OPS435 Assignment 1 for Section C

Overview: du Improved

du is a tool for inspecting directories. It will return the contents of a directory along with how much drive space they are using. However, it can be parse its output quickly, as it usually returns file sizes as a number of bytes:

user@host ~ $ du --max-depth 1 /usr/local/lib

164028	/usr/local/lib/heroku
11072	/usr/local/lib/python2.7
92608	/usr/local/lib/node_modules
8	/usr/local/lib/python3.8
267720	/usr/local/lib

You will therefore be creating a tool called duim (du improved>. Your script will call du and return the contents of a specified directory, and generate a bar graph for each subdirectory. The bar graph will represent the drive space as percent of the total drive space for the specified directory. An example of the finished code your script might produce is this:

user@host ~ $ ./duim.py /usr/local/lib

 61 % [============        ] 160.2 MiB	/usr/local/lib/heroku
  4 % [=                   ] 10.8 MiB	/usr/local/lib/python2.7
 34 % [=======             ] 90.4 MiB	/usr/local/lib/node_modules
  0 % [                    ] 8.0 kiB	/usr/local/lib/python3.8
Total: 261.4 MiB 	 /usr/local/lib

The details of the final output will be up to you, but you will be required to fulfill some specific requirements before completing your script. Read on...

Assignment Requirements

Permitted Modules

Your python script is allowed to import only the os, subprocess and sys modules from the standard library.

Required Functions

You will need to complete the functions inside the provided file called duim.py. The provided checkA1.py will be used to test these functions.

• call_du_sub() should take the target directory as an argument and return a list of strings returned by the command du -d 1<target directory>.
  • Use subprocess.Popen.
  • '-d 1' specifies a max depth of 1. Your list shouldn't include files, just a list of subdirectories in the target directory.
  • Your list should not contain newline characters.
• percent_to_graph() should take two arguments: percent and the total chars. It should return a 'bar graph' as a string.
  • Your function should check that the percent argument is a valid number between 0 and 100. It should fail if it isn't. You can raise ValueError in this case.
  • total chars refers to the total number of characters that the bar graph will be composed of. You can use equal signs = or any other character that makes sense, but the empty space must be composed of spaces, at least until you have passed the first milestone.
  • The string returned by this function should only be composed of these two characters. For example, calling percent_to_graph(50, 10) should return:

   '=====     '

• create_dir_dict should take a list as the argument, and should return a dictionary.
  • The list can be the list returned by call_du_sub().
  • The dictionary that you return should have the full directory name as key, and the number of bytes in the directory as the value. This value should be an integer. For example, using the example of /usr/local/lib, the function would return:

   {'/usr/local/lib/heroku': 164028, '/usr/local/lib/python2.7': 11072, ...}

Additional Functions

You may create any other functions that you think appropriate, especially when you begin to build additional functionality. Part of your evaluation will be on how "re-usable" your functions are, and sensible use of arguments and return values.

Use of GitHub

You will be graded partly on the quality of your Github commits. You may make as many commits as you wish, it will have no impact on your grade. The only exception to this is assignments with very few commits. These will receive low marks for GitHub use and may be flagged for possible academic integrity violations. Assignments that do not adhere to these requirements may not be accepted.

Professionals generally follow these guidelines:

• commit their code after every significant change,
• the code should hopefully run without errors after each commit, and
• every commit has a descriptive commit message.

After completing each function, make a commit and push your code.

After fixing a problem, make a commit and push your code.

GitHub is your backup and your proof of work.

These guidelines are not always possible, but you will be expected to follow these guidelines as much as possible. Break your problem into smaller pieces, and work iteratively to solve each small problem. Test your code after each small change you make, and address errors as soon as they arise. It will make your life easier!

Coding Standard

Your python script must follow the following coding guide:

• PEP-8 -- Style Guide for writing Python Code

Documentation

• Please use python's docstring to document your python script (script level documentation) and each of the functions (function level documentation) you created for this assignment. The docstring should describe 'what' the function does, not 'how' it does.
• Your script should also include in-line comments to explain anything that isn't immediately obvious to a beginner programmer. It is expected that you will be able to explain how each part of your code works in detail.
• Refer to the docstring for after() to get an idea of the function docstrings required.

Authorship Declaration

All your Python code for this assignment must be placed in the provided Python file called assignment1.py. Do not change the name of this file. Please complete the declaration as part of the docstring in your Python source code file (replace "Student Name" with your own name).

Submission Guidelines and Process

Clone Your Repo (ASAP)

The first step will be to clone the Assignment 1 repository. The invite link will be provided to you by your professor. The repo will contain a check script, a README file, and the file where you will enter your code.

The First Milestone (due February 14)

For the first milestone you will have two functions to complete.

• call_du_sub will take one argument and return a list. The argument is a target directory. The function will use subprocess.Popen to run the command du -d l <target_directory>.
• percent_to_graph will take two arguments and return a string.

In order to complete percent_to_graph(), it's helpful to know the equation for converting a number from one scale to another.

(y_out - y_min) / (y_max - y_min) = (x_in - x_min) / (x_max - x_min)

In this equation, ``x`` refers to your input value percent and ``y`` will refer to the number of symbols to print. The max of percent is 100 and the min of percent is 0. Be sure that you are rounding to an integer, and then print that number of symbols to represent the percentage. The number of spaces that you print will be the inverse.

Test your functions with the Python interpreter. Use python3, then:

   import duim
   duim.percent_to_graph(50, 10)

Test with more values, or use the check script.

Second Milestone (due February 21)

For the second milestone you will have one more function to complete.

• create_dir_dict will take your list from call_du_sub and return a dictionary.
  • Every item in your list should create a key in your dictionary.
  • Your dictionary values should be a number of bytes.
  • Again, test using your Python interpreter.

Additional Features

After completing the above, you are expected to add some additional (two or more) functions providing useful information. Some programs you might want to look at are:

• screenfetch/neofetch
• top/htop/Bashtop

It is expected that the additional features you provided should be useful, non-trivial, they should not require super-user privileges and should not require the installation of additional modules or packages. In this part of the assignment, it is better to try for something useful and fail than it is to implement something trivial! I am looking for evidence that you have worked with Linux machines and know what kinds of information are useful to see at a glance.

You might consider:

• Network information/IP addresses
• The state of some important daemons/systemd services
• process information
• information about online users
• number of packages installed
• cpu load

You may even choose to make the output more attractive/colourful etc. If so, you are permitted to use more modules than those specified above, but make sure that the required functions still succeed as they are. You may want to look into default arguments, ask me for clarification if you're interested.

The Assignment (due March 7, 11:59pm)

• As stated before, your code will be inside the file "assignment1.py". Begin by completing the required functions, committing your changes as you go. Complete and test each function before moving to the next.
• When you have completed the task, make sure that all your changes have been committed and pushed to GitHub. In addition, you will submit assignment1.py to Blackboard.

Rubric

Task	Maximum mark	Actual mark
Program Authorship Declaration	5
Check script passed	20
given functions design	5
df/free filtering functions design	10
additional features appropriate	10
additional features implemented	10
docstrings	5
in-line comments	5
First Milestone	10
Debrief	10
github.com repository: Commit messages and use	10
Total	100

Due Date and Final Submission requirement

Please submit the following files by the due date:

• [ ] your algorithm document, named as 'algorithm.txt', in your GitHub repo, by October 19.
• [ ] your python script, named as 'assignment1.py', in your repository, and also submitted to Blackboard, by November 2 at 11:59pm.
• [ ] your debrief answers should be submitted as issues to GitHub by November 24.