Getting Started

Prerequisites

• Python 3.9 or newer (3.12 recommended)

• Conda (recommended) or pip

• HDF5 accelerometry files

Installation

From PyPI

The package is published on PyPI as accelerometry-annotator:

pip install accelerometry-annotator

From source

# Clone the repository
git clone git@github.com:TavoloPerUno/py_visualize_accelerometry.git
cd py_visualize_accelerometry

# Create and activate conda environment
conda create -n panel_app python=3.12
conda activate panel_app

# Install dependencies
pip install -r requirements.txt

Data setup

Place your HDF5 accelerometry files (.h5) in:

visualize_accelerometry/data/readings/

Each file must contain a readings table with columns: timestamp, x, y, z.

Credentials

Create a credentials.json file in the project root:

{
    "annotator1": "password1",
    "annotator2": "password2",
    "admin_user": "adminpass"
}

See credentials.json.example for a template.

Running the app

Once the app is running you will see the branded login page:

Local development

panel serve visualize_accelerometry/app.py \
    --port 5601 \
    --basic-auth credentials.json \
    --cookie-secret $(python -c "import secrets; print(secrets.token_hex(32))") \
    --allow-websocket-origin localhost:5601 \
    --basic-login-template visualize_accelerometry/templates/login.html

Open http://localhost:5601/app in your browser.

HPC (SLURM cluster)

For running on a university SLURM cluster (e.g., Randi at UChicago), use the self-service connect script. It checks for an existing server, starts one if needed, and creates the SSH tunnel automatically:

bash hpc_utils/connect.sh

See Shared server startup for detailed instructions and Slurm deployment guide for the full deployment guide.