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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!

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:

Usage: matrix [OPTIONS]

  Read in a matrix and count the number of negative numbers.

Options:
  --matrix-dir PATH
  --matrix-file PATH
  --install-completion  Install completion for the current shell.
  --show-completion     Show completion for the current shell, to copy it
                        or customize the installation.

  --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 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
---  --  --  --
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 the following section that specifies different executable tasks:

[tool.taskipy.tasks]
black = { cmd = "black matrix tests --check", help = "Run the black checks for source code format" }
flake8 = { cmd = "flake8 matrix tests", help = "Run the flake8 checks for source code documentation" }
mypy = { cmd = "poetry run mypy matrix", help = "Run the mypy type checker for potential type errors" }
pydocstyle = { cmd = "pydocstyle matrix tests", help = "Run the pydocstyle checks for source code documentation" }
pylint = { cmd = "pylint matrix tests", help = "Run the pylint checks for source code documentation" }
test = { cmd = "pytest -x -s", help = "Run the pytest test suite" }
test-silent = { cmd = "pytest -x --show-capture=no", help = "Run the pytest test suite without showing output" }
all = "task black && task flake8 && task pydocstyle && task pylint && task mypy && task test"
lint = "task black && task flake8 && task pydocstyle && task pylint"

This section makes 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 the black tool. If it does not adhere to the standard then you can run the command poetry run black matrix tests and it will automatically reformat the source code.

Along with running tasks like poetry run task lint, you can leverage the relevant instructions in the technical skills to enter into a Docker container and run gradle grade to check your work. If gradle grade shows that all checks pass, you will know that you made progress towards correctly implementing and writing about matrix.

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 like this:

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.


Updated: 2021-10-03   Created: 2021-09-16
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