要進入點。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"mini-project\"></a>\n",
    "\n",
    "## 10.3 迷你專案（An Example Mini-Project）\n",
    "\n",
    "編輯一個 script 儲存成 \"***rotatepoint.py***\" ，內容如以下 cell 中的程式碼。 第一行使用了 `%%writefile` magic 命令，執行該 cell 時就會將 cell 裡的程式碼存成檔案，手動複製程式碼到編輯器的話，可以忽略第一行。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing rotatepoint.py\n"
     ]
    }
   ],
   "source": [
    "%%writefile rotatepoint.py\n",
    "from typing import Tuple\n",
    "from math import cos, sin, radians\n",
    "\n",
    "point2d = Tuple[float, float]\n",
    "\n",
    "def rotate2d(point: point2d, degree: float) -> point2d:\n",
    "    \"\"\"rotate 2D point by specific degree\n",
    "    \"\"\"\n",
    "    x, y = point[0], point[1]\n",
    "    theta = radians(degree)\n",
    "    new_x = x * cos(theta) - y * sin(theta)\n",
    "    new_y = x * sin(theta) + y * cos(theta)\n",
    "    return (new_x, new_y)\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    import argparse\n",
    "    # setup options\n",
    "    parser = argparse.ArgumentParser(prog='rotatepoint', description='''\n",
    "        rotatepoint read a 2D point and angle from command line arguments.\n",
    "            The output is a point transformed by rotating specific degree.\n",
    "        ''')\n",
    "    parser.add_argument('-p', '--point', nargs=2, type=float,\n",
    "        metavar=('X','Y'), required=True,\n",
    "        help='''Specify a (x, y) coordinate poin.''')\n",
    "    parser.add_argument('-a', '--angle', type=float, required=True,\n",
    "        help='''Specify rotating angle in degree.''')\n",
    "    \n",
    "    # parse command line arguments\n",
    "    args = parser.parse_args()\n",
    "\n",
    "    new_point = rotate2d(args.point, args.angle)\n",
    "    print(new_point)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### § 以 Script 方式執行\n",
    "\n",
    "檔案生成後，可以在 notebook 使用 `%run` 來執行。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "usage: rotatepoint [-h] -p X Y -a ANGLE\n",
      "\n",
      "rotatepoint read a 2D point and angle from command line arguments. The output is a point transformed by rotating specific degree.\n",
      "\n",
      "optional arguments:\n",
      "  -h, --help            show this help message and exit\n",
      "  -p X Y, --point X Y   Specify a (x, y) coordinate poin.\n",
      "  -a ANGLE, --angle ANGLE\n",
      "                        Specify rotating angle in degree.\n"
     ]
    }
   ],
   "source": [
    "%run rotatepoint -h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-5.0, 3.0000000000000004)\n"
     ]
    }
   ],
   "source": [
    "%run rotatepoint -p 3 5 -a 90"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### § 把模組當成函式庫 `import`\n",
    "\n",
    "只需要該模組裡面的某些函式，`import` 進來不會去執行 `__name__ == \"__main__\"` 區段的程式碼。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-5.0, 3.0000000000000004)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rotatepoint import rotate2d\n",
    "\n",
    "# 呼叫函式\n",
    "rotate2d((3, 5), 90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"expanding-project\"></a>\n",
    "\n",
    "## 10.4 擴充專案功能練習（Expanding Project Features）\n",
    "\n",
    "學習以下範例使用 `csv` 及 `argparse` 物件的方法，必要時查閱 Python 官網文件，以完成指定的功能。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### § CSV\n",
    "\n",
    "+ [**`csv`**](https://docs.python.org/3/library/csv.html) 模組，Python 官網文件 (https://docs.python.org/3/library/csv.html)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 隨機產生 N 個點資料，輸出成CSV格式\n",
    "import csv\n",
    "from random import randrange, random, sample\n",
    "\n",
    "# csv 物件會自己處理換行，固定要加參數 newline=''\n",
    "with open('points.csv', 'w', newline='') as csvfile:\n",
    "    field_names = ['x', 'y']\n",
    "    # 注意： 沒有 QUOTE_NONNUMERIC 的話，預設輸出文字沒有引號\n",
    "    writer = csv.writer(csvfile, quoting=csv.QUOTE_NONNUMERIC)\n",
    "    # write header\n",
    "    writer.writerow(field_names)\n",
    "    # random sample N points (N == 5)\n",
    "    for _ in range(5):\n",
    "        x, y = randrange(-100,100)+random(), randrange(-100,100)+random()\n",
    "        writer.writerow([x, y])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['x', 'y']\n",
      "[82.93172791107892, 48.90225839878033]\n",
      "[-16.14561496387222, -42.060573887619334]\n",
      "[-83.28140163536176, -65.0394205200153]\n",
      "[-32.43098549252423, 80.10788082697725]\n",
      "[84.96345212346213, 78.05944524112805]\n"
     ]
    }
   ],
   "source": [
    "# 讀入CSV格式檔案，使用csv物件解析每一筆資料\n",
    "with open('points.csv', 'r', newline='') as csvfile:\n",
    "    reader = csv.reader(csvfile, quoting=csv.QUOTE_NONNUMERIC)\n",
    "    for row in reader:\n",
    "        print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[82.93172791107892, -16.14561496387222, -83.28140163536176, -32.43098549252423, 84.96345212346213]\n",
      "[48.90225839878033, -42.060573887619334, -65.0394205200153, 80.10788082697725, 78.05944524112805]\n"
     ]
    }
   ],
   "source": [
    "# 在一次讀入CSV格式檔案，這次使用DictReader物件解析\n",
    "# 將點資料 x, y 分別存成 list\n",
    "X, Y = [], []\n",
    "with open('points.csv', 'r', newline='') as csvfile:\n",
    "    reader = csv.DictReader(csvfile, quoting=csv.QUOTE_NONNUMERIC)\n",
    "    for row in reader:\n",
    "        X.append(row['x'])\n",
    "        Y.append(row['y'])\n",
    "\n",
    "print(X)\n",
    "print(Y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 載入繪圖工具\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2191ab6a910>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(X, Y, 'o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § ArgParse\n",
    "\n",
    "+ [**`argparse`**](https://docs.python.org/3/library/argparse.html) 模組，Python 官網文件 (https://docs.python.org/3/library/argparse.html)\n",
    "\n",
    "使用引數（參數）解譯物件，固定步驟是：\n",
    "1. `parser = argparse.ArgumentParser()` － 建構物件。\n",
    "2. `parser.add_argument()` － 加入程式想要提供的引數選項。\n",
    "3. `args = parser.parse_args()` － 解譯後的引數都在 args 屬性裡。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### § 作業練習\n",
    "\n",
    "1. 使用 IDE（如 VSCode）開發，並學習如何使用 IDE 的除錯功能。\n",
    "2. 【功能一】： 可接受參數指定 (1)產生 N 個隨機座標點及 (2)輸出檔名，但所有的點必須可連成凸多邊形（提示： 使用旋轉轉換），將隨機未排序的點輸出成CSV格式的指定檔名。\n",
    "3. 【功能二】： 可接受參數指定 (1)輸入CSV檔檔名 (2)是否顯示座標及多邊形的繪圖，座標點讀取後計算多邊形的周長並在畫面輸出結果。\n",
    "4. 【功能三】： 可接受參數指定讀入JSON檔案，其他所有參數都套用JSON檔案所設定的內容。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}