Matrix Processing¶
Project Goals¶
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.
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!
Note
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!
Expected Output¶
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 matrix
:
╭─ 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.
Note
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.
Adding Functionality¶
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 matrix
:
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 List
of List
s 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
--- -- -- --
Note
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.
Running Checks¶
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 ....
Note
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.
Project Reflection¶
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.
Project Assessment¶
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.
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.