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.
• call_hostname() and call_uptime() should take no arguments, and should return strings from the shell.
• percent_to_graph(percent) will take an integer 'percent' and return a bar graph that represents this percentage. The bar graph should begin with '[', and end with ']'. Then contents inside should be 20 characters long.
• print_percent_line(name, percent) is provided as a convenience for you. It will print a properly formatted line, such as the one in the example above.

Additional Functions

Your code will need to have some additional functions that will accomplish the following:

• The output from call_df() should be filtered to omit any lines that contain loop or tmpfs. These are not proper file systems and should not be displayed.
• The output from call_free() should be used to calculate a percent of memory used divided by total memory.
• The output from uptime should be in "pretty" format, that is, in weeks, days, and so on. You may create this as a Python function, or you may also want to explore another way to do this.

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. 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 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!

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.

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

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.

Test your functions with the Python interpreter. Use import duim, then call functions with test arguments.

Second Milestone (due February 21)

For the second milestone you will have two more functions to complete.

• create_dir_dict will take your list from call_du_sub and return a dictionary.
• get_total will take two arguments: your list from call_du_sub and the target directory. It will return an integer that is the size of the target directory.

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.