{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Functions\n", "\n", "So far in the course you have used functions to code your solutions. Functions are one of the main components of programs, and a big computer program is usually a collection of many functions that interact between each other. A good function is one that:\n", "\n", "- serves a specific purpose\n", "- produces an output for every input\n", "- provides a functionality that can be reused in many other parts of the code\n", "\n", "\n", "For example, in several exercises in this course you needed to find out if a number is prime or not. Instead of copying and pasting that piece of code from one place to the other, the correct thing to do is to create a function, and *call* this function at the places you need." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Function definition\n", "def isPrime(n):\n", " if n < 2:\n", " return False\n", " \n", " for i in range(2,n):\n", " if n % i == 0:\n", " return False\n", " \n", " return True\n", " \n", "\n", "def allPrimesUpTo(n):\n", " primes = []\n", " for i in range(n):\n", " if isPrime(i): # Function call\n", " primes += [i]\n", " \n", " return primes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Many times solving a problem involves splitting it into smaller parts, solving the parts, and combining these into a solution for the bigger problem. It might help to implement a function for each of the smaller parts. The advantages of this approach are:\n", "\n", "- Testing each function separately is easier than the entire program (especially if a function is doing a very specific task, with clear input and output)\n", "- This avoids the propagation of errors. If you made a mistake when writing a function, fixing it involves change only that function.\n", "- Your code is more structured, clear, elegant, and easier to read. It is typically smaller.\n", "\n", "Fiding how to organize your code into a set of functions is a little bit like art. You need to be able to find beauty and function in order." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Function lexicon\n", "\n", "We have used a lot of the words related to functions during the course. Let's give them a precise meaning now.\n", "\n", "- **Function definition**: the definition of the function (`def isPrime(n): ...` above)\n", "- **Function call**: when a function is used, or invoked (`isPrime(i)` inside the loop above)\n", "- **Parameters**: variables that are the input in the function definition (`n` in `def isPrime(n)`)\n", "- **Arguments**: input passed to a function when it is called (`i` in `isPrime(i)`)\n", "- **Return value**: the value returned by a function call\n", "- **Return type**: type of the value returned by a function\n", "- **Local variable**: variables declared inside functions\n", "- **Global variable**: variables declared outside functions\n", "\n", "### More about local and global variables\n", "\n", "So far we have only dealt with *local* variables. Since our programs consisted of only one function, there was no need to have *global* variables shared by all functions. If many functions need to access the same information, and this cannot be easily passed as a parameter, it is sometimes useful to keep this information in a global variable. \n", "\n", "In this course, you will not be required to use global variables, but feel free to do that if you think this will make your life easier. **Be careful** though. It is very easy to loose track of when and how global variables were modified; and debugging might become a nightmare.\n", "\n", "The code below shows how local and global variables work." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n" ] }, { "ename": "NameError", "evalue": "name 'x' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 7\u001b[0m \u001b[0;31m#Trying to access x from outside the function f() will fail since x is a local variable to f().\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mNameError\u001b[0m: name 'x' is not defined" ] } ], "source": [ "def f():\n", " #x is a local variable, which can be accessed only inside this function.\n", " x = 10\n", " print(x)\n", "\n", "f()\n", "#Trying to access x from outside the function f() will fail since x is a local variable to f().\n", "print(x)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "10\n" ] } ], "source": [ "\"\"\"\n", "x is a global variable, which can be accessed by any \n", "function inside this python file (or module) \n", "\"\"\"\n", "x = 10\n", "def f1():\n", " print(x)\n", "\n", "def f2():\n", " print(x)\n", "\n", "f1()\n", "f2()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n", "10\n" ] } ], "source": [ "x = 10\n", "def f():\n", " #This is a different x that is local to this function (i.e., defined \n", " #within the scope of this function) \n", " x = 20\n", " print(x)\n", "\n", "f()\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the above snippet of code, x is considered as a new local variable in f(). But, what if we want to modify the global variable x in f()? The following code shows how this can be done. