{
"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": [
"[](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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seX5Y8/xZjHUv9JpdrpGkhhnyktQwQ/5Ve0ZdwGWw5vlhzfNnMda9oGt2TV6SGuZMXpIaZshLUsOu6JBP8ttJnuz++16SJ7v2dUkme/p+fcSlviLJzyeZ6KntJ3v6diU5keR4ks2jrPO1kuxO8p0kf5zk60mWd+0L9loDJLm1u54nktwx6nr6SXJ9kj9IcizJ0SSf79ov+lpZCLqfuSNdbYe6trcneTDJ093j1aOuc1qSjT3X8skkP0zyhQV/nV2Tn5Lkl4H/U1W/kGQd8I2qeveIy3qdJD8P/N+q+qXXtN8I/BZwE7AKeAi4oarOv+4kI5Dkw8DvV9W5JP8eoKq+uMCv9RLgT4APASeBbwGfqKqnRlrYayRZCaysqieS/C3gcWAr8FP0ea0sFEm+B4xX1fd72r4C/KCqvty9qV5dVV8cVY0X0702JoB/AHyaBXydr+iZ/LQkYeoH4rdGXcsAtgD3VtWLVfUMcIKpwF8QquqBqjrXbT4CrBllPTN0E3Ciqr5bVS8B9zJ1nReUqjpdVU90z/8SOAYs1j+wsAXY2z3fy9Sb1UJ0M/CnVTWqb+jPmCE/5QPA81X1dE/b+iSHk/xRkg+MqrCL+Fy37HF3zz9nVwPP9exzkoX7g/7TwDd7thfqtV5M1xSYWv4CNgGPdk39XisLRQEPJHk8yY6u7bqqOg1Tb17AtSOr7o1t48JJ4YK9zs2HfJKHkny7z3+9M7JPcOH/sNPA2qraBPxr4DeTvG2B1PxrwI8AP97V+cvTh/U51byuxc3kWie5EzgH3NM1jfRaX8LIr+lsJHkr8DvAF6rqh1z8tbJQvL+q3gt8BLg9yQdHXdBMJHkTcBvwP7qmBX2dm/jzf2+kqm55o/4kS4GPAX+/55gXgRe7548n+VPgBuDQHJb6ikvVPC3JfwW+0W2eBK7v6V4DnBpyaW9oBtd6O/BR4ObqPgwa9bW+hJFf05lKsoypgL+nqvYBVNXzPf29r5UFoapOdY8vJPk6U8tjzydZWVWnu88aXhhpkf19BHhi+vou9Ovc/Ex+Bm4BvlNVJ6cbkqzoPlghyTuBDcB3R1TfBboX/rR/Bny7e34A2JbkqiTrmar5sfmu72KS3Ap8Ebitqv6qp33BXmumPmjdkGR9N3vbxtR1XlC6z5S+Chyrql/pab/Ya2Xkkryl+5CYJG8BPsxUfQeA7d1u24H7R1PhG7rgX/4L+TrDFTCTn4HXrq0BfBD4hSTngPPAZ6vqB/NeWX9fSfLjTC0bfA/4FwBVdTTJfcBTTC2H3L5Q7qzp/CpwFfDgVCbxSFV9lgV8rbs7gT4HHASWAHdX1dERl9XP+4FPAUfS3QYMfAn4RL/XygJxHfD17rWwFPjNqvq9JN8C7kvyGeBZ4OMjrPF1kryZqbuteq9l35/JhcJbKCWpYS7XSFLDDHlJapghL0kNM+QlqWGGvCQ1zJCXpIYZ8pLUsP8Pg0KTR4+0xqsAAAAASUVORK5CYII=\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
}