pamflow
Getting Started
Installation instructions
1. Install miniconda
2. Install Git
3. Clone the repository
4. Install required packages
Beginner’s guide
The tutorial data
1. Download tutorial data
2. Audio recordings
3. Field deployments sheet
4. Target species list
Data preparation
Configure audio path and timezone
Move input files to the pamflow folder
Run the data preparation pipeline
Quality control
Run the quality control pipeline
Check deployment timeline
Check deployment locations
Check survey effort
Timelapses
Species detection
Detection outputs
Audio segments
Data annotation
Wrap-up
Diving deeper
Workflow structure and execution
Overview
Running the workflow
Pipeline details
1. Data preparation
2. Quality control
3. Species detection
4. Acoustic indices
5. Graphical soundscape
Expert’s Guide
Create a custom pipeline
1. Generate the Pipeline
2. Define your Logic in
nodes.py
3. Connect the Nodes in
pipeline.py
4. Register the Pipeline
5. Verify and Run
Troubleshooting Tip: Check your Nodes
Multi‐Season Configuration
Directory Structure
1. Set the Default Output Path
2. Override per Season
3. Adjust Season Configuration
3. Configure Each Season
Field deployment spreadsheet
Update the catalog
Update the audio directory
4. Run using environment
Data standards
Input data standards
File organization
Field deployment sheet
Target species
Output data standards
Deployments
Media
Observations
References
Getting Involved
Contributing guidelines
Table of Contents
Getting Started
How to Contribute
Code Standards
Git Commit Names
Testing
Pull Request Process
Reporting Issues
pamflow
Index
Index