StingrayTools

Tools for processing, organizing, and visualizing NES-LTER Stingray / ISIIS sensor and imaging data.

This package includes sensor-processing utilities, CTD/profile handling, image-link helpers, CSV I/O tools, statistical helpers, and a Dash dashboard for interactive exploration of Stingray cruise data.

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Installation

Clone the repository

git clone https://github.com/anhph95/stingraytools.git
cd StingrayTools

Install with a Python virtual environment

sudo apt update
sudo apt install python3-venv -y
python3 -m venv venv
source venv/bin/activate
pip install -e .

Or install with Conda

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
source ~/.bashrc
conda env create -f environment.yml
conda activate stingray
pip install -e .

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Command-line usage

Process Stingray sensor data

python -m stingray sensors merge \
  --cruise EN706 \
  --start YYYY-MM-DD \
  --end YYYY-MM-DD \
  --root sensor_data \
  --out-dir dash_data/data/stingray_timebinned

Common options:

--cruise CRUISE
    Cruise ID, e.g. EN706.

--start START
    Cruise start date in YYYY-MM-DD format.

--end END
    Cruise end date in YYYY-MM-DD format.

--root ROOT
    Path to the raw sensor-data directory. Default: sensor_data.

--cal-year CAL_YEAR
    Sensor calibration year. Default: 2021.

--time-bin-seconds TIME_BIN_SECONDS
    Time-bin size in seconds. Default: 5.

--out-dir OUT_DIR
    Output directory for dashboard-ready CSV files.

--media-list-dirs MEDIA_LIST_DIRS ...
    Media-list directories for ISIIS image links.

--overwrite-index
    Rebuild cached sensor-file indexes.

--log-level {DEBUG,INFO,WARNING,ERROR}
    Logging level. Default: INFO.

Sensor and image utilities

The package also includes modules for sensor-specific processing and image/media metadata handling:

stingray.sensors.ctd
stingray.sensors.fluorometer
stingray.sensors.par
stingray.sensors.suna
stingray.sensors.merge
stingray.images.frame_timestamp
stingray.images.get_tator_link
stingray.images.abundance
stingray.images.generate_training

Use the relevant module directly or import functions from Python scripts and notebooks as needed.

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Dashboard usage

The dashboard reads data from the configured dash_data/ directory. Expected local structure:

dash_data/
├── data/
│   └── <dataset_name>/
│       └── *.csv
└── misc/
    ├── NESLTER_station_list.csv
    └── NESLTER_transect_bathymetry.csv

Run the dashboard locally

python -m stingray dashboard run --host 0.0.0.0 --port 8050

Then open:

http://localhost:8050

Use the WHOI-hosted dashboard

Dashboard-ready data from dash_data/data can be copied to:

\\vast.whoi.edu\proj\nes-lter\stingray_dashboard\dash_data\data

The dashboard can then be accessed at:

https://stingraydash.whoi.edu/

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Docker usage

The dashboard can also be run with Docker Compose.

Start the dashboard

docker compose up --build

This builds the image, mounts dash_data/, and serves the app at:

http://localhost:8050

Stop the dashboard

docker compose down

Run in detached mode

docker compose up -d

Stop it with:

docker compose down

Updating data

If dash_data/ is mounted as a bind volume, updating CSV files does not require rebuilding the Docker image.

Rebuilding after code changes

Rebuild after modifying source code, dashboard code, assets, dependencies, or Docker configuration:

docker compose up --build

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Development notes

Install in editable mode during development:

pip install -e .

Useful checks:

python -m stingray.process --help
python -m stingray.dashboard.app --help

License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contributors

• Anh Pham
• Sidney Batchelder
• Heidi Sosik

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GitHub Repository: https://github.com/anhph95/StingrayTools