This project invites you to implement a
matrix program that processes a two-dimensional "list of lists" called a matrix. The main feature of the program is that it can count the number of negative numbers inside of a matrix that it inputs from a file specified on its command-line. In addition to implementing part of the command-line interface for
matrix you will add source code that traverses the input matrix and counts the negative numbers. As you enhance your technical skills by implementing and documenting a Python program, you will continue to explore tools such as VS Code and a terminal window and the Python programming language and the Poetry package manager.
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!
If you are an emerging proactive programmer who is not enrolled in a Computer Science class at Allegheny College, you can still work on this assignment! To get started, you should click the "Use this template" button in the matrix-starter GitHub repository and create your own version of this project's source code. After creating your GitHub repository, you can follow all of the other steps!
As previously mentioned, this project invites you to implement a matrix processing program program called
matrix. The program accepts through its command-line interface a
matrix-file parameter that designates a file with a matrix inside of it and the
matrix-dir that is the directory containing the specified file. For this project, you should use the
matrix.txt file inside of the
input directory that contains these contents:
100,19,9,9 10,9,8,7 6,4,2,-1 4,2,0,-1 3,0,-1,-2 -1,-1,-2,-5
After correctly adding all of the required features, you can use Poetry to run the program with the command
poetry run matrix --matrix-dir input --matrix-file matrix.txt and see that it it produces the following output:
✨ Searching for negative numbers in a matrix stored in input/matrix.txt! 📦 The matrix contains the following integer values: --- -- -- -- 100 19 9 9 10 9 8 7 6 4 2 -1 4 2 0 -1 3 0 -1 -2 -1 -1 -2 -5 --- -- -- -- 🧮 The matrix contains 8 negative numbers!
To learn more about how to run this program, you can type the command
poetry run matrix --help to see the following output showing how to use
╭─ Options ─────────────────────────────────────────────────────────────╮ │ --matrix-dir PATH [default: None] │ │ --matrix-file PATH [default: None] │ │ --install-completion [bash|zsh|fish|powe Install completion │ │ rshell|pwsh] for the specified │ │ shell. │ │ [default: None] │ │ --show-completion [bash|zsh|fish|powe Show completion for │ │ rshell|pwsh] the specified shell, │ │ to copy it or │ │ customize the │ │ installation. │ │ [default: None] │ │ --help Show this message │ │ and exit. │ ╰───────────────────────────────────────────────────────────────────────╯
Please note that the provided source code does not contain all of the functionality to produce this output. As explained in the next section, you are invited to add the missing features and ensure that
matrix produces the expected output. Once the program is working correctly, it should produce output similar to that shown in this section.
Don't forget that if you want to run the
matrix program you must use your terminal to first go into the GitHub repository containing this project and then go into the
matrix directory that houses the project's code. Finally, remember that before running the program you must run
poetry install to add the dependencies.
If you study the file
matrix/matrix/main.py you will see that it has many
TODO markers that designate the parts of the program that you need to implement before
matrix will produce correct output. Specifically, you should implement these functions in
def confirm_valid_file(file: Path) -> bool
def count_negatives_in_matrix(matrix: List[List[int]]) -> int
def matrix(matrix_dir: Path = typer.Option(None), matrix_file: Path = typer.Option(None)) -> None
Once you have correctly resolved all of the
TODO markers in the
matrix program, it should produce the expected output described in the previous section. The most important function you need to implement for this project is
count_negatives_in_matrix, which has a signature indicating that it accepts as input a
Lists that contain
int values (i.e.,
List[List[int]]) and returns as output an
int for the number of negative numbers in the file. You may assume that the
matrix parameter that is input to the
confirm_valid_file function is organized such that each row and column of the
matrix is sorted in a non-increasing order.
The following excerpt of output created by a correct implementation of
matrix, which was produced through the use of the python-tabulate package, illustrates the organization of the input matrix. For instance, note that the first column of the matrix contains the values
100, 10, 6, 4, 3, -1 which are organized in a non-increasing manner from the top to the bottom of the matrix. It is also worth noting that all of the other column in the matrix are also organized in the same non-increasing fashion. Moreover, the third-from-the-top row of the matrix contains the values
6, 4, 2, -1 while the last row contains
-1, -1, -2, -5 which are also organized in a non-increasing style.
--- -- -- -- 100 19 9 9 10 9 8 7 6 4 2 -1 4 2 0 -1 3 0 -1 -2 -1 -1 -2 -5 --- -- -- --
Before you start to implement the source code required by this project is worth pausing to remember that the instructor will give advance feedback to any learner who requests it through GitHub and Discord at least 24 hours before the project's due date! Seriously, did you catch that? This policy means that you can have a thorough understanding of ways to improve your project before its final assessment! To learn more about this opportunity, please read the assessment strategy for this site.
If you study the source code in the
pyproject.toml file you will see that it includes a section that specifies different executable tasks like
lint. If you are in the
compare directory that contains the
pyproject.toml file and the
poetry.lock file, the tasks in this section make it easy to 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
black. If it does not meet this standard, then you can run the command
poetry run black compare tests or, alternatively,
poetry run task fixformat, and it will reformat the Python source code!
Along with running tasks like
poetry run task lint, 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 your program has all of the anticipated functionality, you can run the command
poetry run task test and see that the test suite passes and produces output as shown in the following output. Can you think of any additional tests to add to the test suite? If you can, then add them so that you can increase your confident in program correctness!
collected 3 items tests/test_matrix.py ....
Don't forget that when you commit source code or technical writing to your GitHub repository for this project, it will trigger the run of a GitHub Actions workflow. If you are a student at Allegheny College, then running this workflow consumes build minutes for the course's organization! As such, you should only commit to your repository once you have made substantive changes to your project and you are ready to confirm its correctness. Before you commit to your repository, you can still run checks on your own computer by either using Poetry or Docker and GatorGrader.
Once you have finished all 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 tested, compare and contrast the performance of different implementations of the matrix processing algorithm, and answer all of the other questions about your experiences in completing this project.
Since this is a programming project, it is aligned with the applying and analyzing 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 all aspects of the project's specification.
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.