python_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4-1 Python and Jupyter Notebook.\n",
    "Welcom To Jupyter Notebook Guys!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Jupyter notebook is very convenient for a python developer to test their code.\n",
    "Come and be one of us!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter  shortcuts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "up/down arrow keys to move, enter to edit\n",
    "\n",
    "Esc to exit edit mode\n",
    "\n",
    "Shift + enter to run code\n",
    "\n",
    "a/b to add cell above/below\n",
    "\n",
    "dd to delete cell\n",
    "\n",
    "1/2/3... to add comment\n",
    "\n",
    "press h for more shortcuts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "import cv2\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "img = cv2.imread('images/duckietown.jpg')\n",
    "dst1 = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n",
    "plt.subplot(121), plt.imshow(dst1, cmap = 'brg')\n",
    "plt.title('Original Image')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Basic Syntax "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print \"CSP2017\"\n",
    "print \"Hello Duckietown!\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=1\n",
    "print(x)\n",
    "print \"x =\",x\n",
    "print \"I like python\",\n",
    "print \"and I like Duckietown\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in range(1, 10):\n",
    "    print i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A littile challenge : How do you print 1 2 3 4 5 ... 9 ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=['B', 'L', 'F', 'F']\n",
    "print x\n",
    "print (x[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "route = \"\"\n",
    "for i in x:\n",
    "    if i != 'B':\n",
    "        route += i\n",
    "print route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=0\n",
    "if x==1 :\n",
    "    print(\"x equals 1\")\n",
    "else : \n",
    "    print(\"x doesn't equal 1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Playing around with Numpy & Scipy  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy & Scipy are commonly used library for math and science purpose\n",
    "### They are powerful and will save us a lot of trouble"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## array declaration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x = np.array([1, 2, 3])\n",
    "for i in range(x.shape[0]) :\n",
    "    print x[i]\n",
    "print x.shape, type(x), type(x[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Guess what is the output of x.shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "for i in range(x.shape[0]) :\n",
    "    for j in range(x.shape[1]) :\n",
    "        print x[i][j]\n",
    "print x.shape, type(x), type(x[0]), type(x[0][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x = np.zeros((3, 4))\n",
    "print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.ones((3, 4))\n",
    "print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(10, 30 ,5)\n",
    "print x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## array manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "x = np.arange(15)\n",
    "print x\n",
    "x = x.reshape(3,5)\n",
    "print x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## visualization (matplotlib) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.plot([1,2,3], [1,2,3], 'ro')\n",
    "#toggle axis\n",
    "#plt.axis([0, 5, 0, 5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "x = np.arange(100).reshape(10,10)\n",
    "print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "plt.matshow(x)\n",
    "#toggle for gray colormap\n",
    "#plt.matshow(x, cmap=plt.cm.gray)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Let's use it to do our math homework! "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculus "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import integrate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def f(x) :\n",
    "    return x**2\n",
    "result, err = integrate.quad(f, 0, 1)\n",
    "print result\n",
    "print err"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# numpy meshgrid "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nx, ny = (3,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = np.linspace(0, 1, nx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y = np.linspace(0, 1, ny)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xv, yv = np.meshgrid(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "yv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## example for function evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "x = np.arange(-5, 5, 0.1)\n",
    "y = np.arange(-5, 5, 0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xx, yy = np.meshgrid(x, y, sparse = True)\n",
    "#print xx\n",
    "#print yy\n",
    "z = np.sin(xx*2+yy*2)/(xx*2+yy*2)\n",
    "h = plt.contourf(x, y, z)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.linspace(0,5,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.arange(0,5,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## unravel_index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### return value is row and col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.unravel_index([1,2,3,4],(4,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What is the first and second output? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.unravel_index([1,2,3,4],(4,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (array([?, ?, ?, 1]), array([?, ?, ?, ?]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Check Point!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's have some exciting challenge!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 1. Use for loop to Print a multiplication table (from 1~9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# It should look like this \n",
    "# 1*1=1 1*2=2 . . .\n",
    "# 2*1=2 2*2=4 . . ."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hint : You should use two layer of for loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for i in range(1, 9):\n",
    "    print i, '*', i, '=', i*i"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Draw z = cos( x^2 + y^2 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hint : We have done draw z = sin(x*2 + y*2)/(x*2 + y*2), just modify the code!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Step:\n",
    "    #1. import numpy & matplotlib.pyplot\n",
    "    #2. meshgrid the x, y\n",
    "    #3. use the result of meshgrid to draw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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