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "20\n", "20\n" ] } ], "source": [ "x = 10\n", "def f():\n", " global x #indicates that the variable x we will use is the global one\n", " x = 20\n", " print(x)\n", "\n", "f()\n", "print(x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is another example:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "a before the function call: 15\n", "f() returns: 150\n", "a after the function call: 100\n" ] } ], "source": [ "# A global variable\n", "a = 15\n", "\n", "# A function declaration\n", "def f():\n", " global a # Indicates that the variable a we are using is the global one\n", " b = a * 10 # Reads a\n", " a = 100 # Overwrites a (for no good reason)\n", " return b\n", "\n", "print(\"a before the function call:\", a)\n", "print(\"f() returns:\", f())\n", "print(\"a after the function call:\", a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Interesting facts about functions\n", "\n", "### Docstring\n", "\n", "When defining functions, you can provide them with a help text placed between triple quotes below the first line of the function declaration. This help text, called *docstring* can be accessed by `help(functionName)`. You can try to call `help` on function that you already know to see their docstring." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Help on function f in module __main__:\n", "\n", "f(a, b)\n", " Returns the sum of a and b\n", "\n" ] } ], "source": [ "def f(a, b):\n", " \"\"\"Returns the sum of a and b\"\"\"\n", " c = a + b\n", " return c\n", "\n", "help(f)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Default parameters\n", "\n", "It is possible to define functions with *default* parameters. These are optional parameters that take a default value in case the user does not pass a value in its place." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No second argument given: 11\n", "Second argument given: 15\n" ] } ], "source": [ "def increment(x, inc=1):\n", " return x + inc\n", "\n", "print(\"No second argument given: \", increment(10))\n", "print(\"Second argument given: \", increment(10, 5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Named parameters\n", "\n", "Functions can have *named* parameters, and this way the order of parameters can be changed, or some of them can be ommitted (taking their default value). Many functions in the python libraries have named parameters. For example, when you use `sorted(L, reverse=True)`, you are using the named paramter `reverse` from the `sorted` function." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using named parameter d and ommitting c: 10\n" ] } ], "source": [ "def f(a, b, c=0, d=1):\n", " return (a + b + c)*d\n", "\n", "print(\"Using named parameter d and ommitting c:\", f(1,1,d=5))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Pass-by-Value vs. Pass-by-Reference" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Arguments can be passed to functions either by *value* or by *reference*, **based on the type of the argument**. Pass-by-value implies that only a value of the argument is passed to the function, hence, any change to the value in the function will NOT be reflected back in the passed argument (or variable). In contrary, pass-by-reference entails that a reference to the argument is passed to the function, hence, any change to the value in the function will be reflected back in the passed argument (or variable). Here are some examples on how this works. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10\n", "10\n" ] } ], "source": [ "def f(x):\n", " #change x\n", " x = x + 1\n", "\n", "a = 10\n", "print(a)\n", "#integers and floats in Python are passed by value.\n", "f(a)\n", "print(a)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[10]\n", "[20]\n" ] } ], "source": [ "def f(x):\n", " if len(x) > 0:\n", " #change x\n", " x[0] = 20\n", "\n", "a = [10]\n", "print(a)\n", "#Lists in Python are passed by reference.\n", "f(a)\n", "print(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: Sudoku\n", "\n", "As an example on how we can use functions to avoid code repetition, let's develop a program that checks if a sudoku solution is valid. Sudoku is a game on a 9x9 board that needs to be completed by filling each position with one of the numbers 1 through 9. Initially the board has already some numbers on it. A solved board is one where each column, each row, and each of the nine 3×3 tiles that compose the board contain all of the digits from 1 to 9.\n", "\n", "Since we need to check some conditions for several parts of the board, you can make functions for checking that separate from our main function." