artwarp-py 1.0.0

High-performance Python implementation of ARTwarp for bioacoustic signal categorization

ARTwarp-py

A high-performance Python implementation of ARTwarp for automated categorization of tonal animal sounds.

Overview

ARTwarp-py is a complete rewrite of the original MATLAB ARTwarp software, combining Dynamic Time Warping (DTW) for contour similarity with an Adaptive Resonance Theory (ART) neural network for unsupervised clustering, with performance improvements while staying mathematically equivalent to the original algorithm.

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Contents

• Features
• Installation
• Quick Start
• Documentation
• Architecture
• Algorithm Details
• MATLAB Compatibility
• Testing
• Citation
• License & Authors

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Features

Area	Description
Performance	Optimized algorithms using NumPy, with optional Numba JIT compilation
Input formats	.ctr (MATLAB), .csv, .txt; load MATLAB categorization (.mat) via load_mat_categorization()
Visualization	Publication-worthy plots and reports
CLI	Command-line interface for training, plotting, and batch workflows
Quality	Type hints, tests, coverage, and documentation
Future	Parallel processing and batch pipelines (TODO)

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Installation

Requirements: Python 3.8+, NumPy ≥1.20, SciPy ≥1.7, pandas ≥1.3, matplotlib ≥3.4, and optionally Numba ≥0.54 for JIT.

> pip install -e .

For environment setup and virtualenv details, see user/INSTALLATION.md (end-user) and dev/ENVIRONMENT_SETUP.md (developers) within docs/.

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Quick Start

See docs/user/QUICK_REFERENCE.md for a condensed cheat sheet.

Command line

Activate your virtual environment first (e.g. source venv/bin/activate or, in Fish, source venv/bin/activate.fish), then:

Interactive launcher (configure all options via prompts):

> ./run.sh

This opens a menu for Train, Plot, Predict, or Export, then prompts for every parameter (paths, vigilance, learning rate, resampling, etc.) with defaults and validation. You can also call the CLI directly through the script: ./run.sh train -i ./contours -o results.pkl.

Basic commands:

# train on a directory of contour files
> artwarp-py train --input-dir ./contours --output results.pkl \
    --vigilance 85 --learning-rate 0.1 --max-iterations 50

# export reference contours and category assignments
> artwarp-py train --input-dir ./contours --output results.pkl --export-refs --export-categories

Resample with sample interval (seconds) [default = 0.02s]:

# resample contours to uniform temporal resolution (like MATLAB resample option)
> artwarp-py train --input-dir ./contours --output results.pkl --resample --sample-interval 0.02

Altogether (resampling & exporting reference contours / category assignments):

# full command
> artwarp-py train --input-dir ./contours --output results.pkl --resample --sample-interval 0.02 --vigilance 85 --learning-rate 0.1 --max-iterations 50 --export-refs --export-categories

Gener visualizations:

# generate visualizations from Pickle (.pkl) file
> artwarp-py plot --results results.pkl --input-dir ./contours --output-dir ./report

Python API

from artwarp import ARTwarp, load_contours

# load contour data
contours, names = load_contours('path/to/contour/directory', file_format='csv')

# init network
network = ARTwarp(
    vigilance=85.0,
    learning_rate=0.1,
    bias=0.0,
    max_categories=50,
    max_iterations=50,
    warp_factor_level=3
)

# train
results = network.fit(contours, contour_names=names)

# get category assignments
categories = results.categories
reference_contours = results.weight_matrix

# visualize results
from artwarp.visualization import plot_training_summary
plot_training_summary(results)

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Documentation

Document	Description
docs/user/QUICK_REFERENCE.md	(User Guide) Short reference for CLI and common tasks
docs/user/INSTALLATION.md	(User Guide) Install and environment setup
docs/user/API.md	(User Guide) Public API (loaders, exporters, options)
docs/user/ARCHITECTURE.md	(User Guide) Code layout and design
docs/user/VISUALIZATION.md	(User Guide) Plotting and report generation
CHANGELOG.md	Version history
docs/README.md	Docs index and overview
docs/dev/ENVIRONMENT_SETUP.md	(Developers) Detailed environment and tooling
docs/dev/PROJECT_SUMMARY.md	(Developers) Project summary and goals
docs/dev/PERFORMANCE_OPTIMIZATIONS.md	(Developers) Performance notes and benchmarks
docs/dev/TEST_RESULTS.md	(Developers) CI, test matrix, and coverage

