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SmokeyNet Engineering

This project was created for wildfire smoke detection analysis, and aims to improve upon a smoke detection model, SmokeyNet, originally built by the San Diego Supercomputer Center at UCSD.

Note: Processed data may change over time depending on latest camera/weather station updates and if the configured timeframe is changed.

Getting Started

  1. Ensure dependencies installed:
  • python3
  1. With git installed, clone project:

    git clone git@github.com:shaneluna/smokeynet-api.git
    

    Note: You may need to setup an ssh key if first time using git

  2. Change directory into cloned repo:

    cd smokeynet-api
    
  3. Create a virtual environment:

    python -m venv venv
    
  4. Start virutal environment:
    Linux & Mac:

    source venv/bin/activate
    

    Windows:

    ./venv/Scripts/activate
    
  5. Install requirements:

    pip install -r requirements.txt
    
  6. Run the following to fix jupyter lab code formatter:

    jupyter server extension enable --py jupyterlab_code_formatter
    

Naming Standards

  • All folder names + filenames should be lowercase

    • 1 exception is for README files
  • All folder names + filesnames should be snakecase

  • Required notebooks for data getting/processing should have #_ prefix
    I.e. 1_process_camera_metadata.ipynb

  • Save prefix 0_ for notebooks that retrieve initial raw data
    I.e. 0_get_camera_metadata.ipynb

Coding Standards

  • black python formatting

VS Code:
Code > Preferences > Settings > Search python formatting provider > Select black > Search format on save > Check box for Editor: Format on Save

JupyterLab:
JupyterLab Code Formatter has beeng added by default with black and isort formatting.
Make sure to update on save options within JupyterLab: Settings > Advanced Settings Editor > JSON Settings Editor > Jupyterlab Code Formatter > Add the following to User Preferences:
{ "formatOnSave": true }