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Computing Averages

Project Goals

This programming project invites you to combine what you learned about the basics of Python programming to implement a useful program that computes the average of all of the numbers in a file that is provided as input to a program. The program will input the numerical values in a file, iterate through them, and return the average (i.e., arithmetic mean) of all the values. Along with adding documentation to the provided source code, you will create your own Python functions that use both iteration constructs and conditional logic to implement a correct program that passes the test suite and all checks. As you enhance your technical skills, you will program with 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

This project invites you to implement a program called average that performs arithmetic. The program accepts through its command-line a file that contains integer values encoded as text. If you run the program with the command poetry run average --dir input --file numbers.txt it produces this output:

😃 Computing the average of numbers in a file called input/numbers.txt!

😉 Phew, that was hard work!

✨ The average of the input values is -0.95

Although this example shows the average program performing its computation with the numbers.txt file in the input directory, it should work in a general-purpose fashion for any text file that contains integer numbers aligned in a single row like:

-19
-24
-81
12
16

To learn more about how to run this program, you can type the command poetry run average --help to see the following output showing how to use average:

Usage: average [OPTIONS]

  Process a file by computing the average of all the numbers.

Options:
  --dir PATH
  --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 should add all of the missing features to ensure that average produces the expected output. Once the program is working correctly, it should produce all of the expected output described in this section.

Note

Don't forget that if you want to run the average program you must use your terminal window to first go into the GitHub repository containing this project and then go into the average directory that contains the project's source code. Finally, remember that before running the program you must run poetry install to add the dependencies.

Adding Functionality

If you study the file average/average/main.py you will see that it has many TODO markers that designate the parts of the program that you need to implement before average will produce correct output. If you run the provided test suite with the command poetry run task test you will see that it produces output like the following:

    def test_average_computation_five_numbers():
        """Confirm that it is possible to average together five non-zero numbers."""
        number_list = """-72
            29
            61
            -42
            44"""
        average_value = main.compute_average(number_list)
>       assert average_value == ((-72 + 29 + 61 + -42 + 44) / 5)
E       assert 0 == (((((-72 + 29) + 61) + -42) + 44) / 5)

Note that this test case fails because of the fact that, by default, the compute_average function returns 0 instead of the correct arithmetic mean of the numbers specified in the number_list variable. You will need to add source code to the compute_average function so that it correctly calculates the average of the input values!

In summary, you should implement the following functions for the average program:

  • def compute_average(contents: str) -> float:
  • def average(dir: Path = typer.Option(None), file: Path = typer.Option(None)) -> None:

It is worth noting that the compute_average function accepts as input a str that is a one-number-per-line encoding of the file that contains the integer numbers. This means that compute_average will need to iterate through each line in the file and convert the text-based encoding of the number to an int. The compute_average function should also handle the circumstance in which the user-provided file (i.e., numbers.txt) does not have any numbers inside of it! If there were no numbers in the file, then the function can return -1 to indicate that it did not compute an average. As you are finishing your implementation of the compute_average function, you should also ensure that, if all of the numbers inside of the file are 0, then it returns an average of 0.

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 average tests --check", help = "Run the black checks for source code format" }
flake8 = { cmd = "flake8 average tests", help = "Run the flake8 checks for source code documentation" }
mypy = { cmd = "poetry run mypy average", help = "Run the mypy type checker for potential type errors" }
pydocstyle = { cmd = "pydocstyle average tests", help = "Run the pydocstyle checks for source code documentation" }
pylint = { cmd = "pylint average 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 average 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 average.

If your program has all of the anticipated functionality, you can run the command poetry run task test and see that the test suite produces output like this:

collected 5 items

tests/test_average.py .....

You will know that the compute_average function correctly returns 0 when all of the inputs are 0 if the following test case passes:

1
2
3
4
5
6
7
8
9
def test_average_computation_five_numbers_all_zero():
    """Confirm that it is possible to average together five zero numbers."""
    number_list = """0
        0
        0
        0
        0"""
    average_value = main.compute_average(number_list)
    assert average_value == 0

Lines 3 through 7 of this test case define the number_list variable as one that contains a list of 0 values separated by newlines. The purpose of number_list is to represent the string that would arrive from the input file if a person ran the average program on the command-line. Line 8 of this test case calls the compute_average function with the number_list as the input and stores the output in a variable called average_value. Finally, line 9 confirms that compute_average calculates the average of the input as 0.

You will know that the compute_average function correctly returns -1 when there is no input to the function if the follow test case passes:

1
2
3
4
5
def test_average_computation_no_provided_numbers():
    """Confirm that it is possible to average together no numbers."""
    number_list = ""
    average_value = main.compute_average(number_list)
    assert average_value == -1

On line 3 in the above source code, this test defines number_list as an empty string, denoted by "". Finally, on line 4 it calls the compute_average function with number_list as its input and on line 5 it confirms that the computed average_value is -1, as required by the specification of the function under test.

Once all of the test cases pass, you can run the all of the automated checks by typing poetry run task all in your terminal and confirming that there are no errors in the output. If all of the checks pass, then you can run the program with the command poetry run average --dir input --file numbers.txt and then confirm that it produces the expected output, including the average of -0.95.

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 different implementations of the Python function called compute_average, and answer all of the other questions about your experiences in completing this project. One specific goal for this reflection is to ensure that you understand how to accept as input the textual representation of a list of numbers, convert that to a list of numerical values, and then perform an average computation on those values.

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.

Note

Before you finish all of the required deliverables 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.

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: 2022-03-11   Created: 2021-09-10
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