quosi / CleanCode

Clean-up a collection of python scripts for image quality assessment, by applying Clean Code paradigms.

Deploy CleanCode

As this bundle of data analysis code grew to the medium size collection of a python scripts, it is perfect for a little Clean Code exercise. The following steps where applied in order to create an easy to use and clean python application.
>> Go straight to that clean python application & user instructions >>

STEP 1

Some steps to create a nice ditigal working environment for a clean start:

• initialise a git repository for version controll and link it to a remote URL
• create and/or activate your virtual environment for this project
• move working files like *.ipynb out of this repository or at least
• create a .gitignore file do hide certain files like notes.txt from git version controll

• draw diagram to plan code structure
• tea break ☕

STEP 2

Now its about time to dig into this code collection and start cleaning!

• delet unused imports
• restructure and refactor code as planed
• create python classes and modules to include all previously scattered processing functionality
• get rid of redundant / duplicate code
• make all function names camelCase
• rename variables to show there intention
• delet unnecessary functionality
  like the processing of global max, global min, global mean and global average values in the example code
• deleted unnecessary columns from dataframe
  like algo, dm tools, target and
  size values in the example code
• more tea ☕

STEP 3

• write unittests
• solve bugs 🐞
• add main.py script to inherit all processing functionality
• add command line interface for easy handling
• solve more bugs 🐜
• add documentation to functions and classes
• write requirements.txt to collect used python libraries
• write basic user instructions in README.md file for your git repository