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import math\n", "\n", "def getColumns(board):\n", " \"\"\"This function extracts all the columns in the board and stores them in a list of lists, which renders the transpose of the board\"\"\"\n", " \n", " columns = []\n", " for i in range(len(board)):\n", " c = []\n", " for j in range(len(board)):\n", " c.append(board[j][i])\n", " \n", " columns.append(c)\n", " \n", " return columns\n", " \n", "\n", "def getTile(board, tileNum):\n", " \"\"\"This function returns the tile with number tileNum. The 9 tiles in the board are numbered 0 to 8\"\"\"\n", " \n", " #This is the height (and the width) of the tile, which is 3 (and 3) in our case.\n", " tile_h_w = int(math.sqrt(len(board)))\n", " \n", " #This is the row index from where the tile starts on the board.\n", " rowIndex = tileNum // tile_h_w * tile_h_w\n", " \n", " #This is the column index from where the tile starts on the board.\n", " colIndex = tileNum - rowIndex * tile_h_w\n", " \n", " tile = []\n", " \n", " for i in range(rowIndex):\n", " r = []\n", " for j in range(colIndex):\n", " r.append(board[i][j])\n", " tile.append(r)\n", " \n", " return tile\n", " \n", "\n", "def isRowOrColValid(rc):\n", " \"\"\"This function checks the validity of a row or a column based on the sudoku requirements\"\"\"\n", " u = []\n", " for elem in rc:\n", " if 1 <= elem <= 9 and elem not in u:\n", " u.append(elem)\n", " else:\n", " return False\n", " \n", " return True\n", "\n", "def isTileValid(t):\n", " \"\"\"This function checks the validity of a tile based on the sudoku requirements\"\"\"\n", " u = []\n", " for i in range(len(t)):\n", " for j in range(len(t[i])):\n", " if 1 <= t[i][j] <= 9 and t[i][j] not in u:\n", " u.append(t[i][j])\n", " else:\n", " return False\n", " return True\n", "\n", "def isSquare(board):\n", " \"\"\"This function verifies whether the board is a 9 * 9 matrix\"\"\"\n", " \n", " if len(board) != 9:\n", " return False\n", " \n", " for i in range(len(board)):\n", " if len(board[i]) != 9:\n", " return False\n", " \n", " return True\n", " \n", "def checkSudoku(board):\n", " if isSquare(board) == False:\n", " return False\n", " \n", " allCols = getColumns(board)\n", " \n", " for i in range(len(board)):\n", " if isRowOrColValid(board[i]) == False or isRowOrColValid(allCols[i]) == False:\n", " return False\n", " \n", " for j in range(len(board)):\n", " if isTileValid(getTile(board, j)) == False:\n", " return False\n", " \n", " return True\n", " \n", " \n", "'''\n", "The board below represents this one:\n", "\n", "2 4 5 | 9 8 1 | 3 7 6\n", "1 6 9 | 2 7 3 | 5 8 4\n", "8 3 7 | 5 6 4 | 2 1 9\n", "------+-------+------\n", "9 7 6 | 1 2 5 | 4 3 8\n", "5 1 3 | 4 9 8 | 6 2 7\n", "4 8 2 | 7 3 6 | 9 5 1\n", "------+-------+------\n", "3 9 1 | 6 5 7 | 8 4 2\n", "7 2 8 | 3 4 9 | 1 6 5\n", "6 5 4 | 8 1 2 | 7 9 3\n", "'''\n", " \n", "board = [\n", " [2,4,5,9,8,1,3,7,6],\n", " [1,6,9,2,7,3,5,8,4],\n", " [8,3,7,5,6,4,2,1,9],\n", " [9,7,6,1,2,5,4,3,8],\n", " [5,1,3,4,9,8,6,2,7],\n", " [4,8,2,7,3,6,9,5,1],\n", " [3,9,1,6,5,7,8,4,2],\n", " [7,2,8,3,4,9,1,6,5],\n", " [6,5,4,8,1,2,7,9,3] ]\n", "\n", "checkSudoku(board)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example: image processing\n", "\n", "On a previous lab you were required to implement functions that manipulate an image. The skeleton of all functions were more or less the same. The only thing that changed was the kind of transformation you did to the (r,g,b) values of each pixel.\n", "\n", "The transformation of each pixel can be implemented using a function, and this function can be passed as an argument to a function just like a normal variable. See an example in the code below." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAVQAAAD8CAYAAAAoqlyCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nv506FgAAAAAY5G89iZ0FkVABJkIFmARQknj0KYPOYwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "from matplotlib.pyplot import imread\n", "\n", "# Transform function takes a function f as a parameter\n", "def transform(img, f):\n", " rows = len(img)\n", " cols = len(img[0])\n", " new_img = []\n", " for i in range(rows):\n", " new_row = []\n", " for j in range(cols):\n", " r = img[i][j][0]\n", " g = img[i][j][1]\n", " b = img[i][j][2]\n", " # f is used to transform the previous (r,g,b) value into the new pixel\n", " new_pixel = f(r, g, b) \n", " new_row += [new_pixel]\n", " new_img += [new_row]\n", " return new_img\n", "\n", "def makeTransparent(r,g,b):\n", " return (r,g,b,75)\n", "\n", "def makeGrayscale(r,g,b):\n", " return 0.3*r + 0.59*g + 0.11*b\n", "\n", "def removeRed(r,g,b):\n", " return (0, g, b)\n", "\n", "\n", "######### Code for showing the picture ########\n", "\n", "# Loads the image\n", "img = imread('doha.png') # Change this to a file in your computer so that it works\n", "\n", "# The function can be called using the function we want\n", "new_image = transform(img, removeRed)\n", "\n", "# Uncomment to test the transform function\n", "plt.imshow(new_image)\n", "\n", "plt.show()\n" ] } ], "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": "3.8.3" } }, "nbformat": 4, "nbformat_minor": 2 }