ideal-function-selector

This project provides a Python package to select ideal functions from a set of training data and process test data accordingly. It utilizes SQLite for database management and Bokeh for data visualization.

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How to run?

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STEPS:

Clone the repository

https://github.com/abhinandansamal/ideal-function-selector

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STEP 01- Create a conda environment after opening the repository

conda create -n venv python=3.10 -y

conda activate venv

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STEP 02- install the requirements

pip install -r requirements.txt

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STEP 03- run the project

python main.py

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Project Structure

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• config/ - Configuration files.
• data/ - Directory to store data files.
• research/ - Notebooks for research and trials.
• src/ideal_function_selector/ - Source code for the project.
  • components/ - Contains data_loader.py, data_processor.py, data_visualizer.py.
  • data_loader.py - Loads CSV data into the database.
  • data_processor.py - Processes data to find best fit functions and map test data.
  • data_visualizer.py - Visualizes the data using Bokeh.
  • database.py - Handles database-related operations.
• main.py - Main script to run the project.
• test/ - Contains unit test for the project components.
• README.md - Project documentation.
• requirements.txt - List of dependencies.
• setup.py - Setup script for the package.
• template.py - Contains Project structure template.

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License

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MIT

Project Directory Structure

Here is the full directory structure after running template.py and adding the required files.

.project_root/
│
├── .github/
│ └── workflows/
│ └── .gitkeep
│
├── config/
│ └── config.yaml
│
├── research/
│ └── trials.ipynb
│
├── src/
│ └── ideal_function_selector/
│ ├── init.py
│ ├── components/
│ │ ├── init.py
│ │ ├── data_loader.py
│ │ ├── data_processor.py
│ │ ├── data_visualizer.py
│ ├── database
│ │ ├── init.py
│ │ ├── database.py
│
├── test/
│ └── init.py
│ └── test_data_loader.py
│ └── test_data_processor.py
│
├── main.py
├── README.md
├── requirements.txt
├── template.py
├── setup.py
└── LICENSE

### requirements.txt

numpy
pandas
sqlalchemy
bokeh
ipykernel
notebook
pyyaml

### Git Commands

To clone the branch, commit changes, and push to the repository:

```bash
# Clone the repository and checkout the develop branch
git clone -b develop <repository_url>
cd <repository_name>

# Make changes to the code and add a new function
# After making changes, add, commit, and push the changes
git add .
git commit -m "Added new function to process data"
git push origin develop

# Create a pull request on GitHub or your Git management platform