Text Processing¶

Project Goals¶

This engineering effort invites you to investigate how to use various discrete structures, such as the list and the set, to analyze all of the text in a file. This means that your program, called textanalysis, must complete tasks like (i) inputting a text file and storing its contents in a string, (ii) extracting the paragraphs from the text file, and (iii) extracting the words in each paragraph. Next, textanalysis should identify the unique words in each paragraph, the unique words in the entire document, and the words that are evident in all of the paragraphs of the document. To accomplish these tasks your program will use external packages, such as supervenn, that make it possible to better visualize and understand the relationships between the sets that represent each of the paragraphs in the document. As you enhance your knowledge of discrete structures and your technical skills, you will continue to program with VS Code, the Python programming language, and the Poetry package manager. Ultimately, your goal for this project is to create a program that can automatically analyze a complete text document.

Project Access¶

If you are a student enrolled in a Computer Science class at Allegheny College, you can access this assignment by clicking the link provided to you in Discord. Once you click this link it will create a GitHub repository that you can clone to your computer by following the general-purpose instructions in the description of the technical skills. Specifically, you will need to use the git clone command to download the project from GitHub to your computer. Now you are ready to add source code and documentation to the project!

Expected Output¶

As part of this assignment, you are going to implement a textanalysis program that takes as input a complete document stored in a text file and then performs an automated analysis of the document's contents. For instance, here is an excerpt of a text file that you could input into your program. Notably, the entire file has a total of 5 paragraphs that consist of 19 lines, not including the blank lines that separate the paragraphs.

Make enables the end user to build and install your package without knowing
the details of how that is done --- because these details are recorded in the
makefile that you supply.

Make figures out automatically which files it needs to update, based on which
source files have changed. It also automatically determines the proper order
for updating files, in case one non-source file depends on another non-source
file.

If you run the finished version of the textanalysis program with the command poetry run textanalysis --input-file text/input_one.txt --analyze, where text/input_one.txt is the file that contains the above excerpt and the --analyze flag tells the program to perform a textual analysis, you should see the following output in your terminal window. It is worth noting that the program will also produce additional output that explains how the supervenn package analyzed the sets representing each paragraph in order to produce its graphical representation of the text document. You can learn more about both the way in which supervenn uses the FrozenSet discrete structure and how you should interpret the visualization that supervenn produces by visiting the supervenn web site. To see the visualization produced by supervenn you should use a graphics preview program to load the file graphics/set-visualization.png. For the input file called text/input_one.txt, what trends does the visualization show you about the overlap between the words in the document's paragraphs?

✨ Let's characterize the file and its words!

        The input file contains 23 lines, including blank lines!
        The input file contains 19 lines, not including blank lines!
        The input file contains 5 paragraphs!
        The input file contains 118 unique words across all sets!
        The words that are found across all sets are: {'Make', 'to'}

🖌 Saving the visualization in graphics/set-visualization.png

Adding Functionality¶

If you study the file textanalysis/textanalysis/main.py you will see that it has all of the functionality needed to implement the entire command-line interface. Furthermore, the file textanalysis/textanalysis/visualize.py shows that it also has all of the functions needed to use supervenn the create a visualization of the word-level overlap in the provided text file. For this assignment, the textanalysis/textanalysis/extract.py file contains the TODO markers that explain how to implement the following functions.

• def extract_lines_including_blanks(input_lines: str) -> List[str]:
• def extract_lines_not_including_blanks(input_lines: str) -> List[str]:
• def extract_paragraphs(input_lines: str) -> List[str]:
• def extract_unique_words_paragraphs(paragraphs: List[str]) -> List[Set[str]]:
• def extract_unique_words(sets: List[Set[str]]) -> Set[str]:
• def extract_common_words(sets: List[Set[str]]) -> Set[str]:

You can study the source code in the textanalysis/textanalysis/main.py file to see the input and expected output of each of the above functions. For instance, here is the way in which the main function in the main.py file calls the first function in the above list: input_line_count = len(extract.extract_lines_including_blanks(input_text)). Note that it accepts as input the str called input_text that contains the textual input from the file specified on the command-line interface.

The following code segment provides the complete implementation of the extract_lines_including_blanks function. The function signature on line 1 shows that extract_lines_including_blanks will accept a str as input and return a List of str as output, with each index in the List being a single line inside of the input str called input_lines. Line 3 of this function uses the splitlines function to create the required List of str and then line 4 returns it. It is important to note that this function does not filter out the blank lines that splitlines returns — which is the job of the def extract_lines_not_including_blanks(input_lines: str) -> List[str]: function that you also need to create! Once you have implemented the required functions, the textanalysis should produce the expected output.

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def extract_lines_including_blanks(input_lines: str) -> List[str]:
    """Extract all of the lines, including the blanks lines."""
    lines_text = input_lines.splitlines()
    return lines_text

Running Checks¶

As you continue to add and confirm the correctness of datauniqifier's functionality, you should study the source code in the pyproject.toml file. This file contains the specification of several tasks that will help you to easily run checks on your Python source code. Now, you can run commands like poetry run task lint to automatically run all of the linters designed to check the Python source code in your program and its test suite. You can also use the command poetry run task black to confirm that your source code adheres to the industry-standard format defined by the black tool. If it does not adhere to the standard then you can run the command poetry run fixformat and it will automatically reformat the source code. By following a tutorial, you can configure VS Code to use the black tool to automatically reformat the source code when you save a file.

Along with running tasks like poetry run task lint or poetry run task mypy, you can leverage the relevant instructions in the technical skills to run the command gatorgrade --config config/gatorgrade.yml to check your work. If your work meets the baseline requirements and adheres to the best practices that proactive programmers adopt you will see that all the checks pass when you run gatorgrade. You can study the config/gatorgrade.yml file in your repository to learn how the GatorGrade program runs GatorGrader to automatically check your program and technical writing. If there are checks that did not pass correctly, which you can see in either your terminal window or through the logs available from GitHub Actions, then you should read them carefully and take the suggested steps to repair the problems.

Project Reflection¶

Once you have finished both of the previous technical tasks, you can use a text editor to answer all of the questions in the writing/reflection.md file. For instance, you should provide the output of the Python program in a fenced code block, explain the meaning of the Python source code segments that you implemented, and answer all of the other questions about your experiences in completing this project. One of the main goals of the reflection is for you to explain the trends that you see in the different provided text files. You should also discuss how the textanalysis program uses discrete structures like the list and the set to automatically characterize the text of a complete document.

Project Assessment¶

Since this project is an engineering effort, it is aligned with the evaluating and creating levels of Bloom's taxonomy. You can learn more about how a proactive programming expert will assess your work by examining the assessment strategy. From the start to the end of this project you may make an unlimited number of reattempts at submitting source code and technical writing that meet every aspect of the project's specification.

Seeking Assistance¶

Emerging proactive programmers who have questions about this project are invited to ask them in either the GitHub discussions forum or the Proactive Programmers Discord server. Before you ask your question, please read the advice concerning how to best participate in the Proactive Programmers community. If you find a mistake in this project, please describe it and propose a solution by creating an issue in the GitHub Issue Tracker.