MicroSWIFT Dashboard

The MicroSWIFT Dashboard is a Python-based tool designed to work with historical and real-time data from the microSWIFT wave buoy, developed by the University of Washington Applied Physics Laboratory (UW-APL).

MicroSWIFT Dashboard
Note: Currently, it will take 10-30 seconds to load

Contents

This repository contains all the necessary files for running the application both locally and on Render for deployment. To ensure smooth deployment, avoid altering the file structure.

• app.py - The main application file containing all callback functions and layouts. Note: Keep callbacks within this file to avoid issues.
• content_layout.py - Contains the layout templates for displaying data from a single buoy or multiple buoys.
• graphs.py - Manages to create graph objects for visualization.
• Data_retrieval.py - Handles data retrieval from the microSWIFT API, including filtering functions.
• mission_ids.csv - A list of past and current deployments.

Structure

microSWIFT-Dashboard
    |   app.py
    |   card_elements.py
    |   content_layout.py
    |   data_cleaning.py
    |   data_retrieval.py
    |   graphs.py
    |   LICENSE
    |   mission_ids.csv
    |   mission_utils.py
    |   README.md
    |   requirements.txt
    |   spectrogram_plot.py
    |   
    +---assets
    |       SWIFTlogo.png
    |       SWIFTlogo_r.png

Local Installation

To run the application locally:

• Clone the repository:

  git clone <repo_url>

• Install the required dependencies:

  pip install -r requirements.txt

• Run the application:

  python app.py

  The application should be accessible through your hostlocal port.

Running in a Container

Running a container built from source code

To run the application in a container install a Docker compatible container system (ie: Docker or Podman). Then within this directory run

docker-compose up

podman compose up

Running a pre-built container

To run a pre-built container in Docker:

docker pull ghcr.io/apl-uw-software/microswift-dashboard:latest
docker run -p 8080:8080 ghcr.io/apl-uw-software/microswift-dashboard:latest

To run a pre-build container in Podman:

podman pull ghcr.io/apl-uw-software/microswift-dashboard:latest
podman run -p 8080:8080 ghcr.io/apl-uw-software/microswift-dashboard:latest

Deployment

While Dash recommends using Heroku for deployment, we suggest using Render for its simplicity. For deployment on Render, follow their deployment guide. Note: You will need to create a Render account.

If deploying on a private server, we recommend using Gunicorn for serving the application. Learn more about Gunicorn here.

Alternatively, this app can deployed using the provided container image.

Adding a New Mission

To add a new mission:

• Update the mission_ids.csv file with the following details:
  • Mission name
  • List of buoy IDs (separated by spaces)
  • Start time in UTC format
  • End time in UTC format or None (to retrieve the latest data)

Examples:

Mission,Buoy_ids,Starttime,Endtime
Bering Sea June 2024, 040 041 065 077 080 082, 2024-06-01T00:00:00, None
Hurricane Idalia, 029 030 037 046 048, 2023-08-28T00:00:00, 2023-09-15T00:00:00

Adding a New Visualization

To add a new visualization:

• Determine the Type: Decide whether it's for a single buoy or multiple buoys.
• Modify graphs.py: Add the graph object within the appropriate function:

  new_graph = px.<GraphType>(data_frame, x='label', y='label')

  Refer to the Plotly documentation for details on graph types.
• Update content_layout.py: Add an html.Card element to allocate space for the new visual. Use a descriptive ID for the card.
• Modify app.py: Add the new output in the relevant callback:

  Output('id_label', 'children')

License

This project is licensed under the MIT License.