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Architecture

artwarp/
├── core/
│   ├── dtw.py          # Dynamic Time Warping
│   ├── art.py          # ART neural network
│   ├── network.py      # Main ARTwarp algorithm
│   └── weights.py      # Weight update and management
├── io/
│   ├── loaders.py      # Data loading (.ctr, .csv, .txt, .mat)
│   └── exporters.py    # Results export
├── visualization/
│   └── plotting.py     # Plotting and reports
├── utils/
│   ├── validation.py   # Input validation
│   ├── resample.py     # Contour resampling (MATLAB-aligned)
│   └── numba_check.py  # Numba availability + optional install prompt
└── cli/
    └── main.py         # Command-line interface

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Algorithm Details

Dynamic Time Warping

• Optimized DP with Itakura parallelogram constraints
• Vectorized similarity matrix and efficient path backtracing
• Optional Numba JIT for speed

ART neural network

• Bottom-up activation (similarity-based) and top-down matching (vigilance)
• Dynamic category creation and incremental weight updates

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MATLAB compatibility

• Load MATLAB results:
  from artwarp.io import load_mat_categorization
data = load_mat_categorization("ARTwarp85FINAL.mat")
  → weight_matrix, num_categories, and optionally contours / categories / contour_names.
• Export for MATLAB: Use one_based_categories=True or one_based_filenames=True (see API.md).

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Testing

Run the test suite (use the venv’s Python so dependencies are found):

> python -m pytest tests/ -v

With coverage (CI requires ≥80%):

> python -m pytest tests/ --cov=artwarp --cov-report=html --cov-fail-under=80

CI: GitHub Actions runs tests (Python 3.9–3.12), coverage gate, and lint (Black, isort, Flake8, Mypy). See docs/dev/TEST_RESULTS.md for details.

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Citation

If you use this software in your research, please cite:

1. This implementation: [DOI to be assigned]
2. Buck, J. R. & Tyack, P. L. (1993). A quantitative measure of similarity for Tursiops truncatus signature whistles. J. Acoust. Soc. Am. 94, 2497–2506. https://doi.org/10.1121/1.407385
3. Deecke, V. B., Ford, J. K. B. & Spong, P. (1999). Quantifying complex patterns of bioacoustic variation: Use of a neural network to compare killer whale (Orcinus orca) dialects. J. Acoust. Soc. Am. 105, 2499–2507. https://doi.org/10.1121/1.426853
4. Deecke, V. B. & Janik, V. M. (2006). Automated categorization of bioacoustic signals: Avoiding perceptual pitfalls. J. Acoust. Soc. Am. 119, 645–653. https://doi.org/10.1121/1.2139067

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License & Authors

ARTwarp is distributed under the GNU Lesser General Public License v3. See LICENSE or https://www.gnu.org/licenses/lgpl.

• Python implementation: Pedro Gronda Garrigues (2026)
• Original MATLAB: Volker Deecke & Vincent Janik (2006)
• Original MATLAB repo: https://github.com/dolphin-acoustics-vip/artwarp

ARTwarp is distributed under the terms of the GNU Lesser General Public License, version 3, as published by the Free Software Foundation. For details, please refer to the LICENSE file in the root directory of the ARTwarp distribution or see https://www.gnu.org/licenses/lgpl.

Original MATLAB Implementation

This is a complete rewrite of the original ARTwarp MATLAB software available at: https://github.com/dolphin-acoustics-vip/artwarp

Publishing (PyPI/pip): Python packaging tutorial · Stack Overflow: publishing to PyPI

The following are steps (specific to @PedroGGBM) to upload the PyPI package:

# to generate distribution archives (build/)
> conda activate sig-process # or your corresponding venv (e.g., conda, venv, uv, etc)
> cd <base_directory>
> python -m pip install --upgrade setuptools wheel build # ensure latest version of PyPA's build installed
> python -m build

# to upload distribution archive
> python -m pip install --upgrade twine
> python -m twine upload --repository testpypi dist/* # this is to test it
> python -m pip install --index-url https://test.pypi.org/simple/ --no-deps example-package-YOUR-USERNAME-HERE # to test install on test server for PyPI

### !!! Once registered in PyPI and own official API key
> python -m twine upload dist/* # this is to upload it