Preprocess observations and STOFS model data for viewing on sealens-like dashboard.

Installation

Set up conda environment

This package has so far been developed and tested using python 3.12. If needed, use conda to get this:

# Download and set up conda:
wget "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh"
bash Miniforge3-Linux-x86_64.sh -b -p "${HOME}/conda"
source "${HOME}/conda/etc/profile.d/conda.sh"
source "${HOME}/conda/etc/profile.d/mamba.sh"
# Set up a new virtual environment:
mamba create --name=py312 python=3.12
mamba activate py312

Clone repository

git clone https://github.com/oceanmodeling/stofs-event-dashboard.git
# or
git clone [email protected]:oceanmodeling/stofs-event-dashboard.git 

Install dependencies

cd stofs-event-dashboard
python -m venv .venv
source /.venv/bin/activate
pip install -r requirements.txt

Cleanup

When finished, both the venv and (if applicable) conda environments need to be deactivated:

deactivate
# if needed:
mamba deactivate

Usage

Pre-process data

cd stofs-event-dashboard
python milton_example.py

Run dashboard

If running on a remote machine (e.g., AWS, GCP), you need to open a tunnel from your local computer to be able to view the dashboard on a local browser window.

ssh -i ~/.ssh/id_rsa -L8849:localhost:8849 <First.Last>@<cluster_ip_address>

Whether running locally (on your own laptop) or on a remote machine, the command below will start the dashboard. If running remotely, the port number (also repeated at the end of both websocket origins) needs to be the same as in the ssh command above (8849 in this case).

python -m panel serve dashboard*.py --dev --address=127.0.0.1 --port=8849 --allow-websocket-origin=localhost:8849 --allow-websocket-origin=127.0.0.1:8849  --log-level debug

# open dashboard at:
# http://127.0.0.1:8849/dashboard