$1$ Description

In this assignment, you will define a WordProcessor class based on functions defined in a given module, which is stored in a file words.py$.$ Based on your WordProcessor definition, your WordProcessor object should be able to process a textual file by counting the number of words and extracting the vocabulary of words in the file content. A word, in this assignment, is defined as a sequence of adjacent letters separated by punctuations, numbers, or white spaces. $($There is a

function text to words$()$ in words.py capable of identifying words from a string. You can apply it in your class definition to extract words, if necessary.$)$ Note that the vocabulary extracted by the WordProcessor object must consist of words without duplicates. $($This implies you have to remove repeated words when building the vocabulary. The function remove adjacent dups$()$ defined in words.py should be helpful for your vocabulary construction. $)$ In addition to words.py$,$ I also provide a skeleton code file wp skeleton.py$,$ on which you can

define your WordProcessor class.

$2$ Functions in words.py

In this python file words.py you can see three functions defined.

def load string from file$($filename$)$:

#code is missing here

def text to words$($the text$)$:

#code is missing here

def remove adjacent dups$($xs$)$:

#code is missing here

You should practice using the three functions in your Python shell. The following I list some

examples I used in my Python shell.

$>>>$ file content $=$ words.load string from file$("$sawyer$.$txt$")$

$>>>$ wds $=$ words.text to words$($file content$)$

$>>>$ print$($wds$[$:$10])$

$[\text{'}$the$\text{'},$ 'adventures', $\text{'}$of$\text{'},$ 'tom', 'sawyer', 'twain', 'mark', 'electronic', 'text', 'center'$]$

Notice that I have a text file sawyer.txt saved in my current working directory. More

examples:

$>>>$ wds$2=$ words.text to words$("$this is a different example yes!"$)$

$>>>$ print$($wds$2)$

$[\text{'}$this$\text{'},\text{'}$is$\text{'},\text{'}$a$\text{'},$ 'different', 'example', 'yes'$]$

$>>>$ wds$2.$sort$()$

$>>>$ print$($wds$2)$

$[\text{'}$a$\text{'},$ 'different', 'example', $\text{'}$is$\text{'},$ 'this', 'yes'$]$

$>>>$ wds$3=$ words.text to words$("$yes this is a a different example yes!"$)$

$>>>$ wds$3.$sort$()$

$>>>$ print$($wds$3)$

$[\text{'}$a$\text{'},\text{'}$a$\text{'},$ 'different', 'example', $\text{'}$is$\text{'},$ 'this', 'yes', 'yes'$]$

$>>>$ wds$3=$ words.remove adjacent dups$($wds$3)$

$>>>$ print$($wds$3)$

$[\text{'}$a$\text{'},$ 'different', 'example', $\text{'}$is$\text{'},$ 'this', 'yes'$]$

Notice that I used a list method sort$()$ in the above code. In the example

$2$

$>>>$ wds$3.$sort$()$

The sort$()$ method is able to sort the words maintained on the list wds$3$ in place.

$3$ You need to implement your own wp$.$py based on the given wp skeleton.py

Here is the partial code in wp skeleton.py :

import words class WordProcessor: def

init $($self$)$: def process$($self$,$ filename$)$:

#more code here for other methods

When you define the class methods, you can apply the three functions defined in the words.py$.$

You should import words in your class definition so that you can apply the functions. For

example, you can

import words class

WordProcessor:

#some code here

def process$($self$,$ filename$)$:

#some code applying a function in words module file content $=$

words.load string from file$($filename$)$

#more code should follow

The other part of the given code is for testing purpose. Feel free to extend the testing code.

wp $=$ WordProcessor$()$ wp$.$process$("$brooks$.$txt$")$

#display the number of words in the text file print$($wp$.$getWordcount$())$

#display the vocabulary of words in the text file print$($wp$.$getVocabulary$())$

Based on the content of brooks.txt$,$ which is a textual file, the output when running wp$.$py will

be like

$ python$3$ wp$.$py

$31$

$[\text{'}$and$\text{'},\text{'}$be$\text{'},$ 'conceal', 'continue', 'flowcharts', $\text{'}$i$\text{'},\text{'}$ll$\text{'},\text{'}$me$\text{'},$ 'mystified',

'need', 'obvious', 'shall', 'show', $\text{'}$t$\text{'},$ 'tables', 'they', $\text{'}$to$\text{'},$ 'usually', 'won', 'your'$]$