pamflow is a workflow for analyzing passive acoustic monitoring data using Python. It is designed to offer an easy-to-use and reproducible framework for researchers, conservation biologists, citizen scientists, and anyone interested in ecoacoustics and soundscape ecology.

Before You Begin

1. Download or Clone This Repository

git clone https://github.com/pamflow/pamflow.git
cd pamflow

2. Set Up a Working Environment

Ensure you have Python 3.11 installed. Then, create and activate a virtual environment:

conda create -n pamenv python=3.11
conda activate pamenv

Next, install the required dependencies based on your operating system.

On Windows:

pip install -r requirements-win.txt

On macOS:

pip install -r requirements-mac.txt

3. Organize PAM Data

• Audio Data: All audio files must be stored in a dedicated directory. Each subdirectory within this directory should have a unique identifier corresponding to the ID or name of each sensor. This structure ensures that recordings are properly associated with their respective sensors.
• Metadata: Make sure you have a field deployment sheet in an Excel format. This sheet must contain a column named recorderID, where each value matches the names of the subdirectories in the audio data directory. This ensures proper linking between metadata and recorded audio files.

Getting Started

1. Configure Local Settings

Edit the ./conf/local/parameters.yml file to specify the path to your audio files:

DEVICES_ROOT_DIRECTORY: <path to your directory with audio files>

Edit the ./conf/local/catalog.yml file to define the path to your field deployment sheet:

field_deployments_sheet@pandas:
  type: pandas.ExcelDataset
  filepath: <path to your Excel file with deployment information>

2. Run Workflows

The workflow can be executed entirely with the command:

kedro run

However, for the first execution, it is recommended to run one pipeline at a time for better control.

2.1. Prepare data

kedro run --pipeline=prepare_data

2.2. Revise quality of deployments and recordings

kedro run --pipeline=quality_control

2.3. Detect species with AI

kedro run --pipeline=species_detection

2.4. Compute Acoustic Indices

kedro run --pipeline=acoustic_indices

2.5. Generate Graphical Soundscapes

kedro run --pipeline=graphical_soundscape

License

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