Skip to content

Latest commit

 

History

History
28 lines (23 loc) · 1.67 KB

README.md

File metadata and controls

28 lines (23 loc) · 1.67 KB

Extract_course_data Folder

Important

This folder contains code for updating json data

Extracting the course data & preparing for use

Following is the process to prepare the data for this tool :

  1. Head to Pingala => Academic Management => Timetable => Check timetable. Select the Academic Year & Semester, and select the option to download the data as an Excel file
  2. Convert the Excel file into a CSV file for further processing, and store the file under the root directory(any name will work)
  3. Delete the old existing file leaving only one CSV in the folder
  4. Further run the bash script update_data.sh which will automatically parse all the data in the csv file and place it in the js directory.
bash update_data.sh

Files

data_generator.py

This files contain the code that is called to read csv and update the json file, the code uses the packages json, os, numpy, pandas and data_interpreter defined below The key features of this code are:

  • Designed to be called directly from bash
  • Gives a interactive CLI interface to user asking a [Y/n] question
  • Handle Dataframe objects and dumps in json where-ever required

data_interpreter.py

This file act as a package to interpret data wherever required in the data_generator.py file, the code uses the packages datetime, pandas and tqdm. Defined inside are multiple functions that tackle the task of sgregating and making timetables, creating binaries for causes the concepts of which are explained in BACK_END Folder

Important

Each files contains a sample test case under the if __name__ == "__main__": condition for Developers if needed